The theoretical positioning accuracy of multilateration(MLAT) with the time difference of arrival(TDOA) algorithm is very high. However, there are some problems in practical applications. Here we analyze the location ...The theoretical positioning accuracy of multilateration(MLAT) with the time difference of arrival(TDOA) algorithm is very high. However, there are some problems in practical applications. Here we analyze the location performance of the time sum of arrival(TSOA) algorithm from the root mean square error(RMSE) and geometric dilution of precision(GDOP) in additive white Gaussian noise(AWGN) environment. The TSOA localization model is constructed. Using it, the distribution of location ambiguity region is presented with 4-base stations. And then, the location performance analysis is started from the 4-base stations with calculating the RMSE and GDOP variation. Subsequently, when the location parameters are changed in number of base stations, base station layout and so on, the performance changing patterns of the TSOA location algorithm are shown. So, the TSOA location characteristics and performance are revealed. From the RMSE and GDOP state changing trend, the anti-noise performance and robustness of the TSOA localization algorithm are proved. The TSOA anti-noise performance will be used for reducing the blind-zone and the false location rate of MLAT systems.展开更多
Prediction plays a vital role in decision making. Correct prediction leads to right decision making to save the life, energy,efforts, money and time. The right decision prevents physical and material losses and it is ...Prediction plays a vital role in decision making. Correct prediction leads to right decision making to save the life, energy,efforts, money and time. The right decision prevents physical and material losses and it is practiced in all the fields including medical,finance, environmental studies, engineering and emerging technologies. Prediction is carried out by a model called classifier. The predictive accuracy of the classifier highly depends on the training datasets utilized for training the classifier. The irrelevant and redundant features of the training dataset reduce the accuracy of the classifier. Hence, the irrelevant and redundant features must be removed from the training dataset through the process known as feature selection. This paper proposes a feature selection algorithm namely unsupervised learning with ranking based feature selection(FSULR). It removes redundant features by clustering and eliminates irrelevant features by statistical measures to select the most significant features from the training dataset. The performance of this proposed algorithm is compared with the other seven feature selection algorithms by well known classifiers namely naive Bayes(NB),instance based(IB1) and tree based J48. Experimental results show that the proposed algorithm yields better prediction accuracy for classifiers.展开更多
Abnormal driving behavior includes driving distraction,fatigue,road anger,phone use,and an exceptionally happy mood.Detecting abnormal driving behavior in advance can avoid traffic accidents and reduce the risk of tra...Abnormal driving behavior includes driving distraction,fatigue,road anger,phone use,and an exceptionally happy mood.Detecting abnormal driving behavior in advance can avoid traffic accidents and reduce the risk of traffic conflicts.Traditional methods of detecting abnormal driving behavior include using wearable devices to monitor blood pressure,pulse,heart rate,blood oxygen,and other vital signs,and using eye trackers to monitor eye activity(such as eye closure,blinking frequency,etc.)to estimate whether the driver is excited,anxious,or distracted.Traditional monitoring methods can detect abnormal driving behavior to a certain extent,but they will affect the driver’s normal driving state,thereby introducing additional driving risks.This research uses the combined method of support vector machine and dlib algorithm to extract 68 facial feature points from the human face,and uses an SVM model as a strong classifier to classify different abnormal driving statuses.The combined method reaches high accuracy in detecting road anger and fatigue status and can be used in an intelligent vehicle cabin to improve the driving safety level.展开更多
This study proposes a nondestructive optical imaging-based three-dimensional(3D)reconstruction method to analyse electrical tree propagation in polypropylene(PP)cable insulation under mechanical bending.The technique ...This study proposes a nondestructive optical imaging-based three-dimensional(3D)reconstruction method to analyse electrical tree propagation in polypropylene(PP)cable insulation under mechanical bending.The technique combines focus-stacked optical imaging with a feature fusion algorithm to segment in-focus regions across depth layers,enabling 3D reconstruction of electrical trees in PP homopolymer(PPH),block copolymer(PPB)and elastomer-blended(PP/TPE)samples.The results demonstrate that mechanical bending accelerates electrical tree propagation in PPH,and that degradation channels transition from a branch-like to a straight-stick morphology,tending to grow directionally towards stretched regions.With a bending radius of 10 mm,the breakdown time drops from 297.0 min for the undeformed samples to 6.3 min.PPB and PP/TPE delay the time to breakdown by 70.6%and 171.2%,respectively,highlighting their superior resistance under bending stress,which is attributed to maintaining elasticity rather than yield deformation under bending stresses.This study provides a novel tool for evaluating the electrical tree resistance of PP composites under the mechanical stress,guiding the development of recyclable high-voltage direct current cable insulation.展开更多
