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A High-Performance Wearable Piezoresistive Sensor with Wide Temperature Range Via Ti3c2tx Mxene/Au Dual-Layer Conductive Network and Micro-Spike Structure

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SSRN 2024年

作者： Yang, Ning Yin, Xiangyu Liu, Hailian Zhou, Xue Yan, Xin Zhang, Xuenan Zhao, Yong Cheng, Tonglei State Key Laboratory of Synthetical Automation for Process Industries The College of Information Science and Engineering Northeastern University Shenyang110819 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao066004 China Foshan Graduate school of Innovation Northeastern University Guangdong Foshan528311 China Northeastern University China

Currently, with the increasing popularity of wearable electronic devices, there is a growing demand for the performance and application scenarios of flexible pressure sensors. However, effectively balancing sensor's sensitivity and sensing range over a wide temperature range remains an urgent challenge. In this paper, a simple and economical template transfer method was used to prepare micro-spike structures with random distributions, while a dual-layer conductive network was constructed using Ti3C2Tx MXene (two-dimensional transition metal carbides and nitrides) and gold nanoparticles (Au). The working mechanism of the piezoresistive sensor was systematically analyzed. The special surface microstructure and the synergistic effect between the double-layer conductive network enabled the sensor to acquire high sensitivity (497.08 kPa-1) over a wide sensing range (>1000 kPa) and maintain stability for working even at high temperatures (>400 °C). In addition, the obtained piezoresistive sensor had short response time and relaxation time, good linearity, low detection limit, and good fatigue resistance. Furthermore, we demonstrated the application of this sensor in table tennis and electronic sports (eSports) scenarios, showing its bright prospect in human motion pattern recognition. Its insensitivity to temperature also showed great potential for the development of wearable electronic products that can work stably in harsh environments. © 2024, The Authors. All rights reserved.

关键词： Gold nanoparticles

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Distributed Event-Triggered Bandit Convex Optimization With Time-Varying Constraints

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IEEE Transactions on Control of Network Systems 2025年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Ministry of Education Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China Southeast University School of Automation Nanjing210096 China University of Groningen Engineering and Technology Institute Groningen Faculty of Science and Engineering GroningenAG 9747 Netherlands KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden KTH Royal Institute of Technology Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms solving this problem require that each agent broadcasts its decision to its neighbors at each iteration. However, communication resources are often limited. To better utilize communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. © 2014 IEEE.

关键词： Optimization algorithms

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Hybrid mean impact value with nonnegative garrote for input variable selection of multi-layer perceptron 20

Hybrid mean impact value with nonnegative garrote for input ...

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Proceedings of the 2020 4th International Conference on Electronic Information Technology and Computer engineering

作者： Hongxun Wang Fengying Ma Kai Sun School of Electrical Engineering and Automation Qilu University of Technology (shandong Academy of sciences) Jinan China State Key Laboratory of Process Automation in Mining & Metallurgy ganization Beijing China Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China School of Electrical Engineering and Automation Qilu University of Technology (shandong Academy of sciences) Jinan China School of Electrical Engineering and Automation Qilu University of Technology (shandong Academy of sciences) Jinan China State Key Laboratory of Process Automation in Mining & Metallurgy ganization Beijing China Beijing Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

ISBN: (纸本)9781450387811

In the paper, a new adaptive variable selection algorithm for nonlinear regression multi-layer perceptron (MLP) is proposed. The proposed algorithm applies the nonnegative garrote (NNG) to shrink the input weights of the trained MLP. An adaptive indicator is designed by introducing the mean impact value (MIV) algorithm, which can improve the coefficient shrinkage efficiency of the NNG algorithm. The proposed algorithm uses cross-validation and Bayesian information criterion to calculate the optimal shrinkage parameter. The performance of adaptive variable selection algorithm is demonstrated by an example of artificial dataset. Simulation results show that the algorithm has better model accuracy and simplicity than other state-of-art algorithms.

关键词： Variable selection Multi-layer perceptron Mean impact value Nonnegative garrote

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Finite-time asynchronous dissipative filtering of conic-type nonlinear Markov jump systems

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Science China(Information Sciences) 2021年第5期64卷 151-162页

作者： Xiang ZHANG Shuping HE Vladimir STOJANOVIC Xiaoli LUAN Fei LIU Key Laboratory of Intelligent Computing and Signal Processing (Ministry of Education) School of Electrical Engineering and Automation Anhui University Department of Automatic Control Robotics and Fluid Technique Faculty of Mechanical and Civil EngineeringUniversity of Kragujevac Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education) Institute of AutomationJiangnan University

In the present study, the finite-time asynchronous dissipative filter design problem for the Markov jump systems with conic-type nonlinearity is studied. The hidden Markov model can describe the asynchronism embodied in the system modes and the filter modes reasonably. Moreover, a suitable LyapunovKrasovskii function is utilized and linear matrix inequalities are applied to obtain adequate conditions. These techniques guarantee the finite-time boundedness and strict dissipativity of the filtering error dynamic system. Furthermore, the design problems of the passive filter and the H∞ filter are studied by adjusting the three parameters U, G and V. Finally, the filter gains and the optimal index α*are obtained and the correctness and feasibility of the designed approach are verified by a simulation example.

