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Global scale solar energy harnessing: An advanced intra-hourly diffuse solar irradiance predicting framework for solar energy projects

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Neural Computing and Applications 2024年第18期36卷 10585-10598页

作者： El-kenawy, El-Sayed M. Bailek, Nadjem Bouchouicha, Kada Zerouali, Bilel Hassan, Muhammed A. Kuriqi, Alban Jamil, Basharat Colak, Ilhami Khalil, Adel Ibrahim, Abdelhameed Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura35111 Egypt Laboratory of Mathematics Modeling and Applications Department of Mathematics and Computer Science Faculty of Sciences and Technology Ahmed Draia University of Adrar Adrar01000 Algeria Energies and Materials Research Laboratory Faculty of Sciences and Technology University of Tamanghasset Tamanrasset10034 Algeria MEU Research Unit Middle East University Amman Jordan Adrar01000 Algeria Vegetal Chemistry-Water-Energy Laboratory Faculty of Civil Engineering and Architecture Department of Hydraulic Hassiba Benbouali University of Chlef Ouled Fares Chlef02180 Algeria Mechanical Power Engineering Department Faculty of Engineering Cairo University Giza12613 Egypt School of Architecture University of Waterloo 7 Melville St S CambridgeONN1S 2H4 Canada CERIS Instituto Superior Tecnico Universidade de Lisboa Lisbon Portugal Civil Engineering Department University for Business and Technology PristinaKosovo10000 High Temperature Processes Unit IMDEA Energy Institute Av. Ramón de La Sagra 3 Móstoles Madrid28935 Spain Faculty of Engineering and Architectures Nisantasi University Istanbul Turkey Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

Diffuse horizontal irradiance (DHI) forecasts are critical for adopting solar photovoltaic technology. Yet, they can lack reliability given the limited and uncertain meteorological data available for desert areas. This research develops a new sine cosine dipper throated optimization (SCDTO) technique, targeting DHI prediction even given such data constraints. SCDTO uniquely hybridizes sine cosine metaheuristics, adept at exploration, with dipper throat optimization, providing focused exploitation. This hybridization aims to create a robust ensemble model capable of delivering reliable DHI predictions despite climatic uncertainty. The ensemble model, employing various input combinations, was rigorously evaluated across multiple meteorological stations. The SCDTO algorithm exhibited remarkable performance improvements, yielding substantial reductions exceeding 93% in root mean squared error and 98% in mean absolute error at key stations in Morocco, including Tan-Tan, Zagora, Erfoud, and Oujda. Comparative analyses against established optimization algorithms consistently underscored the superior predictive capabilities of SCDTO. Visualizations, including box plots and histograms, demonstrated SCDTO’s efficacy in minimizing prediction errors, particularly for Tan-Tan, Zagora, and Erfoud stations. Also, statistical validation through one-way analysis of variance (ANOVA) further affirmed the significance of the proposed SCDTO method. Therefore, this hybrid metaheuristic optimization enables more accurate DHI predictions from limited meteorological data. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Solar energy

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Dog identification based on textural features and spatial relation of noseprint

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Pattern Recognition 2024年 151卷

作者： Chan, Yung-Kuan Lin, Chuen-Horng Wang, Ching-Lin Tu, Keng-Chang Yang, Shu-Chun Tsai, Meng-Hsiun Yu, Shyr-Shen Department of Management Information Systems National Chung Hsing University 145 Xingda Rd. South Dist. Taichung City402 Taiwan Department of Computer Science and Engineering National Chung Hsing University 145 Xingda Rd. South Dist. Taichung City402 Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology No. 129 Sec. 3 Sanmin Rd North Dist. Taichung City404 Taiwan Department of Information Management National Chin-Yi University of Technology No.57 Sec. 2 Zhongshan Rd. Taiping Dist. Taichung411030 Taiwan

This study proposes dog identification technology based on dog noseprints, which are equivalent to human fingerprints and possess unique characteristics. The aim is to utilize this technology for identifying and managing stray animals. The study presents three processing stages. In the first stage, YOLOv3 detects the dog's nose and nostril regions. The second stage involves enhancing the image's contrast and the contour of the scaly block using the multi-scale line detector. Finally, in the third stage, the shape and spatial features of the scaly block are extracted and utilized for dog identification. The study included a dataset of 276 dogs from multiple animal families and public shelters in Taiwan. The dataset was randomly divided into three groups to determine the optimal parameters for matching the dog noseprint via experimentation. Each dog identification group achieved an accuracy (ACC) exceeding 97.83 %, demonstrating that the proposed parameter-matching method offers high stability. Furthermore, in an additional experimental dataset consisting of dog noseprint images used for dog identification, the proposed method achieved an ACC exceeding 90.22 % in the Top 1 and 94.57 % in the Top 3. The ACC results across different groups consistently demonstrate the proposed method's ability to achieve high accuracy in dog identification. The source code and trained models are publicly available at: https://***/Chuen-HorngLin/Dog-Identification-Noseprint. © 2024 Elsevier Ltd

