Optimal Design of the Modular Joint Drive Train for Enhancing Cobot Load Capacity and Dynamic Performance

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.

Evaluation of the Effectiveness and Efficiency of Extracorporeal Shock Wave Combined with Rehabilitation Training in the Treatment of Muscle Articulation Chronic Pain

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.

A Novel Scheme for Separate Training of Deep Learning-Based CSI Feedback Autoencoders

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. .

Effect of sinew-regulating and bone-setting manipulating combined with exercise training in the treatment of joint injury of distal radio-ulnar

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.

Joint training with local soft attention and dual cross-neighbor label smoothing for unsupervised person re-identification

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.

A Novel Training Sequence Applied to DCS-Based Channel Estimation

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.

Shanghai-Ek Chor Motorcycle Co., Ltd.—An Outstanding Sino-Foreign Joint Venture

Charas Li, deputy general manager of Shanghai-Ek Chor Motorcycle Co. Ltd., which has been named one of the best 10joint ventures in China, enjoys a close association with his employees. Reporter Qian Youwei, talked to Charas Li about the establishment and development of his company. Li said that the international

Weisheng Electronics Co., Ltd.——An Outstanding Sino-foreign Hi-tech Joint Venture

The Weisheng Electronics Co., Ltd., a Sino-foreign hi-tech joint venture, is based in Changsha, capital of Hunan Province. This science-industry-trade integrating hi-tech industrial group has branches in Beijing, Shanghai and Hongkong. Since its founding, the company has developed hitech products in energy, finance and health, intelligent energy measuring products in particular. It has taken the lead in the province’s Sino-foreign hitech enterprises. Using RMB 110 million of funds for its first-phase project, the

Anhui Hongqing Precision Machinery Co.Ltd.——A Sino-foreign Joint Venture

Established in 1991,thecompany exclusively produces andmanages numerical control lathesmajored at processing centres ofvarious specifications.It has a verygood condition for fair competition,with complete production processesfrom casting,machining toassembling,an excellent staffengaged in product development,production,quality control

Wall's Beijing Corporation——A Large,Modern Sino-Foreign Ice Cream Joint Venture

Although it was reported that the title of 5000th Sino-foreign joint venture in Beijing had been conferred on Wall’s, it nevertheless remained a strange name to most Beijingers. However, on one of the hottest days last summer, the name Wall’s was seen in the Beijing streets and lanes, the ice-cream kept in small glass refrigerated cases standing under red and blue umbrellas with the trademark of Wall’s on them. This

Joint On-Demand Pruning and Online Distillation in Automatic Speech Recognition Language Model Optimization

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.

青少年篮球训练运动中关节损伤预警模型

为准确预警青少年在篮球训练中的关节损伤,构建了青少年篮球训练运动中关节损伤预警模型。使用基于独立成分分析和经验模态分解的关节受力信号去噪方法,保留有效信号,在CNN网络中提取关节受力特征后,输入LSTM网络识别关节受力信号的损伤级别后通过蜂鸣器预警。结果显示:所构建模型对青少年篮球训练运动中关节损伤级别识别结果符合实际,预警错误次数为0次,预警结果具有可信度。

美军军事仿真LVC系统发展与启示

针对当前多军兵种联合作战实战化训练的需求,建模和仿真领域的未来目标是创建统一的真实-虚拟-构造(LVC)集成架构,支持快速集成模型和开展仿真,用于联合作战训练、战术协同、拟制作战计划和评估等。通过探究LVC概念内涵和应用领域,分析美军LVC训练网络发展计划,梳理了分布式任务作战、联合仿真环境和美海军模拟训练三个典型LVC系统应用范例。借鉴美军应用LVC解决联合训练的经验做法,给出加快构建我军一体化联合训练的启示,为我军联合训练环境建设提供有价值的参考。

渐进式康复训练在促进乳腺癌术后患者上肢功能恢复中的应用研究

目的:分析渐进式康复训练对乳腺癌术后患者上肢功能恢复的效果。方法:选择2022年4月—2023年4月江门市妇幼保健院乳腺科收治的80例乳腺癌术后患者作为研究对象,按照入院先后顺序编号,将奇数号纳入对照组,偶数号纳入观察组,各40例。对照组给予常规康复护理,观察组在常规康复护理基础上开展渐进式康复训练。比较两组护理前、护理1个月后肩关节活动度、运动耐力、乳腺癌患者生存质量测定量表(FACT-B)评分、肩关节功能评分及术后恢复指标(下床活动时间、排便时间、排气时间)。结果:护理前,两组肩关节活动度、运动耐力指标、FACT-B评分及肩关节功能评分比较,差异无统计学意义(P>0.05)。护理1个月后,两组肩关节活动度优于护理前,运动距离长于护理前,心率恢复时间、最大心率时间短于护理前,FACT-B各项评分及肩关节功能各项评分高于护理前,且观察组肩关节活动度大于对照组,运动距离长于对照组,心率恢复时间、最大心率时间短于对照组,FACT-B各项评分及肩关节功能各项评分高于对照组,差异有统计学意义(P<0.05)。观察组术后下床活动时间、排便时间、排气时间均早于对照组,差异有统计学意义(P<0.05)。结论:乳腺癌患者术后开展渐进式康复训练可有效促进肩关节功能的恢复,还可增强患者的运动耐力并改善其生活质量。

