Wisayataksin, Sumek

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Traffic Signal Control with State-Optimizing Deep Reinforcement Learning and Fuzzy Logic
(2024-09-01)
Meepokgit, Teerapun
;
Wisayataksin, Sumek
Traffic lights are the most commonly used tool to manage urban traffic to reduce congestion and accidents. However, the poor management of traffic lights can result in further problems. Consequently, many studies on traffic light control have been conducted using deep reinforcement learning in the past few years. In this study, we propose a traffic light control method in which a Deep Q-network with fuzzy logic is used to reduce waiting time while enhancing the efficiency of the method. Nevertheless, existing studies using the Deep Q-network may yield suboptimal results because of the reward function, leading to the system favoring straight vehicles, which results in left-turning vehicles waiting too long. Therefore, we modified the reward function to consider the waiting time in each lane. For the experiment, Simulation of Urban Mobility (SUMO) software version 1.18.0 was used for various environments and vehicle types. The results show that, when using the proposed method in a prototype environment, the average total waiting time could be reduced by 18.46% compared with the traffic light control method using a conventional Deep Q-network with fuzzy logic. Additionally, an ambulance prioritization system was implemented that significantly reduced the ambulance waiting time. In summary, the proposed method yielded better results in all environments.
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Configurable Hardware Architecture of Multidimensional Convolution Coprocessor
(2021-01-20)
Boonyuu, Geranun
;
Wisayataksin, Sumek
We propose a configurable coprocessor for the convolutional neural network (CNN) that suit various models of CNN. It can operate 2D standard convolution, 2D depthwise separable convolution, 3D convolution, and a fully connected layer. The proposed processing cluster consists of 72 processing units (PUs) of half-precision floating-point to assist the main processor in embedded systems. The experimental results on Artix-7 FPGA revealed that our design has 12.16 GOPs per cluster. Moreover, this architecture was designed to be scalable for the systems with higher performance.
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Improvement of Electrical Bio-Impedance Measurement: Mixed Signal Approach
(2023-01-01)
Sribua, Phongpitch
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Wisayataksin, Sumek
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Thanachayanont, Apinunt
This article deploys a 5-level shorten rectangular wave technique to measure lock-in electrical bio-impedance (EBI) in medical diagnosis. The new shorten rectangular EBI signal has better properties in eliminating odd harmonics compared to the conventional 3-level shorten rectangular wave technique. The results show that the measurement errors in the 3-component EBI are reduced about 0.3% for R, X, Z and 3% for Phase(Φ) when the 5-level signal is used instead 3-level signal.
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Vegetation Health Monitoring System for Smart Farm using NDVI Analysis
(2022-01-01)
Wisayataksin, Sumek
;
Junta, Nontaput
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Kuanpreeyawat, Jirat
We adapt the technique of normalized difference vegetation index (NDVI) to smart farms for monitoring the health of plants remotely over the IoT network. The system consists of a camera with 2 filters attached. The first one is an RGB filter to capture the visible light image, whereas another one is an infrared range filter. Two images are taken from a camera module and analyzed on Raspberry Pi 4 before being transmitted the NDVI image to the webserver for remote controlling and monitoring. The experimental results from actual plants reveal that our methodology can distinguish the healthy and unhealthy plants easily and efficiently.

Wisayataksin, Sumek

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