Yuangyai, Chumpol

Preferred name

Yuangyai, Chumpol

Alternative Name

Yuangyai, Čhumpol

Yuangyai, C.

Main Affiliation

School of Engineering

chumpol.yu@kmitl.ac.th

Now showing 1 - 10 of 39

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Investigating of antenna selection for the adaptive centroid localization systems
(2015-09-01)
Yuangyai, Chumpol
;
Pan, Chung Yu
;
Lin, Yi Jou
;
Cheng, Chen Yang
Location-based services are widely integrated in our daily lives, and they are used in various ways such as for investigating inventory goods and personal tracking in healthcare. With increasing applications of wireless localization, accuracy of location estimation requirements has become more critical. However, indoor localization suffers from multi-path interference that affects the traditional algorithmic calculation methods based on radio signal strength. The strength of radio signal depends significantly on antenna types and the deployment of wireless sensor network. Therefore, the aim of this paper is to investigate a robust deployment of wireless sensor network considering antennas and optimal signal range to increase signal strength and to reduce receiving signal missing. A bi-response design approach was taken to evaluate antenna selection, signal range, and antenna power rate. The experiment result was applied with existing algorithm to prove effectives. Further, to avoid wireless sensors collision which may result in low accuracy of receiving radio signal, adaptive weight center of gravity localization (AWCG) were proposed. AWCG is based on an assumption of the dynamic relationship between the radio signal strength and the distance in different environments at different times. In the proposed localization algorithm, the error distance was approximately one meter. It is expected to significantly improve the location estimation accuracy with the suggested deployment and proposed algorithm.
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Effect of Si on Microstructure and Corrosion Behavior of CoCrMo Alloys
(2018-06-04)
Peaubuapuan, Chonlawit
;
Tunthawiroon, Phacharaphon
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Kanlayasiri, Kannachai
;
Yuangyai, Chumpol
The aim of this research was to investigate the effect of silicon (Si) on the microstructure and corrosion behavior of CoCrMo alloys. The concentration of Si added in CoCrMo alloys was 0.1, 0.5 and 1 wt %. The corrosion behavior of the present alloys was investigated using potentiodynamic polarization measurements. The normal saline solution with 0.9 wt % was used as an electrolyte. Polarization curves obtained from the polarization test were used to evaluate in terms of corrosion current density (i<inf>corr</inf> ), corrosion potential (E<inf>corr</inf> ) and corrosion parameters that be used to compute corrosion resistant property of CoCrMo alloys. The microstructure of a sample during the polarization test was compared using X-ray diffraction (XRD) and optical microscope (OM). The results indicated that the increase in Si slightly changed the microstructure of CoCrMo alloys and could enhance the resistance to corrosion of CoCrMo alloys in NaCl solution. In addition, the reduction of Cr and Mo concentration in CoCrMo alloys was found to be a significant influence on the decrease in corrosion resistance.
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A Comparative Study of Unbalanced Production Lines Using Simulation Modeling: A Case Study for Solar Silicon Manufacturing
(2022-01-01)
Cheng, Chen Yang
;
Li, Shu Fen
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Lee, Chia Leng
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Jientrakul, Ranon
;
Yuangyai, Chumpol
In the solar silicon manufacturing industry, the production time for crystal growth is ten times longer than at other workstations. The pre-processing time at the ingot-cutting station causes work-in-process (WIP) accumulation and an excessively long cycle time. This study aimed to find the most effective production system for reducing WIP accumulation and shortening the cycle time. The proposed approach considered pull production systems, and the response surface methodology was adopted for performance optimization. A simulation-based optimization technique was used for determining the optimal pull production system. The comparison between the results of various simulated pull production systems and those of the existing solar silicon manufacturing system showed that a hybrid production system in which a kanban station was installed before the bottleneck station with a CONWIP system incorporated for the rest of the production line could reduce the WIP volume by 26% and shorten the cycle time by 16% under the same throughput conditions.
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Blockchain-Based Smart Renewable Energy: Review of Operational and Transactional Challenges
(2022-07-01)
Nepal, Jagdish Prasad
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Yuangyai, Nuttaya
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Gyawali, Saroj
;
Yuangyai, Chumpol
