We investigated the parametric optimization on incremental sheet forming of stainless steel using Grey Relational Analysis(GRA) coupled with Principal Component Analysis(PCA). AISI 316L stainless steel sheets were use...We investigated the parametric optimization on incremental sheet forming of stainless steel using Grey Relational Analysis(GRA) coupled with Principal Component Analysis(PCA). AISI 316L stainless steel sheets were used to develop double wall angle pyramid with aid of tungsten carbide tool. GRA coupled with PCA was used to plan the experiment conditions. Control factors such as Tool Diameter(TD), Step Depth(SD), Bottom Wall Angle(BWA), Feed Rate(FR) and Spindle Speed(SS) on Top Wall Angle(TWA) and Top Wall Angle Surface Roughness(TWASR) have been studied. Wall angle increases with increasing tool diameter due to large contact area between tool and workpiece. As the step depth, feed rate and spindle speed increase,TWASR decreases with increasing tool diameter. As the step depth increasing, the hydrostatic stress is raised causing severe cracks in the deformed surface. Hence it was concluded that the proposed hybrid method was suitable for optimizing the factors and response.展开更多
Deep neural networks excel at image identification and computer vision applications such as visual product search, facial recognition, medical image analysis, object detection, semantic segmentation,instance segmentat...Deep neural networks excel at image identification and computer vision applications such as visual product search, facial recognition, medical image analysis, object detection, semantic segmentation,instance segmentation, and many others. In image and video recognition applications, convolutional neural networks(CNNs) are widely employed. These networks provide better performance but at a higher cost of computation. With the advent of big data, the growing scale of datasets has made processing and model training a time-consuming operation, resulting in longer training times. Moreover, these large scale datasets contain redundant data points that have minimum impact on the final outcome of the model. To address these issues, an accelerated CNN system is proposed for speeding up training by eliminating the noncritical data points during training alongwith a model compression method. Furthermore, the identification of the critical input data is performed by aggregating the data points at two levels of granularity which are used for evaluating the impact on the model output.Extensive experiments are conducted using the proposed method on CIFAR-10 dataset on ResNet models giving a 40% reduction in number of FLOPs with a degradation of just 0.11% accuracy.展开更多
In the present work, the effect of hexagonal cell size of the core on the fundamental natural frequency of FRP honey-comb sandwich panels has been analyzed both experimentally and by finite element technique. Experime...In the present work, the effect of hexagonal cell size of the core on the fundamental natural frequency of FRP honey-comb sandwich panels has been analyzed both experimentally and by finite element technique. Experimental Modal tests were conducted on hexagonal cell honeycombs ranging in size from 8 mm to 20 mm maintaining the facing thickness constant at around 1mm with two different boundary conditions viz C-F-F-F and C-F-C-F. The traditional “strike method” has been used to measure the vibration properties. The modal characteristics of the specimens have been obtained by studying its impulse response. Each specimen has been subjected to impulses through a hard tipped hammer which is provided with a force transducer and the response has been measured through the accelerometer. The impulse and the response are processed through a computer aided FFT Analyzing test system in order to extract the modal parameters with the aid of software. Theoretical investigations have been attempted with appropriate assumptions to understand the behavior of the honeycomb sandwich panels during dynamic loading and to validate experimental results. Finite Element modeling has been done treating the facing as an orthotropic laminate and Core as orthotropic with different elastic constants as recommended in the literature. The results are presented which show that the theoretical model can accurately predict the fundamental frequency and how honeycombs with difference cell size will perform under dynamic loads.展开更多
