A Mixed Binary-Real NSGA II Algorithm Ensuring Both Accuracy and Interpretabi...IJECEIAES
In this work, a Neuro-Fuzzy Controller network, called NFC that implements a Mamdani fuzzy inference system is proposed. This network includes neurons able to perform fundamental fuzzy operations. Connections between neurons are weighted through binary and real weights. Then a mixed binaryreal Non dominated Sorting Genetic Algorithm II (NSGA II) is used to perform both accuracy and interpretability of the NFC by minimizing two objective functions; one objective relates to the number of rules, for compactness, while the second is the mean square error, for accuracy. In order to preserve interpretability of fuzzy rules during the optimization process, some constraints are imposed. The approach is tested on two control examples: a single input single output (SISO) system and a multivariable (MIMO) system.
Low Power 32×32 bit Multiplier Architecture based on Vedic Mathematics Using ...VIT-AP University
In this paper the most significant aspect of the proposed method is that, the developed multiplier architecture is based on vertical and crosswise structure of Ancient Indian Vedic Mathematics. As per this proposed architecture, for two 32-bit numbers; the multiplier and multiplicand, each are grouped as 16-bit numbers so that it decomposes into 16×16 multiplication modules. It is also illustrated that the further hierarchical decomposition of 8×8 modules into 4×4 modules and then 2×2 modules will have a significant VHDL coding of for 32x32 bits multiplication and their used FPGA family Virtex 7 low power implementation by Xilinx Synthesis 16.1 tool done. The synthesis results show that the computation time for calculating the product of 32x32 bits is delay 29.256 ns. (11.499ns logic, 11.994ns route) (48.9% logic, 51.1% route).
Dynamic Memory Allocation, Pointers and Functions, Pointers and StructuresSelvaraj Seerangan
After go through this ppt the learners could be able to know the c programming concepts like dynamic memory allocation, pointers and functions and pointers to structures with examples.
A Mixed Binary-Real NSGA II Algorithm Ensuring Both Accuracy and Interpretabi...IJECEIAES
In this work, a Neuro-Fuzzy Controller network, called NFC that implements a Mamdani fuzzy inference system is proposed. This network includes neurons able to perform fundamental fuzzy operations. Connections between neurons are weighted through binary and real weights. Then a mixed binaryreal Non dominated Sorting Genetic Algorithm II (NSGA II) is used to perform both accuracy and interpretability of the NFC by minimizing two objective functions; one objective relates to the number of rules, for compactness, while the second is the mean square error, for accuracy. In order to preserve interpretability of fuzzy rules during the optimization process, some constraints are imposed. The approach is tested on two control examples: a single input single output (SISO) system and a multivariable (MIMO) system.
Low Power 32×32 bit Multiplier Architecture based on Vedic Mathematics Using ...VIT-AP University
In this paper the most significant aspect of the proposed method is that, the developed multiplier architecture is based on vertical and crosswise structure of Ancient Indian Vedic Mathematics. As per this proposed architecture, for two 32-bit numbers; the multiplier and multiplicand, each are grouped as 16-bit numbers so that it decomposes into 16×16 multiplication modules. It is also illustrated that the further hierarchical decomposition of 8×8 modules into 4×4 modules and then 2×2 modules will have a significant VHDL coding of for 32x32 bits multiplication and their used FPGA family Virtex 7 low power implementation by Xilinx Synthesis 16.1 tool done. The synthesis results show that the computation time for calculating the product of 32x32 bits is delay 29.256 ns. (11.499ns logic, 11.994ns route) (48.9% logic, 51.1% route).
Dynamic Memory Allocation, Pointers and Functions, Pointers and StructuresSelvaraj Seerangan
After go through this ppt the learners could be able to know the c programming concepts like dynamic memory allocation, pointers and functions and pointers to structures with examples.
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.
The importance of cryptography knuckle down to the security in electronic data transmissions has gained an essential relevance during past years. Cryptography security mechanisms uses some algorithms to muddle the data into unreadable text with a key which can only be decoded/decrypted by one who has that associated key for the locked data. Cryptography techniques are of two types: Symmetric & Asymmetric. In this paper we’ve used symmetric cryptography method-Advance Encryption Standard algorithm with 200 bit block size as well as 200 bit key size. We’ve used 5*5 matrix to implement same 128 bit conventional AES algorithm for 200 bit block size. After implementing the algorithm, the proposed work is compared with 128,192 & 256 bits AES techniques in context with Encryption and Decryption Time & Throughput at both Encryption and Decryption ends.
DESIGN AND IMPLEMENTATION OF BINARY NEURAL NETWORK LEARNING WITH FUZZY CLUSTE...cscpconf
In this paper, Design and Implementation of Binary Neural Network Learning with Fuzzy
Clustering (DIBNNFC), is proposed to classify semisupervised data, it is based on the
concept of binary neural network and geometrical expansion. Parameters are updated
according to the geometrical location of the training samples in the input space, and each
sample in the training set is learned only once. It’s a semisupervised based approach, the
training samples are semi-labelled i.e. for some samples, labels are known and for some
samples data labels are not known. The method starts with classification, which is done by
using the concept of ETL algorithm. In classification process various classes are formed.
