The document presents research on using artificial neural networks (ANNs) to model and predict the shear capacity of reinforced concrete deep beams. A database of 270 experimental deep beam tests was used to develop and validate an ANN model. The model takes in 9 input parameters that affect shear capacity and outputs the predicted shear capacity. The model was trained using 170 beams and validated on separate sets of 50 beams. Results showed the ANN model predictions had the lowest average error and variation compared to predictions from 5 national design codes.
Particle Swarm Optimization Based QoS Aware Routing for Wireless Sensor Networksijsrd.com
Efficiency in a Wireless Sensor Network can only be obtained with effective routing mechanisms. This paper uses Particle Swarm Optimization (PSO, a metaheuristic algorithm to perform the process of routing. Since PSO does not have a defined fitness function, it is flexible to incorporate user defined QoS parameters to define the fitness function.
Evolutionary algorithms for optimum design of thin broadband multilayer micro...eSAT Journals
Abstract In this paper we focused on the comparative study of three very popular and most recently developed nature inspired evolutionary algorithms namely Biogeography based optimization algorithm (BBO), Flower pollination algorithm (FPA) and Artificial bee colony optimization algorithm (ABC) for developing a model of 6 layers thin broadband (0.2-20GHz) microwave absorber. The model is optimized for oblique wide angle of incidence (450, 600) taking both TE and TM polarization of the electromagnetic wave under consideration. The primary goal of our design is to minimizing the overall reflection coefficient of the absorber and its total thickness by selecting the proper layer of materials from a predefined database of existing materials. 8 different models are presented and synthesize considering both these design consideration simultaneously and for only overall reflection coefficient of the absorber while total thickness is not taken into consideration during optimization for each cases. The optimum values of all the significant parameters of the multilayer absorber for different models have been compared and tabulated using BBO, FPA and ABC algorithms which established the superiority of our proposed design. Keywords: Multilayer microwave absorber, Oblique incidence, Broadband, Evolutionary algorithms, Arbitrary polarization
Shift Invarient and Eigen Feature Based Image Fusion ijcisjournal
Image fusion is a technique of fusing multiple images for better information and more accurate image
compared input images. Image fusion has applications in biomedical imaging, remote sensing, pattern
recognition, multi-focus image integration, and modern military. The proposed methodology uses benefits
of Stationary Wavelet Transform (SWT) and Principal Component Analysis (PCA) to fuse the two images.
The obtained results are compared with exiting methodologies and shows robustness in terms of entropy,
Peak Signal to Noise Ratio (PSNR) and standard deviation.
A Compact Multiband Metamaterial based Microstrip Patch Antenna for Wireless ...IJERA Editor
In this paper, a metamaterial based compact multiband microstrip antenna is proposed which can give high gain and directivity. Metamaterials are periodic structures and have been intensively investigated due to the particular features such as ultra-refraction phenomenon and negative permittivity and/or permeability. A metamaterialbased microstrip patch antenna with enhanced characteristics and multi band operation will be investigated in this work. The multiple frequency operation will be achieved by varying the capacitance of the metamaterial structure with the help of metallic loadings placed in each metamaterial unit cells. The potential impacts will be miniaturization, reduced cost and reduced power consumption since multiple antennas operating at different frequencies are replaced by a single antenna which can operate at multiple frequencies. The proposed microstrip patch antenna will have its frequencies of operation in the L, S and C bands. The proposed structure is simulated using Agilent Advanced Design System (ADS) 2011.05. It is then fabricated on the FR4 substrate and the performance of the fabricated antenna is measured using the Vector Network Analyzer (VNA)
Efficient And Improved Video Steganography using DCT and Neural NetworkIJSRD
As per the demand of modern communication it is important to establish secret communication which is obtain by seganography .Video Steganography is the technique of hiding some covert message inside a video. The addition of this information to the video is not recognizable through the human eye as modify of a pixel color is negligible. In the proposed method Discrete Cosine Transform (DCT) and neural network is used. Input image is divided into blocks and is processed to generate quantization matrix of cover and stego images by using Discrete Cosine Transform (DCT).And using neural network performance of this method can be further improved. The neural network is trained and on the basis of training and segmentation done, neural network provide efficient positions where data can be merge. The performance and efficiency is measured by PSNR and MSE value.
SAR Image Classification by Multilayer Back Propagation Neural NetworkIJMTST Journal
A novel descriptive feature extraction method of Discrete Fourier transform and neural network classifier for classification of Synthetic Aperture Radar (SAR) images is proposed. The classification process has the following stages (1) Image Segmentation using statistical Region Merging (SRM) (2) Polar transform and Feature extraction using Discrete Fourier Transform (3) Neural Network classification using back propagation. This is generally the first step in image analysis. Segmentation subdivides an image into its constituent parts or objects. The level to which this subdivision is carried depends on the problem being solved. The image segmentation in this study is performed using Statistical Region Merging proposed Richard Nock and Frank Nielsen. The key idea of the Statistical Region Merging model is to formulate image segmentation as an inference problem. Here the merging procedure is based on the theorem. Feature vectors as the input for the neural network. Polar transform is applied to segmented SAR image. The rotation problem under the Cartesian coordinates becomes the translation problem under the polar coordinates.
Particle Swarm Optimization Based QoS Aware Routing for Wireless Sensor Networksijsrd.com
Efficiency in a Wireless Sensor Network can only be obtained with effective routing mechanisms. This paper uses Particle Swarm Optimization (PSO, a metaheuristic algorithm to perform the process of routing. Since PSO does not have a defined fitness function, it is flexible to incorporate user defined QoS parameters to define the fitness function.
