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Femtosecond Laser Enabled Supermetalphobic Double-Side Micropyramids Based Capacitive Tactile Sensor for Highly Sensitive, Antifouling and Robust Biomonitoring

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SSRN 2022年

作者： Zhang, Chengjun Li, Zhikang Li, Haoyu Yang, Qing Wang, Hao Shan, Chao Zhang, Jingzhou Hou, Xun Chen, Feng School of Mechanical Engineering Xi’an Jiaotong University Xi’an710049 China State Key Laboratory for Manufacturing System Engineering Shaanxi Key Laboratory of Photonics Technology for Information School of Electronic Science and Engineering Xi’an Jiaotong University Xi’an710049 China

Pursuing flexible tactile sensor with superior comprehensive performances is highly desired in their practical applications, such as personalized biomonitoring. However, current flexible tactile sensors suffer high-cost and low-efficiency in fabrication, as well as poor robustness and susceptible to contaminant in sensing performances, hindering their applications. Here, a highly sensitive, antifouling and robust all-flexible tactile sensor (AFTS) is presented with capacitive sensing that combines double-side micropyramids dielectric layer and liquid metal (LM) electrode. The double-side micropyramids dielectric layer leads to high sensitivity up to 2.78 kPa -1 , ultra-low limit of detection of ~ 3 Pa, fast response time of 80 ms, and ultra-high cycle durability of over 10000 times. The supermetalphobic micropyramids enable an ultra-high durability of the LM-based electrode due to the abundant nanoparticles induced by femtosecond laser. The used femtosecond laser enables programmable, high-efficiency, low-cost, and large-scale fabrication of supermetalphobic double-side micropyramids, which is difficult to implement using conventional techniques. Furthermore, the outer substrates are superhydrophobically treated by femtosecond laser, endow the AFTS with excellent antifouling ability, and stable sensing signals in the highly humid environment. Successful monitoring of various human physiological and motion signals demonstrates the potential of our developed AFTS for wearable biomonitoring applications. © 2022, The Authors. All rights reserved.

关键词： Efficiency

Controllable Electrical Characteristic of Ti3c2t2/Mos2 (T=Oh, F, O) Interfaces for High Performance Electronics: Insights from First-Principles

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SSRN 2023年

作者： Zhang, Qiang Liu, Yan Zhang, Riguang Lv, Bohan Hao, Runfang Sang, Shengbo Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Electronic Information and Optical Engineering Taiyuan University of Technology Shanxi Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System of The Ministry of Education Taiyuan University of Technology Shanxi Taiyuan030024 China School of Software Taiyuan University of Technology Shanxi Taiyuan030024 China State Key Laboratory of Clean and Efficient Coal Utilization China Laboratory of Aerospace Servo Actuation and Transmission Beijing Institute of Precision Mechatronics and Controls Beijing100000 China

Two-dimensional (2D) semiconductors have shown competitive potential for applications into beyond-silicon electronics. However, the controllable electrical characteristic of metal-semiconductor interfaces is still a challenge for high performance electronics due to Fermi level pinning. To explore Ti3C2T2/MoS2 (T= OH, F, O) for high performance electronics, the vertical electric field and interlayer distance are adopted here to regulate the electrical characteristic of interfaces based on the first-principles calculations. Though this, the electrical characteristic of Ti3C2O2/MoS2 and Ti3C2F2/MoS2 are more sensitive to electric field or interlayer distance than that of Ti3C2(OH)2/MoS2. Especially, the negative electric field with larger than -0.2 V/Å makes Ti3C2O2/MoS2 interface change from p-type Schottky to n-type Schottky contact. Furthermore, the Ti3C2O2/MoS2 interface can not only switch from p-type Schottky to n-type Schottky contact, but also from p-type Schottky to n-type Ohmic contact by varying interlayer distance. Moreover, the tunneling barriers of all interfaces are more sensitive to interlayer distance than to electric field. This work provides vital insights for high-performance electronics of Ti3C2T2/MoS2 (T=OH, F, O) in experiments. © 2023, The Authors. All rights reserved.