These classes classify samples in to two classes after that considers each class as a region and calculates the average of the entire region separately. This average is centres of the region which is used for the purpose of clustering by using FCM algorithm. Once clustering process over labelling of semi supervised data is done, then whole samples would be classify by (DIBNNFC). The method proposes here is exhaustively tested with different benchmark datasets and it is found that, on increasing value of training parameters number of hidden neurons and training time both are getting decrease. The result reported, using real character recognition data set and result will compare with existing semi-supervised classifier, the proposed approach learned with semi-supervised leads to higher classification accuracy.
Implementation of an arithmetic logic using area efficient carry lookahead adderVLSICS Design
An arithmetic logic unit acts as the basic building blocks or cell of a central processing unit of a computer.
And it is a digital circuit comprised of the basic electronics components, which is used to perform various
function of arithmetic and logic and integral operations further the purpose of this work is to propose the
design of an 8-bit ALU which supports 4-bit multiplication. Thus, the functionalities of the ALU in this
study consist of following main functions like addition also subtraction, increment, decrement, AND, OR,
NOT, XOR, NOR also two complement generation Multiplication. And the functions with the adder in the
airthemetic logic unit are implemented using a Carry Look Ahead adder joined by a ripple carry approach.
The design of the following multiplier is achieved using the Booths Algorithm therefore the proposed ALU
can be designed by using verilog or VHDL and can also be designed on Cadence Virtuoso platform.
A comparative study of three validities computation methods for multimodel ap...IJECEIAES
The multimodel approach offers a very satisfactory results in modelling, diagnose and control of complex systems. In the modelling case, this approach passes by three steps: the determination of the model’s library, the validities computation and the establishment of the final model. In this context, this paper focuses on the elaboration of a comparative study between three recent methods of validities computation. Thus, it highlight the method that offers the best performances in term of precision. To achieve this goal, we apply, these three methods on two simulation examples in order to compare their performances.
The Positive Effects of Fuzzy C-Means Clustering on Supervised Learning Class...Waqas Tariq
Selection of inputs is one of the most substantial components of classification algorithms for data mining and pattern recognition problems since even the best classifier will perform badly if the inputs are not selected very well. Big data and computational complexity are main cause of bad performance and low accuracy for classical classifiers. In other words, the complexity of classifier method is inversely proportional with its classification efficiency. For this purpose, two hybrid classifiers have been developed by using both type-1 and type-2 fuzzy c-means clustering with cascaded a classifier. In this proposed classifier, a large number of data points are reduced by using fuzzy c-means clustering before applied to a classifier algorithm as inputs. The aim of this study is to investigate the effect of fuzzy clustering on well-known and useful classifiers such as artificial neural networks (ANN) and support vector machines (SVM). Then the role of positive effects of these proposed algorithms were investigated on applied different data sets.
You will learn about linear data structures like stack and Queue. and also implementation by using array and linked list. at last, we will discuss the various applications of stack and queue and other varients of queues.
Application Of Local Search Methods For Solving A Quadratic Assignment Probl...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/application-of-local-search-methods-for-solving-a-quadratic-assignment-problem-a-case-study/
This paper discusses the design and application of local search methods to a real-life application at a steel cord manufacturing plant. The case study involves a layout problem that can be represented as a Quadratic Assignment Problem (QAP). Due to the nature of the manufacturing process, certain machinery need to be allocated in close proximity to each other. This issue is incorporated into the objective function through assigning high penalty costs to the unfavorable allocations. QAP belongs to one of the most difficult class of combinatorial optimization problems, and is not solvable to optimality as the number of facilities increases. We implement the well-known local search methods, 2-opt, 3-opt and tabu search. We compare the solution performances of the methods to the results obtained from the NEOS server, which provides free access to many optimization solvers on the internet.
Interior Dual Optimization Software Engineering with Applications in BCS Elec...BRNSS Publication Hub
Interior optimization software and algorithms programming methods provide a computational tool with
a number of applications. Theory and computational demonstrations/techniques were primarily shown
in previous articles. The mathematical framework of this new method, (Casesnoves, 2018–2020), was
also proven.The links among interior optimization, graphical optimization (Casesnoves, 2016–7), and
classical methods in non-linear equations systems were developed. This paper is focused on software
engineering with mathematical methods implementation in programming as a primary subject. The
specific details for interior optimization computational adaptation on every specific problem, such as
engineering, physics, and electronics physics, are explained. Second subject is electronics applications
of software in the field of superconductors. It comprises a series of new BCS equation optimization
for Type I superconductors, based on previous research for other different Type I superconductors
previously published. A new dual optimization for two superconductors is simulated. Results are
acceptable with low errors and imaging demonstrations of the interior optimization utility.
