This document describes the development of an artificial neural network (ANN) and graphical user interface (GUI) to estimate fabrication time in rig construction projects. The ANN was trained on data from 960 completed fabrication jobs. It uses height, plate thickness, and inspection criteria as inputs to predict fabrication time in days as the output. Eleven different ANN architectures were tested and the model with 3 input nodes, 50 hidden nodes, and 1 output node performed best with a mean squared error of 1.35337e-2. A GUI was created allowing users to input job parameters and receive a fabrication time prediction without ANN expertise. The developed ANN and GUI provide a data-driven method for fabrication time estimation in rig construction project
Design and development of DrawBot using image processing IJECEIAES
Extracting text from an image and reproducing them can often be a laborious task. We took it upon ourselves to solve the problem. Our work is aimed at designing a robot which can perceive an image shown to it and reproduce it on any given area as directed. It does so by first taking an input image and performing image processing operations on the image to improve its readability. Then the text in the image is recognized by the program. Points for each letter are taken, then inverse kinematics is done for each point with MATLAB/Simulink and the angles in which the servo motors should be moved are found out and stored in the Arduino. Using these angles, the control algorithm is generated in the Arduino and the letters are drawn.
Performance analysis of real-time and general-purpose operating systems for p...IJECEIAES
In general, modern operating systems can be divided into two essential parts, real-time operating systems (RTOS) and general-purpose operating systems (GPOS). The main difference between GPOS and RTOS is the system is time-critical or not. It means that; in GPOS, a high-priority thread cannot preempt a kernel call. But, in RTOS, a low-priority task is preempted by a high-priority task if necessary, even if it’s executing a kernel call. Most Linux distributions can be used as both GPOS and RTOS with kernel modifications. In this study, two Linux distributions, Ubuntu and Pardus, were analyzed and their performances were compared both as GPOS and RTOS for path planning of the multi-robot systems. Robot groups with different numbers of members were used to perform the path tracking tasks using both Ubuntu and Pardus as GPOS and RTOS. In this way, both the performance of two different Linux distributions in robotic applications were observed and compared in two forms, GPOS, and RTOS.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IRJET-Analysis of Face Recognition System for Different ClassifierIRJET Journal
M.Manimozhi, A. John Dhanaseely "Analysis of Face Recognition System for Different Classifier ", International Research Journal of Engineering and Technology (IRJET), Volume2,issue-01 April 2015.e-ISSN:2395-0056, p-ISSN:2395-0072. www.irjet.net .published by Fast Track Publications
Abstract
Face recognition plays vital role for authenticating system. Human Face recognition is a challenging task in computer vision and pattern recognition. Face recognition has attracted much attention due to its potential value in security and law enforcement applications and its theoretical challenges. Different methods are used for feature extraction and classification. Kernel fisher analysis is used for feature extraction. The performance analysis for Euclidean, support vector machine is evaluated. The whole process is done using MATLAB software. A set of 10 person real time images is taken for our work. The classifier recognizes the similar posture as an output.
Algorithm for Modeling Unconventional Machine Tool Machining Parameters using...IDES Editor
Unconventional machining process finds a lot of
application in aerospace and precision industries. It is
preferred over other conventional methods because of the
advent of composite and high strength to weight ratio
materials, complex parts and also because of its high accuracy
and precision. Usually in unconventional machine tools, trial
and error method is used to fix the values of process
parameters. In the proposed work an algorithm which is
developed using Artificial Neural Network (ANN) is proposed
to create mathematical model functionally relating process
parameters and operating parameters of any unconventional
machine tool. This is accomplished by training a feed forward
network with back propagation learning algorithm. The
required data which are used for training and testing the ANN
in the case study is obtained by conducting trial runs in EBW
machine. By adopting the proposed algorithm there will be a
reduction in production time and set-up time along with
reduction in manufacturing cost in unconventional machining
processes. This in general increases the overall productivity.
The programs for training and testing the neural network are
developed, using MATLAB package
Design and development of DrawBot using image processing IJECEIAES
Extracting text from an image and reproducing them can often be a laborious task. We took it upon ourselves to solve the problem. Our work is aimed at designing a robot which can perceive an image shown to it and reproduce it on any given area as directed. It does so by first taking an input image and performing image processing operations on the image to improve its readability. Then the text in the image is recognized by the program. Points for each letter are taken, then inverse kinematics is done for each point with MATLAB/Simulink and the angles in which the servo motors should be moved are found out and stored in the Arduino. Using these angles, the control algorithm is generated in the Arduino and the letters are drawn.
Performance analysis of real-time and general-purpose operating systems for p...IJECEIAES
In general, modern operating systems can be divided into two essential parts, real-time operating systems (RTOS) and general-purpose operating systems (GPOS). The main difference between GPOS and RTOS is the system is time-critical or not. It means that; in GPOS, a high-priority thread cannot preempt a kernel call. But, in RTOS, a low-priority task is preempted by a high-priority task if necessary, even if it’s executing a kernel call. Most Linux distributions can be used as both GPOS and RTOS with kernel modifications. In this study, two Linux distributions, Ubuntu and Pardus, were analyzed and their performances were compared both as GPOS and RTOS for path planning of the multi-robot systems. Robot groups with different numbers of members were used to perform the path tracking tasks using both Ubuntu and Pardus as GPOS and RTOS. In this way, both the performance of two different Linux distributions in robotic applications were observed and compared in two forms, GPOS, and RTOS.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IRJET-Analysis of Face Recognition System for Different ClassifierIRJET Journal
M.Manimozhi, A. John Dhanaseely "Analysis of Face Recognition System for Different Classifier ", International Research Journal of Engineering and Technology (IRJET), Volume2,issue-01 April 2015.e-ISSN:2395-0056, p-ISSN:2395-0072. www.irjet.net .published by Fast Track Publications
Abstract
Face recognition plays vital role for authenticating system. Human Face recognition is a challenging task in computer vision and pattern recognition. Face recognition has attracted much attention due to its potential value in security and law enforcement applications and its theoretical challenges. Different methods are used for feature extraction and classification. Kernel fisher analysis is used for feature extraction. The performance analysis for Euclidean, support vector machine is evaluated. The whole process is done using MATLAB software. A set of 10 person real time images is taken for our work. The classifier recognizes the similar posture as an output.
Algorithm for Modeling Unconventional Machine Tool Machining Parameters using...IDES Editor
Unconventional machining process finds a lot of
application in aerospace and precision industries. It is
preferred over other conventional methods because of the
advent of composite and high strength to weight ratio
materials, complex parts and also because of its high accuracy
and precision. Usually in unconventional machine tools, trial
and error method is used to fix the values of process
parameters. In the proposed work an algorithm which is
developed using Artificial Neural Network (ANN) is proposed
to create mathematical model functionally relating process
parameters and operating parameters of any unconventional
machine tool. This is accomplished by training a feed forward
network with back propagation learning algorithm. The
required data which are used for training and testing the ANN
in the case study is obtained by conducting trial runs in EBW
machine. By adopting the proposed algorithm there will be a
reduction in production time and set-up time along with
reduction in manufacturing cost in unconventional machining
processes. This in general increases the overall productivity.
The programs for training and testing the neural network are
developed, using MATLAB package
This chapter deals with performance analysis of CUDA implementation of an image quality assessment
tool based on structural similarity index (SSI). Since it had been initial created at the University of Texas
in 2002, the Structural SIMilarity (SSIM) image assessment algorithm has become a valuable tool for
still image and video processing analysis. SSIM provided a big giant over MSE (Mean Square Error)
and PSNR (Peak Signal to Noise Ratio) techniques because it way more closely aligned with the results
that would have been obtained with subjective testing. For objective image analysis, this new technique
represents as significant advancement over SSIM as the advancement that SSIM provided over PSNR.
The method is computationally intensive and this poses issues in places wherever real time quality assessment
is desired. We tend to develop a CUDA implementation of this technique that offers a speedup
of approximately 30 X on Nvidia GTX275 and 80 X on C2050 over Intel single core processor.
Development of 3D convolutional neural network to recognize human activities ...journalBEEI
Human activity recognition (HAR) is recently used in numerous applications including smart homes to monitor human behavior, automate homes according to human activities, entertainment, falling detection, violence detection, and people care. Vision-based recognition is the most powerful method widely used in HAR systems implementation due to its characteristics in recognizing complex human activities. This paper addresses the design of a 3D convolutional neural network (3D-CNN) model that can be used in smart homes to identify several numbers of activities. The model is trained using KTH dataset that contains activities like (walking, running, jogging, handwaving handclapping, boxing). Despite the challenges of this method due to the effectiveness of the lamination, background variation, and human body variety, the proposed model reached an accuracy of 93.33%. The model was implemented, trained and tested using moderate computation machine and the results show that the proposal was successfully capable to recognize human activities with reasonable computations.
