Software defect feature selection has problems of feature space dimensionality reduction and large search space.This research proposes a defect prediction feature selection framework based on improved shuffled frog le...Software defect feature selection has problems of feature space dimensionality reduction and large search space.This research proposes a defect prediction feature selection framework based on improved shuffled frog leaping algorithm(ISFLA).Using the two-level structure of the framework and the improved hybrid leapfrog algorithm's own advantages,the feature values are sorted,and some features with high correlation are selected to avoid other heuristic algorithms in the defect prediction that are easy to produce local The case where the convergence rate of the optimal or parameter optimization process is relatively slow.The framework improves generalization of predictions of unknown data samples and enhances the ability to search for features related to learning tasks.At the same time,this framework further reduces the dimension of the feature space.After the contrast simulation experiment with other common defect prediction methods,we used the actual test data set to verify the framework for multiple iterations on Internet of Things(IoT)system platform.The experimental results show that the software defect prediction feature selection framework based on ISFLA is very effective in defect prediction of IoT communication software.This framework can save the testing time of IoT communication software,effectively improve the performance of software defect prediction,and ensure the software quality.展开更多
Load-time series data in mobile cloud computing of Internet of Vehicles(IoV)usually have linear and nonlinear composite characteristics.In order to accurately describe the dynamic change trend of such loads,this study...Load-time series data in mobile cloud computing of Internet of Vehicles(IoV)usually have linear and nonlinear composite characteristics.In order to accurately describe the dynamic change trend of such loads,this study designs a load prediction method by using the resource scheduling model for mobile cloud computing of IoV.Firstly,a chaotic analysis algorithm is implemented to process the load-time series,while some learning samples of load prediction are constructed.Secondly,a support vector machine(SVM)is used to establish a load prediction model,and an improved artificial bee colony(IABC)function is designed to enhance the learning ability of the SVM.Finally,a CloudSim simulation platform is created to select the perminute CPU load history data in the mobile cloud computing system,which is composed of 50 vehicles as the data set;and a comparison experiment is conducted by using a grey model,a back propagation neural network,a radial basis function(RBF)neural network and a RBF kernel function of SVM.As shown in the experimental results,the prediction accuracy of the method proposed in this study is significantly higher than other models,with a significantly reduced real-time prediction error for resource loading in mobile cloud environments.Compared with single-prediction models,the prediction method proposed can build up multidimensional time series in capturing complex load time series,fit and describe the load change trends,approximate the load time variability more precisely,and deliver strong generalization ability to load prediction models for mobile cloud computing resources.展开更多
Along with the rapid development of biometric authentication technology,face recognition has been commercially used in many industries in recent years.However,it cannot be ignored that face recognition-based authentic...Along with the rapid development of biometric authentication technology,face recognition has been commercially used in many industries in recent years.However,it cannot be ignored that face recognition-based authentication techniques can be easily spoofed using various types of attacks such photographs,videos or forged 3D masks.In order to solve this problem,this work proposed a face anti-fraud algorithm based on the fusion of thermal infrared images and visible light images.The normal temperature distribution of the human face is stable and characteristic,and the important physiological information of the human body can be observed by the infrared thermal images.Therefore,based on the thermal infrared image,the pixel value of the pulse sensitive area of the human face is collected,and the human heart rate signal is detected to distinguish between real faces and spoofing faces.In order to better obtain the texture features of the face,an image fusion algorithm based on DTCWT and the improved Roberts algorithm is proposed.Firstly,DTCWT is used to decompose the thermal infrared image and visible light image of the face to obtain high-and low-frequency subbands.Then,the method based on region energy and the improved Roberts algorithm are then used to fuse the coefficients of the high-and low-frequency subbands.Finally,the DTCWT inverse transform is used to obtain the fused image containing the facial texture features.Face recognition is carried out on the fused image to realize identity authentication.Experimental results show that this algorithm can effectively resist attacks from photos,videos or masks.Compared with the use of visible light images alone for face recognition,this algorithm has higher recognition accuracy and better robustness.展开更多