Crop yield prediction helps to enhance the stability of agricultural product supply and promote sustainable agricultural development,both of which are crucial for food production and security.To develop simple yet hig...Crop yield prediction helps to enhance the stability of agricultural product supply and promote sustainable agricultural development,both of which are crucial for food production and security.To develop simple yet highly accurate crop yield prediction models,this study proposed a spring-and summer-maize yield prediction model based on the deep hybrid kernel extreme learning machine(DHKELM)algorithm.In this study,four tree-based feature importance analysis algorithms,including classification and regression tree,gradient boosting decision tree,random forest,and extreme gradient boosting algorithms,were utilized to analyze the importance of the factors affecting the yield of spring and summer maize.Then,based on the analysis of the four algorithms,different combinations of factors were established to obtain the optimal combination of features.Moreover,to improve the prediction accuracy of the machine learning model,this study utilized three optimization algorithms,including the bald eagle search algorithm,chaos game optimization(CGO)algorithm,and carnivorous plant algorithm,to optimize the hyperparameters in the DHKELM algorithm.The results of the study showed that planting density and plant height were important factors affecting maize yield,and net solar radiation(R_(n))received during the reproductive period exhibited the highest relative importance.Appropriate feature combinations can effectively improve model prediction accuracy.The optimal feature combination for spring maize included planting density,plant height,R_(n),mean temperature(T_(mean)),minimum temperature(T_(min)),and cumulative temperature,and the optimal feature combination for summer maize included Rn,plant height,planting density,T_(min),and T_(mean).Among the three optimization algorithms,the CGO algorithm exhibited the best optimization effect and could significantly improve the prediction accuracy of the DHKELM algorithm.When the optimal combination of features was used as input,the CGO-DHKELM model used for maize yield prediction provided the following values:RMSE=1.488 t/hm^(2),R^(2)=0.862,MAE=1.051 t/hm^(2),and NSE=0.852 for spring maize;RMSE=1.498 t/hm^(2),R^(2)=0.892,MAE=1.055 t/hm2,and NSE=0.891 for summer maize.Thus,the findings of the study provide a reference for high-precision prediction of spring and summer maize yields in China.展开更多
Different devices in the recent era generated a vast amount of digital video.Generally,it has been seen in recent years that people are forging the video to use it as proof of evidence in the court of justice.Many kin...Different devices in the recent era generated a vast amount of digital video.Generally,it has been seen in recent years that people are forging the video to use it as proof of evidence in the court of justice.Many kinds of researches on forensic detection have been presented,and it provides less accuracy.This paper proposed a novel forgery detection technique in image frames of the videos using enhanced Convolutional Neural Network(CNN).In the initial stage,the input video is taken as of the dataset and then converts the videos into image frames.Next,perform pre-sampling using the Adaptive Rood Pattern Search(ARPS)algorithm intended for reducing the useless frames.In the next stage,perform preprocessing for enhancing the image frames.Then,face detection is done as of the image utilizing the Viola-Jones algorithm.Finally,the improved Crow Search Algorithm(ICSA)has been used to select the extorted features and inputted to the Enhanced Convolutional Neural Network(ECNN)classifier for detecting the forged image frames.The experimental outcome of the proposed system has achieved 97.21%accuracy compared to other existing methods.展开更多
With rapid developments in platforms and sensors technology in terms of digital cameras and video recordings,crowd monitoring has taken a considerable attentions in many disciplines such as psychology,sociology,engine...With rapid developments in platforms and sensors technology in terms of digital cameras and video recordings,crowd monitoring has taken a considerable attentions in many disciplines such as psychology,sociology,engineering,and computer vision.This is due to the fact that,monitoring of the crowd is necessary to enhance safety and controllable movements to minimize the risk particularly in highly crowded incidents(e.g.sports).One of the platforms that have been extensively employed in crowd monitoring is unmanned aerial vehicles(UAVs),because UAVs have the capability to acquiring fast,low costs,high-resolution and real-time images over crowd areas.In addition,geo-referenced images can also be provided through integration of on-board positioning sensors(e.g.GPS/IMU)with vision sensors(digital cameras and laser scanner).In this paper,a new testing procedure based on feature from accelerated segment test(FAST)algorithms is introduced to detect the crowd features from UAV images taken from different camera orientations and positions.The proposed test started with converting a circle of 16 pixels surrounding the center pixel into a vector and sorting it in ascending/descending order.A single pixel which takes the ranking number 9(for FAST-9)or 12(for FAST-12)was then compared with the center pixel.Accuracy assessment in terms of completeness and correctness was used to assess the performance of the new testing procedure before and after filtering the crowd features.The results show that the proposed algorithms are able to extract crowd features from different UAV images.Overall,the values of Completeness range from 55 to 70%whereas the range of correctness values was 91 to 94%.展开更多
