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Efficient Node Reduction Heuristic for GNN-Based Traffic Speed Forecasting 11

Efficient Node Reduction Heuristic for GNN-Based Traffic Spe...

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11th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2024

作者： Inokuchi, Yuto Gonzalez, Pedro Henrique Aritsugi, Masayoshi Mendonca, Israel Kumamoto University Graduate School of Science and Technology Kumamoto Japan Federal University of Rio de Janeiro System Engineering and Computer Science Program Rio de Janeiro Brazil Kumamoto University Faculty of Advanced Science and Technology Kumamoto Japan

ISBN: (纸本)9798350367300

In this paper, we aim to reduce the number of nodes from Graph Neural Networks (GNNs), thereby simplifying models and reducing computational costs. GNNs are highly effective for various tasks, such as prediction, classification, and clustering, due to their ability to learn node and edge attributes and relationships, and they have been utilized for intelligent transportation systems recently by converting sensor networks into graph structures. Deep spatio-temporal neural networks, including Spatio-Temporal Graph Convolutional Networks (STGCNs), capture spatial and temporal dependencies, making them suitable for traffic speed forecasting, traffic demand prediction, and travel time estimation. Despite their success, GNNs face challenges in industrial applications due to significant memory usage and time consumption. In this paper, we propose a new approach to node reduction that outperforms existing methods in computational efficiency. Our experiments on two real-world traffic datasets demonstrate that using the heuristic and edge information to reduce nodes can cut computation time of optimization up to 95% and, by eliminating noise, can even enhance prediction accuracy. © 2024 IEEE.

关键词： Graph neural networks

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Optimization of Node Reduction Using BRKGA for GNN-Based Traffic Speed Forecasting 13

Optimization of Node Reduction Using BRKGA for GNN-Based Tra...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Inokuchi, Yuto González, Pedro Henrique Mendonça, Israel Aritsugi, Masayoshi Graduate School of Science and Technology Kumamoto University Kumamoto Japan Federal University of Rio de Janeiro System Engineering and Computer Science Program Rio de Janeiro Brazil Kumamoto University Faculty of Advanced Science and Technology Kumamoto Japan

ISBN: (纸本)9798350308365

While Spatio-Temporal Graph Convolutional Networks (STGCNs) are an effective method for traffic speed fore-casting, their training and inference tend to be time-consuming. In this paper, we aim to refine these networks by strategically reducing their number of nodes, thereby boosting computational efficiency. The nodes in these graphs represent data observed for road segments, and by analyzing the interconnections and layout of the graph, we can identify nodes with minimal contribution to overall performance. Removing these nodes can potentially decrease computation time while maintaining the prediction accuracy. We employ the Biased Random-Key Genetic Algorithm (BRKGA) to identify a good set of nodes for removal, based on a predefined percentage reduction of the original graph size (e.g., retaining 95 % of the original graph). We evaluate different graph size configurations, ranging from 95 % to 70 % node retention, to determine the least impactful node set performance. Our experiments on three real-world datasets reveal that reducing nodes can decrease computation time by up to 29%, and as a byproduct of removing noise, even improve the prediction accuracy. © 2024 IEEE.

关键词： Genetic algorithms

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Efficient Hyperparameter Optimization Using Deep Q-Network and BRKGA 13

Efficient Hyperparameter Optimization Using Deep Q-Network a...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Kobayashi, Kosei Aritsugi, Masayoshi González, Pedro Henrique Mendonça, Israel Graduate School of Science and Technology Kumamoto University Kumamoto Japan Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan System Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (纸本)9798350308365