Parkinson’s disease is a neurogenerative disorder and it is difficult to diagnose as no therapies may slow down its progression.This paper contributes a novel analytic system for Parkinson’s Disease Prediction mecha...Parkinson’s disease is a neurogenerative disorder and it is difficult to diagnose as no therapies may slow down its progression.This paper contributes a novel analytic system for Parkinson’s Disease Prediction mechanism using Improved Radial Basis Function Neural Network(IRBFNN).Particle swarm optimization(PSO)with K-means is used to find the hidden neuron’s centers to improve the accuracy of IRBFNN.The performance of RBFNN is seriously affected by the centers of hidden neurons.Conventionally K-means was used to find the centers of hidden neurons.The problem of sensitiveness to the random initial centroid in K-means degrades the performance of RBFNN.Thus,a metaheuristic algorithm called PSO integrated with K-means alleviates initial random centroid and computes optimal centers for hidden neurons in IRBFNN.The IRBFNN uses Particle swarm optimization K-means to find the centers of hidden neurons and the PSO K-means was designed to evaluate the fitness measures such as Intracluster distance and Intercluster distance.Experimentation have been performed on three Parkinson’s datasets obtained from the UCI repository.The proposed IRBFNN is compared with other variations of RBFNN,conventional machine learning algorithms and other Parkinson’s Disease prediction algorithms.The proposed IRBFNN achieves an accuracy of 98.73%,98.47%and 99.03%for three Parkinson’s datasets taken for experimentation.The experimental results show that IRBFNN maximizes the accuracy in predicting Parkinson’s disease with minimum root mean square error.展开更多
In the context of improved navigation for micro aerial vehicles,a new scene recognition visual descriptor,called spatial color gist wavelet descriptor(SCGWD),is proposed.SCGWD was developed by combining proposed Ohta ...In the context of improved navigation for micro aerial vehicles,a new scene recognition visual descriptor,called spatial color gist wavelet descriptor(SCGWD),is proposed.SCGWD was developed by combining proposed Ohta color-GIST wavelet descriptors with census transform histogram(CENTRIST)spatial pyramid representation descriptors for categorizing indoor versus outdoor scenes.A binary and multiclass support vector machine(SVM)classifier with linear and non-linear kernels was used to classify indoor versus outdoor scenes and indoor scenes,respectively.In this paper,we have also discussed the feature extraction methodology of several,state-of-the-art visual descriptors,and four proposed visual descriptors(Ohta color-GIST descriptors,Ohta color-GIST wavelet descriptors,enhanced Ohta color histogram descriptors,and SCGWDs),in terms of experimental perspectives.The proposed enhanced Ohta color histogram descriptors,Ohta color-GIST descriptors,Ohta color-GIST wavelet descriptors,SCGWD,and state-of-the-art visual descriptors were evaluated,using the Indian Institute of Technology Madras Scene Classification Image Database two,an Indoor-Outdoor Dataset,and the Massachusetts Institute of Technology indoor scene classification dataset[(MIT)-67].Experimental results showed that the indoor versus outdoor scene recognition algorithm,employing SVM with SCGWDs,produced the highest classification rates(CRs)—95.48%and 99.82%using radial basis function kernel(RBF)kernel and 95.29%and 99.45%using linear kernel for the IITM SCID2 and Indoor-Outdoor datasets,respectively.The lowest CRs—2.08%and 4.92%,respectively—were obtained when RBF and linear kernels were used with the MIT-67 dataset.In addition,higher CRs,precision,recall,and area under the receiver operating characteristic curve values were obtained for the proposed SCGWDs,in comparison with state-of-the-art visual descriptors.展开更多
Even though wind energy is a deep-rooted technology, but not yet mature and hence there are bounteous scopes for improvement to reduce the cost of wind energy. An experimental investigation has been carried out on 1:2...Even though wind energy is a deep-rooted technology, but not yet mature and hence there are bounteous scopes for improvement to reduce the cost of wind energy. An experimental investigation has been carried out on 1:25 scaled S809 aerofoil blade featuring boundary layer fence at various span wise location. Quantifying electrical power obtained by rotation of wind turbine rotor coupled with dynamic testing system. A baseline model with no flow control and an upgraded model with detachable boundary layer fence have been studied in the wind tunnel. For upgraded model, fences were placed along the location of 40% to 90% of the blade span. The rotor blades are then tested dynamically in wind tunnel at open terrain condition for 7 m/s, 9 m/s and 11 m/s velocities. In order to study the effect of boundary layer fence test has been carried out in the low speed wind tunnel having test section of size 0.9 m × 1.2 m × 2 m. Scope corder DL 750 is used to measure time varying voltage and proximity sensor with its compatible display unit is used to measure the rotor RPM. The flow behaviour was found to be considerably favourable from conventional rotor blades. Installation of fence has been found promising for increased energy extraction from air column by controlling the three dimensional span wise flow. Results demonstrate the potential of the proposed model which can obtain a maximum of about 11.8% increase in the power. In addition, the significance of the location of wing fence and blade pitch angle has been analysed.展开更多
