In order to accomplish the task of object recognition in natural scenes,a new object recognition algorithm based on an improved convolutional neural network(CNN)is proposed.First,candidate object windows are extracted...In order to accomplish the task of object recognition in natural scenes,a new object recognition algorithm based on an improved convolutional neural network(CNN)is proposed.First,candidate object windows are extracted from the original image.Then,candidate object windows are input into the improved CNN model to obtain deep features.Finally,the deep features are input into the Softmax and the confidence scores of classes are obtained.The candidate object window with the highest confidence score is selected as the object recognition result.Based on AlexNet,Inception V1 is introduced into the improved CNN and the fully connected layer is replaced by the average pooling layer,which widens the network and deepens the network at the same time.Experimental results show that the improved object recognition algorithm can obtain better recognition results in multiple natural scene images,and has a higher degree of accuracy than the classical algorithms in the field of object recognition.展开更多
Inspired by genetic algorithm(GA),an improved genetic algorithm(IGA)is proposed.It inherits the main idea of evolutionary computing,avoids the process of coding and decoding inorder to probe the solution in the state ...Inspired by genetic algorithm(GA),an improved genetic algorithm(IGA)is proposed.It inherits the main idea of evolutionary computing,avoids the process of coding and decoding inorder to probe the solution in the state space directly and has distributed computing version.Soit is faster and gives higher precision.Aided by IGA,a new optimization strategy for theflexibility analysis and retrofitting of existing heat exchanger networks is presented.A case studyshows that IGA has the ability of finding the global optimum with higher speed and better preci-sion.展开更多
Energy conservation and congestion control are widely researched topics in Wireless Sensor Networks in recent years. The main objective is to develop a model to find the optimized path on the basis of distance between...Energy conservation and congestion control are widely researched topics in Wireless Sensor Networks in recent years. The main objective is to develop a model to find the optimized path on the basis of distance between source and destination and the residual energy of the node. This paper shows an implementation of nature inspired improved Bat Algorithm to control congestion in Wireless Sensor Networks at transport layer. The Algorithm has been applied on the fitness function to obtain an optimum solution. Simulation results have shown improvement in parameters like network lifetime and throughput as compared with CODA (Congestion Detection and Avoidance), PSO (Particle Swarm Optimization) algorithm and ACO (Ant Colony Optimization).展开更多
The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optim...The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optimalconfiguration of measurement points, this paper presents an optimal configuration scheme for fault locationmeasurement points in DC distribution networks based on an improved particle swarm optimization algorithm.Initially, a measurement point distribution optimization model is formulated, leveraging compressive sensing.The model aims to achieve the minimum number of measurement points while attaining the best compressivesensing reconstruction effect. It incorporates constraints from the compressive sensing algorithm and networkwide viewability. Subsequently, the traditional particle swarm algorithm is enhanced by utilizing the Haltonsequence for population initialization, generating uniformly distributed individuals. This enhancement reducesindividual search blindness and overlap probability, thereby promoting population diversity. Furthermore, anadaptive t-distribution perturbation strategy is introduced during the particle update process to enhance the globalsearch capability and search speed. The established model for the optimal configuration of measurement points issolved, and the results demonstrate the efficacy and practicality of the proposed method. The optimal configurationreduces the number of measurement points, enhances localization accuracy, and improves the convergence speedof the algorithm. These findings validate the effectiveness and utility of the proposed approach.展开更多
