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检索条件"任意字段=International Conference on Electrical Engineering Computing Science and Automatic Control"

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Metaheuristic Algorithm Implementation for PV Array Reconfiguration under Realistic Moving Cloud Condition

Metaheuristic Algorithm Implementation for PV Array Reconfig...

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2023 IEEE international conference on Device Intelligence, computing and Communication Technologies, DICCT 2023

作者： Kapur, Mridul Mahal, Agamdeep Singh Kumar, Priyanshu Pathak, Diwaker Manisha Gaur, Prerna Netaji Subhas University of Technology Formerly Nsit University of Delhi Instrumentation and Control Engineering Department New Delhi110078 India Teerthanker Mahaveer University Faculty of Engineering Department of Electrical Engineering UP Moradabad244001 India Netaji Subhas University of Technology Instrumentation and Control Engineering Department New Delhi110078 India

ISBN: (纸本)9781665474924

Partial shading imposes a major issue while generating power using solar photovoltaics (SPV) and consequences in the formation of hot spots and manifold peaks in the power-voltage (P-V) curve of the SPV arrays. As a result, a main deforming concern arises i.e., The power loss due to mismatch in the SPV system is dependent on and directly caused by the shading pattern. Mitigation of this MPL can be assessed by selecting a proper configuration of the SPV array and optimal shading dispersion over the complete SPV array. This paper focuses on the implementation of renowned metaheuristic algorithms (MAs) to optimally disperse the shading effect for a partially shaded total-cross-tied (TCT) SPV array configuration. A partial shading effect is generated using a realistic moving cloud model by incorporating various affecting environmental parameters. Furthermore, MAs are tested for the designed moving cloud model and a comprehensive analysis is presented among the various SPV array configurations. © 2023 IEEE.

关键词： Solar power generation

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The Smart and Secured Communication Using Applied Cryptography Algorithms 5th

The Smart and Secured Communication Using Applied Cryptograp...

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5th international conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Agarwal, Trapty Jebakumari, S. Adlin Hemlata Ayesha, Amreen Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Information Technology Jain University Karnataka Bangalore India Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

This paper describes the implementation of a at ease communication device the usage of an implemented cryptography algorithm. The gadget is designed to provide comfy statistics and voice communique between endpoints using techniques consisting of encryption and digital signature. The applied cryptography set of rules includes primary components, the symmetric and the uneven algorithms. The symmetric algorithms, along with AES and DES, are used for statistics encryption, even as the uneven algorithms, which include RSA and DSA, are used for virtual signature. The proposed gadget additionally focuses on security of physical places to defend the important thing exchange by means of the use of physical access control measures which include biometric identification. The system additionally employs advanced safety strategies together with message authentication codes and protection protocols like SSL/TLS. The device is examined for robustness and safety using real communique experiments. The results confirm that the proposed machine is cozy and green in supplying comfortable communication among endpoints. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Encryption algorithms

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Gaze Direction Detection Using Dilated Convolutional Neural Network and Transfer Learning

Gaze Direction Detection Using Dilated Convolutional Neural ...

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2024 international automatic control conference, CACS 2024

作者： Huang, Yen-Hsun Lin, Huei-Yung Kuo, Ping-Huan Fan, Yu-Hsiang National Chung Cheng University Department of Electrical Engineering Chiayi621 Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106 Taiwan National Chung Cheng University Department of Mechanical Engineering Chiayi621 Taiwan

ISBN: (纸本)9798350354904

In recent years, many eye-tracking related products are developed, focusing on eye movement and gaze tracking. The former mostly uses more expensive wearable or invasive devices, and the latter sometimes needs to utilize 3D feature points of the head, making data collection more cumbersome. Methods using pure RGB images as input are less common and often require a large amount of data. The direction of gaze is affected by many factors, including head posture as people tend to turn their heads in the direction they are looking. This paper proposes a two-stage gaze direction detection network which consists of the prediction of both head posture and gaze direction. The detection of head and eyes are first conducted, followed by the head posture estimation. We expand the receptive field to acquire more detailed pose information by employing the dilated convolution to HOPENet. For gaze direction prediction, dilated convolutional VGG16 is adopted for feature extraction. The head's facing angle is then incorporated and passed through three fully connected layers to detect the gaze points of the eyes in the images. Due to the lack of public data for evaluation, in this work we create a new image dataset which utilizes an IMU to record the head posture and a projector-camera system to identify the gaze direction. Transfer learning is also employed to compensate for the limited dataset. The experiments using real-scene images have demonstrated the effectiveness of the proposed gaze direction detection technique. © 2024 IEEE.