关键词： Animals

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Fabrication and Characterization of Oriented P3HT/CdS Hybrid Thin Films Prepared by Friction Transfer for Organic Photovoltaics

Fabrication and Characterization of Oriented P3HT/CdS Hybrid...

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International Workshop on Active-Matrix Flatpanel Displays and Devices (AM-FPD)

作者： Yuki Kurokawa Safalmani Pradhan Takehito Kato Shuichi Nagamatsu Shyam S. Pandey Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology 2-4 Hibikino Wakamatsu Kitakyushu Japan Oyama College 771 Nakakuki National Institute of Technology Oyama Tochigi Japan Department of Computer Science and Electronics 680-4 Kawazu Kyushu Institute of Technology Iizuka Japan

Simple, low-cost one-step fabrication donor-acceptor hybrid thin films are highly desired for the practical realization of low-cost organic solar cells. In this work, oriented and hybrid thin films of P3HT/CdS were successfully fabricated by a solvent-free friction transfer (FT) method utilizing Cadmium xanthate [Cd(Xt) 2 ] as CdS precursor and regioregular P3HT in a single step. Complete conversion of [Cd(Xt) 2 ] to CdS with optical band gap of 2.53 eV after thermal annealing at 200 °C was confirmed by absorption spectral investigation. Polarized absorption spectral results revealed that FT-processed hybrid thin films of P3HT/CdS were oriented and extent of molecular orientation was enhanced after the thermal annealing at 200 °C. Highly oriented hybrid thin films with dichroic ratio of 6.95 was prepared under the optimum fabrication conditions such stage speed, stage temperature and applied load of 1200 mm/min, 85°C and 2.4 kg, respectively.

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Network Traffic Co-Movement Assessment via Oriented Basis Signal Processing and Ensemble Decision Trees

Network Traffic Co-Movement Assessment via Oriented Basis Si...

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International Conference on Intelligent Transportation

作者： Mohammad Reza Mohebbi Mohammad Javad Hassan Zada Majid Rostami-Shahrbabaki Mario Döller Iuliia Yamnenko Josef Ressel Center Vision2Move project University of Applied Sciences Kufstein Tirol Austria Department of Computer Science University of Passau Germany Chair of Traffic Engineering and Control Technical University of Munich Germany Department of Electronic Devices and Systems National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Kyiv Ukraine

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Accurate short-term network-wide traffic prediction is essential to guarantee high service quality in urban traffic control systems. Nevertheless, traffic state time series represent network-scale spatiotemporal co-movement patterns and location-specific features. Therefore, hybrid statistical machine learning (ml) algorithms could be utilized to accommodate the aforementioned characteristics. In this paper, a hybrid random forest (rf) and extreme gradient boosting tree (XGBoost) model is introduced for network-wide traffic prediction. Moreover, a multi-noise oriented basis wavelet transform (OBT) filter is employed to pre-process the original time series, and improve the predictive accuracy. The RF model captures the traffic co-movements and the XGBoost extracts the local information. Comparative analysis of the hybrid algorithm and deep learning-based benchmarks indicate better performance of the proposed methodology in hourly traffic state prediction of 30 loop detectors, located in the paris city center. Hence, the hybrid decision-tree-based mechanism is a useful framework for real-time network traffic forecasting offering fewer trainable (hyper-)parameters, and thus, lower computational cost.