Evolutionary algorithms for optimum design of thin broadband multilayer micro...eSAT Journals
Abstract In this paper we focused on the comparative study of three very popular and most recently developed nature inspired evolutionary algorithms namely Biogeography based optimization algorithm (BBO), Flower pollination algorithm (FPA) and Artificial bee colony optimization algorithm (ABC) for developing a model of 6 layers thin broadband (0.2-20GHz) microwave absorber. The model is optimized for oblique wide angle of incidence (450, 600) taking both TE and TM polarization of the electromagnetic wave under consideration. The primary goal of our design is to minimizing the overall reflection coefficient of the absorber and its total thickness by selecting the proper layer of materials from a predefined database of existing materials. 8 different models are presented and synthesize considering both these design consideration simultaneously and for only overall reflection coefficient of the absorber while total thickness is not taken into consideration during optimization for each cases. The optimum values of all the significant parameters of the multilayer absorber for different models have been compared and tabulated using BBO, FPA and ABC algorithms which established the superiority of our proposed design. Keywords: Multilayer microwave absorber, Oblique incidence, Broadband, Evolutionary algorithms, Arbitrary polarization
Shift Invarient and Eigen Feature Based Image Fusion ijcisjournal
Image fusion is a technique of fusing multiple images for better information and more accurate image
compared input images. Image fusion has applications in biomedical imaging, remote sensing, pattern
recognition, multi-focus image integration, and modern military. The proposed methodology uses benefits
of Stationary Wavelet Transform (SWT) and Principal Component Analysis (PCA) to fuse the two images.
The obtained results are compared with exiting methodologies and shows robustness in terms of entropy,
Peak Signal to Noise Ratio (PSNR) and standard deviation.
A Compact Multiband Metamaterial based Microstrip Patch Antenna for Wireless ...IJERA Editor
In this paper, a metamaterial based compact multiband microstrip antenna is proposed which can give high gain and directivity. Metamaterials are periodic structures and have been intensively investigated due to the particular features such as ultra-refraction phenomenon and negative permittivity and/or permeability. A metamaterialbased microstrip patch antenna with enhanced characteristics and multi band operation will be investigated in this work. The multiple frequency operation will be achieved by varying the capacitance of the metamaterial structure with the help of metallic loadings placed in each metamaterial unit cells. The potential impacts will be miniaturization, reduced cost and reduced power consumption since multiple antennas operating at different frequencies are replaced by a single antenna which can operate at multiple frequencies. The proposed microstrip patch antenna will have its frequencies of operation in the L, S and C bands. The proposed structure is simulated using Agilent Advanced Design System (ADS) 2011.05. It is then fabricated on the FR4 substrate and the performance of the fabricated antenna is measured using the Vector Network Analyzer (VNA)
Efficient And Improved Video Steganography using DCT and Neural NetworkIJSRD
As per the demand of modern communication it is important to establish secret communication which is obtain by seganography .Video Steganography is the technique of hiding some covert message inside a video. The addition of this information to the video is not recognizable through the human eye as modify of a pixel color is negligible. In the proposed method Discrete Cosine Transform (DCT) and neural network is used. Input image is divided into blocks and is processed to generate quantization matrix of cover and stego images by using Discrete Cosine Transform (DCT).And using neural network performance of this method can be further improved. The neural network is trained and on the basis of training and segmentation done, neural network provide efficient positions where data can be merge. The performance and efficiency is measured by PSNR and MSE value.
SAR Image Classification by Multilayer Back Propagation Neural NetworkIJMTST Journal
A novel descriptive feature extraction method of Discrete Fourier transform and neural network classifier for classification of Synthetic Aperture Radar (SAR) images is proposed. The classification process has the following stages (1) Image Segmentation using statistical Region Merging (SRM) (2) Polar transform and Feature extraction using Discrete Fourier Transform (3) Neural Network classification using back propagation. This is generally the first step in image analysis. Segmentation subdivides an image into its constituent parts or objects. The level to which this subdivision is carried depends on the problem being solved. The image segmentation in this study is performed using Statistical Region Merging proposed Richard Nock and Frank Nielsen. The key idea of the Statistical Region Merging model is to formulate image segmentation as an inference problem. Here the merging procedure is based on the theorem. Feature vectors as the input for the neural network. Polar transform is applied to segmented SAR image. The rotation problem under the Cartesian coordinates becomes the translation problem under the polar coordinates.
Pixel Recursive Super Resolution.
Ryan Dahl, Mohammad Norouzi & Jonathon Shlens
Google Brain.
Abstract
We present a pixel recursive super resolution model that
synthesizes realistic details into images while enhancing
their resolution. A low resolution image may correspond
to multiple plausible high resolution images, thus modeling
the super resolution process with a pixel independent conditional
model often results in averaging different details–
hence blurry edges. By contrast, our model is able to represent
a multimodal conditional distribution by properly modeling
the statistical dependencies among the high resolution
image pixels, conditioned on a low resolution input. We
employ a PixelCNN architecture to define a strong prior
over natural images and jointly optimize this prior with a
deep conditioning convolutional network. Human evaluations
indicate that samples from our proposed model look
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
Design of a dual-band antenna for energy harvesting applicationjournalBEEI
This report presents an investigation on how to improve the current dual-band antenna to enhance the better result of the antenna parameters for energy harvesting application. Besides that, to develop a new design and validate the antenna frequencies that will operate at 2.4 GHz and 5.4 GHz. At 5.4 GHz, more data can be transmitted compare to 2.4 GHz. However, 2.4 GHz has long distance of radiation, so it can be used when far away from the antenna module compare to 5 GHz that has short distance in radiation. The development of this project includes the scope of designing and testing of antenna using computer simulation technology (CST) 2018 software and vector network analyzer (VNA) equipment. In the process of designing, fundamental parameters of antenna are being measured and validated, in purpose to identify the better antenna performance.
