Automation advancements prompts the extensive integration of collaborative robot(cobot)across a range of industries.Compared to the commonly used design approach of increasing the payload-to-weight ratio of cobot to e...Automation advancements prompts the extensive integration of collaborative robot(cobot)across a range of industries.Compared to the commonly used design approach of increasing the payload-to-weight ratio of cobot to enhance load capacity,equal attention should be paid to the dynamic response characteristics of cobot during the design process to make the cobot more flexible.In this paper,a new method for designing the drive train parameters of cobot is proposed.Firstly,based on the analysis of factors influencing the load capacity and dynamic response characteristics,design criteria for both aspects are established for cobot with all optimization design criteria normalized within the design domain.Secondly,with the cobot in the horizontal pose,the motor design scheme is discretized and it takes the joint motor diameter and gearbox speed ratio as optimization design variables.Finally,all the discrete values of the optimization objectives are obtained through the enumeration method and the Pareto front is used to select the optimal solution through multi-objective optimization.Base on the cobot design method proposed in this paper,a six-axis cobot is designed and compared with the commercial cobot.The result shows that the load capacity of the designed cobot in this paper reaches 8.4 kg,surpassing the 5 kg load capacity commercial cobot which is used as a benchmark.The minimum resonance frequency of the joints is 42.70 Hz.展开更多
Objective: To analyze the effect of combined extracorporeal shock wave and rehabilitation training treatment in patients with muscle articulation chronic pain (MACP). Methods: Ninety-seven MACP patients admitted to ou...Objective: To analyze the effect of combined extracorporeal shock wave and rehabilitation training treatment in patients with muscle articulation chronic pain (MACP). Methods: Ninety-seven MACP patients admitted to our hospital from September 2021 to September 2023 were randomly selected and were divided into Group A (control group, 46 cases, rehabilitation training treatment) and Group B (observation group, 51 cases, extracorporeal shock wave with rehabilitation training treatment), and outcomes of the two groups were compared. Results: The treatment efficiency, post-treatment clinical indexes (upper and lower limb function scores, activities of daily living (ADL) scores, visual analog scale (VAS) scores), and short-form 36 (SF-36) scores of Group B were better than those of Group A (P < 0.05). Conclusion: Combined extracorporeal shock wave and rehabilitation training treatment for MACP patients improved their limb function, daily activities, quality of life, and reduced pain.展开更多
In this paper, we introduce a novel scheme for the separate training of deep learning-based autoencoders used for Channel State Information (CSI) feedback. Our distinct training approach caters to multiple users and b...In this paper, we introduce a novel scheme for the separate training of deep learning-based autoencoders used for Channel State Information (CSI) feedback. Our distinct training approach caters to multiple users and base stations, enabling independent and individualized local training. This ensures the more secure processing of data and algorithms, different from the commonly adopted joint training method. To maintain comparable performance with joint training, we present two distinct training methods: separate training decoder and separate training encoder. It’s noteworthy that conducting separate training for the encoder can pose additional challenges, due to its responsibility in acquiring a compressed representation of underlying data features. This complexity makes accommodating multiple pre-trained decoders for just one encoder a demanding task. To overcome this, we design an adaptation layer architecture that effectively minimizes performance losses. Moreover, the flexible training strategy empowers users and base stations to seamlessly incorporate distinct encoder and decoder structures into the system, significantly amplifying the system’s scalability. .展开更多
Objective: To observe the effect of the joint injury of the distal radio-ulnar joint. Methods: 60 patients with Distal Radioulnar Joint (DRUJ) injury were divided into observation group and control group according to ...Objective: To observe the effect of the joint injury of the distal radio-ulnar joint. Methods: 60 patients with Distal Radioulnar Joint (DRUJ) injury were divided into observation group and control group according to random number method. 30 cases were included in each of the two groups.Before and after treatment in patients with Visual Analogue Scale (Visual Analogue Scale, VAS) score, forearm pronation and supination electromyographic activity, methods of electric integral value (integral electromyogram, iEMG) and Wrist in patients with self assessment Scale (Patient - Rated Wrist Evaluation, PRWE) score evaluation, comparison, and the clinical observation on diagnosis of disease and curative effect of traditional Chinese medicine standard (assessment process by blind method).Results: compared with the two groups before and after treatment, VAS score decreased, forearm pronation and postpronation activity increased, iEMG value increased, and PRWE scale score decreased (all P < 0.05), and the curative effect of the treatment group was better than that of the control group (P < 0.05). The total effective rate of the treatment group [93.3% (28/30)] was higher than that of the control group [50%(15/30), P < 0.05].Conclusion: the combined exercise training of muscle and bone setting technique can effectively alleviate the pain of patients with radial ulnar joint injury, improve the rotation of the forearm, increase the recruitment of the anterior rotatory muscle, and improve the wrist function of patients, and the effect is better than if combined with forearm support fixation.展开更多