Fault-Tolerance Aware Multi Objective Scheduling Algorithm for Task Schedulin...csandit
Computational Grid (CG) creates a large heterogeneous and distributed paradigm to manage and execute the applications which are computationally intensive. In grid scheduling tasks are assigned to the proper processors in the grid system to for its execution by considering the execution policy and the optimization objectives. In this paper, makespan and the faulttolerance of the computational nodes of the grid which are the two important parameters for the task execution, are considered and tried to optimize it. As the grid scheduling is considered to be NP-Hard, so a meta-heuristics evolutionary based techniques are often used to find a solution for this. We have proposed a NSGA II for this purpose. The performance estimation ofthe proposed Fault tolerance Aware NSGA II (FTNSGA II) has been done by writing program in Matlab. The simulation results evaluates the performance of the all proposed algorithm and the results of proposed model is compared with existing model Min-Min and Max-Min algorithm which proves effectiveness of the model.
Enhanced Human Computer Interaction using hand gesture analysis on GPUMahesh Khadatare
This poster represent very active research topic in human
computer interaction (HCI) as automatic hand gesture recognition
using nvidia GPU. In this work neural network based video gesture
are processed and recognize the finger counts. Due to real time
requirement algorithm need to optimize and computationally
efficient. We implemented the MATLAB code, it perform slow when
neural network processing started. Implementing them in a parallel
programming model such as GPU-CUDA would provide the
necessary gain in processing speed. Algorithmic result validation is
done using standard video data set and recognition rate is
calculated. A performance improvement of 15x speed is achieved
which is faster than Intel quad core processor.
DYNAMIC NETWORK ANOMALY INTRUSION DETECTION USING MODIFIED SOMcscpconf
Detection of unexpected and emerging new threats has become a necessity for secured internet
communication with absolute data confidentiality, integrity and availability. Design and
development of such a detection system shall not only be new, accurate and fast but also
effective in a dynamic environment encompassing the surrounding network. In this paper, an algorithm is proposed for anomaly detection through modifying the Self – Organizing Map (SOM), by including new neighbourhood updating rules and learning rate dynamically in order to overcome the fixed architecture and random weight vector assignment. The algorithm initially starts with null network and grows with the original data space as initial weight vectors. New nodes are created using distance threshold parameter and their neighbourhood is identified using connection strength. Employing learning rule, the weight vector updation is carried out for neighbourhood nodes. Performance of the new algorithm is evaluated for using standard bench mark dataset. The result is compared with other neural network methods, shows 98% detection rate and 2% false alarm rate.
Comparative Study of Neural Networks Algorithms for Cloud Computing CPU Sched...IJECEIAES
Cloud Computing is the most powerful computing model of our time. While the major IT providers and consumers are competing to exploit the benefits of this computing model in order to thrive their profits, most of the cloud computing platforms are still built on operating systems that uses basic CPU (Core Processing Unit) scheduling algorithms that lacks the intelligence needed for such innovative computing model. Correspdondingly, this paper presents the benefits of applying Artificial Neural Networks algorithms in regards to enhancing CPU scheduling for Cloud Computing model. Furthermore, a set of characteristics and theoretical metrics are proposed for the sake of comparing the different Artificial Neural Networks algorithms and finding the most accurate algorithm for Cloud Computing CPU Scheduling.
Estimation of Optimized Energy and Latency Constraint for Task Allocation in ...ijcsit
In Network on Chip (NoC) rooted system, energy consumption is affected by task scheduling and allocation
schemes which affect the performance of the system. In this paper we test the pre-existing proposed
algorithms and introduced a new energy skilled algorithm for 3D NoC architecture. An efficient dynamic
and cluster approaches are proposed along with the optimization using bio-inspired algorithm. The
proposed algorithm has been implemented and evaluated on randomly generated benchmark and real life
application such as MMS, Telecom and VOPD. The algorithm has also been tested with the E3S benchmark
and has been compared with the existing mapping algorithm spiral and crinkle and has shown better
reduction in the communication energy consumption and shows improvement in the performance of the
system. On performing experimental analysis of proposed algorithm results shows that average reduction
in energy consumption is 49%, reduction in communication cost is 48% and average latency is 34%.
Cluster based approach is mapped onto NoC using Dynamic Diagonal Mapping (DDMap), Crinkle and
Spiral algorithms and found DDmap provides improved result. On analysis and comparison of mapping of
cluster using DDmap approach the average energy reduction is 14% and 9% with crinkle and spiral.
SPEED-UP IMPROVEMENT USING PARALLEL APPROACH IN IMAGE STEGANOGRAPHYcsandit
This paper presents a parallel approach to improve the time complexity problem associated
with sequential algorithms. An image steganography algorithm in transform domain is
considered for implementation. Image steganography is a technique to hide secret message in
an image. With the parallel implementation, large message can be hidden in large image since
it does not take much processing time. It is implemented on GPU systems. Parallel
programming is done using OpenCL in CUDA cores from NVIDIA. The speed-up improvement
obtained is very good with reasonably good output signal quality, when large amount of data is
processed
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An Efficient Approach for Image Enhancement Based on Image Fusion with Retine...ijsrd.com
Aiming at problems of poor contrast and blurred edges in degraded images, a novel enhancement algorithm is proposed in present research. Image fusion refers to a technique that combines the information from two or more images of a scene into a single fused image.The Algorithm uses Retinex theory and gamma correction to perform a better enhancement of images. The algorithm can efficiently combine the advantages of Retinex and Gamma correction improving both color constancy and intensity of image.
A survey on Efficient Enhanced K-Means Clustering Algorithmijsrd.com
Data mining is the process of using technology to identify patterns and prospects from large amount of information. In Data Mining, Clustering is an important research topic and wide range of unverified classification application. Clustering is technique which divides a data into meaningful groups. K-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. In this paper, we present the comparison of different K-means clustering algorithms.
Comparative study on growth status of Teen age Male Volleyball players on sel...ijsrd.com
The game of Volleyball was invented a long way since 1895 when W. G. Morgan hang a tennis net six feet across the Holyoke "Y" gym and volleyed a basketball bladder back and forth and named it as "mignonette. Initially, the game was managed by Indian Olympic Association and the Interstate volleyball Championship was conducted every two years between the years 1936 and 1950.The purposes of the study were to find out the relationship of growth status between the variables of U-14 years and U-19 years groups. Sixty two (62) male subjects were selected for this study. Among them, thirty one (31) male subjects for U-14 and thirty one (31) male subjects for U-19 years groups. Findings revealed that the relationship between variables of the three growth variables, relationship existed in all of the two age groups except in height and BMI of two groups.
This chapter deals with performance analysis of CUDA implementation of an image quality assessment
tool based on structural similarity index (SSI). Since it had been initial created at the University of Texas
in 2002, the Structural SIMilarity (SSIM) image assessment algorithm has become a valuable tool for
still image and video processing analysis. SSIM provided a big giant over MSE (Mean Square Error)
and PSNR (Peak Signal to Noise Ratio) techniques because it way more closely aligned with the results
that would have been obtained with subjective testing. For objective image analysis, this new technique
represents as significant advancement over SSIM as the advancement that SSIM provided over PSNR.
The method is computationally intensive and this poses issues in places wherever real time quality assessment
is desired. We tend to develop a CUDA implementation of this technique that offers a speedup
of approximately 30 X on Nvidia GTX275 and 80 X on C2050 over Intel single core processor.
Development of 3D convolutional neural network to recognize human activities ...journalBEEI
Human activity recognition (HAR) is recently used in numerous applications including smart homes to monitor human behavior, automate homes according to human activities, entertainment, falling detection, violence detection, and people care. Vision-based recognition is the most powerful method widely used in HAR systems implementation due to its characteristics in recognizing complex human activities. This paper addresses the design of a 3D convolutional neural network (3D-CNN) model that can be used in smart homes to identify several numbers of activities. The model is trained using KTH dataset that contains activities like (walking, running, jogging, handwaving handclapping, boxing). Despite the challenges of this method due to the effectiveness of the lamination, background variation, and human body variety, the proposed model reached an accuracy of 93.33%. The model was implemented, trained and tested using moderate computation machine and the results show that the proposal was successfully capable to recognize human activities with reasonable computations.
