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IEEE International Conference on Industrial Technology (ICIT)

作者： Xiao Liu Luefeng Chen Min Wu Weihua Cao Chengda Lu Witold Pedrycz 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 and the Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Istinye University Sariyer/Istanbul Turkey

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

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.

关键词： Drilling Robust control Uncertainty Robust stability Simulation Coal Resonance

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Traffic Smoothing Controllers for Autonomous Vehicles Using Deep Reinforcement Learning and Real-World Trajectory Data

arXiv

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arXiv 2024年

作者： Lichtlé, Nathan Jang, Kathy Shah, Adit Vinitsky, Eugene Lee, Jonathan W. Bayen, Alexandre M. Department of Electrical Engineering and Computer Sciences UC Berkeley United States CERMICS École des Ponts ParisTech France Department of Mechanical Engineering UC Berkeley United States Institute for Transportation Studies UC Berkeley United States

Designing traffic-smoothing cruise controllers that can be deployed onto autonomous vehicles is a key step towards improving traffic flow, reducing congestion, and enhancing fuel efficiency in mixed autonomy traffic. We bypass the common issue of having to carefully fine-tune a large traffic micro-simulator by leveraging real-world trajectory data from the I-24 highway in Tennessee, replayed in a one-lane simulation. Using standard deep reinforcement learning methods, we train energy-reducing wave-smoothing policies. As an input to the agent, we observe the speed and distance of only the vehicle in front, which are local states readily available on most recent vehicles, as well as non-local observations about the downstream state of the traffic. We show that at a low 4% autonomous vehicle penetration rate, we achieve significant fuel savings of over 15% on trajectories exhibiting many stop-and-go waves. Finally, we analyze the smoothing effect of the controllers and demonstrate robustness to adding lane-changing into the simulation as well as the removal of downstream information. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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EMI Performance Analysis in IRS-Aided Multi-User Wireless Communication Systems

EMI Performance Analysis in IRS-Aided Multi-User Wireless Co...

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Virtual Conference on Communications (VCC), IEEE

作者： Rhana M. Elshishtawy Shimaa S. Ali Adly S. Tag Eldien Mostafa M. Fouda Mohamed I. Ismail Esraa M. Eid Department of Electrical Engineering Faculty of Engineering at Shoubra Benha University Egypt Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA Department of Electronic Engineering & Communication Technology Modern Academy for Engineering Egypt

This paper presents a comprehensive investigation into the effect of Electromagnetic Interference (EMI) on wireless systems employing single and distributed Intelligent Reflecting Surfaces (IRSs) in three distinct conditions by varying the number of antennas, elements, and users. Through extensive numerical simulations, the performance of the wireless systems is rigorously evaluated under different EMI scenarios. Firstly, the influence of varying the number of antennas on the system performance is thoroughly analyzed. The results demonstrate that an increased number of antennas contributes to improved signal reception and reduced interference levels. Moreover, the enhanced beamforming capabilities offered by additional antennas lead to higher signal quality and increased system Achievable Rate (AR). Secondly, the impact of changing the number of elements within the IRS under EMI conditions is investigated in detail. The simulations reveal that an augmented number of elements facilitates efficient EMI mitigation, enabling advanced signal manipulation and more precise signal focusing. As a result, the system exhibits improved performance in terms of signal strength, coverage, and AR. Lastly, the effect of varying the number of users on system performance in the presence of EMI is thoroughly assessed. The findings unveil that an increased number of users under EMI conditions leads to elevated interference levels, degraded signal quality, and diminished system AR. Thus, it is crucial to develop effective EMI mitigation strategies and resource allocation schemes to ensure reliable and efficient communication in IRS-enabled wireless systems.

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Intelligent Energy Management Strategies for Hybrid Electric Transportation

Intelligent Energy Management Strategies for Hybrid Electric...

