Mental task classification is one of the most important problems in Brain-computer interface.This paper studies the classification of five-class mental tasks.The nonlinear parameter of mean period obtained from freque...Mental task classification is one of the most important problems in Brain-computer interface.This paper studies the classification of five-class mental tasks.The nonlinear parameter of mean period obtained from frequency domain information was used as features for classification implemented by using the method of SVM(support vector machines).The averaged classification accuracy of 85.6% over 7 subjects was achieved for 2-second EEG segments.And the results for EEG segments of 0.5s and 5.0s compared favorably to those of Garrett's.The results indicate that the parameter of mean period represents mental tasks well for classification.Furthermore,the method of mean period is less computationally demanding,which indicates its potential use for online BCI systems.展开更多
In this paper, we have proposed a novel model called proximal support matrix machine (PSMM), which is mainly based on the models of proximal support vector machine (PSVM) and low rank support matrix machine (LRSMM). I...In this paper, we have proposed a novel model called proximal support matrix machine (PSMM), which is mainly based on the models of proximal support vector machine (PSVM) and low rank support matrix machine (LRSMM). In design, the PSMM model has comprehensively considered both the relationship between samples of the same class and the structure of rows or columns of matrix data. To a certain extent, our novel model can be regarded as a synthesis of the PSVM model and the LRSMM model. Since the PSMM model is an unconstrained convex problem in essence, we have established an alternating direction method of multipliers algorithm to deal with the proposed model. Finally, since a great deal of experiments on the minst digital database show that the PSMM classifier has a good ability to distinguish two digits with little difference, it encourages us to conduct more complex experiments on MIT face database, INRIA person database, the students face database and Japan female facial expression database. Meanwhile, the final experimental results show that PSMM performs better than PSVM, twin support vector machine, LRSMM and linear twin multiple rank support matrix machine in the demanding image classification tasks.展开更多
This paper proposes a machine-learning-based methodology to automatically classify different types of steel weld defects,including lack of the fusion,porosity,slag inclusion,and the qualified(no defects)cases.This met...This paper proposes a machine-learning-based methodology to automatically classify different types of steel weld defects,including lack of the fusion,porosity,slag inclusion,and the qualified(no defects)cases.This methodology solves the shortcomings of existing detection methods,such as expensive equipment,complicated operation and inability to detect internal defects.The study first collected percussed data from welded steel members with or without weld defects.Then,three methods,the Mel frequency cepstral coefficients,short-time Fourier transform(STFT),and continuous wavelet transform were implemented and compared to explore the most appropriate features for classification of weld statuses.Classic and convolutional neural network-enhanced algorithms were used to classify,the extracted features.Furthermore,experiments were designed and performed to validate the proposed method.Results showed that STFT achieved higher accuracies(up to 96.63%on average)in the weld status classification.The convolutional neural network-enhanced support vector machine(SVM)outperformed six other algorithms with an average accuracy of 95.8%.In addition,random forest and SVM were efficient approaches with a balanced trade-off between the accuracies and the computational efforts.展开更多
Leveraging the power of artificial intelligence to facilitate an automatic analysis and monitoring of heart sounds has increasingly attracted tremendous efforts in the past decade.Nevertheless,lacking on standard open...Leveraging the power of artificial intelligence to facilitate an automatic analysis and monitoring of heart sounds has increasingly attracted tremendous efforts in the past decade.Nevertheless,lacking on standard open-access database made it difficult to maintain a sustainable and comparable research before the first release of the PhysioNet CinC Challenge Dataset.However,inconsistent standards on data collection,annotation,and partition are still restraining a fair and efficient comparison between different works.To this line,we introduced and benchmarked a first version of the Heart Sounds Shenzhen(HSS)corpus.Motivated and inspired by the previous works based on HSS,we redefined the tasks and make a comprehensive investigation on shallow and deep models in this study.First,we segmented the heart sound recording into shorter recordings(10 s),which makes it more similar to the human auscultation case.Second,we redefined the classification tasks.Besides using the 3 class categories(normal,moderate,and mild/severe)adopted in HSS,we added a binary classification task in this study,i.e.,normal and abnormal.In this work,we provided detailed benchmarks based on both the classic machine learning and the state-of-the-art deep learning technologies,which are reproducible by using open-source toolkits.Last but not least,we analyzed the feature contributions of best performance achieved by the benchmark to make the results more convincing and interpretable.展开更多