A New Approach for Solving Inverse Scattering Problems with Overset Grid Gene...TELKOMNIKA JOURNAL
This paper presents a new approach of Forward-Backward Time-Stepping (FBTS)
utilizing Finite-Difference Time-Domain (FDTD) method with Overset Grid Generation (OGG)
method to solve the inverse scattering problems for electromagnetic (EM) waves. The proposed
FDTD method is combined with OGG method to reduce the geometrically complex problem to a
simple set of grids. The grids can be modified easily without the need to regenerate the grid
system, thus, it provide an efficient approach to integrate with the FBTS technique. Here, the
characteristics of the EM waves are analyzed. For the research mentioned in this paper, the
‘measured’ signals are syntactic data generated by FDTD simulations. While the ‘simulated’
signals are the calculated data. The accuracy of the proposed approach is validated. Good
agreements are obtained between simulation data and measured data. The proposed approach
has the potential to provide useful quantitative information of the unknown object particularly for
shape reconstruction, object detection and others.
Comparison Between Levenberg-Marquardt And Scaled Conjugate Gradient Training...CSCJournals
The Internet paved way for information sharing all over the world decades ago and its popularity for distribution of data has spread like a wildfire ever since. Data in the form of images, sounds, animations and videos is gaining users’ preference in comparison to plain text all across the globe. Despite unprecedented progress in the fields of data storage, computing speed and data transmission speed, the demands of available data and its size (due to the increase in both, quality and quantity) continue to overpower the supply of resources. One of the reasons for this may be how the uncompressed data is compressed in order to send it across the network. This paper compares the two most widely used training algorithms for multilayer perceptron (MLP) image compression – the Levenberg-Marquardt algorithm and the Scaled Conjugate Gradient algorithm. We test the performance of the two training algorithms by compressing the standard test image (Lena or Lenna) in terms of accuracy and speed. Based on our results, we conclude that both algorithms were comparable in terms of speed and accuracy. However, the Levenberg- Marquardt algorithm has shown slightly better performance in terms of accuracy (as found in the average training accuracy and mean squared error), whereas the Scaled Conjugate Gradient algorithm faired better in terms of speed (as found in the average training iteration) on a simple MLP structure (2 hidden layers).
A broad ranging open access journal Fast and efficient online submission Expe...ijceronline
nternational Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Optimized Neural Network for Classification of Multispectral ImagesIDES Editor
The proposed work involves the multiobjective PSO
based optimization of artificial neural network structure for
the classification of multispectral satellite images. The neural
network is used to classify each image pixel in various land
cove types like vegetations, waterways, man-made structures
and road network. It is per pixel supervised classification using
spectral bands (original feature space). Use of neural network
for classification requires selection of most discriminative
spectral bands and determination of optimal number of nodes
in hidden layer. We propose new methodology based on
multiobjective particle swarm optimization (MOPSO) to
determine discriminative spectral bands and the number of
hidden layer node simultaneously. The result obtained using
such optimized neural network is compared with that of
traditional classifiers like MLC and Euclidean classifier. The
performance of all classifiers is evaluated quantitatively using
Xie-Beni and â indexes. The result shows the superiority of
the proposed method.
Energy Efficient Zone Divided and Energy Balanced Clustering Routing Protocol...csijjournal
Wireless sensor network has proven its significance in almost every field in today’s era. Wireless sensor network consists of large number of sensor nodes distributed randomly in some areas. In WSN the main objective has been increasing the network lifetime. There is zone divisional approach which has shown sound improvement in increasing the network lifetime over the Leach and EEUC protocols. The proposed protocol Energy efficient zone divided and energy balanced clustering routing protocol (EEZECR) has not only much higher network lifetime as compare to ZECR and it also has much better load balancing in the network. In the EEZECR the concept of double cluster head in a cluster is introduced which reduces the load on cluster head and very efficiently does the task of load balancing in the network thoroughly which makes this protocol favorite for many real time applications. Simulations are performed in MATLAB.
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.
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
Design and implementation of image compression using set partitioning in hier...eSAT Journals
Abstract
To store digital image and video in raw form require large amount of memory space. Image compression means reducing the size of image file without degrading quality of image. Depending on the reconstructed image to be exactly same as the original or some unknown loss may incurred image compression divided into two techniques lossy and lossless techniques. In this paper we present hybrid model which is the combination of several compression techniques. This paper present DWT, DCT and SPIHT implementation. Simulation has been carried out on different images like Lena , Barbra, Cameraman, Test drive . Result analysis is done through parameters like MSE, PSNR and Elapsed time. Values of this parameters will go better than the old algorithms because here we apply SPIHT lossless technique and also due to hybrid combination of DWT and DCT we will get good level of compression. The result analysis shows that the proposed hybrid algorithm performs much better in terms of PSNR with a higher compression ratio as compared to standalone DWT and DCT techniques.