关键词： Electric fields

Ultra-short-term Power Prediction of Photovoltaic based on CNN-AM-BiLSTM

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Ultra-short-term Power Prediction of Photovoltaic based on C...

IEEE Conference on Energy Internet and Energy system Integration (EI2)

作者： Junhui Li Mingxuan Du Haozheng Yu Jie Ma Youwen Zhang Jiatao Gan Xingxu Zhu Cuiping Li Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin China AnYang Power Supply Company State Grid HeNan Electric Power Company AnYang China Economic and Technical Research Institute State Grid HeNan Electric Power Company ZhengZhou China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

The power of photovoltaic power generation is affected by a variety of factors such as weather, radiation and temperature, exhibit a high degree of randomness and uncertainty. To address this problem, this paper proposes a ultra-short-term photovoltaic power prediction model based on Convolutional Neural Networks (CNN) and attention mechanism and bidirectional long short-term memory (Bi-LSTM). Firstly, Pearson's correlation coefficient was used to filter out the meteorological factors which have significant influence on the historical power. Subsequently, in order to capture the spatial features of the input data, a convolutional neural network is utilized for feature extraction and an attention mechanism is introduced to strengthen the model's ability to discriminate key features. Meanwhile, the Bi-LSTM network is utilized to capture the temporal dependencies in the time series data, and finally the PV power prediction value is obtained. Finally, the validation is based on data from actual PV power plants, and the results show that the proposed Convolutional Neural Networks and attention mechanism (CA) and Bi-LSTM Ultra-short-term PV power prediction model outperforms the single model in terms of prediction accuracy, reflecting the effectiveness and superiority of the hybrid model.

关键词： Photovoltaic systems Accuracy Attention mechanisms Time series analysis Weather forecasting Predictive models Feature extraction Data models Hybrid power systems Convolutional neural networks

The Effect of High-Temperature Deformation Twinning on the Work Hardening Behavior of Fe-38mn Alloy During Hot Shear-Compression Deformation

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SSRN 2023年

作者： Sang, Deli Xin, Xiaoli Fu, Ruidong Li, Yijun Jing, Lei College of Materials Science and Engineering Shijiazhuang Tiedao University Hebei Shijiazhuang050000 China State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures Shijiazhuang Tiedao University Hebei Shijiazhuang050043 China State Key Laboratory of Metastable Materials Science and Technology Yanshan University Hebei Qinhuangdao066004 China Northwest Institute for Nonferrous Metal Research Xi’an710016 China

The effect of high-temperature deformation twinning on the work hardening behaviors of Fe-38Mn alloy during hot shear-compression deformation was investigated. The flow curves for hot shear-compression deformation of Fe-38Mn alloy exhibit the characteristics of continuous work hardening. The deformation was uniform shear-compression deformation with no significant strain localization, but the shear action gradually increased with the increase of strain rate. Benefited from such deformation characteristics, the dynamic microstructure evolution was pioneered by "bulged" mechanism and had geometric and sub-grain migration characteristics, which was continuous dynamic recrystallization in nature and accompanied by a large number of micro-shear bands and deformation twins. Particularly, the work hardening rate curve is classified into five stages considering the deformation twinning during hot shear-compression deformation. © 2023, The Authors. All rights reserved.