A hard problem that hinders the movement of waxy crude oil is wax deposition in oil pipelines.To ensure the safe operation of crude oil pipelines,an accurate model must be developed to predict the rate of wax depositi...A hard problem that hinders the movement of waxy crude oil is wax deposition in oil pipelines.To ensure the safe operation of crude oil pipelines,an accurate model must be developed to predict the rate of wax deposition in crude oil pipelines.Aiming at the shortcomings of the ENN prediction model,which easily falls into the local minimum value and weak generalization ability in the implementation process,an optimized ENN prediction model based on the IRSA is proposed.The validity of the new model was confirmed by the accurate prediction of two sets of experimental data on wax deposition in crude oil pipelines.The two groups of crude oil wax deposition rate case prediction results showed that the average absolute percentage errors of IRSA-ENN prediction models is 0.5476% and 0.7831%,respectively.Additionally,it shows a higher prediction accuracy compared to the ENN prediction model.In fact,the new model established by using the IRSA to optimize ENN can optimize the initial weights and thresholds in the prediction process,which can overcome the shortcomings of the ENN prediction model,such as weak generalization ability and tendency to fall into the local minimum value,so that it has the advantages of strong implementation and high prediction accuracy.展开更多
.GN algorithm has high classification accuracy on community detection, but its time complexity is too high. In large scale network, the algorithm is lack of practical values. This paper puts forward an improved GN alg....GN algorithm has high classification accuracy on community detection, but its time complexity is too high. In large scale network, the algorithm is lack of practical values. This paper puts forward an improved GN algorithm. The algorithm firstly get the network center nodes set, then use the shortest paths between center nodes and other nodes to calculate the edge betweenness, and then use incremental module degree as the algorithm terminates standard. Experiments show that, the new algorithm not only ensures accuracy of network community division, but also greatly reduced the time complexity, and improves the efficiency of community division.展开更多
Firstly, the early warning index system of coal mine safety production was given from four aspects as per- sonnel, environment, equipment and management. Then, improvement measures which are additional momentum method...Firstly, the early warning index system of coal mine safety production was given from four aspects as per- sonnel, environment, equipment and management. Then, improvement measures which are additional momentum method, adaptive learning rate, particle swarm optimization algorithm, variable weight method and asynchronous learning factor, are used to optimize BP neural network models. Further, the models are applied to a comparative study on coal mine safety warning instance. Results show that the identification precision of MPSO-BP network model is higher than GBP and PSO-BP model, and MPSO- BP model can not only effectively reduce the possibility of the network falling into a local minimum point, but also has fast convergence and high precision, which will provide the scientific basis for the forewarnin~ management of coal mine safetv production.展开更多
The original fault data of oil immersed transformer often contains a large number of unnecessary attributes,which greatly increases the elapsed time of the algorithm and reduces the classification accuracy,leading to ...The original fault data of oil immersed transformer often contains a large number of unnecessary attributes,which greatly increases the elapsed time of the algorithm and reduces the classification accuracy,leading to the rise of the diagnosis error rate.Therefore,in order to obtain high quality oil immersed transformer fault attribute data sets,an improved imperialist competitive algorithm was proposed to optimize the rough set to discretize the original fault data set and the attribute reduction.The feasibility of the proposed algorithm was verified by experiments and compared with other intelligent algorithms.Results show that the algorithm was stable at the 27th iteration with a reduction rate of 56.25%and a reduction accuracy of 98%.By using BP neural network to classify the reduction results,the accuracy was 86.25%,and the overall effect was better than those of the original data and other algorithms.Hence,the proposed method is effective for fault attribute reduction of oil immersed transformer.展开更多
With more and more researches about improving BP algorithm, there are more improvement methods. The paper researches two improvement algorithms based on quasi-Newton method, DFP algorithm and L-BFGS algorithm. After f...With more and more researches about improving BP algorithm, there are more improvement methods. The paper researches two improvement algorithms based on quasi-Newton method, DFP algorithm and L-BFGS algorithm. After fully analyzing the features of quasi- Newton methods, the paper improves BP neural network algorithm. And the adjustment is made for the problems in the improvement process. The paper makes empirical analysis and proves the effectiveness of BP neural network algorithm based on quasi-Newton method. The improved algorithms are compared with the traditional BP algorithm, which indicates that the imoroved BP algorithm is better.展开更多
