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International Conference on Technology, Informatics, Management, engineering, and Environment (TIME-E)

作者： Ramot Lubis Augie Widyotriatmo Albert Endra Joelianto Doctoral Program in Engineering Physics Faculty of Industrial Technology Institut Teknologi Bandung Bandung Indonesia Instrumentation Control and Automation Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung Indonesia

ISBN: (数字)9798350364750

ISBN: (纸本)9798350364767

This paper presents a microservices-driven digital twin (DT) model for precision farming in controlled indoor environments, addressing the critical need for scalable, modular, and real-time agricultural systems. As the global demand for food rises, integrating Industry 4.0, IoT, edge, and cloud computing technologies becomes essential to improve farming process. The proposed model enhances scalability, modularity, and real-time decision making through decentralized data processing. A master-agent containerized computing architecture was employed to reduce latency and optimize environmental control. Moreover, the integration of a Kalman filter improved the accuracy of key environmental parameter adjustments. This research explored how this model can be scaled for larger farming operations and addresses challenges in system interoperability and implementation. Future work will expand this research to diverse agricultural settings, real-world validation, and ensuring data privacy and security. This study provides a framework for advancing precision farming through digital twin technology.

关键词： Computational modeling Scalability Microservice architectures Real-time systems Digital twins Indoor environment Kalman filters Security Interoperability Farming

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Comparison Performance on SOTA Deep Learning Models for Coffee Beans Grading Inspection

Comparison Performance on SOTA Deep Learning Models for Coff...

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Asian control Conference

作者： Achmad Norman Firdaus Amalia Rakhmawati Vebi Nadhira Suprijanto Endang Juliastuti Gema Nuran Utomo Ayu Gareta Risangtuni Master Program on Instrumentation and Control Faculty of Industrial Technology Bandung Institute of Technology Bandung Indonesia Graduate Program on Engineering Physics Faculty of Industrial Technology Bandung Institute of Technology Bandung Indonesia Instrumentation Control and Automation Research Group Faculty of Industrial Technology Bandung Institute of Technology Bandung Indonesia

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

Several coffee producers rely on manual inspection for quality control, which is prone to inconsistencies and time consumption. Recent studies have proposed Comparison performance on State-of-The-Art (SOTA). Deep learning models were investigated as part of the proposed intelligent computer vision for faster and more reliable coffee bean grading. Performance Deep learning models, i.e., MobileNetV3Small, ResNet50, EfficientNetV2L, and DenseNet201, were evaluated to grade the quality of coffee beans that followed a grading procedure based on Indonesian National Standard (SNI) 01-2907-2008. A specific tray was designed to position coffee beans evenly without overlap for sample preparation, which consist of about 500–600 coffee beans. The process of capturing images is conducted systematically, focusing on each type of coffee bean defect individually. For comparison of SOTA models, five classes of coffee bean defects, With 1053 beans per class, the total dataset comprises 5265 beans. This dataset was divided into 10% for testing and 90% for training. Within the training data, 12% was reserved for validation. The result from this paper shows that MobileNetV3Small achieves the highest accuracy (99.43%) with minimal memory and parameters. Its efficient design allows for robust feature extraction, crucial for distinguishing subtle defect features. While larger models like ResNet50 and EfficientNetV2L also perform well, they exhibit higher memory and parameters.

关键词： Deep learning Training Accuracy Computational modeling Training data Inspection Feature extraction

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Adversarially Robust Reinforcement Learning for Urban Traffic Signal control

Adversarially Robust Reinforcement Learning for Urban Traffi...

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International Conference on Technology, Informatics, Management, engineering, and Environment (TIME-E)

作者： Muhammad Shafly Hamzah Endra Joelianto Herman Yoseph Sutarto Adiyana Putri Instrumentation and Control Master Program Institut Teknologi Bandung Bandung Indonesia Instrumentation Control and Automation Research Group Institut Teknologi Bandung Bandung Indonesia Department of Intelligent System PT. Pusat Riset Energi Bandung Indonesia Doctoral Program of Engineering Physics Institut Teknologi Bandung Bandung Indonesia

ISBN: (数字)9798350364750

ISBN: (纸本)9798350364767

Urban traffic congestion presents critical challenges, resulting in delays, environmental pollution, and significant economic costs. While traditional traffic control methods, such as fixed-time and adaptive systems, have been widely implemented, they often fail in unpredictable conditions, particularly when confronted with noisy or inaccurate data. This research proposes a robust reinforcement learning (RL) solution for traffic signal control using the RADIAL-DQN (Robust Adversarial Loss DQN) algorithm. Integrating adversarial training enhances the system's robustness to real-world uncertainties. Evaluations conducted using a SUMO traffic simulation show that the robust agent produced an increase of only 60.71% in queue length under a projected gradient descent (PGD) adversarial attack, outperforming standard RL agents, which experienced an 89% increase. These results demonstrate the potential of robust RL to improve both the reliability and scalability of urban traffic control systems.

