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第40届中国控制会议

作者： Sheng Gao Guangfu Ma Yanning Guo Wei Zhang Department of Control Science and Engineering Harbin Institute of Technology State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences

For the problem of previous fault-estimation studies that the so-called observer-matching condition should be satisfied,as well as the difficulty to estimate actuator and sensor faults simultaneously,an augmented system fault-estimation observer is proposed for a sort of nonlinear systems subject to process and sensor *** essential idea is to construct an augmented system using a filter and to further create an augmented state vector by considering the system states and simultaneous actuator and sensor *** nonlinearity,process,and sensor disturbances and multiple-fault problem of the actuator and sensor are all taken into ***,an augmented observer is designed for the augmented system by attenuating the disturbances and simultaneously estimating the system states and actuator and sensor *** asymptotic stability is confirmed via the Lyapunov stability theory,and the linear matrix inequality optimization technique is further utilized to solve the designed *** simulations of a single-link flexible-joint robot are performed to illustrate the effectiveness of the proposed robust fault-estimation scheme.

关键词： Augmented observer Fault diagnosis Fault estimation Nonlinear system Linear matrix inequality(LMI)

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Graphene-based silicon modulators

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Frontiers of Information technology & Electronic Engineering 2019年第4期20卷 458-471页

作者： Hao-wen SHU Ming JIN Yuan-sheng TAO Xing-jun WANG State Key Laboratory on Advanced Optical Communication Systems and Networks Department of ElectronicsSchool of Electronics Engineering and Computer Science Peking University Nano-optoelectronics Frontier Center of Ministry of Education at Peking University

Silicon photonics is a promising technology to address the demand for dense and integrated nextgeneration optical interconnections due to its complementary-metal-oxide-semiconductor(CMOS) ***, one of the key building blocks, the silicon modulator, suffers from several drawbacks, including a limited bandwidth, a relatively large footprint, and high power consumption. The graphene-based silicon modulator, which benefits from the excellent optical properties of the two-dimensional graphene material with its unique band structure,has significantly advanced the above critical figures of merit. In this work, we review the state-of-the-art graphenebased silicon modulators operating in various mechanisms, i.e., thermal-optical, electro-optical, and plasmonic. It is shown that graphene-based silicon modulators possess the potential to have satisfactory characteristics in intra-and inter-chip connections.

关键词： Silicon photonics Graphene Optical modulator

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Dynamics of Polar Skyrmion Bubbles under Electric Fields

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Physical Review Letters 2022年第10期129卷 107601-107601页

作者： Ruixue Zhu Zhexin Jiang Xinxin Zhang Xiangli Zhong Congbing Tan Mingwei Liu Yuanwei Sun Xiaomei Li Ruishi Qi Ke Qu Zhetong Liu Mei Wu Mingqiang Li Boyuan Huang Zhi Xu Jinbin Wang Kaihui Liu Peng Gao Jie Wang Jiangyu Li Xuedong Bai Electron Microscopy Laboratory and International Center for Quantum Materials School of Physics Peking University Beijing 100871 China Department of Engineering Mechanics Zhejiang University Hangzhou 310027 Zhejiang China School of Materials Science and Engineering Xiangtan University Xiangtan 411105 Hunan China Hunan Provincial Key Laboratory of Intelligent Sensors and Advanced Sensor Materials School of Physics and Electronics Hunan University of Science and Technology Xiangtan 411201 Hunan China Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing 100190 China Department of Materials Science and Engineering Southern University of Science and Technology Shenzhen 518055 Guangdong China State Key Laboratory for Artificial Microstructure & Mesoscopic Physics School of Physics Peking University Beijing 100871 China Interdisciplinary Institute of Light-Element Quantum Materials and Research Center for Light-Element Advanced Materials Peking University Beijing 100871 China Zhejiang Laboratory Hangzhou 311100 Zhejiang China Guangdong Provincial Key Laboratory of Functional Oxide Materials and Devices Southern University of Science and Technology Shenzhen 518055 Guangdong China School of Physical Sciences University of Chinese Academy of Sciences Beijing 100049 China

Room-temperature polar skyrmions, which have been recently discovered in oxide superlattice, have received considerable attention for their potential applications in nanoelectronics owing to their nanometer size, emergent chirality, and negative capacitance. For practical applications, their manipulation using external stimuli is a prerequisite. Herein, we study the dynamics of individual polar skyrmions at the nanoscale via in situ scanning transmission electron microscopy. By monitoring the electric-field-driven creation, annihilation, shrinkage, and expansion of topological structures in real space, we demonstrate the reversible transformation among skyrmion bubbles, elongated skyrmions, and monodomains. The underlying mechanism and interactions are discussed in conjunction with phase-field simulations. The electrical manipulation of nanoscale polar skyrmions allows the tuning of their dielectric permittivity at the atomic scale, and the detailed knowledge of their phase transition behaviors provides fundamentals for their applications in nanoelectronics.

