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New categorized machine learning models for daily solar irradiation estimation in southern Morocco's, Zagora city

e-Prime - Advances in Electrical Engineering, Electronics and Energy 2024年 10卷

作者： Bounoua, Zineb Chahidi, Laila Ouazzani Mechaqrane, Abdellah SIEDD Laboratory of Intelligent Systems Energy and Sustainable Development Private University of Fez Fez Morocco SIGER Intelligent Systems Georesources and Renewable Energies Laboratory Faculty of Sciences and Technologies Sidi Mohamed Ben Abdellah University Fez PO Box 2202 Morocco LISAC Computer Science Signals Automation and Cognitivism Laboratory Faculty of Sciences Dhar Mehraz Sidi Mohamed Ben Abdellah University PO. Box 1796 Atlas Fez 30003 Morocco

Accurately estimating daily solar irradiation is essential for effectively sizing and simulating solar energy systems. Inaccuracies or discontinuities in solar data can lead to errors in system assessments, potentially resulting in misguided conclusions about their economic feasibility. This study evaluates the performance of various machine learning techniques, including Multilayer Perceptron (MLP), Support Vector Machine Regression (SVMR), and Random Forest (RF), in accurately estimating daily Global and Diffuse Horizontal Irradiations (GHI and DHI), and Direct Normal Irradiation (DNI) in Zagora City, Southern Morocco. The models are categorized based on four classes of data inputs: Temperature-Based Models (GTBMs), Relative Humidity-Based Models (RHBMs) for GHI, Hybrid Parameter-Based Models (HBMs) for GHI, DHI, and DNI, and Clearness Index-Based Models (CIBMs) for DHI and DNI. The results indicate that RF is particularly effective in estimating daily GHI, DHI, and DNI using Hybrid parameters Based Models, achieving validation nRMSEs equal to 8.36 %, 18.55 %, and 10.26 %, respectively. It also performs well in estimating DHI with Clearness index Based Models (nRMSE=20.44 %). The next effective method is MLP, that is the optimal one in estimating daily GHI with Relative Humidity Based Models (nRMSE=11.41 %) and daily DNI using Clearness index Based Models (nRMSE= 12.43 %). Lastly, SVMR is more quitable for estimating daily GHI with Temperature Based Models, with a validation nRMSE of about 9.09 %. The study further demonstrates that empirical models underperform compared to machine learning methods in all included categories. © 2024 The Author(s)

关键词： Clearness index based models (CIBMS) Hybrid parameters based models (HBMS) Machine learning models Relative humidity based models (RHBMs) Solar radiation Temperature based models (GTBMs)

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CEBRA Method: Decoding Brain Activity for Advanced Brain-computer Interface Technology 46

CEBRA Method: Decoding Brain Activity for Advanced Brain-Com...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Yang, Jingcheng Kulwa, Frank Liu, Xuanwei Lu, Yiqing Fu, Yufa Li, Guanglin Huai, Yaping Zhang, Xin Li, Yongcheng Shenzhen Dapeng New District Nanao People's Hospital Guangdong Shenzhen518000 China Shenzhen Longhua District Central Hospital Department of Rehabilitation Medicine Guangdong Shenzhen518000 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Kunming University of Science and Technology Brain Cognition and Brain-Computer Intelligence Integration Group Kunming650500 China Cas Key Laboratory of Human-Machine Intelligence Synergy Systems Shenzhen518055 China University of Chinese Academy of Sciences Shenzhen College of Advanced Technology Shenzhen518055 China Shandong Zhongke Advanced Technology Co. Ltd. Jinan250014 China

ISBN: (纸本)9798350371499

The emerging neurorehabilitation technology, Brain-computer Interface (BCI), provides a novel prospect for stroke recovery. However, decoding brain activity during the movement present substantial challenges, and feature extraction is crutial to build a better decoder. In this study the CEBRA method were employed to extract features first based on electroencephalogram(EEG) data during Motor Execution(ME) and Motor lmagery (Ml) tasks for 20 participants (including 10 stroke patients). The results revealed that, in MI tasks, CEBRA-RF (Random Forests) achieved an average classification accuracy of 91.33%, with an average F1-score of 91.19%, and CEBRA-SVM (Support Vector Machine) achieved an average classification accuracy of 91.32%, with an average F1-score of 90.83%. Compared to other conventional feature extraction methods, CEBRA shows significantly higher accuracy (t-test, p © 2024 IEEE.

