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International Scientific Conference on Power and Electrical engineering of Riga Technical University (RTUCON)

作者： Muhammad Usman Sardar Toomas Vaimann Lauri Kütt Ants Kallaste Bilal Asad Karolina Kudelina Siddique Akbar Department of electrical power engineering and mechatronics Tallinn University of Technology Tallinn Estonia Institute for drives system and power electronics Leibniz University Welfengarten 1 Hannover

The condition monitoring of inverter-fed induction motors plays a crucial role in ensuring their reliable operation and preventing unexpected failures in the industry. This paper presents a preliminary analysis of the condition monitoring of inverter-fed induction motors by investigating key parameters and techniques potentially employed to monitor the operating condition and performance. It focuses on the challenges and complexities of the inverter’s impact parameters, such as highfrequency harmonics, common mode voltages, high voltage gradient (dv/dt) on motor operation, and parasitic capacitance developments. Furthermore, this paper implements the fast Fourier Transform signal processing technique for condition monitoring and data analysis to segregate the fault frequency. For validity, a case study with a laboratory experimental hardware setup is presented to demonstrate the application of the proposed techniques on an inverter-fed induction motor to represent the frequency signatures in a wideband spectrum. This preliminary analysis provides valuable insights into developing comprehensive condition-monitoring strategies for inverter-fed induction motors, aiding in preventing motor failures, minimizing downtime, and optimizing maintenance practices.

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Design of a Novel Safe Approach to Monitor Drivers While Operating Their Cars

Design of a Novel Safe Approach to Monitor Drivers While Ope...

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International Conference on Advances in Biomedical engineering (ICABME)

作者： Aseel Elhawawsha Carla Zeine Roy Abi Zeid Daou Dominik Kalinowski Joseph Khattar Samer Telawi Josef Boercsoek Department of Computer Architecture and System Programming University of Kassel Kassel Germany Mechatronics Eng. Department Saculty of Engineering Université La Sagesse Beirut Lebanon

Road accidents have been known to be one of the leading death causes around the world for a long time. Thus, cars and all kinds of road vehicles form a huge source of danger, and they relate to multiple high risks. There are many factors that can contribute to road traffic accidents such as the health condition of the driver if a medical emergency event happened while driving, drowsiness, or mind-wandering. The proposed system can monitor the behavior of the driver while driving as well as his/her health status. Moreover, it analyzes the monitored data, alerts the driver in a timely manner, and assists him/her in adjusting the driving behavior. Different dangerous and risky situations which can be faced during driving can be captured and avoided by using such a system which leads at the end to increase road traffic safety and reduce vehicle collisions. Notifications are developed in cases of drowsiness, loss of focus, or other concerns that may emerge while driving. However, when the driver's ability to operate the vehicle safely is hampered by a health concern, the implemented system is only able at this stage to alarm the driver itself and the surrounding cars as a first level of alarms.

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Optimal Stabilization of Periodic Orbits

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IFAC-PapersOnLine 2023年第2期56卷 7509-7515页

作者： Fabian Beck Noboru Sakamoto Institute of Robotics and Mechatronics German Aerospace Center (DLR) 82234 Wessling Germany Department of Mechanical Engineering and System Control Nanzan University Yamzato-cho 18 Showa-ku Nagoya 466-8673 Japan

In this contribution the optimal stabilization problem of periodic orbits is studied in a symplectic geometry setting. For this, the stable manifold theory for the point stabilization case is generalized to the case of periodic orbit stabilization. Sufficient conditions for the existence of a normally hyperbolic invariant manifold (NHIM) of the Hamiltonian system are derived. It is shown that the optimal control problem has a solution if the related periodic Riccati equation has a unique periodic solution. For the analysis of the stable and unstable manifold a coordinate transformation is used which is moving along the orbit. As an example, an optimal control problem is considered for a spring-mass oscillator system, which should be stabilized at a certain energy level.