The data in the cloud is protected by various mechanisms to ensure security aspects and user’s privacy.But,deceptive attacks like phishing might obtain the user’s data and use it for malicious purposes.In Spite of m...The data in the cloud is protected by various mechanisms to ensure security aspects and user’s privacy.But,deceptive attacks like phishing might obtain the user’s data and use it for malicious purposes.In Spite of much techno-logical advancement,phishing acts as thefirst step in a series of attacks.With technological advancements,availability and access to the phishing kits has improved drastically,thus making it an ideal tool for the hackers to execute the attacks.The phishing cases indicate use of foreign characters to disguise the ori-ginal Uniform Resource Locator(URL),typosquatting the popular domain names,using reserved characters for re directions and multi-chain phishing.Such phishing URLs can be stored as a part of the document and uploaded in the cloud,providing a nudge to hackers in cloud storage.The cloud servers are becoming the trusted tool for executing these attacks.The prevailing software for blacklisting phishing URLs lacks the security for multi-level phishing and expects security from the client’s end(browser).At the same time,the avalanche effect and immut-ability of block-chain proves to be a strong source of security.Considering these trends in technology,a block-chain basedfiltering implementation for preserving the integrity of user data stored in the cloud is proposed.The proposed Phish Block detects the homographic phishing URLs with accuracy of 91%which assures the security in cloud storage.展开更多
This paper proposes an algorithm for scheduling Virtual Machines(VM)with energy saving strategies in the physical servers of cloud data centers.Energy saving strategy along with a solution for productive resource util...This paper proposes an algorithm for scheduling Virtual Machines(VM)with energy saving strategies in the physical servers of cloud data centers.Energy saving strategy along with a solution for productive resource utilizationfor VM deployment in cloud data centers is modeled by a combination of“VirtualMachine Scheduling using Bayes Theorem”algorithm(VMSBT)and Virtual Machine Migration(VMMIG)algorithm.It is shown that the overall data center’sconsumption of energy is minimized with a combination of VMSBT algorithmand Virtual Machine Migration(VMMIG)algorithm.Virtual machine migrationbetween the active physical servers in the data center is carried out at periodicalintervals as and when a physical server is identified to be under-utilized.In VMscheduling,the optimal data centers are clustered using Bayes Theorem and VMsare scheduled to appropriate data center using the selection policy that identifiesthe cluster with lesser energy consumption.Clustering using Bayes rule minimizesthe number of server choices for the selection policy.Application of Bayestheorem in clustering has enabled the proposed VMSBT algorithm to schedule thevirtual machines on to the physical server with minimal execution time.The proposedalgorithm is compared with other energy aware VM allocations algorithmsviz.“Ant-Colony”optimization-based(ACO)allocation scheme and“min-min”scheduling algorithm.The experimental simulation results prove that the proposedcombination of‘VMSBT’and‘VMMIG’algorithm outperforms othertwo strategies and is highly effective in scheduling VMs with reduced energy consumptionby utilizing the existing resources productively and by minimizing thenumber of active servers at any given point of time.展开更多
Environmental loads that act on marine structures are highly non-deterministic in general.Estimating these loads is a basic requirement for their structural design,but their response is far beyond just counteracting t...Environmental loads that act on marine structures are highly non-deterministic in general.Estimating these loads is a basic requirement for their structural design,but their response is far beyond just counteracting the loads[1,2].The marine environment poses more challenges starting from the choice of material,structural form,design methods,construction techniques,inspection methods,repair,and retrofitting.展开更多