Investigations on Hybrid Learning in ANFISIJERA Editor
Neural networks have attractiveness to several researchers due to their great closeness to the structure of the brain, their characteristics not shared by many traditional systems. An Artificial Neural Network (ANN) is a network of interconnected artificial processing elements (called neurons) that co-operate with one another in order to solve specific issues. ANNs are inspired by the structure and functional aspects of biological nervous systems. Neural networks, which recognize patterns and adopt themselves to cope with changing environments. Fuzzy inference system incorporates human knowledge and performs inferencing and decision making. The integration of these two complementary approaches together with certain derivative free optimization techniques, results in a novel discipline called Neuro Fuzzy. In Neuro fuzzy development a specific approach is called Adaptive Neuro Fuzzy Inference System (ANFIS), which has shown significant results in modeling nonlinear functions. The basic idea behind the paper is to design a system that uses a fuzzy system to represent knowledge in an interpretable manner and have the learning ability derived from a Runge-Kutta learning method (RKLM) to adjust its membership functions and parameters in order to enhance the system performance. The problem of finding appropriate membership functions and fuzzy rules is often a tiring process of trial and error. It requires users to understand the data before training, which is usually difficult to achieve when the database is relatively large. To overcome these problems, a hybrid of Back Propagation Neural network (BPN) and RKLM can combine the advantages of two systems and avoid their disadvantages.
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.
Abstract: Image processing refers to a type of signal processing where the input is an image and output is an image or some of the characteristics of the image such as objects in image, contrast and many more. Edge Detection is considered as one of the most important process in the field of image processing. The existing edge detection algorithms like sobel, prewitt, canny, etc have various limitations. These limitations are overcome using a technique like fuzzy logic. This paper discusses about use of fuzzy logic for edge detection along with some other edge detection techniques incorporated as input the fuzzy system and provides an algorithm for the same.. The paper provides a comparison of the algorithm with varied inputs for real image.
Performance Analysis of Various Activation Functions in Generalized MLP Archi...Waqas Tariq
The activation function used to transform the activation level of a unit (neuron) into an output signal. There are a number of common activation functions in use with artificial neural networks (ANN). The most common choice of activation functions for multi layered perceptron (MLP) is used as transfer functions in research and engineering. Among the reasons for this popularity are its boundedness in the unit interval, the function’s and its derivative’s fast computability, and a number of amenable mathematical properties in the realm of approximation theory. However, considering the huge variety of problem domains MLP is applied in, it is intriguing to suspect that specific problems call for single or a set of specific activation functions. The aim of this study is to analyze the performance of generalized MLP architectures which has back-propagation algorithm using various different activation functions for the neurons of hidden and output layers. For experimental comparisons, Bi-polar sigmoid, Uni-polar sigmoid, Tanh, Conic Section, and Radial Bases Function (RBF) were used.
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.
The importance of cryptography knuckle down to the security in electronic data transmissions has gained an essential relevance during past years. Cryptography security mechanisms uses some algorithms to muddle the data into unreadable text with a key which can only be decoded/decrypted by one who has that associated key for the locked data. Cryptography techniques are of two types: Symmetric & Asymmetric. In this paper we’ve used symmetric cryptography method-Advance Encryption Standard algorithm with 200 bit block size as well as 200 bit key size. We’ve used 5*5 matrix to implement same 128 bit conventional AES algorithm for 200 bit block size. After implementing the algorithm, the proposed work is compared with 128,192 & 256 bits AES techniques in context with Encryption and Decryption Time & Throughput at both Encryption and Decryption ends.
DESIGN AND IMPLEMENTATION OF BINARY NEURAL NETWORK LEARNING WITH FUZZY CLUSTE...cscpconf
In this paper, Design and Implementation of Binary Neural Network Learning with Fuzzy
Clustering (DIBNNFC), is proposed to classify semisupervised data, it is based on the
concept of binary neural network and geometrical expansion. Parameters are updated
according to the geometrical location of the training samples in the input space, and each
sample in the training set is learned only once. It’s a semisupervised based approach, the
training samples are semi-labelled i.e. for some samples, labels are known and for some
samples data labels are not known. The method starts with classification, which is done by
using the concept of ETL algorithm. In classification process various classes are formed.
These classes classify samples in to two classes after that considers each class as a region and calculates the average of the entire region separately. This average is centres of the region which is used for the purpose of clustering by using FCM algorithm. Once clustering process over labelling of semi supervised data is done, then whole samples would be classify by (DIBNNFC). The method proposes here is exhaustively tested with different benchmark datasets and it is found that, on increasing value of training parameters number of hidden neurons and training time both are getting decrease. The result reported, using real character recognition data set and result will compare with existing semi-supervised classifier, the proposed approach learned with semi-supervised leads to higher classification accuracy.
Implementation of an arithmetic logic using area efficient carry lookahead adderVLSICS Design
An arithmetic logic unit acts as the basic building blocks or cell of a central processing unit of a computer.
And it is a digital circuit comprised of the basic electronics components, which is used to perform various
function of arithmetic and logic and integral operations further the purpose of this work is to propose the
design of an 8-bit ALU which supports 4-bit multiplication. Thus, the functionalities of the ALU in this
study consist of following main functions like addition also subtraction, increment, decrement, AND, OR,
NOT, XOR, NOR also two complement generation Multiplication. And the functions with the adder in the
airthemetic logic unit are implemented using a Carry Look Ahead adder joined by a ripple carry approach.
