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Consumer technology (ICCT-Pacific), International Conference on

作者： Wei-Hsin Hung Meng-Hsiun Tsai Ying-Hsuan Huang Guu-Chang Yang dept. of Electrical Engineering National Chung Hsing University Taichung Taiwan dept. of Management Information Systems National Chung Hsing University Taichung Taiwan Aeronautical Systems Research Division National Chung-Shan Institute of Science and Technology Taichung Taiwan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

A digital wind tunnel (WT) can learn from historical aerodynamic coefficients derived from experiments and simulations to build a prediction model. This model enables the prediction of aerodynamic coefficients without requiring additional experiments or simulations, significantly reducing their overall frequency and cost. In 2020, Huang et al. adopts multi-output back-propagation neural network (BPNN) to simultaneously predict lift coefficient and drag coefficient. However, Huang et al. does not investigate about the following issues, (1) abnormal data detection, (2) other aerodynamic coefficients, (3) overfitting problem. In order to solve these problems, this paper proposed a data-validate strategy and re-designed the single-output BPNN. Experimental results showed that both the mean square error and maximum absolute error of the proposed method is smaller than those of Huang et al. 's method.

关键词： Technological innovation Costs Wind tunnels Neural networks Mean square error methods Predictive models Aerodynamics Data models Overfitting

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Dual-Objective Mixed Integer Linear Program and Memetic Algorithm for an Industrial Group Scheduling Problem

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IEEE/CAA Journal of Automatica Sinica 2021年第6期8卷 1199-1209页

作者： Ziyan Zhao Shixin Liu MengChu Zhou Abdullah Abusorrah State Key Laboratory of Synthetical Automation for Process Industries and the College of Information Science and Engineering Northeastern UniversityShenyang 110819China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz UniversityJeddah 21481Saudi Arabia Department of Electrical and Computer Engineering Faculty of Engineeringand Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddah 21481Saudi Arabia

Group scheduling problems have attracted much attention owing to their many practical *** work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-dependent setup time,release time,and due *** is originated from an important industrial process,i.e.,wire rod and bar rolling process in steel production *** objective functions,i.e.,the number of late jobs and total setup time,are minimized.A mixed integer linear program is established to describe the *** obtain its Pareto solutions,we present a memetic algorithm that integrates a population-based nondominated sorting genetic algorithm II and two single-solution-based improvement methods,i.e.,an insertion-based local search and an iterated greedy *** computational results on extensive industrial data with the scale of a one-week schedule show that the proposed algorithm has great performance in solving the concerned problem and outperforms its *** high accuracy and efficiency imply its great potential to be applied to solve industrial-size group scheduling problems.

关键词： Insertion-based local search iterated greedy algorithm machine learning memetic algorithm nondominated sorting genetic algorithm II(NSGA-II) production scheduling

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Smart irrigation system using IoT and machine learning methods

Smart irrigation system using IoT and machine learning metho...

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Novel Intelligent and Leading Emerging sciences Conference (NILES)

作者： Ahmed A. Esmail Manar A. Ibrahim Sayed M. Abdallah Ahmed E. Radwan Haya k. Elsonbaty Mohamed A. Elsayed Noure A. Elnakeib Merit Samir Dawoud Amir El-Ghamry Khaled M. Fouad Ibrahim F. Moawad Artificial Intelligence Science Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Information Technology Faculty of Computer and Information Sciences Mansoura University. Faculty of Computer Science and Engineering New Mansoura University Egypt Faculty of Computer and Information Sciences Mansoura University. Faculty of Computer Science and Engineering New Mansoura University Egypt School of Engineering and Computer Science University of Hertfordshire Hosted by Global Academic Foundation Egypt Information Systems Faculty of Computers and Artificial Intelligence Benha University Egypt Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Information Systems Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