关键词： Convolutional neural networks

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Tuning, control and Path Planning of a Spherical Robot using Stochastic Signals 18

Tuning, Control and Path Planning of a Spherical Robot using...

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18th international conference on electrical engineering, computing science and automatic control (CCE)

作者： Sanchez-Solar, Sergio-Daniel Rodriguez-Gomez, Gustavo Munoz-Melendez, Angelica Martinez-Carranza, Jose Inst Nacl Astrofis Opt & Elect INAOE Comp Sci Dept Luis Enrique Erro 1 Tonantzintla 72840 Puebla Mexico

ISBN: (纸本)9781665400299

Mobile robots have been deployed in civilian applications. From this family of robots, there is one with interesting properties: the spherical robot. controlling this type of robot is challenging;even if non-holonomic, it could navigate uneven terrains where other wheeled robots would get stuck. Given that their components are embedded inside the body of the robot, they can be deployed in hostile environments. Motivated by the latter, the design and implementation of a PID controller for a spherical robot with two degrees of control is presented, namely, lateral and longitudinal motion. Our controller uses stochastical signals to tune and control lateral and longitudinal motion in a robust manner. Our approach has been tested in path planning tasks, moving from one point to another, avoiding obstacles on the way. We use semi-circle trajectories to resolve the path planning and present results in simulations to demonstrate the effectiveness of our approach.

关键词： spherical robot Euler-Langrage equations nonholonomics robots path planning stochastic signals PID controller

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Multiresource allocation in dynamic MEC systems: a continual learning-based approach 3

Multiresource allocation in dynamic MEC systems: a continual...

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3rd international conference on Electronic Information engineering, Big Data, and Computer Technology, EIBDCT 2024

作者： Zhan, Shutao Mao, Jin Chu, Zhipeng Xiong, Ke Fan, Pingyi Letaief, Khaled B. Eng. Res. Center of Network Management Technology for High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China National Engineering Research Center of Advanced Network Technologies Beijing Jiaotong University Beijing100044 China Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing100044 China Department of Electronic Engineering Tsinghua University Beijing100084 China Department of Electrical and Computer Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong

ISBN: (纸本)9781510680449

Recently, mobile edge computing (MEC) has been considered as a promising solution to address the challenges arising from the growing computationally intensive applications and limited computation capability of mobile devices. Thus, the resource allocation is crucial for ensuring the system performance of MEC. Recent deep learning-based resource allocation (DLRA) methods achieve preferable performance in real-time. However, in practical scenarios, the number of users often changes, which leads to a decrease in the performance of DLRA methods. Thus, once the number of users changes, the traditional DLRA methods need to retrain their neural networks, which consumes a lot of computing resources. To address this issue, this paper proposes a continual learning-based resource allocation (CLRA) method to jointly optimize multiple resource including offloading decision, power control, time slot assignment and computation resource distribution in MEC system. Our CLRA method is able to avoid forgetting previous knowledge when the number of users in the MEC system changes by selecting a small number of the most representative samples from the past and relearning. Experimental results show that, our proposed CLRA method achieves a better performance than the traditional DLRA method in terms of the higher quality of service's (QoS's) satisfaction rate and the less energy consumption. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Resource allocation

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A Novel Geometric Evaluation of Building Crack Detection and Reporting System Using Improved Learning Strategy

A Novel Geometric Evaluation of Building Crack Detection and...