This work proposes an improved inertia weight update method and position update method in Particle Swarm Optimization (PSO) to enhance the convergence and mean square error of channel equalizer. The search abilities o...This work proposes an improved inertia weight update method and position update method in Particle Swarm Optimization (PSO) to enhance the convergence and mean square error of channel equalizer. The search abilities of PSO are managed by the key parameter Inertia Weight (IW). A higher value leads to global search whereas a smaller value shifts the search to local which makes convergence faster. Different approaches are reported in literature to improve PSO by modifying inertia weight. This work investigates the performance of the existing PSO variants related to time varying inertia weight methods and proposes new strategies to improve the convergence and mean square error of channel equalizer. Also the position update method in PSO is modified to achieve better convergence in channel equalization. The simulation presents the enhanced performance of the proposed techniques in transversal and decision feedback models. The simulation results also analyze the superiority in linear and nonlinear channel conditions.展开更多
The objective of this paper was to predict the residual strength of post impacted carbon/epoxy composite laminates using an online acoustic emission (AE) monitoring and artificial neural networks (ANN). The laminates ...The objective of this paper was to predict the residual strength of post impacted carbon/epoxy composite laminates using an online acoustic emission (AE) monitoring and artificial neural networks (ANN). The laminates were made from eight-layered carbon (in woven mat form) with epoxy as the binding medium by hand lay-up technique and cured at a pressure of 100 kg/cm2 under room temperature using a 30 ton capacity compression molding machine for 24 h. 21 tensile specimens (ASTM D3039 standard) were cut from the cross ply laminates. 16 specimens were subjected to impact load from three different heights using a Fractovis Plus drop impact tester. Both impacted and non-impacted specimens were subjected to uniaxial tension under the acoustic emission monitoring using a 100 kN FIE servo hydraulic universal testing machine. The dominant AE parameters such as counts, energy, duration, rise time and amplitude are recorded during monitoring. Cumulative counts corresponding to the amplitude ranges obtained during the tensile testing are used to train the network. This network can be used to predict the failure load of a similar specimen subjected to uniaxial tension under acoustic emission monitoring for certain percentage of the average failure load.展开更多
This work presents a machine-learning(ML)algorithm for maximum power point tracking(MPPT)of an isolated photovoltaic(PV)system.Due to the dynamic nature of weather conditions,the energy generation of PV systems is non...This work presents a machine-learning(ML)algorithm for maximum power point tracking(MPPT)of an isolated photovoltaic(PV)system.Due to the dynamic nature of weather conditions,the energy generation of PV systems is non-linear.Since there is no specific method for effectively dealing with the non-linear data,the use of ML methods to operate the PV system at its maximum power point(MPP)is desirable.A strategy based on the decision-tree(DT)regression ML algorithm is proposed in this work to determine the MPP of a PV system.The data were gleaned from the technical specifications of the PV module and were used to train and test the DT.These algorithms predict the maximum power available and the associated voltage of the module for a defined amount of irradiance and temperature.The boost converter duty cycle was determined using predicted values.The simulation was carried out for a 10-W solar panel with a short-circuit current of 0.62 A and an open-circuit voltage of 21.50 V at 1000 W/m^(2) irradiance and a temperature of 25℃.The simulation findings demonstrate that the proposed method compelled the PV panel to work at the MPP predicted by DTs compared to the existing topologies such asβ-MPPT,cuckoo search and artificial neural network results.From the proposed algorithm,efficiency has been improved by>93.93%in the steady state despite erratic irradiance and temperatures.展开更多
文摘We investigated the parametric optimization on incremental sheet forming of stainless steel using Grey Relational Analysis(GRA) coupled with Principal Component Analysis(PCA). AISI 316L stainless steel sheets were used to develop double wall angle pyramid with aid of tungsten carbide tool. GRA coupled with PCA was used to plan the experiment conditions. Control factors such as Tool Diameter(TD), Step Depth(SD), Bottom Wall Angle(BWA), Feed Rate(FR) and Spindle Speed(SS) on Top Wall Angle(TWA) and Top Wall Angle Surface Roughness(TWASR) have been studied. Wall angle increases with increasing tool diameter due to large contact area between tool and workpiece. As the step depth, feed rate and spindle speed increase,TWASR decreases with increasing tool diameter. As the step depth increasing, the hydrostatic stress is raised causing severe cracks in the deformed surface. Hence it was concluded that the proposed hybrid method was suitable for optimizing the factors and response.
