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

共 15627 条记录，以下是1651-1660 订阅

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E-CVP-MVSNet: Effective Hierarchical Multi-View 3D Reconstruction Based on Light-Weight Network 5

E-CVP-MVSNet: Effective Hierarchical Multi-View 3D Reconstru...

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5th international conference on Robotics, Intelligent control and Artificial Intelligence, RICAI 2023

作者： Zhang, Xin Huang, Hongbo Lu, Lu Yan, Xiaoxu Bao, Zhongman Zhao, Qisheng Gong, Meihui Mechanical Electrical Engineering School Beijing Information Science and Technology University Beijing China Computer School Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9798350357950

Multi-view stereo based on cost volume pyramid has achieved good results in depth inference using high-resolution images. However, most approaches require a lot of computing resources, in part because convolutional layers extract redundant features and pay too much attention to some unwanted feature information. In this work, we propose a pyramid network for feature extraction that is both effective and efficient. There are two main mechanisms in the proposed approach. An effective convolution module called SCConv (Spatial and Channel reconstruction Convolution) to reduce duplicate features and encourage representative feature learning. To encode spatial data and processes high-resolution photos more efficiently, Conv2Former is also introduced into the model to enhance feature extraction capabilities. Experiments show that our method can obviously decrease the parameters, complexity and computational costs, thus improve the performance and generative ability remarkably. © 2023 IEEE.

关键词： Attention mechanism Convolution Depth estimation Feature extraction Multi-view stereo

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An Efficient Detection and Classification of Plant Diseases using Deep Learning Approach

An Efficient Detection and Classification of Plant Diseases ...

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2023 international conference on Evolutionary Algorithms and Soft computing Techniques, EASCT 2023

作者： Jagadeesan, S. Deepakraj, E. Ramalingam, Venkadeshan Venkatachalam, Ilayaraja Vivekanandan, Manojkumar Manjula, R. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India University of Technology and Applied Sciences - Shinas Branch Information Technology Department Oman Vellore Institute of Technology School of Computer Science and Engineeing Vellore India School of Engineering and Applied Sciences Srm University-AP Department of Computer Science & Engineering Andhra Pradesh Amaravati India Srm Institute of Science and Technology College of Engineering and Technology Department of Computing Technology Kattankulathur India

ISBN: (纸本)9798350313413

Crop failure caused by disease-causing pests is an important problem. Farmers struggle with disease management and detection due to inadequate interventions. The goal is to create an automatic system to efficiently identify plant diseases from photos while reducing crop losses and increasing productivity. Machine learning algorithms offer a faster and cheaper alternative to visual inspection by experts. The main goal is to perform image analysis for early diagnosis and effective disease control. The use of CNN architecture for plant disease classification and detection offers a promising solution for plant health monitoring and risk mitigation. Given the threats to crop productivity and global food security, reliable methods for early detection and accurate classification are essential. CNNs enable efficient analysis of vast plant image databases, enabling accurate plant disease identification with speed and accuracy. The multi-layer CNN approach extracts feature and refines the representation, facilitating accurate prediction and accurate diagnosis. Transfer learning methods accelerate system development and allow adaptation to plant disease-specific databases. Combining computer vision algorithms with CNN architecture enables real-time monitoring, early disease detection and targeted intervention, reducing yield losses and improving crop management. This approach uses AI, image analysis, and plant pathology to solve the challenges of sustainable agriculture and plant diseases. System performance is measured by various performance metrics. © 2023 IEEE.

关键词： CNN-Based Approach Error Rate Plant disease Precision ROC

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Multiscale Computational Roughness Analysis of InGaAs Growth Process for Solar Cell Applications

Multiscale Computational Roughness Analysis of InGaAs Growth...

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

作者： C. Rodríguez C. Camas D. Valencia I. Zúñiga H. Vilchis Instituto de Investigación e Innovación en Energías Renovables UNICACH Tuxtla Gutiérrez México Ingeniería en Energía Universidad Politécnica de Chiapas Suchiapa México

ISBN: (数字)9798350377545

ISBN: (纸本)9798350377552

In this work, we analyzed the interactions between indium gallium arsenide (In-Ga-As) atoms via Molecular Dynamics (MD) for the growth of thin films through evaporation methods with applications in tandem solar cells. In addition, film roughness and its influence on device performance were determined using the Silvaco ATLAS software. To optimize the growth conditions, we used the MD method with the open-access code LAMMPS to reproduce the thermodynamic phenomena that occur during deposition. We built different models in agreement with the information found in repositories for the interatomic Tersoff potentials of As, Ga, and In. Subsequently, we obtained the film roughness at different stages of the process and performed an electrical stimulation of the tandem solar cell. As part of the results, we obtained long deposition interactions at different temperatures and density flux. In the electrical simulation using Silvaco Atlas, the rms (root-mean-square roughness) values of film growth for models 1 and 2 revealing that Model 2 achieved an efficiency of 19.84%, higher than the 16.79% efficiency of Model 1.