关键词： Domains Dynamical phase transitions Electric polarization Ferroelectricity Ferroelectrics Topological phase transition

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A Multi-objective optimization model for simulation-based acquisition 7

A Multi-objective optimization model for simulation-based ac...

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7th IEEE International Conference on Progress in Informatics and Computing, PIC 2020

作者： Zhang, Xue Xue, Junjie Zhou, Junhua Shi, Guoqiang Yu, Yan Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China Beijing Simulation Center Beijing Complex Product Advanced Manufacturing Engineering Research Center Beijing China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System Engineering Beijing China Science and Technology on Space System Simulation Laboratory Beijing Simulation Center Beijing China

ISBN: (纸本)9781728170862

Large-scale equipment presents the characteristics of technology-intensive, knowledge-intensive and complex, which brings high investment and high risk to equipment acquisition. This paper analyzes the various components of equipment acquisition to fully apply modeling and simulation technology to each stage of it. On the basis of multi-objective optimization theory, the objective function of the lowest risk and lowest cost of simulation-based acquisition (SBA) is proposed. Then establishing a multi-objective optimization model for simulation-based acquisition according to the corresponding constraints. The nondominated sorting genetic algorithm II (NSGA II) and multi-objective particle swarm optimization (MOPSO) algorithms are used to optimize the model, and the majority voting method is used for integration. The comparison of simulation examples proves that the multi-objective optimization model oriented to SBA designed in this paper can effectively realize the optimization of each objective of SBA under cost constraints. And the feasibility and efficiency of optimization solution are guaranteed. © 2020 IEEE.

关键词： Multiobjective optimization

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Plagiarism Detection of Multi-threaded Programs Using Frequent Behavioral Pattern Mining

Plagiarism Detection of Multi-threaded Programs Using Freque...

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作者： Tian, Zhenzhou Wang, Qing Gao, Cong Chen, Lingwei Wu, DInghao School of Computer Science and Technology Xi'An University of Posts and Telecommunications China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an Shaanxi710121 China College of Information Sciences and Technology The Pennsylvania State University University ParkPA16802 United States

Software dynamic birthmark techniques construct birthmarks using the captured execution traces from running the programs, which serve as one of the most promising methods for obfuscation-resilient software plagiarism detection. However, due to the perturbation caused by non-deterministic thread scheduling in multi-threaded programs, such dynamic approaches optimized for sequential programs may suffer from the randomness in multi-threaded program plagiarism detection. In this paper, we propose a new dynamic thread-aware birthmark FPBirth to facilitate multi-threaded program plagiarism detection. We first explore dynamic monitoring to capture multiple execution traces with respect to system calls for each multi-threaded program under a specified input, and then leverage the Apriori algorithm to mine frequent patterns to formulate our dynamic birthmark, which can not only depict the program's behavioral semantics, but also resist the changes and perturbations over execution traces caused by the thread scheduling in multi-threaded programs. Using FPBirth, we design a multi-threaded program plagiarism detection system. The experimental results based on a public software plagiarism sample set demonstrate that the developed system integrating our proposed birthmark FPBirth copes better with multi-threaded plagiarism detection than alternative approaches. Compared against the dynamic birthmark System Call Short Sequence Birthmark (SCSSB), FPBirth achieves 12.4%, 4.1% and 7.9% performance improvements with respect to union of resilience and credibility (URC), F-Measure and matthews correlation coefficient (MCC) metric, respectively. © 2020 World Scientific Publishing Company.

关键词： Semantics

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Machine Learning Model Development with K-Nearest Neighbor Missing Value Imputation for Prognosis Prediction of Patients with Sudden Sensorineural Hearing Loss

SSRN

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SSRN 2024年

作者： Jin, Yabin Li, Meige Li, Minghong Fan, Hao Gong, Haiyan Chen, Wencong Zhang, Minghao Yu, Youjun Luo, Wei Zhang, Xiaotong School of Computer and Communication Engineering Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing100083 China Clinical Research Institute The First People's Hospital of Foshan Guangdong Foshan528000 China Department of Otolaryngology Head and Neck Surgery The First People's Hospital of Foshan Guangdong Foshan528000 China Shunde innovation School University of Science and Technology Beijing Guangdong Foshan528399 China The State Key Laboratory of Medical Molecular Biology Department of Biochemistry and Molecular Biology Institute of Basic Medical Sciences School of Basic Medicine Chinese Academy of Medical Sciences Peking Union Medical College Beijing100005 China Institute for Advanced Materials and Technology University of Science and Technology Beijing Beijing100083 China

