Case retrieval(CR) is critically an important part of case-based design. However few studies attempt to research CR for customer-driven design and analyze the eect of other production factors besides similarity comput...Case retrieval(CR) is critically an important part of case-based design. However few studies attempt to research CR for customer-driven design and analyze the eect of other production factors besides similarity computation. This paper proposes a new CR method for customer-driven design,and requirement-weighting analysis. Fuzzy set theory are integrated into CR process to deal with the fuzzy and imprecision customer requirements. So the proposed method is called weighted fuzzy case retrieval(WFCR). Furthermore,similar case evaluation is added into CR process to demonstrate that the best case is selected not only on the basis of satisifaction for the given requriements,but also on the degree of preference over other cases according to multiple evaluation criteria. Meanwhile,WFCR system is developed and applied to power transformer design to validate its scientificity and practicality. Finally,the paper statistically validated the supriority of WFCR by comparing it with traditional fuzzy case retrieval methods(FCRs),and the comparison indicates that WFCR is more accurate than other FCRs.展开更多
Abnormal gaits including pelvic obliquity gait and knee hyperextension gait are common clinical symptoms related to flat-ground fall among elder adults.This study aimed to determine the feasibility of using lower limb...Abnormal gaits including pelvic obliquity gait and knee hyperextension gait are common clinical symptoms related to flat-ground fall among elder adults.This study aimed to determine the feasibility of using lower limb myoelectrical signals(electromyographic signals,EMG)for gait pattern recognition and to identify the optimal machine learning(ML)algorithms for EMG signal processing.Seven healthy subjects were recruited with their EMG signals collected from eight muscles of the lower limbs during walking with normal and abnormal gaits.Four basic ML algorithms including support vector machine(SVM),K-nearest neighbor(kNN),decision tree(DT),and naive Bayes(NB),and five deep learning models including convolutional neural network(CNN),long-short term memory(LSTM),bidirectional long short-term memory(BiLSTM),and CNN-BiLSTM were used to process the EMG signals recorded under different gaits.Statistical analysis was performed to compare the accuracy of individual ML algorithms in discriminating gait patterns.The overall accuracy was 95.78%for SVM,95.09%for CNN-LSTM,and 96.28%for CNN-BiLSTM,respectively.The overall accuracy was 90.25%for DT,92.62%for kNN,91.27%for NB,and 90.34%for CNN,respectively.The accuracy was 67.39%for LSTM and 74.75%for BiLSTM,respectively.Most ML algorithms in this study had an accuracy greater than 90%in EMG-based abnormal gait pattern recognition except for LSTM and BiLSTM.This study provides novel technology for evaluation of gait pattern recognition related to flat ground fall.展开更多
基金the National Natural Science Foundation of China (Nos. 50775140, 50575142 and 60304015)the National High Technology Research and Development Program (863) of China (No. 2008AA04Z113)+1 种基金the National Basic Research Program (973) of China (No. 2006CB705400)the Shanghai Committee of Science and Technology (No. 08JC1412000)
文摘Case retrieval(CR) is critically an important part of case-based design. However few studies attempt to research CR for customer-driven design and analyze the eect of other production factors besides similarity computation. This paper proposes a new CR method for customer-driven design,and requirement-weighting analysis. Fuzzy set theory are integrated into CR process to deal with the fuzzy and imprecision customer requirements. So the proposed method is called weighted fuzzy case retrieval(WFCR). Furthermore,similar case evaluation is added into CR process to demonstrate that the best case is selected not only on the basis of satisifaction for the given requriements,but also on the degree of preference over other cases according to multiple evaluation criteria. Meanwhile,WFCR system is developed and applied to power transformer design to validate its scientificity and practicality. Finally,the paper statistically validated the supriority of WFCR by comparing it with traditional fuzzy case retrieval methods(FCRs),and the comparison indicates that WFCR is more accurate than other FCRs.
基金supported by National Natural Science Foundation of China(52035007)the Cross Fund for Medical and Engineering of Shanghai Jiao Tong University(YG2021QN118)+1 种基金Rehabilitation Institute of Michigan Foundation(Grant#22-2-003)Hubei Provincial Research Fund(023DJC019).
文摘Abnormal gaits including pelvic obliquity gait and knee hyperextension gait are common clinical symptoms related to flat-ground fall among elder adults.This study aimed to determine the feasibility of using lower limb myoelectrical signals(electromyographic signals,EMG)for gait pattern recognition and to identify the optimal machine learning(ML)algorithms for EMG signal processing.Seven healthy subjects were recruited with their EMG signals collected from eight muscles of the lower limbs during walking with normal and abnormal gaits.Four basic ML algorithms including support vector machine(SVM),K-nearest neighbor(kNN),decision tree(DT),and naive Bayes(NB),and five deep learning models including convolutional neural network(CNN),long-short term memory(LSTM),bidirectional long short-term memory(BiLSTM),and CNN-BiLSTM were used to process the EMG signals recorded under different gaits.Statistical analysis was performed to compare the accuracy of individual ML algorithms in discriminating gait patterns.The overall accuracy was 95.78%for SVM,95.09%for CNN-LSTM,and 96.28%for CNN-BiLSTM,respectively.The overall accuracy was 90.25%for DT,92.62%for kNN,91.27%for NB,and 90.34%for CNN,respectively.The accuracy was 67.39%for LSTM and 74.75%for BiLSTM,respectively.Most ML algorithms in this study had an accuracy greater than 90%in EMG-based abnormal gait pattern recognition except for LSTM and BiLSTM.This study provides novel technology for evaluation of gait pattern recognition related to flat ground fall.