Water loss and improper scheduling are problems with traditional irrigation techniques, making it difficult to meet the growing demand for food production while also preserving precious water resources. To address these challenges, this paper introduces a cutting-edge smart irrigation system that leverages the power of the Internet of Things (IoT), data analysis, and machine learning to determine the most efficient way to apply and schedule water for irrigation. The cornerstone of this innovative system is the utilization of a carefully curated dataset, "Crop Irrigation Scheduling," sourced from Kaggle. This dataset comprises six crucial attributes: Crop Type, Crop Days, Soil Moisture, Temperature, Humidity, and Irrigation. These attributes, described in detail in the dataset’s metadata, provide the foundational information required for precise irrigation management. The system operates seamlessly by deploying IoT sensors to collect real-time data from the field. This data is then preprocessed to ensure its quality and consistency. Subsequently, a machine learning model is trained using this dataset to make intelligent irrigation decisions in real time. The model’s predictions are seamlessly integrated with control mechanisms that govern the irrigation process. Moreover, the system offers an intuitive API for easy data access and management, allowing agricultural professionals to monitor and adjust irrigation strategies effortlessly. Experimental findings validate the system’s utility, with particular emphasis on the decision tree model, which demonstrates the best balance of accuracy, precision, recall, and F1 score. This system maximizes crop yield and minimizes water waste, contributing to a more sustainable and productive agricultural ecosystem.

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Forecasting of Photovoltaic Power with ARO based AI approach

Forecasting of Photovoltaic Power with ARO based AI approach

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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： Mounika Nallur B M Nalini Zabiha Khan S Nayana Prasad N Achyutha G Manjula Masters in Computer Information Systems Murray State University USA Department of Computer Science and Engineering East West Institute of Technology Bengaluru India Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of CSE BGS College of Engineering and Technology Bangalore India

Renewable power from sunlight can power the future smart grid with massive amounts of electricity. systems struggle with solar energy's unpredictability and intermittent nature. Unpredictability of solar electricity hinders smart grid optimization and planning. Photovoltaic (PV) power generation must be accurately estimated to reduce power interruptions. PV power must be accurately predicted to avoid grid disturbances from PV facilities. Thus, we describe a transfer learning and AlexNet-based CNN architecture for short-term power forecasting. Past power, solar radiation, wind speed, and temperature readings determine the input. AlexNet's hyper-parameters are optimized using the artificial rabbit method. By adding selective opposition to ARO, local solution tracking efficiency is improved. CNN input features are created from all input parameters as 2D feature maps. After analyzing real PV data from Limberg, Belgium, the math shows that PV systems work.

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Exploring Equilibrium Strategies in Network Games with Generative AI

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IEEE Network 2024年

作者： Yang, Yaoqi Du, Hongyang Sun, Geng Xiong, Zehui Niyato, Dusit Han, Zhu Army Engineering University of PLA College of Communications Engineering Nanjing210000 China University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Jilin University College of Computer Science and Technology Changchun130012 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore College of Computing and Data Science NTU Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States

Game theory offers a powerful framework for analyzing strategic interactions among decision-makers, providing tools to model, analyze, and predict their behavior. However, implementing game theory can be challenging due to difficulties in deriving solutions, understanding interactions, and ensuring optimal performance. Traditional non-AI and discriminative AI approaches have made valuable contributions but struggle with limitations in handling large-scale games and dynamic scenarios. In this context, generative AI emerges as a promising solution because of its superior data analysis and generation capabilities. This paper comprehensively summarizes the challenges, solutions, and outlooks of combining generative AI with game theory. We start with reviewing the limitations of traditional non-AI and discriminative AI approaches in employing game theory, and then highlight the necessity and advantages of integrating generative AI. Next, we explore the applications of generative AI in various stages of the game theory lifecycle, including model formulation, solution derivation, and strategy improvement. Additionally, from game theory viewpoint, we propose a generative AI-enabled framework for optimizing machine learning model performance against false data injection attacks, supported by a case study to demonstrate its effectiveness. Finally, we outline future research directions for generative AI-enabled game theory, paving the way for its further advancements and development. © 1986-2012 IEEE.