Predicting the blooming season of ornamental plants is significant for guiding adjustments in production decisions and providing viewing periods and routes.The current strategies for observation of ornamental plant bo...Predicting the blooming season of ornamental plants is significant for guiding adjustments in production decisions and providing viewing periods and routes.The current strategies for observation of ornamental plant booming periods are mainly based on manpower and experience,which have problems such as inaccurate recognition time,time-consuming and energy sapping.Therefore,this paper proposes a neural network-based method for predicting the flowering phase of pear tree.Firstly,based on the meteorological observation data of Shijiazhuang Meteorological Station from 2000 to 2019,three principal components(the temperature factor,weather factor,and humidity factor)with high correlation coefficient with the flowering phase of pear tree are obtained by using the principal component analysis method.Then,the three components are used as input factors for the BP neural network.A BP neural network prediction model is constructed based on genetic algorithm optimization.The crossover operator and mutation operator in the adaptive genetic algorithm are improved.Finally,the meteorological sample data from 2013 to 2019 are used to test and verify the algorithm in this paper.The results demonstrate that,the model can solve the local optimization problem of the BP neural network model.The prediction results of the flowering phase of pear tree are evaluated in terms of relevance and prediction accuracy.Both are superior to the traditional effective accumulated temperature and the prediction results of the stepwise regression method.This method can provide more reliable forecast information for the blooming period,which can provide decision-making reference for improving the development of tourism industry.展开更多
To better regulate the speed of brushless DC motors,an improved algorithm based on the original Glowworm Swarm Optimization is proposed.The proposed algorithm solves the problems of poor robustness,slow convergence,an...To better regulate the speed of brushless DC motors,an improved algorithm based on the original Glowworm Swarm Optimization is proposed.The proposed algorithm solves the problems of poor robustness,slow convergence,and low accuracy exhibited by traditional PID controllers.When selecting the glowworm neighborhood set,an optimization scheme based on the growth and competition behavior of weeds is applied to a single glowworm to prevent falling into a local optimal solution.After the glowworm’s position is updated,the league selection operator is introduced to search for the global optimal solution.Combining the local search ability of the invasive weed optimization with the global search ability of the league selection operator enhances the robustness of the algorithm and also accelerates the convergence speed of the algorithm.The mathematical model of the brushless DC motor is established,the PID parameters are tuned and optimized using improved Glowworm Swarm Optimization algorithm,and the speed of the brushless DC motor is adjusted.In a Simulink environment,a double closed-loop speed control model was established to simulate the speed control of a brushless DC motor,and this simulation was compared with a traditional PID control.The simulation results show that the model based on the improved Glowworm Swarm Optimization algorithm has good robustness and a steady-state response speed for motor speed control.展开更多
The appearance of pedestrians can vary greatly from image to image,and different pedestrians may look similar in a given image.Such similarities and variabilities in the appearance and clothing of individuals make the...The appearance of pedestrians can vary greatly from image to image,and different pedestrians may look similar in a given image.Such similarities and variabilities in the appearance and clothing of individuals make the task of pedestrian re-identification very challenging.Here,a pedestrian re-identification method based on the fusion of local features and gait energy image(GEI)features is proposed.In this method,the human body is divided into four regions according to joint points.The color and texture of each region of the human body are extracted as local features,and GEI features of the pedestrian gait are also obtained.These features are then fused with the local and GEI features of the person.Independent distance measure learning using the cross-view quadratic discriminant analysis(XQDA)method is used to obtain the similarity of the metric function of the image pairs,and the final similarity is acquired by weight matching.Evaluation of experimental results by cumulative matching characteristic(CMC)curves reveals that,after fusion of local and GEI features,the pedestrian re-identification effect is improved compared with existing methods and is notably better than the recognition rate of pedestrian re-identification with a single feature.展开更多
In a large-scale wireless sensor network(WSN),densely distributed sensor nodes process a large amount of data.The aggregation of data in a network can consume a great amount of energy.To balance and reduce the energy ...In a large-scale wireless sensor network(WSN),densely distributed sensor nodes process a large amount of data.The aggregation of data in a network can consume a great amount of energy.To balance and reduce the energy consumption of nodes in a WSN and extend the network life,this paper proposes a nonuniform clustering routing algorithm based on the improved K-means algorithm.The algorithm uses a clustering method to form and optimize clusters,and it selects appropriate cluster heads to balance network energy consumption and extend the life cycle of the WSN.To ensure that the cluster head(CH)selection in the network is fair and that the location of the selected CH is not concentrated within a certain range,we chose the appropriate CH competition radius.Simulation results show that,compared with LEACH,LEACH-C,and the DEEC clustering algorithm,this algorithm can effectively balance the energy consumption of the CH and extend the network life.展开更多