In cloud computing Resource allocation is a very complex task.Handling the customer demand makes the challenges of on-demand resource allocation.Many challenges are faced by conventional methods for resource allocatio...In cloud computing Resource allocation is a very complex task.Handling the customer demand makes the challenges of on-demand resource allocation.Many challenges are faced by conventional methods for resource allocation in order tomeet the Quality of Service(QoS)requirements of users.For solving the about said problems a new method was implemented with the utility of machine learning framework of resource allocation by utilizing the cloud computing technique was taken in to an account in this research work.The accuracy in the machine learning algorithm can be improved by introducing Bat Algorithm with feature selection(BFS)in the proposed work,this further reduces the inappropriate features from the data.The similarities that were hidden can be demoralized by the Support Vector Machine(SVM)classifier which is also determine the subspace vector and then a new feature vector can be predicted by using SVM.For an unexpected circumstance SVM model can make a resource allocation decision.The efficiency of proposed SVM classifier of resource allocation can be highlighted by using a singlecell multiuser massive Multiple-Input Multiple Output(MIMO)system,with beam allocation problem as an example.The proposed resource allocation based on SVM performs efficiently than the existing conventional methods;this has been proven by analysing its results.展开更多
With the increasing dimensionality of the data,High-dimensional Feature Selection(HFS)becomes an increasingly dif-ficult task.It is not simple to find the best subset of features due to the breadth of the search space...With the increasing dimensionality of the data,High-dimensional Feature Selection(HFS)becomes an increasingly dif-ficult task.It is not simple to find the best subset of features due to the breadth of the search space and the intricacy of the interactions between features.Many of the Feature Selection(FS)approaches now in use for these problems perform sig-nificantly less well when faced with such intricate situations involving high-dimensional search spaces.It is demonstrated that meta-heuristic algorithms can provide sub-optimal results in an acceptable amount of time.This paper presents a new binary Boosted version of the Spider Wasp Optimizer(BSWO)called Binary Boosted SWO(BBSWO),which combines a number of successful and promising strategies,in order to deal with HFS.The shortcomings of the original BSWO,including early convergence,settling into local optimums,limited exploration and exploitation,and lack of population diversity,were addressed by the proposal of this new variant of SWO.The concept of chaos optimization is introduced in BSWO,where initialization is consistently produced by utilizing the properties of sine chaos mapping.A new convergence parameter was then incorporated into BSWO to achieve a promising balance between exploration and exploitation.Multiple exploration mechanisms were then applied in conjunction with several exploitation strategies to effectively enrich the search process of BSWO within the search space.Finally,quantum-based optimization was added to enhance the diversity of the search agents in BSWO.The proposed BBSWO not only offers the most suitable subset of features located,but it also lessens the data's redundancy structure.BBSWO was evaluated using the k-Nearest Neighbor(k-NN)classifier on 23 HFS problems from the biomedical domain taken from the UCI repository.The results were compared with those of traditional BSWO and other well-known meta-heuristics-based FS.The findings indicate that,in comparison to other competing techniques,the proposed BBSWO can,on average,identify the least significant subsets of features with efficient classification accuracy of the k-NN classifier.展开更多
Apple leaf disease is one of the main factors to constrain the apple production and quality.It takes a long time to detect the diseases by using the traditional diagnostic approach,thus farmers often miss the best tim...Apple leaf disease is one of the main factors to constrain the apple production and quality.It takes a long time to detect the diseases by using the traditional diagnostic approach,thus farmers often miss the best time to prevent and treat the diseases.Apple leaf disease recognition based on leaf image is an essential research topic in the field of computer vision,where the key task is to find an effective way to represent the diseased leaf images.In this research,based on image processing techniques and pattern recognition methods,an apple leaf disease recognition method was proposed.A color transformation structure for the input RGB(Red,Green and Blue)image was designed firstly and then RGB model was converted to HSI(Hue,Saturation and Intensity),YUV and gray models.The background was removed based on a specific threshold value,and then the disease spot image was segmented with region growing algorithm(RGA).Thirty-eight classifying features of color,texture and shape were extracted from each spot image.To reduce the dimensionality of the feature space and improve the accuracy of the apple leaf disease identification,the most valuable features were selected by combining genetic algorithm(GA)and correlation based feature selection(CFS).Finally,the diseases were recognized by SVM classifier.In the proposed method,the selected feature subset was globally optimum.The experimental results of more than 90%correct identification rate on the apple diseased leaf image database which contains 90 disease images for there kinds of apple leaf diseases,powdery mildew,mosaic and rust,demonstrate that the proposed method is feasible and effective.展开更多
