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 21

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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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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
;
Lee, Chia Leng
;
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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Integrating data mining techniques for naïve bayes classification: Applications to medical datasets
(2021-09-01)
Changpetch, Pannapa
;
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
;
Phoungthong, Khamphe
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Prabpayak, Chanwit
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Chowdhury, Md Shahariar
;
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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An auction bidding approach to balance performance bonuses in vehicle routing problems with time windows
(2021-08-02)
Cheng, Chen Yang
;
Ying, Kuo Ching
;
Lu, Chung Cheng
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Yuangyai, Chumpol
;
Chiang, Wan Chen
In the field of operations research, the vehicle routing problem with time windows (VRPTW) has been widely studied because it is extensively used in practical applications. Real-life situations discussed in the relevant research include time windows and vehicle capabilities. Among the constraints in a VRPTW, the practical consideration of the fairness of drivers’ performance bonuses has seldom been discussed in the literature. However, the shortest routes and balanced performance bonuses for all sales drivers are usually in conflict. To balance the bonuses awarded to all drivers, an auction bidding approach was developed to address this practical consideration. The fairness of performance bonuses was considered in the proposed mathematical model. The nearest urgent candidate heuristic used in the auction bidding approach determined the auction price of the sales drivers. The proposed algorithm both achieved a performance bonus balance and planned the shortest route for each driver. To evaluate the performance of the auction bidding approach, several test instances were generated based on VRPTW benchmark data instances. This study also involved sensitivity and scenario analyses to assess the effect of the algorithm’s parameters on the solutions. The results show that the proposed approach efficiently obtained the optimal routes and satisfied the practical concerns in the VRPTW.
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Two-stage stochastic program for supply chain network design under facility disruptions
(2021-03-01)
Kungwalsong, Kanokporn
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Cheng, Chen Yang
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Yuangyai, Chumpol
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Janjarassuk, Udom
A supply chain disruption is an unanticipated event that disrupts the flow of materials in a supply chain. Any given supply chain disruption could have a significant negative impact on the entire supply chain. Supply chain network designs usually consider two stage of decision process in a business environment. The first stage deals with strategic levels, such as to determine facility locations and their capacity, while the second stage considers in a tactical level, such as production quantity, delivery routing. Each stage’s decision could affect the other stage’s result, and it could not be determined individual. However, supply chain network designs often fail to account for supply chain disruptions. In this paper, this paper proposed a two-stage stochastic programming model for a four-echelon global supply chain network design problem considering possible disruptions at facilities. A modified simulated annealing (SA) algorithm is developed to determine the strategic decision at the first stage. The comparison of traditional supply chain network decision framework shows that under disruption, the stochastic solutions outperform the traditional one. This study demonstrates the managerial viability of the proposed model in designing a supply chain network in which disruptive events are proactively accounted for.
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Robust parameter design for multiple-stage nanomanufacturing
(2012-07-01)
Yuangyai, Chumpol
;
Nembhard, Harriet Black
;
Hayes, Gregory
;
Adair, James H.
Process reproducibility is a major concern for scientists and engineers, especially when new processes or new products are transitioned from laboratory-scale to full-scale manufacturing. Robust Parameter Design (RPD) is often used to mitigate this problem. However, in multiple-stage manufacturing process environments, it is difficult to employ the RPD concept because experiments cannot strictly follow the principle of complete randomization. Furthermore, the stages can be located at different sites, leading to multiple sets of noise factors. In the existing literature, only a single set of noise factors is considered. Therefore, in this research, the foundation of using the RPD concept with multistage experiments is developed and discussed. Some optimal design catalogs are provided based on a modified minimum aberration criterion. The context for this work is the development of a medical device made of nanoscale composites using a multiple-stage manufacturing process. © 2012 "IIE".
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Multiple Performance Optimization for Microstrip Patch Antenna Improvement
(2023-05-01)
Chen, Ja Hao
;
Cheng, Chen Yang
;
Chien, Chuan Min
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Yuangyai, Chumpol
;
Chen, Ting Hua
As the Internet of Things (IOT) becomes more widely used in our everyday lives, an increasing number of wireless communication devices are required, meaning that an increasing number of signals are transmitted and received through antennas. Thus, the performance of antennas plays an important role in IOT applications, and increasing the efficiency of antenna design has become a crucial topic. Antenna designers have often optimized antennas by using an EM simulation tool. Although this method is feasible, a great deal of time is often spent on designing the antenna. To improve the efficiency of antenna optimization, this paper proposes a design of experiments (DOE) method for antenna optimization. The antenna length and area in each direction were the experimental parameters, and the response variables were antenna gain and return loss. Response surface methodology was used to obtain optimal parameters for the layout of the antenna. Finally, we utilized antenna simulation software to verify the optimal parameters for antenna optimization, showing how the DOE method can increase the efficiency of antenna optimization. The antenna optimized by DOE was implemented, and its measured results show that the antenna gain and return loss were 2.65 dBi and 11.2 dB, respectively.

Yuangyai, Chumpol

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