关键词： Patient rehabilitation

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Nearest-Better Network for Fitness Landscape Analysis of Continuous Optimization Problems

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IEEE Transactions on Evolutionary Computation 2024年

作者： Diao, Yiya Li, Changhe Zeng, Sanyou Yang, Shengxiang Coello, Carlos A. Coello China University of Geosciences School of Automation Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China Anhui University of Sciences and Technology School of Artificial Intelligence Hefei China China University of Geosciences School of Mechanical Engineering and Electronic Information Wuhan China De Montfort University School of Computer Science and Informatics LeicesterLE1 9BH United Kingdom Department of Computer Science CINVESTAV-IPN 07360 Mexico Tecnologico de Monterrey Faculty of Excellence The School of Engineering and Sciences N.L. Monterrey Mexico

Fitness landscape analysis (FLA) is quite important in evolutionary computation. In this paper, we propose a novel FLA method, the nearest-better network (NBN), which uses the nearest-better relationship to simplify the original fitness landscape of continuous optimization problems. We introduce an efficient algorithm to calculate NBN for continuous problems. We also propose four numerical measurements and a 3D visualization method based on NBN. Experiments show that compared to the other main FLA methods, the four numerical measurements proposed here can effectively measure the four intended features: neutrality, ruggedness, modality, and basin of attraction, respectively, and common features of the fitness landscape can be maintained in 3D NBN visualization, regardless of the scale of the problem. NBN also provides a view of how algorithms search in high-dimensional problems with the help of the 3D NBN visualization. © 1997-2012 IEEE.

关键词： Optimization algorithms

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Blockchain Technologies for Validation of Priority Vehicles in ITS

Blockchain Technologies for Validation of Priority Vehicles ...

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2022 Intelligent Technologies and Electronic Devices in Vehicle and Road Transport Complex, TIRVED 2022

作者： Pomogalova, A.V. Donskov, E.A. Elagin, V.S. Vladyko, A.G. The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Infocommunication Systems Department Saint Petersburg Russia St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences Laboratory of Computer Security Problems Department St. Petersburg Russia The Bonch-Bruevich Saint-Petersburg State University of Telecommunications R&d Department Saint Petersburg Russia The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Faculty of Fundamental Training Saint Petersburg Russia

ISBN: (数字)9798350397703

ISBN: (纸本)9798350397703

The analysis of the possibility of using distributed networks, in particular, the blockchain network, smart contracts, as a control mechanism for controlling messaging and ensuring the digital security of the network is carried out in the work. The paper describes the concept of priority passage and its provision using distributed network mechanisms. Two priority passage scenarios are considered, as well as a messaging scheme for general cases of priority passage and the case of overlapping priority routes. Also, the paper presents mathematical studies of evaluating the possibility of a blockchain network depending on its performance, as well as approbation of calculations on model stands on networks with PoW and LPoS consensus algorithms. © 2022 IEEE.

关键词： Blockchain

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Comparative Analysis of Android Mobile Forensics Tools

Comparative Analysis of Android Mobile Forensics Tools

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2020 IEEE Conference on computer Applications, ICCA 2020

作者： Lwin, Htar Htar Aung, Wai Phyo Lin, Kyaw Kyaw University of Computer Studies Faculty of Computer Systems and Technologies Yangon Myanmar Moscow Automobile and Road Construction State Technical University Department of Automation Control System Russia Defence Services Academy Department of Computer Technology Pyin Oo Lwin Myanmar

