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IEEE International Conference on Systems, Man and Cybernetics

作者： Behzad Akbari Haibin Zhu Ya-Jun Pan Department of Mechanical Engineering Advanced Control and Mechatronics Laboratory Dalhousie University Halifax NS Canada Department of Computer Science and Mathematics Collaborative Systems Laboratory Nipissing University NorthBay ON Canada

Trust evaluation and trust establishment play crucial roles in the management of trust within a multi-agent system. When it comes to collaboration systems, trust becomes directly linked to the specific roles performed by agents. The Role-Based Collaboration (RBC) methodology serves as a framework for assigning roles that facilitate agent collaboration. Within this context, the behavior of an agent with respect to a role is referred to as a process role. This research paper introduces a role engine that incorporates a trust establishment algorithm aimed at identifying optimal and reliable process roles. In our study, we define trust as a continuous value ranging from 0 to 1. To optimize trustworthy process roles, we have developed a consensus-based Gaussian Process Factor Graph (GPFG) tool. Our simulations and experiments validate the feasibility and efficiency of our proposed approach with autonomous robots in unsignalized intersections and narrow hallways.

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Sign Language Recognition and Translation Method based on VTN

Sign Language Recognition and Translation Method based on VT...

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2021 International Conference on Digital Society and Intelligent Systems, DSInS 2021

作者： Qin, Wuyang Mei, Xue Chen, Yuming Zhang, Qihang Yao, Yanyin Hu, Shi Nanjing Tech University College of Electrical Engineering and Control Science Nanjing China Chizhou Vocational and Technical College Department of Mechatronics and Automotive Chizhou China

ISBN: (纸本)9781665406307

Sign language recognition plays an important role in real-time sign language translation, communication for deaf people, education and human-computer interaction. However, vision-based sign language recognition faces difficulties such as insufficient data, huge network models and poor timeliness. We use VTN (Video Transformer Net) to construct a lightweight sign language translation network. We construct the dataset called CSL_BS (Chinese Sign Language-Bank and Station) and two-way VTN to train isolated sign language and compares it with I3D (Inflated three Dimension). Then I3D and VTN are respectively used as feature extraction modules to extract the features of continuous sign language sequences, which are used as the input of the continuous sign language translation decoding network (seq2seq). Based on CSL-BS, two-way VTN achieves 87.9% accuracy while two-way I3D is 84.2%. And the recognition speed is increased by 46.8%. In respect of continuous sign language translation, the accuracy of VTN_seq2seq is 73.5% while I3D_seq2seq is 71.2%, the recognition speed is 13.91s and 26.54s respectively. © 2021 IEEE.

关键词： Feature extraction

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Interpretable deep temporal neural networks for in-situ monitoring under varying conditions in micro-electrical discharge machining

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Journal of Manufacturing Systems 2025年 81卷 222-237页

作者： Long Ye Cheng Guo Ming Wu Jun Qian Nan Yu Dominiek Reynaerts Institute of Materials and Processes School of Engineering University of Edinburgh Edinburgh EH9 3FB UK Guangdong Provincial Key Laboratory of Micro/Nano Optomechatronics Engineering College of Mechatronics and Control Engineering Shenzhen University Shenzhen 518060 PR China Micro & Precision Engineering Group Department of Mechanical Engineering KU Leuven Leuven 3001 Belgium Members Flanders Make Leuven Belgium Department of Computer Science KU Leuven Leuven 3001 Belgium

