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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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FISS GAN:A Generative Adversarial Network for Foggy Image Semantic Segmentation

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IEEE/CAA Journal of Automatica Sinica 2021年第8期8卷 1428-1439页

作者： Kunhua Liu Zihao Ye Hongyan Guo Dongpu Cao Long Chen Fei-Yue Wang School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China State Key Laboratory of Automotive Simulation and Control and the Department of Control Science and Engineering Jilin University(Campus Nanling)Changchun 130025China Mechanical and Mechatronics Engineering Department at the University of Waterloo 200 University Avenue West WaterlooON N2L 3G1Canada State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

Because pixel values of foggy images are irregularly higher than those of images captured in normal weather(clear images),it is difficult to extract and express their *** method has previously been developed to directly explore the relationship between foggy images and semantic segmentation *** investigated this relationship and propose a generative adversarial network(GAN)for foggy image semantic segmentation(FISS GAN),which contains two parts:an edge GAN and a semantic segmentation *** edge GAN is designed to generate edge information from foggy images to provide auxiliary information to the semantic segmentation *** semantic segmentation GAN is designed to extract and express the texture of foggy images and generate semantic segmentation *** on foggy cityscapes datasets and foggy driving datasets indicated that FISS GAN achieved state-of-the-art performance.

关键词： Edge GAN foggy images foggy image semantic segmentation GAN semantic segmentation

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Engine Failure Detection of Raw Mill Machine via Discrete Variational Auto-encoder

Engine Failure Detection of Raw Mill Machine via Discrete Va...

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2022 International Conference on Data and Software engineering, ICoDSE 2022

作者： Abruzi, Izhar Brur Iqbal, Mohammad Mukhlash, Imam Rukmi, Alvida Mustika Kurniati, Nani Kimura, Masaomi Institut Teknologi Sepuluh Nopember Sukolilo Department of Mathematics Surabaya60111 Indonesia Institut Teknologi Sepuluh Nopember Sukolilo Department of Industrial and Systems Engineering Surabaya60111 Indonesia Shibaura Institute of Technology Department of Computer Science and Engineering 3 Chome-7-5 Toyosu Tokyo Koto135-8548 Japan

ISBN: (数字)9798350397055

ISBN: (纸本)9798350397055

We present a deep learning model to detect failure engine state by observing the discrete latent sensor behaviors. Further, we investigate the behaviors from the reconstruction loss of the model until we find its value starting to jump out (anomaly stage). As a result, this work aims to forecast the failure time of the engine as early as possible. To notice the anomaly, we formulate a piecewise function based on alpha -quantile of the loss value inside the proposed model. Unlike the existing studies focusing on the continuous latent, this work draws the discrete latent from discrete variational auto-encoder (DVAE) to predict the failure state better. For evaluation purposes, we evaluated the proposed model on a real dataset from the raw mill machine of a cement factory in Indonesia. From the experiments, we are satisfied to see the proposed model performances detecting the failure state of the raw mill machine as early as possible compared to the state-of-the-art model. © 2022 IEEE.

关键词： Engines

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Introduction of the Up-to-Date Current Development Activities and the Power Beam Control Experiment Project from the LEO for the Realization of the Operational SSPS 74

Introduction of the Up-to-Date Current Development Activitie...

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74th International Astronautical Congress, IAC 2023

作者： Ijichi, Koichi Yanagawa, Hiroki Kitabatake, Hidetoshi Inada, Hitomi Kashimura, Osamu Tanaka, Koji Ishikawa, Ryo Honjo, Kazuhiko Ishimura, Kosei Japan Space Systems 3-5-8 Shibakoen Minato-ku Tokyo105-0011 Japan Japan Aerospace Exploration Agency Institute of Space and Astronautical Science 3-1-1 Yoshinodai Chuo-ku Kanagawa Sagamihara252-5210 Japan Department of Computer and Network Engineering The University of Electro-Communications 1-5-1 Chofugaoka Chofu Tokyo182-8585 Japan Waseda University Dept. of Modern Mechanical Engineering School of Creative Science and Engineering 30401 Okubo Shinjuku-ku Tokyo169-8555 Japan

The outline of the project and the up-to-date status and the progress of the power transmission system towards the realization of the Space Solar Power System (SSPS) will be presented including newly started power transmission demonstration project from the LEO satellite to the ground based on the established technologies by the development projects preceded. Japan Space systems and organized team have been engaged in the SSPS project for more than 30 years and the current power transmission system development project for SSPS was started under METI in the year 2019. The project consisted of three major tasks. The first is to develop system and to establish necessary technologies for so called power generation and transmission panel (PGTP) whose size is about 50 cm square and 10 cm thick, and the target weight should be less than 9 kg. This panel is going to be a unit to consist of the practical power generation and transmission system from the space for the operational SSPS in the GEO as the goal. The second is to improve the total efficiency from DC input to output RF power to exceed 60%. The third is to demonstrate long-range wireless power transmission from flying vehicle to the ground. And then, the power transmission demonstration experiment project from the LEO to the ground with 150kg class satellite started at the end of the year 2022, and is going to be introduced in this paper. This project consisted of the power beaming and configuring technologies from the on the orbit phase array transmission system integrated on the LEO satellite to the ground distributed beam configuration measuring system, and also to demonstrate power transmission based on the preceding development results. Copyright © 2023 by the International Astronautical Federation (IAF). All rights reserved.

