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arXiv 2023年

作者： Cao, Yuxue Zhao, Wenbo Wang, Shengjie Zheng, Xiang Ma, Wenke Wang, Zhaolei Zhang, Tao Beijing Institute of Control Engineering China Department of Automation Tsinghua University China Institute for Interdisciplinary Information Sciences Tsinghua University China Department of Computer Science City University of Hong Kong Hong Kong Qian Xuesen Laboratory of Space Technology China Beijing Aerospace Automatic Control Institute China

Symmetric bi-manual manipulation is an essential skill in on-orbit operationsdue to its potent load capacity. Previous works have applied compliant controlto maintain the stability of manipulations. However, traditional methods haveviewed motion planning and compliant control as two separate modules, which canlead to conflicts with the simultaneous change of the desired trajectory andimpedance parameters in the presence of external forces and ***, the joint usage of these two modules requires experts to manuallyadjust parameters. To achieve high efficiency while enhancing adaptability, wepropose a novel Learning-based Adaptive Compliance algorithm (LAC) thatimproves the efficiency and robustness of symmetric bi-manual ***, the algorithm framework integrates desired trajectory generationand impedance-parameter adjustment under a unified framework to mitigatecontradictions and improve efficiency. Second, we introduce a centralizedActor-Critic framework with LSTM networks preprocessing the force states,enhancing the synchronization of bi-manual manipulation. When evaluated indual-arm peg-in-hole assembly experiments, our method outperforms baselinealgorithms in terms of optimality and robustness. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion planning

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KartalOl: a new deep neural network framework based on transfer learning for iris segmentation and localization task—new dataset for iris segmentation

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Iran Journal of computer Science 2023年第4期6卷 307-319页

作者： Khiarak, Jalil Nourmohammadi Oskouei, Amin Golzari Nasab, Samaneh Salehi Jaryani, Farhang Moafinejad, Seyed Naeim Pourmohamad, Rana Amini, Yasin Noshad, Morteza Institute of Control and Computation Engineering Warsaw University of Technology Ul. Nowowiejska 15/19 Warsaw 00-665 Poland Department of Computer Engineering Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran School of Engineering Lorestan University Aleshtar Campus Aleshtar Iran Faculty of Engineering Arak University Arak Iran Department of Physics Shahid Beheshti University Tehran Iran School of Computing Informatics and Decision Systems Engineering Arizona State University AZ United States Faculty of Electrical and Computer Engineering University of Kharazmi Tehran Iran Stanford University Stanford United States

Iris segmentation and localization in unconstrained environments are challenging due to long distances, illumination variations, limited user cooperation, and moving subjects. Some existing methods in the literature have somehow mitigated some of the above-mentioned issues. In this paper, motivated by these weaknesses, we propose a framework that employs a deep neural network-based approach to iris segmentation and localization. The proposed framework is based on a U-Net architecture initialized with a pre-trained MobileNetV2 model. In addition, to better study the detectors in iris recognition scenarios, we have collected 1000 images. The provided dataset (KartalOl) is made publicly available for the research community. In the proposed framework, to have better generalization, we fine-tuned the MobileNetV2 model on the provided data for NIR-ISL 2021 from the CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa and our dataset. Likewise, data augmentation techniques are applied on images. We chose the binarization threshold for the binary masks by iterating over the images in the provided dataset. The proposed framework is trained and tested in CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa, along with the KartalOl dataset. The experimental results highlight that our method surpasses state-of-the-art methods on mobile-based benchmarks. The implementation source code of KartalOl is made publicly available at https://***/Jalilnkh/KartalOl-NIR-ISL2021031301. © The Author(s) 2023.

关键词： Iris segmentation Localization MobileNetV2 model Unconstrained environment

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Region-aware Half Wavelet Attention Network for Mixed Exposure Correction

Region-aware Half Wavelet Attention Network for Mixed Exposu...

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International Conference on Signal Processing Proceedings (ICSP)

作者： Jingwen Xia Chunjie Zhang Gaopeng Zhang Chuankai Liu School of Computer Science and Technology Beijing Jiaotong University Beijing China Key Laboratory of Spacecraft Optical Imaging and Measurement Technology of Xi’an Xi’an Institute of Optics and Precision Mechanics Chinese Academy of Sciences Xi’an China Beijing Aerospace Control Center Beijing China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Images captured under different exposure conditions may contain both over- and under-exposures. Existing methods mainly focus on addressing over- or under-exposure problem in images. In this paper, we introduce an effective Region-aware Half Wavelet Attention Exposure Correction Network (RHECNet). Specifically, we introduce a region-based feature extraction module that effectively converts the region features in the mixed exposure scene into feature space, which is invariant to exposure. Besides, as incorrect exposure inevitably reduces structure information, we introduce an attention restoration block based on wavelet domain features. It combines exposure-invariant features with those extracted from the wavelet domain to restore structural information. RHECNet has competitive performance results on two image exposure Correction datasets in terms of quantitative metrics and visual quality.