Fault-Tolerance Aware Multi Objective Scheduling Algorithm for Task Schedulin...csandit
Computational Grid (CG) creates a large heterogeneous and distributed paradigm to manage and execute the applications which are computationally intensive. In grid scheduling tasks are assigned to the proper processors in the grid system to for its execution by considering the execution policy and the optimization objectives. In this paper, makespan and the faulttolerance of the computational nodes of the grid which are the two important parameters for the task execution, are considered and tried to optimize it. As the grid scheduling is considered to be NP-Hard, so a meta-heuristics evolutionary based techniques are often used to find a solution for this. We have proposed a NSGA II for this purpose. The performance estimation ofthe proposed Fault tolerance Aware NSGA II (FTNSGA II) has been done by writing program in Matlab. The simulation results evaluates the performance of the all proposed algorithm and the results of proposed model is compared with existing model Min-Min and Max-Min algorithm which proves effectiveness of the model.
Enhanced Human Computer Interaction using hand gesture analysis on GPUMahesh Khadatare
This poster represent very active research topic in human
computer interaction (HCI) as automatic hand gesture recognition
using nvidia GPU. In this work neural network based video gesture
are processed and recognize the finger counts. Due to real time
requirement algorithm need to optimize and computationally
efficient. We implemented the MATLAB code, it perform slow when
neural network processing started. Implementing them in a parallel
programming model such as GPU-CUDA would provide the
necessary gain in processing speed. Algorithmic result validation is
done using standard video data set and recognition rate is
calculated. A performance improvement of 15x speed is achieved
which is faster than Intel quad core processor.
DYNAMIC NETWORK ANOMALY INTRUSION DETECTION USING MODIFIED SOMcscpconf
Detection of unexpected and emerging new threats has become a necessity for secured internet
communication with absolute data confidentiality, integrity and availability. Design and
development of such a detection system shall not only be new, accurate and fast but also
effective in a dynamic environment encompassing the surrounding network. In this paper, an algorithm is proposed for anomaly detection through modifying the Self – Organizing Map (SOM), by including new neighbourhood updating rules and learning rate dynamically in order to overcome the fixed architecture and random weight vector assignment. The algorithm initially starts with null network and grows with the original data space as initial weight vectors. New nodes are created using distance threshold parameter and their neighbourhood is identified using connection strength. Employing learning rule, the weight vector updation is carried out for neighbourhood nodes. Performance of the new algorithm is evaluated for using standard bench mark dataset. The result is compared with other neural network methods, shows 98% detection rate and 2% false alarm rate.
Comparative Study of Neural Networks Algorithms for Cloud Computing CPU Sched...IJECEIAES
Cloud Computing is the most powerful computing model of our time. While the major IT providers and consumers are competing to exploit the benefits of this computing model in order to thrive their profits, most of the cloud computing platforms are still built on operating systems that uses basic CPU (Core Processing Unit) scheduling algorithms that lacks the intelligence needed for such innovative computing model. Correspdondingly, this paper presents the benefits of applying Artificial Neural Networks algorithms in regards to enhancing CPU scheduling for Cloud Computing model. Furthermore, a set of characteristics and theoretical metrics are proposed for the sake of comparing the different Artificial Neural Networks algorithms and finding the most accurate algorithm for Cloud Computing CPU Scheduling.
Estimation of Optimized Energy and Latency Constraint for Task Allocation in ...ijcsit
In Network on Chip (NoC) rooted system, energy consumption is affected by task scheduling and allocation
schemes which affect the performance of the system. In this paper we test the pre-existing proposed
algorithms and introduced a new energy skilled algorithm for 3D NoC architecture. An efficient dynamic
and cluster approaches are proposed along with the optimization using bio-inspired algorithm. The
proposed algorithm has been implemented and evaluated on randomly generated benchmark and real life
application such as MMS, Telecom and VOPD. The algorithm has also been tested with the E3S benchmark
and has been compared with the existing mapping algorithm spiral and crinkle and has shown better
reduction in the communication energy consumption and shows improvement in the performance of the
system. On performing experimental analysis of proposed algorithm results shows that average reduction
in energy consumption is 49%, reduction in communication cost is 48% and average latency is 34%.
Cluster based approach is mapped onto NoC using Dynamic Diagonal Mapping (DDMap), Crinkle and
Spiral algorithms and found DDmap provides improved result. On analysis and comparison of mapping of
cluster using DDmap approach the average energy reduction is 14% and 9% with crinkle and spiral.
SPEED-UP IMPROVEMENT USING PARALLEL APPROACH IN IMAGE STEGANOGRAPHYcsandit
This paper presents a parallel approach to improve the time complexity problem associated
with sequential algorithms. An image steganography algorithm in transform domain is
considered for implementation. Image steganography is a technique to hide secret message in
an image. With the parallel implementation, large message can be hidden in large image since
it does not take much processing time. It is implemented on GPU systems. Parallel
programming is done using OpenCL in CUDA cores from NVIDIA. The speed-up improvement
obtained is very good with reasonably good output signal quality, when large amount of data is
processed
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An Efficient Approach for Image Enhancement Based on Image Fusion with Retine...ijsrd.com
Aiming at problems of poor contrast and blurred edges in degraded images, a novel enhancement algorithm is proposed in present research. Image fusion refers to a technique that combines the information from two or more images of a scene into a single fused image.The Algorithm uses Retinex theory and gamma correction to perform a better enhancement of images. The algorithm can efficiently combine the advantages of Retinex and Gamma correction improving both color constancy and intensity of image.
A survey on Efficient Enhanced K-Means Clustering Algorithmijsrd.com
Data mining is the process of using technology to identify patterns and prospects from large amount of information. In Data Mining, Clustering is an important research topic and wide range of unverified classification application. Clustering is technique which divides a data into meaningful groups. K-means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. In this paper, we present the comparison of different K-means clustering algorithms.
Comparative study on growth status of Teen age Male Volleyball players on sel...ijsrd.com
The game of Volleyball was invented a long way since 1895 when W. G. Morgan hang a tennis net six feet across the Holyoke "Y" gym and volleyed a basketball bladder back and forth and named it as "mignonette. Initially, the game was managed by Indian Olympic Association and the Interstate volleyball Championship was conducted every two years between the years 1936 and 1950.The purposes of the study were to find out the relationship of growth status between the variables of U-14 years and U-19 years groups. Sixty two (62) male subjects were selected for this study. Among them, thirty one (31) male subjects for U-14 and thirty one (31) male subjects for U-19 years groups. Findings revealed that the relationship between variables of the three growth variables, relationship existed in all of the two age groups except in height and BMI of two groups.
Any firm typically made a large investment in a piece of capital machinery and, in theory, it could run 24 hour a day for seven days a week at its optimum Speed. If it did this you gain the maximum value from the investment. In reality there is number of element that can affect the value gained from the investment. So that fully utilization of equipment can be done. Hence for fully utilization of any equipment any firm must have to calculate OEE. This paper represents the methodology applied in increasing the OEE of an Organization by exchanging the feed mechanism from Bowl Feeder to a Conveyor.
Application of Plasma Technology for Coated Textileijsrd.com
More than 99% of the visible matter in the universe is in the plasma state. It can be seen in its natural form on earth as lightning or as polar light in the Arctic and Antarctic. Plasma was first discovered by Irving Langmuir in 1928. Plasma technology is based on a simple physical principle. Matter changes its state when energy is supplied to it: solids become liquid, and liquids become gaseous. If even more energy is supplied to a gas, it is ionized and goes into the energy-rich plasma state, the fourth state of matter.
Design a multi-parameter data acquisition system for an Air Quality Monitoring and publish the data over internet through embedded web server The most common causes of CO exposure are fires, faulty combustion heating systems, exhaust from internal combustion engines and heating gases other than natural gas. Leading cause of accidental poisoning deaths is due to carbon monoxide. CO impairs oxygen delivery and has its most lethal effects on organs requiring high levels of oxygen like the brain and heart. Air quality monitoring system presents a network for indoor and outdoor air quality monitoring. Each node is installed in a different room and includes tin dioxide sensor arrays connected to an acquisition system. The nodes are hardwired or wirelessly connected to a central monitoring unit. To increase the gas concentration measurement accuracy, two gas sensor influence quantities like temperature and humidity are also measured. Therefore, the proposed work is multi parameter data acquisition system for air quality monitoring and to publish the data over internet through embedded web server. The data of the different sensors are transmitted using the wireless transmission technique and it is published over the internet.
Multispectral images are used for space Arial application, target detection and remote sensing application. MS images are very rich in spectral resolution but at a cost of spatial resolution. We propose a new method to increase a spatial resolution MS images. For spatial resolution enhancement of MS images we need to employ a super-resolution technique which uses a Principal Component Analysis (PCA) based approach by learning an edge details from database. Experiments have been carried out on both real multispectral (MS) data and MS data. This experiment is done with the usefulness for hyper spectral (HS) data as a future work.