关键词： LEO satellite Microwave Moon power SSPS WPT

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Deep multi-metric training: the need of multi-metric curve evaluation to avoid weak learning

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Neural Computing and Applications 2024年第30期36卷 18841-18862页

作者： Mamalakis, Michail Banerjee, Abhirup Ray, Surajit Wilkie, Craig Clayton, Richard H. Swift, Andrew J. Panoutsos, George Vorselaars, Bart Department of Psychiatry University of Cambridge Herchel Smith Building Robinson Way CambridgeCB2 0SZ United Kingdom Department of Computer Science University of Sheffield 211 Portobello SheffieldS1 4DP United Kingdom Insigneo Institute for in silico Medicine University of Sheffield The Pam Liversidge Building SheffieldS1 3JD United Kingdom School of Mathematics and Statistics University of Glasgow GlasgowG12 8QQ United Kingdom Department of Infection Immunity and Cardiovascular Disease University of Sheffield SheffieldS10 2HQ United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS10 2TN United Kingdom School of Mathematics and Physics University of Lincoln Brayford Pool LincolnLN6 7TS United Kingdom Department of Engineering Science Institute of Biomedical Engineering University of Oxford OxfordOX3 7DQ United Kingdom Division of Cardiovascular Medicine Radcliffe Department of Medicine University of Oxford OxfordOX3 9DU United Kingdom

The development and application of artificial intelligence-based computer vision systems in medicine, environment, and industry are playing an increasingly prominent role. Hence, the need for optimal and efficient hyperparameter tuning strategies is more than crucial to deliver the highest performance of the deep learning networks in large and demanding datasets. In our study, we have developed and evaluated a new training methodology named deep multi-metric training (DMMT) for enhanced training performance. The DMMT delivers a state of robust learning for deep networks using a new important criterion of multi-metric performance evaluation. We have tested the DMMT methodology in multi-class (three, four, and ten), multi-vendors (different X-ray imaging devices), and multi-size (large, medium, and small) datasets. The validity of the DMMT methodology has been tested in three different classification problems: (i) medical disease classification, (ii) environmental classification, and (iii) ecological classification. For disease classification, we have used two large COVID-19 chest X-rays datasets, namely the BIMCV COVID-19+ and Sheffield hospital datasets. The environmental application is related to the classification of weather images in cloudy, rainy, shine or sunrise conditions. The ecological classification task involves a classification of three animal species (cat, dog, wild) and a classification of ten animals and transportation vehicles categories (CIFAR-10). We have used state-of-the-art networks of DenseNet-121, ResNet-50, VGG-16, VGG-19, and DenResCov-19 (DenRes-131) to verify that our novel methodology is applicable in a variety of different deep learning networks. To the best of our knowledge, this is the first work that proposes a training methodology to deliver robust learning, over a variety of deep learning networks and multi-field classification problems. © Crown 2024.

关键词： COVID-19

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Reduced biquaternion tensors and applications in color video processing

arXiv

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arXiv 2024年

作者： Yu, Cui-E Liu, Xin Luo, Hui Zhang, Yang Macau Institute of Systems Engineering Faculty of Innovation Engineering Macau University of Science and Technology 999078 China School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology 999078 China Department of Mathematics University of Manitoba WinnipegMBR3T 2N2 Canada

In this paper, we introduce the applications of third-order reduced biquaternion tensors in color video processing. We first develop an algorithm for computing the singular value decomposition (SVD) of a third-order reduced biquaternion tensor via a new Ht-product. As theoretical applications, we define the Moore-Penrose inverse of a third-order reduced biquaternion tensor and consider its characterizations via its SVD. Using Moore-Penrose inverses, we mainly discuss the general (or Hermitian) solutions to reduced biquaternion tensor equation A ∗Ht X = B as well as its least-squares solutions. Finally, we develop two algorithms and apply them in color video compression and deblurring, both of which perform better than the compared algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Tensors

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Reciprocal Safety Velocity Cones for Decentralized Collision Avoidance in Multi-Agent systems

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IFAC-PapersOnLine 2023年第2期56卷 8024-8029页

作者： Soulaimane Berkane Dimos V. Dimarogonas Department of Computer Science and Engineering University of Quebec in Outaouais 101 St-Jean Bosco Gatineau QC J8X 3X7 Canada Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology SE-100 44 Stockholm Sweden

