Intelligent 3D garment system of the human body based on deep spiking neural network

Background Intelligent garments,a burgeoning class of wearable devices,have extensive applications in domains such as sports training and medical rehabilitation.Nonetheless,existing research in the smart wearables domain predominantly emphasizes sensor functionality and quantity,often skipping crucial aspects related to user experience and interaction.Methods To address this gap,this study introduces a novel real-time 3D interactive system based on intelligent garments.The system utilizes lightweight sensor modules to collect human motion data and introduces a dual-stream fusion network based on pulsed neural units to classify and recognize human movements,thereby achieving real-time interaction between users and sensors.Additionally,the system incorporates 3D human visualization functionality,which visualizes sensor data and recognizes human actions as 3D models in real time,providing accurate and comprehensive visual feedback to help users better understand and analyze the details and features of human motion.This system has significant potential for applications in motion detection,medical monitoring,virtual reality,and other fields.The accurate classification of human actions contributes to the development of personalized training plans and injury prevention strategies.Conclusions This study has substantial implications in the domains of intelligent garments,human motion monitoring,and digital twin visualization.The advancement of this system is expected to propel the progress of wearable technology and foster a deeper comprehension of human motion.

Point cloud upsampling generative adversarial network based on residual multi-scale off-set attention

Background Owing to the limitations of the working principle of three-dimensional(3D) scanning equipment, the point clouds obtained by 3D scanning are usually sparse and unevenly distributed. Method In this paper, we propose a new generative adversarial network(GAN) that extends PU-GAN for upsampling of point clouds. Its core architecture aims to replace the traditional self-attention(SA) module with an implicit Laplacian offset attention(OA) module and to aggregate the adjacency features using a multiscale offset attention(MSOA)module, which adaptively adjusts the receptive field to learn various structural features. Finally, residual links are added to create our residual multiscale offset attention(RMSOA) module, which utilizes multiscale structural relationships to generate finer details. Result The results of several experiments show that our method outperforms existing methods and is highly robust.

Teaching Reform and Practice of Software Engineering Graduate Students by Implanting Scientific Research Literacy into Classroom Teaching Content

How to cultivate and improve graduate students’innovation and practical abilities in software engineering through the curriculum and teaching mode reform is an important issue.In this paper,a research literacy-driven teaching mode is proposed.It assists in the reform of the curriculum system.Then,a curriculum system construction framework is proposed,which involves the integration of research literacy into classroom teaching content.It assists in the cultivation of research abilities of graduate students in software engineering.The effectiveness of the curriculum reform is demonstrated through questionnaire surveys and research outcomes of the project team.The results show that the methods explored in this paper can serve as valuable references for future course design and teaching practice in computer-related courses for graduates.

Measurement of ^(232)Th(n,γ)cross section at the CSNS Back-n facility in the unresolved resonance region from 4 keV to 100 keV

The neutron capture cross section of ^(232)Th was measured at the neutron time-of-flight facility Back-n of China Spallation Neutron Source(CSNS)for the first time.The measurement was performed with 4 hydrogen-free deuterated benzene C6D6 liquid scintillation detectors,in the ES#2 experiment station on the beam line,at a distance of about 76 m from the neutron-production assembly.The total energy detection principle in combination with the pulse height weighting technique(PHWT)was applied to analyze the measured data.Results of the ^(232)Th(n,γ)reaction cross section in the unresolved resonance region from 4 keV to 100 keV were obtained,which shows a good agreement with the existing experimental data from EXFOR,as well as with the evaluated data from the ENDF/B-VIII.0 and CENDL-3.1.In addition,the excitation function of ^(232)Th(n,γ)^(233)Th reaction in the unresolved resonance region was theoretically calculated by using the code TALYS-1.95.By fitting the experimental cross section and theoretical data,the average parameters in the unresolved resonance region were extracted.

New Standardized Cultivation Techniques for Tea Seed Production

The tea from tea plants,a kind of traditional leaf plant,is deeply loved by people,but the production of tea fruit and its important functional value have been seriously underestimated for a long time.To this end,the oil plant function and comprehensive utilization value of tea oil from tea fruit were introduced,and a set of new standardized cultivation technique of tea plants that could fully exert the potential of tea seed yield was put forward.The technique could increase the tea seed yield per mu from less than 50kg in traditional tea gardens to more than 200 kg,which broke through the production mode of single leaf picking in traditional tea gardens,gave play to the natural reproductive growth advantages of tea plants,achieved"leaf-seed dual harvest",and promoted the improvement of tea industry quality and efficiency,thereby creating a new path to achieve the"leaf-fruit dual use"of tea plants.

上海激光电子伽马源(SLEGS)核共振荧光谱仪设计及探测器性能分析

核共振荧光(NRF)实验可对光核反应低激发能级进行研究。上海激光电子伽马源(SLEGS)设计、建造了一组由2台大尺寸同轴高纯锗(HPGe,ϕ80 mm×70 mm)探测器和2台CLOVER HPGe(4×ϕ50 mm×70 mm)探测器组成的核共振荧光谱仪,大尺寸HPGe探测器获得了大于100%的相对效率和好于0.3%@1332 keV的能量分辨率。谱仪采用Mesytec MDPP-16数字脉冲处理器及MVME获取系统,以读取和记录HPGe前置放大器输出信号的幅度和时间信息。研究了CLOVER HPGe探测器在加和(Add-back)模式和去除(Reduction)模式下的性能提升,结果表明,Add-back重建可以显著提高全能峰效率和峰总比,在1460 keV能量处,Add-back重建后的全能峰效率可以提高21%以上。由于Add-back重建引入了多个晶体之间的耦合,导致探测器能量分辨稍有下降。而Reduction模式可以降低康普顿峰,提高峰总比,并保持和直接(Direct)工作模式同等水平的能量分辨,但是损失了一定的全能峰效率。

High fidelity virtual try-on network via semantic adaptation and distributed componentization

Image-based virtual try-on systems have significant commercial value in online garment shopping.However,prior methods fail to appropriately handle details,so are defective in maintaining the original appearance of organizational items including arms,the neck,and in-shop garments.We propose a novel high fidelity virtual try-on network to generate realistic results.Specifically,a distributed pipeline is used for simultaneous generation of organizational items.First,the in-shop garment is warped using thin plate splines(TPS)to give a coarse shape reference,and then a corresponding target semantic map is generated,which can adaptively respond to the distribution of different items triggered by different garments.Second,organizational items are componentized separately using our novel semantic map-based image adjustment network(SMIAN)to avoid interference between body parts.Finally,all components are integrated to generatethe overall result by SMIAN.A priori dual-modalinformation is incorporated in the tail layers of SMIAN to improve the convergence rate of the network.Experiments demonstrate that the proposed method can retain better details of condition information than current methods.Our method achieves convincing quantitative and qualitative results on existing benchmark datasets.