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Enhanced Robust Low-Order controller for Air Handling Units in HVAC Systems 5

Enhanced Robust Low-Order Controller for Air Handling Units ...

引用

5th IEEE International Conference on Technology, Informatics, Management, Engineering and Environment, TIME-E 2024

作者： Hilmi, Muhammad Utama, Aditya Girza Bandung Institute of Technology Instrumentation and Control Study Program Bandung Indonesia

ISBN: (纸本)9798350364743

Air handling units (AHUs), integral components of HVAC systems, play a crucial role in maintaining indoor air quality. However, implementing AHUs in critical environments poses challenges due to inaccurate modeling of these complex systems, load changes from external factors, and inherent system uncertainties, potentially leading to suboptimal control performance. In this paper, we adopt a robust control approach using μ-synthesis to address these uncertainties and disturbances, aiming to achieve robust stability and performance of the closed-loop system. To facilitate practical implementation, we reduce the complexity of the high-order controller resulting from the robust method by applying Hankel-norm approximation to obtain a reduced-order controller. Validation through simulation demonstrates a robust system response to exogenous disturbances, with root mean square errors of 2.6416 × 10-4 °C and energy savings of 8.16 % in average power consumption. The reduced-order controller is justified by its comparable robustness and accuracy to the high-order controller, with additional advantages in energy savings and computational load. The results demonstrate the proposed method's efficacy, showcasing its potential to significantly improve the control capabilities of HVAC systems. © 2024 IEEE.

关键词： HVAC

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Potato Leaf Disease Detection by Means of Vision Transformer and CoAtNet Architecture 5

Potato Leaf Disease Detection by Means of Vision Transformer...

引用

5th IEEE International Conference on Technology, Informatics, Management, Engineering and Environment, TIME-E 2024

作者： Kenan Sergio, Mesa Anugerah Stevano Siallagan, Ariel Brandon Joelianto, Endra Institut Teknologi Bandung Engineering Physics Study Program Bandung Indonesia Institut Teknologi Bandung Instrumentation Control and Automation Research Group Bandung Indonesia

ISBN: (纸本)9798350364743

Potatoes are a food source that is beneficial for humans in various aspects, such as having nutritional and economic value. However, potato plants are susceptible to many diseases. To address this issue, various researchers have suggested methods utilizing machine learning and image processing for automatic disease type detection. This paper introduces a disease detection model for potato leaf images, employing two computer vision techniques: Vision Transformer (ViT) and CoAtNet. The dataset used in this paper consisted of 30,760 augmented images divided into seven classes. The ViT model had a test accuracy of 99.32% and a validation accuracy of99.12%. Meanwhile, the CoAtNEt model had a test accuracy of 99.58 % and a validation accuracy of 99.48 %. These results show that both models can be used in the detection of potato leaf diseases. © 2024 IEEE.

关键词： CoAtNet image processing leaf disease potato vision transformer

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Enhanced Robust Low-Order controller for Air Handling Units in HVAC Systems

Enhanced Robust Low-Order Controller for Air Handling Units ...

引用

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

作者： Muhammad Hilmi Aditya Girza Utama Instrumentation and Control Study Program Bandung Institute of Technology Bandung Indonesia

ISBN: (数字)9798350364750

ISBN: (纸本)9798350364767

Air handling units (AHUs), integral components of HVAC systems, play a crucial role in maintaining indoor air quality. However, implementing AHUs in critical environments poses challenges due to inaccurate modeling of these complex systems, load changes from external factors, and inherent system uncertainties, potentially leading to suboptimal control performance. In this paper, we adopt a robust control approach using µ-synthesis to address these uncertainties and disturbances, aiming to achieve robust stability and performance of the closed-loop system. To facilitate practical implementation, we reduce the complexity of the high-order controller resulting from the robust method by applying Hankel-norm approximation to obtain a reduced-order controller. Validation through simulation demonstrates a robust system response to exogenous disturbances, with root mean square errors of 2.6416 × 10 −4 °C and energy savings of 8.16 % in average power consumption. The reduced-order controller is justified by its comparable robustness and accuracy to the high-order controller, with additional advantages in energy savings and computational load. The results demonstrate the proposed method's efficacy, showcasing its potential to significantly improve the control capabilities of HVAC systems.

关键词： Robust control HVAC Uncertainty Accuracy Energy conservation Stability analysis Robustness Complexity theory Complex systems Root mean square

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Optimizing Indoor Vertical Farming with DDPG Reinforcement Learning: Enhancing control of Nutrient Levels, Climate Conditions, and Plant Health 5

Optimizing Indoor Vertical Farming with DDPG Reinforcement L...

引用

5th IEEE International Conference on Technology, Informatics, Management, Engineering and Environment, TIME-E 2024

作者： Albert Panggabean, Good Fried Romadhon, Rahmat Lubis, Ramot Joelianto, Endra Institut Teknologi Bandung Doctoral Program in Engineering Physics Bandung Indonesia Institut Teknologi Del Faculty of Informatics and Electrical Engineering Toba Indonesia Institut Teknologi Bandung Engineering Physics Study Program Bandung Indonesia Institut Teknologi Bandung Instrumentation Control and Automation Research Group Bandung Indonesia

ISBN: (纸本)9798350364743

In indoor vertical farming systems, managing dynamic and complex environments poses significant challenges. Traditional control strategies often fall short in adapting to rapid changes in critical parameters such as nutrient levels, climate conditions, and plant health indicators. To address these challenges, this paper presents a novel approach for optimizing indoor vertical farming using Deep Deterministic Policy Gradient (DDPG), a reinforcement learning algorithm well-suited for environments with continuous action spaces. Our study focused on the control and monitoring of key parameters, including nutrient levels (pH and EC), climate conditions (temperature, humidity, light, and CO2 levels), and plant health indicators (biomass accumulation and leaf size structure). By defining a comprehensive state and action space, we simulated an indoor farming environment and trained the DDPG model to maximize crop yield and health. © 2024 IEEE.

