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Performance of SNMP Protocol in Sensor Network Measurement and IoT Web-based HMI Features 8

Performance of SNMP Protocol in Sensor Network Measurement a...

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8th International Conference on instrumentation, control, and Automation, ICA 2023

作者： Nurmalia, Enung Bashar, Labid Bin Joelianto, Endra Institut Teknologi Bandung Instrumentation and Control Master Program Bandung Indonesia Institut Teknologi Bandung Instrumentation and Control Research Group Bandung Indonesia

ISBN: (纸本)9798350301274

The Simple Network Management Protocol (SNMP) is a networking protocol used for the management and monitoring of all connected devices in internet standard protocol networks. Recently, the utilization and capability of SNMP has been enhanced to be able to handle data exchange across a network from the control level to the human machine interface level. Some controllers, including micro-controllers, have upgraded their features and coverage to include SNMP in their communications protocol. The utilization of SNMP is also supported by Open Platform Communications (OPC) Server, which plays an important role as data exchange server for various controllers or systems with different gateways and drivers. This paper presents the performance assessment of SNMP implementation in a network, from controller to OPC server, comparing hardwired and wireless automation architectures. The results show that in both cases the round-Trip times (RTT) of SNMP were acceptable according to network criteria, with an average of less than 100 ms. Hence, SNMP is suitable as an industrial automation protocol. © 2023 IEEE.

关键词： Electronic data interchange

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Performances of Human Activity Recognition Classification Using LSTM and 1D-2D CNN 9

Performances of Human Activity Recognition Classification Us...

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9th IEEE Information Technology International Seminar, ITIS 2023

作者： Agung, Muhammad Iqbal Anggoro Nurmalia, Enung Joelianto, Endra Institut Teknologi Bandung Instrumentation and Control Master Program Bandung Indonesia Institut Teknologi Bandung Instrumentation and Control Research Group Bandung Indonesia

ISBN: (纸本)9798350306835

The Human Activity Recognition (HAR) System endeavors to proficiently categorize input data acquired from sensors affixed to the human body, meticulously measuring movement and assigning it to specific activity categories. While traditional techniques have long struggled to capture the intricacies of HAR, especially in instances with subtle microactivity sequences, the integration of machine learning within the realm of Artificial Intelligence has proven itself adept at handling such complex data-related challenges. This article considers the utilization of machine learning techniques to enhance human activities by examining datasets for HAR classification on realtime applications. A carefully curated dataset is chosen as the primary input for the processes of training, testing, and evaluation, facilitated by sophisticated models that include Long Short-Term Memory (LSTM), 1D Convolution Neural Network (CNN) for time series data, 1D CNN for the frequency domain, and 2D Image CNN models. These models are meticulously scrutinized, with their performance metrics meticulously compared to determine the most suitable HAR model for the task at hand. Through comprehensive simulations, the results affirm the supremacy of the 1D CNN frequency domain model in terms of performance with 93.28% accuracy, meanwhile the LSTM model showing the best results in term of the computation cost with the lowest time/step of 0.505ms/step. This outcome underscores the potential of advanced machine learning techniques in revolutionizing the field of HAR, particularly when confronted with nuanced and intricate movement sequences. © 2023 IEEE.

关键词： Fast Fourier transforms

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Trajectory-Tracking and Centralized control for Mapping Tasks Using Multiple Drones 11

Trajectory-Tracking and Centralized Control for Mapping Task...

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11th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2024

作者： Argiliana, Shania Ekawati, Estiyanti Mukhlish, Faqihza Institut Teknologi Bandung Instrumentation and Control Master Program Faculty of Industrial Technology Indonesia Center for Instrumentation Technology and Automation Institut Teknologi Bandung Faculty of Industrial Technology Indonesia

ISBN: (纸本)9798350355314

This study focuses on improving multiple drones' trajectory tracking accuracy and completion time in mapping assignments. A centralized control system is applied to drones to achieve such performance. The system tracking accuracy is supported by dense square fiducial markers (SFM) as positioning media. The drone's position is assumed to be at the camera's center, leading to more precise calculations. Detecting multiple markers also ensures that the drones do not lose positional information over long distances, resulting in more accurate position estimation. The test is conducted indoors with two drones, with trajectory tracking tests performed along the x-axis and y-axis within a 5 × 4 m2 area. The test shows that the root means squared tracking error (RMSTE) when the drones moved along the positive x-axis was 5.2 cm, less than 20% of the drone's diameter. During the mapping assignment, the drones traced an S-shaped route and obtained RMSTE values of 12.3 cm for drone A and 22.5 cm for drone B, with a travel time of 51 seconds from takeoff to landing. Therefore, detecting multiple markers improves trajectory tracking capabilities, enabling drones to perform mapping tasks efficiently, even in confined areas. © 2024 IEEE.

关键词： Drones

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Adversarially Robust Reinforcement Learning for Urban Traffic Signal control 5

Adversarially Robust Reinforcement Learning for Urban Traffi...

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

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

ISBN: (纸本)9798350364743

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. © 2024 IEEE.

