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Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

Computers, Materials & Continua 2023年第11期77卷 2579-2594页

作者： Abdennasser Dahmani Yamina Ammi Nadjem Bailek Alban Kuriqi Nadhir Al-Ansari Salah Hanini Ilhami Colak Laith Abualigah El-Sayed M.El-kenawy Department of Mechanical Engineering Faculty of Science and TechnologyGIDD Industrial Engineering and Sustainable Development LaboratoryUniversity of RelizaneBourmadiaRelizane48000Algeria Laboratory of Biomaterials and Transport Phenomena(LBMPT) University of MedeaUrban PoleMedea26000Algeria Energies and Materials Research Laboratory Faculty of Sciences and TechnologyUniversity of TamanghassetTamanrasset10034Algeria Sustainable Development and Computer Science Laboratory Faculty of Sciences and TechnologyAhmed Draia University of AdrarAdrarAlgeria CERIS Instituto Superior TécnicoUniversidade de LisboaLisbon1049-001Portugal Civil Engineering Department University for Business and TechnologyPristina10000Kosovo Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Engineering and Architecture Faculty Nisantasi UniversityIstanbulTurkey Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains MalaysiaPulau Pinang11800Malaysia Department of Computing and Information Systems School of Engineering and TechnologySunway University MalaysiaPetaling Jaya27500Malaysia Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Increasing global energy consumption has become an urgent problem as natural energy sources such as oil,gas,and uranium are rapidly running *** into renewable energy sources such as solar energy is being pursued to counter *** energy is one of the most promising renewable energy sources,as it has the potential to meet the world’s energy needs *** study aims to develop and evaluate artificial intelligence(AI)models for predicting hourly global *** hyperparameters were optimized using the Broyden-FletcherGoldfarb-Shanno(BFGS)quasi-Newton training algorithm and STATISTICA *** from two stations in Algeria with different climatic zones were used to develop the *** error measurements were used to determine the accuracy of the prediction models,including the correlation coefficient,the mean absolute error,and the root mean square error(RMSE).The optimal support vector machine(SVM)model showed exceptional efficiency during the training phase,with a high correlation coefficient(R=0.99)and a low mean absolute error(MAE=26.5741 Wh/m^(2)),as well as an RMSE of 38.7045 Wh/m^(2) across all ***,this study highlights the importance of accurate prediction models in the renewable energy,which can contribute to better energy management and planning.

关键词： Renewable energy energy prediction global irradiation artificial intelligence BFGS quasi-Newton training algorithm

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Vehicle Trajectory-Based Intersection Classification Using Deep Transfer Learning

Vehicle Trajectory-Based Intersection Classification Using D...

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Intelligent computing, Communication, Networking and Services (ICCNS), International Conference on

作者： Abanoub Kased Rana Rabee Akram Fahmy Hussein Mohamed Marco Yacoub Mohammed Elhenawy Huthaifa I. Ashqar Anas Alsobeh Mahmoud Masoud Abdallah A. Hassan Computers and Systems Department Faculty of Engineering Minia University Minia Egypt Centre for Accident Research and Road Safety Queensland (CARRS-Q) Australia Civil Engineering Department Arab American University Jenin Palestine School of Computing Southern illinois university Carbondale Carbondale IL USA Department of Information Systems & Operations Management Center for Smart Mobility and Logistics King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia

ISBN: (数字)9798350354690

ISBN: (纸本)9798350354706

This document is a model and instructions for M-TEX. The accelerated expansion of global road networks necessitates the availability of accurate and up-to-date cartographic data for connected and automated vehicles. Conventional mapping techniques, such as satellite imagery and field surveys, are inher-ently time-consuming and labor-intensive. This study proposes a novel approach that makes use of the ubiquity of GPS-embedded devices to facilitate the efficient updating of road maps. We present a classifier that identifies the types of intersections from vehicle trajectories, which represents a crucial initial step in the generation of automated maps. The methodology employs five distinct convolutional deep neural network architectures for the detection and classification of road intersection types. The experimental results, which were obtained using three real-world vehicle trajectory datasets, demonstrate classification accuracy levels that range from 84 % to 91 %. It is noteworthy that these results were achieved using less than 10% of the data that is typically required for comparable accuracy levels, which highlights the efficiency of the feature extraction approach that was employed. This research contributes to the advancement of intelligent transportation systems by offering a more efficient and scalable method for road network mapping and updating.