关键词： Measurement Visualization Wavelet domain Video sequences Signal processing Feature extraction Image restoration Data mining

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Partial Discharge Classification with Transformer Neural Networks

Partial Discharge Classification with Transformer Neural Net...

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International Conference on Properties and Applications of Properties and Applications of Dielectric Materials

作者： Thepjit Cheypoca Wiboon Promphanich Aung Ye Thway Angelina Phimpissada Hankae Siwakorn Jeenmuang Norasage Pattanadech Instrumentation and Control Engineering Dept. School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Computer Engineering Dept. School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Electrical Engineering Dept. School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Mechatronics Engineering Dept. School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand

ISBN: (数字)9798350374605

ISBN: (纸本)9798350386165

This paper introduces an approach with the Transformer Neural Networks model for partial discharge patterns classification, that consists of corona discharge, internal discharge and surface discharge. The PD measuring circuit suggested in IEC 60270:2000 is used to record Partial discharge signals. Independent parameters such as phase and charge of PD patterns were recorded. The phase value will be encoded into the charge array and Transformer Neural Network is constructed using Positional Embedding and Transformer Encoder Layer. 80% of the recorded data will be used as a training data and 20% recorded data was used for testing of the classification models. Impacts of neuron numbers and network architecture on the PD classification performance will be observed

关键词： Partial discharges Phased arrays Partial discharge measurement Surface discharges Training data Pattern classification Transformers Discharges (electric) Biological neural networks Testing

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Cooperative Task Allocation of Multiple VA Vs Based on Greedy Algorithm 4

Cooperative Task Allocation of Multiple VA Vs Based on Greed...

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4th IEEE International Conference on computer and Communication Engineering technology, CCET 2021

作者： Chen, Yuheng C., Du J., Chen W., Yu Northwestern Poly Technical University Department of Computer Science Xi' an China North Automatic Control Technology Institute Department of Software System Development Taiyuan030000 China

ISBN: (纸本)9781665438902

Multiple VAVs can perform certain large-scale tasks by constructing VA V clusters in practical applications since its excellent performance such as strong operability and composability. However, it is difficult to determine the specific execution of each drone before performing the cluster task in a cluster composed of multiple drones. This paper studies the task assignment problem under the constraint of multi-VA V cooperative flight, and proposes a task planning algorithm based on the shortest flight path or time optimal under the condition of ensuring the cooperative flight between VAVs. The algorithm combines the directed graph in graph theory to represent the cooperative flight constraint relationship between tasks, and adopts the breadth-first search strategy to realize the traversal assignment of tasks. During the assignment process, according to the greedy algorithm, the tasks are locally optimally allocated to obtain the approximate optimal solution. Finally, a simulation experiment is performed on the constructed task scene to verify the effectiveness of the algorithm. © 2021 IEEE.

关键词： Drones

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Data-Driven Adequacy and Security Assessment: Machine Learning Trends for Reliable Smart Energy Networks

Data-Driven Adequacy and Security Assessment: Machine Learni...

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Smart Grid Conference (SGC)

作者： Mohammad Mohsen Hayati Hassan Majidi Hossein Shahinzadeh Mehdi Abapour Mahmud Fotuhi-Firuzabad Gevork B. Gharehpetian Faculty of Electrical and Computer Engineering Energy Systems Research Institute (ESRI) University of Tabriz Tabriz Iran Department of Electrical Engineering Amirkabir University of Technology Tehran Iran Center of Excellence in Power System Management and Control (CEPSMC) Sharif University of Technology Tehran Iran

ISBN: (数字)9798331511333

ISBN: (纸本)9798331511340

Smart energy networks (SENs) face numerous challenges related to security, reliability, operational uncertainties, and increasing penetration of renewable energies (REs), necessitating advanced methodologies for adequacy and security assessment. This study delves into the utilization of machine learning-driven approaches (MLDAs) to quantify reliability and gauge the dependability of future sustainable SENs. By autonomously acquiring knowledge from datasets, MLDAs offer swift and reliable evaluations, surpassing traditional methodologies. The integration of artificial intelligence (AI) in enhancing power system reliability is highlighted, emphasizing the importance of extensive datasets and evolving AI algorithms. Data-driven approaches empower energy network operators to proactively identify and mitigate security risks, enhancing the reliability of critical infrastructure systems. The research underscores the significance of constructing reliable databases, addressing challenges in real-time operations, and ensuring the performance and trustworthiness of machine learning (ML) algorithms. Overall, the study showcases the potential of ML techniques in revolutionizing reliability management and risk assessment in smart energy networks, paving the way for sustainable and reliable power systems.