关键词： dissipative filtering hidden Markov model finite-time boundedness Markov jump systems conic-type nonlinearity

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Grade Monitoring using Semantic Features of Flotation Froth Image

Grade Monitoring using Semantic Features of Flotation Froth ...

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Chinese Control and Decision Conference, CCDC

作者： Xu Wang Jingmin Lian Daoxi Liu Yutian Lei School of Information Science and Engineering Northeastern University Shenyang China BGRIMM Technology Group Beijing China State Key Laboratory of Process Automation in Mining & Metallurgy Beijing China

ISBN: (纸本)9781665478977

In the industrial flotation circuits, grades are the key performance indicators for flotation condition recognition and operations. Modeling based on machine vision is an effective tool for grade monitoring, and froth image feature extraction as a model input plays a crucial role. Given the low efficiency of hand-crafted features and the poor interpretability of high-dimensional features extracted by convolutional neural networks, a grade monitoring method with human-understandable semantic features as input is proposed in this paper. First, based on the combination of generative adversarial networks and encoder, we design a new network structure that maps the froth images data space to the latent semantic space. Then the semantic features of froth images can be automatically extracted by decomposing the constructed matrix with rich latent semantics. Finally, as demonstrated in the industrial experiment, the extracted semantic features can not only be visually interpreted but also can be effectively used in grade monitoring.

关键词： Machine vision Semantics Key performance indicator process control Predictive models Feature extraction Generative adversarial networks

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Semi-Supervised Multiview Fuzzy Broad Learning

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SSRN 2023年

作者： Xi, Chao Peng, Cheng Fan, Zizhu Liu, Qiang Wang, Hui Key Laboratory of Advanced Control and Optimization of Jiangxi Province East China Jiaotong University Nanchang330013 China Department of Engineering King’s College London London4616 United Kingdom State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Liaoning110819 China

Semi-supervised learning models often rely on the restricted assumptions, and can easily suffer from corvariate shift or noise. Few studies have explored the use of fuzzy rule-based methods in the semi-supervised discipline. In this paper, we first build a semi-supervised fuzzy broad learning model called MT-FBLS to address these issues. Further, a trusted multiview semisupervised classification method, termed TMSSC, is proposed by integrating the MT-FBLS with a multiview fusion network to enhance the robustness of the model. Under the Mean-Teacher framework, MT-FBLS involves the Takagi-Sugeno-Kang(TSK) fuzzy model which can effectively deal with imprecision and uncertainty, and broad learning system(BLS) which has strong rule learning ability and high computational efficiency. TMSSC utilizes a trusted mechanism to blend multiple views, so as to enhance its learning ability in the semi-supervised *** on the benchmark datasets demonstrate that the proposed methods have better anti-noise ability, competitive classification accuracy, as well as fast running speed. © 2023, The Authors. All rights reserved.

关键词： Learning systems

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A diffusion model based on multi-scale spatial Mamba for medical image segmentation

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engineering Applications of Artificial Intelligence 2025年 156卷

作者： Li, Chun Sun, Qiule Zhang, Muqing Zhang, Jianxin School of Medical Information Wannan Medical College Wuhu Anhui241002 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang Liaoning110819 China School of Information and Communication Engineering Dalian University of Technology Dalian Liaoning116024 China School of Computer Science and Engineering Dalian Minzu University Dalian Liaoning116600 China

Medical image segmentation plays a key role in disease diagnosis, treatment planning, and monitoring disease progression. Recently, denoising diffusion models have shown significant promise in generating accurate pixel-level semantic representations. In this study, we introduce a diffusion model based on multi-scale spatial mamba (MSM-Diff) designed for precise medical image segmentation. MSM-Diff integrates the strengths of diffusion models, Mamba architecture, and convolutional neural networks to efficiently capture both global and local contextual information from complex volumetric data. The core of MSM-Diff is the Mamba-based U-shaped feature encoder (MUFE), which combines the three-dimensional multi-scale spatial Mamba model (MS-Mamba) with extracted features for enhanced multi-scale and global feature extraction. By using the mamba architecture, the model maintains linear computational complexity. Additionally, MSM-Diff incorporates a multi-scale gated spatial convolution (MS-GSC) module within MUFE to further refine spatial feature representations. Extensive evaluations of three public datasets demonstrate that MSM-Diff consistently outperforms current methods, achieving state-of-the-art performance in DSC and HD95. This model provides a robust solution for medical image segmentation by effectively capturing global context and accurately delineating boundaries, thereby improving diagnostic and treatment planning outcomes for radiologists and clinicians. © 2025 Elsevier Ltd