关键词： Training Accuracy Roads Feature extraction Data models Real-time systems Trajectory Satellite images Convolutional neural networks Vehicle dynamics

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A Bibliometric Analysis of Artificial Intelligence and Electrical Energy Reliability

A Bibliometric Analysis of Artificial Intelligence and Elect...

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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Samuel-Soma M. Ajibade Kehinde Ehineni Angela Lee Siew Hoong Muhammed Basheer Jasser Oluwadare Joshua Oyebode Department of Data Science and Artificial Intelligence School of Engineering and Technology Sunway University Selangor Malaysia Research Centre for Nanomaterials and Energy Technology (RCNMET) Sunway University Selangor Malaysia Industrial Business Unit Littelfuse The Galleria Saskatoon Canada Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Malaysia Civil and Environmental Engineering Department Afe Babalola University Ado Ekiti Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

The escalating dependence on artificial intelligence (AI) in energy and electrical power dependability systems has garnered significant interest in both academic and industrial circles. This study provides a bibliometric examination of artificial intelligence (AI) applications in the field of electrical dependability, covering the time frame from 2002 to 2023. The main aim of this study is to examine the patterns of publication, notable authors, institutions, and funding organizations that contribute to research in artificial intelligence for electrical dependability. A further objective of the endeavor is to chart the co-authorship networks, keyword frequencies, and citation trends in the field. Extraction of data from the Scopus database was carried out using an extensive search approach, and subsequently, network visualization was performed using VOSviewer. The findings indicated a substantial rise in research productivity throughout the last ten years, accompanied with a distinguishable expansion in joint research endeavors. China has established itself as the foremost nation in research on electrical reliability powered by artificial intelligence, with the United States and India following closely after. In addition, the National Natural Science Foundation of China was recognized as the most productive and influential funding institution. The thematic analysis revealed many prominent subjects in the sector, such as predictive maintenance, problem detection, and energy consumption optimization via artificial intelligence. The paper offers invaluable insights into the present research landscape and emphasizes developing trends, providing a clear plan for future research objectives in the fields of artificial intelligence and electrical dependability.

关键词： Productivity Technological innovation Bibliometrics Organizations Reinforcement learning Market research Robustness Visual databases Artificial intelligence Predictive maintenance

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Diagnosis of COVID-19 Pneumonia via a Novel Deep Learning Architecture

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Journal of Computer Science & Technology 2022年第2期37卷 330-343页

作者： Xin Zhang Siyuan Lu Shui-Hua Wang Xiang Yu Su-Jing Wang Lun Yao Yi Pan Yu-Dong Zhang Department of Medical Imaging The Fourth People's Hospital of Huai'anHuai'an 223002China School of Informatics University of LeicesterLeicesterLE17RHU.K. School of Architecture Building and Civil Engineering Loughborough UniversityLoughboroughLE113TUU.K. School of Mathematics and Actuarial Science University of LeicesterLeicesterLE17RHU.K. Key Laboratory of Behavior Sciences Institute of PsychologyChinese Academy of SciencesBeijing 100101China Department of Psychology University of the Chinese Academy of SciencesBeijing 100101China Department of Infection Diseases The Fourth People's Hospital of Huai'anHuaian 223002China Department of Computer Science Georgia State UniversityAtlanta 30302-5060U.S.A. Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz University Jeddah 21589Saudi Arabia