The biodegradability evaluation of petrochemical wastewater is vital for regulating the petrochemical wastewater treatment process.Nevertheless,the essential datasets derived by instruments with different sampling sca...The biodegradability evaluation of petrochemical wastewater is vital for regulating the petrochemical wastewater treatment process.Nevertheless,the essential datasets derived by instruments with different sampling scales are characterized by multiple time scales,making it challenging for the existing data-driven biodegradability evaluation methods to achieve feasible results.In this paper,an intelligent evaluation method is proposed based on multiple time-scale analyses to ensure realtime and accurate biodegradability evaluation of the petrochemical wastewater treatment process.Firstly,a multiple time-scale reconfiguration method is introduced to regularize the datasets consistently by regulating the time-series characteristics of the collected variables.Moreover,missing data for large time-scale variables are supplemented by linear interpolation.Secondly,a multi-scale feature extraction algorithm based on partial least squares is designed to obtain biodegradability feature variables and remove noise and redundant information.Thirdly,an intelligent evaluation model based on a dynamic fuzzy min-max neural network is established to realize the classification of biodegradability.Finally,the proposed evaluation method is applied to the practical petrochemical wastewater treatment process.The experimental results demonstrate that the proposed method can provide real-time and accurate evaluation of the petrochemical wastewater biodegradability.展开更多
A stall diagnosis method based on the entropy feature extraction algorithm is developed in axial compressors.The reliability of the proposed method is determined and a parametric sensitivity analysis is experimentally...A stall diagnosis method based on the entropy feature extraction algorithm is developed in axial compressors.The reliability of the proposed method is determined and a parametric sensitivity analysis is experimentally conducted for two different types of compressor stall diagnoses.A collection of time‐resolved pressure sensors is mounted circumferentially and along the chord direction to measure the dynamic pressure on the casing.Results show that the stall and prestall precursor embedded in the dynamic pressures are identified through nonlinear feature perturbation extraction using the entropy feature extraction algorithm.Further analysis demonstrates that the prestall precursor with the peak entropy value is related to the unsteady tip leakage flow for the spike‐type stall diagnosis.The modal wave inception with increasing amplitude is identified by the considerable increase of the entropy value.The flow field in the tip region indicates that the modal wave corresponds to the flow separation in the suction side of the rotor blade.The warning time is 100–300 rotor revolutions for both types of stall diagnoses,which is beneficial for stall control in different axial compressors.Moreover,a parametric study of the embedding dimension m,similar tolerance n,similar radius r,and data length N in the fuzzy entropy method is conducted to determine the optimal parameter setting for stall diagnosis.The stall warning based on the entropy feature extraction algorithm provides a new stall diagnosis approach in the axial compressor with different stall types.This stall warning can also be adopted as an online stability monitoring index when using the concept of active stall control.展开更多
基金supported by the Joint Civil Aviation Fund of National Natural Science Foundation of China(Nos.U1533108 and U1233112)
文摘The theoretical positioning accuracy of multilateration(MLAT) with the time difference of arrival(TDOA) algorithm is very high. However, there are some problems in practical applications. Here we analyze the location performance of the time sum of arrival(TSOA) algorithm from the root mean square error(RMSE) and geometric dilution of precision(GDOP) in additive white Gaussian noise(AWGN) environment. The TSOA localization model is constructed. Using it, the distribution of location ambiguity region is presented with 4-base stations. And then, the location performance analysis is started from the 4-base stations with calculating the RMSE and GDOP variation. Subsequently, when the location parameters are changed in number of base stations, base station layout and so on, the performance changing patterns of the TSOA location algorithm are shown. So, the TSOA location characteristics and performance are revealed. From the RMSE and GDOP state changing trend, the anti-noise performance and robustness of the TSOA localization algorithm are proved. The TSOA anti-noise performance will be used for reducing the blind-zone and the false location rate of MLAT systems.
