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In-situ forming ultra-mechanically sensitive materials for high-sensitivity stretchable fiber strain sensors

National science Review 2024年第6期11卷 254-263页

作者： Rouhui Yu Changxian Wang Xiangheng Du Xiaowen Bai Yongzhong Tong Huifang Chen Xuemei Sun Jing Yang Naoji Matsuhisa Huisheng Peng Meifang Zhu Shaowu Pan State Key Laboratory for Modification of Chemical Fibers and Polymer Materials College of Materials Science and Engineering Donghua University MOE Key Lab of Disaster Forecast and Control in Engineering School of Mechanics and Construction Engineering Jinan University State Key Laboratory of Molecular Engineering of Polymers Department of Macromolecular Science and Institute of Fiber Materials and Devices Fudan University Department of Cardiology Shanghai Xuhui Central HospitalZhongshan-Xuhui Hospital Fudan University Research Center for Advanced Science and Technology and Institute of Industrial Science The University of Tokyo

Fiber electronics with flexible and weavable features can be easily integrated into textiles for wearable applications. However, due to small sizes and curved surfaces of fiber materials, it remains challenging to load robust active layers, thus hindering production of high-sensitivity fiber strain sensors. Herein, functional sensing materials are firmly anchored on the fiber surface in-situ through a hydrolytic condensation *** anchoring sensing layer with robust interfacial adhesion is ultra-mechanically sensitive, which significantly improves the sensitivity of strain sensors due to the easy generation of microcracks during stretching. The resulting stretchable fiber sensors simultaneously possess an ultra-low strain detection limit of 0.05%, a high stretchability of 100%, and a high gauge factor of 433.6, giving 254-folds enhancement in sensitivity. Additionally, these fiber sensors are soft and lightweight, enabling them to be attached onto skin or woven into clothes for recording physiological signals, e.g. pulse wave velocity has been effectively obtained by them. As a demonstration, a fiber sensor-based wearable smart healthcare system is designed to monitor and transmit health status for timely intervention. This work presents an effective strategy for developing high-performance fiber strain sensors as well as other stretchable electronic devices.

关键词： fiber strain sensor stretchable wearable healthcare

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Efficient Design and Torque Prediction for Similar PMaSynRMs with a Generalized Attention Convolutional Network

Efficient Design and Torque Prediction for Similar PMaSynRMs...

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2024 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific 2024

作者： Ma, Yidan Song, Zaixin Liang, Yongtao Cao, Jianfu The Hong Kong Polytechnic University State Key Lab Of Ultra-Precision Machining Technology Department Of Industrial And Systems Engineering Hong Kong Hong Kong Xi'an Jiaotong University School Of Automation Science And Engineering Xi'an China

ISBN: (纸本)9798331529277

Reducing computational load in the design optimization of similarly structured electric motors is crucial for industrial production. Recent advancements, particularly in data-driven surrogate models like neural networks, have shown significant potential in this area. This paper introduces a generalized attention convolutional network (GACN) for optimizing permanent magnet-assisted synchronous reluctance motors (PMaSynRMs) that vary in the number and shape of flux barrier layers. The network effectively identifies commonalities in the electromagnetics and flux barrier structures of similar PMaSynRMs within a high-dimensional feature space. Specifically, it utilizes ResNet34 as the backbone, enhanced with spatial and channel attention blocks for feature extraction, to discern motor structures independent of geometric interference. Additionally, the network incorporates a local maximum mean discrepancy block to measure data distribution similarities and adjust the nonlinear functions that correlate cross-sectional images with torque performance in similar motors. Experimental results demonstrate that, after training on a dataset of four-layer flux barrier PMaSynRM images, the proposed method accurately predicts torque and assesses geometric validity in PMaSynRMs with two- and three-layer barriers. © 2024 IEEE.