Human eye detection has become an area of interest in the field of computer vision with an extensive range of applications in human-computer interaction,disease diagnosis,and psychological and physiological studies.Ga...Human eye detection has become an area of interest in the field of computer vision with an extensive range of applications in human-computer interaction,disease diagnosis,and psychological and physiological studies.Gaze-tracking systems are an important research topic in the human-computer interaction field.As one of the core modules of the head-mounted gaze-tracking system,pupil positioning affects the accuracy and stability of the system.By tracking eye movements to better locate the center of the pupil,this paper proposes a method for pupil positioning based on the starburst model.The method uses vertical and horizontal coordinate integral projections in the rectangular region of the human eye for accurate positioning and applies a linear interpolation method that is based on a circular model to the reflections in the human eye.In this paper,we propose a method for detecting the feature points of the pupil edge based on the starburst model,which clusters feature points and uses the RANdom SAmple Consensus(RANSAC)algorithm to perform ellipse fitting of the pupil edge to accurately locate the pupil center.Our experimental results show that the algorithm has higher precision,higher efficiency and more robustness than other algorithms and excellent accuracy even when the image of the pupil is incomplete.展开更多
The purpose of introducing blockchain into electronic archives sharing and utilization is to break the information barrier between electronic archives sharing departments by relying on technologies such as smart contr...The purpose of introducing blockchain into electronic archives sharing and utilization is to break the information barrier between electronic archives sharing departments by relying on technologies such as smart contract and asymmetric encryption.Aiming at the problem of dynamic permission management in common access control methods,a new access control method based on smart contract under blockchain is proposed,which improves the intelligence level under blockchain technology.Firstly,the Internet attribute access control model based on smart contract is established.For the dynamic access of heterogeneous devices,the management contract,permission judgment contract and access control contract are designed;Secondly,the access object credit evaluation algorithm based on particle swarm optimization radial basis function(PSO-RBF)neural network is used to dynamically generate the access node credit threshold combined with the access policy,so as to realize the intelligent access right management method.Finally,combined with the abovemodels and algorithms,the workflow of electronic archives sharing and utilization model of multi blockchain is constructed.The experimental results show that the timeconsuming of the process increases linearly with the number of continuous access to electronic archives blocks,and the secure access control of sharing and utilization is feasible,secure and effective.展开更多
基金This work was supported by Liaoning Natural Fund Guidance Plan Project(No.20180550021)Dalian Science and Technology Star Project(No.2017RQ021)2019 Qingdao Binhai University-level Science and Technology Plan Research Project(No.2019KY09).
文摘Software defect feature selection has problems of feature space dimensionality reduction and large search space.This research proposes a defect prediction feature selection framework based on improved shuffled frog leaping algorithm(ISFLA).Using the two-level structure of the framework and the improved hybrid leapfrog algorithm's own advantages,the feature values are sorted,and some features with high correlation are selected to avoid other heuristic algorithms in the defect prediction that are easy to produce local The case where the convergence rate of the optimal or parameter optimization process is relatively slow.The framework improves generalization of predictions of unknown data samples and enhances the ability to search for features related to learning tasks.At the same time,this framework further reduces the dimension of the feature space.After the contrast simulation experiment with other common defect prediction methods,we used the actual test data set to verify the framework for multiple iterations on Internet of Things(IoT)system platform.The experimental results show that the software defect prediction feature selection framework based on ISFLA is very effective in defect prediction of IoT communication software.This framework can save the testing time of IoT communication software,effectively improve the performance of software defect prediction,and ensure the software quality.
基金This work was supported by Shandong medical and health science and technology development plan project(No.202012070393).
文摘Load-time series data in mobile cloud computing of Internet of Vehicles(IoV)usually have linear and nonlinear composite characteristics.In order to accurately describe the dynamic change trend of such loads,this study designs a load prediction method by using the resource scheduling model for mobile cloud computing of IoV.Firstly,a chaotic analysis algorithm is implemented to process the load-time series,while some learning samples of load prediction are constructed.Secondly,a support vector machine(SVM)is used to establish a load prediction model,and an improved artificial bee colony(IABC)function is designed to enhance the learning ability of the SVM.Finally,a CloudSim simulation platform is created to select the perminute CPU load history data in the mobile cloud computing system,which is composed of 50 vehicles as the data set;and a comparison experiment is conducted by using a grey model,a back propagation neural network,a radial basis function(RBF)neural network and a RBF kernel function of SVM.As shown in the experimental results,the prediction accuracy of the method proposed in this study is significantly higher than other models,with a significantly reduced real-time prediction error for resource loading in mobile cloud environments.Compared with single-prediction models,the prediction method proposed can build up multidimensional time series in capturing complex load time series,fit and describe the load change trends,approximate the load time variability more precisely,and deliver strong generalization ability to load prediction models for mobile cloud computing resources.