关键词： Training Adaptive systems Uncertainty Scalability Urban areas Traffic control Robustness Standards Traffic congestion Testing

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Automatic Facial Asymmetry Analysis for Elderly Stroke Detection by using Cosine Similarity 19

Automatic Facial Asymmetry Analysis for Elderly Stroke Detec...

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19th International Conference on Electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2022

作者： Kaewmahanin, Watchara Rassameecharoenchai, Teerameth Jutharee, Wisanu Tongskulroongruang, Thienrawit Wiphunawat, Paphatchaya Jennawasin, Tanagorn Kaewkamnerdpong, Boonserm Naresuan University Faculty of Allied Health Sciences Department of Cardiothoracic Technology Phitsanulok Thailand King Mongkut's University of Technology Thonburi Institute of Field Robotics and Automation Bangkok Thailand Mongkut's University of Technology Thonburi Department of Control Systems and Instrumentation Engineering Bangkok Thailand King Mongkut's University of Technology Thonburi Biological Engineering Program Faculty of Engineering Bangkok Thailand

ISBN: (纸本)9781665485845

For acute stroke patients, time to recognize the stroke symptom onset is crucial for the lifesaving treatment. The automatic detection of stroke signs has been increasingly developed for practical use. The better timely classification results could allow better detection of the symptoms and avoid morbidity and mortality. In this study, we proposed using cosine similarity between the left and right sides of the face as a part of features for classifying the stroke elderly patients from healthy elderly participants. We employed our method on images from the Toronto NeuroFace dataset. The results showed high classification accuracy (the average accuracy of 97.8998% from 6 classification methods). These results indicate that the cosine similarity features can be useful to detect facial asymmetry and pose very promising potential for elderly stroke detection. © 2022 IEEE.

关键词： Patient treatment

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Comparative Study on Fall Detection using Machine Learning Approaches 19

Comparative Study on Fall Detection using Machine Learning A...

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19th International Conference on Electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2022

作者： Tongskulroongruang, Thienrawit Wiphunawat, Paphatchaya Jutharee, Wisanu Kaewmahanin, Watchara Rassameecharoenchai, Teerameth Jennawasin, Tanagorn Kaewkamnerdpong, Boonserm King Mongkut's University of Technology Thonburi Department of Control Systems and Instrumentation Engineering Bangkok Thailand Institute of Field Robotics and Automation King Mongkut's University of Technology Thonburi Bangkok Thailand Naresuan University Faculty of Allied Health Sciences Department of Cardiothoracic Technology Phitsanulok Thailand King Mongkut's University of Technology Thonburi Biological Engineering Program Faculty of Engineering Bangkok Thailand

ISBN: (纸本)9781665485845

Falls are one of the most dangerous problems for the elderly. A reliable fall detection system can aid in reducing the harmful repercussions of an unintentional fall. The focus of this paper is on a dataset that includes signals from portable devices. We propose a simple feature selection approach to reduce the number of effective input attributes based on a voting strategy. The dataset is classified into three categories, i.e., pre-fall, fall and post-fall using various machine learning approaches. The computational time is significantly reduced for most of the classification algorithms, and the relative reduction reaches to 66.67% with decision tree algorithm. Classification accuracy can reach as high as 95.17% when using neural network, while the relative reductions on the classification accuracy due to the feature selection do not exceed 1.50%. © 2022 IEEE.

关键词： Decision trees

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Tuning of FP-PID controller based on PSO Algorithm Applied to a Human Gait

Tuning of FP-PID Controller based on PSO Algorithm Applied t...

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IEEE Latin American Robotics Symposium, LARS

作者： Rodrigo B. de Medeiros Daniel M. Muñoz Department of Mechanical Engineering Mechatronics Graduate Program Automation and Control Group/GRACO University of Brasilia Brasilia DF Brazil

ISBN: (数字)9781665462808

ISBN: (纸本)9781665462815

Robotic manipulators are multi-input multi-output (MIMO) systems with nonlinear points affected by numerous uncertainties and disturbances. PID controllers are widely used in industry for kinematic and dynamic control. However, when applied to MIMO systems, they are not easy to tune and require performance improvements. In this work, a PID controller is proposed with a fuzzy precompensator (FP-PID), both tuned by the bioinspired particle swarm optimization (PSO) algorithm to a two-degree of freedom (2-DOF) robotic manipulator representing a human leg. To validate the system, two real datasets of human gait were used: normal walking and stair climbing to estimate the error trajectory of the manipulator. The statistical analysis of the PSO algorithm with 16 experiments was satisfactory, and the addition of the fuzzy precompensator to the conventional PID resulted in a reduction of the mean square error of one of the manipulator links by up to 73 percent.

关键词： Legged locomotion Uncertainty Statistical analysis Service robots Stairs Trajectory Robots

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Human Following Robot Based on Object Detection for Aerospace Application

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

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Aerospace Electrical engineering and Informatics (ICAAEEI), International Conference of Adisutjipto on

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

ISBN: (数字)9798331507930

ISBN: (纸本)9798331507947

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.