关键词： Uncertainty Machine learning algorithms Databases Machine learning Power system reliability Smart grids Management Reliability Security Risk management

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Robust control of Feeding Speed for Coal Mine Tunnel Drilling Machines 25

Robust Control of Feeding Speed for Coal Mine Tunnel Drillin...

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25th IEEE International Conference on Industrial technology, ICIT 2024

作者： Liu, Xiao Chen, Luefeng Wu, Min Cao, Weihua Lu, Chengda Pedrycz, Witold School of Automation China University of Geosciences Wuhan China School of Automation The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Department of Computer Engineering Sariyer Istanbul34396 Turkey

Changes in coal seam hardness cause fluctuations in the feed resistance at the drill bit during the drilling process, leading to unstable feeding speed. This paper proposes a robust dynamic output feedback controller to suppress disturbances caused by the variations in coal seam hardness in the feed system. Firstly, an unknown parameter measuring coal seam hardness is introduced, and an uncertain model of the feeding system is established based on the finite element model of the drill string. By designing weighted functions based on industrial field requirements and constructing a generalized plant, the controller achieves loop shaping, reducing the low-frequency impact of coal seam hardness variations on the feed system and suppressing the systems resonance peak. Simulation results demonstrate that the controller effectively suppresses parameter variations and external disturbances caused by changes in coal seam hardness, achieving stable control of the drilling speed. © 2024 IEEE.

关键词： Robust control

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Design and Simulation of Nano Scale FIN FET Using Silvaco TCAD

Design and Simulation of Nano Scale FIN FET Using Silvaco TC...

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2021 International Conference Engineering and Telecommunication, En and T 2021

作者： Saurabh, Raman Tripathi, Pramod Narayan Bhasme, Sarang Balasaheb Nazarov, Alexey Moscow Institute of Physics and Technology Department of Radio Engineering and Cybernetics Moscow Russia Federal Research Center Computer Science and Control of Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9781728183909

We present design and simulation of a single gate Fin-FET structure on the SOI substrate using Silvaco TCAD. A 45 nm channel length Fin-FET structure is compared with conventional MOSFET structure having same channel length. Different structural and process parameters such as length, width, height, dielectric thickness, doping etc has been optimized for designing the Fin-FET. The simulated structure shows the threshold voltage of 0.37643, lower DIBL, higher drain saturation current and improved parameters as compared to the conventional MOSFET structure. © 2021 IEEE.

关键词： Fins (heat exchange)

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Automation 4.0 for Water Level Monitoring System

Automation 4.0 for Water Level Monitoring System

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International Conference on Information technology (INCIT)

作者： Warangkhana Kimpan Attapon Palananda Boonhatai Kruekaew Department of Computer Science School of Science King Mongkut’s Institute of Technology Ladkrabang Bangkok Thailand Department of Intelligent Control Innovation Faculty of Science and Technology Nakhon Pathom Rajabhat University Nakhon Pathom Thailand

This paper proposed the concept of using automation 4.0 for monitoring the water level. The water level warning system specifications are to measure the water level using ultrasonic sensors and measure the amount of rainfall using a weighing rain gauge. The system automatically controls the measurement of the level of the flood using a Programmable Logic controller (PLC) via PROFINET. Then the water level is monitored, and the results will be displayed through HMI technology via Web panel trainer, Node-Red dashboard, and transfer data via PROFICLOUD. Moreover, the warning information will be sent via LINE notification on mobile to people who live near water sources or staff in charge of preventing disasters.

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Modular Verification of SPARCv8 Code

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Journal of computer Science & technology 2020年第6期35卷 1382-1405页

作者： Jun-Peng Zha Xin-Yu Feng Lei Qiao Department of Computer Science and Technology Nanjing UniversityNanjing 210023China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China Beijing Institute of Control Engineering Beijing 100080China

Inline assembly code is common in system software to interact with the underlying hardware platforms. The safety and correctness of the assembly code is crucial to guarantee the safety of the whole system. In this paper, we propose a practical Hoare-style program logic for verifying SPARC (Scalable Processor Architecture) assembly code. The logic supports modular reasoning about the main features of SPARCv8 ISA (instruction set architecture), including delayed control transfers, delayed writes to special registers, and register windows. It also supports relational reasoning for refinement verification. We have applied it to verify that there is a contextual refinement between a context switch routine in SPARCv8 and a switch primitive. The program logic and its soundness proof have been mechanized in Coq.

关键词： Scalable Processor Architecture Version 8(SPARCv8) assembly code verification context switch Coq refinement verification

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