Background: Sudden sensorineural hearing loss (SSNHL) is a common disease with heterogeneous treatment response. The study aimed to comprehensively investigate the prognostic factors of SSNHL, and develop machine-learning models with K-Nearest Neighbor (KNN) missing value imputation to predict different response ***: A retrospective study was conducted for 600 SSNHL patients who received standardized treatment. The clinical features, blood tests, concurrent symptoms, body measures, and audiometric features were collected. The missing values were imputed using the KNN algorithm. Six classifiers and four classification tasks were explored. The model performance was evaluated by accuracy and area under the receiver operating characteristic curve. In addition, a feature importance analysis was conducted for model explanation and complexity ***: The difference in age, time from onset to treatment, mean hearing threshold, vertigo, ear blockage sensation, curve type, revive, auditory brainstem response, and distortion product evoked otoacoustic emission response was statistically significant among the response groups. The Random Forest classifier achieved the best performance for all classification tasks, with significant improvements from the KNN. The feature importance analysis revealed new audiometric features that can guide treatment delivery, and facilitate etiological ***: Machine-learning models can effectively predict the treatment response of SSNHL patients based on clinical features, especially audiometric features. This can help clinicians deliver personalized and precise treatment for SSNHL ***: This study was supported by National key R&D Program of China (2023YFB3812901);Project funded by China Postdoctoral science Foundation (GZC20230239);National Funding Program for postdoctoral researchers (2023M740219);Natural science Foundation of Guangdong Province (2021A1515220032).Declaration of Interest: All

关键词： Otoacoustic emissions

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Nonreciprocal thermal radiation in ultrathin magnetized epsilon-near-zero semiconductors

arXiv

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arXiv 2022年

作者： Liu, Mengqi Xia, Shuang Wan, Wenjian Qin, Jun Li, Hua Zhao, Changying Bi, Lei Qiu, Cheng-Wei Institute of Engineering Thermophysics MOE Key Laboratory for Power Machinery and Engineering School of Mechanical Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore National Engineering Research Center of Electromagnetic Radiation Control Materials University of Electronic Science and Technology of China Chengdu610054 China State Key Laboratory of Electronic Thin-Films and Integrated Devices University of Electronic Science and Technology of China Chengdu610054 China Key Laboratory of Terahertz Solid State Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai2000509 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Science Beijing1000049 China

Spectral/angular emissivity e and absorptivity α of an object are widely believed to be identical by Kirchhoff's law of thermal radiation in reciprocal systems, but this introduces an intrinsic and inevitable energy loss for energy conversion and harvesting devices. So far, experimental evidences of breaking this well-known balance are still absent, and previous theoretical proposals are restricted to narrow single-band nonreciprocal radiation. Here we observe for the first time, to our knowledge, the violation of Kirchhoff's law using ultrathin (0.6 has been experimentally demonstrated under a moderate external magnetic field. Moreover, based on magnetized ENZ building blocks supporting asymmetrically radiative Berreman and surface ENZ modes, we show versatile shaping of nonreciprocal thermal radiation: single-band, dual-band, and broadband nonreciprocal emission spectra at different wavebands. Our findings of breaking Kirchhoff's law will advance the conventional understanding of emission and absorption processes of natural objects, and lay a solid foundation for more comprehensive studies in designing various nonreciprocal thermal emitters. The reported recipe of diversely shaping nonreciprocal emission will also breed new possibilities in renovating next-generation nonreciprocal energy devices in the areas of solar cells, thermophotovoltaic, radiative cooling, etc. Copyright © 2022, The Authors. All rights reserved.

关键词： III-V semiconductors

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A RIS-Based Vehicle DOA Estimation Method With Integrated Sensing and Communication System

arXiv

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arXiv 2022年

作者： Chen, Zhimin Chen, Peng Guo, Ziyu Zhang, Yudong Wang, Xianbin The School of Electronic and Information Shanghai Dianji University Shanghai201306 China State Key Laboratory of Integrated Services Networks Xidian University Xi’an710071 China The State Key Laboratory of Millimeter Waves Southeast University Nanjing210096 China The School of Information Science and Technology Fudan University Shanghai200433 China The State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai200433 China The School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom School of Computer Science and Engineering Southeast University Nanjing210096 China The Department of Electrical and Computer Engineering Western University LondonONN6A 5B9 Canada

