Winding is one of themost important components in power transformers.Ensuring the health state of the winding is of great importance to the stable operation of the power system.To efficiently and accurately diagnose t...Winding is one of themost important components in power transformers.Ensuring the health state of the winding is of great importance to the stable operation of the power system.To efficiently and accurately diagnose the disc space variation(DSV)fault degree of transformer winding,this paper presents a diagnostic method of winding fault based on the K-Nearest Neighbor(KNN)algorithmand the frequency response analysis(FRA)method.First,a laboratory winding model is used,and DSV faults with four different degrees are achieved by changing disc space of the discs in the winding.Then,a series of FRA tests are conducted to obtain the FRA results and set up the FRA dataset.Second,ten different numerical indices are utilized to obtain features of FRA curves of faulted winding.Third,the 10-fold cross-validation method is employed to determine the optimal k-value of KNN.In addition,to improve the accuracy of the KNN model,a comparative analysis is made between the accuracy of the KNN algorithm and k-value under four distance functions.After getting the most appropriate distance metric and kvalue,the fault classificationmodel based on theKNN and FRA is constructed and it is used to classify the degrees of DSV faults.The identification accuracy rate of the proposed model is up to 98.30%.Finally,the performance of the model is presented by comparing with the support vector machine(SVM),SVM optimized by the particle swarmoptimization(PSO-SVM)method,and randomforest(RF).The results show that the diagnosis accuracy of the proposed model is the highest and the model can be used to accurately diagnose the DSV fault degrees of the winding.展开更多
On the basis of machine leaning,suitable algorithms can make advanced time series analysis.This paper proposes a complex k-nearest neighbor(KNN)model for predicting financial time series.This model uses a complex feat...On the basis of machine leaning,suitable algorithms can make advanced time series analysis.This paper proposes a complex k-nearest neighbor(KNN)model for predicting financial time series.This model uses a complex feature extraction process integrating a forward rolling empirical mode decomposition(EMD)for financial time series signal analysis and principal component analysis(PCA)for the dimension reduction.The information-rich features are extracted then input to a weighted KNN classifier where the features are weighted with PCA loading.Finally,prediction is generated via regression on the selected nearest neighbors.The structure of the model as a whole is original.The test results on real historical data sets confirm the effectiveness of the models for predicting the Chinese stock index,an individual stock,and the EUR/USD exchange rate.展开更多
In this paper,Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)based methods are to be applied on fault diagnosis in a robot manipulator.A comparative study between the two classifiers in terms of successfully det...In this paper,Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)based methods are to be applied on fault diagnosis in a robot manipulator.A comparative study between the two classifiers in terms of successfully detecting and isolating the seven classes of sensor faults is considered in this work.For both classifiers,the torque,the position and the speed of the manipulator have been employed as the input vector.However,it is to mention that a large database is needed and used for the training and testing phases.The SVM method used in this paper is based on the Gaussian kernel with the parametersγand the penalty margin parameter“C”,which were adjusted via the PSO algorithm to achieve a maximum accuracy diagnosis.Simulations were carried out on the model of a Selective Compliance Assembly Robot Arm(SCARA)robot manipulator,and the results showed that the Particle Swarm Optimization(PSO)increased the per-formance of the SVM algorithm with the 96.95%accuracy while the KNN algo-rithm achieved a correlation up to 94.62%.These results showed that the SVM algorithm with PSO was more precise than the KNN algorithm when was used in fault diagnosis on a robot manipulator.展开更多