The design of the following multiplier is achieved using the Booths Algorithm therefore the proposed ALU
can be designed by using verilog or VHDL and can also be designed on Cadence Virtuoso platform.
A comparative study of three validities computation methods for multimodel ap...IJECEIAES
The multimodel approach offers a very satisfactory results in modelling, diagnose and control of complex systems. In the modelling case, this approach passes by three steps: the determination of the model’s library, the validities computation and the establishment of the final model. In this context, this paper focuses on the elaboration of a comparative study between three recent methods of validities computation. Thus, it highlight the method that offers the best performances in term of precision. To achieve this goal, we apply, these three methods on two simulation examples in order to compare their performances.
The Positive Effects of Fuzzy C-Means Clustering on Supervised Learning Class...Waqas Tariq
Selection of inputs is one of the most substantial components of classification algorithms for data mining and pattern recognition problems since even the best classifier will perform badly if the inputs are not selected very well. Big data and computational complexity are main cause of bad performance and low accuracy for classical classifiers. In other words, the complexity of classifier method is inversely proportional with its classification efficiency. For this purpose, two hybrid classifiers have been developed by using both type-1 and type-2 fuzzy c-means clustering with cascaded a classifier. In this proposed classifier, a large number of data points are reduced by using fuzzy c-means clustering before applied to a classifier algorithm as inputs. The aim of this study is to investigate the effect of fuzzy clustering on well-known and useful classifiers such as artificial neural networks (ANN) and support vector machines (SVM). Then the role of positive effects of these proposed algorithms were investigated on applied different data sets.
You will learn about linear data structures like stack and Queue. and also implementation by using array and linked list. at last, we will discuss the various applications of stack and queue and other varients of queues.
Application Of Local Search Methods For Solving A Quadratic Assignment Probl...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/application-of-local-search-methods-for-solving-a-quadratic-assignment-problem-a-case-study/
This paper discusses the design and application of local search methods to a real-life application at a steel cord manufacturing plant. The case study involves a layout problem that can be represented as a Quadratic Assignment Problem (QAP). Due to the nature of the manufacturing process, certain machinery need to be allocated in close proximity to each other. This issue is incorporated into the objective function through assigning high penalty costs to the unfavorable allocations. QAP belongs to one of the most difficult class of combinatorial optimization problems, and is not solvable to optimality as the number of facilities increases. We implement the well-known local search methods, 2-opt, 3-opt and tabu search. We compare the solution performances of the methods to the results obtained from the NEOS server, which provides free access to many optimization solvers on the internet.
Interior Dual Optimization Software Engineering with Applications in BCS Elec...BRNSS Publication Hub
Interior optimization software and algorithms programming methods provide a computational tool with
a number of applications. Theory and computational demonstrations/techniques were primarily shown
in previous articles. The mathematical framework of this new method, (Casesnoves, 2018–2020), was
also proven.The links among interior optimization, graphical optimization (Casesnoves, 2016–7), and
classical methods in non-linear equations systems were developed. This paper is focused on software
engineering with mathematical methods implementation in programming as a primary subject. The
specific details for interior optimization computational adaptation on every specific problem, such as
engineering, physics, and electronics physics, are explained. Second subject is electronics applications
of software in the field of superconductors. It comprises a series of new BCS equation optimization
for Type I superconductors, based on previous research for other different Type I superconductors
previously published. A new dual optimization for two superconductors is simulated. Results are
acceptable with low errors and imaging demonstrations of the interior optimization utility.
Investigations on Hybrid Learning in ANFISIJERA Editor
Neural networks have attractiveness to several researchers due to their great closeness to the structure of the brain, their characteristics not shared by many traditional systems. An Artificial Neural Network (ANN) is a network of interconnected artificial processing elements (called neurons) that co-operate with one another in order to solve specific issues. ANNs are inspired by the structure and functional aspects of biological nervous systems. Neural networks, which recognize patterns and adopt themselves to cope with changing environments. Fuzzy inference system incorporates human knowledge and performs inferencing and decision making. The integration of these two complementary approaches together with certain derivative free optimization techniques, results in a novel discipline called Neuro Fuzzy. In Neuro fuzzy development a specific approach is called Adaptive Neuro Fuzzy Inference System (ANFIS), which has shown significant results in modeling nonlinear functions. The basic idea behind the paper is to design a system that uses a fuzzy system to represent knowledge in an interpretable manner and have the learning ability derived from a Runge-Kutta learning method (RKLM) to adjust its membership functions and parameters in order to enhance the system performance. The problem of finding appropriate membership functions and fuzzy rules is often a tiring process of trial and error. It requires users to understand the data before training, which is usually difficult to achieve when the database is relatively large. To overcome these problems, a hybrid of Back Propagation Neural network (BPN) and RKLM can combine the advantages of two systems and avoid their disadvantages.