关键词： Generative adversarial networks

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Multi-Uncertainty Aware Autonomous Cooperative Planning

arXiv

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arXiv 2024年

作者： Zhang, Shiyao Li, He Zhang, Shengyu Wang, Shuai Ng, Derrick Wing Kwan Xu, Chengzhong Research Institute for Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Macau China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore School of Electrical Engineering and Telecommunications The University of New South Wales Australia

Autonomous cooperative planning (ACP) is a promising technique to improve the efficiency and safety of multi-vehicle interactions for future intelligent transportation systems. However, realizing robust ACP is a challenge due to the aggregation of perception, motion, and communication uncertainties. This paper proposes a novel multi-uncertainty aware ACP (MUACP) framework that simultaneously accounts for multiple types of uncertainties via regularized cooperative model predictive control (RC-MPC). The regularizers and constraints for perception, motion, and communication are constructed according to the confidence levels, weather conditions, and outage probabilities, respectively. The effectiveness of the proposed method is evaluated in the Car Learning to Act (CARLA) simulation platform. Results demonstrate that the proposed MUACP efficiently performs cooperative formation in real time and outperforms other benchmark approaches in various scenarios under imperfect knowledge of the environment. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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General Trans-Compiler based Mobile Applications Converter 3

General Trans-Compiler based Mobile Applications Converter

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3rd Novel Intelligent and Leading Emerging sciences Conference, NILES 2021

作者： Shaban, Nourhan M. Yousef, Ahmed H. Mohamed, Hoda K. Ain Shams University Department of Computer and Systems Engineering Cairo Egypt Egypt School of Information Technology and Computer Science Nile University Giza Egypt

ISBN: (纸本)9781665421577

Deployment on different platforms has been a great issue for mobile companies that aim to maximize the return on investments by making their mobile applications available on different mobile platforms. Consequently, the app may be developed several times to match different platforms. Therefore, there is a need to have solutions that enable the developers to develop the app once, and run it everywhere to reduce the cost of development and reach out to maximum users across several platforms. In this paper a tool is provided with the most popular languages /frameworks (native, cross platform) based on their market share to select the desired framework among them to code with, get the developed application written in the target language that meets their business requirements, and give the ability to convert native application to cross platform one. A trans-compiler based solution is introduced to convert the UI and UI-related backend code of application from a set of languages to any other language in the set and ability of adding more languages and frameworks in the future to achieve the concept of generalization of code conversion. © 2021 IEEE.

关键词： Android (operating system)

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Fast least-squares prestack time migration via accelerating the explicit calculation of Hessian matrix with dip-angle Fresnel zone

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Petroleum science 2022年第3期19卷 1031-1047页

作者： Bo-Wu Jiang Jiang-Jie Zhang Hao Zhang Wen-Kai Lu College of Mechanical and Electrical Engineering Beijing University of Chemical TechnologyBeijing100029China The Institute for Artificial Intelligence(THUAI) Beijing100084China State Key Laboratory of Intelligent Technology and Systems Beijing100084China Beijing National Research Center for Information Science and Technology(BNRist) Beijing100084China The Department of Automation Tsinghua UniversityBeijing100084China Key Laboratory of Petroleum Resource Research Institute of Geology and GeophysicsChinese Academy of SciencesBeijing100029China Institute of Geomechanics Chinese Academy of Geological SciencesBeijing100081China

Amplitude versus offset analysis is a fundamental tool for determining the physical properties of reservoirs but generally hampered by the blurred common image gathers(CIGs).The blurring can be optimally corrected using the blockwise least-squares prestack time migration(BLS-PSTM),where common-offset migrated sections are divided into a series of blocks related to the explicit offsetdependent Hessian matrix and the following inverse filtering is iteratively applied to invert the corresponding ***,calculating the Hessian matrix is *** present a fast BLS-PSTM via accelerating Hessian calculation with dip-angle Fresnel zone(DFZ).DFZ is closely related to optimal migration aperture,which significantly attenuates migration swings and reduces the computational cost of ***,our fast BLS-PSTM is implemented as a two-stage ***,we limit the aperture for any imaging point with an approximated the projected Fresnel zone before calculating the Hessian ***,we determine whether a seismic trace contributes to the imaging point via DFZ during calculating the Hessian *** tests on synthetic and field data validate the distinct speedup with higher-quality CIGs compared to BLS-PSTM.