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2024 international conference on Innovative computing, Intelligent Communication and Smart electrical Systems, ICSES 2024

作者： Govindaraj, R. Mondal, Kalimuddin Bharathi, C.R. Sheela, D. Priya, B.Sudha Saveetha Unviersity Dept of Ece Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India Saveetha University Department of Ece Chennai India Care College of Engineering Department of Civil Engineering Trichy620009 India

ISBN: (纸本)9798331543617

The ability to identify cracks is crucial for determining the safety of building structures during inspections. Consequently, it is crucial to have professionals conduct building inspections on a regular basis to guarantee the dependability and durability of buildings. Traditional building inspection methods have relied on human visual examination with AI approaches that have achieved remarkable achievement in computer vision in the past several years. It is currently impossible to guarantee that the quality of the detection is up to standard and accurate when engineering personnel primarily use simple mechanical monitoring instruments to monitor concrete cracks. Human inspection is bound to be impacted by psychological, physical, and external factors, as well as by emotions. An intelligent system for detecting cracks in building structures may be built using the new approach known as Improved Learning Strategy for Building Crack Detection (ILSBCD), which is a combination of digital image processing and deep learning techniques. In addition, this work suggests a method to improve the known partly entangled states of constructing micro-particles by using the entanglement exchange mechanism that is based on the deep learning methods between two of these particles. Based on computational picture processing techniques and multi-scale evaluation of features, the testing findings demonstrate that the automatic crack detection approach works well. Initiating the device, seeing real-time photos, taking pictures of a crack, measuring its breadth, and evaluating its safety are all done using the system's graphical user interface. In order to improve the image's contrast using binarization techniques, the photos and their findings are saved for reference and then used to execute contrast enhancement. Lastly, the picture is segmented using the Improved Learning Strategy for Building Crack Detection (ILSBCD) method, which allows for more accurate crack detection. In order to det

关键词： Graphical user interfaces

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Mobile Edge computing Issues and Proposed Solution 2

Mobile Edge Computing Issues and Proposed Solution

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2nd IEEE international Maghreb Meeting of the conference on sciences and Techniques of automatic control and Computer engineering, MI-STA 2022

作者： Sharf, Musbah A. Aldawibi, Omar O. Higher Institute for Engineering Technology Computer Technology Dept. Zliten Libya Higher Institute of Engineering Technology Electrical and ElectronicTechnology Dept Zliten Libya

ISBN: (数字)9781665479189

ISBN: (纸本)9781665479189

Due to the rapid growth of Internet-based services, the access to these services has quickly become one of the most critical goals. Mobile Edge computing (MEC) allows content providers, application developers, and information technology services to be performed at the mobile network's edges, rather than at the network's core. This paper demonstrates how to take use of MEC technology and provide excellent performance to public networks in data cache. As a result, response time (latency) will be reduced significantly, network bandwidth will be enhanced, and Quality of Experience (QoE) of end user will improve. © 2022 IEEE.

关键词： Mobile edge computing

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Real-Time Object Detection and Robotic Manipulation for Agriculture Using a YOLO-Based Learning Approach 25

Real-Time Object Detection and Robotic Manipulation for Agri...

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25th IEEE international conference on Industrial Technology, ICIT 2024

作者： Zhao, Hongyu Tang, Zezhi Li, Zhenhong Dong, Yi Si, Yuancheng Lu, Mingyang Panoutsos, George Imperial College The Department Of Physics London United Kingdom University Of Sheffield The Department Of Automatic Control And Systems Engineering SheffieldS1 3JD United Kingdom University Of Manchester The Department Of Electrical And Electronic Engineering Manchester United Kingdom University Of Southampton The Department Of Electronics And Computer Science Southampton United Kingdom Fudan University The Department Of Economics Shanghai200433 China AmesIA50011 United States

ISBN: (纸本)9798350340266

The optimization of crop harvesting processes for commonly cultivated crops is of great importance in the aim of agricultural industrialization. Nowadays, the utilization of machine vision has enabled the automated identification of crops, leading to the enhancement of harvesting efficiency, but challenges still exist. This study presents a new framework that combines two separate architectures of Convolutional Neural Networks (CNNs) in order to simultaneously accomplish the tasks of crop detection and harvesting (robotic manipulation) inside a simulated environment. Crop images in the simulated environment are subjected to random rotations, cropping, brightness, and contrast adjustments to create augmented images for dataset generation. The You Only Look Once(YOLO) algorithmic framework is employed with traditional Rectangular Bounding Boxes (R-Bbox) for crop localization. The proposed method subsequently utilises the acquired image data via a visual geometry group model in order to reveal the grasping positions for the robotic manipulation. © 2024 IEEE.