The Feixianguan Formation reservoirs in northeastern Sichuan are mainly a suite of carbonate platform deposits.The reservoir types are diverse with high heterogeneity and complex genetic mechanisms.Pores,vugs and frac...The Feixianguan Formation reservoirs in northeastern Sichuan are mainly a suite of carbonate platform deposits.The reservoir types are diverse with high heterogeneity and complex genetic mechanisms.Pores,vugs and fractures of different genetic mechanisms and scales are often developed in association,and it is difficult to classify reservoir types merely based on static data such as outcrop observation,and cores and logging data.In the study,the reservoirs in the Feixianguan Formation are grouped into five types by combining dynamic and static data,that is,karst breccia-residual vuggy type,solution-enhanced vuggy type,fractured-vuggy type,fractured type and matrix type(non-reservoir).Based on conventional logging data,core data and formation microscanner image(FMI)data of the Qilibei block,northeastern Sichuan Basin,the reservoirs are classified in accordance with fracture-vug matching relationship.Based on the principle of cluster analysis,K-Nearest Neighbor(KNN)classification templates are established,and the applicability of the model is verified by using the reservoir data from wells uninvolved in modeling.Following the analysis of the results of reservoir type discrimination and the production of corresponding reservoir intervals,the contributions of various reservoir types to production are evaluated and the reliability of reservoir type classification is verified.The results show that the solution-enhanced vuggy type is of high-quality sweet spot reservoir in the study area with good physical property and high gas production,followed by the fractured-vuggy type,and the fractured and karst breccia-residual vuggy types are the least promising.展开更多
The growing usage of Android smartphones has led to a significant rise in incidents of Android malware andprivacy breaches.This escalating security concern necessitates the development of advanced technologies capable...The growing usage of Android smartphones has led to a significant rise in incidents of Android malware andprivacy breaches.This escalating security concern necessitates the development of advanced technologies capableof automatically detecting andmitigatingmalicious activities in Android applications(apps).Such technologies arecrucial for safeguarding user data and maintaining the integrity of mobile devices in an increasingly digital world.Current methods employed to detect sensitive data leaks in Android apps are hampered by two major limitationsthey require substantial computational resources and are prone to a high frequency of false positives.This meansthat while attempting to identify security breaches,these methods often consume considerable processing powerand mistakenly flag benign activities as malicious,leading to inefficiencies and reduced reliability in malwaredetection.The proposed approach includes a data preprocessing step that removes duplicate samples,managesunbalanced datasets,corrects inconsistencies,and imputes missing values to ensure data accuracy.The Minimaxmethod is then used to normalize numerical data,followed by feature vector extraction using the Gain ratio andChi-squared test to identify and extract the most significant characteristics using an appropriate prediction model.This study focuses on extracting a subset of attributes best suited for the task and recommending a predictivemodel based on domain expert opinion.The proposed method is evaluated using Drebin and TUANDROMDdatasets containing 15,036 and 4,464 benign and malicious samples,respectively.The empirical result shows thatthe RandomForest(RF)and Support VectorMachine(SVC)classifiers achieved impressive accuracy rates of 98.9%and 98.8%,respectively,in detecting unknown Androidmalware.A sensitivity analysis experiment was also carriedout on all three ML-based classifiers based on MAE,MSE,R2,and sensitivity parameters,resulting in a flawlessperformance for both datasets.This approach has substantial potential for real-world applications and can serve asa valuable tool for preventing the spread of Androidmalware and enhancing mobile device security.展开更多
In this paper, a memetic algorithm with competition(MAC) is proposed to solve the capacitated green vehicle routing problem(CGVRP). Firstly, the permutation array called traveling salesman problem(TSP) route is used t...In this paper, a memetic algorithm with competition(MAC) is proposed to solve the capacitated green vehicle routing problem(CGVRP). Firstly, the permutation array called traveling salesman problem(TSP) route is used to encode the solution, and an effective decoding method to construct the CGVRP route is presented accordingly. Secondly, the k-nearest neighbor(k NN) based initialization is presented to take use of the location information of the customers. Thirdly, according to the characteristics of the CGVRP, the search operators in the variable neighborhood search(VNS) framework and the simulated annealing(SA) strategy are executed on the TSP route for all solutions. Moreover, the customer adjustment operator and the alternative fuel station(AFS) adjustment operator on the CGVRP route are executed for the elite solutions after competition. In addition, the crossover operator is employed to share information among different solutions. The effect of parameter setting is investigated using the Taguchi method of design-ofexperiment to suggest suitable values. Via numerical tests, it demonstrates the effectiveness of both the competitive search and the decoding method. Moreover, extensive comparative results show that the proposed algorithm is more effective and efficient than the existing methods in solving the CGVRP.展开更多