Keywords- DWT, DCT, SPIHT, PSNR, MSE
INVERSIONOF MAGNETIC ANOMALIES DUE TO 2-D CYLINDRICAL STRUCTURES –BY AN ARTIF...ijsc
Application of Artificial Neural Network Committee Machine (ANNCM) for the inversion of magnetic
anomalies caused by a long-2D horizontal circular cylinder is presented. Although, the subsurface targets
are of arbitrary shape, they are assumed to be regular geometrical shape for convenience of mathematical
analysis. ANNCM inversion extract the parameters of the causative subsurface targets include depth to the
centre of the cylinder (Z), the inclination of magnetic vector(Ɵ)and the constant term (A)comprising the
radius(R)and the intensity of the magnetic field(I). The method of inversion is demonstrated over a
theoretical model with and without random noise in order to study the effect of noise on the technique and
then extended to real field data. It is noted that the method under discussion ensures fairly accurate results
even in the presence of noise. ANNCM analysis of vertical magnetic anomaly near Karimnagar, Telangana,
India, has shown satisfactory results in comparison with other inversion techniques that are in vogue.The
statistics of the predicted parameters relative to the measured data, show lower sum error (<9.58%) and
higher correlation coefficient (R>91%) indicating that good matching and correlation is achieved between
the measured and predicted parameters.
Study of Properties of Concrete when its Fine Aggregate is replaced by Glass ...ijsrd.com
Use of waste material in concrete achieves a new height in the present construction world. In concrete all their ingredients are partially or fully replaced by many waste materials like Cement is replaced by Fly Ash, Rice Husk Ash, Wheat Straw Ash, etc., Fine aggregate is replaced by Saw Dust Ash, Quarry Fines, and Glass Powder etc. And coarse aggregate is replaced by cockle shell, tire rubber, recycle aggregate etc. In this paper study of Compressive strength, Split Tensile Strength, Workability and water absorption of concrete is done when its fine aggregate is replaced by Glass Powder. 150 * 150 * 150 mm cube and 150 * 300 mm cylinders are cased of M 25 grade of concrete.
Production of shell eggs enriched with n-3 fatty acidsiosrphr_editor
Unsaturated long chain fatty acids (n-3 FAs) have been proposed in a human diet to reduce the risk of atherosclerosis and therefore the risk of stroke. N-3 FAs also play an important role in retinal and brain tissue development in the neonate. The main natural source of n-3 FA is marine fish. The aim of this work was to create shell egg enriched with n-3 FAs using natural golden marine algae (MA) as a supplement in hen's diet. Three experiments were conducted: (1) hundred hens from the hybrid Lohmann Brown were fed with diet containing 1.27% MA; (2) hundred hens from the same hybrid were fed with diet containing 1.77% MA; (3) hundred hens were the control group. The duration of the experiments was 4 weeks. Slight enriching of the shell egg yolk at the both groups fed with diet containing MA happened after the end of the second week. The concentration of docosahexaenoic acid (DHA; C22:6, n=3) at the 1st experimental group was 90.3 mg/100 g of egg mass, and 112.1 mg/100 g of egg mass at the 2nd experimental group. The concentration of DHA at the control group was 54.5 mg/100 g of egg mass. After the 3rd week the concentration of DHA at the 1st group increased to 201.2 mg/100 g of egg mass and to 304.9 mg/100 g of egg mass at the 2nd group. At that time the concentration of the DHA at the control group remained unchanged. At the end of the 4th week the concentration of DHA reached the maximum level: 224.5 mg/100 g of egg mass at the 1st group and 328.4 mg/ 100 g of egg mass at the 2nd group. The concentration of the DHA at the control group was 51.9 mg/100 g egg mass. It is interested to note that eicosapentaenoic acid (EPA; C20:5, n=3) appeared in low concentrations of 10-15 mg/100g of egg mass at the end of the 4th week of the experiment at the 2nd experimental group.
Pixel Recursive Super Resolution.
Ryan Dahl, Mohammad Norouzi & Jonathon Shlens
Google Brain.
Abstract
We present a pixel recursive super resolution model that
synthesizes realistic details into images while enhancing
their resolution. A low resolution image may correspond
to multiple plausible high resolution images, thus modeling
the super resolution process with a pixel independent conditional
model often results in averaging different details–
hence blurry edges. By contrast, our model is able to represent
a multimodal conditional distribution by properly modeling
the statistical dependencies among the high resolution
image pixels, conditioned on a low resolution input. We
employ a PixelCNN architecture to define a strong prior
over natural images and jointly optimize this prior with a
deep conditioning convolutional network. Human evaluations
indicate that samples from our proposed model look
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
Design of a dual-band antenna for energy harvesting applicationjournalBEEI
This report presents an investigation on how to improve the current dual-band antenna to enhance the better result of the antenna parameters for energy harvesting application. Besides that, to develop a new design and validate the antenna frequencies that will operate at 2.4 GHz and 5.4 GHz. At 5.4 GHz, more data can be transmitted compare to 2.4 GHz. However, 2.4 GHz has long distance of radiation, so it can be used when far away from the antenna module compare to 5 GHz that has short distance in radiation. The development of this project includes the scope of designing and testing of antenna using computer simulation technology (CST) 2018 software and vector network analyzer (VNA) equipment. In the process of designing, fundamental parameters of antenna are being measured and validated, in purpose to identify the better antenna performance.
A New Approach for Solving Inverse Scattering Problems with Overset Grid Gene...TELKOMNIKA JOURNAL
This paper presents a new approach of Forward-Backward Time-Stepping (FBTS)
utilizing Finite-Difference Time-Domain (FDTD) method with Overset Grid Generation (OGG)
method to solve the inverse scattering problems for electromagnetic (EM) waves. The proposed
FDTD method is combined with OGG method to reduce the geometrically complex problem to a
simple set of grids. The grids can be modified easily without the need to regenerate the grid
system, thus, it provide an efficient approach to integrate with the FBTS technique. Here, the
characteristics of the EM waves are analyzed. For the research mentioned in this paper, the
‘measured’ signals are syntactic data generated by FDTD simulations. While the ‘simulated’
signals are the calculated data. The accuracy of the proposed approach is validated. Good
agreements are obtained between simulation data and measured data. The proposed approach
has the potential to provide useful quantitative information of the unknown object particularly for
shape reconstruction, object detection and others.