“校企联培制”模式下应用型卓越人才培养路径探究——以湖北大学知行学院为例

近年来地方应用型本科高校以服务地方经济和产业需求为己任,着重培养学生的工程和创新技术应用能力,高校与企业合作,发挥各自优势,实现生产、教学、科研的深度合作,共同完成人才培养。介绍机械类专业在新工科背景下,以培养应用型卓越人才为目标,基于“校企联培制”的总体思路,实施学校专业教学计划与企业工程师培养计划“双计划”,在人才培养体系设计、校企资源整合、就业平台搭建和师资力量提升等方面进行改革,实施产教融合,提升就业能力,最终实现人才的高质量培养和就业。

基于CenterNet的多教师联合知识蒸馏

介绍了一种基于轻量化CenterNet的多教师联合知识蒸馏方案。所提方案能有效解决模型轻量化带来的性能恶化问题,可以显著缩小教师模型和学生模型之间的性能差距。将大规模复杂模型作为教师模型,指导训练轻量化学生模型。相比于模型的传统训练方案,使用所提知识蒸馏训练方案可以在相同的训练轮数后使轻量化模型达到更优的检测性能。主要贡献是提出了适用于CenterNet目标检测网络的新型知识蒸馏训练方案——多教师联合知识蒸馏。在后续实验中,进一步引入了蒸馏注意力机制,从而优化了多教师联合知识蒸馏的训练效果。在VOC2007数据集(Visual Object Classes 2007 Dataset)上,以MobileNetV2轻量化网络作为主干网络为例,相较于传统的CenterNet(主干网络为ResNet50),所提方案在参数量指标上压缩了74.7%,推理速度提升了70.5%,在平均精度上只有1.99的降低,取得了更好的“性能-速度”平衡。实验证明,同样经过100轮训练,使用多教师联合知识蒸馏训练方案的轻量化模型相较于普通训练方案,平均精度提升了11.30。

不同方法干预3~6岁轻中度孤独症谱系障碍儿童共同注意的效果

目的比较回合操作教学法(DTT)、关键反应训练(PRT)及DTT结合PRT干预3~6岁轻中度孤独症谱系障碍(ASD)儿童共同注意的效果。方法2023年1月至2024年3月,上海市泰亿格儿童康复中心36~72月龄ASD儿童39例,随机分为DTT组、PRT组和混合组,分别接受DTT、PRT以及DTT结合PRT,共10周。干预前后采用ASD儿童共同注意评估量表进行评定。结果DTT组脱落2例,PRT组脱落1例,最终纳入36例。干预方法(F=11.225,P<0.001)和干预阶段(F=416.935,P<0.001)主效应均显著,交互效应显著(F=10.501,P<0.001),混合组干预期和迁移期评分最优(P<0.05)。结论DTT和PRT均能提升ASD儿童共同注意,两者结合效果更佳。

基于联合研究院的专业学位研究生培养模式探索与实践

随着我国专业学位研究生的招生规模逐步扩大,如何实现专业学位与学术学位研究生分类培养,已成为各高校高质量发展过程中面临的突出问题。本研究基于广州大学-广东腐蚀科学与技术创新研究院联合研究院,积极探索了高校与新型研发机构联合培养专业学位硕士研究生的模式。文章梳理了校内外双导师三段式研究生联合培养要素,介绍了通过承担合作科研项目与构建专业方向模块等手段凝练人才培养方向,并逐步建立研究生联合培养有效运行机制和保障制度的实践探索。实践表明,联合研究院的建设,显著提升了校内外研究生导师队伍水平,丰富了以工程技术难题和产业技术问题为导向的研究生课题,提高了材料与化工专业学位研究生培养质量。

面向复杂雪天场景的单幅图像去雪方法

降雪是一种复杂的天气现象,雪花多尺度、运动轨迹复杂以及远景呈现雾天效果.然而现有的单幅图像去雪方法以及数据集,并没有考虑到这些复杂的情况,从而限制了去雪方法在真实雪天图像上的表现.基于此,首先根据图像的深度信息构造了一种联合雪花和雾的雪天成像模型,合成了具有真实感的雪天图像数据集.然后设计了一种端到端的图像恢复网络,将去雾模块和去雪模块级联,并进行联合训练.其中去雪模块采用了双层U型网络用于解决雪花多尺度和轨迹复杂的问题.最后将该方法同其它最先进的方法进行实验对比,证明该方法在合成数据集和真实图像中都具有非常好的效果,且能够提升后续高级视觉任务性能.