关键词： Traffic signals

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Single Bean Region of Interest Auto-Detection in a Coffee Beans Image Towards Thorough Quality Inspection 8

Single Bean Region of Interest Auto-Detection in a Coffee Be...

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8th International Conference on instrumentation, control, and Automation, ICA 2023

作者： Utomo, Gema Nuran Rakhmawati, Amalia Nadhira, Vebi Juliastuti, Endang Suprijanto, Suprijanto Institut Teknologi Bandung Master's Program in Instrumentation and Control Bandung40132 Indonesia Institut Teknologi Bandung Doctoral Program in Engineering Physics Bandung40132 Indonesia Institut Teknologi Bandung Instrumentation and Control Reaserch Group Bandung40132 Indonesia

ISBN: (纸本)9798350301274

Indonesia was declared the world's fourth-largest coffee bean producer in 2020 and yields consistently increase each year. Indonesia also has the Indonesian National Standard (SNI) 01-2907-2008, which specifies various defects with the defect scores of coffee beans that determine their quality. Common coffee bean defects listed in SNI 01-2907-2008 include black beans, broken black beans, partly black beans, and brown coffee beans. These defects degrade the quality and affect the market value. Previous studies have proposed computer vision systems as a non-invasive testing method for grading coffee beans based on trained images. However, obtaining the detail in a single bean image based on the region of interest could have been more comprehensively explained in those studies, making them less informative and requiring a more detailed conceptual breakdown. This study describes several coffee beans captured as images using a digital camera with a specific field of view, F/25 f-number, 1/60s exposure time, ISO400, and a lighting system reaching 12,000 lux. These images are processed based on the region of interest and centroid, resulting in single bean images with a resolution of 220 x 220 pixels ready for training. The samples used in this study are Robusta Lampung coffee beans, which are commonly found in Indonesia and exhibit various types of defects based on the criteria in SNI 01-2907-2008, such as black beans, broken black beans, partly black beans, and brown coffee beans. © 2023 IEEE.

关键词： Defects

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Type-2 Fuzzy Sliding Mode control Design for Self-Balancing Mobile Robot 6

Type-2 Fuzzy Sliding Mode Control Design for Self-Balancing ...

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6th International Conference on Information and Communications Technology, ICOIACT 2023

作者： Hartono Leksono, Edi Joelianto, Endra Instrumentation and Control Master Program Institut Teknologi Bandung Bandung Indonesia Engineering Physics Research Group Institut Teknologi Bandung Bandung Indonesia Instrumentation and Control Research Group Institut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9798350315639

Mobile robots have been a popular research topic in recent decades due to their many potential applications. One of the common types of mobile robots is the two-wheeled self-balancing robot, which operates based on the basic concept of an inverted pendulum. This concept represents an unstable system, so a proper controller is required to stabilize the system. This paper proposes a controller that combines a type2 fuzzy logic system and sliding mode control to stabilize and maintain the robot's body balance in a straight upright position. Sliding mode control is a well-known robust controller with the ability to handle system disturbances. However, the presence of a switching law in sliding mode control can lead to the occurrence of chattering phenomena. Hence, a type-2 fuzzy logic system is applied to reduce the chattering effect by adjusting the switching gain of the sliding mode control. Additionally, the system's stability is analyzed using the Lyapunov theorem. The simulation results demonstrate that the designed type-2 fuzzy sliding mode control not only reduces chattering but also decreases energy consumption by approximately 59.92% and reduces the undershoot of the pitching rate by about 33.5%, with only a 0.115-second longer settling time for pitching angle compared to conventional sliding mode control. © 2023 IEEE.

关键词： Mobile robots

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Treatment on Missing Data of Wind Speed Measurement in Automatic Weather Station Using Multivariate Imputation Chained Equation 8

Treatment on Missing Data of Wind Speed Measurement in Autom...

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8th International Conference on instrumentation, control, and Automation, ICA 2023

作者： Wicaksana, Haryas Subyantara Winarko, Shodiq Kurniadi, Deddy Institut Teknologi Bandung Master Program in Instrumentation and Control Bandung Indonesia Meteorology Climatology and Geophysics Agency of Indonesia Center for Instrumentation Calibration and Engineering Jakarta Indonesia Institut Teknologi Bandung Instrumentation and Control Research Group Faculty of Industrial Technology Bandung Indonesia

ISBN: (纸本)9798350301274

Automatic Weather Station (AWS) has an anemometer to measure surface wind speed. In operation, the anemometer has a potential to damage internal components, causing data loss. Imputation is an alternative solution for missing data treatment. This study aims to utilize the spatial relationship of wind speed measurement on multi sites AWS in the imputation model. The imputation of the wind speed dataset uses Multivariate Imputation Chained Equation (MICE) algorithm with the input being the results of anemometer measurements at several surrounded points. The research focuses on the AWS Cisurupan site in the period 1 January 2019 - 31 December 2022. The wind speed dataset has a sampling interval of 10 minutes. MICE algorithm is able to overcome missing data on AWS wind speed measurement using the AWS neighbors input. MICE-based data imputation performance still meets WMO operational requirements for wind speed measurements with a maximum percentage of missing data of 15%. Mean average percentage error (MAPE) value obtained is less than 10%. © 2023 IEEE.