Comparison Between Levenberg-Marquardt And Scaled Conjugate Gradient Training...CSCJournals
The Internet paved way for information sharing all over the world decades ago and its popularity for distribution of data has spread like a wildfire ever since. Data in the form of images, sounds, animations and videos is gaining users’ preference in comparison to plain text all across the globe. Despite unprecedented progress in the fields of data storage, computing speed and data transmission speed, the demands of available data and its size (due to the increase in both, quality and quantity) continue to overpower the supply of resources. One of the reasons for this may be how the uncompressed data is compressed in order to send it across the network. This paper compares the two most widely used training algorithms for multilayer perceptron (MLP) image compression – the Levenberg-Marquardt algorithm and the Scaled Conjugate Gradient algorithm. We test the performance of the two training algorithms by compressing the standard test image (Lena or Lenna) in terms of accuracy and speed. Based on our results, we conclude that both algorithms were comparable in terms of speed and accuracy. However, the Levenberg- Marquardt algorithm has shown slightly better performance in terms of accuracy (as found in the average training accuracy and mean squared error), whereas the Scaled Conjugate Gradient algorithm faired better in terms of speed (as found in the average training iteration) on a simple MLP structure (2 hidden layers).
A broad ranging open access journal Fast and efficient online submission Expe...ijceronline
nternational Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Optimized Neural Network for Classification of Multispectral ImagesIDES Editor
The proposed work involves the multiobjective PSO
based optimization of artificial neural network structure for
the classification of multispectral satellite images. The neural
network is used to classify each image pixel in various land
cove types like vegetations, waterways, man-made structures
and road network. It is per pixel supervised classification using
spectral bands (original feature space). Use of neural network
for classification requires selection of most discriminative
spectral bands and determination of optimal number of nodes
in hidden layer. We propose new methodology based on
multiobjective particle swarm optimization (MOPSO) to
determine discriminative spectral bands and the number of
hidden layer node simultaneously. The result obtained using
such optimized neural network is compared with that of
traditional classifiers like MLC and Euclidean classifier. The
performance of all classifiers is evaluated quantitatively using
Xie-Beni and â indexes. The result shows the superiority of
the proposed method.
Energy Efficient Zone Divided and Energy Balanced Clustering Routing Protocol...csijjournal
Wireless sensor network has proven its significance in almost every field in today’s era. Wireless sensor network consists of large number of sensor nodes distributed randomly in some areas. In WSN the main objective has been increasing the network lifetime. There is zone divisional approach which has shown sound improvement in increasing the network lifetime over the Leach and EEUC protocols. The proposed protocol Energy efficient zone divided and energy balanced clustering routing protocol (EEZECR) has not only much higher network lifetime as compare to ZECR and it also has much better load balancing in the network. In the EEZECR the concept of double cluster head in a cluster is introduced which reduces the load on cluster head and very efficiently does the task of load balancing in the network thoroughly which makes this protocol favorite for many real time applications. Simulations are performed in MATLAB.
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.
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
Design and implementation of image compression using set partitioning in hier...eSAT Journals
Abstract
To store digital image and video in raw form require large amount of memory space. Image compression means reducing the size of image file without degrading quality of image. Depending on the reconstructed image to be exactly same as the original or some unknown loss may incurred image compression divided into two techniques lossy and lossless techniques. In this paper we present hybrid model which is the combination of several compression techniques. This paper present DWT, DCT and SPIHT implementation. Simulation has been carried out on different images like Lena , Barbra, Cameraman, Test drive . Result analysis is done through parameters like MSE, PSNR and Elapsed time. Values of this parameters will go better than the old algorithms because here we apply SPIHT lossless technique and also due to hybrid combination of DWT and DCT we will get good level of compression. The result analysis shows that the proposed hybrid algorithm performs much better in terms of PSNR with a higher compression ratio as compared to standalone DWT and DCT techniques.
Keywords- DWT, DCT, SPIHT, PSNR, MSE
INVERSIONOF MAGNETIC ANOMALIES DUE TO 2-D CYLINDRICAL STRUCTURES –BY AN ARTIF...ijsc
Application of Artificial Neural Network Committee Machine (ANNCM) for the inversion of magnetic
anomalies caused by a long-2D horizontal circular cylinder is presented. Although, the subsurface targets
are of arbitrary shape, they are assumed to be regular geometrical shape for convenience of mathematical
analysis. ANNCM inversion extract the parameters of the causative subsurface targets include depth to the
centre of the cylinder (Z), the inclination of magnetic vector(Ɵ)and the constant term (A)comprising the
radius(R)and the intensity of the magnetic field(I). The method of inversion is demonstrated over a
theoretical model with and without random noise in order to study the effect of noise on the technique and
then extended to real field data. It is noted that the method under discussion ensures fairly accurate results
even in the presence of noise. ANNCM analysis of vertical magnetic anomaly near Karimnagar, Telangana,
India, has shown satisfactory results in comparison with other inversion techniques that are in vogue.The
statistics of the predicted parameters relative to the measured data, show lower sum error (<9.58%) and
higher correlation coefficient (R>91%) indicating that good matching and correlation is achieved between
the measured and predicted parameters.