COVID-19 is a contagious infection that has severe effects on the global economy and our daily *** diagnosis of COVID-19 is of importance for consultants,patients,and *** this study,we use the deep learning network AlexNet as the backbone,and enhance it with the following two aspects:1)adding batch normalization to help accelerate the training,reducing the internal covariance shift;2)replacing the fully connected layer in AlexNet with three classifiers:SNN,ELM,and ***,we have three novel models from the deep COVID network(DC-Net)framework,which are named DC-Net-S,DC-Net-E,and DC-Net-R,*** comparison,we find the proposed DC-Net-R achieves an average accuracy of 90.91%on a private dataset(available upon email request)comprising of 296 images while the specificity reaches 96.13%,and has the best performance among all three proposed *** addition,we show that our DC-Net-R also performs much better than other existing algorithms in the literature.

关键词： pneumonia COVID-19 convolutional neural network AlexNet deep learning

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Utilization of Operation Research Techniques and Innovative Testing Methods for Sustainable Construction Materials

Utilization of Operation Research Techniques and Innovative ...

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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Oluwadare Joshua Oyebode Sunday Apeh Samuel-Soma M. Ajibade Chukwudulue Jedidiah Anomnakachukwu Imade Princess Okokpujie Akinniyi Tunde Akinyemi Adedotun Adetunla Olusola Anthony Ibitoye Stephen Gekwu Udeagbara Civil and Environmental Engineering Department Afe Babalola University Ado-Ekiti Ekiti State Nigeria Department of Computing and Information Systems School of Engineering and Technology Sunway University Malaysia Department of Mechanical and Mechatronics Engineering Afe Babalola University Ado-Ekiti Ekiti State Nigeria Department of Architecture Afe Babalola University Ado-Ekiti Ekiti-State Nigeria Department of Mechanical Engineering Sciences University of Johannesburg South Africa Department of Finance Afe Babalola University Ado-Ekiti Ekiti State Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Innovative technology and local content cannot be achieved without appropriate techniques and testing methods to realize sustainable civil infrastructure in a challenging economy. This paper examines the utilization of operation research techniques and innovative testing methods for sustainable construction materials. Pertinent literature and empirical studies were combined to explore the theoretical foundations and real-world applications of operation research methodologies and novel testing methods for sustainable construction. Findings revealed that operation research techniques play a significant role in maximizing the consumption of sustainable construction materials. In the construction sector, ultrasonic testing (UT) is a popular non-destructive testing (NDT) method for evaluating the characteristics and structural integrity of materials like composites, steel, and concrete. Radiographic testing (RT), Computed tomography (CT), digital image correlation (DIC), and laser scanning are Ekiti State, Nigeria beneficial. A non-invasive geophysical technique called ground-penetrating radar (GPR) is used to find and map subsurface features, utilities, and irregularities in soil and building materials. Despite the advancements in operation research techniques and innovative testing methods for sustainable construction materials, several challenges persist, and future directions warrant attention to further enhance their effectiveness and applicability in construction projects. In conclusion, integrating operation research techniques and innovative testing methods represents a transformative opportunity to build a more sustainable future for the construction industry and society. Supportive policies and regulations can drive the adoption of sustainable practices and innovative technologies in the construction industry.

关键词： Operations research Correlation Digital images Computed tomography Maintenance Sustainable development Optimization civil engineering Testing Construction industry

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GIM and BIM

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PFG – Journal of Photogrammetry, Remote Sensing and geoinformation Science 2020年第1期88卷 33-42页

作者： Herle, Stefan Becker, Ralf Wollenberg, Raymond Blankenbach, Jörg Geodetic Institute and Chair for Computing in Civil Engineering & Geo Information Systems RWTH Aachen University Aachen Germany

geospatial information modelling (GIM) is used for decades to document phenomena of the real world. Visualizing and analysing GIM data are usually accomplished by geographic information system tools. The construction industry, on the other hand, uses usually computer-aided design (CAD) tools to plan buildings. With the introduction of building information modelling (BIM), modelling in CAD was enhanced to the entire life cycle of constructions. BIM and GIM are not independent of each other, e.g. BIM uses geospatial data for planning purposes. However, integrating both is challenging since the modelling methods differ. The paper describes approaches to establish interoperability between models of both domains. A literature review reveals the problems and challenges different researchers tackled to achieve interoperability.