Vehicle type recognition(VTR)is an important research topic due to its significance in intelligent transportation systems.However,recognizing vehicle type on the real-world images is challenging due to the illuminatio...Vehicle type recognition(VTR)is an important research topic due to its significance in intelligent transportation systems.However,recognizing vehicle type on the real-world images is challenging due to the illumination change,partial occlusion under real traffic environment.These difficulties limit the performance of current state-of-art methods,which are typically based on single-stage classification without considering feature availability.To address such difficulties,this paper proposes a two-stage vehicle type recognition method combining the most effective Gabor features.The first stage leverages edge features to classify vehicles by size into big or small via a similarity k-nearest neighbor classifier(SKNNC).Further the more specific vehicle type such as bus,truck,sedan or van is recognized by the second stage classification,which leverages the most effective Gabor features extracted by a set of Gabor wavelet kernels on the partitioned key patches via a kernel sparse representation-based classifier(KSRC).A verification and correction step based on minimum residual analysis is proposed to enhance the reliability of the VTR.To improve VTR efficiency,the most effective Gabor features are selected through gray relational analysis that leverages the correlation between Gabor feature image and the original image.Experimental results demonstrate that the proposed method not only improves the accuracy of VTR but also enhances the recognition robustness to illumination change and partial occlusion.展开更多
Text categorization is a significant technique to manage the surging text data on the Internet.The k-nearest neighbors(kNN) algorithm is an effective,but not efficient,classification model for text categorization.In t...Text categorization is a significant technique to manage the surging text data on the Internet.The k-nearest neighbors(kNN) algorithm is an effective,but not efficient,classification model for text categorization.In this paper,we propose an effective strategy to accelerate the standard kNN,based on a simple principle:usually,near points in space are also near when they are projected into a direction,which means that distant points in the projection direction are also distant in the original space.Using the proposed strategy,most of the irrelevant points can be removed when searching for the k-nearest neighbors of a query point,which greatly decreases the computation cost.Experimental results show that the proposed strategy greatly improves the time performance of the standard kNN,with little degradation in accuracy.Specifically,it is superior in applications that have large and high-dimensional datasets.展开更多
To accurately identify soybean pests and diseases, in this paper, a kind of deep convolution network model was used to determine whether or not a soybean crop possessed pests and diseases. The proposed deep convolutio...To accurately identify soybean pests and diseases, in this paper, a kind of deep convolution network model was used to determine whether or not a soybean crop possessed pests and diseases. The proposed deep convolution network could learn the highdimensional feature representation of images by using their depth. An inception module was used to construct a neural network. In the inception module, multiscale convolution kernels were used to extract the distributed characteristics of soybean pests and diseases at different scales and to perform cascade fusion. The model then trained the SoftMax classifier in a uniformed framework. This realized the model of soybean pests and diseases so as to verify the effectiveness of this method. In this study, 800 images of soybean leaf images were taken as the experimental objects. Of these 800 images, 400 were selected for network training, and the remaining 400 images were used for the network test. Furthermore, the classical convolutional neural network was optimized. The accuracies before and after optimization were 96.25% and 95.81%, respectively, in terms of extracting image features. This type of research might be applied to achieve a degree of automation in agricultural field management.展开更多