基金This research was funded by the Hebei Science and Technology Support Program Project(Grant No.19273703D)the Hebei Higher Education Science and Technology Research Project(Grant No.ZD2020318).
文摘Along with the rapid development of biometric authentication technology,face recognition has been commercially used in many industries in recent years.However,it cannot be ignored that face recognition-based authentication techniques can be easily spoofed using various types of attacks such photographs,videos or forged 3D masks.In order to solve this problem,this work proposed a face anti-fraud algorithm based on the fusion of thermal infrared images and visible light images.The normal temperature distribution of the human face is stable and characteristic,and the important physiological information of the human body can be observed by the infrared thermal images.Therefore,based on the thermal infrared image,the pixel value of the pulse sensitive area of the human face is collected,and the human heart rate signal is detected to distinguish between real faces and spoofing faces.In order to better obtain the texture features of the face,an image fusion algorithm based on DTCWT and the improved Roberts algorithm is proposed.Firstly,DTCWT is used to decompose the thermal infrared image and visible light image of the face to obtain high-and low-frequency subbands.Then,the method based on region energy and the improved Roberts algorithm are then used to fuse the coefficients of the high-and low-frequency subbands.Finally,the DTCWT inverse transform is used to obtain the fused image containing the facial texture features.Face recognition is carried out on the fused image to realize identity authentication.Experimental results show that this algorithm can effectively resist attacks from photos,videos or masks.Compared with the use of visible light images alone for face recognition,this algorithm has higher recognition accuracy and better robustness.
基金This research was funded by the Science and Technology Support Plan Project of Hebei Province(Grant Number 19273703D)the Science and Technology Research Project of Hebei Province(Grant Number ZD2020318).
文摘Predicting the blooming season of ornamental plants is significant for guiding adjustments in production decisions and providing viewing periods and routes.The current strategies for observation of ornamental plant booming periods are mainly based on manpower and experience,which have problems such as inaccurate recognition time,time-consuming and energy sapping.Therefore,this paper proposes a neural network-based method for predicting the flowering phase of pear tree.Firstly,based on the meteorological observation data of Shijiazhuang Meteorological Station from 2000 to 2019,three principal components(the temperature factor,weather factor,and humidity factor)with high correlation coefficient with the flowering phase of pear tree are obtained by using the principal component analysis method.Then,the three components are used as input factors for the BP neural network.A BP neural network prediction model is constructed based on genetic algorithm optimization.The crossover operator and mutation operator in the adaptive genetic algorithm are improved.Finally,the meteorological sample data from 2013 to 2019 are used to test and verify the algorithm in this paper.The results demonstrate that,the model can solve the local optimization problem of the BP neural network model.The prediction results of the flowering phase of pear tree are evaluated in terms of relevance and prediction accuracy.Both are superior to the traditional effective accumulated temperature and the prediction results of the stepwise regression method.This method can provide more reliable forecast information for the blooming period,which can provide decision-making reference for improving the development of tourism industry.
基金This research was funded by the Hebei Science and Technology Support Program Project(19273703D)the Hebei Higher Education Science and Technology Research Project(ZD2020318).
文摘To better regulate the speed of brushless DC motors,an improved algorithm based on the original Glowworm Swarm Optimization is proposed.The proposed algorithm solves the problems of poor robustness,slow convergence,and low accuracy exhibited by traditional PID controllers.When selecting the glowworm neighborhood set,an optimization scheme based on the growth and competition behavior of weeds is applied to a single glowworm to prevent falling into a local optimal solution.After the glowworm’s position is updated,the league selection operator is introduced to search for the global optimal solution.Combining the local search ability of the invasive weed optimization with the global search ability of the league selection operator enhances the robustness of the algorithm and also accelerates the convergence speed of the algorithm.The mathematical model of the brushless DC motor is established,the PID parameters are tuned and optimized using improved Glowworm Swarm Optimization algorithm,and the speed of the brushless DC motor is adjusted.In a Simulink environment,a double closed-loop speed control model was established to simulate the speed control of a brushless DC motor,and this simulation was compared with a traditional PID control.The simulation results show that the model based on the improved Glowworm Swarm Optimization algorithm has good robustness and a steady-state response speed for motor speed control.
基金This research was funded by the Science and Technology Support Plan Project of Hebei Province(grant numbers 17210803D and 19273703D)the Science and Technology Spark Project of the Hebei Seismological Bureau(grant number DZ20180402056)+1 种基金the Education Department of Hebei Province(grant number QN2018095)the Polytechnic College of Hebei University of Science and Technology.
