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Thermally Conductive and UV-EMI Shielding Electronic Textiles for Unrestricted and Multifaceted Health Monitoring

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Nano-Micro Letters 2024年第10期16卷 149-162页

作者： Yidong Peng Jiancheng Dong Jiayan Long Yuxi Zhang Xinwei Tang Xi Lin Haoran Liu Tuoqi Liu Wei Fan Tianxi Liu Yunpeng Huang Key laboratory of Synthetic and Biological Colloids Ministry of EducationSchool of Chemical and Material EngineeringJiangnan UniversityWuxi 214122People's Republic of China

Skin-attachable electronics have garnered considerable research attention in health monitoring and artificial intelligence domains,whereas susceptibility to elec-tromagnetic interference(EMI),heat accumulation issues,and ultraviolet(UV)-induced aging problems pose significant constraints on their potential ***,an ultra-elas-tic,highly breathable,and thermal-comfortable epidermal sensor with exceptional UV-EMI shielding performance and remarkable thermal conductivity is developed for high-fidelity monitoring of multiple human electrophysiological *** filling the elastomeric microfibers with thermally conductive boron nitride nanoparticles and bridging the insulating fiber interfaces by plating Ag nanoparticles(NPs),an interwoven thermal con-ducting fiber network(0.72 W m^(-1) K^(-1))is constructed benefiting from the seamless thermal interfaces,facilitating unimpeded heat dissipation for comfort skin *** excitingly,the elastomeric fiber substrates simultaneously achieve outstanding UV protection(UPF=143.1)and EMI shielding(SET>65,X-band)capabilities owing to the high electrical conductivity and surface plasmon resonance of Ag ***,an electronic textile prepared by printing liquid metal on the UV-EMI shielding and thermally conductive nonwoven textile is finally utilized as an advanced epidermal sensor,which succeeds in monitoring different electrophysiological signals under vigorous electromagnetic *** research paves the way for developing protective and environmentally adaptive epidermal electronics for next-generation health regulation.

关键词： Skin electronics Thermal regulating textiles Electromagnetic interference shielding Ultraviolet proof Health monitoring

Traffic Anomaly DetectionMethod Based on Improved GRU and EFMS-Kmeans Clustering

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Computer Modeling in Engineering & Sciences 2021年第3期126卷 1053-1091页

作者： Yonghua Huo Yi Cao Zhihao Wang Yu Yan Zhongdi Ge Yang Yang Science and Technology on Communication networks laboratory The 54th Research Institute of CETCShijiazhuangChina Department of Military Representative Office of General Military Equipment Development Shijiazhuang ShijiazhuangChina State Key laboratory of networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing100876China

In recent years,with the continuous development of information technology and the rapid growth of network scale,network monitoring and management become more and more *** traffic is an important part of network *** order to ensure the normal operation of the network,improve the availability of the network,find network faults in time and deal with network attacks;it is necessary to detect the abnormal traffic in the *** traffic detection is of great significance in the actual network ***,in order to improve the accuracy and efficiency of network traffic anomaly detection,this paper proposes a comprehensive anomaly detection method based on improved GRU traffic prediction and improved K-means clustering,and cascade the traffic prediction and clustering to achieve the purpose of anomaly ***,an improved highway-GRU algorithm HS-GRU(An improved Gate Recurrent Unit neural network based on Highway network and STL algorithm,HS-GRU)is proposed,which combines STL decomposition algorithm with highway GRU neural network and uses this improved algorithm to predict *** then,we proposed the EFMS-Kmeans algorithm(An improved clustering algorithmthat combined Mean Shift algorithmbased on electrostatic force with K-means clustering)to solve the shortcoming of the traditional K-means clustering which cannot automatically determine the number of *** sum of the squared errors(SSE)method and the contour coefficient method were used to double test the clustering *** determining the clustering center,the potential energy gradient was directly used for anomaly detection by using the threshold method,which considered the local characteristics of the data and ensured the accuracy of anomaly *** simulation results show that the anomaly detection algorithm based on HS-GRU and EFMS-Kmeans clustering proposed in this paper can effectively improve the accuracy of flow anomaly detection and has impo

关键词： Anomaly detection gated recurrent unit clustering mean shift K-means

A memristive neural network based matrix equation solver with high versatility and high energy efficiency

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Science China(Information Sciences) 2023年第2期66卷 198-208页

作者： Jiancong LI Houji ZHOU Yi LI Xiangshui MIAO School of Optical and Electronic Information Huazhong University of Science and Technology School of Integrated Circuits Huazhong University of Science and Technology Wuhan National laboratory for Optoelectronics

As the main topic in modern scientific computing and machine learning tasks, matrix equation solving is suffering high computational latency and tremendous power consumption due to the frequent data movement in traditional von Neumann computers. Although the in-memory computing paradigms have shown the potential to accelerate the execution of solving matrix equations, the existing memristive matrix equation solvers are still limited by the low system versatility and low computation precision of the memristor arrays. In this work, we demonstrate a hybrid architecture for accurate, as well as efficient, matrix equation solving problems, where the memristive crossbar arrays are used for the parallel vector-matrix multiplication and the digital computer for accuracy. The linear neural-network solving(NNS) method is adopted here and its versatility for various types of matrix equations is proved. The weight-slice computation method is developed to perform the analog matrix multiplication with high efficiency and high robustness in the array. The solution results confirmed that typical matrix equations can be solved by this memristive matrix equation solver with high accuracy. Further performance benchmarking demonstrates that the generalized memristive matrix equation solver has low solving time-complexity while outperforming the state-of-the-art CMOS and in-memory processors.

