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International Conference on Advanced Cloud and Big Data (CBD)

作者： Yongkang Li Xiaodong Chen Xiaohui Wan Jin Sun Peng Zheng Yunchang Wang Zebin Wu School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Department of Industry Development Quzhou Transportation Bureau Quzhou China Department of Quality Control and Testing Chang shan Inspection and Testing institute Quzhou China

ISBN: (数字)9798331511074

ISBN: (纸本)9798331511081

K-SVD (K-Singular Value Decomposition)is a com-monly used dictionary learning method that progressively opti-mizes dictionaries to better represent data by iterating sparse coding and dictionary update steps. However, with the increasing data size, the traditional K -SVD method faces significant challenges in terms of computational efficiency. Therefore, this paper proposes an asynchronous distributed parallel K-SVD method based on Ray framework to cope with the computational efficiency problem in large-scale data processing. This method achieves efficient parallel computation by decomposing the sparse coding and dictionary updating steps of the K-SVD method into multiple independent tasks and utilizing the distributed scheduling capability of the Ray framework. Since the zero elements of the coefficient matrix are unevenly distributed, we introduce a stride partitioning approach when performing the decomposition. In addition, in order to avoid the method falling into local optima and failing to find global optima due to parallelism, this paper synchronizes with the global at the same time as the atomic update of the dictionary. Meanwhile, for the purpose of avoiding the overhead caused by data synchronization, a parameter server is introduced to realize asynchronous updating of dictionary and coefficient matrix to reduce the overhead caused by communication. The experimental results show that, compared with the traditional K-SVD method, the method proposed in this paper has a significant advantage in computational efficiency and is able to efficiently process hyperspectral image datasets under the premise of guaranteeing the sparse representation accuracy. Compared with the synchronous parallel K-SVD method, the proposed method has faster error convergence and does not fall into local optima.

关键词： Dictionaries Image coding Parallel processing Data processing Computational efficiency Sparse matrices Synchronization Servers Hyperspectral imaging Convergence

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Real Time Nanoscale Cleaning Phenomenon Observation During Enforcing MHz Wave By Evanescent Field 241

Real Time Nanoscale Cleaning Phenomenon Observation During E...

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241st ECST Meeting

作者： Terayama, Yutaka Khajornrungruang, Panart Suzuki, Keisuke Mori, Ryotaro Hamada, Satomi Wada, Yutaka Hiyama, Hirokuni Mechanical Science and Technology Division Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka iizuka820-8502 Japan Mechanical Science and Technology Division Department of Intelligent and Control Systems Ebara Corporation Ltd. Kanagawa fujisawa144-8510 Japan

ISBN: (纸本)9781607685395

Post-CMP Cleaning phenomenon is considered as the detachment of the nanoparticle from the substrate surface to be cleaned, and the occasional reattachment of the nanoparticle to surface in nanoscale. However, residual contamination on the substrate has been usually inspected only after the cleaning process. Therefore, the cleaning mechanism of nanoparticles has not already clearly known. Hence, we have established dynamically observation of cleaning phenomena near the surface by applying the evanescent light which occurred a few hundred nm from the surface. In this article, detachments of φ30, 50, 70 nm silica particles and the acoustic cavitation effect were observed during enforcing 1.1 MHz waves in 60 frames per second. The main phenomenon was described that some of each silica nanoparticles were randomly intermittently detached from the surface when the cavitation effect repeatedly irregularly occurs and decays near the surface or remained on the surface during enforcing MHz wave. © 2022 ECS - The Electrochemical Society.

关键词： Silica nanoparticles

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Dubins Vehicle Intercept of a Brownian Target on a Sphere

Dubins Vehicle Intercept of a Brownian Target on a Sphere

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IEEE Conference on Decision and control

作者： Dejan Milutinović David W. Casbeer Department of Electrical and Computer Engineering University of California Santa Cruz CA USA Control Science Center Air Force Research Laboratory Wright-Patterson AFB OH USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper focuses on a navigation of a Dubins vehicle (DV) to intercept a moving target on a sphere. The uncertainty in the target motion is described by a Brownian motion model. An Itô-type stochastic differential equation for the relative motion between DV and the Brownian moving target is derived. This stochastic differential equation is used to formulate an intercept controller as a minimum time optimal controller. The properties of the controller are incorporated into the numerical computations of the controller, which by its construction can intercept a Brownian target. Our numerical simulation results show that the computed controller can intercept both stationary and moving targets on the sphere.