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Renewable Energy and Hydrogen Technologies (GlobConHT), IEEE IAS Global Conference on

作者： Pankaj Yadav Vikas Vikash Kumar Saini Ameena S. Al-Sumaiti Rajesh Kumar Department of Electrical Engineering Malaviya National Institute of Technology Jaipur India Center for Energy and Environment Malaviya National Institute of Technology Jaipur India Electrical Engineering and Computer Science Advanced Power and Energy Center Khalifa University UAE

Shortage of fossil fuels around the world, oil prices, and environmental concerns like climate change and air pollution are big challenges for the transport sector. Hybrid electric vehicles (HEVs) are an alternative solution to overcome the above challenge. HEVs require an efficient energy management strategy (EMS) to promote consumption efficiency in the different test cycles. The learning-based algorithms are frequently used to control how efficiently HEVs use energy. The tremendous processing intensity, the extensive data training, and the stringent necessity of accurately predicting the future state of operation all work against the maximum use of these systems. In this study, the model-free reinforcement control mechanism is used for the energy management of HEVs. A deep $\mathbf{Q}$ network (DQN) and deep deterministic policy gradient (DDPG) are utilized to the enhance the learning process and reliability of the EMS framework. Since it is a memoryless random process, the Markovian decision process has been used to formulate the issue. Both learning algorithms are compared with different test driving cycles. The simulation results present that the deep DDPG performance is more reliable and fast converges than the deep $\mathbf{Q}$ learning algorithm.

关键词： Training Renewable energy sources Simulation Transportation Reinforcement learning Robustness Real-time systems

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Detection of Anomaly Detection with high dimensional data using volume and velocity aspects 23

Detection of Anomaly Detection with high dimensional data us...

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Ravi Kumar Tata Koushik Chakraborty Rashima Mahajan Deepak Dudeja R Ramkumar Upasana Gupta Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation India Office of the Registrar Adamas University India Department of Computer Science and Engineering Manav Rachna International Institute of Research and Studies India Mulana Maharishi Markandeshwar Engineering College India Department of Electrical and Electronics Engineering_School of Engineering and Technology Dhanalakshmi Srinivasan University India CSE BBDU India

ISBN: (纸本)9798400709418

Abstract: Now a days detection of various anomalies finds all the unexpected events in the computing information over the internet are defined as anomalous events or inconsistency events. While detecting the various inconsistencies will help in getting the useful computing information system. Inconsistency type locations and exception investigation is the way of information mining prepared for recognizing information focuses that drop exterior anomaly. While doing so a standard is set up to anticipated design in a dataset. This location preparation is crucial since peculiarities like these are frequently a marker of bizarre behavior, such as potential false endeavors, security breaches, or cyber security interruptions. Organizations use information mining methods to identify inconsistencies and screen information points inputs and outputs moving over their IT framework and frameworks. On a very basic level, peculiarity discovery makes a difference organization address security spill, dodge information introduction, and set cautions against unordinary behavior that goes astray from the built-up design. This research paper deals with various techniques and ways for several anomalies inside the information. The finding of the research is how to detect the anomalies.

关键词： Anomaly Detection

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Exploring Convolution Neural Networks for Image Classification in Medical Imaging

Exploring Convolution Neural Networks for Image Classificati...

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Intelligent and Innovative Technologies in Computing, electrical and Electronics (IITCEE), International Conference

作者： Vishwa Priya V Padmini Chattu K. Sivasankari Dnyaneshwar Tukaram Pisal Bantupalli Renuka Sai D. Suganthi Department of CS Vels Institute Science Technology and Advanced Studies Chennai Tamilnadu India ACE Engineering College Department of ECE Sri Sai Ram Institute Of Technology Chennai Tamilnadu India SVPMs Institute of Management Malegaon Baramati Pune Maharashtra India Computer Science and Engineering Raghu Engineering College Visakhapatnam Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Thandalam Chennai