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.
Abstract: Image processing refers to a type of signal processing where the input is an image and output is an image or some of the characteristics of the image such as objects in image, contrast and many more. Edge Detection is considered as one of the most important process in the field of image processing. The existing edge detection algorithms like sobel, prewitt, canny, etc have various limitations. These limitations are overcome using a technique like fuzzy logic. This paper discusses about use of fuzzy logic for edge detection along with some other edge detection techniques incorporated as input the fuzzy system and provides an algorithm for the same.. The paper provides a comparison of the algorithm with varied inputs for real image.
Performance Analysis of Various Activation Functions in Generalized MLP Archi...Waqas Tariq
The activation function used to transform the activation level of a unit (neuron) into an output signal. There are a number of common activation functions in use with artificial neural networks (ANN). The most common choice of activation functions for multi layered perceptron (MLP) is used as transfer functions in research and engineering. Among the reasons for this popularity are its boundedness in the unit interval, the function’s and its derivative’s fast computability, and a number of amenable mathematical properties in the realm of approximation theory. However, considering the huge variety of problem domains MLP is applied in, it is intriguing to suspect that specific problems call for single or a set of specific activation functions. The aim of this study is to analyze the performance of generalized MLP architectures which has back-propagation algorithm using various different activation functions for the neurons of hidden and output layers. For experimental comparisons, Bi-polar sigmoid, Uni-polar sigmoid, Tanh, Conic Section, and Radial Bases Function (RBF) were used.
Although fuzzy systems demonstrate their ability to
solve different kinds of problems in various applications, there is an increasing interest on developing solid mathematical implementations suitable for control applications such as that used in fuzzy logic controllers (FLC). It is well known that, wide range of parameters is needed to be specified before the construction of a fuzzy system. To simplify in a systematic way the design and construction of a general fuzzy system, and without loss for generality a full parameterization process for a singleton type FLC is proposed in this paper. The resented methodology is very helpful in developing a universal computing algorithm for a standard fuzzy like PID controllers. An illustrative example shows the simplicity of applying the new paradigm.
ALGORITHMIC AND ARCHITECTURAL OPTIMIZATION OF A 3D RECONSTRUCTION MEDICAL IMA...IJCSEIT Journal
This paper presents an optimization of an FPGA circuit implementation of 3D reconstruction algorithm of
medicals images. It is based on an algorithmic specification in the shape of a Factorized and Conditioned
Data Dependences Graph (GFCDD). An automatic and optimized implementation of the algorithm of «
Marching Cubes » has been carried out. The repetitive property of the algorithm has been exploited, as
much as possible, by means of the methodology “Adequacy Algorithm Structures”.
This paper is focused on developing a platform that
helps researchers to create verify and implement their
machine learning algorithms to a humanoid robot in real
environment. The presented platform is durable, easy to fix,
upgrade, fast to assemble and cheap. Also, using this platform
we present an approach that solves a humanoid balancing
problem, which uses only fully connected neural network as a
basic idea for real time balancing. The method consists of 3
main conditions: 1) using different types of sensors detect the
current position of the body and generate the input
information for the neural network, 2) using fully connected
neural network produce the correct output, 3) using servomotors make movements that will change the current position
to the new one. During field test the humanoid robot can
balance on the moving platform that tilts up to 10 degrees to
any direction. Finally, we have shown that using our platform
we can do research and compare different neural networks in
similar conditions which can be important for the researchers
to do analyses in machine learning and robotics.
11 Construction productivity and cost estimation using artificial BenitoSumpter862
11 Construction productivity and cost estimation using artificial neural networks
Introduction
Because of the uncertainties and complexities involved in construction projects, expert system applications and artificial intelligence are helpful in the context of construction engineering and management. This chapter focuses on the applications of artificial neural networks (ANNs) for productivity and cost estimations, which are among the most crucial tasks of construction managers and general estimators.
The main objective of this chapter is to provide practical explanations of how to design, develop, analyse and validate ANNs as robust and reliable tools for productivity and cost estimations. An introduction to ANNs is provided, and several examples from the literature that have used ANNs in different areas of construction productivity and cost predictions are listed. As a result, a framework is presented to serve as a general guide on how to develop ANNs, and based on that, a detailed example is discussed to show the application in a real construction project setting. By the end of the chapter, readers should have some basic background about ANNs and should be able to develop a simple but efficient ANN for their own construction projects.
Artificial neural networks (ANNs)
An artificial neural network (ANN) can be defined as a massive parallel distributed processor composed of simple processing units (neurons) which are capable of storing experiential knowledge and retrieving it for future use (Haykin 1999). Neurons communicate by sending signals to each other over a large number of weighted connections. Thus, ANNs can be considered as an information processing technology that, by learning from different experiences and generalizing from previous examples, can simulate the human brain system. A simple schematic diagram of a neuron is shown in Figure 11.1.
Figure 11.1 shows that each neuron has two distinct segments: a summing junction that sums up the received inputs from neighbours and an activation function that computes the output signal, which is propagated to other neurons. The activation function can be theoretically in any form such as signum, linear or semilinear, hyperbolic tangent and sigmoid functions.