关键词： Space vehicles Uncertainty Planets Space debris Stochastic processes Optimal control Differential equations Satellite navigation systems Numerical simulation Numerical models

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Efficient Photothermal Catalytic Oxidation Enabled by Three-Dimensional Nanochannel Substrates

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Environmental science and Technology 2024年第11期58卷 5153-5161页

作者： Li, Yifei Zhang, Qianpeng Chong, Yanan Huang, Wei-Hsiang Chen, Chi-Liang Jin, Xiaojing Chen, Guangxu Fan, Zhiyong Qiu, Yongcai Ye, Daiqi School of Environment and Energy State Key Laboratory of Luminescent Materials and Devices Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control South China University of Technology Guangdong Guangzhou510000 China State Key Laboratory of Photovoltaic Science and Technology Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception Institute of Optoelectronics Fudan University Shanghai200433 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon999077 Hong Kong State Key Laboratory of Advanced Displays and Optoelectronics Technologies The Hong Kong University of Science and Technology Clear Water Bay Kowloon999077 Hong Kong National Synchrotron Radiation Research Center 101 Hsin-Ann Road Hsinchu Science Park Hsinchu30076 Taiwan Taipei10607 Taiwan College of Light Chemical Industry and Materials Engineering Shunde Polytechnic Foshan528333 China

Photothermal catalysis exhibits promising prospects to overcome the shortcomings of high-energy consumption of traditional thermal catalysis and the low efficiency of photocatalysis. However, there is still a challenge to develop catalysts with outstanding light absorption capability and photothermal conversion efficiency for the degradation of atmospheric pollutants. Herein, we introduced the Co3O4 layer and Pt nanoclusters into the three-dimensional (3D) porous membrane through the atomic layer deposition (ALD) technique, leading to a Pt/Co3O4/AAO monolithic catalyst. The 3D ordered nanochannel structure can significantly enhance the solar absorption capacity through the light-trapping effect. Therefore, the embedded Pt/Co3O4 catalyst can be rapidly heated and the O2 adsorbed on the Pt clusters can be activated to generate sufficient O2- species, exhibiting outstanding activity for the diverse VOCs (toluene, acetone, and formaldehyde) degradation. Optical characterization and simulation calculation confirmed that Pt/Co3O4/AAO exhibited state-of-the-art light absorption and a notable localized surface plasmon resonance (LSPR) effect. In situ diffuse reflectance infrared Fourier transform spectrometry (in situ DRIFTS) studies demonstrated that light irradiation can accelerate the conversion of intermediates during toluene and acetone oxidation, thereby inhibiting byproduct accumulation. Our finding extends the application of AAO’s optical properties in photothermal catalytic degradation of air pollutants. © 2024 American Chemical Society.

关键词： Catalysis

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Efficacy of Local Cache for Performance Improvement of Reliable Data Transmission in Aggregated Robot Processing Architecture

Efficacy of Local Cache for Performance Improvement of Relia...

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International Conference on control, Automation and Systems ( ICCAS)

作者： Abdul Jalil Jun Kobayashi Department of Computer Science and Systems Engineering Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan Department of Intelligent and Control Systems Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan

This study aims to improve the performance of message data transmission in Aggregated Robot Processing (ARP) architecture where the RELIABLE option of the RELIABILITY QoS (Quality of Service) policy is applied to a ROS 2 (Robot Operating System 2) node communication. Basically, the RELIABLE option guarantees that a publisher properly send all message data to a subscriber. However, the publisher could fail to transmit some message data to the subscriber even in the RELIABLE QoS option unless the buffer size of the publisher is enough. We introduce local cache to a sensing component to alleviate the issue in the case that the sensors output the same value in a row. The experimental results showed that the local cache improved the latency and reduced the message loss in node communication.

关键词： Performance evaluation Operating systems Process control Quality of service Robot sensing systems control systems Sensors

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A Feature Extraction and Analysis Method for Battery Health Monitoring

A Feature Extraction and Analysis Method for Battery Health ...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Jilun Tian Jiusi Zhang Hao Luo Congsheng Huang Mo-Yuen Chow Yuchen Jiang Shen Yin Department of Control Science and Engineering Harbin Institute of Technology Harbin China Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan R.O.C Department of Electrical and Computer Engineering North Carolina State University Raleigh NC U.S.A Faculty of Engineering Norwegian University of Science and Technology Trondheim Norway

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

Batteries are widely applied in industrial systems, promoting industrial production efficiency. The system prognosis needs to establish a battery health monitoring model and maintain regular maintenance, improving reliability and safety while reducing unnecessary economic losses. An accurate data-driven state-of-health (SOH) estimation method is crucial, as it mainly relies on the information related to battery SOH values in the extracted features. Therefore, we propose a statistical-based feature extraction method, which makes it physically meaningful and effective. To explore more information, we propose a feature analysis framework to analyze battery features, including visualization, correlation, and importance analysis. We utilize a random forest regression model as an effective learner and validate it based on NASA’s real-world dataset. The feature analysis contents are beneficial for understanding the role and importance of features in SOH estimation tasks, which can also provide an extended scenario for feature extraction of batteries.

关键词： Training Visualization Correlation Estimation Voltage Feature extraction Batteries

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SS-DBSCAN: Epsilon Estimation with Stratified Sampling for Density-Based Spatial Clustering of Applications with Noise

SS-DBSCAN: Epsilon Estimation with Stratified Sampling for D...