Monitoring is critical enabler of digitalization in modern manufacturing, supporting enhanced process control, quality assurance, and real-time decision-making. By integrating data-driven techniques with the powerful capabilities of deep learning, monitoring systems can efficiently extract valuable insights from complex, high-dimensional time-series data. However, traditional data-driven approaches often lack interpretability, limiting their adoption in industrial applications that demand high reliability and accountability. To address this challenge, this paper proposes an interpretable monitoring framework based on a deep temporal neural network (DTNN). Designed with a modular architecture, the DTNN integrates key components for embedding, temporal feature learning and classification, enabling it to effectively capture complex underlying patterns in temporal process data and overcome the limitations of conventional methods. The DTNN’s capabilities are demonstrated in the context of micro-electrical discharge machining (micro-EDM), a prominent non-traditional machining process known for producing intricate and high-precision components. Through a pulse discrimination task utilizing a large dataset of reliable labels, the DTNN achieves superior classification accuracy under varying processing parameters while providing interpretable insights into discharge phenomena. Furthermore, the DTNN monitoring approach is applied to a deep-hole drilling process in micro-EDM, enabling closed-loop control of discharge status and ensuring long-term process stability. The DTNN’s modular design, interpretability and real-time adaptability underscore its potential for advancing data-driven monitoring systems in digital manufacturing.

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Sentiment Classification of Chinese Railway Review Text Based on Multi-Feature Fusion Gated Recurrent Unit

Sentiment Classification of Chinese Railway Review Text Base...

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2021 International Conference on Information Science, Parallel and Distributed Systems, ISPDS 2021

作者： Su, Chenglang Jiang, Lekang Pi, Shaorui Shenzhen University College of Mechatronics and Control Engineering Shenzhen China University of Nottingham Department of Computer Science Nottingham United Kingdom University of Liverpool Department of Electrical Engineering and Electronics Liverpool United Kingdom

ISBN: (纸本)9781665417389

Posting comments through online platforms has become the main channel for many users to express their opinions. Many Chinese private companies have rectified their marketing strategies by collecting online user comments and using them as a reference. However, since many Chinese state-owned enterprises are within the system, they can only rectify and reform according to the plan provided by the government but with little effect. China's railway system is faced with such problems. Traditional sentiment classification methods may encounter some problems when processing short texts. Our research provides a Gated Recurrent Unit (GRU) sentiment classification method based on multi-feature fusion and sentiment classification of user reviews about China's railway system. And through classification, we get 1244 comments with negative emotions and 919 non-emotional comments © 2021 IEEE.

关键词： Railroads

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A Real-Time System for Classification of Pakistan Sign Language using Machine Learning

A Real-Time System for Classification of Pakistan Sign Langu...

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International Conference on Open Source Systems and Technologies (ICOSST)

作者： Muhammad Zia Ur Rahman Muhammad Azeem Akbar Muhammad Usman Muhammad Hurrirah Tahir Shahzaib Muhammad Tanveer Riaz Muhammad Abbas Khan Dept. of Mechanical Mechatronics and Manufacturing Engineering University of Engineering & Technology (UET) Lahore Faisalabad Campus Faisalabad Pakistan Department of Information Technology Lappeenranta University of Technology Lappeenranta Finland Dept. of Mechatronics and Control Engineering University of Engineering & Technology (UET) Lahore Faisalabad Campus Faisalabad Pakistan Department of Electrical Engineering Balochistan university of information technology engineering and management sciences Quetta Pakistan

Sign language is used by the deaf to interact with hearing individuals in the community. Despite the fact that sign language is often used, the general population is unaware of it, and only hearing-impaired people are familiar with its usage. In this article, we have created a system for real-time sign language recognition in order to communicate with folks who are deaf or hard of hearing by using Pakistan Sign Language (PSL) as its sign language. Ten types or indications of PSL are differentiated in this study. Flex sensors in the glove can detect the position of certain fingers. The MPU-6050, a micro-electro-mechanical device is used to capture the unique characteristics of the hand's movement in a 3D space. In this setup, a Raspberry Pi 3B serves as the microcontroller. MPU-6050 and Flex sensors performance are integrated with additional elements including mean, energy, variance, and correlation for classification. In our investigation, we have collected information from over 3,000 movement feature samples from different people. To increase the precision of the proposed methodology, we have incorporated several machine learning classification algorithms.

