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3rd International Conference on Emerging Trends in electrical, Control, and Telecommunication engineering, ETECTE 2024

作者： Wahab, Adeel Khan, Umar Shahbaz Nawaz, Tahir Akbar, Hassan Shah, Syed Tayyab Hussain Khan, Zulfiqar Hasan Department of Mechatronics Engg National University of Sciences and Technology IslamabadH-12 Pakistan National Centre of Robotics and Automation National University of Sciences and Technology IslamabadH-12 Pakistan Department of Basic Sciences and Humanities National University of Sciences and Technology IslamabadH-12 Pakistan Faculty of Electrical Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technology Topi Pakistan

ISBN: (纸本)9798331504861

Steady-state visual evoked potential (SSVEP) is a brain response to flickering visual stimuli in the occipital area. SSVEP detection methods usually utilize multichannel configurations, which can be intricate and uncomfortable. Conversely, single-channel configurations are simple and user-friendly, but attaining the necessary accuracy is difficult, requiring the development of a reliable and accurate single-channel technique. For this purpose, this study introduces a hybrid VMD-LGBM approach to single-channel SSVEP detection. The proposed technique utilizes VMD as a preprocessor that decomposes the SSVEP signal into harmonic, intrinsic mode functions (IMFs), and noisy IMFs. To separate harmonic IMFs from noisy IMFs, the crest factor of the cross-correlation function was introduced. Then the restored SSVEP signal is used to extract features using power spectral density analysis (Welch's approach), followed by classification using a Light Gradient Boosting Machine. Our proposed approach performs better than available techniques in terms of classification accuracy and information transfer rate. The proposed single-channel approach provides better efficiency and user-friendliness for practical applications. ©2024 IEEE.

关键词： Harmonic analysis

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Cross-Modality Image Transformation Using Generative Adversarial Network 24th

Cross-Modality Image Transformation Using Generative Adversa...

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24th Conference on Man-Machine-Environment System engineering, MMESE 2024

作者： Liu, Haoting Yu, Shiqi Hou, Qingwen Chen, Shuai Guo, Kuiyuan Wang, Xu Li, Qing Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China CATARC Technology Co. Ltd. Shenzhen518118 China

ISBN: (纸本)9789819771387

A cross-modality image transformation method using the Generative Adversarial Network (GAN) is proposed in this paper. First, the preprocessing methods are performed to the original image data. The computational steps include adjusting image size and suppressing image noises. Second, a kind of GAN is constructed. Both the generator network and discriminator network are created. Third, the GAN is trained and the back propagation optimization is performed continuously. Finally, the trained GAN can be used for data enhancement. To test the effects of computational steps above, the image transformation from the visible light image to other noise-contaminated image is performed. Lots of experiments have illustrated the correctness of our method. © Beijing KeCui Man-Machine-Environment System engineering Technology research Academy 2024.

关键词： Generative adversarial networks

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Generating Realistic VRU-AV Scenarios via Social Force-Based Gradient Optimization 27

Generating Realistic VRU-AV Scenarios via Social Force-Based...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Long, Tingting Zong, Mengru Zhang, Chi Liu, Yuehu Ma, Shuangxun Li, Li National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Shaanxi Xi'an China School of Electrical Engineering Xi' An Jiaotong University Shaanxi Xi'an China School of Future Transportation Changan University Xi'an710064 China BNRist Tsinghua University Department of Automation Beijing100084 China

ISBN: (纸本)9798331505929

Validating safe and reliable interaction with vulnerable road users (VRUs) is imperative for autonomous vehicles, especially in simulation testing procedures before on-road applications. However, existing VRU agents are either with predefined routes or with collision-oriented behaviors, which limits the fidelity and diversity of scenarios. Bridging the gap between the simulated world and reality requires realistic VRU behaviors. In this paper, we apply social force to capture natural human behavior, considering how individuals are influenced by their surroundings and adhere to real-world rules. To this end, we propose HybridSFM, an improved social force model designed for characterizing human-vehicle interacting behavior. Specifically, the steering and acceleration of the VRUs are parameterized and integrated with social norms to forecast trajectories. Based on this, we further implement a gradient optimization framework for generating VRU-AV safety-critical scenarios. Firstly, VRUs are positioned randomly around the ego-agent. Differentiable simulation updates states and unrolls trajectories while backpropagation optimizes the actions of VRUs. The modified scenario is further optimized in the subsequent time step until a collision happens. The experiments demonstrate that our approach can enhance realistic safety-critical scenarios at intersection scenarios, facilitating the testing of interaction capabilities for autonomous vehicles. © 2024 IEEE.

