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8th International Conference on engineering and Emerging Technologies, ICEET 2022

作者： Pimpetch, Khwanchanok Srithorn, Jongkol School of Industrial Engineering Institute of Engineering Suranaree University of Technology System Engineering Program Nakhon Ratchasima Province Thailand

ISBN: (纸本)9781665491068

This research aims to improve the plasma cutting process to increase the efficiency of the process by applying the MES system (Manufacturing Execution systems). This study focused on increasing efficiency in the plasma cutting process for supporting future customers' needs, reducing the cost of waste in the plasma cutting process, and creating a more standardized production process. The MES system directly linked pc and PLC (programmable Logic Controller) to increase operational efficiency. The system allows auto-input of all parameters needed for the plasma cutting process through the connection. Furthermore, this system enables the robot to receive the cutting program command based on the customer's command data in the MES Server by creating conditions from the Kepware application. The experimental results show that improvement by the application of the MES system can reduce the production time by %07.55. The amount of waste caused by the plasma cutting process due to cutting errors by %100. Furthermore, it allows the company to reduce the cost of repairing time and reduce the cost of producing new parts to replace the scrap. © 2022 IEEE.

关键词： Industrial research

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A Q-Learning Hybrid BRKGA Applied to the Knapsack Problem with Forfeits 13

A Q-Learning Hybrid BRKGA Applied to the Knapsack Problem wi...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Souto, Gabriel Aritsugi, Masayoshi Mendonca, Israel Simonetti, Luidi González, Pedro Henrique 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

This paper presents a hybrid BRKGA (Q-HBRKGA) that combines BRKGA with Q-learning and a Local Branching technique to solve the Knapsack Problem with Forfeits(KPF). The aim is to tackle this problem, a vari-ant of the 0-1 Knapsack Problem, where a penalty cost is imposed when both items of a forfeit pair are included in the solution. Notably, Q-HBRKGA achieves superior results compared to existing approaches, establishing a valid strategy for KPF problem-solving. Computational experiments, utilizing benchmark instances, demonstrate the efficacy of Q-HBRKGA by outperforming state-of-the-art methods documented in the literature. © 2024 IEEE.

关键词： Reinforcement learning

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Unassisted photoelectrochemical CO_(2)reduction by employingⅢ-Ⅴphotoelectrode with 15%solar-to-fuel efficiency

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Carbon Energy 2025年第3期7卷 1-10页

作者： Karthik Peramaiah Purushothaman Varadhan Vinoth Ramalingam Bilawal Khan Pradip Kumar Das Hao Huang Hui-Chun Fu Xiulin Yang Vincent Tung Kuo-Wei Huang Jr-Hau He Chemistry Program Center for Renewable Energy and Storage Technologies(CREST)and Division of Physical Science and EngineeringKing Abdullah University of Science and Technology(KAUST)ThuwalSaudi Arabia Institute of Sustainability for Chemicals Energyand Environment(ISCE2)Agency for ScienceTechnologyand Research(A*STAR)Singapore School of Computing Engineering and TechnologyRobert Gordon UniversityAberdeenUK Department of Materials Science and Engineering City University of Hong KongKowloonSARHong KongChina Guangxi Key Laboratory of Low Carbon Energy Materials School of Chemistry and Pharmaceutical SciencesGuangxi Normal UniversityGuilinChina Department of Chemical System and Engineering University of TokyoTokyoJapan

Solar-driven carbon dioxide reduction reaction(CO_(2)RR)provides an oppor tunity to produce value-added chemical feedstocks and ***,achieving efficient and stable photoelectrochemical(PEC)CO_(2)RR into selec tive products is challenging owing to the difficulties associated with the optical and the electrical configuration of PEC devices and electrocatalyst ***,we construct an efficient,concentrated sunlight-driven CO_(2)RR setup consisting of InGaP/GaAs/Ge triple-junction cell as a photoanode and oxide-derived Au(Ox-Au)as a cathode to perform the unassisted PEC CO_(2)*** one-sun illumination,a maximum operating current density of 11.5 mA cm^(-2) with an impressive Faradaic efficiency(FE)of~98%is achieved for carbon monoxide(CO)production,leading to a solar-to-fuel conversion efficiency of~15%.Under concentrated intensity of 10 sun,the photoanode records a maximum current density of~124 mAcm^(-2) and maintains~60%of FE for CO *** results demonstrate crucial advancements in usingⅢ-Ⅴbased photoanodes for concentrated PEC CO_(2)RR.