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Robust Car-Following control of Connected and Autonomous Vehicles: A Stochastic Model Predictive control Approach

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Zhang, Peiyu Tian, Daxin Zhou, Jianshan Chang, Mai Duan, Xuting Zhao, Dezong Cao, Dongpu Leung, Vicor C.M. Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing China James Watt School of Engineering University of Glasgow Glasgow U.K Department of Mechanical and Mechatronics Engineering University of Waterloo Waterloo ON Canada Computer Science and Software Engineering Shenzhen University Shenzhen China

Vehicle platooning has attracted growing attention for its potential to enhance traffic capacity and road safety. This paper proposes an innovative distributed Stochastic Model Predictive control (SMPC) for a vehicle platoon system to enhance the robustness and safety of the vehicles in uncertain traffic environments. In particular, considering the similarity between the acceleration or deceleration behaviour of neighbouring vehicles and the spring-scale properties, we use a two-mass spring system for the first time to construct an uncertain dynamic model of a formation system. In the presence of uncertain perturbations with known distributional attributes (expectation, variance), we propose an objective function in the form of expectation along with probabilistic chance constraints. Subsequently, a state feedback control mechanism is devised accordingly. Under the cumulative probability distribution function of stochastic perturbations, we theoretically derive a computationally tractable equivalent of the SMPC model. Finally, simulation experiments are designed to validate the control performance of the SMPC platoon controllers, along with an analysis of the stability performance under varying probabilities. The experimental findings demonstrate that the model can be efficiently solved in real-time with appropriately chosen prediction horizon lengths, ensuring robust and safe longitudinal vehicle formation control. IEEE

关键词： Random processes

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DECENTRALIZED DIFFUSION-BASED LEARNING UNDER NON-PARAMETRIC LIMITED PRIOR KNOWLEDGE

arXiv

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arXiv 2023年

作者： Wachel, Pawel Kowalczyk, Krzysztof Rojas, Cristian R. Department of Control Systems and Mechatronics Wroclaw University of Science and Technology Wroclaw Poland School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

We study the problem of diffusion-based network learning of a nonlinear phenomenon, m, from local agents’ measurements collected in a noisy environment. For a decentralized network and information spreading merely between directly neighboring nodes, we propose a non-parametric learning algorithm, that avoids raw data exchange and requires only mild a priori knowledge about m. Non-asymptotic estimation error bounds are derived for the proposed method. Its potential applications are illustrated through simulation experiments. Copyright © 2023, The Authors. All rights reserved.

关键词： Electronic data interchange

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Quasi-in-situ observing the rare earth texture evolution in an extruded Mg-Zn-Gd alloy with bimodal microstructure

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Journal of Magnesium and Alloys 2021年第5期9卷 1818-1827页

作者： M.G.Jiang C.Xu H.Yan T.Nakata Z.W.Chen C.S.Lao R.S.Chen S.Kamado E.H.Han College of Mechatronics and Control Engineering Shenzhen University3688 Nanhai AveShenzhen 518060China The Group of Magnesium Alloys and Their Applications Institute of Metal ResearchChinese Academy of Sciences62 Wencui RoadShenyang 110016China School of Materials Science and Engineering Harbin Institute of TechnologyHarbin 150001China Department of Mechanical Engineering Nagaoka University of TechnologyNagaoka 940-2188Japan

The static recrystallization and associated texture evolution were investigated in an extruded Mg-Zn-Gd alloy with bimodal microstructure based on a quasi-in-situ electron back-scatter diffraction(EBSD)*** typical rare earth(RE)texture formed during annealing,evolving from the bimodal microstructure with[1010]basal fiber texture that consisted of fine recrystallized(RXed)grains and coarse unrecrystallized(un RXed)grains elongated along the extrusion *** both RXed and un RXed regions,the RXed nucleation produced randomized orientations without preferred selection and the RXed grains with RE texture orientation had more intensive growth ability than those with basal fiber orientation,thereby leading to the preferred selection of RE texture orientation during grain *** relationships between stored strain energy,solute drag,grain growth and texture evolution are discussed in *** study provided direct evidence of the RE texture evolution in an extruded Mg-RE alloy,which assists in understanding the formation mechanisms for RE texture during extrusion and better developing wrought Mg alloys with improved formability.

