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4th International Conference on Materials Chemistry and Environmental engineering, CONF-MCEE 2024

作者： Hu, Xinyu Pei, Haoyu School of Computer and Electronic Information Science Nanjing Normal University Nanjing210046 China College of Mechanical Engineering Dalian University of Science and Technology Dalian116036 China

A multiple robots system provides advantages over a single robot when it comes to exploring an unfamiliar area. Due to its applications in localization and mapping, search and rescue, etc. This technology has attracted a lot of study interest. The main challenge is properly distributing target points among several robots so that they can each investigate a separate section of the environment simultaneously and ensure the shortest possible total exploration time. However, more work still needs to be done to incorporate research issues in a real setting, such as how to eliminate duplicate exploration areas by giving robots multiple target locations or how to avoid unanticipated barriers in new situations. The allocation algorithms, motion planning techniques, and exploration strategies used in multi-robot collaboration (CME) are reviewed in this paper. Additionally, it provides an explanation of the methods research teams employ to optimize traditional whale optimization algorithm (WOA), how they enhance exploration performance and speed, and a comparative analysis of these methods. We also discussed a cooperative exploration method based on dynamic Voronoi partitioning. However, we also need some obstacle avoidance algorithms to help understand the gaps and issues that will need to be solved in the upcoming years, namely how to handle unexpected impediments in unfamiliar surroundings. Lastly, a summary of potential future lines of inquiry and uses is given. © Published under licence by IOP Publishing Ltd.

关键词： Multipurpose robots

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Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition

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植物表型组学（英文） 2024年第4期6卷 828-840页

作者： Jianwu Lin Xin Zhang Yongbin Qin Shengxian Yang Xingtian Wen Tomislav Cernava Quirico Migheli Xiaoyulong Chen Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China School of Biological Sciences Faculty of Environmental and Life SciencesUniversity of SouthamptonSouthampton S017 1BJUK Dipartimento di Agraria and NRD-Nucleo di Ricerca sulla Desertificazione Università degli Studi di SassariSassariItaly Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Life Sciences Guizhou UniversityGuiyang 550025China

Accurate identification of plant diseases is important for ensuring the safety of agricultural *** neural networks(CNNs)and visual transformers(VTs)can extract effective representations of images and have been widely used for the intelligent recognition of plant disease ***,CNNs have excellent local perception with poor global perception,and VTs have excellent global perception with poor local *** makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition *** this paper,we propose a local and global feature-aware dual-branch network,named LGNet,for the identification of plant *** specifically,we first design a dual-branch structure based on CNNs and VTs to extract the local and global ***,an adaptive feature fusion(AFF)module is designed to fuse the local and global features,thus driving the model to dynamically perceive the weights of different ***,we design a hierarchical mixed-scale unit-guided feature fusion(HMUFF)module to mine the key information in the features at different levels and fuse the differentiated information among them,thereby enhancing the model's multiscale perception ***,extensive experiments were conducted on the Al Challenger 2018 dataset and the self-collected corn disease(SCD)*** experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the Al Challenger 2018 dataset and the SCD dataset,with accuracies of 88.74％and 99.08％,respectively.

关键词： global plant disease dual-branch feature-aware local networks recognition

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Optimizing mmWave UAV Networks: Mobility-Aware Deployment and Resource Allocation 100

Optimizing mmWave UAV Networks: Mobility-Aware Deployment an...

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100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Li, Sicong Yoshii, Kazutoshi Shimamoto, Shigeru Ho, Tu Dac Waseda University Dept. of Communications and Computer Engineering Tokyo Japan Norwegian University of Science and Technology Dept. of Information Security and Communication Technology Trondheim Norway

ISBN: (纸本)9798331517786

Application of Unmanned Aerial Vehicles (UAVs) as small airborne base stations is gradually becoming a research hotspot in the field of wireless communications. With the flexible deployment, wide coverage and low cost, UAV communications show great potential in the fields of emergency rescue, environmental monitoring and military operations. In particular, the easier availability of line-of-sight (LOS) links for UAVs makes its combination with millimetre wave (mmWave) technology significantly enhance the communication bandwidth and data transfer rate. However, the very limited coverage of mmWave communications greatly affects the actual data rates provided by small aerial base stations of mmWave UAVs, especially considering user movement on the ground. In this paper, we study the joint optimisation problem of UAV deployment, user association, spectrum resource allocation in millimetre-wave heterogeneous networks, and explore the impact of different user movement models on system performance. Given that this problem is NP-hard, our paper proposes an MCR-SAC method to maximize the network's total throughput. Simulation results demonstrate an average 18% improvement in total transmission rate compared to the conventional. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Blockchain and Artificial Intelligence Applications to Defeat COVID-19 Pandemic

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computer Systems science & engineering 2022年第2期40卷 691-702页