ON Fuzzy Linear Programming Technique Applicationijsrd.com
A solution approach based on fuzzy linear programming is proposed and applied to optimal machine scheduling problem. In this solution approach errors in the demand of various products during the next production period are considered to be fuzzy in nature. In conventional linear programming approach it is assumed that there is no error in the expected demand of various products. A fuzzy linear programming approach is proposed to obtain an optimal solution under fuzzy conditions. In the proposed method expected demand of products & profit are expressed by fuzzy set notations. The proposed fuzzy linear programming formulation is then transformed to an equivalent conventional linear programming problem and solutions obtained by solving this transformed linear programming problem. For illustration purpose the proposed method is applied to a profit maximization related machine scheduling problem.
A Survey of Techniques Used To Detect Selfish Nodes in MANETijsrd.com
An mobile Ad Hoc network is a collection of mobile nodes. They do not have any existing infrastructure and they do not have any centralized administrator. So the MANET is self-creating, self-organizing and self-administrative wireless network. In MANET each node acts as router. In practice some of the nodes may act as the selfish nodes. These nodes use the network and its services but they do not cooperate with other nodes. Such selfish nodes do not consume any energy such as CPU power, battery and bandwidth for retransmitting the data of other nodes. They will preserve the resources for their own use. In this paper we have provide the comparative study of different type of methods to increase the selfish node detection and the network throughput
Analysis of Different Parking Space and its Comparisonijsrd.com
In the "Analysis of different parking space and its comparison" we collected data from different parking space of our institute N.I.T Rourkela. Initially we figured out what is the variation of PCU with a certain time and then we compared all these data with the help of "t- test" to find out whether these parking pattern and demand are same or different. In another part we find out the "spatial and temporal distribution" of main road traffic vehicle, here "spatial distribution" is the variation of PCU (passenger car unit) with distance and in "temporal distribution" variation of PCU with time.
Hydrostatic Continuous Variable Power Transmission Drive for Two wheelers usi...ijsrd.com
Power transmission is an extremely important factor for the automotive industry today. In vehicles, the power transmission system is the major source of energy losses. This is an intentionally compact review for a module addressing basic Fluid Mechanics for incompressible fluids within the context of Applied Energy Systems. Rather than attempting to cover Fluid Mechanics in a very broad and general way, two practical areas are selected in the use of fluids, hydrostatic power transmission systems and the flow of fluids through pipes and fittings. Thus readers are prepared for applying the same and similar principles to a much broader range of practical applications in the future. The present review work relates to a rotary pump and motor transmission system, which permits a change in ratio between the speed of the driver and driven shafts from direct drive to neutral position. This transmission, which may be adopted for many uses, such as vehicles and machine tools, is endowed with the utmost ease of operation even under load, is of a simple and rugged construction and offers safety of operation even after an extended use.
Introduction to VIP with PCI Express Technologyijsrd.com
This paper describes latest technology PCI Express and VIP for reusability purpose as it is necessary for today's faster verification needs. It is explained using PCIe Verification IP. This verification is achieved by developing Device reference module. PCIe is high speed serial bus that supports 2.5 GT/s to 16 GT/s. PCIe is point to point device with lane and link concept that support full duplex communications between two devices. Verification Intellectual Property is component that behaves exactly like PCIe design and used for verification of the design. Additionally it has several features like generation, checking and coverage. PCIe VIP is architecture using system Verilog HVL.
Clustering Algorithm for Gujarati Languageijsrd.com
Natural language processing area is still under research. But now a day it is on platform for worldwide researchers. Natural language processing includes analyzing the language based on its structure and then tagging of each word appropriately with its grammar base. Here we have 50,000 tagged words set and we try to cluster those Gujarati words based on proposed algorithm, we have defined our own algorithm for processing. Many clustering techniques are available Ex. Single linkage , complete, linkage ,average linkage, Hear no of clusters to be formed are not known, so it's all depends on the type of data set provided .Clustering is preprocess for stemming . Stemming is the process where root is extracted from its word. Ex. cats= cat+S, meaning. Cat: Noun and plural form.
Improved Frequent Pattern Mining Algorithm using Divide and Conquer Technique...ijsrd.com
Frequent patterns are patterns such as item sets, subsequences or substructures that appear in a data set frequently. A Divide and Conquer method is used for finding frequent item set mining. Its core advantages are extremely simple data structure and processing scheme. Divide the original dataset in the projected database and find out the frequent pattern from the dataset. Split and Merge uses a purely horizontal transaction representation. It gives very good result for dense dataset. The researchers introduce a split and merge algorithm for frequent item set mining. There are some problems with this algorithm. We have to modify this algorithm for getting better results and then we will compare it with old one. We have suggested different methods to solve problem with current algorithm. We proposed two methods (1) Method I and (2) Method II for getting solution of problem. We have compared our algorithm with the currently worked algorithm SaM. We examine the performance of SaM and Modified SaM using real datasets. We have taken results for both dense and sparse datasets.
A Decision Tree Based Classifier for Classification & Prediction of Diseasesijsrd.com
In this paper, we are proposing a modified algorithm for classification. This algorithm is based on the concept of the decision trees. The proposed algorithm is better then the previous algorithms. It provides more accurate results. We have tested the proposed method on the example of patient data set. Our proposed methodology uses greedy approach to select the best attribute. To do so the information gain is used. The attribute with highest information gain is selected. If information gain is not good then again divide attributes values into groups. These steps are done until we get good classification/misclassification ratio. The proposed algorithms classify the data sets more accurately and efficiently.
A review on techniques for optimizing process parameters for TIG Welding Alum...ijsrd.com
Tungsten inert gas welding is one of the widely used techniques for joining ferrous and non-ferrous metals. TIG welding offers several advantages like joining of dissimilar metals, low heat affected zone, absence of slag etc. Gas tungsten arc welding, GTAW, uses a non consumable electrode to produce the weld. Weld area is protected from atmospheric contamination by a shielding gas (usually inert gas such as argon) and a filler material is normally used. The weld pool is easily controlled such that unbaked root passes can be made, the arc is stable at very low welding currents enabling thin components to be welded and the process produces very good quality weld metal, although highly skilled welders are required for the best results. The welding parameters are selected by operator based on experience or from a handbook. However, this does not ensure that the selected welding process parameters can produce the optimal or near optimal weld pool geometry for that particular welding machine and environment. The aim of this paper is to review the techniques of optimizing process parameters of TIG welding process.
A Review Study on Methods of Tunneling in Hard Rocksijsrd.com
This article presents a review on the different methodologies that are used for tunnels excavations in hard rocks in present era. Growing needs for modern transportation and utility networks have increased the demand for a more extensive and elaborate use of underground space or through high mountains / hills. As a result, more projects have to be completed in various ground conditions and one of which is more challenging is to carry out excavation work in hard rocks. Significant technological advances have rendered these projects possible, but have also given rise to new challenges as many of these projects have to be completed in difficult conditions, with very strict environmental constraints, particularly in urban areas where the potential impact of tunneling on existing structures is a major concern. This paper addresses the main aspects of tunneling and underground works performed in hard rocks. A summary is presented of the more recent advances and widely adopted techniques in these regards.
EFFICIENT USE OF HYBRID ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM COMBINED WITH N...csandit
This research study proposes a novel method for automatic fault prediction from foundry data
introducing the so-called Meta Prediction Function (MPF). Kernel Principal Component
Analysis (KPCA) is used for dimension reduction. Different algorithms are used for building the
MPF such as Multiple Linear Regression (MLR), Adaptive Neuro Fuzzy Inference System
(ANFIS), Support Vector Machine (SVM) and Neural Network (NN). We used classical
machine learning methods such as ANFIS, SVM and NN for comparison with our proposed
MPF. Our empirical results show that the MPF consistently outperform the classical methods.
AN IMPROVED METHOD FOR IDENTIFYING WELL-TEST INTERPRETATION MODEL BASED ON AG...IAEME Publication
This paper presents an approach based on applying an aggregated predictor formed by multiple versions of a multilayer neural network with a back-propagation optimization algorithm for helping the engineer to get a list of the most appropriate well-test interpretation models for a given set of pressure/ production data. The proposed method consists of three stages: (1) data decorrelation through principal component analysis to reduce the covariance between the variables and the dimension of the input layer in the artificial neural network, (2) bootstrap replicates of the learning set where the data is repeatedly sampled with a random split of the data into train sets and using these as new learning sets, and (3) automatic reservoir model identification through aggregated predictor formed by a plurality vote when predicting a new class. This method is described in detail to ensure successful replication of results. The required training and test dataset were generated by using analytical solution models. In our case, there were used 600 samples: 300 for training, 100 for cross-validation, and 200 for testing. Different network structures were tested during this study to arrive at optimum network design. We notice that the single net methodology always brings about confusion in selecting the correct model even though the training results for the constructed networks are close to 1. We notice also that the principal component analysis is an effective strategy in reducing the number of input features, simplifying the network structure, and lowering the training time of the ANN. The results obtained show that the proposed model provides better performance when predicting new data with a coefficient of correlation approximately equal to 95% Compared to a previous approach 80%, the combination of the PCA and ANN is more stable and determine the more accurate results with lesser computational complexity than was feasible previously. Clearly, the aggregated predictor is more stable and shows less bad classes compared to the previous approach.