关键词： Wavelet transforms Radio frequency Accuracy Machine learning algorithms Urban areas Signal processing algorithms Detectors Prediction algorithms Decision trees Random forests

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Safe vehicle motion design with learning for moving in environment with uncertainties ⁎

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IFAC-PapersOnLine 2024年第4期58卷 228-233页

作者： Dénes Tompos Balázs Németh Tamás Hegedűs Vu Van Tan Péter Gáspár Institute for Computer Science and Control (SZTAKI) Hungarian Research Network (HUN-REN) Kende u. 13-17. 1111 Budapest Hungary Department of Control for Transportation and Vehicle Systems Budapest University of Technology and Economics Stoczek u. 2. 1111 Budapest Hungary Department of Automotive Mechanical Engineering Faculty of Mechanical Engineering University of Transport and Communications 3 Cau Giay Street 100000 Hanoi Vietnam

In this paper a motion profile design for unmanned aerial vehicles is proposed which method is able to guarantee safe collision-free motion. The motivations of the work are provided by the uncertainties of covered areas by the vehicles, and also the need of high performance fast vehicle motion. The uncertain information on the environment for detecting Conflict areas is processed through clustering and Mahalanobis-distance-based filtering methods. The resulted Conflict areas are involved in the motion design method, which is facilitated through reinforcement learning. This paper shows the application of the method on a drone that moves together with a mobile robot in the same environment. The safe and high performance motion of the drone is illustrated through simulation example.

关键词： Unmanned aerial vehicles safe motion clustering point clouds

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Supervised Representation Learning From Problem-Solving Behavior in Virtual Reality Manufacturing Environment Via Long Short-Term Memory

SSRN

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SSRN 2024年

作者： Zhu, Rui Aqlan, Faisal Zhao, Richard Yang, Hui Li, Yifu School of Industrial and Systems Engineering The University of Oklahoma NormanOK73019 United States Department of Industrial Engineering University of Louisville LouisvilleKY40292 United States Department of Computer Science University of Calgary CalgaryABT2N 1N4 Canada Complex Systems Monitoring Modeling and Control Laboratory The Pennsylvania State University University ParkPA16802 United States

The number of manufacturing jobs has been consistently increasing thanks to the rapidly evolving landscape and the newly adopted strategic plan of US manufacturing. However, there have been rising concerns in recent years about the problem-solving skills of engineering students, who will soon become the next generation of the US workforce. Such circumstances call for systematic evaluation and a better understanding of engineering students’ problem-solving. This paper builds a virtual reality (VR)-based manufacturing environment and adopts a data-driven analytical workflow to evaluate engineering students’ problem-solving performance. Specifically, students perform assembly tasks to produce car toys that satisfy the pre-defined requirements within the VR craft-production manufacturing system. We collect in situ eye-tracking data containing spatial-temporal dynamics of eye movements and abstract their latent features via a long short-term memory (LSTM)-based supervised representation learning for problem-solving performance evaluation. This work outperforms the traditional performance metrics-based evaluation by incorporating the nonlinear dynamics summarized from the in situ production process. Experimental results show that learned feature representations provide significantly more distinguishable patterns when categorizing the students’ problem-solving performance than traditional performance metrics. The proposed evaluation workflow has a broader impact on facilitating the problem-solving assessment across a wide range of manufacturing systems and worker training programs. © 2024, The Authors. All rights reserved.

关键词： Eye tracking

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Integration of Robust Control with Reinforcement Learning for Safe Autonomous Vehicle Motion

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IFAC-PapersOnLine 2023年第2期56卷 1101-1106页

作者： Attila Lelkó Balázs Németh Dániel Fényes Péter Gáspár Institute for Computer Science and Control (SZTAKI) Eötvös Loránd Research Network (ELKH) 13-17. Kende u. H-1111 Budapest Hungary Department of Control for Transportation and Vehicle Systems Budapest University of Technology and Economics 2 Stoczek u. H-1111 Budapest Hungary

This paper presents a control design framework for the integration of robust control and reinforcement learning-based (RL) control agent. The proposed integration method is applied for motion control of autonomous road vehicles, providing safe motion. In the integrated motion control, longitudinal and lateral dynamics are incorporated. The high-performance motion of the vehicle, e.g., high-velocity motion, path following, and reduction of lateral acceleration, through the RL-based control agent is achieved. The training through Proximal Policy Optimization during episodes is performed. Safe motion with guaranteed performances, i.e., keeping limits on lateral error, through the robust control and the supervisor is achieved. The robust control is designed through the H ∞ method, and in the supervisor, a constrained quadratic programming task is performed. As a result, lateral and longitudinal control inputs of the vehicle are calculated by the integrated control system. The effectiveness of the proposed control method using simulation scenarios and test scenarios on a small-scaled test vehicle is illustrated.