Existing unsupervised person re-identification approaches fail to fully capture thefine-grained features of local regions,which can result in people with similar appearances and different identities being assigned the...Existing unsupervised person re-identification approaches fail to fully capture thefine-grained features of local regions,which can result in people with similar appearances and different identities being assigned the same label after clustering.The identity-independent information contained in different local regions leads to different levels of local noise.To address these challenges,joint training with local soft attention and dual cross-neighbor label smoothing(DCLS)is proposed in this study.First,the joint training is divided into global and local parts,whereby a soft attention mechanism is proposed for the local branch to accurately capture the subtle differences in local regions,which improves the ability of the re-identification model in identifying a person’s local significant features.Second,DCLS is designed to progressively mitigate label noise in different local regions.The DCLS uses global and local similarity metrics to semantically align the global and local regions of the person and further determines the proximity association between local regions through the cross information of neighboring regions,thereby achieving label smoothing of the global and local regions throughout the training process.In extensive experiments,the proposed method outperformed existing methods under unsupervised settings on several standard person re-identification datasets.展开更多
Studies have indicated that the distributed compressed sensing based(DCSbased) channel estimation can decrease the length of the reference signals effectively. In block transmission, a unique word(UW) can be used as a...Studies have indicated that the distributed compressed sensing based(DCSbased) channel estimation can decrease the length of the reference signals effectively. In block transmission, a unique word(UW) can be used as a cyclic prefix and reference signal. However, the DCS-based channel estimation requires diversity sequences instead of UW. In this paper, we proposed a novel method that employs a training sequence(TS) whose duration time is slightly longer than the maximum delay spread time. Based on proposed TS, the DCS approach perform perfectly in multipath channel estimation. Meanwhile, a cyclic prefix construct could be formed, which reduces the complexity of the frequency domain equalization(FDE) directly. Simulation results demonstrate that, by using the method of simultaneous orthogonal matching pursuit(SOMP), the required channel overhead has been reduced thanks to the proposed TS.展开更多
Automatic speech recognition(ASR)systems have emerged as indispensable tools across a wide spectrum of applications,ranging from transcription services to voice-activated assistants.To enhance the performance of these...Automatic speech recognition(ASR)systems have emerged as indispensable tools across a wide spectrum of applications,ranging from transcription services to voice-activated assistants.To enhance the performance of these systems,it is important to deploy efficient models capable of adapting to diverse deployment conditions.In recent years,on-demand pruning methods have obtained significant attention within the ASR domain due to their adaptability in various deployment scenarios.However,these methods often confront substantial trade-offs,particularly in terms of unstable accuracy when reducing the model size.To address challenges,this study introduces two crucial empirical findings.Firstly,it proposes the incorporation of an online distillation mechanism during on-demand pruning training,which holds the promise of maintaining more consistent accuracy levels.Secondly,it proposes the utilization of the Mogrifier long short-term memory(LSTM)language model(LM),an advanced iteration of the conventional LSTM LM,as an effective alternative for pruning targets within the ASR framework.Through rigorous experimentation on the ASR system,employing the Mogrifier LSTM LM and training it using the suggested joint on-demand pruning and online distillation method,this study provides compelling evidence.The results exhibit that the proposed methods significantly outperform a benchmark model trained solely with on-demand pruning methods.Impressively,the proposed strategic configuration successfully reduces the parameter count by approximately 39%,all the while minimizing trade-offs.展开更多
基金Supported by National Key Research and Development Program of China (Grant Nos.2022YFB4703000,2019YFB1309900)。