ISBN: (纸本)9781728159256

This paper performs a comparative analysis of Android mobile forensics tools which are used for acquisition and analyzing of Android mobile devices. The major challenges of Android forensics investigation are manufacturing of Android devices with various operating system versions and there is no single tool which can be used for all sorts of Android devices. Aiming to overcome these challenges and increase more accuracy and integrity in Android forensic investigation, we made comparative analysis on both open source tools and one commercial tool. Logical and physical acquisition methods were utilized to acquire data from Android devices. Android Debug Bridge backup, Linux Data Duplicator utility tool, Magnet Acquire and Belkasoft Acquisition tools were used for acquisition. Two popular analyzing tools such as Autopsy and Belkasoft Evidence Center were utilized to analyze acquired data. The results show that using multiple tools can get more accuracy and integrity of artifacts which is forensically sound. © 2020 IEEE.

关键词： Android (operating system)

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Developing and Analysis of Cyber Security Models for Security Operation Center in Myanmar

Developing and Analysis of Cyber Security Models for Securit...

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2020 IEEE Conference on computer Applications, ICCA 2020

作者： Aung, Wai Phyo Lwin, Htar Htar Lin, Kyaw Kyaw Moscow Automobile and Road Construction State Technical University Department of Automation Control System Russia University of Computer Studies Faculty of Computer Systems and Technologies Yangon Myanmar Defence Services Academy Department of Computer Technology Pyin Oo Lwin Myanmar

ISBN: (纸本)9781728159256

In counteraction to the increasing threat of cyber terrorism, the modeling to be predicted in guessing the predictive models for estimating the incidence of cyber-attacks for enterprise network in Myanmar are seriously needed. Although we need these models, there is no record of attacks, defenseless, outcome and threat to utilize the developing predictive models and authentication. The main purpose of this research is to determine whether SOC (Security Operation Center) manager uses cyber security model by using SOC results figures to prepare further cyber defense and incident response plan. The goal of this study was achieved by conducting experiments on various cyber-attacks occurred in security operation center of Industrial Control System (ICS). © 2020 IEEE.

关键词： Terrorism

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Distributed Event-Triggered Bandit Convex Optimization with Time-Varying Constraints

arXiv

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arXiv 2024年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China The Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States The Department of Mathematics Southeast University Nanjing210096 China The Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms require that each agent broadcasts its decision to its neighbors at each iteration. To better utilize the limited communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. Copyright © 2024, The Authors. All rights reserved.

关键词： Convex optimization

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UGNA-VPR: A Novel Training Paradigm for Visual Place Recognition Based on Uncertainty-Guided NeRF Augmentation

arXiv

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arXiv 2025年

作者： Shen, Yehui Zhang, Lei Li, Qingqiu Zhao, Xiongwei Wang, Yue Lu, Huimin Chen, Xieyuanli College of Intelligence Science and Technology National University of Defense Technology China Faculty of Robot Science and Engineering Northeastern University China School of Electronic and Information Engineering Harbin Institute of Technology Shenzhen China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University China SIASUN Robot& Automation Co. Ltd China School of Computer Science Fudan University China

Visual place recognition (VPR) is crucial for robots to identify previously visited locations, playing an important role in autonomous navigation in both indoor and outdoor environments. However, most existing VPR datasets are limited to single-viewpoint scenarios, leading to reduced recognition accuracy, particularly in multi-directional driving or feature-sparse scenes. Moreover, obtaining additional data to mitigate these limitations is often expensive. This paper introduces a novel training paradigm to improve the performance of existing VPR networks by enhancing multi-view diversity within current datasets through uncertainty estimation and NeRF-based data augmentation. Specifically, we initially train NeRF using the existing VPR dataset. Then, our devised self-supervised uncertainty estimation network identifies places with high uncertainty. The poses of these uncertain places are input into NeRF to generate new synthetic observations for further training of VPR networks. Additionally, we propose an improved storage method for efficient organization of augmented and original training data. We conducted extensive experiments on three datasets and tested three different VPR backbone networks. The results demonstrate that our proposed training paradigm significantly improves VPR performance by fully utilizing existing data, outperforming other training approaches. We further validated the effectiveness of our approach on self-recorded indoor and outdoor datasets, consistently demonstrating superior results. Our dataset and code have been released at https://***/nubot-nudt/UGNA-VPR. Copyright © 2025, The Authors. All rights reserved.