Figure 11.1 Schematic diagram of a neuron
To form a network, neurons are grouped into several layers, namely input, hidden and output layers. Two types of network topologies are shown in Figure 11.2:
· Feed-forward networks: data flows strictly from input to output layers, and no feedback connections are allowed.
· Recurrent networks: feedback connections are allowed to provide data flow from the following layers to the preceding layers.
Figure 11.2 Different topologies of ANNs (Alavala 2006)
An ANN should be arranged in such a way that it can provide the desired outputs for a set of inputs presented to the network. To do so, either connection weights should be set using prior knowledge or the network should be trained by training sa ...
11 Construction productivity and cost estimation using artificial AnastaciaShadelb
11 Construction productivity and cost estimation using artificial neural networks
Introduction
Because of the uncertainties and complexities involved in construction projects, expert system applications and artificial intelligence are helpful in the context of construction engineering and management. This chapter focuses on the applications of artificial neural networks (ANNs) for productivity and cost estimations, which are among the most crucial tasks of construction managers and general estimators.
The main objective of this chapter is to provide practical explanations of how to design, develop, analyse and validate ANNs as robust and reliable tools for productivity and cost estimations. An introduction to ANNs is provided, and several examples from the literature that have used ANNs in different areas of construction productivity and cost predictions are listed. As a result, a framework is presented to serve as a general guide on how to develop ANNs, and based on that, a detailed example is discussed to show the application in a real construction project setting. By the end of the chapter, readers should have some basic background about ANNs and should be able to develop a simple but efficient ANN for their own construction projects.
Artificial neural networks (ANNs)
An artificial neural network (ANN) can be defined as a massive parallel distributed processor composed of simple processing units (neurons) which are capable of storing experiential knowledge and retrieving it for future use (Haykin 1999). Neurons communicate by sending signals to each other over a large number of weighted connections. Thus, ANNs can be considered as an information processing technology that, by learning from different experiences and generalizing from previous examples, can simulate the human brain system. A simple schematic diagram of a neuron is shown in Figure 11.1.
Figure 11.1 shows that each neuron has two distinct segments: a summing junction that sums up the received inputs from neighbours and an activation function that computes the output signal, which is propagated to other neurons. The activation function can be theoretically in any form such as signum, linear or semilinear, hyperbolic tangent and sigmoid functions.
Figure 11.1 Schematic diagram of a neuron
To form a network, neurons are grouped into several layers, namely input, hidden and output layers. Two types of network topologies are shown in Figure 11.2:
· Feed-forward networks: data flows strictly from input to output layers, and no feedback connections are allowed.
· Recurrent networks: feedback connections are allowed to provide data flow from the following layers to the preceding layers.
Figure 11.2 Different topologies of ANNs (Alavala 2006)
An ANN should be arranged in such a way that it can provide the desired outputs for a set of inputs presented to the network. To do so, either connection weights should be set using prior knowledge or the network should be trained by training sa ...
A Proposed Method to Develop Shared Papers for Researchers at Conferenceiosrjce
In conferences, the topics of interest for papers include variety of subjects, if the researcher wants to
write a shared researched paper on specific subject with another researcher who is also interested in the same
subject and wants to participate in the same conference, here the problem will arise especially when the topics
of interest and number of researchers become large. The aim of the paper is to solve this problem by finding a
suitable representation of researcher information of topics of interest that can be easily represented and then
found shared researcher on the same topics of interest. Two proposed system algorithms are implemented to
find the shared researchers in conference which gives an easy and efficient implementation.
Alexis AUBRY, Mario LEZOCHE. Enterprise Information Systems: a proposition fo...Milan Zdravković
Presentation from the 1st Workshop on Future Internet Enterprise Systems - FINES 2010: Ontologies and Interoperability, made at 10.11.2010 in Faculty of Mechanical Engineering, Laboratory for Intelligent Manufacturing Systems
Mlp mixer image_process_210613 deeplearning paper review!taeseon ryu
안녕하세요 딥러닝논문읽기모임 입니다!
오늘 소개드릴 논문은 MLP-Mixer라는 제목의 논문입니다.
해당 논문은 아직 아카이브에만 올라와 있고 구글 브레인팀에서 발표한 논문입니다.
CNN은 컴퓨터 비전에서 널리 사용하고 있는 레이어지만, 최근에는 Transformer와 같은 네트워크도 비전영역에 들어오기 시작하고, 몇몇 분야에서는 SOTA를 달성하기도 했습니다. 해당 논문은 Multi layer perceptron만을 사용하여 최신 논문들과 경쟁력이 있는 결과를 달성하는대 성공하였습니다.
논문에 디테일한 설명을 이미지처리팀 허다운님이 자세한 리뷰를 도와주셨습니다! 오늘도 많은 관심 미리 감사드립니다!