关键词： Shear bands

Fair: Frequency-Aware Image Restoration for Industrial Visual Anomaly Detection

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SSRN 2024年

作者： Liu, Tongkun Li, Bing Du, Xiao Jiang, Bingke Geng, Leqi Wang, Feiyang Zhao, Zhuo State Key Laboratory for Manufacturing System Engineering Xi’an Jiaotong University No.99 Yanxiang Road Yanta District Shaanxi Xi’an710054 China International Joint Research Laboratory for Micro/Nano Manufacturing and Measurement Technologies Xi’an Jiaotong University No.99 Yanxiang Road Yanta District Shaanxi Xi’an710054 China Anhui Province Key Laboratory of Machine Vision Inspection Yangtze River Delta HIT Robot Technology Research Institute Wuhu24100 China

Image reconstruction-based anomaly detection models are widely explored in industrial visual inspection. However, existing models usually suffer from the trade-off between normal reconstruction fidelity and abnormal reconstruction distinguishability, which damages the performance. In this paper, we find that the above trade-off can be better mitigated by leveraging the distinct frequency biases between normal and abnormal reconstruction errors. To this end, we propose \textbf{F}requency-\textbf{a}ware \textbf{I}mage \textbf{R}estoration (FAIR), a novel self-supervised image restoration task that restores images from their high-frequency components. It enables precise reconstruction of normal patterns while mitigating unfavorable generalization to anomalies. Using only a simple vanilla UNet, FAIR achieves convincing performance with high efficiency on various industrial visual inspection datasets. Code: https://***/liutongkun/FAIR. © 2024, The Authors. All rights reserved.

关键词： Image reconstruction

Provably Convergent Federated Trilevel Learning

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arXiv

arXiv 2023年

作者： Jiao, Yang Yang, Kai Wu, Tiancheng Jian, Chengtao Huang, Jianwei Department of Computer Science and Technology Tongji University China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University China Shanghai Research Institute for Intelligent Autonomous Systems China School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China

Trilevel learning, also called trilevel optimization (TLO), has been recognized as a powerful modelling tool for hierarchical decision process and widely applied in many machine learning applications, such as robust neural architecture search, hyperparameter optimization, and domain adaptation. Tackling TLO problems has presented a great challenge due to their nested decision-making structure. In addition, existing works on TLO face the following key challenges: 1) they all focus on the non-distributed setting, which may lead to privacy breach;2) they do not offer any non-asymptotic convergence analysis which characterizes how fast an algorithm converges. To address the aforementioned challenges, this paper proposes an asynchronous federated trilevel optimization method to solve TLO problems. The proposed method utilizes µ-cuts to construct a hyper-polyhedral approximation for the TLO problem and solve it in an asynchronous manner. We demonstrate that the proposed µ-cuts are applicable to not only convex functions but also a wide range of non-convex functions that meet the µ-weakly convex assumption. Furthermore, we theoretically analyze the non-asymptotic convergence rate for the proposed method by showing its iteration complexity to obtain ϵ-stationary point is upper bounded by (Formula Presented). Extensive experiments on real-world datasets have been conducted to elucidate the superiority of the proposed method, e.g., it has a faster convergence rate with a maximum acceleration of approximately 80%. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

Flow Sensing-Based Underwater Target Detection Using Distributed Mobile Sensors

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Flow Sensing-Based Underwater Target Detection Using Distrib...

IEEE Conference on Decision and Control

作者： Jun Wang Tongsheng Shen Dexin Zhao Feitian Zhang Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing China National Innovation Institute of Defense Technology Beijing China Department of Advanced Manufacturing and Robotics and the State Key Laboratory of Turbulence and Complex Systems College of Engineering Peking University Beijing China

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper presents a novel bioinspired underwater target detection method with distributed mobile sensors. Inspired by the fish's lateral line system, distributed mobile sensors are proposed to measure their respective local flow pressure and collectively estimate the parameters (e.g., position and dimension) of underwater targets with dynamic sensor trajectory planning. This paper formulates an optimal trajectory planning problem for a group of mobile sensors using information entropy as the cost function, with an aim to maximize the confidence level in the estimated target parameters. This paper in particular investigates the detection of a circular target in a uniform flow. A Bayesian filter is adopted to assimilate the distributed pressure measurements dynamically to estimate the position and radius of given circular targets. To verify the effectiveness of the proposed method, simulation is conducted with both fixed and moving circular targets in uniform flow. Simulation results are presented and analyzed.