文摘The appearance of pedestrians can vary greatly from image to image,and different pedestrians may look similar in a given image.Such similarities and variabilities in the appearance and clothing of individuals make the task of pedestrian re-identification very challenging.Here,a pedestrian re-identification method based on the fusion of local features and gait energy image(GEI)features is proposed.In this method,the human body is divided into four regions according to joint points.The color and texture of each region of the human body are extracted as local features,and GEI features of the pedestrian gait are also obtained.These features are then fused with the local and GEI features of the person.Independent distance measure learning using the cross-view quadratic discriminant analysis(XQDA)method is used to obtain the similarity of the metric function of the image pairs,and the final similarity is acquired by weight matching.Evaluation of experimental results by cumulative matching characteristic(CMC)curves reveals that,after fusion of local and GEI features,the pedestrian re-identification effect is improved compared with existing methods and is notably better than the recognition rate of pedestrian re-identification with a single feature.
基金This research was funded by the Science and Technology Support Plan Project of Hebei Province(grant numbers 17210803D and 19273703D)the Science and Technology Spark Project of the Hebei Seismological Bureau(grant number DZ20180402056)+1 种基金the Education Department of Hebei Province(grant number QN2018095)the Polytechnic College of Hebei University of Science and Technology.
文摘In a large-scale wireless sensor network(WSN),densely distributed sensor nodes process a large amount of data.The aggregation of data in a network can consume a great amount of energy.To balance and reduce the energy consumption of nodes in a WSN and extend the network life,this paper proposes a nonuniform clustering routing algorithm based on the improved K-means algorithm.The algorithm uses a clustering method to form and optimize clusters,and it selects appropriate cluster heads to balance network energy consumption and extend the life cycle of the WSN.To ensure that the cluster head(CH)selection in the network is fair and that the location of the selected CH is not concentrated within a certain range,we chose the appropriate CH competition radius.Simulation results show that,compared with LEACH,LEACH-C,and the DEEC clustering algorithm,this algorithm can effectively balance the energy consumption of the CH and extend the network life.
基金This research was funded by the Science and Technology Support Plan Project of Hebei Province(grant numbers 17210803D and 19273703D)the Science and Technology Spark Project of the Hebei Seismological Bureau(grant number DZ20180402056)+1 种基金the Education Department of Hebei Province(grant number QN2018095)the Polytechnic College of Hebei University of Science and Technology.
文摘Human eye detection has become an area of interest in the field of computer vision with an extensive range of applications in human-computer interaction,disease diagnosis,and psychological and physiological studies.Gaze-tracking systems are an important research topic in the human-computer interaction field.As one of the core modules of the head-mounted gaze-tracking system,pupil positioning affects the accuracy and stability of the system.By tracking eye movements to better locate the center of the pupil,this paper proposes a method for pupil positioning based on the starburst model.The method uses vertical and horizontal coordinate integral projections in the rectangular region of the human eye for accurate positioning and applies a linear interpolation method that is based on a circular model to the reflections in the human eye.In this paper,we propose a method for detecting the feature points of the pupil edge based on the starburst model,which clusters feature points and uses the RANdom SAmple Consensus(RANSAC)algorithm to perform ellipse fitting of the pupil edge to accurately locate the pupil center.Our experimental results show that the algorithm has higher precision,higher efficiency and more robustness than other algorithms and excellent accuracy even when the image of the pupil is incomplete.
基金supported by Shandong Social Science Planning and Research Project in 2021(No.21CPYJ40).
文摘The purpose of introducing blockchain into electronic archives sharing and utilization is to break the information barrier between electronic archives sharing departments by relying on technologies such as smart contract and asymmetric encryption.Aiming at the problem of dynamic permission management in common access control methods,a new access control method based on smart contract under blockchain is proposed,which improves the intelligence level under blockchain technology.Firstly,the Internet attribute access control model based on smart contract is established.For the dynamic access of heterogeneous devices,the management contract,permission judgment contract and access control contract are designed;Secondly,the access object credit evaluation algorithm based on particle swarm optimization radial basis function(PSO-RBF)neural network is used to dynamically generate the access node credit threshold combined with the access policy,so as to realize the intelligent access right management method.Finally,combined with the abovemodels and algorithms,the workflow of electronic archives sharing and utilization model of multi blockchain is constructed.The experimental results show that the timeconsuming of the process increases linearly with the number of continuous access to electronic archives blocks,and the secure access control of sharing and utilization is feasible,secure and effective.