A new method for power quality(PQ)disturbances identification is brought forward based on combining a neural network with least square(LS)weighted fusion algorithm.The characteristic components of PQ disturbances are ...A new method for power quality(PQ)disturbances identification is brought forward based on combining a neural network with least square(LS)weighted fusion algorithm.The characteristic components of PQ disturbances are distilled through an improved phase-located loop(PLL)system at first,and then five child BP ANNs with different structures are trained and adopted to identify the PQ disturbances respectively.The combining neural network fuses the identification results of these child ANNs with LS weighted fusion algorithm,and identifies PQ disturbances with the fused result finally.Compared with a single neural network,the combining one with LS weighted fusion algorithm can identify the PQ disturbances correctly when noise is strong.However,a single neural network may fail in this case.Furthermore,the combining neural network is more reliable than a single neural network.The simulation results prove the conclusions above.展开更多
Enhancing the accuracy of real-time ship roll prediction is crucial for maritime safety and operational efficiency.To address the challenge of accurately predicting the ship roll status with nonlinear time-varying dyn...Enhancing the accuracy of real-time ship roll prediction is crucial for maritime safety and operational efficiency.To address the challenge of accurately predicting the ship roll status with nonlinear time-varying dynamic characteristics,a real-time ship roll prediction scheme is proposed on the basis of a data preprocessing strategy and a novel stochastic trainer-based feedforward neural network.The sliding data window serves as a ship time-varying dynamic observer to enhance model prediction stability.The variational mode decomposition method extracts effective information on ship roll motion and reduces the non-stationary characteristics of the series.The energy entropy method reconstructs the mode components into high-frequency,medium-frequency,and low-frequency series to reduce model complexity.An improved black widow optimization algorithm trainer-based feedforward neural network with enhanced local optimal avoidance predicts the high-frequency component,enabling accurate tracking of abrupt signals.Additionally,the deterministic algorithm trainer-based neural network,characterized by rapid processing speed,predicts the remaining two mode components.Thus,real-time ship roll forecasting can be achieved through the reconstruction of mode component prediction results.The feasibility and effectiveness of the proposed hybrid prediction scheme for ship roll motion are demonstrated through the measured data of a full-scale ship trial.The proposed prediction scheme achieves real-time ship roll prediction with superior prediction accuracy.展开更多
The fault diagnosis model for FMS based on multi layer feedforward neural networks was discussed An improved BP algorithm,the tactic of initial value selection based on genetic algorithm and the method of network st...The fault diagnosis model for FMS based on multi layer feedforward neural networks was discussed An improved BP algorithm,the tactic of initial value selection based on genetic algorithm and the method of network structure optimization were presented for training this model ANN(artificial neural network)fault diagnosis model for the robot in FMS was made by the new algorithm The result is superior to the rtaditional algorithm展开更多
基金Supported by the National Natural Science Foundation of China(61701029)Basic Research Foundation of Beijing Institute of Technology(20170542008)Industry-University Research Innovation Foundation of the Science and Technology Development Center of the Ministry of Education(2018A02012)。
文摘In order to accomplish the task of object recognition in natural scenes,a new object recognition algorithm based on an improved convolutional neural network(CNN)is proposed.First,candidate object windows are extracted from the original image.Then,candidate object windows are input into the improved CNN model to obtain deep features.Finally,the deep features are input into the Softmax and the confidence scores of classes are obtained.The candidate object window with the highest confidence score is selected as the object recognition result.Based on AlexNet,Inception V1 is introduced into the improved CNN and the fully connected layer is replaced by the average pooling layer,which widens the network and deepens the network at the same time.Experimental results show that the improved object recognition algorithm can obtain better recognition results in multiple natural scene images,and has a higher degree of accuracy than the classical algorithms in the field of object recognition.
文摘Inspired by genetic algorithm(GA),an improved genetic algorithm(IGA)is proposed.It inherits the main idea of evolutionary computing,avoids the process of coding and decoding inorder to probe the solution in the state space directly and has distributed computing version.Soit is faster and gives higher precision.Aided by IGA,a new optimization strategy for theflexibility analysis and retrofitting of existing heat exchanger networks is presented.A case studyshows that IGA has the ability of finding the global optimum with higher speed and better preci-sion.
