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23rd International Conference on Control, Automation and systems, ICCAS 2023

作者： Utsula, Bizza S. Ashedananta, Muhammad D. Azis, Nadana A. Nazaruddin, Yul Y. Nadhira, Vebi Institut Teknologi Bandung Faculty of Industrial Technology Department of Engineering Physics Bandung40132 Indonesia Institut Teknologi Bandung Engineering Physics Doctoral Program Faculty of Industrial Technology Bandung40132 Indonesia Instrumentation and Control Research Group Institut Teknologi Bandung Faculty of Industrial Technology Bandung40132 Indonesia

ISBN: (纸本)9788993215274

The increasing number of autonomous vehicles will result in a safety factor that must be maintained. One of the crucial prerequisites for this is proper localization, especially at intersections where most road accidents occur each year. This research is proposed to improve the localization process, which has drawbacks such as the inaccurate Global Positioning system (GPS) when satellite signals are blocked, and Light Detection and Ranging (LiDAR), which is expensive and needs heavy computing time. This is done by implementing the High Definition Map (HD Map) method, which maps the condition of an area that is stored as memory on an autonomous vehicle computer. The proposed system uses a monocular camera to be processed into a semantic segmentation feature map at the pixel level, then uses point cloud to reconstruct a 3D intersection model, which is then segmented semantically as well so that position data is obtained after merging using the occupancy grid and scoring function. The results of this CNN-based approach localization produce less accuracy and Mean Intersection over Union (MIoU) values with 60% average accuracy and 62.3 MioU when compared to GPS data and other 3D semantic segmentation systems, but the proposed approach can save costs and computational burden. © 2023 ICROS.

关键词： Autonomous vehicles

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Graphene-and Multi-Walled Carbon Nanotubes-Coated Tapered Plastic Optical Fiber for Detection of Lard Adulteration in Olive Oil

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Photonic Sensors 2022年第4期12卷 1-10页

作者： Ika PUSPITA Ninik IRAWATI Kartika Anoraga MADURANI Fredy KURNIAWAN Sekartedjo KOENTJORO Agus Muhamad HATTA Department of Engineering Physics Faculty of Industrial Technology and System EngineeringInstitut Teknologi Sepuluh NopemberSurabaya 60111Indonesia Department of Physics National Taiwan UniversityTaipei 10617China Department of Chemistry Faculty of Science and Data AnalyticsInstitut Teknologi Sepuluh NopemberSurabaya 60111Indonesia

A rapid and high-performance sensor for lard adulterant in edible oil was developed using the tapered plastic optical fiber(POF)coated with graphene and multi-walled carbon *** coating material was deposited onto a tapered POF with a taper waist diameter and a taper length of 0.45 mm and 1 cm,*** addition of the coating material was used to increase the sensitivity and selectivity coefficient of the tapered POF toward the lard *** sensing mechanism is based on a simultaneous interaction of lard substance and an evanescent wave of tapered POF with the coating *** results showed that graphene coating on the tapered POF increased the selectivity coefficient of the tapered POF towards lard substance from 33.54 to 324.19,and it gave a sensitivity of 0.427 dBm/%.In comparison,multi-walled carbon nanotubes coating increased the selectivity coefficient to 71.65 and increased its sensitivity to 1.189 dBm/%.Thus,the proposed configuration of the tapered POF with the coating material offered a simple configuration for a rapid,high sensitivity and selectivity detection of lard adulterant in edible oils.

关键词： Lard adulteration tapered optical fiber sensor graphene carbon nanotubes food safety

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DDPG-Based PID Optimal Controller for Position and Sway Angle of RTGC 23

DDPG-Based PID Optimal Controller for Position and Sway Angl...

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23rd International Conference on Control, Automation and systems, ICCAS 2023

作者： Bandong, Steven Nazaruddin, Yul Y. Joelianto, Endra Institut Teknologi Bandung Engineering Physics Doctoral Program Faculty of Industrial Technology Bandung40132 Indonesia Instrumentation and Control Research Group Faculty of Industrial Technology Bandung40132 Indonesia

ISBN: (纸本)9788993215274

Increasing global trade requires fast and accurate container transfers by minimizing positional errors and swing angles on the Rubber Tyred Gantry Crane (RTGC) at the container yard causing that the optimization of the controller is a necessity. Currently developing artificial intelligence, namely reinforcement learning based Deep Deterministic Policy Gradient (DDPG) approach, can provide continuous action or output that can be used for controller optimization problems. DDPG can learn from its interactions with the environment, allowing it to identify the best optimal parameter region. This paper proposes DDPG as an PID controller optimizer for position and swing angle of RTGC tested on six episode variations and a trajectory reference. The results garnered from this proposed methodology reveal that the optimal PID parameters perform well across a spectrum of reference trajectories, as well as under varying system parameters such as container masses and rope lengths, as demonstrated by metrics like position and swing angle error, rise time, settling time, and overshoot. © 2023 ICROS.