Stochastic Computing Correlation Utilization in Convolutional Neural Network ...TELKOMNIKA JOURNAL
In recent years, many applications have been implemented in embedded systems and mobile Internet of Things (IoT) devices that typically have constrained resources, smaller power budget, and exhibit "smartness" or intelligence. To implement computation-intensive and resource-hungry Convolutional Neural Network (CNN) in this class of devices, many research groups have developed specialized parallel accelerators using Graphical Processing Units (GPU), Field-Programmable Gate Arrays (FPGA), or Application-Specific Integrated Circuits (ASIC). An alternative computing paradigm called Stochastic Computing (SC) can implement CNN with low hardware footprint and power consumption. To enable building more efficient SC CNN, this work incorporates the CNN basic functions in SC that exploit correlation, share Random Number Generators (RNG), and is more robust to rounding error. Experimental results show our proposed solution provides significant savings in hardware footprint and increased accuracy for the SC CNN basic functions circuits compared to previous work.
Application of ann for ultimate shear strength of fly ash concrete beamseSAT Journals
Abstract
The application of artificial neural networks (ANN) for ultimate shear strength of fly ash concrete beams with transverse
reinforcement is investigated in this paper. An ANN model is built, trained and tested using the available test data of 216 RC beams
collected from the literature also the experimental data of twenty seven fly ash concrete beams under shear. The experimental shear
strength were also compared to those obtained using building codal equations and empirical equations proposed by various
researchers. The ANN model was found to predict satisfactorily when compared to available analytical predictions.
Keywords: Artificial Neural Network (ANN), Building codes, Comparison, Charts, Empirical Equations, Fly ash
Concrete, Shear Strength.
Application of ann for ultimate shear strength of fly ash concrete beamseSAT Journals
Abstract
The application of artificial neural networks (ANN) for ultimate shear strength of fly ash concrete beams with transverse
reinforcement is investigated in this paper. An ANN model is built, trained and tested using the available test data of 216 RC beams
collected from the literature also the experimental data of twenty seven fly ash concrete beams under shear. The experimental shear
strength were also compared to those obtained using building codal equations and empirical equations proposed by various
researchers. The ANN model was found to predict satisfactorily when compared to available analytical predictions.
Keywords: Artificial Neural Network (ANN), Building codes, Comparison, Charts, Empirical Equations, Fly ash
Concrete, Shear Strength.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
AN ARTIFICIAL NEURAL NETWORK-BASED APPROACH COUPLED WITH TAGUCHI'S METHOD FOR...IAEME Publication
Nowadays, project duration prediction has become of crucial importance for managers since it points out the expectancy-life of project realization. To this end, the Neural Network-based approach coupled with the Taguchi method is used to predict the necessary time, which allows the fulfillment of the targeted project within the prescribed span without delay. Accordingly, the whole process for modeling the targeted problem is described, in which the modeling and simulation of the activities network are introduced for calculating the total average time of project. Then, the neural network approach is adopted to predict the total time for finishing the considered project within the deadlines, where the neural network’s input variables are composed of success probability, improvement and learning factors. While, the output variable is the total average project duration, which is the critical data during design phase. After that, the well-known Taguchi method is purposefully used to optimize the already obtained target by neural network. Finally, Simulation analysis through MATLAB are used to show the efficiency of the proposed approach regarding the workability of the approach when it comes to estimating the deadline of the targeted project.
Similar to Artificial Neural Network Based Graphical User Interface for Estimation of Fabrication Time in Rig Construction Project (20)
Due to availability of internet and evolution of embedded devices, Internet of things can be useful to contribute in energy domain. The Internet of Things (IoT) will deliver a smarter grid to enable more information and connectivity throughout the infrastructure and to homes. Through the IoT, consumers, manufacturers and utility providers will come across new ways to manage devices and ultimately conserve resources and save money by using smart meters, home gateways, smart plugs and connected appliances. The future smart home, various devices will be able to measure and share their energy consumption, and actively participate in house-wide or building wide energy management systems. This paper discusses the different approaches being taken worldwide to connect the smart grid. Full system solutions can be developed by combining hardware and software to address some of the challenges in building a smarter and more connected smart grid.
A Survey Report on : Security & Challenges in Internet of Thingsijsrd.com
In the era of computing technology, Internet of Things (IoT) devices are now popular in each and every domains like e-governance, e-Health, e-Home, e-Commerce, and e-Trafficking etc. Iot is spreading from small to large applications in all fields like Smart Cities, Smart Grids, Smart Transportation. As on one side IoT provide facilities and services for the society. On the other hand, IoT security is also a crucial issues.IoT security is an area which totally concerned for giving security to connected devices and networks in the IoT .As, IoT is vast area with usability, performance, security, and reliability as a major challenges in it. The growth of the IoT is exponentially increases as driven by market pressures, which proportionally increases the security threats involved in IoT The relationship between the security and billions of devices connecting to the Internet cannot be described with existing mathematical methods. In this paper, we explore the opportunities possible in the IoT with security threats and challenges associated with it.
In today’s emerging world of Internet, each and every thing is supposed to be in connected mode with the help of billions of smart devices. By connecting all the devises used in our day to day life, make our life trouble less and easy. We are incorporated in a world where we are used to have smart phones, smart cars, smart gadgets, smart homes and smart cities. Different institutes and researchers are working for creating a smart world for us but real question which we need to emphasis on is how to make dumb devises talk with uncommon hardware and communication technology. For the same what kind of mechanism to use with various protocols and less human interaction. The purpose is to provide the key area for application of IoT and a platform on which various devices having different mechanism and protocols can communicate with an integrated architecture.
Study on Issues in Managing and Protecting Data of IOTijsrd.com
This paper discusses variety of issues for preserving and managing data produced by IoT. Every second large amount of data are added or updated in the IoT databases across the heterogeneous environment. While managing the data each phase of data processing for IoT data is exigent like storing data, querying, indexing, transaction management and failure handling. We also refer to the problem of data integration and protection as data requires to be fit in single layout and travel securely as they arrive in the pool from diversified sources in different structure. Finally, we confer a standardized pathway to manage and to defend data in consistent manner.
Interactive Technologies for Improving Quality of Education to Build Collabor...ijsrd.com
Today with advancement in Information Communication Technology (ICT) the way the education is being delivered is seeing a paradigm shift from boring classroom lectures to interactive applications such as 2-D and 3-D learning content, animations, live videos, response systems, interactive panels, education games, virtual laboratories and collaborative research (data gathering and analysis) etc. Engineering is emerging with more innovative solutions in the field of education and bringing out their innovative products to improve education delivery. The academic institutes which were once hesitant to use such technology are now looking forward to such innovations. They are adopting the new ways as they are realizing the vast benefits of using such methods and technology. The benefits are better comprehensibility, improved learning efficiency of students, and access to vast knowledge resources, geographical reach, quick feedback, accountability and quality research. This paper focuses on how engineering can leverage the latest technology and build a collaborative learning environment which can then be integrated with the national e-learning grid.
Internet of Things - Paradigm Shift of Future Internet Application for Specia...ijsrd.com
In the world more than 15% people are living with disability that also include children below age of 10 years. Due to lack of independent support services specially abled (handicap) people overly rely on other people for their basic needs, that excludes them from being financially and socially active. The Internet of Things (IoT) can give support system and a better quality of life as well as participation in routine and day to day life. For this purpose, the future solutions for current problems has been introduced in this paper. Daunting challenges have been considered as future research and glimpse of the IoT for specially abled person is given in the paper.
A Study of the Adverse Effects of IoT on Student's Lifeijsrd.com
Internet of things (IoT) is the most powerful invention and if used in the positive direction, internet can prove to be very productive. But, now a days, due to the social networking sites such as Face book, WhatsApp, twitter, hike etc. internet is producing adverse effects on the student life, especially those students studying at college Level. As it is rightly said, something which has some positive effects also has some of the negative effects on the other hand. In this article, we are discussing some adverse effects of IoT on student’s life.