Blockchain has peculiar characteristics among various digital technologies due to its decentralised and cryptographic properties. The combination of intelligent energy systems and blockchain can innovate new forms of transactive energy and navigate the digital journey to transform the future of renewable energy systems. This review studies various blockchain implementations in the smart energy domain and presents the findings on operational and transactional challenges in a blockchain-based smart renewable energy system. We also identify the differences between operations and transactions in smart energy systems. Furthermore, we identify the most pronounced cryptocurrencies in different studies. The findings highlighted various challenges concerning the implementation of blockchain-based smart energy systems. We identified how these challenges spawn across operational and transactional deliverables. Building on these findings, we discuss various challenges impacting the operational and transactional domains, which we believe have significant value for researchers, practitioners, policy makers, entrepreneurs, and start-ups. It will provide long-term benefits to humankind in fulfilling energy requirements, promoting sustainable energy use by developing countermeasures to combat identified challenges and leveraging the optimal use of blockchain technology.
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Statistical process control with autocorrelated data using neural networks
(2011-10-24)
Yuangyai, Chumpol
;
Abrahams, Rachel
Statistical Process Control (SPC) is widely used for monitoring the performance of processes in manufacturing. Traditional SPC methods require trained individuals to read data which results in slow and limited detection. Much research has been devoted into developing an online automated system for SPC, so that the abnormality can be detected quickly and corrected by the process operation. To build a system as such, artificial neural networks (ANN) are widely used as tools where complex patterns can be difficult to recognize. Many research projects involve using random data patterns for training and recognition of patterns for ANN/SPC applications. However, many manufacturing processes involve autocorrelated data, to determine the effect of autocorrelated data, green sand data was analyzed and a neural network was built and trained to analyze a number of out of control patterns. Overall, the network performed best for detecting larger mean shifts. © 2011 IEEE.
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Integrating data mining techniques for naïve bayes classification: Applications to medical datasets
(2021-09-01)
Changpetch, Pannapa
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Pitpeng, Apasiri
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Hiriote, Sasiprapa
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Yuangyai, Chumpol
In this study, we designed a framework in which three techniques—classification tree, association rules analysis (ASA), and the naïve bayes classifier—were combined to improve the per-formance of the latter. A classification tree was used to discretize quantitative predictors into cate-gories and ASA was used to generate interactions in a fully realized way, as discretized variables and interactions are key to improving the classification accuracy of the naïve Bayes classifier. We applied our methodology to three medical datasets to demonstrate the efficacy of the proposed method. The results showed that our methodology outperformed the existing techniques for all the illustrated datasets. Although our focus here was on medical datasets, our proposed methodology is equally applicable to datasets in many other areas.
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Barriers to electric vehicle adoption in Thailand
(2021-11-01)
Kongklaew, Chanwit
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Phoungthong, Khamphe
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Prabpayak, Chanwit
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Chowdhury, Md Shahariar
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Khan, Imran
Electric vehicles (EVs) are considered to be a solution for sustainable transportation. EVs can reduce fossil fuel consumption, greenhouse gas emissions, and the negative impacts of climate change and global warming, as well as help improve air quality. However, EV adoption in Thailand is quite low. Against this backdrop, this study investigates barriers and motivators for EV adoption and their public perception in Thailand. A total of 454 responses were collected through an online questionnaire. The results indicate that the top three concerns of respondents about EVs are public infrastructure and vehicle performance in terms of charge range and battery life. Respondents with more than five years of driving experience in the age range of 26–35 years old could be key targets for early EV adoption.
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Integrating Spatial Risk Factors with Social Media Data Analysis for an Ambulance Allocation Strategy: A Case Study in Bangkok
(2022-08-01)
Jientrakul, Ranon
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Yuangyai, Chumpol
