Building indoor dangerous behavior recognition is a specific application in the field of abnormal human recognition.A human dangerous behavior recognition method based on LSTM-GCN with attention mechanism(GLA)model wa...Building indoor dangerous behavior recognition is a specific application in the field of abnormal human recognition.A human dangerous behavior recognition method based on LSTM-GCN with attention mechanism(GLA)model was proposed aiming at the problem that the existing human skeleton-based action recognition methods cannot fully extract the temporal and spatial features.The network connects GCN and LSTMnetwork in series,and inputs the skeleton sequence extracted by GCN that contains spatial information into the LSTM layer for time sequence feature extraction,which fully excavates the temporal and spatial features of the skeleton sequence.Finally,an attention layer is designed to enhance the features of key bone points,and Softmax is used to classify and identify dangerous behaviors.The dangerous behavior datasets are derived from NTU-RGB+D and Kinetics data sets.Experimental results show that the proposed method can effectively identify some dangerous behaviors in the building,and its accuracy is higher than those of other similar methods.展开更多
The basic theory of YOLO series object detection algorithms is discussed, the dangerous driving behavior dataset is collected and produced, and then the YOLOv7 network is introduced in detail, the deep separable convo...The basic theory of YOLO series object detection algorithms is discussed, the dangerous driving behavior dataset is collected and produced, and then the YOLOv7 network is introduced in detail, the deep separable convolution and CA attention mechanism are introduced, the YOLOv7 bounding box loss function and clustering algorithm are optimized, and the DB-YOLOv7 network structure is constructed. In the first stage of the experiment, the PASCAL VOC public dataset was utilized for pre-training. A comparative analysis was conducted to assess the recognition accuracy and inference time before and after the proposed improvements. The experimental results demonstrated an increase of 1.4% in the average recognition accuracy, alongside a reduction in the inference time by 4 ms. Subsequently, a model for the recognition of dangerous driving behaviors was trained using a specialized dangerous driving behavior dataset. A series of experiments were performed to evaluate the efficacy of the DB-YOLOv7 algorithm in this context. The findings indicate a significant enhancement in detection performance, with a 4% improvement in accuracy compared to the baseline network. Furthermore, the model’s inference time was reduced by 20%, from 25 ms to 20 ms. These results substantiate the effectiveness of the DB-YOLOv7 recognition algorithm for detecting dangerous driving behaviors, providing comprehensive validation of its practical applicability.展开更多
[Objectives]To observe the clinical efficacy of Dingjing Pills on patients with schizophrenia accompanied by risky behaviors.[Methods]Two hundred patients diagnosed with schizophrenia and risky behaviors were divided ...[Objectives]To observe the clinical efficacy of Dingjing Pills on patients with schizophrenia accompanied by risky behaviors.[Methods]Two hundred patients diagnosed with schizophrenia and risky behaviors were divided into two groups based on the random and double-blinded principle:the treatment group(100 cases)treated with Dingjing Pills combined with risperidone,and the control group(100 cases)treated with risperidone alone.The observation course was 6 weeks.The clinical efficacy was compared using brief psychiatric rating scale(BPRS),modified overt aggression scale(revised edition)(MAOS),treatment emergent symptom scale(TESS),and blood routine,liver function,kidney function,and electrocardiogram examinations were conducted.[Results]After treatment,Dingjing Pills significantly reduced the scores of brief psychiatric rating scale,modified overt aggression scale and treatment emergent symptom scale in patients with schizophrenia and dangerous behaviors,and had no significant toxic or side effects.The total effective rate in the treatment group was 88.8%,while the total effective rate in the control group was 77.1%.There was a significant difference in therapeutic efficacy between the two groups(P<0.05).[Conclusions]Dingjing Pills has an intervention and therapeutic effect on high-risk behaviors of schizophrenia,with minimal side effects and easy acceptance by patients.展开更多
文摘Building indoor dangerous behavior recognition is a specific application in the field of abnormal human recognition.A human dangerous behavior recognition method based on LSTM-GCN with attention mechanism(GLA)model was proposed aiming at the problem that the existing human skeleton-based action recognition methods cannot fully extract the temporal and spatial features.The network connects GCN and LSTMnetwork in series,and inputs the skeleton sequence extracted by GCN that contains spatial information into the LSTM layer for time sequence feature extraction,which fully excavates the temporal and spatial features of the skeleton sequence.Finally,an attention layer is designed to enhance the features of key bone points,and Softmax is used to classify and identify dangerous behaviors.The dangerous behavior datasets are derived from NTU-RGB+D and Kinetics data sets.Experimental results show that the proposed method can effectively identify some dangerous behaviors in the building,and its accuracy is higher than those of other similar methods.
文摘The basic theory of YOLO series object detection algorithms is discussed, the dangerous driving behavior dataset is collected and produced, and then the YOLOv7 network is introduced in detail, the deep separable convolution and CA attention mechanism are introduced, the YOLOv7 bounding box loss function and clustering algorithm are optimized, and the DB-YOLOv7 network structure is constructed. In the first stage of the experiment, the PASCAL VOC public dataset was utilized for pre-training. A comparative analysis was conducted to assess the recognition accuracy and inference time before and after the proposed improvements. The experimental results demonstrated an increase of 1.4% in the average recognition accuracy, alongside a reduction in the inference time by 4 ms. Subsequently, a model for the recognition of dangerous driving behaviors was trained using a specialized dangerous driving behavior dataset. A series of experiments were performed to evaluate the efficacy of the DB-YOLOv7 algorithm in this context. The findings indicate a significant enhancement in detection performance, with a 4% improvement in accuracy compared to the baseline network. Furthermore, the model’s inference time was reduced by 20%, from 25 ms to 20 ms. These results substantiate the effectiveness of the DB-YOLOv7 recognition algorithm for detecting dangerous driving behaviors, providing comprehensive validation of its practical applicability.
文摘[Objectives]To observe the clinical efficacy of Dingjing Pills on patients with schizophrenia accompanied by risky behaviors.[Methods]Two hundred patients diagnosed with schizophrenia and risky behaviors were divided into two groups based on the random and double-blinded principle:the treatment group(100 cases)treated with Dingjing Pills combined with risperidone,and the control group(100 cases)treated with risperidone alone.The observation course was 6 weeks.The clinical efficacy was compared using brief psychiatric rating scale(BPRS),modified overt aggression scale(revised edition)(MAOS),treatment emergent symptom scale(TESS),and blood routine,liver function,kidney function,and electrocardiogram examinations were conducted.[Results]After treatment,Dingjing Pills significantly reduced the scores of brief psychiatric rating scale,modified overt aggression scale and treatment emergent symptom scale in patients with schizophrenia and dangerous behaviors,and had no significant toxic or side effects.The total effective rate in the treatment group was 88.8%,while the total effective rate in the control group was 77.1%.There was a significant difference in therapeutic efficacy between the two groups(P<0.05).[Conclusions]Dingjing Pills has an intervention and therapeutic effect on high-risk behaviors of schizophrenia,with minimal side effects and easy acceptance by patients.