关键词： Permanent magnets

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High-precision flexible sweat self-collection sensor for mental stress evaluation

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npj Flexible Electronics 2024年第1期8卷 417-426页

作者： Chenhao Wang Zhengyu Wang Wei Wei Zhenjie Zhang Anne Ailina Li Guanghao Huang Xian Li Shuzhi Sam Ge Lianqun Zhou Hui Kong Institute for Future School of AutomationShandong Key Laboratory of Industrial Control TechnologyQingdao UniversityQingdao266071China ETH Zürich Department of Biosystems Science and EngineeringBio Engineering LaboratoryBasel4056Switzerland The University of Sydney 2006 SydneyAustralia Natl Univ Singapore Dept Elect&Comp EngnSingapore117576Singapore Suzhou Institute of Biomedical Engineering and Technology Chinese.Academy of SciencesSuzhou215163China

As a stress hormone existing in the human body,cortisol can reflect the psychological stress and health status in daily life,and is a potential biomarker of the body’s stress *** effectively collect sweat and accurately identify the target,this paper reports a flexible wearable cortisol detection device with outstanding reliability and *** imprinted polymer(MIP)ensures cortisol *** carbon nanotubes(CNT)on electrodes increase sensitivity,expanding the detection range to 10^(−3) to 10^(4) nM,with sensitivity at 189.2 nA/lg(nM).In addition,porous chitosan hydrogel(PCSH)collects sweat effectively,its adhesive properties and 80%swelling rate offer a low-cost alternative to *** printed circuit board(FPCB)and serpentine electrode(SE)ensure device *** non-invasive,highly sensitive device offers a novel method for mental stress monitoring and clinical diagnosis,advancing human physiological state monitoring.

关键词： stress durability swelling

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Detecting PLC Intrusions Using control Invariants

Detecting PLC Intrusions Using Control Invariants

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作者： Yang, Zeyu He, Liang Yu, Hua Zhao, Chengcheng Cheng, Peng Chen, Jiming Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China University of Colorado Department of Computer Science and Engineering DenverCO80204 United States

Programmable logic controllers (PLCs), i.e., the core of control systems, are well-known to be vulnerable to a variety of cyber attacks. To mitigate this issue, we design PLC-Sleuth, a novel noninvasive intrusion detection/localization system for PLCs, which is built on a set of control invariants - i.e., the correlations between sensor readings and the concomitantly triggered PLC commands - that exist pervasively in all control systems. Specifically, taking the system's supervisory control and data acquisition log as input, PLC-Sleuth abstracts/identifies the system's control invariants as a control graph using data-driven structure learning, and then monitors the weights of graph edges to detect anomalies thereof, which is in turn, a sign of intrusion. We have implemented and evaluated PLC-Sleuth using both a platform of ethanol distillation system (EDS) and a realistically simulated Tennessee Eastman (TE) process. The results show that PLC-Sleuth can: 1) identify control invariants with 100%/98.11% accuracy for EDS/TE;2) detect PLC intrusions with 98.33%/0.85 ‰ true/false positives (TPs/FPs) for EDS and 100%/0% TP/FP for TE;and 3) localize intrusions with 93.22%/96.76% accuracy for EDS/TE. © 2014 IEEE.

关键词： Ethanol

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Rhizoma paridis saponins protected against liver injury in diethylnitrosamine-induced mice

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Traditional Medicine Research 2023年第2期8卷 7-12页

作者： Chen Luo Jing-Wen Yao Shu-Li Man Wen-Yuan Gao Key Laboratory of Industrial Microbiology Ministry of Education/Tianjin Key Laboratory of Industry Microbiology/National and Local United Engineering Lab of Metabolic Control Fermentation Technology/College of BiotechnologyTianjin University of Science&TechnologyTianjin 300457China Tianjin Key Laboratory for Modern Drug Delivery and High Efficiency School of Pharmaceutical Science and TechnologyTianjin UniversityTianjin 300072China