文摘Prediction plays a vital role in decision making. Correct prediction leads to right decision making to save the life, energy,efforts, money and time. The right decision prevents physical and material losses and it is practiced in all the fields including medical,finance, environmental studies, engineering and emerging technologies. Prediction is carried out by a model called classifier. The predictive accuracy of the classifier highly depends on the training datasets utilized for training the classifier. The irrelevant and redundant features of the training dataset reduce the accuracy of the classifier. Hence, the irrelevant and redundant features must be removed from the training dataset through the process known as feature selection. This paper proposes a feature selection algorithm namely unsupervised learning with ranking based feature selection(FSULR). It removes redundant features by clustering and eliminates irrelevant features by statistical measures to select the most significant features from the training dataset. The performance of this proposed algorithm is compared with the other seven feature selection algorithms by well known classifiers namely naive Bayes(NB),instance based(IB1) and tree based J48. Experimental results show that the proposed algorithm yields better prediction accuracy for classifiers.
文摘Abnormal driving behavior includes driving distraction,fatigue,road anger,phone use,and an exceptionally happy mood.Detecting abnormal driving behavior in advance can avoid traffic accidents and reduce the risk of traffic conflicts.Traditional methods of detecting abnormal driving behavior include using wearable devices to monitor blood pressure,pulse,heart rate,blood oxygen,and other vital signs,and using eye trackers to monitor eye activity(such as eye closure,blinking frequency,etc.)to estimate whether the driver is excited,anxious,or distracted.Traditional monitoring methods can detect abnormal driving behavior to a certain extent,but they will affect the driver’s normal driving state,thereby introducing additional driving risks.This research uses the combined method of support vector machine and dlib algorithm to extract 68 facial feature points from the human face,and uses an SVM model as a strong classifier to classify different abnormal driving statuses.The combined method reaches high accuracy in detecting road anger and fatigue status and can be used in an intelligent vehicle cabin to improve the driving safety level.
基金supported by National Natural Science Foundation of China(Grants 52477151 and 52522702).
文摘This study proposes a nondestructive optical imaging-based three-dimensional(3D)reconstruction method to analyse electrical tree propagation in polypropylene(PP)cable insulation under mechanical bending.The technique combines focus-stacked optical imaging with a feature fusion algorithm to segment in-focus regions across depth layers,enabling 3D reconstruction of electrical trees in PP homopolymer(PPH),block copolymer(PPB)and elastomer-blended(PP/TPE)samples.The results demonstrate that mechanical bending accelerates electrical tree propagation in PPH,and that degradation channels transition from a branch-like to a straight-stick morphology,tending to grow directionally towards stretched regions.With a bending radius of 10 mm,the breakdown time drops from 297.0 min for the undeformed samples to 6.3 min.PPB and PP/TPE delay the time to breakdown by 70.6%and 171.2%,respectively,highlighting their superior resistance under bending stress,which is attributed to maintaining elasticity rather than yield deformation under bending stresses.This study provides a novel tool for evaluating the electrical tree resistance of PP composites under the mechanical stress,guiding the development of recyclable high-voltage direct current cable insulation.