关键词： Crops

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An Extensive Examination of Methods for Detecting and Classifying Malignant and Benign Skin Cancers Using Deep Learning Techniques 3

An Extensive Examination of Methods for Detecting and Classi...

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3rd IEEE international conference on Distributed computing and electrical Circuits and Electronics, ICDCECE 2024

作者： Pasumarthy, Rambabu Gondi, Lakshmeeswari Gitam School Of Technology Gitam Deemed To Be University Department Of Computer Science And Engineering Andhra Pradesh Vishakhapatnam India

ISBN: (纸本)9798350318609

Skin cancer is a pressing health concern responsible for numerous fatalities in today's world, often diagnosed in its advanced stages, making treatment planning and patient survival challenging. Medical image interpretation, such as MRI as well as CT scans, is crucial for skin cancer diagnosis, but the manual analysis is costly, time-consuming, and can be influenced by biases. DL (Deep learning), a cutting-edge technology, is gaining attention as it allows for the automatic evaluation and interpretation of medical images, reducing the need for human intervention. Deep learning approaches have demonstrated remarkable outcomes in various domains by automatically extracting intrinsic image features, excluding the need for manual feature extraction. This review delves into the application of DL approaches in the diagnosis & detection of cancer, with a particular focus on skin cancer. Detecting melanoma from samples of dermoscopic skin is a formidable task, but employing a machine vision tool, like a deep learning approach, can address some of these challenges. The study introduces an automated melanoma classifier on the basis of DCNN (Deep Convolutional Neural Network) to accurately differentiate malignant from benign melanoma. It also outlines the various deep learning models and steps involved in utilizing these models for skin cancer detection. Recent advancements in the skin cancer detection Deep Learning (DL) methods are explored and summarized, shedding light on the critical challenges that have been faced in accurately detecting the cancer of skin in its early stages. For this research, dermoscopic images from the international Skin Imaging Collaboration datastores (ISIC 2016, ISIC 2017, and ISIC 2020), containing diverse cancer samples, were obtained and evaluated based on metrics like precision, accuracy, specificity, recall, and F1-score. © 2024 IEEE.

关键词： Convolutional neural networks

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Energy Sustainability: Using Particle Swarm Optimization to Evaluate Hydrogen Exchange Membrane Fuel Cells in altering instances

Energy Sustainability: Using Particle Swarm Optimization to ...

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electrical engineering and automatic control (ICEEAC), international conference on

作者： Assala Bouguerra Abd Essalam Badoud Saad Mekheilef Badreddine Kanouni Department of Electrical Engineering Automatic Laboratory of Setif University of Setif 1 Setif Algeria School of Science Computing and Engineering Technologies Swinburne University of Technology Hawthorn VIC Australia

ISBN: (数字)9798350349740

ISBN: (纸本)9798350349757

In the race for renewable energy, Protons-Exchange Membrane Fuel Cells (PEMFCs) stand out as an intriguing rival due to their low environmental footprint and superior energy conversion efficiency. However, enhancing the efficiency of power tracking in PEMFCs remains challenging, especially when dealing with unpredictable operational factors like changes in pressure or temperature. This research investigates the potential to identify the optimal operating conditions for hydrogen exchange membranes in fuel cells utilizing PSO or, (Particle Swarm Optimization). For the purpose of learning more about the complex relationship between temperature, pressure, and output tracking efficiency. Thorough evaluations demonstrate that PSO enhances power tracking performance in many settings, resulting in more efficient and reliable energy systems using PEMFCs.

关键词： electrical engineering Renewable energy sources Fault tolerance Temperature Hydrogen Fuel cells Particle swarm optimization

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