Autism spectrum disorder(ASD)is a multifaceted neurological developmental condition that manifests in several ways.Nearly all autistic children remain undiagnosed before the age of three.Developmental problems affecti...Autism spectrum disorder(ASD)is a multifaceted neurological developmental condition that manifests in several ways.Nearly all autistic children remain undiagnosed before the age of three.Developmental problems affecting face features are often associated with fundamental brain disorders.The facial evolution of newborns with ASD is quite different from that of typically developing children.Early recognition is very significant to aid families and parents in superstition and denial.Distinguishing facial features from typically developing children is an evident manner to detect children analyzed with ASD.Presently,artificial intelligence(AI)significantly contributes to the emerging computer-aided diagnosis(CAD)of autism and to the evolving interactivemethods that aid in the treatment and reintegration of autistic patients.This study introduces an Ensemble of deep learning models based on the autism spectrum disorder detection in facial images(EDLM-ASDDFI)model.The overarching goal of the EDLM-ASDDFI model is to recognize the difference between facial images of individuals with ASD and normal controls.In the EDLM-ASDDFI method,the primary level of data pre-processing is involved by Gabor filtering(GF).Besides,the EDLM-ASDDFI technique applies the MobileNetV2 model to learn complex features from the pre-processed data.For the ASD detection process,the EDLM-ASDDFI method uses ensemble techniques for classification procedure that encompasses long short-term memory(LSTM),deep belief network(DBN),and hybrid kernel extreme learning machine(HKELM).Finally,the hyperparameter selection of the three deep learning(DL)models can be implemented by the design of the crested porcupine optimizer(CPO)technique.An extensive experiment was conducted to emphasize the improved ASD detection performance of the EDLM-ASDDFI method.The simulation outcomes indicated that the EDLM-ASDDFI technique highlighted betterment over other existing models in terms of numerous performance measures.展开更多
In recent years, urban rail transit （URT） systems have rapidly developed in China, however, their existing strategies for vehicle maintenance are still based on experiential and qualitative methods which result in e...In recent years, urban rail transit （URT） systems have rapidly developed in China, however, their existing strategies for vehicle maintenance are still based on experiential and qualitative methods which result in either high cost or emergencies. In this paper, a tentative attempt at introducing the fuzzy set theory into quantitative analysis and assessment of URT trains＇ failures was presented. Based on the proposed FMEA-fuzzy model, a com- puter aided system for URT maintenance optimization was developed. The overall structure and procedure of the system were described in detail, and the important issues, including the development environment, improvement to FMEA table, acquisition of weight distribution matrix P, and setting of fuzzy vector R, were also discussed. Initial application into the vehicle maintenance of Shanghai Metro System shows, that the proposed model and computer aided system have a good performance and consequently are worth further development.展开更多
Acute Lymphoblastic Leukemia(ALL)is a fatal malignancy that is featured by the abnormal increase of immature lymphocytes in blood or bone marrow.Early prognosis of ALL is indispensable for the effectual remediation of...Acute Lymphoblastic Leukemia(ALL)is a fatal malignancy that is featured by the abnormal increase of immature lymphocytes in blood or bone marrow.Early prognosis of ALL is indispensable for the effectual remediation of this disease.Initial screening of ALL is conducted through manual examination of stained blood smear microscopic images,a process which is time-consuming and prone to errors.Therefore,many deep learning-based computer-aided diagnosis(CAD)systems have been established to automatically diagnose ALL.This paper proposes a novel hybrid deep learning system for ALL diagnosis in blood smear images.The introduced system integrates the proficiency of autoencoder networks in feature representational learning in latent space with the superior feature extraction capability of standard pretrained convolutional neural networks(CNNs)to identify the existence of ALL in blood smears.An augmented set of deep image features are formed from the features extracted by GoogleNet and Inception-v3 CNNs from a hybrid dataset of microscopic blood smear images.A sparse autoencoder network is designed to create an abstract set of significant latent features from the enlarged image feature set.The latent features are used to perform image classification using Support Vector Machine(SVM)classifier.The obtained results show that the latent features improve the classification performance of the proposed ALL diagnosis system over the original image features.Moreover,the classification performance of the system with various sizes of the latent feature set is evaluated.The retrieved results reveal that the introduced ALL diagnosis system superiorly compete the state of the art.展开更多
When a direct-current （DC） machine runs at extremely low speed or standstill, the reduction in the armature resistance and the armature flux linkage due to the short circuited coils by the brushes on the commutator ...When a direct-current （DC） machine runs at extremely low speed or standstill, the reduction in the armature resistance and the armature flux linkage due to the short circuited coils by the brushes on the commutator should not be neglected. Taking this reduction effect into account, the average values of the reduction coefficients relate to the machine parameters in complicated forms. In this paper, an effective algorithm for the precise computation of the average values of these reduction coefficients is proposed. Furthermore, in the algorithm, the effect of the insulation thickness between the commutator segments and the multiplicity of the wave winding are considered for the first time. The proposed algorithm can also be accommodated into the computer-aided design （CAD） of a DC machine, which normally runs at extremely low speed or standstill.展开更多
基金Researchers supporting Project number(RSPD2025R1107),King Saud University,Riyadh,Saudi Arabia.