关键词： Prestack time migration Fresnel zone Hessian Least squares migration Migrated gather

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RDG-TE: Link reliability-aware DRL-GNN-based traffic engineering in SDN

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Expert systems with Applications 2025年 265卷

作者： Farhan, Muhammad Shah, Nadir Wang, Lei Muntean, Gabriel-Miro Song, Houbing Herbert School of Software Dalian University of Technology Dalian China Department of Computer Science COMSATS University Islamabad Wah Campus Pakistan Key Lab of Ubiquitous Network and Service Software of Liaoning Province China School of Electronic Engineering Dublin City University Dublin Ireland Department of Information Systems University of Maryland Baltimore United States

The existing advanced machine learning approaches based on Graph Neural Networks (GNN) for efficient traffic engineering (TE) in Software Defined Networking (SDN) overlook consideration of link reliability values. Link reliability is a very important parameter, directly linked to end-to-end delay for data packet transmission, and can be used to improve the delivery performance. This research article proposes two versions of RDG-TE, a novel model that integrates Deep Reinforcement Learning (DRL) and GNN in order to solve efficiently network TE problems by considering link reliability in both model training and reward computation. The proposed model enables improved performance by more accurately predicting the SDN network behavior in case of link failure. Testing results show that, in comparison to the closest state-of-art approach, our proposed approach reduces the number of disturbed flows by 24.19% and the hop count by 35.38% while also increases the reliable route prediction accuracy by 40.17%. © 2024 Elsevier Ltd

关键词： Deep reinforcement learning

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Deep learning-based Soft word embedding approach for sentiment analysis 28th

Deep learning-based Soft word embedding approach for sentime...

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28th International Conference on Knowledge Based and Intelligent information and engineering systems, KES 2024

作者： Ouni, Chafika Benmohamed, Emna Ltifi, Hela REGIM-Lab.: Research Groups in Intelligent Machines LR11ES48 3038 Faculty of Sciences Economics and Management of Sfax University of Sfax Tunisia Department of Computer Science College of Engineering and Information Technology Onaizah Colleges P.O. Box 5371 Onaizah Saudi Arabia Computer Science and Mathematics Department Faculty of Sciences and techniques of Sidi Bouzid University of Kairouan Tunisia

Word Embeddings (WE) play a crucial role in capturing the meanings of words. They provide continuous vector representations that encode semantic and syntactic information. To accurately convert words into meaningful vectors, in this paper, we propose a novel approach called Soft EMBedding method (SoftEMB). SoftEMB combines the strengths of the Glove and Word2Vec methods through a Soft-Voting algorithm. The SoftEMD approach aims to improve the performance of word embedding, particularly in the context of Sentiment Analysis (SA) hybrid models. To evaluate the SoftEMD performance, we test it on various SA models based on CNN-LSTM, CNN-GRU, CNN-biLSTM, and CNN-bi-GRU. Our results demonstrate a substantial enhancement in accuracy when evaluating movie reviews, with scores of 88.29%, 88.33%, 88.27%, and 88.27%. Similarly, for Sentiment140 dataset, our proposal shows improved results, achieving accuracy rates of 83.27%, 82.78%, 82.76%, and 82.51%. These results highlight the significant progress made in accurately analyzing both movie reviews and the Sentiment140 dataset. © 2024 The Authors.

关键词： Semantics

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