关键词： Hamiltonian Dynamics Nonlinear Control Periodic Orbit Optimal Control

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Reward-Driven Clustered Federated Learning for DRL Based UAV-RIS Networks

Reward-Driven Clustered Federated Learning for DRL Based UAV...

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Recent Advances and Innovations in engineering (ICRAIE)

作者： Yi Jie Wong Mau-Luen Tham Ban-Hoe Kwan Amjad Iqbal Ying Loong Lee Yoong Choon Chang Department of Electrical and Electronic Engineering Universiti Tunku Abdul Rahman Kajang Malaysia Department of Mechatronics and Biomedical Engineering Universiti Tunku Abdul Rahman Kajang Malaysia Department of System and Computer Engineering Carleton University Canada

Deep reinforcement learning (DRL) has empowered numerous reconfigurable intelligent surface (RIS) aided millimeter-wave unmanned aerial vehicle (UAV) communications. However, most existing works often employ single-task DRL, that could only handle one specific environment (or task). This hurdles the large-scale adoption of DRL-based UAV-RIS networks, since the environment has infinite variations. In this paper, we aim to solve diverse UAV-RIS tasks with a minimum number of DRLs required. Specifically, we proposed a reward-driven clustering technique to autonomously cluster all 50 simulated tasks into 10 clusters. For each cluster, we train a multi-task DRL using Federated Learning to handle all tasks in the cluster. Simulation shows that given 50 unique tasks, the proposed method could reduce the total agents required from 50 to 10, which is a 5-fold reduction.

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Attention-Driven Graph Convolutional Networks for Deadline-Constrained Virtual Machine Task Allocation in Edge Computing

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IEEE Transactions on Consumer Electronics 2025年

作者： Ali, Ahmad Ullah, Inam Singh, Sushil Kumar Jiang, Weiwei Alturise, Fahad Bai, Xiaoshan Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Beijing University of Posts and Telecommunications Beijing China Qassim University College of Computer Department of Cybersecurity Buraydah Saudi Arabia

Effective load balancing is critical in contemporary mobile computing environments to ensure optimal resource utilization and adherence to strict deadlines. This study presents a novel framework for deadline-constrained load balancing, termed DCLD-net, which leverages the integration of Graph Convolutional Networks (GCN) and an attention-based Bidirectional Gated Recurrent Unit (BiGRU). The proposed framework utilizes the spatial modeling capabilities of GCNs to capture intricate network topologies, while the temporal dynamics and attention mechanisms of BiGRU enable the identification of critical patterns in traffic flow data. By incorporating key factors such as sticky sessions, load distribution, content-based factors such as videos or file downloads, instance health checks, and other relevant parameters, the DCLD-net model achieves high accuracy in predicting task schedules. These schedules allocate tasks to virtual machines (VMs), enabling dynamic traffic routing through a load balancer that efficiently assigns tasks. Extensive experimental evaluations demonstrate that the proposed attention-based BiGRU-GCN model significantly outperforms traditional methods in both deadline compliance and load balancing efficiency, offering a robust solution to the challenges of modern cloud computing. Experimental results demonstrate that DCLD-net outperforms traditional methods by 27% in deadline compliance and 29.3% in load balancing efficiency. © 1975-2011 IEEE.

关键词： Mobile edge computing

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Design of Seaweed Dryer Using Sugeno's Fuzzy Logic Control Approach

Design of Seaweed Dryer Using Sugeno's Fuzzy Logic Control A...