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Spiral coverage path planning for Multi-UAV photovoltaic panel inspection applications

Spiral coverage path planning for Multi-UAV photovoltaic pan...

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International Conference on Unmanned Aircraft systems (ICUAS)

作者： Marco Andrés Luna Mohammad Sadeq Ale Isaac Miguel Fernandez-Cortizas Carlos Santos Ahmed Refaat Ragab Martin Molina Pascual Campoy Computer Vision and Aerial Robotics Group (CVAR) Centre for Automation and Robotics (C.A.R.) Universidad Politécnica de Madrid (UPM-CSIC) Madrid Spain Department of Signal Theory and Communications University of Alcala (UAH) Madrid Spain Department of Network Faculty of Information Systems and Computer Science October 6 University Giza Egypt Department of Artificial Intelligence Universidad Politécnica de Madrid Madrid Spain

This paper deals with the problem of coverage path planning for multiple UAVs in disjoint regions. For this purpose, a spiral-coverage path planning algorithm is proposed. Additionally, task assignment methods for multi-region inspection with a swarm of UAVs are applied. The centralized system architecture is described, and an adaptive sliding mode controller is designed. Furthermore, we evaluate the performance of the proposed techniques by obtaining numerical results and simulations with the controller. The results show that the spiral pattern optimizes the cost of the mission and improves the task distribution of the mission planning system. Additionally, the performance of the proposed controller is robust to simulated disturbances.

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PolarBEVU: Multi-Camera 3D Object Detection in Polar Bird's-Eye View via Unprojection

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Hou, Minghui Lyu, Chuanhao Wang, Gang Ma, Baorui Xu, Rongtao Hu, Jue Fan, Xiaopeng Jilin University College of Computer Science and Technology Changchun China Tsinghua University School of Software Beijing China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing China Harbin Institute of Technology National Key Laboratory of Science and Technology on Advanced Composites in Special Environments Harbin150080 China Harbin Institute of Technology Centre for Composite Materials and Structures Harbin150001 China Harbin Institute of Technology Faculty of Computing Harbin150001 China

3D object detection from a Bird's Eye View (BEV) has emerged as a novel perception paradigm for autonomous driving scenarios. While most current 3D object detection methods still rely on the conventional Cartesian coordinates, they fail to align with the non-aligned coordinate system inherent in image geometry. The Polar coordinates, on the other hand, better fit with the geometric shape corresponding to the perception of cameras. However, transforming between coordinate systems introduces distortions in the perception information, resulting in issues such as "Weak Adaptability to Heatmap Distribution"and "Offset in the Center Point of the Bounding Box."To address these challenges, this paper proposes a cutting-edge 3D object detection model named PolarBEVU, which leverages the bird's-eye view under the Polar coordinates along with multi-camera unprojection. The model introduces an innovative "Deformable Uniform Heatmap Distribution"method that adjusts heatmap computations based on box shapes, generating high-quality heatmaps and effectively resolving the issue of "Weak Adaptability to Heatmap Distribution."Moreover, the model incorporates the concept of "Dynamic High-risk Regression Region"to enhance the accuracy and robustness of the center point regression at the bounding box, thus mitigating the issue of "Offset in the Center Point of the Bounding Box."In extensive experiments on the nuScenes dataset, PolarBEVU achieves impressive results with 49.9% mAP and 57.4% NDS on the test set, surpassing other comparative approaches and reaching the state-of-the-art (SOTA) performance among methods utilizing Polar coordinates. This clearly demonstrates the efficacy and superiority of PolarBEVU. In addition, the model is successfully deployed on Nvidia Jetson AGX Orin, showcasing real-time inference speeds of 31.42ms. These findings affirm PolarBEVU's potential for practical applications. Code is available at https: //***/JLUrob/PolarBEVU. © 2025 IEEE.

关键词： Object tracking

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