关键词： DDPG indoor vertical farming precision agriculture reinforcement learning smart farming

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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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Potato Leaf Disease Detection by Means of Vision Transformer and CoAtNet Architecture

Potato Leaf Disease Detection by Means of Vision Transformer...

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

作者： Mesa Anugerah Kenan Sergio Ariel Brandon Stevano Siallagan Endra Joelianto Engineering Physics Study Program Institut Teknologi Bandung Bandung Indonesia Instrumentation Control and Automation Research Group Institut Teknologi Bandung Bandung Indonesia

ISBN: (数字)9798350364750

ISBN: (纸本)9798350364767

关键词： Productivity Computer vision Accuracy Computational modeling Image processing Machine learning Transformers Informatics Monitoring Diseases

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Optimisation-based sampling time selection for modelling the three interacting tanks

Optimisation-based sampling time selection for modelling the...

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2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023

作者： Rahadian, Helmy Burohman, Azka Muji Widyotriatmo, Augie Joelianto, Endra Institut Teknologi Bandung Engineering Physics Doctoral Program Bandung Indonesia Universitas Dian Nuswantoro Electrical Engineering Study Program Semarang Indonesia Research Center for Photonics Tangerang Selatan Indonesia Institut Teknologi Bandung Instrumentation and Control Research Group Bandung Indonesia University Center of Excellence Ai on Vision Nlp & Big Data Analytics Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9798350309225

This paper considers parameters and sampling time identification of a three interacting tanks system. A novel tailor-made model is introduced to deal with the real plant in the laboratory. Experimental data are then exploited to perform parameter identification via particle swarm optimisation (PSO) method. In addition to the system parameters, we propose to simultaneously optimize the sampling selection in the optimisation process. Even though several well known sampling time selection are established, a more precise selection is required. It is shown in this paper that a meta-heuristic optimisation method (PSO) can be an efficient procedure for this problem. Moreover, three different error measures for performing optimisation are also discussed. © 2023 IEEE.

关键词： Heuristic methods

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Optimizing Indoor Vertical Farming with DDPG Reinforcement Learning: Enhancing control of Nutrient Levels, Climate Conditions, and Plant Health

Optimizing Indoor Vertical Farming with DDPG Reinforcement L...

引用

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

作者： Albert Good Fried Panggabean Rahmat Romadhon Ramot Lubis Endra Joelianto Doctoral Program in Engineering Physics Institut Teknologi Bandung Bandung Indonesia Faculty of Informatics and Electrical Engineering Institut Teknologi Del Toba Indonesia Engineering Physics Study Program Institut Teknologi Bandung Bandung Indonesia Instrumentation Control and Automation Research Group Institut Teknologi Bandung Bandung Indonesia

ISBN: (数字)9798350364750

ISBN: (纸本)9798350364767

关键词： Smart agriculture Precision agriculture Reinforcement learning Humidity Aerospace electronics Nutrients Temperature control Informatics Monitoring Farming

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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

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

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Exploring the Influence of Temperature on Sodium Titanate Mineral Formation Using X-Ray Diffraction 2

Exploring the Influence of Temperature on Sodium Titanate Mi...

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2nd International Conference on Physics and Applied Physics, ICP and AP 2023

作者： Noer, Zikri Sihombing, Yuan Alfinsyah Rajagukguk, Juniastel Idamayanti, Dewi Taufik, Dede Amri, Fauzan Agus, Muhammad Abduh Akram Department of Physics Faculty of Vocational Universitas Sumatera Utara Jl. Bioteknologi No. 2 Sumatera Utara Medan20155 Indonesia Department of Physics Faculty of Mathematics and Natural Sciences Universitas Sumatera Utara Jl. Bioteknologi No. 1 Sumatera Utara Medan20155 Indonesia Department of Physics Faculty of Mathematics and Natural Sciences Universitas Negeri Medan Medan Indonesia Advance Materials Engineering Study Program Department of Foundry Engineering Politeknik Manufaktur Bandung Bandung40135 Indonesia Center for Ceramics Ministry of Industry of Indonesia Jl. Achmad Yani No. 392 Jawa Barat Bandung40272 Indonesia Instrumentation and Control Engineering Technology Politeknik Negeri Indramayu Jawa Barat Indonesia

In this study, an inquiry was conducted to examine the influence of temperature on the crystal structure of sodium titanate (NTO). NTO was synthesized using NaCl, TTIP, citric acid, and ethylene glycol as precursors. The NaCl-citric acid precursor was dissolved in purified water, while the titanium precursor was prepared by blending ethylene glycol with TTIP. After vigorous stirring, the precursor solution was placed in a Teflon-coated stainless steel autoclave, subjected to preheating, and subsequently dried to form a gel. The gel underwent calcination at temperatures ranging from 800 to 1000 °C for 3 hours each. The resulting samples were rinsed with deionized water and dried to eliminate residual NaCl. Based on the results of XRD characterization, the NaTi4O8 phase experienced decomposition into rutile form when the calcination temperature was increased from 800-1000 °C and began to be identified as a rutile phase at a temperature of 900 °C. © Published under licence by IOP Publishing Ltd.

关键词： Sodium chloride

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