Background:Diethylnitrosamine,one of food additives,possessed a strong carcinogenic effect in *** paridis saponins,as the main active components of Paris polyphylla,have a good anti-cancer effect in our previous *** verify their inhibitory effect on diethylnitrosamine-induced liver cancer,we carried out this ***:We established diethylnitrosamine-induced mouse hepatocarcinoma models to evaluate antitumor of Rhizoma paridis ***,gas chromatography-mass spectrometry was applied to analyze the metabolites in the urine and serum ***:Rhizoma paridis saponins alleviated diethylnitrosamine-induced *** the one hand,Rhizoma paridis saponins down-regulated the levels of liver function markers,such as alanine aminotransferase,aspartate transaminase and alpha *** the other hand,Rhizoma paridis saponins reduced metabolic disorders by increasing fructose and mannose metabolism,and decreasing pentose and glucuronate interconversion,inositol phosphate metabolism,and the process of saturated fatty acids transforming to unsaturated fatty acids,which based on the regulating mRNA expression of glucose transporter type 4,lactate dehydrogenase A,fatty acid synthetas,acetyl-CoA carboxylase and apolipoprotein ***:Rhizoma paridis saponins has the potential application to inhibit chemical-induced hepatocarcinogenesis in the future.

关键词： Rhizoma paridis saponins liver injury diethylnitrosamine metabolites

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Remaining Useful Life Prediction for a Multi-stack Solid Oxide Fuel Cell System with Degradation Interactions

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CSEE Journal of Power and Energy Systems 2022年第4期8卷 1207-1214页

作者： Xiaojuan Wu Liangfei Xu Yang Huang Danan Yang Junhao Wang Houjun Wang Xi Li School of Automation Engineering University of Electronic Science and Technology of ChinaChengdu 610054China State Key Lab of Automotive Safety and Energy School of Vehicle and MobilityTsinghua UniversityBeijing 100084China School of Artificial Intelligencei and Automation Key Laboratory of Image Processing and Intelligent Control of Education MinistryHuazhong University of Science and TechnologyWuhan 430074China

Due to the constraints of manufacturing and materials,high-power plants cannot rely on only one solid oxide fuel cell stack.A multi-stack system is a solution for a highpower system,which consists of multiple fuel cell stacks.A short lifetime is one of the main challenges for the fuel cell before largescale commercial applications,and prognostic is an important method to improve the reliability of fuel *** from the traditional prognostic approaches applied to single-stack fuel cell systems,the key problem in multi-stack prediction is how to solve the correlation of multi-stack degradation,which can directly affect the accuracy of *** response to this difficulty,a standard Brownian motion is added to the traditional Wiener process to model the degradation of each stack,and then the probability density function of the remaining useful life(RUL)of each stack is ***,a Copula function is adopted to reflect the dependence between life distributions,so as to obtain the remaining useful life for the whole multi-stack system.1 The simulation results show that compared with the traditional prediction model,the proposed approach has a higher prediction accuracy for multi-stack fuel cell systems.

关键词： Degradation interaction multi-stack remaining useful life(RUL) solid oxide fuel cell(SOFC)

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Analytical-Chemistry-Informed Transformer for Infrared Spectra Modeling 39

Analytical-Chemistry-Informed Transformer for Infrared Spect...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Huang, Shiluo Jin, Yining Jin, Wei Mu, Ying School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Chengdu China Department of Electrical and Computer Engineering University of Alberta EdmontonAB Canada Research Centre for Analytical Instrumentation State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (纸本)157735897X