Study of Properties of Concrete when its Fine Aggregate is replaced by Glass ...ijsrd.com
Use of waste material in concrete achieves a new height in the present construction world. In concrete all their ingredients are partially or fully replaced by many waste materials like Cement is replaced by Fly Ash, Rice Husk Ash, Wheat Straw Ash, etc., Fine aggregate is replaced by Saw Dust Ash, Quarry Fines, and Glass Powder etc. And coarse aggregate is replaced by cockle shell, tire rubber, recycle aggregate etc. In this paper study of Compressive strength, Split Tensile Strength, Workability and water absorption of concrete is done when its fine aggregate is replaced by Glass Powder. 150 * 150 * 150 mm cube and 150 * 300 mm cylinders are cased of M 25 grade of concrete.
Production of shell eggs enriched with n-3 fatty acidsiosrphr_editor
Unsaturated long chain fatty acids (n-3 FAs) have been proposed in a human diet to reduce the risk of atherosclerosis and therefore the risk of stroke. N-3 FAs also play an important role in retinal and brain tissue development in the neonate. The main natural source of n-3 FA is marine fish. The aim of this work was to create shell egg enriched with n-3 FAs using natural golden marine algae (MA) as a supplement in hen's diet. Three experiments were conducted: (1) hundred hens from the hybrid Lohmann Brown were fed with diet containing 1.27% MA; (2) hundred hens from the same hybrid were fed with diet containing 1.77% MA; (3) hundred hens were the control group. The duration of the experiments was 4 weeks. Slight enriching of the shell egg yolk at the both groups fed with diet containing MA happened after the end of the second week. The concentration of docosahexaenoic acid (DHA; C22:6, n=3) at the 1st experimental group was 90.3 mg/100 g of egg mass, and 112.1 mg/100 g of egg mass at the 2nd experimental group. The concentration of DHA at the control group was 54.5 mg/100 g of egg mass. After the 3rd week the concentration of DHA at the 1st group increased to 201.2 mg/100 g of egg mass and to 304.9 mg/100 g of egg mass at the 2nd group. At that time the concentration of the DHA at the control group remained unchanged. At the end of the 4th week the concentration of DHA reached the maximum level: 224.5 mg/100 g of egg mass at the 1st group and 328.4 mg/ 100 g of egg mass at the 2nd group. The concentration of the DHA at the control group was 51.9 mg/100 g egg mass. It is interested to note that eicosapentaenoic acid (EPA; C20:5, n=3) appeared in low concentrations of 10-15 mg/100g of egg mass at the end of the 4th week of the experiment at the 2nd experimental group.
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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
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Step by step process of uploading presentation videos Hoopeer Hoopeer
Deep neural network, compressive sensing, floating gate techniques can be efficiently employed to increase voltage swing and reduce supply voltage requirements of class AB regulated cascode current mirrors, implement extreme low power analog circuits with this process. /also have good references for subthreshold region.
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International Journal of Engineering Inventions (IJEI),
1. International Journal of Engineering Inventions
ISSN: 2278-7461, www.ijeijournal.com
Volume 1, Issue 9 (November2012) PP: 20-26
Modeling of Deep Beams Using Neural Network
Dr. M. A. Tantary1 & F. A. Baba2
1
Associate professor, department of civil engineering, NIT Srinagar
2
PG Student, NIT Srinagar
Abstract:––The fundamental problem of the reinforced concrete deep beams is that a number of parameters affecting shear
behavior have led to a limited understanding of shear failure mechanism and prediction of exact shear capacity. Although, a
large number of researchers carried out work, but there is no agreed rational procedure to predict the shear capacity of deep
beams. This is mainly due to the non-linear behavior associated with the failure of reinforced concrete deep beams.
Artificial Neural Networks are widely used to approximate complex systems that are difficult to model using
conventional modeling techniques such as mathematical modeling. They have been successfully applied by many
researchers in several civil engineering problems, structural, geotechnical, management etc. Civil and structural engineers
attempt to improve the analysis, design, and control of the behavior of structural systems. The behavior of structural systems,
however, is complex and often governed by both known and unknown multiple variables, with their interrelationship
generally unknown, nonlinear, and sometimes very complicated. The traditional approach used by most researchers in
modeling starts with an assumed form of an empirical or analytical equation and is followed by a regression analysis using
experimental data to determine unknown coefficients such that the equation will fit the data. In the last two decades,
researchers explored the potential of artificial neural networks (ANNs) as an analytical alternative to conventional
techniques, which are often limited by strict assumptions of normality, linearity, homogeneity, variable independence, etc.
Researchers found ANNs particularly useful for function approximation and mapping problems, which are tolerant of some
imprecision and have a considerable amount of experimental data available. In a strict mathematical sense, ANNs do not
provide closed-form solutions for modeling problems but offer a complex and accurate solution based on a representative set
of historical examples of the relationship. Advantages of ANNs include the ability to learn and generalize from examples,
produce meaningful solutions to problems even when input data contain errors or are incomplete, adapt solutions over time
to compensate for changing circumstances, process information rapidly, and transfer readily between computing systems
(Flood and Kartam 1994).
While many efforts have been conducted to understand the shear behavior of reinforced concrete deep beams and (or) to
derive equations for estimating such shear capacity, some researchers explored the application of ANNs for such predictions.
For example, Oreta (2004) applied ANNs to a set of 155 experimental tests to simulate the size effect on the shear strength
of reinforced concrete beams without transverse reinforcement.