关键词： Containers

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Constrained and Unconstrained Nonlinear Control of Rubber Tyred Gantry Crane (RTGC) with DC Motor Using Sliding Mode Controller 23

Constrained and Unconstrained Nonlinear Control of Rubber Ty...

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23rd International Conference on Control, Automation and systems, ICCAS 2023

作者： Napitupulu, Cristian Mp Bandong, Steven Nazaruddin, Yul Yunazwin Siregar, Parsaulian I. Institut Teknologi Bandung Faculty of Industrial Technology Department of Engineering Physics Bandung40132 Indonesia Institut Teknologi Bandung Engineering Physics Doctoral Program Faculty of Industrial Technology Bandung40132 Indonesia Institut Teknologi Bandung Instrumentation and Control Research Group Department of Engineering Physics Bandung40132 Indonesia

ISBN: (纸本)9788993215274

The increasing exchange of goods between countries and continents necessitates efficient transportation facilities, with cargo seaports playing a vital role. Within each cargo seaport, the Rubber Tyred Gantry Crane (RTGC) stands out as crucial heavy equipment that requires automation to manage heavy cargo traffic and eliminate bottlenecks. While linear control methods are commonly employed to address this challenge, the inherent nonlinear behavior of the system must be considered. This paper focuses on designing a nonlinear control method, specifically the Sliding Mode Controller (SMC), to create an effective MIMO controller for managing the position, cable length, and sway of the gantry crane system. Two DC motors serve as actuators for controlling the trolley position and cable length, which hoists the container. The nonlinear gantry crane model, integrated with the nonlinear DC motor model, is elaborately derived. Subsequently, the designed non-linear SMC control law and sliding surface are simulated to assess control performance. Acknowledging the limitation of the power source and DC motor specifications, the paper also explores control performance when the voltage to control R TGC is constrained within a specified range. The results demonstrate that the proposed method yields excellent control performance for both constrained and unconstrained control outputs. © 2023 ICROS.

关键词： Sliding mode control

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Gelatine-Based Phantom for Simulating A-Line Reverberations in Lung Ultrasonogram 8

Gelatine-Based Phantom for Simulating A-Line Reverberations ...

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8th International Conference on Instrumentation, Control, and Automation, ICA 2023

作者： Nabila, Citra Alfa Bahri, Saeful Adhika, Damar Rastri Suprijanto, Suprijanto Institut Teknologi Bandung Master Program of Instrumentation and Control Faculty of Industrial Technology Bandung Indonesia Institut Teknologi Bandung Doctoral Program of Engineering Physics Faculty of Industrial Technology Bandung Indonesia Institut Teknologi Bandung Advanced Functional Material Research Group Faculty of Industrial Technology Research Center of Nanosciences and Nanotechnology Bandung Indonesia Institute Teknologi Bandung Instrumentation and Control Research Group Faculty of Industrial Technology Bandung Indonesia

ISBN: (纸本)9798350301274

To understand typical features that could be imaged using ultrasonography (USG) in the training phases of medical students or practitioners, a lung phantom is required. The research team suggests using a gelatine-based phantom to demonstrate A-line reverberations in lung ultrasound. The ordinary gelatine-based phantom has the disadvantage of being too fragile and prone to breaking. The specific process was attempted on gelatine with a plasticizer and carrier agent to increase the gelatine's strength and elasticity. To simulate the A-lines, two different textures of gelatine layers are used, with other processes in between. A special procedure was used to set off the various acoustic impedances affecting reverberation physics with those varied textures. Based on USG image analysis, the proposed lung phantom material has some advantages: it is inexpensive and easy to find, despite its complicated procedures. Furthermore, it could be processed to have the desired properties and recycled after hardening with water bath melting. © 2023 IEEE.

关键词： Textures

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Normalized Traffic Features Using Graph Signal Processing for Traffic Flow Prediction 9

Normalized Traffic Features Using Graph Signal Processing fo...

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9th IEEE Information technology International Seminar, ITIS 2023

作者： Putri, Adiyana Joelianto, Endra Doctoral Program of Engineering Physics Institut Teknologi Bandung Faculty of Industrial technology Bandung Indonesia Instrumentation and Control Research Group Institut Teknologi Bandung Faculty of Industrial Technology Bandung Indonesia

ISBN: (纸本)9798350306835

Traffic forecasting is essential for the intelligent transportation system and is applied to managing traffic networks. Recently, there has been a growing interest in incorporating spatial and temporal variables into the methodology of traffic forecasting. The primary emphasis of modern research lies in the complex architecture of graph neural networks. In contrast, preprocessing data for the input is also essential. Especially traffic data, such as traffic flows, have high nonlinearity and complex patterns. The traffic graph signal is attributes that are associated with the nodes. In this paper, the step of preprocessing data utilized graph signal processing (GSP) to filter noise from traffic signal data before the traffic flow data was applied for prediction tasks. The adjusted parameter for filtering noise was used. In this paper, the parameter was set from 0.1 to 10. The result shows that the increasing parameter filter of the graph data was smoothed filtered. Moreover, the smooth data is affected by the time horizons. For the increasing time horizon, we can use the large parameter and get the prediction error smaller than the shorter time horizon. © 2023 IEEE.