基金National Natural Science Foundation of China(Grant No.52309050,32372680)Youth Backbone Teacher Project of Henan University of Science and Technology(Grant No.13450013 and 3450010)+1 种基金Key Scientific Research Projects of Colleges and Universities in Henan Province(Grant No.24B416001)Innovative Research Team(Science and Technology)in the University of Henan Province(Grant No.23IRTSTHN024).
文摘Crop yield prediction helps to enhance the stability of agricultural product supply and promote sustainable agricultural development,both of which are crucial for food production and security.To develop simple yet highly accurate crop yield prediction models,this study proposed a spring-and summer-maize yield prediction model based on the deep hybrid kernel extreme learning machine(DHKELM)algorithm.In this study,four tree-based feature importance analysis algorithms,including classification and regression tree,gradient boosting decision tree,random forest,and extreme gradient boosting algorithms,were utilized to analyze the importance of the factors affecting the yield of spring and summer maize.Then,based on the analysis of the four algorithms,different combinations of factors were established to obtain the optimal combination of features.Moreover,to improve the prediction accuracy of the machine learning model,this study utilized three optimization algorithms,including the bald eagle search algorithm,chaos game optimization(CGO)algorithm,and carnivorous plant algorithm,to optimize the hyperparameters in the DHKELM algorithm.The results of the study showed that planting density and plant height were important factors affecting maize yield,and net solar radiation(R_(n))received during the reproductive period exhibited the highest relative importance.Appropriate feature combinations can effectively improve model prediction accuracy.The optimal feature combination for spring maize included planting density,plant height,R_(n),mean temperature(T_(mean)),minimum temperature(T_(min)),and cumulative temperature,and the optimal feature combination for summer maize included Rn,plant height,planting density,T_(min),and T_(mean).Among the three optimization algorithms,the CGO algorithm exhibited the best optimization effect and could significantly improve the prediction accuracy of the DHKELM algorithm.When the optimal combination of features was used as input,the CGO-DHKELM model used for maize yield prediction provided the following values:RMSE=1.488 t/hm^(2),R^(2)=0.862,MAE=1.051 t/hm^(2),and NSE=0.852 for spring maize;RMSE=1.498 t/hm^(2),R^(2)=0.892,MAE=1.055 t/hm2,and NSE=0.891 for summer maize.Thus,the findings of the study provide a reference for high-precision prediction of spring and summer maize yields in China.
文摘Different devices in the recent era generated a vast amount of digital video.Generally,it has been seen in recent years that people are forging the video to use it as proof of evidence in the court of justice.Many kinds of researches on forensic detection have been presented,and it provides less accuracy.This paper proposed a novel forgery detection technique in image frames of the videos using enhanced Convolutional Neural Network(CNN).In the initial stage,the input video is taken as of the dataset and then converts the videos into image frames.Next,perform pre-sampling using the Adaptive Rood Pattern Search(ARPS)algorithm intended for reducing the useless frames.In the next stage,perform preprocessing for enhancing the image frames.Then,face detection is done as of the image utilizing the Viola-Jones algorithm.Finally,the improved Crow Search Algorithm(ICSA)has been used to select the extorted features and inputted to the Enhanced Convolutional Neural Network(ECNN)classifier for detecting the forged image frames.The experimental outcome of the proposed system has achieved 97.21%accuracy compared to other existing methods.
文摘With rapid developments in platforms and sensors technology in terms of digital cameras and video recordings,crowd monitoring has taken a considerable attentions in many disciplines such as psychology,sociology,engineering,and computer vision.This is due to the fact that,monitoring of the crowd is necessary to enhance safety and controllable movements to minimize the risk particularly in highly crowded incidents(e.g.sports).One of the platforms that have been extensively employed in crowd monitoring is unmanned aerial vehicles(UAVs),because UAVs have the capability to acquiring fast,low costs,high-resolution and real-time images over crowd areas.In addition,geo-referenced images can also be provided through integration of on-board positioning sensors(e.g.GPS/IMU)with vision sensors(digital cameras and laser scanner).In this paper,a new testing procedure based on feature from accelerated segment test(FAST)algorithms is introduced to detect the crowd features from UAV images taken from different camera orientations and positions.The proposed test started with converting a circle of 16 pixels surrounding the center pixel into a vector and sorting it in ascending/descending order.A single pixel which takes the ranking number 9(for FAST-9)or 12(for FAST-12)was then compared with the center pixel.Accuracy assessment in terms of completeness and correctness was used to assess the performance of the new testing procedure before and after filtering the crowd features.The results show that the proposed algorithms are able to extract crowd features from different UAV images.Overall,the values of Completeness range from 55 to 70%whereas the range of correctness values was 91 to 94%.