Hyperparameter optimization (HPO) is paragon to maximize performance when designing machine learning models. Among different HPO methods, Genetic Algorithm (GA) based optimization is considered effective because it allows a wide and diverse range of solutions to be explored. However, GA's exploratory nature makes this type of algorithm to evaluate many solutions that do not improve the overall performance. This is specially costly when the objective function to be evaluated is time-consuming, like in the HPO field. In this paper, we propose an efficient hybrid algorithm that is able to reduce computational cost by combining deep reinforcement learning with the Biased Random Key Genetic Algorithm (BRKGA), a variant of genetic algorithms. Our reinforcement learning agent has a decision-making role during the population's fitness calculation, in which it filters out chromosomes that would not improve the overall fitness of the population. The agent uses small amounts of pre-trained data to identify trends in potentially good solutions, and carry out its decision process. We conduct experiments on eight different datasets to assess the effectiveness of the proposed method, and the results show that the proposed method can significantly reduce the computation time of hyperparameter search using BRKGA (up to 44% reduction in computational time) without compromising the quality of the solution (no statistically difference in results). © 2024 IEEE.

关键词： Genetic algorithms

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Altitude Hold Controls on Multi-Rotor sUAV with Barometer Effects

Altitude Hold Controls on Multi-Rotor sUAV with Barometer Ef...

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Cybernetics and Innovations (ICCI), International Conference on

作者： Jirayuth Jantawong Prasongsuk Songsree Warinthorn Kirathihiran Faculty of Engineering and Technology Technology Program in Electrical Technology and Automatic Control System Siam Technology College Bangkok Thailand

ISBN: (数字)9798331533267

ISBN: (纸本)9798331533274

This paper presents an accurate altitude estimation on a small multirotor UAV. That is crucial for the stability and performance of UAV, particularly in altitude hold control. Barometric pressure sensors are widely used to estimate altitude based on atmospheric pressure, but their readings are highly susceptible to environmental fluctuations, temperature variations, and dynamic airflow effects. To mitigate these altitude errors, this study focuses on enhanced flight control installation techniques, including the physical protection of barometric sensors inside enclosed, well-ventilated compartments to shield them from wind disturbances and rapid pressure changes. The proposed mitigation strategy is validated through ground testing and flight testing, where altitude hold performance is assessed under various environmental conditions. The results confirm that enclosing the barometric sensor within a protective, ventilated chamber significantly reduces altitude estimation errors, leading to more stable altitude hold control with reduced vertical oscillations. These findings highlight the importance of integrated sensor protection in UAV flight control system design and provide a practical framework for improving altitude estimation reliability in both consumer and industrial UAV applications.

关键词： Temperature sensors Wind Technological innovation Fluctuations Autonomous aerial vehicles Barometers Protection Thermal stability Aerospace control Testing

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Design and Development of Electric Motor Torque Meter Using Microcontroller

Design and Development of Electric Motor Torque Meter Using ...

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Research, Invention, and Innovation Congress (RI2C)

作者： Peerawat Meesuk Natthaphong Nak-Udom Kittiphong Phimyotha Program of Electrical Engineering and Automation Control System Faculty of Engineering and Industrial Technology Bansomdejchaopraya Rajabhat University Bangkok Thailand Program of Electromechanic Manufacturing Engineering Faculty of Engineering and Industrial Technology Bansomdejchaopraya Rajabhat University Bangkok Thailand

ISBN: (数字)9798331506018

ISBN: (纸本)9798331506025

This article presents the development of electric motor torque meter using microcontroller together with dynamometer process and application of light process to find out revolutions per minute (RPM) which torque is a variable that affects the effectiveness of electrical machine. A torque meter is, therefore, a tool that has an important role to work with mechanical part. The torque meter and RPM was tested and found that there is an error of torque measure at 13.74 percent and found that the torque has higher errors when it exceeds 2 N-m and there is an error of RPM at 5.36 percent.

关键词： Technological innovation Torque Microcontrollers Measurement uncertainty Size measurement Pollution measurement Electrical resistance measurement Tachometers Standards Electric motors

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The Impact Perceived Desirability, Propensity to Act, and Perceived Feasibility towards Technoprenurial Commitment Based on A Entreprenurial Event Theory 6

The Impact Perceived Desirability, Propensity to Act, and Pe...