The detection and recognition of radar signals play a critical role in the maintenance of future electronic warfare(EW).So far,however,there are still problems with signal detection and recognition,especially in the l...The detection and recognition of radar signals play a critical role in the maintenance of future electronic warfare(EW).So far,however,there are still problems with signal detection and recognition,especially in the low probability of intercept(LPI)radar.This paper explores the usefulness of such an algorithm in the scenario of LPI radar signal detection and recognition based on visibility graphs(VG).More network and feature information can be extracted in the VG two-dimensional space,this algorithm can solve the problem of signal recognition using the autocorrelation function.Wavelet denoising processing is introduced into the signal to be tested,and the denoised signal is converted to the VG domain.Then,the signal detection is performed by using the constant false alarm of the VG average degree.Next,weight the converted graph.Finally,perform feature extraction on the weighted image,and use the feature to complete the recognition.It is testified that the proposed algorithm offers significant improvements,such as robustness to noise,and the detection and recognition accuracy,over the recent researches.展开更多
In this paper,the application of an algorithm for precipitation retrieval based on Himawari-8 (H8) satellite infrared data is studied.Based on GPM precipitation data and H8 Infrared spectrum channel brightness tempera...In this paper,the application of an algorithm for precipitation retrieval based on Himawari-8 (H8) satellite infrared data is studied.Based on GPM precipitation data and H8 Infrared spectrum channel brightness temperature data,corresponding "precipitation field dictionary" and "channel brightness temperature dictionary" are formed.The retrieval of precipitation field based on brightness temperature data is studied through the classification rule of k-nearest neighbor domain (KNN) and regularization constraint.Firstly,the corresponding "dictionary" is constructed according to the training sample database of the matched GPM precipitation data and H8 brightness temperature data.Secondly,according to the fact that precipitation characteristics in small organizations in different storm environments are often repeated,KNN is used to identify the spectral brightness temperature signal of "precipitation" and "non-precipitation" based on "the dictionary".Finally,the precipitation field retrieval is carried out in the precipitation signal "subspace" based on the regular term constraint method.In the process of retrieval,the contribution rate of brightness temperature retrieval of different channels was determined by Bayesian model averaging (BMA) model.The preliminary experimental results based on the "quantitative" evaluation indexes show that the precipitation of H8 retrieval has a good correlation with the GPM truth value,with a small error and similar structure.展开更多
Filament-induced breakdown spectroscopy(FIBS)combined with machine learning algorithms was used to identify five aluminum alloys.To study the effect of the distance between focusing lens and target surface on the iden...Filament-induced breakdown spectroscopy(FIBS)combined with machine learning algorithms was used to identify five aluminum alloys.To study the effect of the distance between focusing lens and target surface on the identification accuracy of aluminum alloys,principal component analysis(PCA)combined with support vector machine(SVM)and Knearest neighbor(KNN)was used.The intensity and intensity ratio of fifteen lines of six elements(Fe,Si,Mg,Cu,Zn,and Mn)in the FIBS spectrum were selected.The distances between the focusing lens and the target surface in the pre-filament,filament,and post-filament were 958 mm,976 mm,and 1000 mm,respectively.The source data set was fifteen spectral line intensity ratios,and the cumulative interpretation rates of PC1,PC2,and PC3 were 97.22%,98.17%,and 95.31%,respectively.The first three PCs obtained by PCA were the input variables of SVM and KNN.The identification accuracy of the different positions of focusing lens and target surface was obtained,and the identification accuracy of SVM and KNN in the filament was 100%and 90%,respectively.The source data set of the filament was obtained by PCA for the first three PCs,which were randomly selected as the training set and test set of SVM and KNN in 3:2.The identification accuracy of SVM and KNN was 97.5%and 92.5%,respectively.The research results can provide a reference for the identification of aluminum alloys by FIBS.展开更多
A motion information analysis system based on the acceleration data is proposed in this paper,consisting of filtering,feature extraction and classification.The Kalman filter is adopted to eliminate the noise.With the ...A motion information analysis system based on the acceleration data is proposed in this paper,consisting of filtering,feature extraction and classification.The Kalman filter is adopted to eliminate the noise.With the time-domain and frequency-domain analysis,acceleration features like the amplitude,the period and the acceleration region values are obtained.Furthermore,the accuracy of the motion classification is improved by using the k-nearest neighbor (KNN) algorithm.展开更多
基金supported in part by Shaanxi Natural Science Foundation Project (2023-JC-QN-0438)in part by Fundamental Research Funds for the Central Universities (2452021050).
