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Normalized Traffic Features Using Graph Signal Processing for Traffic Flow Prediction

Normalized Traffic Features Using Graph Signal Processing fo...

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Information technology International Seminar (ITIS)

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

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.

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Software Interference Prevention and SWaP-C Minimization in Cyber-Physical systems Using Hypervisor technology

Software Interference Prevention and SWaP-C Minimization in ...

引用

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

作者： Bambang Sridadi Endra Joelianto Yul Yunazwin Nazaruddin 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

Currently, hypervisor technology is an emerging topic of research. Virtualization technology, especially hypervisors and virtual machines (VM), has been widely studied but is still little applied in the cyber-physical systems (CPS) field. A hypervisor, or VM monitor (VMM), is a software program used to run multiple VMs on a single physical machine through space and time partitioning in a real-time operating system. This paper discusses the hypervisor technology to prevent software interference and to minimize size, weight, and power-cost (SWaP-C) in cyber physical systems, as well as, the application of hypervisor technology in the aerospace, automotive and control fields. By partitioning, a hypervisor can increase software safety because application programs with different criticality levels do not affect each other in failure when run in partitioned VMs.

关键词： Virtual machine monitors Prevention and mitigation Operating systems Interference Cyber-physical systems Virtual machines Real-time systems Software safety Safety Aerospace control

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A Microservices-Driven Digital Twin Model for Precision Farming in a Controlled Indoor Environment

A Microservices-Driven Digital Twin Model for Precision Farm...

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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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Study on Standard Human Motion Based on Phase Plane for Non-Holonomic Vehicle Interface

Study on Standard Human Motion Based on Phase Plane for Non-...

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Conference on Human system Interactions, HSI

作者： Sho Yokota Hiroshi Hashimoto Daisuke Chugo Faculty of Science and Engineering Setsunan University Osaka Japan Master Program of Innovation for Design and Engineering Advanced Institute of Industrial Technology Shinagawa Tokyo Japan School of Science and Engineering Kwansei Gakuin University Sanda Hyogo Japan

Since the structure and D.O.F. of human and machine are different, the motion of human and machine is essentially different. This study defines "Pseudo Human Motion" as approximated machine's motions referring to the standard human motion. If the pseudo human motion is realized, the operation of complex machine becomes easy, because the motion of complex machine becomes human-like motion. In order to realize pseudo human motion, we derive the standard human motion which is to be a model for the machine's motion. For this purpose, we measured the human motions of plural subjects by using motion capture system. The human motion is different in each subject about the motion time, therefore it is difficult to directly use measured human motion as a standard human motion. We adopts following solutions for this problem. The fundamental components of each subject are extracted from measured human motion data by using DFT(Discrete Fourier Transform). And the fundamental components are plotted on the phase plane. There are no big differences among the human motion of each subject on the phase plane. Thus we do ellipse fitting to the each subject motion on the phase plane, and we call it the standard human motion. This ellipse trajectory, which is standard human motion, can evaluate motions even if different motion in time domain, and can absorb different motion cycle and amplitude caused by individual physical characteristics and peculiarities.

关键词： Humans Standards Vehicles Trajectory Time domain analysis Motion measurement Data mining

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On Max Pressure Urban Traffic Control with Learning

On Max Pressure Urban Traffic Control with Learning

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Information technology International Seminar (ITIS)

作者： Adiyana Putri Endra Joelianto Herman Y Sutarto Faculty of Industrial technology Institut Teknologi Bandung Doctoral Program of Engineering Physics Bandung Indonesia Faculty of Industrial Technology Institut Teknologi Bandung Instrumentation and Control Research Group Bandung Indonesia Department of Intelligent System PT. Pusat Riset Energi (PRE) Bandung Indonesia

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.

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Low cost vision-based 3D localization system for indoor unmanned aerial vehicles

Low cost vision-based 3D localization system for indoor unma...

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IEEE International Conference on Robotics, Biomimetics, and Intelligent Computational systems (ROBIONETICS)

作者： Stefio Yosse Andrean Endra Joelianto Augie Widyotriatmo Widyawardana Adiprawita Faculty of Industrial Technology Engineering Physics Study Program Bandung Indonesia Instrumentation & Control Research Group ITB Bandung Indonesia School of Electrical Engineering and Informatics Institute of Technology Bandung Bandung Indonesia

The paper presents a method to determine a position of objects in three dimentional coordinates. The proposed method uses a vision-based localization using two low cost cameras. The formulation is derived by using a pin-hole camera principal to project a location of a point in a 2D image into 3D space. This method can be used for indoor unmanned aerial vehicles localization to provide position information for control and navigation purposes.