Pedagogy for Effective use of ICT in English Language Learningijsrd.com
The use of information and communications technology (ICT) in education is a relatively new phenomenon and it has been the educational researchers' focus of attention for more than two decades. Educators and researchers examine the challenges of using ICT and think of new ways to integrate ICT into the curriculum. However, there are some barriers for the teachers that prevent them to use ICT in the classroom and develop supporting materials through ICT. The purpose of this study is to examine the high school English teachers’ perceptions of the factors discouraging teachers to use ICT in the classroom.
In recent years usage of private vehicles create urban traffic more and more crowded. As result traffic becomes one of the important problems in big cities in all over the world. Some of the traffic concerns are traffic jam and accidents which have caused a huge waste of time, more fuel consumption and more pollution. Time is very important parameter in routine life. The main problem faced by the people is real time routing. Our solution Virtual Eye will provide the current updates as in the real time scenario of the specific route. This research paper presents smart traffic navigation system, based on Internet of Things, which is featured by low cost, high compatibility, easy to upgrade, to replace traditional traffic management system and the proposed system can improve road traffic tremendously.
Ontological Model of Educational Programs in Computer Science (Bachelor and M...ijsrd.com
In this work there is illustrated an ontological model of educational programs in computer science for bachelor and master degrees in Computer science and for master educational program “Computer science as second competence†by Tempus project PROMIS.
Understanding IoT Management for Smart Refrigeratorijsrd.com
Lately the concept of Internet of Things (IoT) is being more elaborated and devices and databases are proposed thereby to meet the need of an Internet of Things scenario. IoT is being considered to be an integral part of smart house where devices will be connected to each other and also react upon certain environmental input. This will eventually include the home refrigerator, air conditioner, lights, heater and such other home appliances. Therefore, we focus our research on the database part for such an IoT’ fridge which we called as smart Fridge. We describe the potentials achievable through a database for an IoT refrigerator to manage the refrigerator food and also aid the creation of a monthly budget of the house for a family. The paper aims at the data management issue based on a proposed design for an intelligent refrigerator leveraging the sensor technology and the wireless communication technology. The refrigerator which identifies products by reading the barcodes or RFID tags is proposed to order the required products by connecting to the Internet. Thus the goal of this paper is to minimize human interaction to maintain the daily life events.
DESIGN AND ANALYSIS OF DOUBLE WISHBONE SUSPENSION SYSTEM USING FINITE ELEMENT...ijsrd.com
Double wishbone designs allow the engineer to carefully control the motion of the wheel throughout suspension travel. 3-D model of the Lower Wishbone Arm is prepared by using CAD software for modal and stress analysis. The forces and moments are used as the boundary conditions for finite element model of the wishbone arm. By using these boundary conditions static analysis is carried out. Then making the load as a function of time; quasi-static analysis of the wishbone arm is carried out. A finite element based optimization is used to optimize the design of lower wishbone arm. Topology optimization and material optimization techniques are used to optimize lower wishbone arm design.
A Review: Microwave Energy for materials processingijsrd.com
Microwave energy is a latest largest growing technique for material processing. This paper presents a review of microwave technologies used for material processing and its use for industrial applications. Advantages in using microwave energy for processing material include rapid heating, high heating efficiency, heating uniformity and clean energy. The microwave heating has various characteristics and due to which it has been become popular for heating low temperature applications to high temperature applications. In recent years this novel technique has been successfully utilized for the processing of metallic materials. Many researchers have reported microwave energy for sintering, joining and cladding of metallic materials. The aim of this paper is to show the use of microwave energy not only for non-metallic materials but also the metallic materials. The ability to process metals with microwave could assist in the manufacturing of high performance metal parts desired in many industries, for example in automotive and aeronautical industries.
Web Usage Mining: A Survey on User's Navigation Pattern from Web Logsijsrd.com
With an expontial growth of World Wide Web, there are so many information overloaded and it became hard to find out data according to need. Web usage mining is a part of web mining, which deal with automatic discovery of user navigation pattern from web log. This paper presents an overview of web mining and also provide navigation pattern from classification and clustering algorithm for web usage mining. Web usage mining contain three important task namely data preprocessing, pattern discovery and pattern analysis based on discovered pattern. And also contain the comparative study of web mining techniques.
APPLICATION OF STATCOM to IMPROVED DYNAMIC PERFORMANCE OF POWER SYSTEMijsrd.com
Application of FACTS controller called Static Synchronous Compensator STATCOM to improve the performance of power grid with Wind Farms is investigated .The essential feature of the STATCOM is that it has the ability to absorb or inject fastly the reactive power with power grid . Therefore the voltage regulation of the power grid with STATCOM FACTS device is achieved. Moreover restoring the stability of the power system having wind farm after occurring severe disturbance such as faults or wind farm mechanical power variation is obtained with STATCOM controller . The dynamic model of the power system having wind farm controlled by proposed STATCOM is developed . To validate the powerful of the STATCOM FACTS controller, the studied power system is simulated and subjected to different severe disturbances. The results prove the effectiveness of the proposed STATCOM controller in terms of fast damping the power system oscillations and restoring the power system stability.
Making model of dual axis solar tracking with Maximum Power Point Trackingijsrd.com
Now a days solar harvesting is more popular. As the popularity become higher the material quality and solar tracking methods are more improved. There are several factors affecting the solar system. Major influence on solar cell, intensity of source radiation and storage techniques The materials used in solar cell manufacturing limit the efficiency of solar cell. This makes it particularly difficult to make considerable improvements in the performance of the cell, and hence restricts the efficiency of the overall collection process. Therefore, the most attainable maximum power point tracking method of improving the performance of solar power collection is to increase the mean intensity of radiation received from the source used. The purposed of tracking system controls elevation and orientation angles of solar panels such that the panels always maintain perpendicular to the sunlight. The measured variables of our automatic system were compared with those of a fixed angle PV system. As a result of the experiment, the voltage generated by the proposed tracking system has an overall of about 28.11% more than the fixed angle PV system. There are three major approaches for maximizing power extraction in medium and large scale systems. They are sun tracking, maximum power point (MPP) tracking or both.
A REVIEW PAPER ON PERFORMANCE AND EMISSION TEST OF 4 STROKE DIESEL ENGINE USI...ijsrd.com
In day today's relevance, it is mandatory to device the usage of diesel in an economic way. In present scenario, the very low combustion efficiency of CI engine leads to poor performance of engine and produces emission due to incomplete combustion. Study of research papers is focused on the improvement in efficiency of the engine and reduction in emissions by adding ethanol in a diesel with different blends like 5%, 10%, 15%, 20%, 25% and 30% by volume. The performance and emission characteristics of the engine are tested observed using blended fuels and comparative assessment is done with the performance and emission characteristics of engine using pure diesel.
Study and Review on Various Current Comparatorsijsrd.com
This paper presents study and review on various current comparators. It also describes low voltage current comparator using flipped voltage follower (FVF) to obtain the single supply voltage. This circuit has short propagation delay and occupies a small chip area as compare to other current comparators. The results of this circuit has obtained using PSpice simulator for 0.18 μm CMOS technology and a comparison has been performed with its non FVF counterpart to contrast its effectiveness, simplicity, compactness and low power consumption.
Reducing Silicon Real Estate and Switching Activity Using Low Power Test Patt...ijsrd.com
Power dissipation is a challenging problem for today's system-on-chip design and test. This paper presents a novel architecture which generates the test patterns with reduced switching activities; it has the advantage of low test power and low hardware overhead. The proposed LP-TPG (test pattern generator) structure consists of modified low power linear feedback shift register (LP-LFSR), m-bit counter, gray counter, NOR-gate structure and XOR-array. The seed generated from LP-LFSR is EXCLUSIVE-OR ed with the data generated from gray code generator. The XOR result of the sequence is single input changing (SIC) sequence, in turn reduces the switching activity and so power dissipation will be very less. The proposed architecture is simulated using Modelsim and synthesized using Xilinx ISE9.2.The Xilinx chip scope tool will be used to test the logic running on FPGA.
Defending Reactive Jammers in WSN using a Trigger Identification Service.ijsrd.com
In the last decade, the greatest threat to the wireless sensor network has been Reactive Jamming Attack because it is difficult to be disclosed and defend as well as due to its mass destruction to legitimate sensor communications. As discussed above about the Reactive Jammers Nodes, a new scheme to deactivate them efficiently is by identifying all trigger nodes, where transmissions invoke the jammer nodes, which has been proposed and developed. Due to this identification mechanism, many existing reactive jamming defending schemes can be benefited. This Trigger Identification can also work as an application layer .In this paper, on one side we provide the several optimization problems to provide complete trigger identification service framework for unreliable wireless sensor networks and on the other side we also provide an improved algorithm with regard to two sophisticated jamming models, in order to enhance its robustness for various network scenarios.
Defending Reactive Jammers in WSN using a Trigger Identification Service.