In this research program, one of the largest, reliable and most confident database of 270 deep beams was utilized to
investigate the applicability of the ANN technique to predict the shear capacity of deep beams for a widest range of all
affecting parameters. The incorporated variables were width, effective depth, shear span, shear span to depth ratio,
compressive strength of concrete, percentage of longitudinal steel, percentage of vertical steel, percentage of horizontal web
steel and yield strength of steel. For this important structural criteria, the proposed model predictions were compared with
experimental values and five national codes, viz, KBCS, EC-2, CIRIA Guide-2, CSA and ACI-318 and in all the cases, a
good confidence level of the proposed model was observed.
I. INTRODUCTION
An artificial neural network is a network of large number of highly connected processing units called neurons. The
neurons are connected by unidirectional communication channels (connections). The strength of connections between the
neurons is represented by numerical values called weights. Knowledge is stored in the form of a collection of weights. Each
neuron has an activation value that is a function of the sum of inputs received from other neurons through the weighted
connections. Matlab (2007) was used to develop the neural network model.
Pre-processing of Data:
A comprehensive study was carried out on the collected experimental data to choose the data which can be used in
the training of neural network model. A reliable data base of test results of 270 deep beams was obtained for developing the
neural network model. The statistics of database is shown in table 1.1
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2. Modeling of Deep Beams Using Neural Network
Table: 1.1; Statistics of Experimental Data Base:
width Effective Shear a/d Cylinder % % % Hor. Yield Shear
(mm) Depth Span Strength Long. Vert. Steel Strength Capacity
(mm) (mm) ( Mpa) Steel Steel of Steel (kN)
(N/mm2)
Max 915.00 1750.00 3500.00 3.20 120.00 4.25 2.86 3.17 605.00 8396.00
Min 100.00 125.00 125.01 0.27 14.00 0.01 0.00 0.00 376.00 14.00
Mean 201.70 497.13 672.01 1.30 35.13 1.30 0.33 0.35 430.65 908.67
Stand.
Dev 174.76 295.140 553.9 0.37 19.384 1.019 0.404 0.4913 48.220 1209.294
II. SCALING OF DATA
Data scaling is an essential step for neural networks. In a multi-layered NN having a back-propagation algorithm,
the combination of nonlinear and linear transform functions can result in well trained process. In the present NNs, tan-
sigmoid and linear transform functions were employed in the hidden and output layers, respectively. As upper and lower
bounds of tan-sigmoid function output are +1 and -1, respectively, input and output in the database were normalized by
dividing each data parameter by the maximum value of the respective parameter in the data base. Also, after training and
simulation, outputs having the same units as the original database can be obtained by multiplying the same maximum value
of the respective parameters to the simulated output.
III. DIVISION OF DATA
An important factor that can significantly influence the ability of a network to learn and generalize is the number
of specimens (beams) in the training set. Although it increases the time required to train a network, increasing the number of
training specimens provides more information about the shape of the solution surface and thus increases the potential level of
accuracy that can be achieved by the network. Since Back Propagation is most widely used in civil engineering, So it was
decided to use feed forward back propagation algorithm for developing the neural network. Back propagation recommends
dividing the data set into three sets, training, validation and testing sets. So, it was decided to use 170 specimen of the data
for training, 50 for testing and 50 for validation out of the 270 specimens. First of all, training data was selected randomly,
and checked to make sure that it satisfies a good distribution within the problem domain.
IV. ARCHITECTURE OF NEURAL NETWORK
The neural network was designed to have an input layer that consists of nine input neurons representing the most
important parameters that affect the shear capacity of reinforced concrete beams. Based on careful study of recent
approaches for the shear phenomena in concrete members, it was decided to design the input layer to consist of the said nine
parameters. The output layer consisted of one neuron representing the ultimate shear capacity of the of the deep beam. There
are two hidden layers, the first layer is having nine neurons and second hidden layer has eighteen neurons. The transfer
function used is tansig while as it is purelin for output layer. The complete architecture of the network is shown in figure 1.1.
Figure: 1.1; Architecture of Neural Network
V. TRAINING OF NEURAL NETWORK:
In a multilayer feed-forward neural network, training refers to the iterative process involving the presentation of
training data to the network, the invocation of learning rules to modify the connection weights, and, usually, the evolution of
the network architecture, such that the knowledge embedded in the training data is appropriately captured by the weight
structure of the network. During the training phase, the training data consist of input and associated output pairs representing
the problem that we want the network to learn. The training set is used to reduce the ANN error. The error on the validation
set is monitored during the training process. The validation set error will normally decrease during the initial phase of
training, as does the training set error. However, when the network begins to overfit the data, the error on the validation set
will typically begin to rise. When the validation set error increases for a specified number of epochs, the training is stopped.
The test set is used as further check for generalization, but has not any effect on the training. Over fittings and predictions
ISSN: 2278-7461 www.ijeijournal.com P a g e | 21
3. Modeling of Deep Beams Using Neural Network
in training and outputs of NNs are commonly influenced by the number of hidden layers and neurons in each hidden layer.
Therefore, trial and error approach was carried out to choose an adequate number of hidden layers and number of neurons in
each hidden layer as shown in figure 1.1 above. In addition, NN performance is significantly dependent on initial conditions,
such as initial weights and biases, back-propagation algorithms, and learning rate.
In this study, the training phase of ANN is implemented by using the back-propagation learning algorithm
“trainlm”. Trainlm is a network training function that updates weight and bias values according to Levenberg-Marquardt
optimization. A backpropagation network typically starts out with a random set of weights. The network adjusts its weights
each time it sees an input–output pair. Each pair requires two stages: a forward pass and a backward pass. The forward pass
involves presenting a sample input to the network and letting activations flow until they reach the output layer. During the
backward pass, the network’s actual output (from the forward pass) is compared with the target output and error estimates
are computed for the output units. The weights connected to the output units can be adjusted to reduce those errors. We can
then use the error estimates of the output units to derive error estimates for the units in the hidden layers. Lastly, errors are
propagated back to the connections stemming from the input units.