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Boonkul, Klongkwan
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Chaicharoenwut, Pakinai
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Nilsang, Suriyaphong
Emergency medical service (EMS) base allocation plays a critical role in emergency medical service systems. Fast arrival of an EMS unit to an incident scene increases the chance of survival and reduces the chance of victim disability. However, recently, the allocation strategy has been performed by experts using past data and experiences. This may lead to ineffective planning due to a lack of consideration of a recent and relevant data, such as disaster events, population density, public transportation stations, and public events. Therefore, we propose an approach of the integration of using spatial risk factors and social media factors to identify EMS bases. These factors are combined into a single domain by using the kernel density estimation technique, resulting in a heatmap. Then, the heatmap is used in a modified maximizing covering location problem with a heatmap (MCLP-Heatmap) to allocate ambulance base. To acquire recent data, social media is then used for collecting road accidents, traffic, flood, and fire incidents. Additionally, another data source, spatial risk information, is collected from Bangkok GIS. These data are analyzed using the kernel density estimation method to construct a heatmap before being sent to the MCLP-heatmap to identify EMS bases in the area of interest. In addition, the proposed integrated approach is applied to the Bangkok area with a smaller number of EMS bases than that of the existing approach. The simulated results indicated that the number of covered EMS requests was increased by 3.6% and the number of ambulance bases in action was reduced by approximately 26%. Additionally, the bases defined by the proposed approach covered more area than those of the existing approach.
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Carbon Footprint Assessment of Food Waste Disposal Methods in a Thai Hypermarket’s Fresh Food Department
(2026-04-01)
Hutangkoon, Thunyanat
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Yuangyai, Chumpol
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Puttongsiri, Tongchai
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Filimonau, Viachaslau
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Koiwanit, Jarotwan
The global urgency to mitigate environmental degradation and promote sustainable resource use necessitates effective waste management strategies, particularly in the retail sector, which is a significant contributor to food waste. This study explores the carbon ramifications of food waste disposal methods within a hypermarket’s fresh food department in Bangkok, Thailand. Using the method of life cycle assessment (LCA) under the CML2001 framework, this study evaluates three food waste management methods: anaerobic digestion (AD), sanitary landfill, and mechanical and biological waste treatment (MBT). The analysis is structured to quantify the carbon footprint associated with each waste management strategy, measured in kilograms (kg) of carbon dioxide (CO<inf>2</inf>) equivalent (eq.) per kg of food waste. The estimated carbon footprint is 0.0066 kg CO<inf>2</inf> eq./kg of food waste for MBT, 0.1221 kg CO<inf>2</inf> eq./kg of food waste for AD, and 1.4667 kg CO<inf>2</inf> eq./kg of food waste for sanitary landfill. These values were derived from defined system boundaries, modeling assumptions, and available operational data used to construct the life cycle inventory (LCI). In addition, a formal sensitivity analysis was not conducted in this study. Therefore, the reported values should be interpreted within the context of the modeling assumptions and data sources applied.
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Multiple-performance optimization for human-robot collaboration: A translation motion in a tele-sandblasting maintenance system
(2016-01-01)
Jientrakul, Ranon
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Yuangyai, Chumpol
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Chotiprayanakul, Pholchai
;
Limnararat, Sunpasit
Human-robot collaboration is a vital part of teleoperation systems. Determining the design parameters satisfying the optimal human performance is very important in the design of a teleoperation system for a specific task. Specifically, to thoroughly represent human performance for a task, multiple performance measures in a teleoperation system are evaluated. The experiment was also conducted on a mixed-reality tele-sandblasting system which is setup to study 2D-motion translation in tele-sandblasting task. Then, identifying the design setting to satisfy the optimal measures are difficult due to conflicts among them. Therefore, this paper presents a multipleperformance optimization using a desirability function. The results provide the range of setting condition promoting the optimal human performance measures. © IEOM Society International.

Yuangyai, Chumpol

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