文摘Deep neural networks excel at image identification and computer vision applications such as visual product search, facial recognition, medical image analysis, object detection, semantic segmentation,instance segmentation, and many others. In image and video recognition applications, convolutional neural networks(CNNs) are widely employed. These networks provide better performance but at a higher cost of computation. With the advent of big data, the growing scale of datasets has made processing and model training a time-consuming operation, resulting in longer training times. Moreover, these large scale datasets contain redundant data points that have minimum impact on the final outcome of the model. To address these issues, an accelerated CNN system is proposed for speeding up training by eliminating the noncritical data points during training alongwith a model compression method. Furthermore, the identification of the critical input data is performed by aggregating the data points at two levels of granularity which are used for evaluating the impact on the model output.Extensive experiments are conducted using the proposed method on CIFAR-10 dataset on ResNet models giving a 40% reduction in number of FLOPs with a degradation of just 0.11% accuracy.
文摘In the present work, the effect of hexagonal cell size of the core on the fundamental natural frequency of FRP honey-comb sandwich panels has been analyzed both experimentally and by finite element technique. Experimental Modal tests were conducted on hexagonal cell honeycombs ranging in size from 8 mm to 20 mm maintaining the facing thickness constant at around 1mm with two different boundary conditions viz C-F-F-F and C-F-C-F. The traditional “strike method” has been used to measure the vibration properties. The modal characteristics of the specimens have been obtained by studying its impulse response. Each specimen has been subjected to impulses through a hard tipped hammer which is provided with a force transducer and the response has been measured through the accelerometer. The impulse and the response are processed through a computer aided FFT Analyzing test system in order to extract the modal parameters with the aid of software. Theoretical investigations have been attempted with appropriate assumptions to understand the behavior of the honeycomb sandwich panels during dynamic loading and to validate experimental results. Finite Element modeling has been done treating the facing as an orthotropic laminate and Core as orthotropic with different elastic constants as recommended in the literature. The results are presented which show that the theoretical model can accurately predict the fundamental frequency and how honeycombs with difference cell size will perform under dynamic loads.
文摘Parkinson’s disease is a neurogenerative disorder and it is difficult to diagnose as no therapies may slow down its progression.This paper contributes a novel analytic system for Parkinson’s Disease Prediction mechanism using Improved Radial Basis Function Neural Network(IRBFNN).Particle swarm optimization(PSO)with K-means is used to find the hidden neuron’s centers to improve the accuracy of IRBFNN.The performance of RBFNN is seriously affected by the centers of hidden neurons.Conventionally K-means was used to find the centers of hidden neurons.The problem of sensitiveness to the random initial centroid in K-means degrades the performance of RBFNN.Thus,a metaheuristic algorithm called PSO integrated with K-means alleviates initial random centroid and computes optimal centers for hidden neurons in IRBFNN.The IRBFNN uses Particle swarm optimization K-means to find the centers of hidden neurons and the PSO K-means was designed to evaluate the fitness measures such as Intracluster distance and Intercluster distance.Experimentation have been performed on three Parkinson’s datasets obtained from the UCI repository.The proposed IRBFNN is compared with other variations of RBFNN,conventional machine learning algorithms and other Parkinson’s Disease prediction algorithms.The proposed IRBFNN achieves an accuracy of 98.73%,98.47%and 99.03%for three Parkinson’s datasets taken for experimentation.The experimental results show that IRBFNN maximizes the accuracy in predicting Parkinson’s disease with minimum root mean square error.