文摘Energy conservation and congestion control are widely researched topics in Wireless Sensor Networks in recent years. The main objective is to develop a model to find the optimized path on the basis of distance between source and destination and the residual energy of the node. This paper shows an implementation of nature inspired improved Bat Algorithm to control congestion in Wireless Sensor Networks at transport layer. The Algorithm has been applied on the fitness function to obtain an optimum solution. Simulation results have shown improvement in parameters like network lifetime and throughput as compared with CODA (Congestion Detection and Avoidance), PSO (Particle Swarm Optimization) algorithm and ACO (Ant Colony Optimization).
基金the National Natural Science Foundation of China(52177074).
文摘The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optimalconfiguration of measurement points, this paper presents an optimal configuration scheme for fault locationmeasurement points in DC distribution networks based on an improved particle swarm optimization algorithm.Initially, a measurement point distribution optimization model is formulated, leveraging compressive sensing.The model aims to achieve the minimum number of measurement points while attaining the best compressivesensing reconstruction effect. It incorporates constraints from the compressive sensing algorithm and networkwide viewability. Subsequently, the traditional particle swarm algorithm is enhanced by utilizing the Haltonsequence for population initialization, generating uniformly distributed individuals. This enhancement reducesindividual search blindness and overlap probability, thereby promoting population diversity. Furthermore, anadaptive t-distribution perturbation strategy is introduced during the particle update process to enhance the globalsearch capability and search speed. The established model for the optimal configuration of measurement points issolved, and the results demonstrate the efficacy and practicality of the proposed method. The optimal configurationreduces the number of measurement points, enhances localization accuracy, and improves the convergence speedof the algorithm. These findings validate the effectiveness and utility of the proposed approach.
文摘A hard problem that hinders the movement of waxy crude oil is wax deposition in oil pipelines.To ensure the safe operation of crude oil pipelines,an accurate model must be developed to predict the rate of wax deposition in crude oil pipelines.Aiming at the shortcomings of the ENN prediction model,which easily falls into the local minimum value and weak generalization ability in the implementation process,an optimized ENN prediction model based on the IRSA is proposed.The validity of the new model was confirmed by the accurate prediction of two sets of experimental data on wax deposition in crude oil pipelines.The two groups of crude oil wax deposition rate case prediction results showed that the average absolute percentage errors of IRSA-ENN prediction models is 0.5476% and 0.7831%,respectively.Additionally,it shows a higher prediction accuracy compared to the ENN prediction model.In fact,the new model established by using the IRSA to optimize ENN can optimize the initial weights and thresholds in the prediction process,which can overcome the shortcomings of the ENN prediction model,such as weak generalization ability and tendency to fall into the local minimum value,so that it has the advantages of strong implementation and high prediction accuracy.
文摘.GN algorithm has high classification accuracy on community detection, but its time complexity is too high. In large scale network, the algorithm is lack of practical values. This paper puts forward an improved GN algorithm. The algorithm firstly get the network center nodes set, then use the shortest paths between center nodes and other nodes to calculate the edge betweenness, and then use incremental module degree as the algorithm terminates standard. Experiments show that, the new algorithm not only ensures accuracy of network community division, but also greatly reduced the time complexity, and improves the efficiency of community division.
文摘Firstly, the early warning index system of coal mine safety production was given from four aspects as per- sonnel, environment, equipment and management. Then, improvement measures which are additional momentum method, adaptive learning rate, particle swarm optimization algorithm, variable weight method and asynchronous learning factor, are used to optimize BP neural network models. Further, the models are applied to a comparative study on coal mine safety warning instance. Results show that the identification precision of MPSO-BP network model is higher than GBP and PSO-BP model, and MPSO- BP model can not only effectively reduce the possibility of the network falling into a local minimum point, but also has fast convergence and high precision, which will provide the scientific basis for the forewarnin~ management of coal mine safetv production.
基金Sponsored by the National Natural Science Foundation of China(Grant No.51504085)the Natural Science Foundation for Returness of Heilongjiang Province of China(Grant No.LC2017026).