关键词： matrix equation solving memristor linear neural network matrix-multiplication analog computing

Optimization-Based Fragmental Transmission Method for Video Data in Opportunistic networks

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Tsinghua Science and Technology 2017年第4期22卷 389-399页

作者： Peng Li Xiaoming Wang Junling Lu Lichen Zhang Zaobo He the Key laboratory Modern Teaching Technology Ministry of Education School of Computer Science Shaanxi Normal University Department of Computer Science Georgia State University

Multimedia data have become popularly transmitted content in opportunistic networks. A large amount of video data easily leads to a low delivery ratio. Breaking up these big data into small pieces or fragments is a reasonable option. The size of the fragments is critical to transmission efficiency and should be adaptable to the communication capability of a network. We propose a novel communication capacity calculation model of opportunistic network based on the classical random direction mobile model, define the restrain facts model of overhead, and present an optimal fragment size algorithm. We also design and evaluate the methods and algorithms with video data fragments disseminated in a simulated environment. Experiment results verified the effectiveness of the network capability and the optimal fragment methods.

关键词： opportunistic network communication capabilities fragment video data

Energy saving performance improvement of intelligent connected PHEVs via NN-based lane change decision

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Science China(Technological Sciences) 2021年第6期64卷 1203-1211页

作者： HUANG ChangYao LI Liang FANG ShengNan CHENG Shuo CHEN Zheng State Key laboratory of Automotive Safety and Energy Tsinghua UniversityBeijing 100084China School of Sciences Ningbo University of TechnologyNingbo 315211China

Plug-in hybrid electric vehicles(PHEVs) unite the advantages of the engine and electric motor which could provide great potential in saving energy. However, the fuel economy performance of the PHEVs is highly associated with the driving condition, especially for parallel PHEVs because they could not decouple the engine work status from the driving condition. Meanwhile, fuel economy performance is not only a longitudinal issue but also related to lane selection. Lane selection is an important driving behavior and the algorithm of lane selection is necessary for the development progress of intelligent connected vehicles. Energy consumption cost is an important part of the vehicle’s using consumption cost. Therefore, lane selection strategies must consider this *** the development of intelligent connected vehicle technology, such as V2X(Vehicle to Everything), the potential of energy consumption performance of intelligent connected PHEVs could be improved by taking environment information from V2X and smart sensors into lane selection. In this paper, a neural network(NN) based method is proposed to predict the future status of the local vehicle using the information from V2X, and then another network is used to estimate the future energy consumption of each lane. The lane selection is decided on energy consumption estimation. Lastly, the effectiveness of the method is validated by simulation using Matlab combined with SUMO(Simulation of Urban MObility).

关键词： PHEV energy saving lane selection neural network

Hybrid Control of Delay Induced Hopf Bifurcation of Dynamical Small-World network

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Journal of Shanghai Jiaotong university(Science) 2017年第2期22卷 206-215页

作者：丁大为张肖芸王年梁栋 Key laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electronics and Information Engineering Anhui University

In this paper, we focus on the Hopf bifurcation control of a small-world network model with time-delay. With emphasis on the relationship between the Hopf bifurcation and the time-delay, we investigate the effect of time-delay by choosing it as the bifurcation parameter. By using tools from control and bifurcation theory, it is proved that there exists a critical value of time-delay for the stability of the model. When the time-delay passes through the critical value, the model loses its stability and a Hopf bifurcation occurs. To enhance the stability of the model, we propose an improved hybrid control strategy in which state feedback and parameter perturbation are used. Through linear stability analysis, we show that by adjusting the control parameter properly, the onset of Hopf bifurcation of the controlled model can be delayed or eliminated without changing the equilibrium point of the model. Finally, numerical simulations are given to verify the theoretical analysis. © 2017, Shanghai Jiaotong University and Springer-Verlag Berlin Heidelberg.

关键词： Hopf bifurcation hybrid control small-world network bifurcation control

Optimization-based topology identification of complex networks

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Chinese Physics B 2011年第11期20卷 127-133页

作者：唐圣学陈丽何怡刚 Province-Ministry Joint Key laboratory of Electromagnetic Field and Electrical Apparatus Reliability Hebei University of Technology College of Electrical and Information Engineering Hunan University

In many cases, the topological structures of a complex network are unknown or uncertain, and it is of significance to identify the exact topological structure. An optimization-based method of identifying the topological structure of a complex network is proposed in this paper. Identification of the exact network topological structure is converted into a minimal optimization problem by using the estimated network. Then, an improved quantum-behaved particle swarm optimization algorithm is used to solve the optimization problem. Compared with the previous adaptive synchronization- based method, the proposed method is simple and effective and is particularly valid to identify the topological structure of synchronization complex networks. In some cases where the states of a complex network are only partially observable, the exact topological structure of a network can also be identified by using the proposed method. Finally, numerical simulations are provided to show the effectiveness of the proposed method.