Background:The paper examines the state and prospects of using 3D modeling in solving identification,classification,diagnostic and situational tasks of forensic examination.Aims and Objectives:The aim of this study is...Background:The paper examines the state and prospects of using 3D modeling in solving identification,classification,diagnostic and situational tasks of forensic examination.Aims and Objectives:The aim of this study is to analyze the world expert practice of using scientific and technical means of three-dimensional modeling in solving problems of forensic examination,using the example of our country,the leading countries of Europe,as well as the United States.Materials and Methods:The empirical basis of the study is the results of the systematization of scientific and technical means for 3D modeling in solving identification,classification,diagnostic and situational problems of forensic examinations used in the expert practice of Ukraine,the United Kingdom,France,Germany,and the USA.Results:The systematization of modem scientific and technical means for 3D modeling,used in solving identification,classification,diagnostic and situational tasks of forensic examination,has been carried out.We analyzed and identified 3D modeling software that most fully meets the requirements of effective forensic expert activities.The features of the use of effective 3D modeling mean for solving identification,classification,diagnostic and situational tasks of forensic examination,namely the software systems"ToolScan"and"TrasoScan",and the SketchUp program have been disclosed.Conclusion:The introduction of the SketchUp 8 software into forensic expert activities will increase the effectiveness of the modeling method in forensic examinations,which,in turn,will have an impact on the effectiveness of expert conclusions,increase their evidentiary value and,as a result,contribute to the entire process of a criminal investigation.展开更多
基金This work was supportedin part by the National Natural Science Foundation of China(No.60271025,No.30370395)in part by the Science and Technology Depart ment of Shaanxi Province(No.2003K10-G24).
文摘Mental task classification is one of the most important problems in Brain-computer interface.This paper studies the classification of five-class mental tasks.The nonlinear parameter of mean period obtained from frequency domain information was used as features for classification implemented by using the method of SVM(support vector machines).The averaged classification accuracy of 85.6% over 7 subjects was achieved for 2-second EEG segments.And the results for EEG segments of 0.5s and 5.0s compared favorably to those of Garrett's.The results indicate that the parameter of mean period represents mental tasks well for classification.Furthermore,the method of mean period is less computationally demanding,which indicates its potential use for online BCI systems.
文摘In this paper, we have proposed a novel model called proximal support matrix machine (PSMM), which is mainly based on the models of proximal support vector machine (PSVM) and low rank support matrix machine (LRSMM). In design, the PSMM model has comprehensively considered both the relationship between samples of the same class and the structure of rows or columns of matrix data. To a certain extent, our novel model can be regarded as a synthesis of the PSVM model and the LRSMM model. Since the PSMM model is an unconstrained convex problem in essence, we have established an alternating direction method of multipliers algorithm to deal with the proposed model. Finally, since a great deal of experiments on the minst digital database show that the PSMM classifier has a good ability to distinguish two digits with little difference, it encourages us to conduct more complex experiments on MIT face database, INRIA person database, the students face database and Japan female facial expression database. Meanwhile, the final experimental results show that PSMM performs better than PSVM, twin support vector machine, LRSMM and linear twin multiple rank support matrix machine in the demanding image classification tasks.
基金support of Shanghai Pinlan Data Technology Co.,Ltd.,and Open Fund of Shanghai Key Laboratory of Engineering Structure Safety,SRIBS(No.2021-KF-06).