In this paper, we solve the inter-agent collision avoidance problem in an arbitrary n-dimensional Euclidean space using reciprocal safety velocity cones (RSVCs). We propose a decentralized feedback control strategy that guarantees simultaneously asymptotic stabilization to a reference and collision avoidance. Our algorithm is purely decentralized in the sense that each agent uses only local information about its neighbouring agents. Moreover, the proposed solution can be implemented using only inter-agent bearing measurements. Therefore, the algorithm is a sensor-based control strategy which is practically implementable using a wide range of sensors such as vision systems and range scanners. Simulation results in a two dimensional environment cluttered with agents shows that the number of possible deadlocks is marginal and decrease with the decrease in the clutteredness of the workspace.

关键词： Collision avoidance multi-agent systems sensor-based methods motion planning feedback control

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Urban Digital Twins for Intelligent Road Inspection

Urban Digital Twins for Intelligent Road Inspection

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Fan, Rui Zhang, Yikang Guo, Sicen Li, Jiahang Feng, Yi Su, Shuai Zhang, Yanting Wang, Wenshuo Jiang, Yu Junaid Bocus, Mohammud Zhu, Xingyi Chen, Qijun Department of Control Science & Engineering Frontiers Science Center for Intelligent Autonomous Systems State Key Laboratory of Intelligent Autonomous Systems Shanghai201804 China Donghua University Department of Computer Science & Technology Shanghai201620 China McGill University Department of Civil Engineering MontrealH3A 0C3 Canada Cto Office at ClearMotion BillericaMA01821 United States University of Bristol Department of Electrical & Electronic Engineering BristolBS8 1UB United Kingdom Tongji University Key Laboratory of Road and Traffic Engineering Ministry of Education Shanghai200092 China

ISBN: (纸本)9781665480451

Urban digital twin (UDT) technologies offer new opportunities for intelligent road inspection (IRI). This paper first reviews the state-of-the-art algorithms used in the two key components of UDT-based IRI systems: (1) multi-temporal, multi-dimension, multi-score, and heterogeneous road data acquisition, and (2) road distress detection. This paper then summarizes the UDTIRI competition, organized in conjunction with IEEE Bigdata 2022. More details on our competition are available at ***/view/udtiri-workshop/bigdata-2022. © 2022 IEEE.

关键词： Data acquisition

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Efficient Information Extraction in Few-Shot Relation Classification through Contrastive Representation Learning

arXiv

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arXiv 2024年

作者： Borchert, Philipp De Weerdt, Jochen Moens, Marie-Francine IESEG School of Management 3 Rue de la Digue Lille59000 France Research Centre for Information Systems Engineering KU Leuven Belgium Department of Computer Science KU Leuven Belgium

Differentiating relationships between entity pairs with limited labeled instances poses a significant challenge in few-shot relation classification. Representations of textual data extract rich information spanning the domain, entities, and relations. In this paper, we introduce a novel approach to enhance information extraction combining multiple sentence representations and contrastive learning. While representations in relation classification are commonly extracted using entity marker tokens, we argue that substantial information within the internal model representations remains untapped. To address this, we propose aligning multiple sentence representations, such as the [CLS] token, the [MASK] token used in prompting, and entity marker tokens. Our method employs contrastive learning to extract complementary discriminative information from these individual representations. This is particularly relevant in low-resource settings where information is scarce. Leveraging multiple sentence representations is especially effective in distilling discriminative information for relation classification when additional information, like relation descriptions, are not available. We validate the adaptability of our approach, maintaining robust performance in scenarios that include relation descriptions, and showcasing its flexibility to adapt to different resource constraints. © 2024, CC BY.

关键词： Classification (of information)

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Application of LabVIEW to Determine Characteristics of Two-Terminal Passive Components 14

Application of LabVIEW to Determine Characteristics of Two-T...

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14th International Conference on Measurement, MEASUREMENT 2023

作者： Otomanski, Przemyslaw Pawlowski, Eligiusz Szlachta, Anna Poznan University of Technology Institute of Electrical Engineering and Electronics Piotrowo street 3A Poznań60-965 Poland Lublin University of Technology Faculty of Electrical Engineering and Computer Science Nadbystrzycka street 38A Lublin20-618 Poland Rzeszow University of Technology Department of Metrology and Measurement Systems W. Pola street 2 Rzeszow35-959 Poland

ISBN: (纸本)9788097262976

The paper discusses the topic of determining the current-voltage characteristics of two-terminal passive components. A virtual measurement instrument created in the LabVIEW environment using the NI USB-6211 data acquisition device is presented, as well as the results of tests performed for example elements. The described approach can also be applied to other measurement situations, such as for studying the self-heating effect of RTD. © 2023 Institute of Measurement science, Slovak Academy o.

关键词： Current voltage characteristics

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