关键词： Autonomous vehicles

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DC FAULT RIDE-THROUGH for WIND POWER INTEGRATION VIA MMC-HVDC BASED on PRECISE CONTROL of ENERGY STORAGE CONFIGURED INSIDE WIND FARM

DC FAULT RIDE-THROUGH for WIND POWER INTEGRATION VIA MMC-HVD...

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Renewable Power Generation and Future Power Systems Conference 2023, RPG 2023 UK

作者： Sun, Yinfeng Guo, Yuhang Fan, Fulin Fan, Jiabin Li, Guoqing Wang, Zhenhao Wu, Xueguang Jilin China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China Department of Electronic and Electrical Engineering University of Strathclyde Glasgow United Kingdom State Grid Smart Grid Research Institute Beijing China Beijing Key Laboratory of DC Power Grid Technology and Simulation Beijing China

ISBN: (纸本)9781839539848

The modular multilevel converter-based high voltage direct current (MMC-HVDC) overhead lines transmission scheme is the preferred option for wind power integration, though it is susceptible to DC faults. To ensure the connection of a wind farm (WF) to the grid during DC faults, this paper proposes a coordinated control strategy for DC fault ride-Through (FRT) based on the energy storage system configured inside the WF for wind power integration through a bipolar MMC-HVDC. In the case of an instantaneous DC fault, the non-fault pole is regulated to operate at full power state to maximise wind power transmission. The exceedance of available wind power outputs over the transmission capacity of the non-fault pole is then accurately allocated to the internal energy storage units (ESUs) of wind generators (WGs) in order to ensure the power balance of the system and reduce the state of charge variance of ESUs as soon as possible. In order to reduce the energy capacities of ESUs required to store the excessive wind power in the event of a permanent DC fault, the method of step-by-step removing WGs is adopted to ensure the system power remaining balanced when the internal ESUs of the disconnected WGs are out of operation. The model is developed and carried out on the PSCAD/EMTDC simulation platform to verify the effectiveness of the proposed control strategies. © The Institution of engineering & Technology 2023.

关键词： Poles

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Dynamic Interaction Analysis of Coupled Axial-Torsional-Lateral Mechanical Vibrations in Rotary Drilling Systems

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Structural Durability & Health Monitoring 2025年第1期19卷 77-103页

作者： Sabrina Meddah Sid Ahmed Tadjer Abdelhakim Idir Kong Fah Tee Mohamed Zinelabidine Doghmane Madjid Kidouche Applied Automation Laboratory Faculty of Hydrocarbons and ChemistryUniversity of BoumerdesBoumerdes35000Algeria Institute of Electrical and Electronic Engineering(IGEE) University of BoumerdesBoumerdes35000Algeria Laboratory of the Electrification of Industrial Enterprises University of BoumerdesBoumerdes35000Algeria Electrical Engineering Department Mohamed Boudiaf University of M’silaM’sila28000Algeria Department of Civil and Environmental Engineering King Fahd University of Petroleum&MineralsDhahran31261Saudi Arabia Interdisciplinary Research Center for Construction and Building Materials King Fahd University of Petroleum&Minerals(KFUPM)Dhahran31261Saudi Arabia

Maintaining the integrity and longevity of structures is essential in many industries,such as aerospace,nuclear,and *** achieve the cost-effectiveness of large-scale systems in petroleum drilling,a strong emphasis on structural durability and monitoring is *** study focuses on the mechanical vibrations that occur in rotary drilling systems,which have a substantial impact on the structural integrity of drilling *** study speciﬁcally investigates axial,torsional,and lateral vibrations,which might lead to negative consequences such as bit-bounce,chaotic whirling,and high-frequency *** events not only hinder the efﬁciency of drilling but also lead to exhaustion and harm to the system’s components since they are difﬁcult to be detected and controlled in real *** study investigates the dynamic interactions of these vibrations,speciﬁcally in their high-frequency modes,usingﬁeld data obtained from measurement while ***ﬁndings have demonstrated the effect of strong coupling between the high-frequency modes of these vibrations on drilling sys-tem *** obtained results highlight the importance of considering the interconnected impacts of these vibrations when designing and implementing robust control ***,integrating these compo-nents can increase the durability of drill bits and drill strings,as well as improve the ability to monitor and detect ***,by exploiting theseﬁndings,the assessment of structural resilience in rotary drilling systems can be ***,the study demonstrates the capacity of structural health monitoring to improve the quality,dependability,and efﬁciency of rotary drilling systems in the petroleum industry.