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A Bibliometric Analysis of Artificial Intelligence and Electrical Energy Reliability 5

A Bibliometric Analysis of Artificial Intelligence and Elect...

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5th IEEE International Conference on Electro-computing Technologies for Humanity, NIGERCON 2024

作者： Ajibade, Samuel-Soma M. Ehineni, Kehinde Hoong, Angela Lee Siew Jasser, Muhammed Basheer Oyebode, Oluwadare Joshua Sunway University School of Engineering and Technology Department of Data Science and Artificial Intelligence Selangor Malaysia Selangor Malaysia Industrial Business Unit Littelfuse The Galleria 15 Innovation Blvd #140 SaskatoonSKS7N 2X8 Canada Sunway University School of Engineering and Technology Department of Computing and Information Systems Selangor Malaysia Afe Babalola University Civil and Environmental Engineering Department Ekiti Ado Nigeria

ISBN: (纸本)9798331542559

关键词： Artificial intelligence Bibliometric Analysis Electrical Reliability Energy

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Estimating and Mitigating the Congestion Effect of Curbside Pick-ups and Drop-offs: A Causal Inference Approach

arXiv

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arXiv 2022年

作者： Liu, Xiaohui Qian, Sean Teo, Hock-Hai Ma, Wei Department of Information Systems and Analytics School of Computing National University of Singapore Singapore Department of Civil and Environmental Engineering H. John Heinz III Heinz College Carnegie Mellon University PittsburghPA United States Department of Civil and Environmental Engineering The Hong Kong Polytechnic University Hong Kong Hong Kong

Curb space is one of the busiest areas in urban road networks. Especially in recent years, the rapid increase of ride-hailing trips and commercial deliveries has induced massive pick-ups/drop-offs (PUDOs), which occupy the limited curb space that was designed and built decades ago. These PUDOs could jam curbside utilization and disturb the mainline traffic flow, evidently leading to significant negative societal externalities. However, there is a lack of an analytical framework that rigorously quantifies and mitigates the congestion effect of PUDOs in the system view, particularly with little data support and involvement of confounding effects. To bridge this research gap, this paper develops a rigorous causal inference approach to estimate the congestion effect of PUDOs on general regional networks. A causal graph is set to represent the spatio-temporal relationship between PUDOs and traffic speed, and a double and separated machine learning (DSML) method is proposed to quantify how PUDOs affect traffic congestion. Additionally, a re-routing formulation is developed and solved to encourage passenger walking and traffic flow re-routing to achieve system optimization. Numerical experiments are conducted using real-world data in the Manhattan area. On average, 100 additional units of PUDOs in a region could reduce the traffic speed by 3.70 and 4.54 mph on weekdays and weekends, respectively. Re-routing trips with PUDOs on curb space could respectively reduce the system-wide total travel time by 2.44% and 2.12% in Midtown and Central Park on weekdays. Sensitivity analysis is also conducted to demonstrate the effectiveness and robustness of the proposed framework. © 2022, CC BY.

关键词： Drops

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Computational Study of the Structural, Mechanical, Electronic, Optical and Thermal Properties of Balix (X =P, as, Sb) Perovskites

SSRN

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SSRN 2024年

作者： Rahman, Md. Zillur Hasan, Sayed Sahriar Akter, Shamima Mukhtar, Nurhakimah Mohd Absar, Nazifa Hasan, Md Akib Islam, Md Ariful Ichibha, Tom Maezono, Ryo Hongo, Kenta Department of Electrical Systems Engineering Universiti Malaysia Perlis Perlis02600 Malaysia School of Information Science JAIST Ishikawa Nomi923-1292 Japan Department of Electrical and Electronic Engineering International Islamic University Chittagong Kumira Chittagong4318 Bangladesh Chittagong College Chittagong4203 Bangladesh Research Center for Advanced Computing Infrastructure JAIST Ishikawa Nomi923-1292 Japan Institute of Mining and Special Civil Engineering TU Bergakademie Freiberg Freiberg09599 Germany