关键词： Trajectory planning Simulation Object detection Robot sensing systems Sensor systems Entropy Sensors

Influence of Box-in Structure on Heat Dissipation Performance of UHV Converter Transformer 4

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Influence of Box-in Structure on Heat Dissipation Performanc...

4th IEEE China International Electrical and Energy Conference, CIEEC 2021

作者： Cheng, Lin Wen, Hao Zhang, Jie Liu, Cong Hao, Jian Li, Wenping Electric Power Research Institute Nari Group Corporation/State Grid Nanjing China Chongqing University State Key Laboratory of Power Transmission Equipment System Security and New Technology Chongqing China Baoding Tianwei Baobian Electrical Limited Company Baoding China

ISBN: (纸本)9781728171494

The Box-in structure is a structure used to reduce the noise effect of the converter transformer, but it also brings about the problem of reduced heat dissipation performance of the converter transformer. To study influences of Box-in structure on the heat dissipation and temperature distribution of Ultra-High Voltage(UHV) converter transformers so as to guide the heat dissipation of converter transformer and ensure its safe and stable operation, the following workwas done in this paper: Based on the finite element principle, the commercial software COMSOL was used to establish the electromagnetic-thermal-flow field simulation model of the converter transformer and its surrounding environment. Then, the influence of the Box-in structure on the temperature rise of the converter transformer and its surrounding environment under different ambient wind speeds was analyzed. After the above work, the following conclusions were obtained: the existence of the box-in structure significant reduces the convection heat dissipation capacity of the converter transformer box wall, resulting in that when the converter transformer with Box-in structure is compared with the one without Box-in structure, its average box wall temperature is 3.7K higher, and the local temperature is 7.7-17.55K higher;the hot-spot temperature rise of the converter transformer box wall decreases with the increase of ambient wind speeds. The research results obtained in this paper can be used to guide the heat dissipation of the box-in structure UHV converter transformer to ensure its stable operation. © 2021 IEEE.

关键词： Computer software

Multi-block SSD based on small object detection for UAV railway scene surveillance

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Chinese Journal of Aeronautics 2020年第6期33卷 1747-1755页

作者： Yundong LI Han DONG Hongguang LI Xueyan ZHANG Baochang ZHANG Zhifeng XIAO North China University of Technology Beijing 100144China Key Laboratory of Large Structure Health Monitoring and Control Shijiazhuang 050043China Unmanned System Research Institute Beihang UniversityBeijing 100083China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100083China State Key Laboratory of Information Engineering in Surveying Wuhan UniversityWuhan 430079China

A method of multi-block Single Shot Multi Box Detector(SSD)based on small object detection is proposed to the railway scene of unmanned aerial vehicle *** address the limitation of small object detection,a multi-block SSD mechanism,which consists of three steps,is ***,the original input images are segmented into several overlapped ***,each patch is separately fed into an SSD to detect the ***,the patches are merged together through two *** the first stage,the truncated object of the sub-layer detection result is *** the second stage,a sub-layer suppression and filtering algorithm applying the concept of non-maximum suppression is utilized to remove the overlapped boxes of *** boxes that are not detected in the main-layer are *** addition,no sufficient labeled training samples of railway circumstance are available,thereby hindering the deployment of SSD.A two-stage training strategy leveraging to transfer learning is adopted to solve this *** deep learning model is preliminarily trained using labeled data of numerous auxiliaries,and then it is refined using only a few samples of railway scene.A railway spot in China,which is easily damaged by landslides,is investigated as a case *** results show that the proposed multi-block SSD method produces an overall accuracy of 96.6%and obtains an improvement of up to 9.2%compared with the traditional SSD.

关键词： Deep learning Multi-block Single Shot MultiBox Detector(SSD) Objection detection Railway scene Unmanned aerial vehicle remote sensing