Modern healthcare relies on medical imaging to diagnose and cure diseases. Convolution Neural Networks (CNNs) have excelled at image categorization, and their use in medical imaging could change illness diagnosis. This study examines CNN's medical picture categorization performance. This study used X-rays, CT scans, MRIs, and histopathology slides. Traditional CNNs, transfer learning models, and bespoke networks were constructed and trained. The training data included lung ailments, cancer detection, bone fractures, and neurological disorders. Our investigations showed that CNNs can extract complex characteristics from medical photos, improving classification accuracy. Transfer learning, where pre-trained models were fine-tuned on medical data, performed well with 94.8% accuracy. We used cutting-edge data augmentation and attention strategies to improve model generalization and interpretability. In addition to high classification accuracy, we studied model explainability using gradient-based methods and visualization to highlight medical picture regions of relevance that influenced model predictions. Building trust with medical practitioners and understanding deep learning model decision-making requires interpretability. In clinical settings, CNN deployment raises ethical and practical issues such as data privacy, model robustness, and regulatory compliance. According to our research, CNNs may increase medical picture classification accuracy and speed. X-rays had the best accuracy at 95.2%, followed by CT scans at 92.7% and MRIs at 94.1%. Histopathological slides exhibited 88.6% accuracy; however, this shows that CNNs can diagnose diseases from microscopic images.

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Optimizing Reconfigurable Intelligent Surface-Assisted Integrated Sensing and Communication Systems

Optimizing Reconfigurable Intelligent Surface-Assisted Integ...

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Virtual Conference on Communications (VCC), IEEE

作者： Mohamed I. Ismail Abdullah M. Shaheen Mostafa M. Fouda Ahmed S. Alwakeel Department of Electronic Engineering & Communication Technology Modern Academy for Engineering Egypt Department of Electrical and Communication Engineering Suez University Suez Egypt Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA

The integrated sensing and communication (ISAC) system merged with reconfigurable intelligent surfaces (RIS) has recently received much attention. This paper uses different optimization algorithms for a suggested beamforming optimization framework in ISAC systems with RIS. The signal is transmitted by a dual-function base station (BS) that is integrated with the RIS, which performs both communication and sensing functions simultaneously. The ISAC system is designed to optimize the signal transmission through the RIS by maximizing the SNR to increase the overall performance. Moreover, the different optimization algorithms is tested on 28 different numerical benchmark functions related to the IEEE Congress on Evolutionary Computation 2017 (CEC'17) test suite. The effectiveness of the simulation results in solving the CEC'17 test suite is assessed. The simulation results show that the Artificial Ecosystem Optimizer (AEO) improves the SNR of different users for different numbers of RIS elements. The SNR reaches 25 dB when using 200 RIS elements.

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Lane Tracking for Autonomous Road Pavement Inspection with Unmanned Aerial Vehicles

Lane Tracking for Autonomous Road Pavement Inspection with U...

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Applied Computational Intelligence in Information Systems (ACIIS), International Conference on

作者： Melody Mae O. Maluya Earl Ryan M. Aleluya Joel G. Opon Carl John O. Salaan Department of Computer Applications College of Computer Studies Mindanao State University - Iligan Institute of Technology Iligan City Philippines Department of Computer Engineering and Mechatronics College of Engineering Mindanao State University - Iligan Institute of Technology Iligan City Philippines Department of Civil Engineering College of Engineering Mindanao State University - Iligan Institute of Technology Iligan City Philippines Department of Electrical Engineering College of Engineering Mindanao State University - Iligan Institute of Technology Iligan City Philippines

Pavement distress assessment is a critical aspect of road infrastructure maintenance and safety. Traditional methods relying on manual surveys have proven time-consuming, labor-intensive, costly, and prone to human errors. Emerging developments in computer vision and deep learning techniques have opened new avenues for automated pavement distress detection using drones. Autonomous road inspection scheme using UAVs requires intelligent detection and tracking of road lanes. In effect, the navigation of the UAV will not require direct control from the human operator. This paper highlights the efficiency of road lane tracking using the instance segmentation approach of the You Only Look Once (YOLOv8) architecture. It also introduces a dataset of UAV images from complex environments within the highway roads in Northern Mindanao of the Philippines. The trained YOLO model garnered 99.98% precision, 80.12% recall, and 87.21% mean average precision. Its network performance showed a reliable self-correcting navigation mechanism for the autonomous UAV. The proposed approach could aid the UAV in autonomous maneuvering during inspection. Thus, future work includes experimentation with the feedback control system of the UAV where the road lane tracking is one component.