文摘Autism spectrum disorder(ASD)is a multifaceted neurological developmental condition that manifests in several ways.Nearly all autistic children remain undiagnosed before the age of three.Developmental problems affecting face features are often associated with fundamental brain disorders.The facial evolution of newborns with ASD is quite different from that of typically developing children.Early recognition is very significant to aid families and parents in superstition and denial.Distinguishing facial features from typically developing children is an evident manner to detect children analyzed with ASD.Presently,artificial intelligence(AI)significantly contributes to the emerging computer-aided diagnosis(CAD)of autism and to the evolving interactivemethods that aid in the treatment and reintegration of autistic patients.This study introduces an Ensemble of deep learning models based on the autism spectrum disorder detection in facial images(EDLM-ASDDFI)model.The overarching goal of the EDLM-ASDDFI model is to recognize the difference between facial images of individuals with ASD and normal controls.In the EDLM-ASDDFI method,the primary level of data pre-processing is involved by Gabor filtering(GF).Besides,the EDLM-ASDDFI technique applies the MobileNetV2 model to learn complex features from the pre-processed data.For the ASD detection process,the EDLM-ASDDFI method uses ensemble techniques for classification procedure that encompasses long short-term memory(LSTM),deep belief network(DBN),and hybrid kernel extreme learning machine(HKELM).Finally,the hyperparameter selection of the three deep learning(DL)models can be implemented by the design of the crested porcupine optimizer(CPO)technique.An extensive experiment was conducted to emphasize the improved ASD detection performance of the EDLM-ASDDFI method.The simulation outcomes indicated that the EDLM-ASDDFI technique highlighted betterment over other existing models in terms of numerous performance measures.
基金supported by the Research Program of Science and Technology Commission in Shanghai under Grant No.10dz1122701
文摘In recent years, urban rail transit （URT） systems have rapidly developed in China, however, their existing strategies for vehicle maintenance are still based on experiential and qualitative methods which result in either high cost or emergencies. In this paper, a tentative attempt at introducing the fuzzy set theory into quantitative analysis and assessment of URT trains＇ failures was presented. Based on the proposed FMEA-fuzzy model, a com- puter aided system for URT maintenance optimization was developed. The overall structure and procedure of the system were described in detail, and the important issues, including the development environment, improvement to FMEA table, acquisition of weight distribution matrix P, and setting of fuzzy vector R, were also discussed. Initial application into the vehicle maintenance of Shanghai Metro System shows, that the proposed model and computer aided system have a good performance and consequently are worth further development.
文摘Acute Lymphoblastic Leukemia(ALL)is a fatal malignancy that is featured by the abnormal increase of immature lymphocytes in blood or bone marrow.Early prognosis of ALL is indispensable for the effectual remediation of this disease.Initial screening of ALL is conducted through manual examination of stained blood smear microscopic images,a process which is time-consuming and prone to errors.Therefore,many deep learning-based computer-aided diagnosis(CAD)systems have been established to automatically diagnose ALL.This paper proposes a novel hybrid deep learning system for ALL diagnosis in blood smear images.The introduced system integrates the proficiency of autoencoder networks in feature representational learning in latent space with the superior feature extraction capability of standard pretrained convolutional neural networks(CNNs)to identify the existence of ALL in blood smears.An augmented set of deep image features are formed from the features extracted by GoogleNet and Inception-v3 CNNs from a hybrid dataset of microscopic blood smear images.A sparse autoencoder network is designed to create an abstract set of significant latent features from the enlarged image feature set.The latent features are used to perform image classification using Support Vector Machine(SVM)classifier.The obtained results show that the latent features improve the classification performance of the proposed ALL diagnosis system over the original image features.Moreover,the classification performance of the system with various sizes of the latent feature set is evaluated.The retrieved results reveal that the introduced ALL diagnosis system superiorly compete the state of the art.
文摘When a direct-current （DC） machine runs at extremely low speed or standstill, the reduction in the armature resistance and the armature flux linkage due to the short circuited coils by the brushes on the commutator should not be neglected. Taking this reduction effect into account, the average values of the reduction coefficients relate to the machine parameters in complicated forms. In this paper, an effective algorithm for the precise computation of the average values of these reduction coefficients is proposed. Furthermore, in the algorithm, the effect of the insulation thickness between the commutator segments and the multiplicity of the wave winding are considered for the first time. The proposed algorithm can also be accommodated into the computer-aided design （CAD） of a DC machine, which normally runs at extremely low speed or standstill.