关键词： Adaptive and robust control of automotive systems Autonomous vehicles

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Obfuscation Mechanism for Simultaneous Public Information Release and Private Information Hiding in Discrete Event systems

SSRN

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SSRN 2024年

作者： Duan, Wei Hadjicostis, Christoforos N. Li, Zhiwu School of Electro-Mechanical Engineering Xidian University 2 South Taibai Road Xi’an710071 China Department of Electrical and Computer Engineering University of Cyprus 75 Kallipoleos Avenue Nicosia1678 Cyprus Institute of Systems Engineering Macau University of Science and Technology Avenida Wai Long Taipa999078 China

In this paper, we study the problem of simultaneous public information release and private information hiding in discrete event systems modeled by partially observed non-deterministic finite automata, where the public and private information is respectively defined as unobservable fault and secret events. The notion of D-C-compossibility is introduced to characterize whether a system simultaneously conceals the occurrences of secret events while releasing the occurrences of fault events. When such a property does not hold, we investigate its enforcement through a defensive function. Specifically, our focus is on prioritizing the concealment of the secret events while maximizing the detection of the fault events through the use of defensive functions. The notion of D-C-enforceability that refers to the capability of a defensive function to employ a strategy for manipulating observations of a system to enforce D-C-compossibility is proposed. That is, a D-C-enforcing defensive function ensures that all occurrences of fault events can be revealed while concealing all occurrences of secret events. A D-C-diagnoser construction is proposed to enumerate all feasible defensive actions following system behavior. By taking advantage of the D-C-diagnoser, we obtain a necessary and sufficient condition for D-C-enforceability, along with a corresponding obfuscation strategy for defensive functions. © 2024, The Authors. All rights reserved.

关键词： Discrete event simulation

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Fractional-order Fixed-time Non-singular Sliding Mode Control for Nonlinear Robotic Manipulators

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IFAC-PapersOnLine 2023年第2期56卷 8105-8110页

作者： Saim Ahmed Ahmad Taher Azar Ibrahim A. Hameed Automated Systems and Soft Computing Lab (ASSCL) College of Computer and Information Sciences Prince Sultan University Riyadh 11586 Saudi Arabia Faculty of Computers and Artificial Intelligence Benha University Benha 13518 Egypt Department of ICT and Natural Sciences Norwegian University of Science and Technology Larsgårdsve-gen 2 6009 Ålesund Norway

In this study, fractional-order non-singular fixed-time terminal sliding mode control (FoFxNTSM) for nonlinear robotic manipulators in the existence of uncertainties and external disturbances is examined. To begin, the concept of fractional-order fixed-time non-singular terminal sliding mode control is introduced. This method combines the benefits of NTSM (which provides fast convergence speed, smooth and singularity-free control inputs) with the advantages of a fractional-order constants (which improves position tracking effectiveness). Lyapunov analysis yields the fixed-time stability of the closed-loop system. To evaluate and illustrate the performance of the proposed strategy, the relevant simulation results are presented.

关键词： Fixed-time SMC nonlinear robotic manipulators fractional-order control fast convergence

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Collaborative Computing in Non-Terrestrial Networks: A Multi-Time-Scale Deep Reinforcement Learning Approach

arXiv

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arXiv 2024年

作者： Cao, Yang Lien, Shao-Yu Liang, Ying-Chang Niyato, Dusit Shen, Xuemin School of Information Science and Technology Southwest Jiaotong University Chengdu611756 China University of Electronic Science and Technology of China Chengdu611731 China Institute of Intelligent Systems National Yang Ming Chiao Tung University Tainan City711 Taiwan School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Department of Electrical and Computer Engineering University of Waterloo 200 University Avenue West WaterlooONN2L 3G1 Canada

Constructing earth-fixed cells with low-earth orbit (LEO) satellites in non-terrestrial networks (NTNs) has been the most promising paradigm to enable global coverage. The limited computing capabilities on LEO satellites however render tackling resource optimization within a short duration a critical challenge. Although the sufficient computing capabilities of the ground infrastructures can be utilized to assist the LEO satellite, different time-scale control cycles and coupling decisions between the space- and ground-segments still obstruct the joint optimization design for computing agents at different segments. To address the above challenges, in this paper, a multi-time-scale deep reinforcement learning (DRL) scheme is developed for achieving the radio resource optimization in NTNs, in which the LEO satellite and user equipment (UE) collaborate with each other to perform individual decision-making tasks with different control cycles. Specifically, the UE updates its policy toward improving value functions of both the satellite and UE, while the LEO satellite only performs finite-step rollout for decision-makings based on the reference decision trajectory provided by the UE. Most importantly, rigorous analysis to guarantee the performance convergence of the proposed scheme is provided. Comprehensive simulations are conducted to justify the effectiveness of the proposed scheme in balancing the transmission performance and computational complexity. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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