关键词： Forecasting

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On Max Pressure Urban Traffic Control with Learning 9

On Max Pressure Urban Traffic Control with Learning

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9th IEEE Information technology International Seminar, ITIS 2023

作者： Putri, Adiyana Joelianto, Endra Sutarto, Herman Y Doctoral Program of Engineering Physics Faculty of Industrial Technology Institut Teknologi Bandung Bandung Indonesia Instrumentation and Control Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung Indonesia Bandung Indonesia

ISBN: (纸本)9798350306835

Managing the traffic system is a multifaceted challenge, imposing various solutions to prevent congestion. Traffic management aims to create an efficient control system with numerous advantages, including reduced fuel emissions, lower stress levels, and enhanced driving discipline. Max Pressure stands as one of the techniques for traffic control, designed to adapt and enhance network throughput by considering pressure, defined as the difference between upstream and downstream queue lengths. Recent developments have yielded improved performance in Max Pressure through learning. In this adaptive control mechanism, the concept of Max Pressure is integrated as a reward function within reinforcement learning. This study introduces the "Max Pressure learning"concept, revealing its potential for creating adaptable traffic controllers. By modifying the pressure equation and transforming it into a performance metric, the original Max Pressure concept effectively responds to diverse traffic conditions. This paper showcases research findings demonstrating the surprising effectiveness of pressure as a control mechanism in the context of the learning process. Ultimately, using pressure enables controllers to efficiently alleviate gridlock situations in traffic, highlighting its promising role in optimizing traffic management and enhancing overall traffic system performance. © 2023 IEEE.

关键词： Traffic congestion

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Path Planning Method for Mobile Robots in Pedestrian-Friendly Environments Using Drone Bird's-Eye View

Path Planning Method for Mobile Robots in Pedestrian-Friendl...

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2025 IEEE/SICE International Symposium on system Integration, SII 2025

作者： Ye, Linghao Chugo, Daisuke Muramatsu, Satoshi Yokota, Sho Hashimoto, Hiroshi Kwansei Gakuin University Graduate School of Science and Technology Hyogo Sanda6691330 Japan Tokai University School of Information Science and Technology Kanagawa Hiratsuka2591292 Japan Toyo University Faculty of Science and Engineering Saitama Kawagoe3508585 Japan Advanced Institute of Industrial Technology Master Program of Innovation for Design and Engineering Shinagawa Tokyo1400011 Japan

ISBN: (纸本)9798331531614

The objective of this paper is to develop a robot system that can transmit the video taken by the drone to the computer in real time, use YOLO to recognize the image of pedestrians, and enable the ground robot to plan the path to avoid pedestrians. External sensors, such as laser range finders (LRF) and CCD cameras, are often used on mobile robots. As we all know, these sensors are susceptible to occlusion. Therefore, even if the mobile robot avoids the pedestrian in front of it, it is likely to collide with other pedestrians that cannot be detected due to occlusion. In this study, we used a drone to shoot videos at high altitudes, transferred the videos to a computer, used YOLO on the computer to perform image recognition of pedestrians around the robot, and passed the recognition results to ROS, which enabled it to plan a path to avoid pedestrians around it. As a result, showed that the videos shot at high altitudes could effectively identify pedestrians around the robot after being recognized by YOLO. © 2025 IEEE.

关键词： Drones

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Parameter Identification of Signal Attenuation for High Speed Rail Communication system Using Grey Box Neural Network 8

Parameter Identification of Signal Attenuation for High Spee...

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8th International Conference on Instrumentation, Control, and Automation, ICA 2023

作者： Lukman, Selvi Nazaruddin, Yul Yunazwin Joelianto, Endra Institut Teknologi Bandung Engineering Physics Doctoral Program Faculty of Industrial Technology Bandung Indonesia Institut Teknologi Bandung Instrumentation Control Research Group Department of Engineering Physics Bandung Indonesia National Center for Sustainable Transportation Technology Bandung Indonesia

ISBN: (纸本)9798350301274

A reliable connected digital railways must face a huge challenge of network and frequency planning in order to deploy a dedicated infrastructure for High Speed Rail (HSR). This paper investigates the parameter identification of signal attenuation for HSR in a free space propagation case. A neural network based optimization of Grey Box Neural Network (GBNN) is utilized to validate the parameter identification results. Satisfying results of minimum convergences of RMSE = 1.6371 and MAPE = 0.015 have been achieved. Furthermore, the ability of identified parameter in adjusting the dynamic scenario of the real signal attenuation measurement dataset is displayed when the distance is 118.45 meter and the path loss value is 116.82 dB. Through this applied neural network based technique, network designers are able to plan and design the best possible of network performances and frequency planning for HSR. © 2023 IEEE.

关键词： Parameter estimation

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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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