Artificial Neural Network Based Graphical User Interface for Estimation of Fabrication Time in Rig Construction Project
1. IJSRD - International Journal for Scientific Research & Development| Vol. 1, Issue 4, 2013 | ISSN (online): 2321-0613
All rights reserved by www.ijsrd.com 1011
Abstract—This paper addresses the problem of estimation of
fabrication time in Rig construction projects through
application of Artificial Neural Network (ANNs) as this is
the most crucial activity for successful project management
planning. ANN is a non-linear, data driven, self adaptive
approach as opposed to the traditional model based
methods, also fast becoming popular in forecasting where
relationship between input and output is not known but vast
collection of data is available. Around 960 data regarding
fabrication activity has been collected from ABG Shipyard
Ltd., Dahej. 3 input parameters have been considered for
estimation of output as fabrication time. 11 Feed Forward
Back Propagation neural networks with different network
architectures were made. Network N10 was able to predict
the output with MSE 1.35337e-2. Coding was done for the
Graphical User Interface (GUI) so that the GUI runs,
simulates network N10, and displays the fabrication time
for different combination of inputs.
Keywords: Fabrication time, Artificial Neural Network,
Graphical User Interface, GUI coding.
I. INTRODUCTION
In the construction projects (ex. Rig building) it is crucial to
minimize risks in the project estimation phase. This is an
early project stage in which different resources are
estimated. One of the important estimations is also the
necessary number of fabrication/construction hours or days.
The estimation phase is commonly a human expert driven
(intuitive method) activity which is sensitive to the expert’s
bias (judgement / experience). This bias can lead to an
underestimation of project resources, when the estimator is
overconfident, or to over-estimation of project resources
when the estimator does not have sufficient confidence that
all aspects of the project can be properly covered. Both
scenarios, based on the expert’s estimation, have a negative
impact on the future business decisions. In case of
underestimation, the project will bring economic loss, and in
case of overestimation, it will most likely be assigned to a
competitive supplier. The estimator’s key competence is to
properly collect and evaluate all the information which is
significant for making the project estimation. The paradigm
lies in the fact that the estimator should spend minimal time
necessary on estimation activity.
One of the main obstacles in this process is to
accurately define the relationship between product
characteristics and the construction/fabrication time
necessary to manufacture the product. Earlier studies
(Zhang and Fu, 2009) showed that the scientific and
reasonable performance evaluation is advantageous to
promote the comprehensive management level of
engineering projects. At present the fields of academia and
engineering had been achieved some results on this issue.
Iranmanesh and Zarezadeh (2008) stated that the researchers
have done lot of research in the area of application of
Artificial Neural Network (ANN) in project success, project
evaluation, project cost forecasting. As there are a few
studies on application of ANN in project estimation, there is
a good opportunity to accurately forecast time of fabrication
in construction projects through applying ANN approach.
The main aim of this paper is to develop the model
that fits into intuitive method for estimating fabrication
time. Three input variables are considered as height of job,
max. plate thickness of job and inspection criteria of job.
Inspection criteria is dependent on the value of max. plate
thickness. From this input value output as productivity
factor is decided by the intuitive method. Fabrication time in
number of days is calculated by dividing quantity of job
(tonnes) by productivity factor. We have developed detailed
step by step neural network model of the expert driven
estimation approach. Through this model, an effort is made
to capture the experience of the data available, which can be
further used to predict new combination of inputs. We have
also developed a Graphical User Interface(GUI) so that the
ANN model can be used without any prior knowledge of
ANN.
II. METHODOLOGY
A. Data
To train the ANN, 960 readings from already completed
jobs were collected from ABG Shipyard Ltd., Dahej,
Bharuch. Out of them 15 sample readings are shown below
(see Table I)
Sr
.
N
o.
Activity/
Job
Hei
ght
(m )
Max
Thick
ness
Of
Plates
(mm )
Inspec
tion
Criteri
a
(%)
Producti
vity
Factor
Quan
tity
Of
Activ
ity
(Ton
nes )
Durat
ion
(
Days
)
1 BOW 8.20 16.00
20.00
%
0.8
108.5
0
136
2 CS1 5.00 63.00
100.00
%
0.5
147.1
8
294
3 CS2P 8.00 80.00
100.00
%
0.5 50.65 101
4 CS3S 7.50 75.00
100.00
%
0.5 71.16 142
5 MD1 1.50 25.40
50.00
%
1 33.22 33
6 LB2P 2.00 22.50
50.00
%
1 26.65 27
7 LB2S 1.00 22.50
50.00
%
1 24.90 25
8 CS7 8.00 29.00
50.00
%
0.5 58.39 117
Artificial Neural Network Based Graphical User Interface for Estimation of
Fabrication Time in Rig Construction Project
D. H. Patel1
A. H. Makwana2
A. A. Mehta3
1, 2
Department of Mechanical Engineering, Government Engineering College, Dahod, India.
3
Department of Civil Engineering, Bhagwan Mahavir College of Engg. & Tech., Surat, India.
2. Artificial Neural Network Based Graphical User Interface for Estimation of Fabrication Time in Rig Construction Project
(IJSRD/Vol. 1/Issue 4/ 2013/0047)
All rights reserved by www.ijsrd.com 1012
9
SPUDCA
NFW
6.00 57.00
100.00
%
0.8
346.7
6
495
10 CSG2P 2.00 89.00
100.00
%
0.2 1.66 8
11 CSG3P 3.30 57.00
100.00
%
0.2 10.52 53
12 CSG4P 2.00 89.00
100.00
%
0.2 1.76 9
13 CSG5P 1.50 32.00
50.00
%
0.2 4.41 22
14 LQ5
10.0
0
19.00
20.00
%
0.8 90.62 113
15 LQ6 6.50 25.40
50.00
%
0.8 54.38 68
Min.(all 960
jobs)
1.00 6.00 20% 0.2 0.50
-
Max.(all 960
jobs)
30.0
0
90.00 100% 1 350
Table. 1: Data on 15 samples of activities from ABG
Shipyard Ltd., Dahej, Bhrauch
B. Application of Artificial Neural Network
Earlier studies (Jha) stated that ANNs are non-linear data
driven self adaptive approach as opposed to the traditional
model based methods. ANN is one of the branches of
Artificial Intelligence (AI). ANNs are powerful tools for
modelling, especially when the underlying data relationship
is unknown. ANNs can identify and learn correlated patterns
between input data sets and corresponding target values.
After training, ANNs can be used to predict the outcome of
new independent input data. ANNs imitate the learning
process of the human brain and can process problems
involving non-linear and complex data even if the data are
imprecise and noisy.
Neural network fitting tool (nftool-MATLAB
(7.8.0) R2009a) was used for creating the network. The
multilayer Feed forward back-propagation neural networks
were selected for the modelling as it’s the most common and
suitable for this study. Different parameters were carefully
selected to achieve the best performance.
11 different Neural Networks (N1 to N11) with
different neural network architectures were created. All the
networks consist of three layers of neurons with three
neurons for three inputs in input layer and one neuron for
one output in the output layer. The number of neurons in the
hidden layer varies in different neural networks (Fig. 1).
Fig. 1: Structure of the neural network
1) Transfer function
Each neuron has its own transfer function ƒ, which produces
the output a of that neuron based on the net input n from the
previous layer.
a = ƒ(n) …(i)
where, n = Σ (piwi +bi) …(ii)
p= scalar input
w=weight
b=bias
i = 1 to N,
N = Number of inputs.
Tan-Sigmoid & Pure-linear transfer functions were used as
an activation or transfer function. Tan-Sigmoid squashes the
output between -1 to 1 by using equation
ƒ (n) = [2 / (1 + e-2n
)] – 1 ...(iii)
and Pure-linear uses the equation
ƒ (n) = n ...(iv)
2) Training
All the networks were trained using Levenberg-Marquardt
back propagation algorithm which uses the following
equation to update weight and bias of the network:
...(v)
Where, xi = matrix of all weights and bias in ith
training
cycle
J = Matrix that contains first derivatives of the
network errors with respect to the weights and biases
μ = scalar value (μ α performance function)
I = Identity Matrix
All the network networks were trained for 1000 epochs
(training cycle). The initial value for µ was taken as 0.001. µ
is multiplied by µ decrease whenever a step would reduce
the performance function and multiplied by µ increase
whenever a step would increase the performance function.
3) Weights and Bias
Each neuron has its bias b and weights (w) equal to neurons
in the previous layer. Initial weights and bias were selected
randomly by the software. After the training, all the weights
and bias were saved for all the networks.
4) Performance function
At the end of each cycle, the performance of the network is
calculated by its performance function. Mean Squared Error
(MSE) was taken as the performance which uses the
following equation.