This study explores the structural, optical, mechanical, electronic, and thermal characteristics of ionic semiconductor compounds BaLiX (X =P, As, Sb) using Density Functional Theory (DFT). A comprehensive analysis of BaLiX (X =P, As, Sb) is conducted, proving that the estimated and observed lattice parameters coincide well and confirming mechanical stability through elastic stiffness constants. Electronic band structures reveal direct bandgap semiconductor properties with values of 0.70 eV, 0.30 eV, and 0.95 eV for BaLiP, BaLiAs, and BaLiSb, respectively, suggesting specific applications such as mid-infrared photodetectors, terahertz devices, and near-infrared sensors. Optical property analyses, including energy loss function, reflectivity, refractive index, and absorption coefficient, highlight the potential of these materials for optoelectronic and photovoltaic applications. The materials exhibit potential for use as thermal barrier coatings (TBC) due to their comparatively lower Debye temperature (D), minimum thermal conductivity (Kmin) and lattice thermal conductivity (kph). © 2024, The Authors. All rights reserved.

关键词： Perovskite

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A Heuristics-Based Cost Model for Scientic Workow Scheduling in Clou

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Computers, Materials & Continua 2021年第6期67卷 3265-3282页

作者： Ehab Nabiel Al-Khanak Sai Peck Lee Saif Ur Rehman Khan Navid Behboodian Osamah Ibrahim Khalaf Alexander Verbraeck Hans van Lint Department of Transport and Planning Faculty of Civil Engineering and Geosciences(CiTG)Delft University of TechnologyDelftNetherlands Faculty of Computer Science and Information Technology University of MalayaKuala LumpurMalaysia Department of Computer Science COMSATS University Islamabad(CUI)IslamabadPakistan Faculty of Computing and Digital Technology HELP UniversityKuala LumpurMalaysia Al-Nahrain Nanorenewable Energy Research Centre Al-Nahrain UniversityBaghdadIraq Department Multi-Actor Systems Faculty of TechnologyPolicy and Management(TPM)Delft University of TechnologyDelftNetherlands

Scientic Workow Applications(SWFAs)can deliver collaborative tools useful to researchers in executing large and complex scientic ***,Scientic Workow Scheduling(SWFS)accelerates the computational procedures between the available computational resources and the dependent workow jobs based on the researchers’***,cost optimization is one of the SWFS challenges in handling massive and complicated tasks and requires determining an approximate(near-optimal)solution within polynomial computational *** by this,current work proposes a novel SWFS cost optimization model effective in solving this *** proposed model contains three main stages:(i)scientic workow application,(ii)targeted computational environment,and(iii)cost optimization *** model has been used to optimize completion time(makespan)and overall computational cost of SWFS in cloud computing for all considered scenarios in this research *** will ultimately reduce the cost for service *** the same time,reducing the cost has a positive impact on the protability of service providers towards utilizing all computational resources to achieve a competitive advantage over other cloud service *** evaluate the effectiveness of this proposed model,an empirical comparison was conducted by employing three core types of heuristic approaches,including Single-based(i.e.,Genetic Algorithm(GA),Particle Swarm Optimization(PSO),and Invasive Weed Optimization(IWO)),Hybrid-based(i.e.,Hybrid-based Heuristics Algorithms(HIWO)),and Hyper-based(i.e.,Dynamic Hyper-Heuristic Algorithm(DHHA)).Additionally,a simulation-based implementation was used for SIPHT SWFA by considering three different sizes of *** proposed model provides an efcient platform to optimally schedule workow tasks by handing data-intensiveness and computational-intensiveness of *** results reveal that the proposed cost optimization model attained an optimal Job completio

关键词： Scientic workow scheduling empirical comparison cost optimization model heuristic approach cloud computing

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