关键词： Semantic Segmentation

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Sensitivity Analysis-Based Explainable Diffusion Transformers for Anomaly Detection in Consumer Electronics Manufacturing

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IEEE Transactions on Consumer Electronics 2025年

作者： Yi, Ling Liu, Shiyu Zhou, Li Ning, Zhaolong Song, Jiajie Chen, Qingda Zhang, Ke Ding, Jinliang Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China China National University of Defense Technology College of Electronic Science and Technology Changsha410073 China Inner Mongolia University School of Electronic Information Engineering Hohhot China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China

Consumer electronics play a crucial role in the artificial intelligence internet of things (AIoT), with anomaly detection (AD) being particularly critical for the consumer product manufacturing industry. However, existing AD methods suffer from limitations such as poor detection accuracy and lack of explainability, hindering their widespread adoption in industrial manufacturing. To address these issues, we propose SADiTAD, a Sensitivity Analysis-based Diffusion Transformer for Anomaly Detection. This model comprises a diffusion transformer (DiT)-based reconstruction enhancement sub-network and a vision transformer (ViT)-based detection sub-network. In the DiT sub-network, we introduce a structural similarity index measure (SSIM)-guided one-step denoising method to expedite the denoising process. Additionally, to enhance the model's explainability, we develop a sensitivity analysis-based ViT (SA-ViT) model, which evaluates the sensitivity of input embeddings to various image regions to determine if the fault region is being accurately identified during anomaly detection. The proposed SADiTAD model has been evaluated on public datasets MVTec AD and VisA, demonstrating superior performance over existing state-of-The-Art anomaly detection methods and providing enhanced explainability. © 1975-2011 IEEE.

关键词： Sensitivity analysis

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Learning-Based Switched Reliable Control of Cyber-Physical Systems With Intermittent Communication Faults

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IEEE/CAA Journal of Automatica Sinica 2020年第3期7卷 711-724页

作者： Xin Huang Jiuxiang Dong College of Information Science and Engineering Northeastern UniversityShenyang 110819China State Key Laboratory of Synthetical Automation of Process Industries Northeastern UniversityShenyang 110819China Key Laboratory of Vibration and Control of Aero-Propulsion System Ministry of Education Northeastern UniversityShenyang 110819China IEEE

This study deals with reliable control problems in data-driven cyber-physical systems(CPSs) with intermittent communication faults, where the faults may be caused by bad or broken communication devices and/or cyber attackers. To solve them, a watermark-based anomaly detector is proposed, where the faults are divided to be either detectable or ***, the fault's intermittent characteristic is described by the average dwell-time(ADT)-like concept, and then the reliable control issues, under the undetectable faults to the detector, are converted into stabilization issues of switched systems. Furthermore,based on the identifier-critic-structure learning algorithm, a datadriven switched controller with a prescribed-performance-based switching law is proposed, and by the ADT approach, a tolerated fault set is given. Additionally, it is shown that the presented switching laws can improve the system performance degradation in asynchronous intervals, where the degradation is caused by the fault-maker-triggered switching rule, which is unknown for CPS operators. Finally, an illustrative example validates the proposed method.

关键词： Adaptive dynamic programming(ADP) communication fault cyber-physical systems(CPSs) data-driven control reliable control

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Information Modelling Method of As-built process Data

Information Modelling Method of As-built Process Data

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2020 International Conference on Artificial Intelligence and Application Technologies, AIAT 2020

作者： Dai, Sheng Zhao, Gang Yu, Yong Bao, Qiangwei School of Mechanical Engineering and Automation Beihang University Beijing100191 China Key Laboratory of Aeronautics Smart Manufacturing Beihang University Beijing100191 China Beijing Engineering Technological Research Center of High-Efficient and Green CNC Machining Process Beijing100191 China

The digital twin concept plays a significant role in the Industry 4.0 era since it was proposed. One key enabling technology for digital twin creation is the information modelling of physical products. Therefore, the modelling approach to modelling as-built process data of physical products, which faithfully reflects the real machining status, emerges to be pivotal. This paper addresses the problem of modelling as-built process data in machining process, which is difficult to accomplish by relevant methods, and hinders data reuse and the long-term archiving. Furthermore, an ontology-based information modelling method of as-built process data is proposed as the recommendation to represent and record the data in the cyberspace. It provides a standardized process for companies to model as-built process data by specifying the contents to be modeled, and the modelling method. To validate the effectiveness, a case study is undertaken in an aviation manufacturing plant at last. The result shows that the proposed information modelling methodology is readily to the virtualization of as-built machining process data. © Published under licence by IOP Publishing Ltd.

关键词： Machining

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