关键词： Performance evaluation Photovoltaic cells Computational modeling Process control Atoms Indium gallium arsenide Surface roughness Numerical models Rough surfaces Optimization

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Security Issues in Software-Defined Networking (SDN) Environments 23

Security Issues in Software-Defined Networking (SDN) Environ...

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23rd RoEduNet conference: Networking in Education and Research, RoEduNet 2024

作者： Yalda, Khirota Gorgees Hamad, Diyar Jamal Tapus, Nicolae Okumus, Ibrahim Taner Politehnica University of Bucharest Faculty of Automatic Control and Computer Science Bucharest Romania Erbil Polytechnic University Kurdistan Region IT Department Soran Technical College Erbil Iraq Kahramanmaras Sutcu Imam University Kahramanmaras Computer Engineering Faculty of Engineering Turkey

ISBN: (纸本)9798331540388

Software-Defined Networking (SDN) represents a significant shift in network architecture, providing exceptional programmability, flexibility, and simplified management. However, this paradigm shift introduces a unique set of security challenges that must be addressed to fully realize the potential of SDN. This paper examines the security issues in SDN environments, detailing the threats and vulnerabilities at various layers of the SDN architecture, including the control plane, data plane, and application plane. Through an extensive review of current literature, critical security challenges such as controller attacks, data plane breaches, and vulnerabilities in inter-plane communications are identified. Existing security solutions and mitigation strategies, such as authentication and authorization mechanisms, encryption techniques, and intrusion detection systems, are also explored. Furthermore, the paper discusses recent advances and emerging trends in SDN security, offering insights into ongoing research and future directions. The findings underscore the importance of robust security measures in ensuring the reliability and integrity of SDN deployments, providing a foundation for future innovation and development in this dynamic field. © 2024 IEEE.

关键词： Network intrusion

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Industrial Automation Home-Lab (IAH-Lab) for Teaching/Learning During Covid-19 Pandemic 15th

Industrial Automation Home-Lab (IAH-Lab) for Teaching/Learni...

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15th APCA international conference on automatic control and Soft computing (controlO)

作者： Palma, Luis Brito Antunes, Rui Azevedo Gil, Paulo NOVA Sch Sci & Technol Caparica Portugal Polytech Inst Setubal ESTSetubal Setubal Portugal Ctr Technol & Syst Uninova Caparica Portugal

ISBN: (纸本)9783031100475;9783031100468

Covid-19 pandemic has led to temporary disruptions to face-to-face teaching/learning at universities and other institutions, and a general shift to online or hybrid education and training. In this paper, the authors propose a new pedagogic methodology for teaching and learning based on a remote industrial automation laboratory located in the teacher's house, to which it was given the acronym IAH-Lab. The main contributions are the proposed remote teaching/learning methodology, the architectures, and new remote setup (IAH-Lab) mounted for this purpose. Simulations and experimental results elucidate the potential and performance of the proposed remote laboratory (IAH-Lab).

关键词： Industrial automation and control Remote laboratory Distance education Simulation Covid-19 pandemic

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Flow-Level Bandwidth Allocation on P4 Tofino Switch with In-Network DRL Inference

Flow-Level Bandwidth Allocation on P4 Tofino Switch with In-...

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7th international conference on Artificial Intelligence in Information and Communication, ICAIIC 2025

作者： Irfan, Muhammad Hu, Hang Lee, Myung J. Qadeer, Arslan Kim, Yang G. Ahmed, Kazi Nobayashi, Daiki City College of New York Department of Electrical Engineering New YorkNY10031 United States School of Computing & Data Science Wentworth Institute of Technology BostonMA02115 United States NYCCT Department of Computer Engineering Technology BrooklynNY11201 United States NYIT Department of Electrical & Computer Engineering Technology New YorkNY10023 United States Kyushu Institute of Technology Department of Electrical and Electronic Engineering Fukuoka804-8550 Japan

ISBN: (纸本)9798331507022

The Software-Defined Networking (SDN) paradigm has significantly improved network efficiency by integrating machine learning (ML) capabilities into the control plane (CP). This integration allows adaptive management of network resources in response to dynamic traffic conditions. However, the significant geographical distance between the CP and data plane (DP) causes considerable round-trip latency, measured in milliseconds, which poses a major challenge to time-sensitive traffic. To address this challenge, our research proposes a novel in-network Reinforcement Learning (RL) inference framework. This framework extends programmability from the CP to the DP, enabling timely and precise control of network resources to meet the stringent Quality of Service (QoS) requirements of mission-critical applications. The in-network RL inference is implemented using match-action tables within the Protocol Independent Switch Architecture (PISA) framework in the DP and validated through hardware deployment of protocol-independent packet processors (P4). To allocate bandwidth precisely based on QoS requirements, a P4 meter extern is used to differentiate the unique demands of individual traffic flows. Our enhanced deep-deterministic policy gradient (eDDPG)-based RL inference achieves superior performance with minimal processing overhead, reducing latency by 88.7% and jitter by 89.1 % compared to systems without RL inference. © 2025 IEEE.

关键词： Quality of service

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Artificial Neural Network-Based Fast Power Reserve control for Active Power Balancing

Artificial Neural Network-Based Fast Power Reserve Control f...