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6th International Conference on Cybernetics and Intelligent system, ICORIS 2024

作者： Rumangkit, Stefanus Irianto, Aloysius Bagas Pradipta Hadi, Antonius Satria Management Department BINUS Business School Bina Nusantara University Jakarta Indonesia Information System Department Faculty of Engineering Atma Jaya University Yogyakarta Indonesia Study Program of Entrepreneurship Faculty of Economy University of Widya Mataram Yogyakarta Indonesia

ISBN: (纸本)9798331539603

Technopreneurship represent a form of entrepreneurship that emphasizes entrepreneurial skills and technology. Studies on technopreneurship have been conducted, but research investigating antecedents of technopreneurship based on the entrepreneurial event model (EEM), is still limited. Most research on technopreneurship is dominated by the use of the theory of planned behavior (TPB). Therefore, this study aims to investigate the driving factors in EEM, namely perceived desirability, propensity to act, and perceived feasibility to technopreneurship intention. In addition, another purpose of this study is to explore technopreneurship intention towards technopreneurship commitment. To achieve this goal, the study used quantitative research design, primary data sources, and data collection methods using questionnaires. The sample in this study amounted to 240, with the sampling method using purposive sampling. Reliability, validity, and hypothesis testing using Smart-PLS software version 3. The results showed that perceived desirability affects technoprenurial intention, propensity to act affects technoprenurial intention, perceived feasibility affects technoprenurial intention, and technoprenurial intention affects technopreneurial commitment. © 2024 IEEE.

关键词： Entrepreneurship Intention Entreprenurial Event Model Technopreneurship

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A Robust Higher-order Sliding Mode Controller Design for Underwater Vehicles in the Presence of Uncertainties

A Robust Higher-order Sliding Mode Controller Design for Und...

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International Conference on Control, Automation and systems ( ICCAS)

作者： Tegen Eyasu Derbew Nak Yong Ko Dept. of Electronic Engineering Interdisciplinary Program in IT-Bio Convergence System Chosun University Gwangju South Korea

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

This paper presents trajectory following of a 6 degrees of freedom (DOFs) underwater vehicle using a robust generalized super-twisting algorithm (GSTA)-based sliding mode controller (SMC) (GSTA-SMC). A Lyapunov stability analysis framework was applied to verify the finite-time convergence of the system trajectories to the sliding manifold. Numerical simulations, both with and without the presence of external disturbances, were conducted using MATLAB/Simulink software. The tracking and chattering effect suppressing capabilities of the proposed controller were compared with the standard super-twisting algorithm (STA)-based sliding mode controller (STA-SMC). The simulation results demonstrated the effectiveness and the robustness of the proposed control method.

关键词： Uncertainty Software algorithms Numerical simulation Stability analysis Robustness 6-DOF Trajectory Underwater vehicles Numerical stability Standards

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The Combined Localization Based on TDOA and FDOA for High-Speed Moving Object

The Combined Localization Based on TDOA and FDOA for High-Sp...

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International Symposium on Soft Computing and Intelligent systems (SCIS)

作者： Seul-Bi Jeon Tae-Ho Jo Jong-Hwan Park Suk-Seung Hwang Dept. of Electronic Engineering Interdisciplinary Program in IT-Bio Convergence System Chosun University Gwangju Korea Satellite System Lab Soletop Co. Ltd Daejeon Korea Interdisciplinary Program in IT-Bio Convergence System School of Electronic Eigineering Chosun University Gwangju Korea

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

Detecting the location of high-speed moving objects is a critical issue in various fields including the medical, emergence service, aerospace, defense areas, etc. In general, Time Difference of Arrival (TDOA) and Frequency Difference of Arrival (FDOA) techniques are primarily used for estimating the location of a moving object. Although these techniques provide excellent estimation performance for the stationary or low speed moving object, they may have significant location estimation errors for the high-speed object moving. In order to improve this problem, this paper introduces the combined localization method based on TDOA and FDOA for estimating the location of high-speed moving objects efficiently.

关键词： Location awareness Time-frequency analysis Estimation error Time difference of arrival Computer simulation Frequency estimation

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Techno-Economic Analysis of Utilization Offshore Platform in the Java Sea to Produce Hydrogen with Offshore Wind Turbine-Based Energy Sources 7

Techno-Economic Analysis of Utilization Offshore Platform in...