关键词： photoelectrochemical oxide derived Au concentrated sunlight selec tive products III V photoelectrode pec devices CO reduction solar fuel efficiency

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Comparison of Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) Models on the Poverty Levels in Central Java in 2023

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IAENG International Journal of Applied Mathematics 2024年第12期54卷 2746-2757页

作者： Alya, Najma Attaqiya Almaulidiyah, Qothrotunnidha Farouk, Bailey Reshad Rantini, Dwi Ramadan, Arip Othman, Fazidah Engineering Department Data Science Technology Study Program Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multi-discipline Universitas Airlangga Indonesia Information System Study Program Department of Industrial and System Engineering Telkom University Surabaya Campus Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia

Poverty is considered a serious global issue that must be immediately eradicated by Sustainable Development Goals (SDGs) 1, namely ending poverty anywhere and in any form. As a developing country, poverty is a complex issue faced by Indonesia. Tackling poverty in Indonesia has become a key focus of the government’s strategic priorities. This issue of poverty is experienced by all provinces in Indonesia, including Central Java. The poverty rate in Central Java stands at 10.77% or 3.79 million people. The causes of poverty are explained by several factors. Using these various factors, a mapping model can be conducted to determine the number of poor populations in Central Java. This research employs Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) approaches to compare the effectiveness of these models in analyzing the number of poor populations in Central Java in 2023. The kernel function weighting used in this study is Fixed Gaussian. The results showed that the MGWR model outperformed both the OLS regression and GWR models, achieving an Akaike Information Criterion (AIC) of 62.766, an R2 of 82.3%, and a Mean Squared Error (MSE) of 0.177. Consequently, it can be inferred that the MGWR model was more suitable for explaining the poverty levels in Central Java. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Logistic regression

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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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Modeling the Percentage of NEET in Indonesia with Spatial Cauchy Regression through the Bayesian Analysis Approach

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IAENG International Journal of Applied Mathematics 2024年第7期54卷 1288-1295页

作者： Rantini, Dwi Fakhruzzaman, Muhammad Noor Ningrum, Ratih Ardiati Othman, Fazidah Choir, Achmad Syahrul Ramadan, Arip Alya, Najma Attaqiya Putri, Elfira Rahma Pratama, Muhammad Alfian Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology andMultidiscipline Universitas Airlangga Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia Statistical Computing StudyProgram Politeknik Statistika STIS Indonesia Information System Study Program School of Industrial and System Engineering Telkom University Indonesia

Indonesia has entered a period of demographic bonus. Human resources must be optimized. The number of children who do not in employment, education or training (NEET) in each province needs attention. Several factors that could contribute to the decline in the NEET percentage are literacy rates and the number of adolescents with computer skills. Increasing these two factors is believed to be able to reduce the percentage of NEET in every province in Indonesia. To find out the relationship between these two factors and how much influence they have on the percentage of NEET, this research is modeled by Cauchy regression and includes spatial effects. The results of the analysis show that the best model is when the spatial effect is modeled by Fernandez Steel Skew Normal conditionally autoregressive (FSSN CAR). This result is seen from the smallest value of the Watanabe Akaike Information Criterion (WAIC) in this model, which is 190.5. The parameter estimated shows that the higher the literacy rate and the number of adolescents with computer skills, the lower the percentage of NEET in each province in Indonesia. The results of this research can be useful for the Indonesian government to increase the number of educational facilities related to these two factors. © (2024), (International Association of Engineers). All rights reserved.