关键词： Magnesium alloy Rare earth texture Extrusion Annealing Recrystallization

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Aircraft wing vibration parameters measurement system using MEMS IMUs and closed-loop optimal correction

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Aerospace Systems 2022年第3期5卷 473-480页

作者： Maamo, M.S. Afonin, A.A. Sulakov, A.S. Department of Control Systems Informatics and Power Engineering Moscow Aviation Institute (National Research University) Volokolamskoe shosse 4 Moscow 125993 Russian Federation Faculty of Robotics and Mechatronics Moscow University for Industry and Finance «Synergy» Street Meshchanskaya 9/14 Moscow 129090 Russian Federation

Nowadays, there is still a need for the development of a high-precision measurement system for aircraft structural elements vibrations, this is due to the fact that many aviation problems are associated with analyzing vibration characteristics of aircraft mechanical structures during its main modes of operation, including flight, particularly the airplane wing health monitoring. This paper presents preliminary results of research carried out toward building a promising system designed to measure vibration parameters of airplane wing. Comparing it with the existing analogues systems, the proposed system features the use of approaches that are traditional for solving orientation and navigation problems for vibration measurements. The article discusses the principles of building such system based on micromechanical inertial measurements units, displacement sensors, on-board aircraft navigation system and sensor data fusion technology. The paper provides a brief overview of the existing solutions in this field of research, and substantiates the relevance and feasibility of the proposed technical solution. The paper presents the basic hardware components of the proposed system, presents the fundamentals of its operation based on the data collected from many sources, including displacement sensors, inertial units and the optimal Kalman estimation and correction algorithm. Besides, the paper presents the mathematical errors models of the system main components. The main algorithms of the system are shown, including algorithms for inertial measurements, estimation and correction in a closed-loop scheme for including optimal Kalman filter, and indirect calculations of vibration parameters. Finally, the initial simulation results of system operation are shown, demonstrating its operability and expected accuracy characteristics, which confirms the system effectiveness and the prospects of the chosen direction of research. © 2022, Shanghai Jiao Tong University.

关键词： Airplane wing Closed-loop optimal correction MEMS IMU Navigation and orientation Optimal Kalman filter Sensor data fusion Vibration measurement

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Dual-Purpose dc-dc/ac PWM Modular Power Converter as Grid-Forming Unit in a Droop-controlled ac Nanogrid

Dual-Purpose dc-dc/ac PWM Modular Power Converter as Grid-Fo...

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Annual Conference of Industrial Electronics Society

作者： Carlos Roncero-Clemente Javier-Gutiérrez Escalona Oleksandr Matiushkin V. Fernão Pires María Isabel Milanés-Montero Enrique Romero-Cadaval Electrical Electronic and Control Engineering Department University of Extremadura Spain Badajoz Spain Department of Electrical Power Engineering and Mechatronics Tallin University of Technology Tallinn Estonia DEE Sustain. RD ESTSetúbal Instituto Politécnico Setúbal INESC-ID Lisboa

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

This paper presents the design and implementation of a grid-forming control for a dual-purpose dc-dc/ac PWM modular power converter tailored for three-phase three-wire ac conversion in nanogrid applications. The study emphasizes the integration of grid-forming and droop control mechanisms to ensure system stability and controlled power sharing. Extensive simulations and analyses demonstrate the effectiveness of the proposed control strategy, yielding swift and precise responses to dynamic power demand changes. The research offers valuable insights into the operation and functionality of this power converter topology within ac nanogrids, contributing to enhanced grid flexibility and reliability in renewable energy systems.

关键词： Renewable energy sources Analytical models Power demand Pulse width modulation Power system stability control systems Stability analysis Topology Reliability Grid forming

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