文摘In cloud computing Resource allocation is a very complex task.Handling the customer demand makes the challenges of on-demand resource allocation.Many challenges are faced by conventional methods for resource allocation in order tomeet the Quality of Service(QoS)requirements of users.For solving the about said problems a new method was implemented with the utility of machine learning framework of resource allocation by utilizing the cloud computing technique was taken in to an account in this research work.The accuracy in the machine learning algorithm can be improved by introducing Bat Algorithm with feature selection(BFS)in the proposed work,this further reduces the inappropriate features from the data.The similarities that were hidden can be demoralized by the Support Vector Machine(SVM)classifier which is also determine the subspace vector and then a new feature vector can be predicted by using SVM.For an unexpected circumstance SVM model can make a resource allocation decision.The efficiency of proposed SVM classifier of resource allocation can be highlighted by using a singlecell multiuser massive Multiple-Input Multiple Output(MIMO)system,with beam allocation problem as an example.The proposed resource allocation based on SVM performs efficiently than the existing conventional methods;this has been proven by analysing its results.
基金supported from the Deanship of Research and Graduate Studies(DRG)at Ajman University,Ajman,UAE(Grant No.2023-IRG-ENIT-34).
文摘With the increasing dimensionality of the data,High-dimensional Feature Selection(HFS)becomes an increasingly dif-ficult task.It is not simple to find the best subset of features due to the breadth of the search space and the intricacy of the interactions between features.Many of the Feature Selection(FS)approaches now in use for these problems perform sig-nificantly less well when faced with such intricate situations involving high-dimensional search spaces.It is demonstrated that meta-heuristic algorithms can provide sub-optimal results in an acceptable amount of time.This paper presents a new binary Boosted version of the Spider Wasp Optimizer(BSWO)called Binary Boosted SWO(BBSWO),which combines a number of successful and promising strategies,in order to deal with HFS.The shortcomings of the original BSWO,including early convergence,settling into local optimums,limited exploration and exploitation,and lack of population diversity,were addressed by the proposal of this new variant of SWO.The concept of chaos optimization is introduced in BSWO,where initialization is consistently produced by utilizing the properties of sine chaos mapping.A new convergence parameter was then incorporated into BSWO to achieve a promising balance between exploration and exploitation.Multiple exploration mechanisms were then applied in conjunction with several exploitation strategies to effectively enrich the search process of BSWO within the search space.Finally,quantum-based optimization was added to enhance the diversity of the search agents in BSWO.The proposed BBSWO not only offers the most suitable subset of features located,but it also lessens the data's redundancy structure.BBSWO was evaluated using the k-Nearest Neighbor(k-NN)classifier on 23 HFS problems from the biomedical domain taken from the UCI repository.The results were compared with those of traditional BSWO and other well-known meta-heuristics-based FS.The findings indicate that,in comparison to other competing techniques,the proposed BBSWO can,on average,identify the least significant subsets of features with efficient classification accuracy of the k-NN classifier.
基金Natural Science Foundation of China(grant Nos.61473237,61202170,and 61402331)It is also supported by the Shaanxi Provincial Natural Science Foundation Research Project(2014JM2-6096)+3 种基金Tianjin Research Program of Application Foundation and Advanced Technology(14JCYBJC42500)Tianjin science and technology correspondent project(16JCTPJC47300)the 2015 key projects of Tianjin science and technology support program(No.15ZCZDGX00200)the Fund of Tianjin Food Safety&Low Carbon Manufacturing Collaborative Innovation Center.
