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Big Data Enhancement of R0.01Reliability for Rain Attenuation Model Optimization in Thailand
(2025-01-01)
Pa-In, Wetchaphat
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Chodkaveekityada, Peeramed
Rain attenuation prediction is crucial for satellite communication, particularly in tropical regions like Thailand. A key parameter in rain attenuation modeling is the rainfall rate exceeded for 0.01% of the time (R0.01). This study collects big data from rain gauges across Thailand, recorded at 1-minute intervals over three years (2022-2024), and analyzes the Data Reception Rate (DRR) by comparing annual rainfall rates. This analysis also monitors the development and quality of the rain gauge network. The results, showing a high DRR, indicate the reliability of R0.01. This reliable R0.01 can then be used to accurately calculate the predicted attenuation exceeded for 0.01% of the time (A0.01), derived from slant-path measurements following the ITU-R model, using existing satellites serving Thailand.
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Technical Scheme for Optimizing Urban and Rural Logistics Operations and Improving the Informatization of Rural Logistics
(2024-02-13)
Zheng, Zhong
;
He, Wanxian
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Wei, Ganglan
In order to optimize the core problems encountered in the operation of urban and rural logistics and improve the informatization degree of rural logistics, the authors of this paper conduct research on the application of big data, cloud computing, and other technologies in urban and rural logistics and informatization. Based on the current problems, the authors adopt the hierarchical design, C/S architecture, and cloud API technology to improve the low level of informatization in urban and rural logistics, as well as optimize the dispersion and repetitive construction of logistics resources for logistics enterprises in urban and rural areas. Overall, a feasible sharing technology solution for urban-rural logistics informatization has been provided, providing a certain reference for optimizing urban-rural logistics operations and improving rural informatization issues.
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Adaptive big data maturity model using latent class analysis for small and medium businesses in Thailand
(2022-11-15)
Limpeeticharoenchot, Santisook
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Cooharojananone, Nagul
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Chavarnakul, Thira
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Charoenruk, Nuttirudee
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Atchariyachanvanich, Kanokwan
Big data analytics (BDA) is widely adopted in large enterprises. However, very few small- and medium-sized enterprises (SMEs) have adopted BDA because they lack the relevant knowledge, which makes BDA development expensive and unsuitable. A big data maturity model (BDMM) is a tool for assessing the stage for using big data in a company, and it acts as a guide for improvement. However, most BDMMs are designed for large enterprises using rule-based scoring, which is static over time. Developing a suitable BDMM for SMEs is a challenging task for professionals in terms of acquiring small-scale expertise owing to the lack of case studies for verifying the maturity level. This study proposes a new BDMM for Thai SMEs and a new methodology for developing a dynamic model using latent class analysis (LCA), which explains the behaviour of each latent class and provides non-rule-based scoring. We define four types of capabilities in SMEs: organizational and attitude factors, information technology, technology, and people readiness. Data are collected from 135 SMEs in Thailand. We introduce a methodology for developing multiple building stages of the BDMM. Further, we experiment with several clusters suitable for SMEs using statistic-based and data visualization approaches. The proposed BDMM is validated via a secondary evaluation of 11 firms, nine months after the initial evaluation. Further, we introduce a web-based application for respondents to obtain their firm's assessment results. The visualization-based result helps the respondents compare their business with other companies at the same or higher maturity level. In summary, SMEs can use the proposed BDMM to plan for continuous self-improvement and thus optimize their business using BDA to maximize its value to the organization.
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The Application of Data-Driven Technologies to Enhance Supply Chain Resilience in the Context of COVID-19
(2021-01-01)
Zheng, Zhong
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Lin, Yun
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Li, Liwei
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Lu, Liuqing
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Pan, Yanfang
The sudden outbreak of COVID-19 has wreaked havoc on global supply chains. Fortunately, digital drive technology can improve supply chain resilience. This paper is based on literature review to research the impact of COVID-19 to supply chain, to research the interplay of supply chain resilience and data-driven technology, focuses on the operation mechanism of data-driven technology (digital twin, blockchain, big data) and integration them together to enhance the resilience of supply chain. This paper try to presents an effective perspective of supply chain resilience by data-driven technology, and it can provide some reference and guidance for improving the resilience of supply chain during and after the COVID-19.
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The Grid-Based Spatial ARIMA Model: An Innovation for Short-Term Predictions of Ocean Current Patterns with Big HF Radar Data
(2020-01-01)
Pongto, Ratchanont
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Wiwattanaphon, Nopparat
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Lekpong, Peerapon
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Lawawirojwong, Siam
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Srisonphan, Siwapon
Marine natural disasters have direct impacts on countries as well as their residents living on and near the coast. Warning and monitoring system can aid in reducing the loss of lives in the event of a disaster. HF (high frequency) radar, an IoT-enabled ocean surface current monitoring system, implementation is one of the first attempts towards achieving this goal. Although HF systems can monitor sea current patterns in terms of speed and direction for each of the pixels of the coverage area, it fails to predict future values, which are essential to many applications such as oil-spill trajectory prediction (using the GNOME suite: General NOAA Operational Modeling Environment), water quality control and management, and optimized sea navigation. In this paper, we propose a model, called the grid-based spatial ARIMA (auto-regressive integrated moving average), to estimate the forecast values. As a result, the full potential of the HF systems can be utilized. The method considers not only observations of POI (point of interest), but also its neighboring pixels when predicting future values. The proposed method is implemented and compared with other existing approaches, including baseline, kNN, traditional ARIMA model, and LSTM (long short-term memory) techniques. The experimental results showed that our approach outperformed other methods in V comp prediction (with RMSEs of 6.23265) with a configuration of (2, 0, 1) as (p, d, q) and a historical dataset of 1 day and 7Â h prior. This configuration was found to be the best combination.
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Discovery of incremental association rules based on a new FP-growth algorithm
(2019-02-01)
Kreesuradej, Worapoj
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Thurachon, Wannasiri
In this paper, we propose a new FP-Growth algorithm for incremental association rule discovery. We also design a new FPISC-tree based on the FUFP-tree structure. The new FPISC-tree is more suitable for the task of incremental association rule discovery than FUFP-tree structure. The basic ideas of the proposed algorithm are to retrieve the frequent itemsets from the original database and to use their support count in the update of the new support count of the incremental database so that the original paths do not need to be reprocessed as well as to strategically use them to discover frequent itemsets from the FPISC-tree. Experimental results show that the proposed algorithm was able to reduce the number of constructed subtrees and the execution time was significantly less than those of the FP-Growth and FUFP-tree.
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Analyzing technique for electrical energy monitoring system in Thailand hospital
(2017-07-12)
Sarikprueck, Piampoom
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Attaphong, Chodchanok
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Lumyong, Pichit
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Ngamwatthanasilpa, Bandit
An electrical energy monitoring system helps improve the security and reliability of a hospital operating system which can also indirectly enhance energy efficiency itself. However, a large hospital has a complex electrical system which leads to the big data issue from the installed monitoring system in this size-scaled hospital. Therefore, this paper proposes K-mean clustering technique, which is one of the effective data mining techniques, to analyze the big data from the electrical energy monitoring system in hospital. The case study is a large hospital with 200 in-patient beds in Thailand. Without loss of generality, electrical load profile is used for analyzing instead of using the electrical energy. Finally, the proposed data clustering technique is able to characterize electrical load profiles effectively for each hospital floor. This technique also identify the abnormality of these characterized electrical load profiles in various scenarios which hospital system operators can use them to consider the security, reliability, and energy efficiency of their operating systems.

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