作者： Mohammed Baz Sabita Khatri Abdullah Baz Hosam Alhakami Alka Agrawal Raees Ahmad Khan Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif 21944Saudi Arabia Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknow226025India Department of Computer Engineering College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia

The rapid emergence of novel virus named SARS-CoV2 and unchecked dissemination of this virus around the world ever since its outbreak in 2020,provide critical research criteria to assess the vulnerabilities of our current health *** paper addresses our preparedness for the management of such acute health emergencies and the need to enhance awareness,about public health and healthcare *** view of this unprecedented health crisis,distributed ledger and AI technology can be seen as one of the promising alternatives for fighting against such epidemics at the early stages,and with the higher *** the implementation level,blockchain integration,early detection and avoidance of an outbreak,identity protection and safety,and a secure drug supply chain can be *** the opposite end of the continuum,artificial intelligence methods are used to detect corona effects until they become too serious,avoiding costly drug *** paper explores the application of blockchain and artificial intelligence in order to fight with COVID-19 epidemic *** paper analyzes all possible newly emerging cases that are employing these two technologies for combating a pandemic like COVID-19 along with major challenges which cover all technological and motivational *** paper has also discusses the potential challenges and whether further production is required to establish a health monitoring system.

关键词： Artificial intelligence information privacy machine learning coronavirus(COVID-19) SARS-CoV-2 epidemic

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The Assessment of the Design Risk for ORAN

The Assessment of the Design Risk for ORAN

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Chen, Shiang-Jiun Chang, YaHan Raksasook, Nattapol Wu, Wei-Liang National Taipei University of Technology Department of Computer Science and Information Engineering Taipei Taiwan National Taipei University of Technology International Electrical Engineering and Computer Science Taipei Taiwan University of Taipei Department of Mathematics Taiwan

ISBN: (纸本)9798350324174

The Open Radio Access Network (O-RAN) uses a virtualization architecture to disaggregate components and connect them through open interfaces, and also uses software defined networking (SDN) technology for decoupling. The O-RAN architecture is an innovative telecommunication architecture, which also brings some potential problems for security, including but not limited to the followings: new architecture (disaggregation, Real Time RAN intelligent controllers (Near-RT RIC), virtualization), the open interfaces (O1, A1, and E2, etc.), artificial intelligent and machine learning (AIML) workflow, and standardization issues. Facing the new structure, in addition to the original risks, new risks also need to be considered. "Secure in design"is imperative to mitigate the information security risks. This study will look at the risk assessment of O-RAN from the perspective of testing and do security analysis. © 2023 IEEE.

关键词： Machine learning

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A Progress Prediction Model Using Res-zLSTM for LOCA in Nuclear Power Plants

A Progress Prediction Model Using Res-zLSTM for LOCA in Nucl...

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2024 International Conference on Electrical Power Systems and Intelligent Control, EPSIC 2024

作者： Liu, Liang She, Jingke Shu, Lingyu Zhao, Yizhong College of Computer Science & Electronic Engineering Hunan University Changsha China Hunan 6906 Information Technology Co. Ltd Yueyang China

ISBN: (纸本)9798350366075

Predicting the changes in various operation conditions following a loss of coolant accident (LOCA) in a nuclear power plant is of great significance. To enhance the accuracy of prediction, this paper proposes a novel prediction model called Res-zLSTM. The Res-zLSTM model incorporates residual structure and attention mechanisms to effectively mitigate the gradient vanishing problem in traditional Long Short-Term Memory (LSTM) models. Additionally, it maintains a low network complexity while simultaneously strengthening its ability to learn from long-term sequence data. This allows for accurate predictions of key parameter trends in LOCA conditions. In validation experiments, the Res-zLSTM model was used to predict multiple important operational parameters under different LOCA conditions. The experimental results demonstrate that the Res-zLSTM model exhibits superior prediction accuracy, achieving a maximum improvement of 54% compared to the zLSTM model. While the prediction accuracy of the Res-zLSTM model may be influenced by the changing rate of operation data, its outstanding performance in predicting LOCA conditions provides a novel approach for predicting operation conditions in complex systems. ©2024 IEEE.

关键词： Prediction models

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Collaborative Research on Customer Electricity Demand Forecasting and Power System Planning on Basis of Fuzzy Algorithms 5

Collaborative Research on Customer Electricity Demand Foreca...