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Information Technology International Seminar (ITIS)

作者： Weny Findiastuti Firmansyah Adiputra Rullie Annisa Firmansyah Hanafi Samsul Hidayat Department of Industrial Engineering University of Trunojoyo Madura Bangkalan Indonesia Department of Information System University of Trunojoyo Madura Bangkalan Indonesia Department of Mechatronics Engineering University of Trunojoyo Madura Bangkalan Indonesia

This research aims to develop an innovative seaweed dryer with Sugeno Fuzzy Logic Control. In Indonesia, seaweed, especially Eucheuma cottonii, has high economic value, but traditional drying risks reducing its quality. This research focuses on developing an efficient and effective drying mechanism using the fuzzy logic concept. With Sugeno Fuzzy Logic Control, this system can overcome environmental uncertainties such as temperature and humidity, and adapt well. As a result, the quality and efficiency of seaweed drying are significantly improved, potentially providing benefits to the seaweed industry and expanding the understanding of fuzzy logic in industrial control.

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Study on wear mechanism of small ball-end grinding wheel and surface integrity of complex component under high-speed machining condition

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Wear 2025年 570卷

作者： Qin, Biao Liu, Henan Cheng, Jian Tian, Jinchuan Sun, Jiangang Chen, Mingjun School of Mechatronics Engineering Harbin Institute of Technology Heilongjiang Harbin150001 China State Key Laboratory of Robotics and System Harbin Institute of Technology Heilongjiang Harbin150001 China Zhengzhou Research Institute Harbin Institute of Technology Zhengzhou450000 China

The small ball-end fine diamond grinding wheel is well-suited for machining complex components with small curved surface structures, demonstrating high-precision and high-quality machining capabilities. By increasing the speed of the workpiece, the issue of poor grinding performance caused by the small diameter of the ball-end grinding wheel can be effectively overcome. However, this strategy inevitably increases wheel wear and affects surface integrity. To address these challenges, this paper systematically investigates the grinding characteristics of the small ball-end grinding wheel. First, grinding experiments were performed to investigate the influence of workpiece speed, feed rate, and grinding depth on the material removal rate, wheel wear, and surface integrity. Then, the wear mechanism of the grinding wheel was analyzed. The wear modes of the grinding wheel at high workpiece speeds include metal adhesive wear and abrasive grain fracture, which enhance the self-sharpening of the grinding wheel while accelerating its wear. Furthermore, the surface morphology creation mechanism of fused silica under different material removal modes was revealed. Moreover, the correlation between material removal rate and surface roughness caused by wheel wear was revealed. A high-speed grinding strategy was subsequently proposed based on the relationship between the grinding characteristics and grinding parameters of the small ball-end grinding wheel under various grinding conditions. Finally, the feasibility of this grinding strategy was validated through its application to the machining of the complex curved fused silica hemispherical resonator (HSR). Experimental results indicate that under the high workpiece speed condition, the best surface roughness of HSR decreases from 90.5 nm to 62.2 nm, while the maximum grinding wheel wear increases from 9.7 μm to 11.5 μm, with minimal impact on surface integrity. Therefore, the high-speed grinding strategy proposed in this study ho

关键词： Fused silica

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Comparison of Biocode Based Machine Learning and Segmentation Model for Automated Prawn Size Prediction for Real Prawn Farm 5

Comparison of Biocode Based Machine Learning and Segmentatio...

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5th Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2024

作者： Zariful, Muhammad Wafiq Abd Tashim, Najeebah Az-Zahra Lim, Tiong Hoo Fakhrurroja, Hanif Chuprat, Suriayati Putra, Seno Adi Liu, Pengcheng Basri, Aida Maryam Universiti Technologi Brunei Electrical and Electronic Engineering Bandar Seri Begawan Brunei National Research and Innovation Agency Research Center for Smart Mechatronics Bandung Indonesia Universiti Teknologi Malaysia Razak Faculty of Technology and Informatics Kuala Lumpur Malaysia Telkom Universiti School of Industrial and System Engineering Bandung Indonesia University of York Department of Computer Science York United Kingdom Universiti Technologi Brunei School of Applied Science and Matematics Bandar Seri Begawan Brunei