∑ ...(vi)
Where,
Error, e = actual output - network output
Q = no. of readings used for training = 672
5) Testing and Comparison
After 1000 training cycles, All the networks were simulated
with training set of 672 (70%) readings and unknown
validation and testing set of 144 (15%) and 144 (15%)
reading respectively. Comparison of networks was made on
the bases of MSE and Regression values given by MATLAB
for training and testing dataset (see Table II).
Network set
Network
Architecture
Input-hidden-output
(Transfer Functions)
MSE
Regression
value
N1
train
1
( t ) ( l )
2.62710e-
2
7.81577e-1
test
2.88462e-
2
7.82512e-1
N2 train
1 1
( t ) ( l )
2.68554e-
2
7.79208e-1
3. Artificial Neural Network Based Graphical User Interface for Estimation of Fabrication Time in Rig Construction Project
(IJSRD/Vol. 1/Issue 4/ 2013/0047)
All rights reserved by www.ijsrd.com 1013
test
2.24624e-
2
8.18538e-1
N3
train
1 1
( t ) ( l )
2.48288e-
2
8.07704e-1
test
2.62807e-
2
7.67928e-1
N4
train
2 1
( t ) ( l )
2.21643e-
2
8.26944e-1
test
3.05855e-
2
7.52987e-1
N5
train
2 1
( t ) ( l )
2.43133e-
2
8.07415e-1
test
2.55221e-
2
8.02421e-1
N6
train
1
( t ) ( l )
1.94257e-
2
8.46247e-1
test
3.57585e-
2
7.13734e-1
N7
train
1
( t ) ( l )
2.16734e-
2
8.31115e-1
test
2.24649e-
2
8.15882e-1
N8
train
1
( t ) ( l )
1.76361e-
2
8.65306e-1
test
2.05272e-
2
8.38827e-1
N9
train
1
( t ) ( l )
1.81221e-
2
8.57222e-1
test
2.56533e-
2
7.98515e-1
N10
train
( t ) ( l )
1.35337e-
2
9.0077e-1
test
1.95865e-
2
8.53534e-1
N11
train
1
( t ) ( l )
1.58471e-
2
8.78835e-1
test
2.53753e-
2
7.98257e-1
Table. 2: Performance comparison of 11 different network
after 1000 training cycles
t = tan-sigmoid
l = pure linear
C. Graphical User Interface Development
The GUI was developed using GUIDE (MATLAB's
Graphical User Interface Development Environment).
GUIDE stores GUIs in two files: MATLAB Figure file
(ANN.fig) and MATLAB M file (ANN.m), which are
generated when the GUI is saved or run for the first time.
1) GUI layout
GUI figure file (MATLAB figure file) contains the GUI
figure layout and the components of the GUI. There are 3
inputs and 1 output. For each input, three objects were
created : Static Text, Slider and Edit Text. Static text object
contains label of the input (Fig. 2).
Fig. 2: Objects in GUI
A desired value of an input can be entered in Edit text
object. Slider object can also be used to change the value
with slider. Slider and edit text objects have their value
range. For each slider and edit text, Min and Max value were
selected based on the training dataset. Inspection criteria is
dependent on max. plate thickness of the job so its value is
directly displayed by the tool depending upon the value of
max. plate thickness.
D. GUI Coding
GUI Code file (MATLAB M-file) contains the code that
controls the GU, including the call-backs for its components.
This is referred as GUI M-file. Complete coding was done in
such a manner that the GUI will not run the network N10
and display an error message if the inputs are not in range. A
call-back was assign to analyze the push button. If any of the
input parameters is not in range, an error message will be
displayed, otherwise the call-back will display the final
output as the number of days by simulating the network.
III. RESULTS
Among the 11 different network, network N10 with
architecture 3-50-1 was selected on the bases of least MSE
value (see Table II) 1.35337e-2. The performance graph of
network N10 with respect to the epochs (training cycle) was
generated. Epochs were shown on X-axis and network’s
performance (MSE) was shown on the Y-axis. The best
validation performance was 0.021178 at 93 epoch (Fig. 3).
Fig. 3 : Performance graphs of the network N10
IV. CONCLUSION
In this paper a neural network approach to estimate time of
fabrication in Rig construction project was studied.
Multilayer feed forward back propagation network can be
implemented successfully to estimate number of days of the
fabrication activity. The result show least errors (see Table
II) in 960 sample activities are acceptable and it can be
hopeful for researcher to applying this method with new
hypothesis on network structure and product characteristics.
It is notable that this study is an introductory study and can
be extended in various areas such as changing NN structure,
input variables, variable range and so on. The GUI
developed for the model would proved to be efficient to be
used by the end user without prior knowledge of ANN.
However, this research has some limitations. First,
the fabrication activity dataset is limited to one industry
4. Artificial Neural Network Based Graphical User Interface for Estimation of Fabrication Time in Rig Construction Project
(IJSRD/Vol. 1/Issue 4/ 2013/0047)
All rights reserved by www.ijsrd.com 1014
ABG Shipyard only. Therefore it is necessary to collect
more sample datasets from various-sized industries. Second,
the variables for estimating fabrication time are used with
restricted project inputs i.e. height, max. plate thickness and
inspection criteria of job that too within given range (see
Table I). Therefore, it is necessary to conduct more studies
using more input parameters and a wide range of each input
parameters. Third, GUI can be run by MATLAB's GUIDE
tool only. An independent software tool can be developed
which can run the network and the GUI which does not need
MATLAB to run the network.
REFERENCES
[1] Anish Kumar K.N, 'ENGINEERING,
PROCUREMENT AND CONSTRUCTION OF
JU2000A JACK-UP DRILLING RIG', ABG Shipyard
Ltd. Offshore Division training, 27th June 2008.
[2] Seyed Hossein Iranmanesh & Mansoureh Zarezadeh,
'Application of ANN to forecast actual cost of a project
to improve earned value management system', World
academy of science, engineering & technology, 2008.
[3] Jha Girish Kumar, 'Artificial Neural Networks and its
applications', I.A.R.I. New Delhi.
[4] Mehta A. A., Dr. Desai A. K., 'Artificial Neural
Network based graphic user interface for steel fibre
reinforced concrete flexural member', SVNIT, Surat,
2009.
[5] Qinghua Zhang and Qiang Fu, 'Performance evaluation
model of engineering project management based on
improved wavelet NN", J. Serv. & Management
(scientific research publishing) 2009.
[6] Karl Kuzman and Blaz Florjanic, 'Estimation of time for
manufacturing of injection moulds using ANN based
model', 2012.
[7] Chaudhari V R., 'Application of artificial neural
network in manufacturing in prediction of process
parameters and comparison with regression analysis',
Department of mech. Engineering, SVNIT, 2011.
[8] Zhigang Ji & Yajing Li, 'The application of RBE NN on
construction cost forecasting', Second international
workshop on knowledge discovery & data mining,
2009.
[9] Ismaail Elsawy, Hossain Hosny & Mohammed Abdel
Razek, 'A neural network model for construction
projects site overhead cost estimating in Egypt', IJCSI
international journal of computer science issue, vol. 8,
issue 3, no.1,2011.
[10]Seyed Hossein Iranmanesh & Mansoureh Zarezadeh,
'Application of ANN to forecast actual cost of a project
to improve earned value management system', World
academy of science, engineering & technology, 2008.
[11]Gwang-Hee Kim, Sung-Hoon An & Kyung-In Kang,
'Comparison of construction cost estimating model
based on regression analysis, NN & case-based
reasoning', Building and environment (science direct)
2004.
[12]Jing Xu & Jianguo Chen, 'Application study on the
performance evaluation for engineering project
management based on BP NN', International forum on
information technology and applications, 2009.
[13]Yu-Ren Wang & Chun-Yin Yu, 'Predicting project
success using ANN-Ensemble classification models',
IEEE,2011.
[14]Se Hun Lim & Kyungdoo Nam, 'ANN modelling in
forecasting successful implementation of ERP system',
International journal of computational intelligence
research, 2006.
[15]Qinghua Zhang & Qiang Fu, 'Performance evaluation
model of engineering project management based on
improved wavelet NN', J. Serv. & Management
(scientific research publishing), 2009.
[16]Ivo M.L. Ferreira & Paulo J. S. Gil, 'Application and
performance analysis of NN for decision support in
conceptual design', Experts systems with applications,
2012.
[17]Olanrewaju O.A., Jimoh A.A & Kholopane P.A.,
'Comparison between regression analysis and ANN in
project selection", IEEE, 2011.
[18]Krishnamoorthy C.S., Rajeev S., 'Artificial Intelligence
and Expert Systems for Engineers', CRC Press LLC
ISBN: 0849391253 Pub Date:08/01/96.