文摘Apple leaf disease is one of the main factors to constrain the apple production and quality.It takes a long time to detect the diseases by using the traditional diagnostic approach,thus farmers often miss the best time to prevent and treat the diseases.Apple leaf disease recognition based on leaf image is an essential research topic in the field of computer vision,where the key task is to find an effective way to represent the diseased leaf images.In this research,based on image processing techniques and pattern recognition methods,an apple leaf disease recognition method was proposed.A color transformation structure for the input RGB(Red,Green and Blue)image was designed firstly and then RGB model was converted to HSI(Hue,Saturation and Intensity),YUV and gray models.The background was removed based on a specific threshold value,and then the disease spot image was segmented with region growing algorithm(RGA).Thirty-eight classifying features of color,texture and shape were extracted from each spot image.To reduce the dimensionality of the feature space and improve the accuracy of the apple leaf disease identification,the most valuable features were selected by combining genetic algorithm(GA)and correlation based feature selection(CFS).Finally,the diseases were recognized by SVM classifier.In the proposed method,the selected feature subset was globally optimum.The experimental results of more than 90%correct identification rate on the apple diseased leaf image database which contains 90 disease images for there kinds of apple leaf diseases,powdery mildew,mosaic and rust,demonstrate that the proposed method is feasible and effective.
基金supported by the National Key Research and Development Project(Grant No.2018YFC1900800-5)the National Natural Science Foundation of China(Grant Nos.61890930-5,61622301,61903010,62021003,62103012)Beijing Nova Program(Grant No.20240484694)。
文摘The biodegradability evaluation of petrochemical wastewater is vital for regulating the petrochemical wastewater treatment process.Nevertheless,the essential datasets derived by instruments with different sampling scales are characterized by multiple time scales,making it challenging for the existing data-driven biodegradability evaluation methods to achieve feasible results.In this paper,an intelligent evaluation method is proposed based on multiple time-scale analyses to ensure realtime and accurate biodegradability evaluation of the petrochemical wastewater treatment process.Firstly,a multiple time-scale reconfiguration method is introduced to regularize the datasets consistently by regulating the time-series characteristics of the collected variables.Moreover,missing data for large time-scale variables are supplemented by linear interpolation.Secondly,a multi-scale feature extraction algorithm based on partial least squares is designed to obtain biodegradability feature variables and remove noise and redundant information.Thirdly,an intelligent evaluation model based on a dynamic fuzzy min-max neural network is established to realize the classification of biodegradability.Finally,the proposed evaluation method is applied to the practical petrochemical wastewater treatment process.The experimental results demonstrate that the proposed method can provide real-time and accurate evaluation of the petrochemical wastewater biodegradability.
基金National Natural Science Foundation of China,Grant/Award Number:51922098,51727810National Science and TechnologyMajor Project of China,Grant/Award Number:J2019‐II‐0020‐0041Special Fund for the Member of Youth Innovation Promotion Association of Chinese Academy of Sciences,Grant/Award Number:2018173。
文摘A stall diagnosis method based on the entropy feature extraction algorithm is developed in axial compressors.The reliability of the proposed method is determined and a parametric sensitivity analysis is experimentally conducted for two different types of compressor stall diagnoses.A collection of time‐resolved pressure sensors is mounted circumferentially and along the chord direction to measure the dynamic pressure on the casing.Results show that the stall and prestall precursor embedded in the dynamic pressures are identified through nonlinear feature perturbation extraction using the entropy feature extraction algorithm.Further analysis demonstrates that the prestall precursor with the peak entropy value is related to the unsteady tip leakage flow for the spike‐type stall diagnosis.The modal wave inception with increasing amplitude is identified by the considerable increase of the entropy value.The flow field in the tip region indicates that the modal wave corresponds to the flow separation in the suction side of the rotor blade.The warning time is 100–300 rotor revolutions for both types of stall diagnoses,which is beneficial for stall control in different axial compressors.Moreover,a parametric study of the embedding dimension m,similar tolerance n,similar radius r,and data length N in the fuzzy entropy method is conducted to determine the optimal parameter setting for stall diagnosis.The stall warning based on the entropy feature extraction algorithm provides a new stall diagnosis approach in the axial compressor with different stall types.This stall warning can also be adopted as an online stability monitoring index when using the concept of active stall control.