文摘Automation advancements prompts the extensive integration of collaborative robot(cobot)across a range of industries.Compared to the commonly used design approach of increasing the payload-to-weight ratio of cobot to enhance load capacity,equal attention should be paid to the dynamic response characteristics of cobot during the design process to make the cobot more flexible.In this paper,a new method for designing the drive train parameters of cobot is proposed.Firstly,based on the analysis of factors influencing the load capacity and dynamic response characteristics,design criteria for both aspects are established for cobot with all optimization design criteria normalized within the design domain.Secondly,with the cobot in the horizontal pose,the motor design scheme is discretized and it takes the joint motor diameter and gearbox speed ratio as optimization design variables.Finally,all the discrete values of the optimization objectives are obtained through the enumeration method and the Pareto front is used to select the optimal solution through multi-objective optimization.Base on the cobot design method proposed in this paper,a six-axis cobot is designed and compared with the commercial cobot.The result shows that the load capacity of the designed cobot in this paper reaches 8.4 kg,surpassing the 5 kg load capacity commercial cobot which is used as a benchmark.The minimum resonance frequency of the joints is 42.70 Hz.
文摘Objective: To analyze the effect of combined extracorporeal shock wave and rehabilitation training treatment in patients with muscle articulation chronic pain (MACP). Methods: Ninety-seven MACP patients admitted to our hospital from September 2021 to September 2023 were randomly selected and were divided into Group A (control group, 46 cases, rehabilitation training treatment) and Group B (observation group, 51 cases, extracorporeal shock wave with rehabilitation training treatment), and outcomes of the two groups were compared. Results: The treatment efficiency, post-treatment clinical indexes (upper and lower limb function scores, activities of daily living (ADL) scores, visual analog scale (VAS) scores), and short-form 36 (SF-36) scores of Group B were better than those of Group A (P < 0.05). Conclusion: Combined extracorporeal shock wave and rehabilitation training treatment for MACP patients improved their limb function, daily activities, quality of life, and reduced pain.
文摘In this paper, we introduce a novel scheme for the separate training of deep learning-based autoencoders used for Channel State Information (CSI) feedback. Our distinct training approach caters to multiple users and base stations, enabling independent and individualized local training. This ensures the more secure processing of data and algorithms, different from the commonly adopted joint training method. To maintain comparable performance with joint training, we present two distinct training methods: separate training decoder and separate training encoder. It’s noteworthy that conducting separate training for the encoder can pose additional challenges, due to its responsibility in acquiring a compressed representation of underlying data features. This complexity makes accommodating multiple pre-trained decoders for just one encoder a demanding task. To overcome this, we design an adaptation layer architecture that effectively minimizes performance losses. Moreover, the flexible training strategy empowers users and base stations to seamlessly incorporate distinct encoder and decoder structures into the system, significantly amplifying the system’s scalability. .
基金Key project of nature fund of anhui department of education(No.KJ2018a0273).
文摘Objective: To observe the effect of the joint injury of the distal radio-ulnar joint. Methods: 60 patients with Distal Radioulnar Joint (DRUJ) injury were divided into observation group and control group according to random number method. 30 cases were included in each of the two groups.Before and after treatment in patients with Visual Analogue Scale (Visual Analogue Scale, VAS) score, forearm pronation and supination electromyographic activity, methods of electric integral value (integral electromyogram, iEMG) and Wrist in patients with self assessment Scale (Patient - Rated Wrist Evaluation, PRWE) score evaluation, comparison, and the clinical observation on diagnosis of disease and curative effect of traditional Chinese medicine standard (assessment process by blind method).Results: compared with the two groups before and after treatment, VAS score decreased, forearm pronation and postpronation activity increased, iEMG value increased, and PRWE scale score decreased (all P < 0.05), and the curative effect of the treatment group was better than that of the control group (P < 0.05). The total effective rate of the treatment group [93.3% (28/30)] was higher than that of the control group [50%(15/30), P < 0.05].Conclusion: the combined exercise training of muscle and bone setting technique can effectively alleviate the pain of patients with radial ulnar joint injury, improve the rotation of the forearm, increase the recruitment of the anterior rotatory muscle, and improve the wrist function of patients, and the effect is better than if combined with forearm support fixation.