文摘In the context of improved navigation for micro aerial vehicles,a new scene recognition visual descriptor,called spatial color gist wavelet descriptor(SCGWD),is proposed.SCGWD was developed by combining proposed Ohta color-GIST wavelet descriptors with census transform histogram(CENTRIST)spatial pyramid representation descriptors for categorizing indoor versus outdoor scenes.A binary and multiclass support vector machine(SVM)classifier with linear and non-linear kernels was used to classify indoor versus outdoor scenes and indoor scenes,respectively.In this paper,we have also discussed the feature extraction methodology of several,state-of-the-art visual descriptors,and four proposed visual descriptors(Ohta color-GIST descriptors,Ohta color-GIST wavelet descriptors,enhanced Ohta color histogram descriptors,and SCGWDs),in terms of experimental perspectives.The proposed enhanced Ohta color histogram descriptors,Ohta color-GIST descriptors,Ohta color-GIST wavelet descriptors,SCGWD,and state-of-the-art visual descriptors were evaluated,using the Indian Institute of Technology Madras Scene Classification Image Database two,an Indoor-Outdoor Dataset,and the Massachusetts Institute of Technology indoor scene classification dataset[(MIT)-67].Experimental results showed that the indoor versus outdoor scene recognition algorithm,employing SVM with SCGWDs,produced the highest classification rates(CRs)—95.48%and 99.82%using radial basis function kernel(RBF)kernel and 95.29%and 99.45%using linear kernel for the IITM SCID2 and Indoor-Outdoor datasets,respectively.The lowest CRs—2.08%and 4.92%,respectively—were obtained when RBF and linear kernels were used with the MIT-67 dataset.In addition,higher CRs,precision,recall,and area under the receiver operating characteristic curve values were obtained for the proposed SCGWDs,in comparison with state-of-the-art visual descriptors.
文摘Even though wind energy is a deep-rooted technology, but not yet mature and hence there are bounteous scopes for improvement to reduce the cost of wind energy. An experimental investigation has been carried out on 1:25 scaled S809 aerofoil blade featuring boundary layer fence at various span wise location. Quantifying electrical power obtained by rotation of wind turbine rotor coupled with dynamic testing system. A baseline model with no flow control and an upgraded model with detachable boundary layer fence have been studied in the wind tunnel. For upgraded model, fences were placed along the location of 40% to 90% of the blade span. The rotor blades are then tested dynamically in wind tunnel at open terrain condition for 7 m/s, 9 m/s and 11 m/s velocities. In order to study the effect of boundary layer fence test has been carried out in the low speed wind tunnel having test section of size 0.9 m × 1.2 m × 2 m. Scope corder DL 750 is used to measure time varying voltage and proximity sensor with its compatible display unit is used to measure the rotor RPM. The flow behaviour was found to be considerably favourable from conventional rotor blades. Installation of fence has been found promising for increased energy extraction from air column by controlling the three dimensional span wise flow. Results demonstrate the potential of the proposed model which can obtain a maximum of about 11.8% increase in the power. In addition, the significance of the location of wing fence and blade pitch angle has been analysed.
文摘The data in the cloud is protected by various mechanisms to ensure security aspects and user’s privacy.But,deceptive attacks like phishing might obtain the user’s data and use it for malicious purposes.In Spite of much techno-logical advancement,phishing acts as thefirst step in a series of attacks.With technological advancements,availability and access to the phishing kits has improved drastically,thus making it an ideal tool for the hackers to execute the attacks.The phishing cases indicate use of foreign characters to disguise the ori-ginal Uniform Resource Locator(URL),typosquatting the popular domain names,using reserved characters for re directions and multi-chain phishing.Such phishing URLs can be stored as a part of the document and uploaded in the cloud,providing a nudge to hackers in cloud storage.The cloud servers are becoming the trusted tool for executing these attacks.The prevailing software for blacklisting phishing URLs lacks the security for multi-level phishing and expects security from the client’s end(browser).At the same time,the avalanche effect and immut-ability of block-chain proves to be a strong source of security.Considering these trends in technology,a block-chain basedfiltering implementation for preserving the integrity of user data stored in the cloud is proposed.The proposed Phish Block detects the homographic phishing URLs with accuracy of 91%which assures the security in cloud storage.