关键词： Rotary drilling systems mechanical vibrations structural durability dynamic interaction analysis ﬁeld data analysis

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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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BCLH2Pro:A novel computational tools approach for hydrogen production prediction via machine learning in biomass chemical looping processes

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Energy and AI 2024年第4期18卷 13-26页

作者： Thanadol Tuntiwongwat Sippawit Thammawiset Thongchai Rohitatisha Srinophakun Chawalit Ngamcharussrivichai Somboon Sukpancharoen Department of Mechanical Engineering Faculty of EngineeringKhon Kaen UniversityKhon Kaen 40002Thailand Department of Automation Robotics and Intelligent System Faculty of EngineeringKhon Kaen UniversityKhon Kaen 40002Thailand Department of Chemical Engineering Faculty of EngineeringKasetsart UniversityBangkok 10900Thailand Center of Excellence in Catalysis for Bioenergy and Renewable Chemicals(CBRC) Faculty of ScienceChulalongkorn UniversityPathumwanBangkok 10330Thailand Institute of Nanoscience and Nanotechnology(ION2) Universiti Putra MalaysiaSerdang 43400SelangorMalaysia Department of Agricultural Engineering Faculty of EngineeringKhon Kaen UniversityKhon Kaen 40002Thailand Center for Alternative Energy Research and Development Khon Kaen UniversityKhon Kaen 40002Thailand

This study optimizes biomass chemical looping processes(BCLpro),a technique for converting biomass to energy,through machine learning(ML)for sustainable energy *** study proposes an integrated Fe_(2)O_(3)-based,BCLpro combining steam gasification for H2 *** Plus is used as the primary tool to generate extensive datasets covering 24 biomass types with 18 feature inputs in a supervised model.A methodology involving K-Nearest Neighbors(KNN),Extreme Gradient Boosting(XGB),Light Gradient Boosting Machine(LGBM),Support Vector Machine(SVM),Random Forest(RF),and CatBoost(CB)algorithms was employed to predict H_(2)yields in the BCLpro,utilizing 10-fold cross-validation for robust model *** highlight the CB algorithm’s superior performance,achieving up to 98%predictive accuracy,with carbon content,reducer temperature,and Fe_(2)O_(3)/Al_(2)O_(3)mass ratio identified as crucial *** algorithm has been developed into a user-friendly tool,BCLH2Pro,accessible via a web *** tool is designed to assist in reducing costs,optimizing biomass selection,and planning operational conditions to maximize H2 yield in BCLpro *** to the tool can be obtained through the following link:http://***/.

关键词： Artificial intelligence Supervised learning Process simulation Chemical looping Agricultural waste

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Toolpath and Tool Design Innovations in Deformation Machining for Superior AA-7075 T6 Fins 6

Toolpath and Tool Design Innovations in Deformation Machinin...

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6th International Conference on Advancements in Materials and Manufacturing, ICAAMM 2024

作者： Ponnusamy, Prasanth Seenivasan, S. Loganathan, Ganesh Babu Magesh Babu, D. Sivalingam, A. Nanthakumar, S. Girimurugan, R. Department of Mechanical Engineering Tagore Institute of Engineering and Technology Tamilnadu Salem636 112 India Department of Mechanical Engineering Rathinam Technical Campus Tamilnadu Coimbatore641 021 India Department of Robotics and Automation Rajalakshmi Engineering College Tamilnadu Chennai602 105 India Department of Mechatronics Engineering Velammal Institute of Technology Tamilnadu Panjetty601 204 India Department of Mechatronics Engineering Sona College of Technology Tamilnadu Salem636 005 India Department of Mechanical Engineering PSG Institute of Technology and Applied Research Tamilnadu Coimbatore641 062 India Department of Mechanical Engineering Nandha College of Technology Tamilnadu Perundurai638 052 India