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Secure Image Encryption using Two Dimensional Logistic Map
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Mathematical and computer science Dep., Faculty of Science,
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1. Combinatory Logic and language
engineering
Ismail Biskri, Adam Joly and Boucif Amar Bensaber
LAMIA, Université du Québec à Trois-Rivières
2. Introduction
Language engineering:
Everything related to the NLP and the knowledge extraction
Main goal: help humans to access to knowledge contained in texts
Definition:
The study and the description of the concepts, the approaches, the methods and the
techniques that allow data extraction and knowledge modeling and acquisition from
texts
Knowledge acquisition from text needs to be assisted by analysis tools for
corpus, such as:
Semantic or syntactic analyzers
Marker tracking tools supported by contextual exploration
Statistical analyzers
Etc.
Numerous application fields since the development of the Web and
office tools
2 Biskri, Joly & Amar Bensaber, ICGST 2011
3. Introduction (2/3)
Many generations of tools:
At the beginning (about 40 years ago):
Applications focusing on 1 functionality
Since the 90’s:
More complex approaches are required by the industry for text analysis
There is an interest for functions and operations assembling in complex processing
chains (Hallab & al. 2000; Moscarola & al., 2002)
Most of the tools proposed then offer various functionalities
Despite some success with scientists and industries, they have many
important limits:
The technologies offer a closed and limited set of functionalities
They are designed as autonomous entities that can hardly or simply not be
integrated into more complex processing chains
They can be unusable by researchers with particular analysis needs (lack of
adaptability)
3 Biskri, Joly & Amar Bensaber, ICGST 2011
4. Introduction (3/3)
Recently, a new generation of software platforms for language engineering
has started to emerge
Statistical analysis:
Aladin (Seffah & al., 1995)
T2K and Knime (Warr, 2007)
Linguistic Analysis:
Context (Crispino & al., 1999)
Gate (Cunningham & al, 2002)
From these new platforms emerge new interests on processing chains
about:
Their coherence
Their flexibility
Their adaptability
Etc.
4 Biskri, Joly & Amar Bensaber, ICGST 2011
5. General Framework
Processing chain:
Integrated sequence of computational modules dedicated to specific processing,
assembled in a pertinent order according to a processing goal determined by the
language engineer
A module accomplishes an operation which applies to one or many object
entities from a given type and returns other object entities from another
type
A processing chain allow the composition of modules
We need a formal system that can answer 2 fundamental questions:
Given a set of modules, what are the allowable arrangements which lead to
coherent processing chains?
Given a coherent processing chain, how can we automate (as much as possible)
its assessment (in the sense of its calculability)?
Such a system will be at the center of our theoretical model
5 Biskri, Joly & Amar Bensaber, ICGST 2011
6. General Framework
Theoretical general framework chosen: Applicative
Grammars (Desclés, 1990; Shaumyan, 1998)
Instead of designing a rewritten grammar for syntactical
validation of the processing chain, we use a typed logic.
Types are given to inputs/outputs (integer, char, …)
Types constraint the possibilities of modules composition
Main advantages of this formalism:
Assures a firm compositionality of the different modules in the different processing chains,
by validating the types attributed to the modules
Allows to compose an infinity of modules
6 Biskri, Joly & Amar Bensaber, ICGST 2011
7. Combinatory Logic
Combinator Role -Reduction ruleβ
B Composition B x y z x (y z)→
C Permutation C x z y x y z→
Φ Distribution Φ x y z u x (y u) (z u)→
W Duplication W x y x y y→
From the works of Schöfinkel (1924) and Curry and Feys (1958)
Eliminate the need for variables in mathematics
Combinators:
Abstract operators that apply to other operators in order to build more
complex operators;
Act as functions over arguments, in an operator-operands structure
Each specific action is represented by a unique rule that defines the
equivalence between a logical expression with a combinator versus one
without a combinator ( -reduction rule)β
7 Biskri, Joly & Amar Bensaber, ICGST 2011
8. Complex combinators:
We can combine recursively many elementary combinators together
to form an infinitely range of complex combinators
The global action is determined by the successive application of the
combinators (from left to right)
Example:
i. B B C x y z u v
ii. B (C x) y z u v
iii. C x (y z) u v
iv. x u (y z) v
Power combinators (χn
):
Reiterates n times the action of the combinator χ
Distance combinators (χn):
Postpones the action of a combinator of n stepsχ
Combinatory Logic (2/3)
8 Biskri, Joly & Amar Bensaber, ICGST 2011
9. Combinatory Logic (3/3)
Combinatory logic fills 2 major goals:
It gives an interoperable and formal representation of the solution;
Combinatory logic expressions formally represent the composition of the
modules of the processing chain and gives the direct execution order