关键词： Bayesian CAR Cauchy Education NEET

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Human Following Robot Based on Object Detection for Aerospace Application 1

Human Following Robot Based on Object Detection for Aerospac...

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1st International Conference of Adisutjipto on Aerospace Electrical engineering and Informatics, ICAAEEI 2024

作者： Sumari, Arwin Datumava Wahvudi Aryandika, Ahnaf Bagus Purnami, Ndaru Atmi Dermawan, Denny Syamsiana, Ika Noer State Polytechnic of Malang Cognitive Artificial Intelligence Research Group Department of Electrical Engineering Malang Indonesia Adisutjipto Institute of Aerospace Technology Electrical Engineering Study Program Yogyakarta Indonesia Adisutjipto Institute of Aerospace Technology Aerospace Navigation Instrumentation Control Automation and Robotics System Research Group Electrical Engineering Study Program Yogyakarta Indonesia Adisutjipto Institute of Aerospace Technology Electrical Engineering Study Program Aerosp. Digit. Syst. Microprocessor Microcontroller and Programmable Logic Devices Research Group Yogyakarta Indonesia

ISBN: (纸本)9798331507930

In this research, the Human Following Robot (HFR) prototype has been designed and implemented using YOLO v3-Tiny and Tensor Flow Lite on Raspberry Pi hardware named Rewang. The HFR Rewang is designed to assist aircraft technicians by bringing maintenance tool kits to aircraft shelters. Rewang's movement is based on a camera as a vision sensor following the movement of human entities. Image input is processed by YOLO v3-Tiny through the determination of the bounding box, the calculation of Euclidean Distance, and the determination of the pixel value of the camera as the basis of Rewang's movement which is regulated by an L298N driver motor that controls two Direct Current (DC) motors for the left and right wheels. The test results show that Rewang can move at an average speed of 0.18 m/s at 2 m with a travel time of 11 seconds with a light intensity of more than 250 lux. The minimum and maximum distance of object detection is as far as 120 cm and 400 cm, with an object detection accuracy of 95.8%. Rewang allows the HFR to have a larger carrying capacity to bring more complete aircraft maintenance tools and is prospective for other aerospace and general applications. © 2024 IEEE.

关键词： Travel time

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Coprecipitation Strategy for Halide-Based Solid-State Electrolytes and Atmospheric-Dependent In Situ Analysis

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ACS Applied Materials and Interfaces 2024年第21期16卷 27394-27399页

作者： Bilo, Josanelle Angela V. Chang, Chung-Kai Chuang, Yu-Chun Fang, Mu-Huai Research Center for Applied Sciences Academia Sinica Taipei11529 Taiwan Department of Engineering and System Science National Tsing Hua University Hsinchu30013 Taiwan Nano Science and Technology Program Taiwan International Graduate Program Academia Sinica and National Tsinghua University Hsinchu30013 Taiwan Department of Science and Technology Philippine Textile Research Institute Taguig City1631 Philippines National Synchrotron Radiation Research Center Hsinchu300 Taiwan

In the continuous pursuit of an energy-efficient alternative to the energy-intensive mechanochemical process, we developed a coprecipitation strategy for synthesizing halide-based solid-state electrolytes that warrant both structural control and commercial scalability. In this study, we propose a new coprecipitation approach to synthesized Li3InCl6, exhibiting both structural and electrochemical performance stability, with a high ionic conductivity of 1.42 × 10-3 S cm-1, comparable to that of traditionally prepared counterparts. Through the in situ synchrotron X-ray diffraction technique, we unveil the stability mechanisms and rapid chemical reactions of Li3InCl6 under dry Ar, dry O2, and high-humidity atmosphere, which were not previously reported. Furthermore, the fast reversibility capability of moisture-exposed Li3InCl6 was tracked under vacuum, revealing the optimal recovery conditions at low temperatures (150-200 °C). This work addresses the critical challenges in structural engineering and sustainable mass production and provides insights into chemical reactions under real-world conditions. © 2024 The Authors. Published by American Chemical Society.

关键词： Coprecipitation

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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 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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