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Self-Immolative Electrochemical Redox Substrates: Emerging Artificial Receptors in Sensing and Biosensing

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ACS Measurement Science Au 2024年第2期4卷 163-183页

作者： Manibalan, Kesavan Arul, Ponnusamy Wu, Hsin-Jay Huang, Sheng-Tung Mani, Veerappan Department of Materials Science and Engineering National Yang Ming Chiao Tung University Hsinchu 30010 Taiwan Institute of Biochemical and Biomedical Engineering Department of Chemical Engineering and Biotechnology National Taipei University of Technology Taipei 10608 Taiwan Advanced Membranes and Porous Materials Center (AMPMC) Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955-6900 Saudi Arabia High-Value Biomaterials Research and Commercialization Center National Taipei University of Technology No. 1 Sec. 3 Zhongxiao E. Rd. Taipei 10608 Taiwan

The development of artificial receptors has great significance in measurement science and technology. The need for a robust version of natural receptors is getting increased attention because the cost of natural receptors is still high along with storage difficulties. Aptamers, imprinted polymers, and nanozymes are some of the matured artificial receptors in analytical chemistry. Recently, a new direction has been discovered by organic chemists, who can synthesize robust, activity-based, self-immolative organic molecules that have artificial receptor properties for the targeted analytes. Specifically designed trigger moieties implant selectivity and sensitivity. These latent electrochemical redox substrates are highly stable, mass-producible, inexpensive, and eco-friendly. Combining redox substrates with the merits of electrochemical techniques is a good opportunity to establish a new direction in artificial receptors. This Review provides an overview of electrochemical redox substrate design, anatomy, benefits, and biosensing potential. A proper understanding of molecular design can lead to the development of a library of novel self-immolative redox molecules that would have huge implications for measurement science and technology. © 2024 The Authors. Published by American Chemical Society.

关键词： alkaline phosphatase artificial receptors electrochemical mediators electrochemical sensors and biosensors galactosidase hydrogen peroxide neuraminidase organic electrochemistry ratiometric sensors self-immolative sensors

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Fault Detection using Opposition-based Learning Orca Predator Algorithm with Random Forest

Fault Detection using Opposition-based Learning Orca Predato...

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Distributed Computing and electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： C. Umarani Mohammed I. Habelalmateen Sujata N Patil Rekha Phadke Srinath Chandramohan Department of Computer Science and Applications Christ Academy Institute for Advanced Studies Bangalore India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Radiation Oncology Department (Pathology Lab) Thomas Jefferson University Philadelphia USA Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

Machinery equipment stability and safety operations heavily depend on the implementation of intelligent fault detection systems. The system enhances production efficiency while decreasing maintenance costs together with its most significant benefit of preventing safety accidents. Intelligent compound fault detection methods require additional research regarding their interpretability features. The detection results prove difficult to understand which impedes their general acceptance for industrial use in the real world. Therefore, this research proposes an Opposition-based Learning Orca Predator Algorithm (OLOPA) with Random Forest (RF) for fault detection. The OLOPA exploration-exploitation control improves worldwide search performance while reducing convergence time thus enhancing both accuracy and stability in feature extraction. High-dimensional data management and strong generalization abilities characterize the RF classifier during its performance. The combined hybrid method provides dependable and interpretable fault detection capabilities which enables its use in complex industrial environments. Furthermore, this research extracts meaningful features from signals through time-domain and frequency domain to enhance the feature detection and signal quality maintenance. The OLOPA-RF achieves 99.91% accuracy for CWRU dataset which is better than state-of-the-art methods.

关键词： Vibrations Accuracy High dimensional data Fault detection Feature extraction Safety Maintenance Random forests Classification tree analysis Convergence

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