文摘This paper proposes an algorithm for scheduling Virtual Machines(VM)with energy saving strategies in the physical servers of cloud data centers.Energy saving strategy along with a solution for productive resource utilizationfor VM deployment in cloud data centers is modeled by a combination of“VirtualMachine Scheduling using Bayes Theorem”algorithm(VMSBT)and Virtual Machine Migration(VMMIG)algorithm.It is shown that the overall data center’sconsumption of energy is minimized with a combination of VMSBT algorithmand Virtual Machine Migration(VMMIG)algorithm.Virtual machine migrationbetween the active physical servers in the data center is carried out at periodicalintervals as and when a physical server is identified to be under-utilized.In VMscheduling,the optimal data centers are clustered using Bayes Theorem and VMsare scheduled to appropriate data center using the selection policy that identifiesthe cluster with lesser energy consumption.Clustering using Bayes rule minimizesthe number of server choices for the selection policy.Application of Bayestheorem in clustering has enabled the proposed VMSBT algorithm to schedule thevirtual machines on to the physical server with minimal execution time.The proposedalgorithm is compared with other energy aware VM allocations algorithmsviz.“Ant-Colony”optimization-based(ACO)allocation scheme and“min-min”scheduling algorithm.The experimental simulation results prove that the proposedcombination of‘VMSBT’and‘VMMIG’algorithm outperforms othertwo strategies and is highly effective in scheduling VMs with reduced energy consumptionby utilizing the existing resources productively and by minimizing thenumber of active servers at any given point of time.
文摘Environmental loads that act on marine structures are highly non-deterministic in general.Estimating these loads is a basic requirement for their structural design,but their response is far beyond just counteracting the loads[1,2].The marine environment poses more challenges starting from the choice of material,structural form,design methods,construction techniques,inspection methods,repair,and retrofitting.
文摘This work proposes an improved inertia weight update method and position update method in Particle Swarm Optimization (PSO) to enhance the convergence and mean square error of channel equalizer. The search abilities of PSO are managed by the key parameter Inertia Weight (IW). A higher value leads to global search whereas a smaller value shifts the search to local which makes convergence faster. Different approaches are reported in literature to improve PSO by modifying inertia weight. This work investigates the performance of the existing PSO variants related to time varying inertia weight methods and proposes new strategies to improve the convergence and mean square error of channel equalizer. Also the position update method in PSO is modified to achieve better convergence in channel equalization. The simulation presents the enhanced performance of the proposed techniques in transversal and decision feedback models. The simulation results also analyze the superiority in linear and nonlinear channel conditions.
文摘The objective of this paper was to predict the residual strength of post impacted carbon/epoxy composite laminates using an online acoustic emission (AE) monitoring and artificial neural networks (ANN). The laminates were made from eight-layered carbon (in woven mat form) with epoxy as the binding medium by hand lay-up technique and cured at a pressure of 100 kg/cm2 under room temperature using a 30 ton capacity compression molding machine for 24 h. 21 tensile specimens (ASTM D3039 standard) were cut from the cross ply laminates. 16 specimens were subjected to impact load from three different heights using a Fractovis Plus drop impact tester. Both impacted and non-impacted specimens were subjected to uniaxial tension under the acoustic emission monitoring using a 100 kN FIE servo hydraulic universal testing machine. The dominant AE parameters such as counts, energy, duration, rise time and amplitude are recorded during monitoring. Cumulative counts corresponding to the amplitude ranges obtained during the tensile testing are used to train the network. This network can be used to predict the failure load of a similar specimen subjected to uniaxial tension under acoustic emission monitoring for certain percentage of the average failure load.
文摘This work presents a machine-learning(ML)algorithm for maximum power point tracking(MPPT)of an isolated photovoltaic(PV)system.Due to the dynamic nature of weather conditions,the energy generation of PV systems is non-linear.Since there is no specific method for effectively dealing with the non-linear data,the use of ML methods to operate the PV system at its maximum power point(MPP)is desirable.A strategy based on the decision-tree(DT)regression ML algorithm is proposed in this work to determine the MPP of a PV system.The data were gleaned from the technical specifications of the PV module and were used to train and test the DT.These algorithms predict the maximum power available and the associated voltage of the module for a defined amount of irradiance and temperature.The boost converter duty cycle was determined using predicted values.The simulation was carried out for a 10-W solar panel with a short-circuit current of 0.62 A and an open-circuit voltage of 21.50 V at 1000 W/m^(2) irradiance and a temperature of 25℃.The simulation findings demonstrate that the proposed method compelled the PV panel to work at the MPP predicted by DTs compared to the existing topologies such asβ-MPPT,cuckoo search and artificial neural network results.From the proposed algorithm,efficiency has been improved by>93.93%in the steady state despite erratic irradiance and temperatures.