基金supported by the National Natural Science Foundation of China under Grant Nos.62076117 and 62166026the Jiangxi Key Laboratory of Smart City under Grant No.20192BCD40002Jiangxi Provincial Natural Science Foundation under Grant No.20224BAB212011.
文摘Existing unsupervised person re-identification approaches fail to fully capture thefine-grained features of local regions,which can result in people with similar appearances and different identities being assigned the same label after clustering.The identity-independent information contained in different local regions leads to different levels of local noise.To address these challenges,joint training with local soft attention and dual cross-neighbor label smoothing(DCLS)is proposed in this study.First,the joint training is divided into global and local parts,whereby a soft attention mechanism is proposed for the local branch to accurately capture the subtle differences in local regions,which improves the ability of the re-identification model in identifying a person’s local significant features.Second,DCLS is designed to progressively mitigate label noise in different local regions.The DCLS uses global and local similarity metrics to semantically align the global and local regions of the person and further determines the proximity association between local regions through the cross information of neighboring regions,thereby achieving label smoothing of the global and local regions throughout the training process.In extensive experiments,the proposed method outperformed existing methods under unsupervised settings on several standard person re-identification datasets.
基金support by National Key Technology Research and Development Program of the Ministry of Science and Technology of China (2015BAK05B01)
文摘Studies have indicated that the distributed compressed sensing based(DCSbased) channel estimation can decrease the length of the reference signals effectively. In block transmission, a unique word(UW) can be used as a cyclic prefix and reference signal. However, the DCS-based channel estimation requires diversity sequences instead of UW. In this paper, we proposed a novel method that employs a training sequence(TS) whose duration time is slightly longer than the maximum delay spread time. Based on proposed TS, the DCS approach perform perfectly in multipath channel estimation. Meanwhile, a cyclic prefix construct could be formed, which reduces the complexity of the frequency domain equalization(FDE) directly. Simulation results demonstrate that, by using the method of simultaneous orthogonal matching pursuit(SOMP), the required channel overhead has been reduced thanks to the proposed TS.
基金supported by Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2022-0-00377,Development of Intelligent Analysis and Classification Based Contents Class Categorization Technique to Prevent Imprudent Harmful Media Distribution).
文摘Automatic speech recognition(ASR)systems have emerged as indispensable tools across a wide spectrum of applications,ranging from transcription services to voice-activated assistants.To enhance the performance of these systems,it is important to deploy efficient models capable of adapting to diverse deployment conditions.In recent years,on-demand pruning methods have obtained significant attention within the ASR domain due to their adaptability in various deployment scenarios.However,these methods often confront substantial trade-offs,particularly in terms of unstable accuracy when reducing the model size.To address challenges,this study introduces two crucial empirical findings.Firstly,it proposes the incorporation of an online distillation mechanism during on-demand pruning training,which holds the promise of maintaining more consistent accuracy levels.Secondly,it proposes the utilization of the Mogrifier long short-term memory(LSTM)language model(LM),an advanced iteration of the conventional LSTM LM,as an effective alternative for pruning targets within the ASR framework.Through rigorous experimentation on the ASR system,employing the Mogrifier LSTM LM and training it using the suggested joint on-demand pruning and online distillation method,this study provides compelling evidence.The results exhibit that the proposed methods significantly outperform a benchmark model trained solely with on-demand pruning methods.Impressively,the proposed strategic configuration successfully reduces the parameter count by approximately 39%,all the while minimizing trade-offs.