文摘Winding is one of themost important components in power transformers.Ensuring the health state of the winding is of great importance to the stable operation of the power system.To efficiently and accurately diagnose the disc space variation(DSV)fault degree of transformer winding,this paper presents a diagnostic method of winding fault based on the K-Nearest Neighbor(KNN)algorithmand the frequency response analysis(FRA)method.First,a laboratory winding model is used,and DSV faults with four different degrees are achieved by changing disc space of the discs in the winding.Then,a series of FRA tests are conducted to obtain the FRA results and set up the FRA dataset.Second,ten different numerical indices are utilized to obtain features of FRA curves of faulted winding.Third,the 10-fold cross-validation method is employed to determine the optimal k-value of KNN.In addition,to improve the accuracy of the KNN model,a comparative analysis is made between the accuracy of the KNN algorithm and k-value under four distance functions.After getting the most appropriate distance metric and kvalue,the fault classificationmodel based on theKNN and FRA is constructed and it is used to classify the degrees of DSV faults.The identification accuracy rate of the proposed model is up to 98.30%.Finally,the performance of the model is presented by comparing with the support vector machine(SVM),SVM optimized by the particle swarmoptimization(PSO-SVM)method,and randomforest(RF).The results show that the diagnosis accuracy of the proposed model is the highest and the model can be used to accurately diagnose the DSV fault degrees of the winding.
基金supported by the Social Science Foundation of China under Grant No.17BGL231。
文摘On the basis of machine leaning,suitable algorithms can make advanced time series analysis.This paper proposes a complex k-nearest neighbor(KNN)model for predicting financial time series.This model uses a complex feature extraction process integrating a forward rolling empirical mode decomposition(EMD)for financial time series signal analysis and principal component analysis(PCA)for the dimension reduction.The information-rich features are extracted then input to a weighted KNN classifier where the features are weighted with PCA loading.Finally,prediction is generated via regression on the selected nearest neighbors.The structure of the model as a whole is original.The test results on real historical data sets confirm the effectiveness of the models for predicting the Chinese stock index,an individual stock,and the EUR/USD exchange rate.
基金supported by Taif University Researchers Supporting Project(Number TURSP-2020/122),Taif University,Taif,Saudi Arabia.
文摘In this paper,Support Vector Machine(SVM)and K-Nearest Neighbor(KNN)based methods are to be applied on fault diagnosis in a robot manipulator.A comparative study between the two classifiers in terms of successfully detecting and isolating the seven classes of sensor faults is considered in this work.For both classifiers,the torque,the position and the speed of the manipulator have been employed as the input vector.However,it is to mention that a large database is needed and used for the training and testing phases.The SVM method used in this paper is based on the Gaussian kernel with the parametersγand the penalty margin parameter“C”,which were adjusted via the PSO algorithm to achieve a maximum accuracy diagnosis.Simulations were carried out on the model of a Selective Compliance Assembly Robot Arm(SCARA)robot manipulator,and the results showed that the Particle Swarm Optimization(PSO)increased the per-formance of the SVM algorithm with the 96.95%accuracy while the KNN algo-rithm achieved a correlation up to 94.62%.These results showed that the SVM algorithm with PSO was more precise than the KNN algorithm when was used in fault diagnosis on a robot manipulator.