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international conference of the TC Electrimacs Committee, ELECTRIMACS 2024

作者： Tannous, Antonella Razi, Reza Binot, Ferréol Francois, Bruno Univ. Lille Arts et Metiers Institute of Technology Centrale Lille JUNIA Lille France

ISBN: (纸本)9783031739200

Microgrids play a crucial role in modernizing the power grid by facilitating the integration of renewable energy sources. However, these sources exhibit high intermittency and stochastic behaviour, leading to challenges in effectively managing a microgrid amidst varying load demand and unexpected grid events. To address these uncertainties, local advanced control methods that leverage real-time data and enhanced computing capabilities are required. Frequency droop controllers generate additional power faster than the allocated power reserve to achieve an instantaneous balance of the electrical system. The automatic frequency restoration consists in making other generators participate gradually after 30 seconds. This paper proposes an intelligent control technique designed to enhance the static frequency droop controller, aiming to achieve active power balancing while minimizing CO2 emissions and operating costs. Consequently, an artificial neural network-based adaptive module is developed to anticipate and substitute the diesel-based reserve with a low carbon footprint reserve. This module considers new influencing input factors to anticipate and adjust the power setpoints of a stationary storage unit. The effectiveness of the proposed method is demonstrated on an islanded AC microgrid. The real-time simulation is conducted and validated on Opal-RT simulator, showing improved active power balancing while reducing both costs and CO2 emissions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Microgrids

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Research on Regularization Network Optimization Based on Wavelet Function 8th

Research on Regularization Network Optimization Based on Wav...

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8th international conference on computing, control and Industrial engineering, CCIE 2024

作者： Bai, Hongbao Mao, Weiwei Zhang, Zhiguo Shen, Xiangzhong Pang, Teng The Fifth Department Naval Petty Officer Academy Bengbu China School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9789819769339

In order to solve overfitting of modeling in noisy circumstance, a normal function with corresponding training algorithm is proposed for wavelet networks based on wavelet sampling theory. Since such an algorithm can use sample distributions and errors respectively to train input and output weights, learning efficiencies of wavelet networks are improved greatly. In addition, the related theorems have also been given to demonstrate that the novel cost function can ensure minimization of the approximation error, the theories and experiments show that this novel cost function can ensure generalizations of wavelet networks. Data management module is responsible for data communication in the test process. It can obtain data from external or instrument drive module and transmit data to signal processing module for processing. When completion of the test, the test results are edited and printed through the affiliated function modules. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cost functions

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Large-Format 3D Printing with Hardware and Software Complex 'RoboPrint' 4

Large-Format 3D Printing with Hardware and Software Complex ...

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4th international Youth conference on Radio Electronics, electrical and Power engineering, REEPE 2022

作者： Mazaev, Rodion E. Kulikov, Roman S. Lyovushkin, Vladislav A. Ivanov, Michael G. Danilov, Vladimir A. Vakhitov, Rodion R. National Research University 'MPEI' Radio Technical Systems Department Moscow Russia 3D-InTech Moscow Russia National Research University 'MPEI' Computing Machines Systems and Networks Department Moscow Russia

ISBN: (纸本)9781665414340

This paper presents the RoboPrint software and hardware complex, which implements the approach of creating large parts and tooling from composite thermoplastic materials using 3D printing. The complex can produce parts larger than 10 meters in size for each of the XYZ measurements. This was achieved by abandoning the frame and placing the print heads on freely moving unmanned wheeled robots. In turn, the rejection of the frame became possible thanks to the use of the kinematic scheme of the printer developed by us, and control over the positioning of robots is carried out by the radio navigation system, which is on board each robot. The coordination of the work of the complex is carried out by the swarm control method, and allows the production of a large part by simultaneous printing with a variety of printheads. The use of multiple print heads significantly reduces the production time of the part. The key systems of each printer are equipped with feedback, as well as an automatic quality control system. © 2022 IEEE.

关键词： 3D printers

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Robust Underwater Direction-of-Arrival Tracking with Uncertain Measurement Noise

Robust Underwater Direction-of-Arrival Tracking with Uncerta...

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international conference on Guidance, Navigation and control, ICGNC 2022

作者： Zhang, Boxuan Gao, Guangen Ding, Xiaokun Hou, Xianghao School of Marine Science and Technology Northwestern Polytechnical University Shaanxi Xi’an710072 China AVIC Xi’an Flight Automatic Control Research Institute Xi’an China

ISBN: (纸本)9789811966125

A robust underwater direction-of-arrival (DOA) tracking technique is presented by the paper. Firstly, considering the port and starboard ambiguity problem, the measurement model is founded as a non-linear sound pressure model based on a uniform circular array. Then, considering the unknown underwater environment can sometimes cause uncertain measurement noise, a robust DOA tracking technique based on variational Bayesian (VB) and extended Kalman filter (EKF) method is proposed. By utilizing proposed VB-EKF to estimate uncertain measurement noise online, DOA tracking technique can achieve a reliable tracking accuracy. Finally, the proposed VB-EKF is tested by simulations to verify its robustness and tracking performance. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Extended Kalman filters

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