关键词： Cameras

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Traffic Flow Estimation using Graph Neural Network with Aggregation of Traffic Features

Traffic Flow Estimation using Graph Neural Network with Aggr...

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International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Adiyana Putri Ferio Brahmana Endra Joelianto Herman Y. Sutarto Engineering Physics Doctoral Program Faculty of Industrial Technology Institut Teknologi Bandung Indonesia Artificial Intelligence Control and Automation Laboratory Faculty of Industrial Technology Institut Teknologi Bandung Indonesia Instrumentation and Control Research Group Faculty of Industrial Technology Institut Teknologi Bandung Indonesia Department of Intelligent System PT. Pusat Riset Energi (PRE) Bandung Indonesia

ISBN: (纸本)9781665476881

The increasing vehicle volume every year affects the prediction of the traffic system. The purpose of predicting traffic flow is to estimate the lost data caused by sensor malfunctions due to connection disruptions or ages. In estimating traffic flow, obtaining and explaining the historical data from the nearest segment sensor is necessary. The graph model can describe the spatial relationship between segments by connecting nodes with edges. Graph Neural Network (GNN) has been used as a learning method to predict the traffic flow in the segment sensor. GNN gets input in the form of spatial data built through a graph model, and the features are traffic density, average vehicle speed, and delay time on the road network. This paper shows that GNN's capability performs better in extracting spatial features and predicting the traffic flow by aggregating the features through connectivity nodes and edges.

关键词： Learning systems Roads Estimation Feature extraction Robot sensing systems Graph neural networks Spatial databases

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Optimal RTGC Controller using Robust PID H∞ Integral-Backstepping under Payload Mass and Rope Length Uncertainties

Optimal RTGC Controller using Robust PID H∞ Integral-Backst...

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International Conference on Control, Automation, Robotics and Vision (ICARCV)

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

ISBN: (纸本)9781665476881

The increase in goods traffic between countries and islands requires the availability of port automation so that the distribution of goods is faster and the quality is guaranteed. One of the control problems for automating the port is controlling the position and sway angle of the container on the Rubber Tyred Gantry Crane (RTGC). However, the RTGC system's model is often uncertain because of the uncertainty in the mass of the payload and the length of the hauling rope when operating. This paper proposes a robust PID controller based on $H$ ∞ integral backstepping to overcome this problem. Particle Swarm Optimization (PSO), Simulated Annealing (SA) and Genetic Algorithms (GA) are used to optimize the parameter of $H$ ∞ integral backstepping so that the system response follows the desired trajectory. The results show that the control system designed can provide a satisfactory solution to variations in the mass and length of the rope. PSO and SA give better optimal and robust performance than GA.

关键词： Backstepping Uncertainty Automation Simulated annealing Control systems Robustness Trajectory

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

Parameter Identification of Signal Attenuation for High Spee...

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International Conference on Instrumentation Control and Automation (ICA)

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

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.

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Development of an Optical Biosensor Based on Gold Nanoparticles

Development of an Optical Biosensor Based on Gold Nanopartic...

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IEEE Conference on Nanotechnology

作者： Amanda Anatasya Yong-Joon Choi Tsugumi Sakae Gilang Gumilar Ni Luh Wulan Septiani Go Kawamura Kazuaki Sawada Brian Yuliarto Faculty of Industrial Technology Master's Program in Engineering Physics Institut Teknologi Bandung Bandung Indonesia Department of Electrical and Electronic Information Engineering Toyohashi University of Technology Aichi Japan Engineering Physics Department Advanced Functional Materials (AFM) Laboratory Institut Teknologi Bandung Bandung Indonesia Research Center for Advanced Materials National Research and Innovation (BRIN) Komplek Puspiptek South Tangerang Indonesia

This paper describes an experimental study of SARS-CoV-2 detection using plasmonic gold nanoparticles, which overcomes the limitations of traditional detection techniques known for their time-consuming and a tendency to produce high false negative results. The transmission spectrum of gold nanoparticles (AuNPs) was recently estimated to be in filter-free wavelength sensors as a miniaturization detection system. Additionally, we demonstrate that wide-ranging absorption will manifest within the visible spectrum when the virus is partially coated with gold nanoparticles at a particular percentage of coverage. This extensive absorption can be harnessed to direct the advancement of a reliable and precise colorimetric plasmon sensor for the detection of COVID-19. The transmission value resulting from the gold nano seed is around 507–540 nm. In addition, plasmonic gold nanoparticles can detect SARS-CoV-2 well from the range of 100-1,000 ng/ml. We expect a miniaturization biosensor system with qualitative diagnosis to be realized by the development method.

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