文摘The original fault data of oil immersed transformer often contains a large number of unnecessary attributes,which greatly increases the elapsed time of the algorithm and reduces the classification accuracy,leading to the rise of the diagnosis error rate.Therefore,in order to obtain high quality oil immersed transformer fault attribute data sets,an improved imperialist competitive algorithm was proposed to optimize the rough set to discretize the original fault data set and the attribute reduction.The feasibility of the proposed algorithm was verified by experiments and compared with other intelligent algorithms.Results show that the algorithm was stable at the 27th iteration with a reduction rate of 56.25%and a reduction accuracy of 98%.By using BP neural network to classify the reduction results,the accuracy was 86.25%,and the overall effect was better than those of the original data and other algorithms.Hence,the proposed method is effective for fault attribute reduction of oil immersed transformer.
文摘With more and more researches about improving BP algorithm, there are more improvement methods. The paper researches two improvement algorithms based on quasi-Newton method, DFP algorithm and L-BFGS algorithm. After fully analyzing the features of quasi- Newton methods, the paper improves BP neural network algorithm. And the adjustment is made for the problems in the improvement process. The paper makes empirical analysis and proves the effectiveness of BP neural network algorithm based on quasi-Newton method. The improved algorithms are compared with the traditional BP algorithm, which indicates that the imoroved BP algorithm is better.
基金Sponsored by the Teaching and Research Award Programfor Outstanding Young Teachers in High Education Institutions of MOE China(Grant No.ZDXM03006).
文摘A new method for power quality(PQ)disturbances identification is brought forward based on combining a neural network with least square(LS)weighted fusion algorithm.The characteristic components of PQ disturbances are distilled through an improved phase-located loop(PLL)system at first,and then five child BP ANNs with different structures are trained and adopted to identify the PQ disturbances respectively.The combining neural network fuses the identification results of these child ANNs with LS weighted fusion algorithm,and identifies PQ disturbances with the fused result finally.Compared with a single neural network,the combining one with LS weighted fusion algorithm can identify the PQ disturbances correctly when noise is strong.However,a single neural network may fail in this case.Furthermore,the combining neural network is more reliable than a single neural network.The simulation results prove the conclusions above.
基金supported by the National Natural Science Foundation of China(Grant Nos.52231014 and 52271361)the Natural Science Foundation of Guangdong Province of China(Grant No.2023A1515010684).
文摘Enhancing the accuracy of real-time ship roll prediction is crucial for maritime safety and operational efficiency.To address the challenge of accurately predicting the ship roll status with nonlinear time-varying dynamic characteristics,a real-time ship roll prediction scheme is proposed on the basis of a data preprocessing strategy and a novel stochastic trainer-based feedforward neural network.The sliding data window serves as a ship time-varying dynamic observer to enhance model prediction stability.The variational mode decomposition method extracts effective information on ship roll motion and reduces the non-stationary characteristics of the series.The energy entropy method reconstructs the mode components into high-frequency,medium-frequency,and low-frequency series to reduce model complexity.An improved black widow optimization algorithm trainer-based feedforward neural network with enhanced local optimal avoidance predicts the high-frequency component,enabling accurate tracking of abrupt signals.Additionally,the deterministic algorithm trainer-based neural network,characterized by rapid processing speed,predicts the remaining two mode components.Thus,real-time ship roll forecasting can be achieved through the reconstruction of mode component prediction results.The feasibility and effectiveness of the proposed hybrid prediction scheme for ship roll motion are demonstrated through the measured data of a full-scale ship trial.The proposed prediction scheme achieves real-time ship roll prediction with superior prediction accuracy.
文摘The fault diagnosis model for FMS based on multi layer feedforward neural networks was discussed An improved BP algorithm,the tactic of initial value selection based on genetic algorithm and the method of network structure optimization were presented for training this model ANN(artificial neural network)fault diagnosis model for the robot in FMS was made by the new algorithm The result is superior to the rtaditional algorithm