关键词： YOLO Tensors Accuracy Prototypes Wheels Cameras Hardware Maintenance Aircraft Aerospace industry

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Regenerative Braking System in E-Trike: Interval Type-2 Fuzzy Logic control to Enhance Driving Range

Regenerative Braking System in E-Trike: Interval Type-2 Fuzz...

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International Conference on Technology, Informatics, Management, engineering, and Environment (TIME-E)

作者： Muhammad Rizalul Wahid Endra Joelianto Bentang Arief Budiman Doctoral Program in Engineering Physics Institut Teknologi Bandung Bandung Indonesia Mechatronics and Artificial Intelligence Universitas Pendidikan Indonesia Bandung Indonesia Instrumentation Control and Automation Research Group Institut Teknologi Bandung Bandung Indonesia Faculty of Mechanical and Aerospace Engineering Institut Teknologi Bandung Bandung Indonesia

ISBN: (数字)9798350364750

ISBN: (纸本)9798350364767

Electric vehicles (EV s) boast zero tailpipe emissions, directly improving air quality and lowering greenhouse gas emissions. Despite the promise of EVs for sustainable transportation, their limited range is a key challenge. Regenerative braking is a promising approach for enhancing the driving range. This research proposes a study of Interval Type-2 Fuzzy Logic (IT2FL) control to improve regenerative braking efficiency in an electric trike. Employing IT2FL control for regenerative braking recovers 35.84% of energy, translating to a 91.053 km increase in driving range. The IT2FL method achieved a range of 50 km/kWh, representing a significant 41.7% improvement. Furthermore, utilizing the Nie-Tan Method reducer within the IT2FL framework could extend the range by an additional 2.3 km, resulting in a 3 % increase in energy recovery. These findings demonstrate the effectiveness of IT2FL control in enhancing regenerative braking efficiency for the electric trike.

关键词： Fuzzy logic Electric potential Transportation Electric vehicles Air quality Informatics Greenhouse gases

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Optimization for vibration analysis in rotating machines

Renewable Energy and Power Quality Journal

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Renewable Energy and Power Quality Journal 2022年 20卷 369-373页

作者： Calderón Ch, J. Alan Tafur Sotelo, Julio C. Gamarra, Benjamín Barriga Alencastre, Jorge Lozano, John Urbizagástegui, Rodrigo Solano, Gonzalo Menacho, Daniel Applied Nanophysics Institute for Physics Technical University of Ilmenau Ilmenau98693 Germany Control Engineering and Automation Master Program Mechatronic Engineering Master Program Engineering Department Pontificia Universidad Católica del Perú Peru Federal University named after MV Lomonosov Arkhangelsk Russia

A not stable mechanical movement transmission between systems produces equilibrium losses, such as a rotor of motors that are coupled in rotating machines. This can be studied as a disturbance "vibration" either as characteristic of the movement transmission due to controlled displacement over rotors, which transmits the movement. Therefore, in this research is presented an analysis for an optimal control of the rotor axis displacement that includes "vibration" as the part of the movement transmission. It implies mathematical modelling and specific sensors selections to correlate the vibration in this control task. Furthermore, in order to verify the proposed analysis, it was simulated and tested in a hybrid magnetic bearing system. © 2022, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

关键词： Vibration analysis

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Linear Fresnel reflector technology in Brazil: a techno-economic evaluation

Renewable Energy and Power Quality Journal

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Renewable Energy and Power Quality Journal 2023年 21卷 329-336页

作者： Azevedo, G.F. Lopes, M.E. Menezes, B.A. Rabelo, O.H.F. Brito, L.B. Dias, A.B. Filho, A.L.F. Domingues, E.G. Department of Electrical Engineering University of Brasília Darcy Ribeiro University Campus – Gleba A Avenida L3 Norte DF Brasília70.910-900 Brazil Electrical Engineering/Control and Automation Engineering Program Federal Institute of Goiás Câmpus Goiânia Rua 75 nº46-Centro GO Goiânia74055-110 Brazil

This study aims to present the results of applying a computational tool developed to perform a technical-economic feasibility analysis of a large-scale Linear Fresnel Reflector (LFR) solar power plant. The viability analysis indicators were obtained considering that the electricity produced trades at the Regulated Contracting Environment of the Brazilian Electricity Market. The parametric structural optimization method is used to optimize the technical parameters of the plant. A case study was conducted to analyze the viability of Implementing a 100 MW LFR power plant in the five regions of Brazil. The results indicated that Northeastern is the best region to install this power plant. However, the project is not viable in all cities analyzed. A sensitivity analysis (SA) of some strategically selected variables was conducted to evaluate its impacts on the project's net present value of an optimized LFR plant located in the city with the best technical performance. Finally, an optimistic scenario was evaluated considering the net present value's response to the parameters investigated in the SA. For this scenario, the results indicated the project's economic viability. © 2023, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

关键词： Sensitivity analysis

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