文摘This paper proposes a machine-learning-based methodology to automatically classify different types of steel weld defects,including lack of the fusion,porosity,slag inclusion,and the qualified(no defects)cases.This methodology solves the shortcomings of existing detection methods,such as expensive equipment,complicated operation and inability to detect internal defects.The study first collected percussed data from welded steel members with or without weld defects.Then,three methods,the Mel frequency cepstral coefficients,short-time Fourier transform(STFT),and continuous wavelet transform were implemented and compared to explore the most appropriate features for classification of weld statuses.Classic and convolutional neural network-enhanced algorithms were used to classify,the extracted features.Furthermore,experiments were designed and performed to validate the proposed method.Results showed that STFT achieved higher accuracies(up to 96.63%on average)in the weld status classification.The convolutional neural network-enhanced support vector machine(SVM)outperformed six other algorithms with an average accuracy of 95.8%.In addition,random forest and SVM were efficient approaches with a balanced trade-off between the accuracies and the computational efforts.
基金partially supported by the Ministry of Science and Technology of the People's Republic of China with the STI2030-Major Projects(2021ZD0201900)the National Natural Science Foundation of China(No.62227807 and 62272044)+3 种基金the Teli Young Fellow Program from the Beijing Institute of Technology,Chinathe Natural Science Foundation of Shenzhen University General Hospital(No.SUGH2018QD013),Chinathe Shenzhen Science and Technology Innovation Commission Project(No.JCYJ20190808120613189),Chinathe Grants-in-Aid for Scientific Research(No.20H00569)from the Ministry of Education,Culture,Sports,Science and Technology(MEXT),Japan.
文摘Leveraging the power of artificial intelligence to facilitate an automatic analysis and monitoring of heart sounds has increasingly attracted tremendous efforts in the past decade.Nevertheless,lacking on standard open-access database made it difficult to maintain a sustainable and comparable research before the first release of the PhysioNet CinC Challenge Dataset.However,inconsistent standards on data collection,annotation,and partition are still restraining a fair and efficient comparison between different works.To this line,we introduced and benchmarked a first version of the Heart Sounds Shenzhen(HSS)corpus.Motivated and inspired by the previous works based on HSS,we redefined the tasks and make a comprehensive investigation on shallow and deep models in this study.First,we segmented the heart sound recording into shorter recordings(10 s),which makes it more similar to the human auscultation case.Second,we redefined the classification tasks.Besides using the 3 class categories(normal,moderate,and mild/severe)adopted in HSS,we added a binary classification task in this study,i.e.,normal and abnormal.In this work,we provided detailed benchmarks based on both the classic machine learning and the state-of-the-art deep learning technologies,which are reproducible by using open-source toolkits.Last but not least,we analyzed the feature contributions of best performance achieved by the benchmark to make the results more convincing and interpretable.
文摘Background:The paper examines the state and prospects of using 3D modeling in solving identification,classification,diagnostic and situational tasks of forensic examination.Aims and Objectives:The aim of this study is to analyze the world expert practice of using scientific and technical means of three-dimensional modeling in solving problems of forensic examination,using the example of our country,the leading countries of Europe,as well as the United States.Materials and Methods:The empirical basis of the study is the results of the systematization of scientific and technical means for 3D modeling in solving identification,classification,diagnostic and situational problems of forensic examinations used in the expert practice of Ukraine,the United Kingdom,France,Germany,and the USA.Results:The systematization of modem scientific and technical means for 3D modeling,used in solving identification,classification,diagnostic and situational tasks of forensic examination,has been carried out.We analyzed and identified 3D modeling software that most fully meets the requirements of effective forensic expert activities.The features of the use of effective 3D modeling mean for solving identification,classification,diagnostic and situational tasks of forensic examination,namely the software systems"ToolScan"and"TrasoScan",and the SketchUp program have been disclosed.Conclusion:The introduction of the SketchUp 8 software into forensic expert activities will increase the effectiveness of the modeling method in forensic examinations,which,in turn,will have an impact on the effectiveness of expert conclusions,increase their evidentiary value and,as a result,contribute to the entire process of a criminal investigation.