A hybrid manufacturing process, deformation machining (DM) combines subtractive manufacturing with incremental forming. Monolithic components with complex profiles are created through the hybridization of manufacturing technologies, which also reduces the wastage of raw materials. The properties of geometry and surface quality of the fin made from the aluminum alloy Al-7075 T6 using the DM process's bending mode was examined in this research, as were the effects of various approaches, toolpath methods, and tool design. To achieve the desired shape for the machined fin, various combinations of arcuate and slanting fin toolpath techniques were explored utilizing both top-down and bottom-up methods. The bottom-up method and the arcuate toolpath strategy were used to investigate various tool profiles. Using the best combination of toolpath strategy and tool profile, named as the T3 tool profile, which has a 0.6 mm nose radius and a circular sector, bottom-to-top (BTT)technique, and arcuate toolpath method, components with better geometrical features and surface roughness (SR) were produced. The analysis of the forming force in the bending approach of the DM progression was further carried out using this optimal combination. © Published under licence by IOP Publishing Ltd.

关键词： Bending (forming)

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Reinforcement Fuzzy Q-Learning Incorporated with Genetic Kinematics Analysis for Self-organizing Holonomic Motion Control of Six-Link Stewart Platforms

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International Journal of Fuzzy Systems 2023年第3期25卷 1239-1255页

作者： Huang, Hsu-Chih Xu, Sendren Sheng-Dong Chen, Yu-Xiang Chen, Chien-Ming Department of Electrical Engineering National Ilan University Yilan260007 Taiwan Graduate Institute of Automation and Control National Taiwan University of Science and Technology Taipei106335 Taiwan Advanced Manufacturing Research Center National Taiwan University of Science and Technology Taipei106335 Taiwan

This paper contributes to the development of reinforcement fuzzy Q-learning incorporated with genetic kinematics analysis for self-organizing holonomic motion control of six-link Stewart platforms. The fuzzy theory is integrated with the Q-learning algorithm to develop a hybrid reinforcement learning strategy. Both the actions and the Q-functions in the classical Q-learning algorithm are inferred by means of fuzzy rules. The proposed hybrid reinforcement learning is then applied to self-organizing holonomic motion control of parallel Stewart platforms for industrial applications. After the kinematics analysis using a geometrical approach and genetic algorithm (GA)-based Newton–Raphson formulation, this study develops a practical control scheme that combines fuzzy Q-learning and proportional–integral–differential (PID) control scheme, called FQPID. In the proposed FQPID holonomic motion control method, the PID control gains are online-tuned via the fuzzy Q-learning computation process. An experimental Stewart platform consisting of six servo motors, adjustable links, and two platforms is constructed to illustrate the effectiveness of the presented methods. The experimental results are compared with some existing control methods using the same evaluation criterion. Through the experimental results and comparative works, the superiority of the proposed FQPID control scheme is presented and discussed. © 2023, The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association.

关键词： Reinforcement learning

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Interactive Learning of Physical Object Properties Through Robot Manipulation and Database of Object Measurements

Interactive Learning of Physical Object Properties Through R...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Andrej Kruzliak Jiri Hartvich Shubhan P. Patni Lukas Rustler Jan Kristof Behrens Fares J. Abu-Dakka Krystian Mikolajczyk Ville Kyrki Matej Hoffmann Department of Cybernetics Faculty of Electrical Engineering Czech Technical University in Prague Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Mechanical Engineering Department Faculty of Engineering New York University Abu Dhabi UAE Department of Electrical and Electronic Engineering Imperial College London London UK Department of Electrical Engineering and Automation Intelligent Robotics Group School of Electrical Engineering Aalto University

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This work presents a framework for automatically extracting physical object properties, such as material composition, mass, volume, and stiffness, through robot manipulation and a database of object measurements. The framework involves exploratory action selection to maximize learning about objects on a table. A Bayesian network models conditional dependencies between object properties, incorporating prior probability distributions and uncertainty associated with measurement actions. The algorithm selects optimal exploratory actions based on expected information gain and updates object properties through Bayesian inference. Experimental evaluation demonstrates effective action selection compared to a baseline and correct termination of the experiments if there is nothing more to be learned. The algorithm proved to behave intelligently when presented with trick objects with material properties in conflict with their appearance. The robot pipeline integrates with a logging module and an online database of objects, containing over 24,000 measurements of 63 objects with different grippers. All code and data are publicly available, facilitating automatic digitization of objects and their physical properties through exploratory manipulations.

关键词： Uncertainty Databases Volume measurement Pipelines Measurement uncertainty Inference algorithms Probability distribution Bayes methods Intelligent robots Material properties

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