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Automation, control and Electronics engineering (CACEE), International Conference on

作者： Gloriana Joseph Monko Masaomi Kimura Department of Functional Control Systems Graduate School Shibaura Institute of Technology Tokyo Japan Department of Computer Science and Engineering Shibaura Institute of Technology Tokyo Japan

Clustering algorithms are crucial in uncovering hidden patterns and structures within datasets. Among the density-based clustering algorithms, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) has gained considerable attention for its effectiveness in various applications. However, determining appropriate parameter values for this algorithm remains a challenging task. This paper presents a novel methodology for eps parameter estimation for an improved DBSCAN, namely SS-DBSCAN. The experimental results across nine datasets demonstrate the efficacy of our proposed method in accurately determining clusters with eps value from SS-DBSCAN algorithm. The clusters identified using estimated eps values by SS-DBSCAN align well with the inherent structure of the datasets, yielding better cluster results than the manually set parameters and other methods used for automatic estimations of the eps for DBSCAN. Our approach adapted well to the peculiarities of each dataset, whether dealing with different scales, dimensions, or densities; it proved the versatility and robustness across various datasets, thereby emphasizing its generalizability and potential for broader applications.

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Deep Learning for Personalized Electrocardiogram Diagnosis: A Review

arXiv

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arXiv 2024年

作者： Ding, Cheng Yao, Tianliang Wu, Chenwei Ni, Jianyuan Department of Biomedical Engineering Georgia Institution of Technlogy Atlanta United States Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University No. 1239 Siping Road Shanghai200092 China Department of Biomedical Engineering University of Rochester Rochester United States Department of Information Technology and Computer Science Juniata College Huntingdon United States

The electrocardiogram (ECG) remains a fundamental tool in cardiac diagnostics, yet its interpretation traditionally reliant on the expertise of cardiologists. The emergence of deep learning has heralded a revolutionary era in medical data analysis, particularly in the domain of ECG diagnostics. However, inter-patient variability prohibit the generalibility of ECG-AI model trained on a population dataset, hence degrade the performance of ECG-AI on specific patient or patient group. Many studies have address this challenge using different deep learning technologies. This comprehensive review systematically synthesizes research from a wide range of studies to provide an in-depth examination of cutting-edge deep-learning techniques in personalized ECG diagnosis. The review outlines a rigorous methodology for the selection of pertinent scholarly articles and offers a comprehensive overview of deep learning approaches applied to personalized ECG diagnostics. Moreover, the challenges these methods encounter are investigated, along with future research directions, culminating in insights into how the integration of deep learning can transform personalized ECG diagnosis and enhance cardiac care. By emphasizing both the strengths and limitations of current methodologies, this review underscores the immense potential of deep learning to refine and redefine ECG analysis in clinical practice, paving the way for more accurate, efficient, and personalized cardiac diagnostics. © 2024, CC BY.

关键词： Generative adversarial networks

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Preface: Security and safety in artificial intelligence

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Security and Safety 2024年第4期3卷 4-5页

作者： Hao Zhang Yu-Gang Jiang Claudio Melchiorri Gerhard Rigoll Department of Control Science and Engineering Tongji UniversityShanghai 200092China Shanghai Institute of Intelligent Science and Technology Shanghai 201210China School of Computer Science Fudan UniversityShanghai 200433China Department of Electrical Electronic and Information EngineeringUniversity of BolognaBologna 40136Italy Institute for Human-Machine Communication Technical University of MunichMunich 80539Germany

This special topic centers on cutting-edge advancements in the security and safety of artificial intelligence(AI),with a focus on critical applications across domains such as autonomous systems,federated learning,and network *** rapid evolution of AI algorithms,enabled by advances in hardware and software,has led to transformative applications but also revealed significant vulnerabilities and security risks.

关键词： artificial hardware autonomous

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Tangentially-Magnetized-Slot-PM-Assisted Magnet Saturation Relieving Design for Doubly Salient Reluctance Machine Position Sensorless Drive Electric Propulsion Applications

Tangentially-Magnetized-Slot-PM-Assisted Magnet Saturation R...

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Electrical engineering and control Technologies (CEECT), Conference on

作者： Weiyu Wang Xiaodong Zhang Shuangxia Niu Xing Zhao Jianfei Pan Weinong Fu School of Engineering Great Bay University Dongguan China Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong China Department of Electronic Engineering University of York York United Kingdom College of Mechatronics and Control Engineering Shenzhen University Shenzhen China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

ISBN: (数字)9798331528096

ISBN: (纸本)9798331528102

For doubly salient reluctance machines, the magnetic saturation effect easily occurs, thereby the saturated inductance with complex analytical expression brings difficulties to the inductance modeling procedure during position sensorless control. To tackle this issue, a direct solution from electrical machine design is introduced to regulate magnetic flux distribution. The key is to insert tangentially-magnetized-slot-permanent-magnet into stator slots, thus relieving the magnetic saturation in the stator teeth. Consequently, unsaturated inductance characteristics can be acquired through the magnetic saturation relieving effect, thereby the inductance modeling effort can be significantly decreased, and such design is friendly for doubly salient reluctance machine position sensorless drive application.

关键词： Magnetic flux Inductance Analytical models Windings Stator windings Sensorless control Reluctance machines Magnetic analysis Saturation magnetization Load modeling

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