文摘The Feixianguan Formation reservoirs in northeastern Sichuan are mainly a suite of carbonate platform deposits.The reservoir types are diverse with high heterogeneity and complex genetic mechanisms.Pores,vugs and fractures of different genetic mechanisms and scales are often developed in association,and it is difficult to classify reservoir types merely based on static data such as outcrop observation,and cores and logging data.In the study,the reservoirs in the Feixianguan Formation are grouped into five types by combining dynamic and static data,that is,karst breccia-residual vuggy type,solution-enhanced vuggy type,fractured-vuggy type,fractured type and matrix type(non-reservoir).Based on conventional logging data,core data and formation microscanner image(FMI)data of the Qilibei block,northeastern Sichuan Basin,the reservoirs are classified in accordance with fracture-vug matching relationship.Based on the principle of cluster analysis,K-Nearest Neighbor(KNN)classification templates are established,and the applicability of the model is verified by using the reservoir data from wells uninvolved in modeling.Following the analysis of the results of reservoir type discrimination and the production of corresponding reservoir intervals,the contributions of various reservoir types to production are evaluated and the reliability of reservoir type classification is verified.The results show that the solution-enhanced vuggy type is of high-quality sweet spot reservoir in the study area with good physical property and high gas production,followed by the fractured-vuggy type,and the fractured and karst breccia-residual vuggy types are the least promising.
基金Princess Nourah bint Abdulrahman University and Researchers Supporting Project Number(PNURSP2024R346)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The growing usage of Android smartphones has led to a significant rise in incidents of Android malware andprivacy breaches.This escalating security concern necessitates the development of advanced technologies capableof automatically detecting andmitigatingmalicious activities in Android applications(apps).Such technologies arecrucial for safeguarding user data and maintaining the integrity of mobile devices in an increasingly digital world.Current methods employed to detect sensitive data leaks in Android apps are hampered by two major limitationsthey require substantial computational resources and are prone to a high frequency of false positives.This meansthat while attempting to identify security breaches,these methods often consume considerable processing powerand mistakenly flag benign activities as malicious,leading to inefficiencies and reduced reliability in malwaredetection.The proposed approach includes a data preprocessing step that removes duplicate samples,managesunbalanced datasets,corrects inconsistencies,and imputes missing values to ensure data accuracy.The Minimaxmethod is then used to normalize numerical data,followed by feature vector extraction using the Gain ratio andChi-squared test to identify and extract the most significant characteristics using an appropriate prediction model.This study focuses on extracting a subset of attributes best suited for the task and recommending a predictivemodel based on domain expert opinion.The proposed method is evaluated using Drebin and TUANDROMDdatasets containing 15,036 and 4,464 benign and malicious samples,respectively.The empirical result shows thatthe RandomForest(RF)and Support VectorMachine(SVC)classifiers achieved impressive accuracy rates of 98.9%and 98.8%,respectively,in detecting unknown Androidmalware.A sensitivity analysis experiment was also carriedout on all three ML-based classifiers based on MAE,MSE,R2,and sensitivity parameters,resulting in a flawlessperformance for both datasets.This approach has substantial potential for real-world applications and can serve asa valuable tool for preventing the spread of Androidmalware and enhancing mobile device security.
基金supported by the National Science Fund for Distinguished Young Scholars of China(61525304)the National Natural Science Foundation of China(61873328)
文摘In this paper, a memetic algorithm with competition(MAC) is proposed to solve the capacitated green vehicle routing problem(CGVRP). Firstly, the permutation array called traveling salesman problem(TSP) route is used to encode the solution, and an effective decoding method to construct the CGVRP route is presented accordingly. Secondly, the k-nearest neighbor(k NN) based initialization is presented to take use of the location information of the customers. Thirdly, according to the characteristics of the CGVRP, the search operators in the variable neighborhood search(VNS) framework and the simulated annealing(SA) strategy are executed on the TSP route for all solutions. Moreover, the customer adjustment operator and the alternative fuel station(AFS) adjustment operator on the CGVRP route are executed for the elite solutions after competition. In addition, the crossover operator is employed to share information among different solutions. The effect of parameter setting is investigated using the Taguchi method of design-ofexperiment to suggest suitable values. Via numerical tests, it demonstrates the effectiveness of both the competitive search and the decoding method. Moreover, extensive comparative results show that the proposed algorithm is more effective and efficient than the existing methods in solving the CGVRP.