The back-propagation algorithm updates its weights incrementally, after seeing each input–output pair. After it has
seen all the input–output pairs (and adjusted its weights many times), it is said that one epoch has been completed. Training a
back-propagation network usually requires many thousands of epochs. An error criteria for the network output is usually
chosen and the maximum number of iterations is set to provide a condition for terminating the learning process. The
performance of ANN can be monitored by monitoring the training error with respect to the number of iterations. If the
network “learns,” the error will approach a minimum value. After the training phase, the ANN can be tested for the other set
of patterns, which the network has never seen, where the final values of the weights obtained in the training phase are used.
No weight modification is involved in the testing phase.
During training of the NN, the MSE (mean square error) of the training set was reduced to less than 0.0004 and
MSE of validation set was reduced to less than 0.02 as shown in figure 1.2. After 15 epochs, the validation set error started
to rise. So, training was stopped after 15 epochs and developed neural network model saved. Then, developed neural
network was validated with the new data to check the generalization of network, discussed in the next section.
In this way, the model was developed for predicting the shear capacity of deep beams, and henceforth, the said neural
network model is referred as “PROPOSED MODEL”.
Performance is 6.56791e-005, Goal is 1e-006
1
10
Goa l
0
10 Te st
Va lida tion
-1 Tra ining
10
-2
10
-3
MSE
10
-4
10
-5
10
-6
10
-7
10
0 5 10 15
E p o c h s
Figure: 1.2; Training session of Network
VI. VALIDATION OF PROPOSED MODEL:
Validation of the proposed model is equally important as its development. Without validation, we can’t rely on the
model. For this purpose, the predictions of shear capacity by the proposed model were compared with the experimental
results (from literature) of sixty beams. The model was also compared with the predictions of five national codes, viz ACI-
318, CIRIA Guide-2, EC-2, CSA and KBCS. The results of the proposed model are closer to the experimental values than
any other national code. The mean error of the proposed model was found equal to 17.41 % which was lowest error,
compared to the five national codes. The root mean square deviation of proposed model was 326.21, which was again the
lowest. Hence, the confidence level of the said model is best, when compared with the expressions of five national codes.
The detailed comparison is presented in Table 1.2.
Table: 1.2; Comparison of predictions with experimental results
Expe
Proposed
rime KBCS EC-2 CIRIA CSA ACI
Beam Model
ntal
Designa
Shea Shea % Shea Shear % Shea Shea % Shea %
tion % %
r r Erro r Capac Erro r r Erro r Erro
Error Error
Capa Capa r Capa ity r Capa Capa r Capa r
ISSN: 2278-7461 www.ijeijournal.com P a g e | 22
5. Modeling of Deep Beams Using Neural Network
% Mean
44.6 50.16 36.2 46.47 40.1 17.4
Error
RMS 872.3 718.5 797.9 511.9 326.
As seen the shear capacity predicted by proposed model was much closer to the experimental results as compared
to different codes. The mean error and root mean square deviation of shear capacity predicted by the proposed model was
much lesser as compared to different codes. Therefore, proposed model was used for carrying out the parametric study,
whereby influence of various parameters on shear capacity was studied.
Influence of a/d interms of depth
3000
2500
2000
Shear capacity (kN)
1500
1000
500
0
0.5 1 1.5 2
a/d ratio
Influence of a/d in terms of shear span
3000
2500
2000
Shear Capacity (kN)
1500
1000
500
0
0.5 1 1.5 2
a/d Ratio
Influence of percentage of Longitudinal steel
3000
2500
2000
Shear Capacity (kN)
1500
1000
500
0
0.5 1 1.5 2 2.5
Percentage of Longitudinal steel
ISSN: 2278-7461 www.ijeijournal.com P a g e | 24
6. Modeling of Deep Beams Using Neural Network
Influence of a/d in terms of both shear span and depth
3500
3000
2500
Shear Capacity (kN)
2000
1500
1000
500
0
0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8
a/d Ratio
Influence of Compressive Strength of Concrete
1900
1800
1700
Shear Capacity (kN)
1600
1500
1400
1300
1200
30 32 34 36 38 40 42 44 46 48 50
Compressive Strength of Concrete (N/mm2)
Influence of percentage of Vertical Steel
2800
2600
2400
2200
Shear Capacity (kN)
2000
1800
1600
1400
1200
1000
0.6 0.8 1 1.2 1.4 1.6
Percentage of Vertical Steel
ISSN: 2278-7461 www.ijeijournal.com P a g e | 25
7. Modeling of Deep Beams Using Neural Network
Influence of Percentage of Horizontal web steel
5000
4500
4000
Shear Capacity (kN)
3500
3000
2500
0.5 1 1.5 2 2.5
Percentage of Horizontal web steel
VII. CONCLUSIONS
The proposed model was studied by comparing the shear strength predictions with experimental data (from
technical literature) and five national codes viz, KBCS, EC-2, CIRIA Guide -2, CSA and ACI-318 in general. The above
comparisons were also made through parametric study. In both the cases proposed model showed good agreements,
indicating the consistency of the proposed model. The proposed model adequately predicts the shear capacity of deep beams
for different values of influencing parameters like longitudinal steel, shear span to depth ratio etc. Neural Networks have a
great capacity of providing the solution to complex problems like deep beams.
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ISSN: 2278-7461 www.ijeijournal.com P a g e | 26