关键词： complex networks topology identification optimization particle swarm

Exploring prognostic potential of long noncoding RNAs in colorectal cancer based on a competing endogenous RNA network

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World Journal of Gastroenterology 2020年第12期26卷 1298-1316页

作者： Zhi-Dong Yang Hui Kang Department of laboratory Medicine The First Affiliated Hospital of China Medical UniversityShenyang 110001Liaoning ProvinceChina

BACKGROUND Colorectal cancer(CRC)is one of the most prevalent tumors ***,long noncoding RNAs(lncRNAs)have been shown to influence tumorigenesis and tumor progression by acting as competing endogenous RNAs(ceRNAs).It is difficult to extract prognostic lncRNAs and useful bioinformation from most ceRNA networks constructed *** To construct a prognostic related ceRNA regulatory network and lncRNA related signature based on risk score in *** RNA transcriptome profile and clinical information of 506 CRC patients were downloaded from the Cancer Genome Atlas database.R packages and Perl program were used for data *** regression analysis was used for prognostic model *** real-time polymerase chain reaction was used to detect the expression of *** A prognostic-related ceRNA network was constructed,including 9 lncRNAs,44 mRNAs,and 30 *** addition,a four-lncRNA model was constructed using multivariate Cox regression analysis,which could be an independent prognostic model in *** risk score for each patient was calculated,and the 506 patients were divided into high and low-risk groups(253 for each group)based on the median risk *** results of the survival analysis showed that patients with a high-risk score had a poor survival ***,the predictive value of the four-lncRNA model was evaluated in *** survival probabilities could be better predicted when combing the risk score and clinical *** Set Enrichment Analysis results verified that a number of cancer-related signaling pathways were enriched with a high-risk score in ***,we validated a novel lncRNA(LINC00488)using quantitative real-time polymerase chain reaction in 22 paired CRC patient tumor tissues compared to adjacent non-tumor *** The four-lncRNA model could give better predictive value for CRC *** understanding of the lncRNA-related ceRNA regulatory mechanism coul

关键词： Colorectal cancer Long noncoding RNA miRNA mRNA Transcription factor Competing endogenous RNA Survival

Bioinspired polysaccharide-based nanocomposite membranes with robust wet mechanical properties for guided bone regeneration

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National Science Review 2024年第3期11卷 215-226页

作者： Jian-Hong Xiao Zhen-Bang Zhang JiaHao Li Si-Ming Chen Huai-Ling Gao YinXiu Liao Lu Chen ZiShuo Wang YiFan Lu YuanZhen Hou HengAn Wu DuoHong Zou Shu-Hong Yu Department of Chemistry New Cornerstone Science Laboratory Institute of Biomimetic Materials&Chemistry Anhui Engineering Laboratory of Biomimetic MaterialsDivision of Nanomaterials&Chemistry Hefei National Research Center for Physical Sciences at the Microscale University of Science and Technology of China Department of Dental Implant Center Stomatologic Hospital and College Key Laboratory of Oral Diseases Research of Anhui Province Anhui Medical University CAS Key laboratory of Mechanical Behavior and Design of Materials Department of Modern Mechanics CAS Center for Excellence in Complex System Mechanics University of Science and Technology of China Department of Oral Surgery College of Stomatology National Clinical Research Center for Oral DiseasesShanghai Key Laboratory of Stomatology Shanghai Research Institute of Stomatology Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Institute of Innovative Materials(I2M) Department of Chemistry Department of Materials Science and Engineering Southern University of Science and Technology

Polysaccharide-based membranes with excellent mechanical properties are highly desired. However, severe mechanical deterioration under wet conditions limits their biomedical applications. Here, inspired by the structural heterogeneity of strong yet hydrated biological materials, we propose a strategy based on heterogeneous crosslink-and-hydration（HCH） of a molecule/nano dual-scale network to fabricate polysaccharide-based nanocomposites with robust wet mechanical properties. The heterogeneity lies in that the crosslink-and-hydration occurs in the molecule-network while the stress-bearing nanofiber-network remains unaffected. As one demonstration, a membrane assembled by bacterial cellulose nanofiber-network and Ca2+-crosslinked and hydrated sodium alginate molecule-network is designed. Studies show that the crosslinked-and-hydrated molecule-network restricts water invasion and boosts stress transfer of the nanofiber-network by serving as interfibrous bridge. Overall, the molecule-network makes the membrane hydrated and flexible; the nanofiber-network as stress-bearing component provides strength and *** HCH dual-scale network featuring a cooperative effect stimulates the design of advanced biomaterials applied under wet conditions such as guided bone regeneration membranes.

关键词： bioinspiration heterogeneous crosslink-and-hydration dual-scale network nanocomposite membrane wet mechanical properties guided bone regeneration