关键词： Wind speed

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Enhanced Feature Matching in Image Stitching Using ORB for Image-Based Mapping Using UAV 1

Enhanced Feature Matching in Image Stitching Using ORB for I...

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1st Engineering and Science International Conference 2024, ESIC 2024

作者： Anoraga, Muhammad Imron Catur Ekawati, Estiyanti Mukhlish, Faqihza Instrumentation and Control Master Program Faculty of Industrial Technology Institut Teknologi Bandung Bandung Indonesia Engineering Physics Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung Indonesia Center for Instrumentation Technology and Automation Institut Teknologi Bandung Bandung Indonesia

Image-based region mapping requires an image-stitching process to produce a complete map while preserving the details of the mapped objects. Image acquisition can be done using UAVs such as drones, and image stitching can be performed using the Oriented FAST and Rotated BRIEF (ORB) techniques. However, this algorithm has a drawback: There are many mismatches during feature matching. In this study, a maximum pixel displacement constraint between images was applied during the feature-matching process. Applying this constraint improved the feature-matching results due to a significant reduction of the mismatch rate from 84.28% to 25.17%. This reduction positively impacted subsequent stages, including map stitching. The mismatch removal process and the acquisition of transformation models during image registration became faster, reducing the processing time from 17m 36.8s to 1m 17.2s. Additionally, there was an improvement in the SSIM metric score, indicating that the stitched image results had a higher similarity to the reference image captured by a digital camera, increasing from 0.8423 to 0.8489. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Design of Image-Based Autonomous Landing instrumentation and control System for Unmanned Quadrotor Vehicles 10

Design of Image-Based Autonomous Landing Instrumentation and...

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10th IEEE Information Technology International Seminar, ITIS 2024

作者： Fauzan, Ihsan M. Mukhlish, Faqihza Prayoga, Aditya Ahmad, Robby Azhari Institut Teknologi Bandung Instrumentation and Control Master Program Faculty of Industrial Technology Indonesia Institut Teknologi Bandung Engineering Physics Research Group Center for Instrumentation Technology and Automation Indonesia PT. Sas Aero Sishan Engineering Department Indonesia

ISBN: (纸本)9798331521295

The study designs an instrumentation and control system for autonomous quadrotor landing using a visual marker as the landing target. For landing process, the marker used as a landing target is an embedded ArUco marker, capable for detection for both long range and close-range detection. Position of the quadrotor relative from the ArUco marker is estimated using Perspective-N-Point pose estimation. The estimation errors compared to actual measurements are under 20 cm, which is less than the distance between ArUco border and its center. To keep camera for facing downward during the flight, the 2 axis inertially stabilization platform (ISP). The designed ISP can maintain the camera orientation even when quadrotor angle has changed. The landing control system is made for two different scenarios. First scenario is for landing with Global Positioning System (GPS) support, and the second scenario is without GPS support. The landing control system in GPS support scenario uses velocity setpoint to control the quadrotor while in without GPS support scenario, control system uses angle-thrust setpoint to control quadrotor. Quadrotor landing control system can attempt landing on top of the ArUco marker in both scenarios. The trajectory Mean Average Error (MAE) of x and y coordinates and radius of the landing process is (0.108, 0.326, 0.359) m for GPS support scenario and (0.301, 0.251, 0.436) m for without GPS support scenario. © 2024 IEEE.

关键词： Global positioning system

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IoT-Based Temperature and Relative Humidity Monitoring System Using Simple Network Management Protocol 7

IoT-Based Temperature and Relative Humidity Monitoring Syste...

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7th International Conference on instrumentation, control, and Automation, ICA 2021

作者： Aji, Nurvita Nazuwatussya'diyah Joelianto, Endra Institut Teknologi Bandung Master Program of Instrumentation and Control Bandung Indonesia

ISBN: (纸本)9781665432955

This paper proposes a system architecture design for the Internet of Things (IoT) implemented in temperature and relative humidity measurement using DHT 11 temperature and humidity sensor. Simple Network Management Protocol (SNMP) is used to manage different network devices and transfer information so that the ESP32 Wi-Fi microcontroller connected to the sensor can communicate with Open Platform Communication (OPC) server. In addition, the network variables are identified by Object Identifiers (OIDs) then collected to Management Information Base (MIB). These two elements are required by the SNMP monitoring tool for enabling the user to monitor network infrastructure and run troubleshooting. Data acquired from the ESP32 Wi-Fi microcontroller are accumulated on the OPC server using SNMP and subsequently transferred to the OPC Client such as Human Machine Interface (HMI) and OPC data logger. The OPC server has a ping channel feature to determine network latency measured as a round trip time - the time taken for data to get its destination and back again. Later on, the ESP32 microcontroller was also connected to a mobile application via Wi-Fi for displaying real-time data simultaneously with the HMI. The SNMP network latency test on the proposed system architecture design results in 126,33 ms for 13 minutes without a corrupt packet of data. The result indicates SNMP can be used as an alternative protocol for IoT data communication. © 2021 IEEE.

关键词： Internet of things

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