Combinators provides operators to support the different types of
interactions between modules:
B: expresses the composition of 2 interconnected modules
C: assures that all combinators and modules of the expression appear together
to the left and all inputs to the right (ordering)
Φ: distributes the same input to 2 or more different modules
9 Biskri, Joly & Amar Bensaber, ICGST 2011
10. Processing Chains
Our model builds systems using metaprogramming:
The metaprograms act as controllers over the programs (modules) by specifying the
interactions between modules and their execution flow
The goal is to be able to easily replace a module by another one with
compatible inputs and outputs
Module:
It acts like a math function:
It takes arguments as inputs
It processes a specific action
It returns a result as output
Each module is independent (black box: we know what it does but we are not interesting
in how)
It must have the capacity to communicate with other modules following a protocol
10 Biskri, Joly & Amar Bensaber, ICGST 2011
11. Processing Chains (2/2)
A controller supervises the flow of communication:
It verifies the validity of connections between modules (if the processing chain is
syntactically correct):
It determines the execution order of modules (following the combinatory
expression)
It triggers the execution of a module (one at a time only)
Processing chain 2
Processing chain 1
M1M1
M2M2
O1
O2
I1
I2 I4
I3
M3/C2M3/C2 O3 M4M4 O4I5
Controller 1
M1M1 O1 M2/C3M2/C3 O2I3
I2
I1
…
By abstraction, a processing
chain (the controller and
modules) can be considered
as a (super or meta) module
by itself)
Thus it can be used as a
module in another processing
chain
11 Biskri, Joly & Amar Bensaber, ICGST 2011
12. Basic Processing Chains (1 module)
M1M1 O1I1
M1M1 O1
I2
I1
In
…
12 Biskri, Joly & Amar Bensaber, ICGST 2011
1 input:
No combinator needed
O1 is obtained by applying M1 to I1
O1 = M1 I1
n inputs:
We add the inputs at the end of the expression
O1 = M1 I1 I2 … In
13. Serial processing chains
Relation of composition between modules (B)
2 connected modules:
O1 = M1 I1
O2 = M2 I2
I2 = O1
O2 = M2 (M1 I1)
O2 = B M2 M1 I1
3 connected modules:
O3 = M3 I3
I3 = O2
O3 = M3 (B M2 M1 I1)
O3 = B3
M3 B M2 M1 I1
O3 = C B3
B M3 M2 M1 I1
4 connected modules: O4 = C B4
(C B3
B) M4 M3 M2 M1 I1
(…)
The power of B is induced by the number of modules in the chain
M1M1 O1I1 M2M2 O2I2
M1M1 O1I1 M2M2 O2I2 M3M3 O3I3
13 Biskri, Joly & Amar Bensaber, ICGST 2011
14. Parallel processing chains
Contains modules that have many inputs
Module connected on the 1st input of a 2nd module:
O2 = M2 I2 I3
O1 = M1 I1
I2 = O1
O2 = M2 (M1 I1) I3
O2 = B M2 M1 I1 I3
2 modules connected to a 3rd module:
O3 = M3 I3 I4
I3 = M1 I1
I4 = M2 I2
O3 = M3 (M1 I1) (M2 I2)
O3 = B M3 M1 I1 (M2 I2)
O3 = C2 B M3 M1 (M2 I2) I1
O3 = B3 C2 B M3 M1 M2 I2 I1
3 modules connected to a 4th module: B7 C6 C6 B3 C2 B M4 M1 M2 M3 I3 I2 I1
(…)
The distance of combinators B and C can be induced by the number of modules
M1M1 O1I1
M2M2 O2
I2
I3
M1M1
M2M2
O1
O2
I1
I2 I4
I3
M3M3 O3
14 Biskri, Joly & Amar Bensaber, ICGST 2011
16. SATIM
Following these formalisms and principles, we have implemented a prototype
(work in progress) named SATIM.
SATIM: « Système d’Analyse et de Traitement de l’Information Multidimensionnelle »
(Multidimensional Data Analysis and processing System)
The architecture of this modular platform postulates 3 levels of interaction with a
language engineer:
1. Workshop:
Contains various modules, procedures and functions and their assigned applicative categories
Possibility to add or delete modules to a « database » of modules
1. Laboratory:
Allows an engineer to build his processing chain and adjust it using tests and according to his
objective
1. Application:
It is the output of the previous level: the processing chain is then an autonomous software that
contains a coherent and well organized subset of modules
16 Biskri, Joly & Amar Bensaber, ICGST 2011
17. Conclusion
We are at a prototypal stage/test phase
Eventually, it will become the full-size project within which we aspire
to design tools for language engineering and other tools for NLP in
general
The strong foundations (formalism and principles) at the heart of SATIM
are aimed to address the need for coherence, flexibility, adaptability and
easy communication between programs (processing chains):
Modules are independents: we can easily replace a module by another one
with compatible inputs and output to change some parts of a given program
We believe that the approach could help research teams to collaborate
together by sharing components
17 Biskri, Joly & Amar Bensaber, ICGST 2011
Editor's Notes
The composition combinator B combines together two operators x and y in order to form the complex operator B x y that acts on an operand z according to the β-reduction rule. The permutation combinator C uses an operator x in order to build the complex operator C x such as if x acts on the operands y and z, C x will act on those operands in the reverse order, that is to say z and y. Given the three operators x, y and z and the operand u, the distribution combinator Φ distributes the operand with the two precedent operators. Finally, given the binary operators x, and the operand y, the combinator W duplicates y so that the operator x will have its two arguments.