ISBN: (纸本)9798350374407

The aquaculture industry has witnessed a significant surge in technological integration over the past decade, leveraging advancements in the Internet of Things (IoT) and Artificial Intelligence (AI) to enhance production efficiency. Monitoring the growth of aquatic cultures, such as fishes and prawns, plays a pivotal role in optimizing production. However, traditional methods, notably in prawn aquaculture in Asia, still heavily rely on stress-inducing techniques like netting and manual measurements. This paper proposes a novel approach using YOLO-based detection algorithm with biocode reference point based on the ArUco design to predict prawn size based on the length and are dimension to analysed the improvement in predicting the size. The propose solution is compared extensively using statistical tests against segmentation model with and without fine-tuning. Initial results show that both YOLOv8 segmentation and YOLOv8 with Segment Anything Model (SAM) give similar results with the actual mean length of 15.7 cm and predicted mean lengths are 20.5 cm and 20.8 cm respectively. However, after fine-tuning the parameter, the accuracy of predicted results improved significantly with 15.8 cm for YOLOv8 segmentation and 16 cm for YOLOv8 with SAM. The mean absolute error of 0.93 cm for YOLOv8 segmentation and 1.10 cm for YOLOv8 with SAM, and the width of 0.48 cm and 0.78 cm respectively. © 2024 IEEE.

关键词： Deep learning

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A Probabilistic Approach based on Combination of Distance Metrics and Distribution Functions for Human Postures Classification

A Probabilistic Approach based on Combination of Distance Me...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Xin He Vibekananda Dutta Teresa Zielinska Takafumi Matsumaru Graduate School of Information Production and System Waseda University Kitakyushu Fukuoka Japan Faculty of Mechatronics and Faculty of Power and Aeronautical Engineering Warsaw University of Technology Warsaw Poland

The article proposes a method for classifying human postures using an improved probabilistic neural network (PNN) with different distance measures and different probabilistic distribution functions (PDF). We found that the PNN with angular distance provides better accuracy, precision, and recall for the postures classification tasks than the PNN with conventional Euclidean distance. The k Nearest Neighbors (kNN) method gives slightly better prediction results than PNN, but our PNN is much faster. Such good computational performance is beneficial for posture recognition tasks that require real-time functions. An example is the needs of cobots or service robots. The article also proposes a method for selecting the distribution smoothing parameter ($\sigma$) using the sub-optimization process based on the improved Gray Wolf optimization (I-GWO) algorithm. It was found that the impact of PDF differences on the quality of the results can be reduced by choosing the best possible $\sigma$. In order to evaluate the developed method, selected human activities were recorded. The datasets were created using two different RGB-D systems located in two different laboratories.

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Design and Control for WLR-3P:A Hydraulic Wheel-Legged Robot

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Cyborg and Bionic systems 2023年第1期4卷 243-259页

作者： Xu Li Haoyang Yu Haibo Feng Songyuan Zhang Yili Fu State Key Laboratory of Robotics and System College of Mechatronics EngineeringHarbin Institute of TechnologyHarbin 150001China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang UniversityHangzhou 310027China Intelligent Robot Research Center Zhejiang LaboratoryHangzhou 311100China

The robot used for disaster rescue or field exploration requires the ability of fast moving on flat road and adaptability on complex *** hybrid wheel-legged robot(WLR-3P,prototype of the third-generation hydraulic wheel-legged robot)has the characteristics of fast and efficient mobility on flat surfaces and high environmental adaptability on rough *** this paper,3 design requirements are proposed to improve the mobility and environmental adaptability of the *** meet these 3 requirements,2 design principles for each requirement are put ***,for light weight and low inertia with high stiffness,3-dimensional printing technology and lightweight material are ***,the integrated hydraulically driven unit is used for high power density and fast response ***,the microhydraulic power unit achieves power autonomy,adopting the hoseless design to strengthen the reliability of the hydraulic *** is more,the control system including hierarchical distributed electrical system and control strategy is *** mobility and adaptability of WLR-3P are demonstrated with a series of ***,the robot can achieve a speed of 13.6 km/h and a jumping height of 0.2 m.

关键词： robot Robot terrain

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