With the development of intelligent transportation, growing attention has been received to integrated sensing and communication (ISAC) systems. In this paper, we formulate a novel passive sensing technique to obtain information on the vehicle’s direction of arrival (DOA) using reconfigurable intelligent surfaces (RIS). A novel estimation method is proposed in the scenario with a receiver using only one full-functional channel, where multiple measurements for the DOA estimation are achieved by controlling the reflection matrix (measurement matrix) in the RIS. Moreover, different from the existing estimation methods, we also consider the interference signals introduced by wireless communication in the ISAC system. Then, we propose a novel atomic norm-based method to remove the interference signals and reconstruct the sparse signal. Additionally, a novel Hankel-based multiple signal classification (MUSIC) method is formulated to obtain the DOA information after the interference removal. To reduce the interference signals more efficiently and improve the performance of the sparse reconstruction, we optimize the measurement matrix to improve the signal-to-interference-plus-noise ratio (SINR). Finally, the theoretical Cram’er-Rao lower bound (CRLB) is derived for the ISAC system on the vehicle DOA estimation. Simulation results show that the proposed method can achieve better performance in the DOA estimation, and the corresponding CRLB with different distributions of the sensing nodes are shown. The code for the proposed method is available online https://***/chenpengseu/***. Copyright © 2022, The Authors. All rights reserved.

关键词： Vehicles

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EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review

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Computational Intelligence and Neuroscience 2022年第0期2022卷 6486570页

作者： Ahmad, Ijaz Wang, Xin Zhu, Mingxing Wang, Cheng Pi, Yao Khan, Javed Ali Khan, Siyab Samuel, Oluwarotimi Williams Chen, Shixiong Li, Guanglin CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Shenzhen China School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China School of Biomedical Engineering Sun Yat-Sen University Guangzhou China Department of Software Engineering University of Science and Technology Khyber Pakhtunkhwa Bannu Pakistan Institute of Computer Science and Information Technology The University of Agriculture Khyber Pakhtunkhwa Peshawar Pakistan

Epileptic seizure is one of the most chronic neurological diseases that instantaneously disrupts the lifestyle of affected individuals. Toward developing novel and efficient technology for epileptic seizure management, recent diagnostic approaches have focused on developing machine/deep learning model (ML/DL)-based electroencephalogram (EEG) methods. Importantly, EEG's noninvasiveness and ability to offer repeated patterns of epileptic-related electrophysiological information have motivated the development of varied ML/DL algorithms for epileptic seizure diagnosis in the recent years. However, EEG's low amplitude and nonstationary characteristics make it difficult for existing ML/DL models to achieve a consistent and satisfactory diagnosis outcome, especially in clinical settings, where environmental factors could hardly be avoided. Though several recent works have explored the use of EEG-based ML/DL methods and statistical feature for seizure diagnosis, it is unclear what the advantages and limitations of these works are, which might preclude the advancement of research and development in the field of epileptic seizure diagnosis and appropriate criteria for selecting ML/DL models and statistical feature extraction methods for EEG-based epileptic seizure diagnosis. Therefore, this paper attempts to bridge this research gap by conducting an extensive systematic review on the recent developments of EEG-based ML/DL technologies for epileptic seizure diagnosis. In the review, current development in seizure diagnosis, various statistical feature extraction methods, ML/DL models, their performances, limitations, and core challenges as applied in EEG-based epileptic seizure diagnosis were meticulously reviewed and compared. In addition, proper criteria for selecting appropriate and efficient feature extraction techniques and ML/DL models for epileptic seizure diagnosis were also discussed. Findings from this study will aid researchers in deciding the most efficient ML/DL m

关键词： Electroencephalography

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Deep Credible Metric Learning for Unsupervised Domain Adaptation Person Re-identification 16th

Deep Credible Metric Learning for Unsupervised Domain Adapta...

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16th European Conference on computer Vision, ECCV 2020

作者： Chen, Guangyi Lu, Yuhao Lu, Jiwen Zhou, Jie Department of Automation Tsinghua University Beijing China State Key Lab of Intelligent Technologies and Systems Beijing China Beijing National Research Center for Information Science and Technology Beijing China Tsinghua Shenzhen International Graduate School Tsinghua University Beijing China School of Computer Science Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9783030585976

The trained person re-identification systems fundamentally need to be deployed on different target environments. Learning the cross-domain model has great potential for the scalability of real-world applications. In this paper, we propose a deep credible metric learning (DCML) method for unsupervised domain adaptation person re-identification. Unlike existing methods that directly finetune the model in the target domain with pseudo labels generated by the source pre-trained model, our DCML method adaptively mines credible samples for training to avoid the misleading from noise labels. Specifically, we design two credibility metrics for sample mining including the k-Nearest Neighbor similarity for density evaluation and the prototype similarity for centrality evaluation. As the increasing of the pseudo label credibility, we progressively adjust the sampling strategy in the training process. In addition, we propose an instance margin spreading loss to further increase instance-wise discrimination. Experimental results demonstrate that our DCML method explores credible and valuable training data and improves the performance of unsupervised domain adaptation. © 2020, Springer Nature Switzerland AG.

关键词： Deep learning

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