基金supported in part by the National Natural Science Foundation of China(Nos.61304205 and 61502240)the Natural Science Foundation of Jiangsu Province(BK20191401)the Innovation and Entrepreneurship Training Project of College Students(202010300290,202010300211,202010300116E).
文摘Vehicle type recognition(VTR)is an important research topic due to its significance in intelligent transportation systems.However,recognizing vehicle type on the real-world images is challenging due to the illumination change,partial occlusion under real traffic environment.These difficulties limit the performance of current state-of-art methods,which are typically based on single-stage classification without considering feature availability.To address such difficulties,this paper proposes a two-stage vehicle type recognition method combining the most effective Gabor features.The first stage leverages edge features to classify vehicles by size into big or small via a similarity k-nearest neighbor classifier(SKNNC).Further the more specific vehicle type such as bus,truck,sedan or van is recognized by the second stage classification,which leverages the most effective Gabor features extracted by a set of Gabor wavelet kernels on the partitioned key patches via a kernel sparse representation-based classifier(KSRC).A verification and correction step based on minimum residual analysis is proposed to enhance the reliability of the VTR.To improve VTR efficiency,the most effective Gabor features are selected through gray relational analysis that leverages the correlation between Gabor feature image and the original image.Experimental results demonstrate that the proposed method not only improves the accuracy of VTR but also enhances the recognition robustness to illumination change and partial occlusion.
基金Project (No. 2012BAH18B05) supported by the National Key Technology R&D Program of China
文摘Text categorization is a significant technique to manage the surging text data on the Internet.The k-nearest neighbors(kNN) algorithm is an effective,but not efficient,classification model for text categorization.In this paper,we propose an effective strategy to accelerate the standard kNN,based on a simple principle:usually,near points in space are also near when they are projected into a direction,which means that distant points in the projection direction are also distant in the original space.Using the proposed strategy,most of the irrelevant points can be removed when searching for the k-nearest neighbors of a query point,which greatly decreases the computation cost.Experimental results show that the proposed strategy greatly improves the time performance of the standard kNN,with little degradation in accuracy.Specifically,it is superior in applications that have large and high-dimensional datasets.
基金Supported by 2017 Harbin Application Technology Research and Development Funds Innovation Talent Project(2017RAQXJ079)
文摘To accurately identify soybean pests and diseases, in this paper, a kind of deep convolution network model was used to determine whether or not a soybean crop possessed pests and diseases. The proposed deep convolution network could learn the highdimensional feature representation of images by using their depth. An inception module was used to construct a neural network. In the inception module, multiscale convolution kernels were used to extract the distributed characteristics of soybean pests and diseases at different scales and to perform cascade fusion. The model then trained the SoftMax classifier in a uniformed framework. This realized the model of soybean pests and diseases so as to verify the effectiveness of this method. In this study, 800 images of soybean leaf images were taken as the experimental objects. Of these 800 images, 400 were selected for network training, and the remaining 400 images were used for the network test. Furthermore, the classical convolutional neural network was optimized. The accuracies before and after optimization were 96.25% and 95.81%, respectively, in terms of extracting image features. This type of research might be applied to achieve a degree of automation in agricultural field management.
基金This work was supported by the National Defence Pre-research Foundation of China(30502010103).