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7th Astechnova International Energy Conference, ASTECHNOVA 2023

作者： Franika, Resistentio Vembre Ridwan, Muhhamad Kholid Perdana, Abram Master Program in System Engineering Universitas Gadjah Mada Jalan Teknika Utara 3 Yogyakarta55281 Indonesia Department of Physics Engineering Universitas Gadjah Mada Jalan Grafika 2 Yogyakarta55284 Indonesia Renewable Energy Systems HertfordshireWD4 8LR United Kingdom

Indonesia's geographical condition, which consists of 70 percent ocean territory, holds the potential for utilizing offshore wind turbines to provide cleaner energy. In this study, economic calculations were conducted for offshore wind turbines to generate green electricity and green hydrogen. The chosen ocean area for this endeavor is around the Papa offshore platform in the Java Sea. This area has experienced an economic decline, making it viable for repurposing as a sub-station for a powerhouse and an electrolyzer plant. Based on meteorological data analysis results, the area around the Papa offshore platform exhibits an average at height of 119 m;the wind speed is 5.5 m/*** prevailing wind direction is East Southeast (ESE), with a frequency of 25.2%. Further analysis of wind farms aimed at generating a capacity of 100.8 MW revealed an optimal potential energy generation of approximately 141,656.9 MWh per year. For scenario 1 (electricity production), the cost is $0.192/kWh. The levelized cost of hydrogen (LCOH) for scenario 2 (offshore electrolyzer) and scenario 3 (onshore electrolyzer) is $9.44/kg and $10.1/kg, respectively. To assess investment feasibility, a Net Present Value (NPV) analysis was conducted. This analysis yielded a selling price of electricity at $0.0271/kWh for the first scenario. For the second scenario, the selling price of hydrogen was calculated at $17.11/kg, and for the third scenario, it was $16.99/kg. Considering its flexibility and potential response to restrictions, the third scenario was deemed the most suitable for offshore utilization to produce clean energy. © Published under licence by IOP Publishing Ltd.

关键词： Offshore wind turbines

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Multi Task Classification Using Deep Learning Approaches for Big Five Personality Traits Prediction: A Review 11

Multi Task Classification Using Deep Learning Approaches for...

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11th International Conference on Information Technology, Computer and Electrical engineering, ICITACEE 2024

作者： Nudin, Salamun Rohman Gernowo, Rahmat Somantri, Maman Wibowo, Adi Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Physics Semarang Indonesia Diponegoro University Faculty of Engineering Department of Electrical Engineering Semarang Indonesia Diponegoro University Faculty of Science and Mathematics Department of Informatics Semarang Indonesia

ISBN: (纸本)9798350389289

This paper presents a comprehensive review of deep learning methods for the Big Five personality traits prediction using multi-task classification. The purpose of the review is to determine the performance of models developed in previous research to describe research opportunities and challenges in the future. The Big Five model is a framework in psychology that categorizes human personality into five dimensions: openness, conscientiousness, extraversion, agreeableness, and neuroticism. The review begins by analyzing 22 articles from the Science Direct, IEEE Xplore, and Scopus databases by applying screening using the Big Five Personality keywords. Then study the concepts of deep learning for model development such as: input, architecture, method, dataset, and performance to identify the strengths and limitations of each approach. The review results of the highest average accuracy by modality such as unimodal text with essay dataset (80.98%), unimodal image with ImageNet dataset (95.36%), unimodal audio with SSPNet dataset (79.48%), bimodal image and audio with Chalearn Video First Impression dataset (91.67%), multimodal text, image, and audio with Chalearn Video First Impression dataset (91.88%). The average accuracy of the Big Five Personality traits was highest (95.36%) and lowest (59%). This highlights the need for standard benchmarks, interpretable models and interdisciplinary collaboration to improve the accuracy, reliability and ethical implications of personality prediction. Future research is expected to pave the way for advancements in understanding human personality through deep learning approaches using other modalities such as EEG, GSR, and PPG. © 2024 IEEE.

关键词： Contrastive Learning

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