Infrared (IR) spectroscopy is a fundamental technique in analytical chemistry. Recently, deep learning (DL) has drawn great interest as the modeling method of infrared spectral data. However, unlike vision or language tasks, IR spectral data modeling is faced with the problem of calibration transfer and has distinctive characteristics. Introducing the prior knowledge of IR spectroscopy could guide the DL methods to learn representations aligned with the domain-invariant characteristics of spectra, and thus improve the performance. Despite such potential, there is a notable absence of DL methods that incorporate such inductive bias. To this end, we propose Analytical-Chemistry-Informed Transformer (ACT) with two modules informed by the field knowledge in analytical chemistry. First, ACT includes learnable spectral processing inspired by chemometrics, which comprises spectral pre-processing, tokenization, and post-processing. Second, a straightforward yet effective representation learning mechanism, namely spectral-attention, is incorporated into ACT. Spectral-attention utilizes the intra-spectral and inter-spectral correlations to extract spectral representations. Empirical results show that ACT has achieved competitive results in 9 analytical tasks covering applications across pharmacy, chemistry, and agriculture. Compared with existing networks, ACT reduces the root mean square error of prediction (RMSEP) by more than 20% in calibration transfer tasks. These results indicate that DL methods in IR spectroscopy could benefit from the integration of prior knowledge in analytical chemistry. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep learning

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Fe nanoclusters anchored in biomass waste-derived porous carbon nanosheets for high-performance supercapacitor

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Chinese Chemical Letters 2024年第8期35卷 362-366页

作者： Yuchen Wang Yaoyu Liu Xiongfei Huang Guanjie He Kai Yan Guangdong Provincial Key Laboratory of Environmental Pollution Control and Remediation Technology School of Environmental Science and EngineeringSun Yat-sen UniversityGuangzhou 510275China Key Laboratory of Advanced Energy Materials Chemistry(Ministry of Education) Nankai UniversityTianjin 300071China Electrochemical Innovation Lab Department of Chemical EngineeringUniversity College LondonLondonWC1E 7JEUnited Kingdom

Metal-nanocluster materials have gradually become a promising electrode candidate for supercapaci-tor *** high-efficient and rational architecture of these metal-nanocluster electrode mate-rials with satisfied supercapacitive performance are full of ***,Fe-nanocluster anchored porous carbon(FAPC)nanosheets were constructed through a facile and low-cost impregnation-activation *** characterization methods documented that FAPC nanosheets possessed a mesopore-dominated structure with large surface area and abundant Fe-N4 active sites,which are crucial for su-percapacitive energy *** optimal FAPC electrode exhibited a high specific capacitance of 378 F/g at a specific current of 1 A/g and an excellent rate capability(271 F/g at 10 A/g),which are comparable or even superior to that of most reported carbon ***,the FAPC-based device achieved a desired specific energy of 14.8 Wh/kg at a specific power of 700 W/*** work opens a new avenue to design metal-nanocluster materials for high-performance biomass waste-based supercapacitors.

关键词： Fe nanoclusters Biomass waste Porous carbon nanosheets Supercapacitor Charge transfer

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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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Design of Intentional Eye-blink Signal Acquisition and control System Based on Pressure Sensor 7

Design of Intentional Eye-blink Signal Acquisition and Contr...

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7th International Conference on Intelligent Computing and Signal Processing, ICSP 2022

作者： Shao, Xueqi Ning, Haonan Yang, Wenjun Niu, Yafeng Southeast University School of Mechanical Engineering Department of Industrial Design Nanjing China National Key Laboratory of Science and Technology on Aircraft Control Xi'an China

ISBN: (纸本)9781665478571

Eye-blink control means that users blink intentionally to realize human-computer interaction. An intentional eye-blink signal acquisition and control system based on flexible pressure sensor is designed, which aims to control the object through simple eye movements such as blinking and winking. The system uses two pressure sensors to collect the blink signals at the orbicularis oculi muscles of the left and right eyes respectively. The software part realizes the acquisition and recognition of six intentional eye-blink signals, and transmits blink-control instruction signals through Bluetooth, so as to realize the control of the remote-controlled vehicle. The advantage of this system is that it is not affected by illumination and does not need GUI interface for control. The availability of the system is qualified after system test. © 2022 IEEE.

关键词： Human computer interaction

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