文摘The detection and recognition of radar signals play a critical role in the maintenance of future electronic warfare(EW).So far,however,there are still problems with signal detection and recognition,especially in the low probability of intercept(LPI)radar.This paper explores the usefulness of such an algorithm in the scenario of LPI radar signal detection and recognition based on visibility graphs(VG).More network and feature information can be extracted in the VG two-dimensional space,this algorithm can solve the problem of signal recognition using the autocorrelation function.Wavelet denoising processing is introduced into the signal to be tested,and the denoised signal is converted to the VG domain.Then,the signal detection is performed by using the constant false alarm of the VG average degree.Next,weight the converted graph.Finally,perform feature extraction on the weighted image,and use the feature to complete the recognition.It is testified that the proposed algorithm offers significant improvements,such as robustness to noise,and the detection and recognition accuracy,over the recent researches.
基金Supported by National Natural Science Foundation of China(41805080)Natural Science Foundation of Anhui Province,China(1708085QD89)+1 种基金Key Research and Development Program Projects of Anhui Province,China(201904a07020099)Open Foundation Project Shenyang Institute of Atmospheric Environment,China Meteorological Administration(2016SYIAE14)
文摘In this paper,the application of an algorithm for precipitation retrieval based on Himawari-8 (H8) satellite infrared data is studied.Based on GPM precipitation data and H8 Infrared spectrum channel brightness temperature data,corresponding "precipitation field dictionary" and "channel brightness temperature dictionary" are formed.The retrieval of precipitation field based on brightness temperature data is studied through the classification rule of k-nearest neighbor domain (KNN) and regularization constraint.Firstly,the corresponding "dictionary" is constructed according to the training sample database of the matched GPM precipitation data and H8 brightness temperature data.Secondly,according to the fact that precipitation characteristics in small organizations in different storm environments are often repeated,KNN is used to identify the spectral brightness temperature signal of "precipitation" and "non-precipitation" based on "the dictionary".Finally,the precipitation field retrieval is carried out in the precipitation signal "subspace" based on the regular term constraint method.In the process of retrieval,the contribution rate of brightness temperature retrieval of different channels was determined by Bayesian model averaging (BMA) model.The preliminary experimental results based on the "quantitative" evaluation indexes show that the precipitation of H8 retrieval has a good correlation with the GPM truth value,with a small error and similar structure.
基金Project supported by the Natural Science Foundation of Jilin Province,China(Grant No.2020122348JC)。
文摘Filament-induced breakdown spectroscopy(FIBS)combined with machine learning algorithms was used to identify five aluminum alloys.To study the effect of the distance between focusing lens and target surface on the identification accuracy of aluminum alloys,principal component analysis(PCA)combined with support vector machine(SVM)and Knearest neighbor(KNN)was used.The intensity and intensity ratio of fifteen lines of six elements(Fe,Si,Mg,Cu,Zn,and Mn)in the FIBS spectrum were selected.The distances between the focusing lens and the target surface in the pre-filament,filament,and post-filament were 958 mm,976 mm,and 1000 mm,respectively.The source data set was fifteen spectral line intensity ratios,and the cumulative interpretation rates of PC1,PC2,and PC3 were 97.22%,98.17%,and 95.31%,respectively.The first three PCs obtained by PCA were the input variables of SVM and KNN.The identification accuracy of the different positions of focusing lens and target surface was obtained,and the identification accuracy of SVM and KNN in the filament was 100%and 90%,respectively.The source data set of the filament was obtained by PCA for the first three PCs,which were randomly selected as the training set and test set of SVM and KNN in 3:2.The identification accuracy of SVM and KNN was 97.5%and 92.5%,respectively.The research results can provide a reference for the identification of aluminum alloys by FIBS.
基金supported by the In-shoe Triaxial Pressure Measurement (Grant No.07DZ12077)and the Shanghai Innovation Project
文摘A motion information analysis system based on the acceleration data is proposed in this paper,consisting of filtering,feature extraction and classification.The Kalman filter is adopted to eliminate the noise.With the time-domain and frequency-domain analysis,acceleration features like the amplitude,the period and the acceleration region values are obtained.Furthermore,the accuracy of the motion classification is improved by using the k-nearest neighbor (KNN) algorithm.