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5th International Conference on Advances in Electrical engineering and computer Applications, AEECA 2024

作者： Wu, Fangquan Liu, Yichi Li, Xiong North China Electric Power University School of Computer Science and Technology Baoding071066 China Hunan University Information Science and Engineering Changsha410082 China University of Electronic Science and Technology School of Electronics and Communication Engineering Chengdu611731 China

ISBN: (纸本)9798350355253

Accurate prediction of consumer demand for electricity has always been a goal pursued by the power industry. Rational planning of power system resources so as to achieve power supply reliability and improve energy utilization efficiency is the purpose of this paper. This study is based on fuzzy algorithm to forecast consumer electricity demand through fuzzy reasoning and fuzzy logic methods, and synergize with power system planning. The article collects and organizes historical electricity demand data and related environmental factors data. Then a fuzzy inference model is established and fuzzy sets and fuzzy rules are applied to demand forecasting. The experimental results show that the root-mean-square error of the fuzzy algorithm is 0.01-0.03. The algorithm is able to capture the vagueness and uncertainty of demand with high prediction accuracy and comprehensiveness. The algorithm in this paper can meet the goals pursued by the power industry and bring contribution to the power industry. © 2024 IEEE.

关键词： Demand response

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Energy Efficiency Enhancement in a Small Industrial Facility 32nd

Energy Efficiency Enhancement in a Small Industrial Facility

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32nd International Conference on Organization and technology of Maintenance, OTO 2023

作者： Kljajić, Ružica Kraus, Zorislav Fekete, Krešimir Marić, Predrag Faculty of Electrical Engineering Computer Science and Information Technology Osijek University of Osijek Kneza Trpimira 2b Osijek31000 Croatia

ISBN: (纸本)9783031514937

Small industrial utilities, connected to the distribution grid, can cause various disturbances that need to be eliminated or mitigated. Due to the complexity of the system and the large number of motors, industrial consumers may have problems with the voltage deviations caused by the excessive amount of reactive energy which is a natural consequence of their operation and construction. Since industrial consumers have to pay for both active and reactive power delivered from the grid, the goal is to reduce those costs as well as to reduce feeder loadings. Delivered energy can be reduced by installing compensation devices or by connecting photovoltaic power plant, which would produce active and reactive energy at the point of consumption. This paper analyzes the power flows and voltage conditions of a small industrial facility in three scenarios: the basic scenario, the second scenario with built-in compensation devices and in the third scenario with integrated photovoltaic power plant. For all three scenarios, power flow analysis is performed and results are presented in the form of tables and graphically. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Energy efficiency

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L1Norm Minimization in ISAR Imaging 23

L1Norm Minimization in ISAR Imaging

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23rd International Symposium on Electrical Apparatus and Technologies, SIELA 2024

作者： Lazarov, Andon Minchev, Dimitar Nikola Vaptzarov Naval Academy Information Technology Department Varna Bulgaria Burgas Free University Computer Science and Engineering Faculty Burgas Bulgaria

ISBN: (纸本)9798350382365

This work deals with an ISAR (Inverse Synthetic Aperture Radar) image enhancement algorithm using target's sparse image and image's l1 norm minimization. As calculation of l1 norm of two-dimensional (2D) image matrix is too complicated, target's image matrix is resized to one-dimensional (1D) one. According to ISAR theory, the ISAR signal matrix is a Fourier transform of the target's image matrix. Thus, the ISAR signal is obtained one dimensional one while modelling by Fourier transform of a certain target's image column matrix. In the image reconstruction algorithm, 12 norm constrained function and l12 objective function are defined. Simulation experimental results prove functionality of the developed algorithm. © 2024 IEEE.

关键词： Radar imaging

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Improving Oil Spill Detection in Marine Environments Through Deep Learning Approaches 4

Improving Oil Spill Detection in Marine Environments Through...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Shanmukh, M. Phani Priya, S. Baghavathi Madeswaran, Tamilselvi Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India University of Technology and Applied Sciences Department of Information Technology Nizwa Oman

ISBN: (纸本)9798350343670

Oil spills represent a major environmental threat, particularly in marine ecosystems. Timely detection and segmentation of oil spills from satellite imagery is crucial for directing rapid response and remediation *** research concludes with an insights by developing a deep learning approach for automated detection and segmentation of oil spills from synthetic aperture radar (SAR) *** research implements and compares three algorithms PSPNET,DeepLapV3 and fully convolutional networks (FCN) with U-Net for semantic segmentation of SAR *** the networks are trained on a dataset of SAR images with pixel-wise labels indicating oil spill regions. This research also evaluates the segmentation performance using common metrics like intersection-over-union (IoU) and pixel-wise accuracy. Our results indicate that both FCN and U-Net can effectively detect and delineate oil spill regions, with U-Net demonstrating slightly higher IoU scores and with and accuracy of 95%. The deep learning models generalize well to unseen SAR images. Overall, this work demonstrates the feasibility of deep convolutional neural networks for automated analysis of SAR imagery to enable rapid oil spill response at marine environment. © 2024 IEEE.

关键词： Deep Learning Algorithms Oil Spill Detection Rapid Oil Spill Response Semantic Segmentation Synthetic Aperture Radar (SAR) Imagery

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