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Feasibility of Reverse Vending Machine for PET Bottle Recycling in Case of ABC Hypermarket
(2024-01-03)
Jungthawan, Siripong
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Tiyarattanachai, Ronnachai
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Anantavrasilp, Isara
The prevalence of polyethylene terephthalate (PET) in beverage packaging has increased microplastic (MP) accumulation in the environment. MP can become a component of air pollution, especifically of particulate matter with a diameter of less than 2.5 microns (PM2.5). Unfortunately, Thailand has a low recycling rate for plastic bottles, with only 4.5% being recycled. Reverse Vending Machines (RVMs) were introduced in the early 2010s as a potential solution to this problem. RVMs incentivize recycling by providing rewards or refunds for each bottle deposited, thereby reducing the amount of PET waste and MP in the environment. This study analyzed usage and waste collection data from RVMs located at 15 locations of a hypermarket chain in Thailand from June 2020 to December 2022. The results showed that the average PET bottles collected from each machine was approximately 670 kg per year, which was lower than the break-even point of 3,200 kg per year. Economic feasibility indicators also suggested that the use of RVMs might not be economically sound. This study proposed suggestions to improve the business model of RVMs and offered policy recommendations to the government on how to enhance the effectiveness of RVMs.
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Factors influencing the usage of bicycles on university campuses: A case study of universities in Thailand
(2023-12-01)
Pakdeewanich, Chitsanu
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Anantavrasilp, Isara
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Tiyarattanachai, Ronnachai
University campuses usually consist of many buildings with different functions, e.g., classrooms, laboratories, offices, and cafeterias. In turn, students and university employees may commute between these buildings several times a day. Organizing in-campus transportation suitable for the campus’ characteristics and the travel behavior of the community is, therefore, a critical and challenging task. This paper focuses on using a bicycle as a mode of transport on university campuses. We analyzed the traveler's demographic, behaviors, and their attitudes toward cycling by conducting a survey covering students, faculty, and staff in several universities. In total, we have collected 1,433 responses from 19 universities across Thailand. According to the responses, most undergrad students use motorcycles and public transport as their primary modes of transport. In contrast, postgrad students, faculty, and staff mainly use private cars. Only 2.9% of all respondents use bicycles regularly. By applying Binary Logistic Regression to the responses data, we found that the demographic factors, including residency and mode of transport, are important determinants of bicycle use on university campuses. Also, lacking dedicated bicycle lanes and long travel times are main obstacles preventing people from cycling within university campuses. Based on these results, the study suggests that improving the availability and accessibility of bicycle facilities could encourage more people to use bicycles as a sustainable mode of transport on university campuses.
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Hybrid Multi-Model Fuzzy Ensemble Approach for Cardiovascular Diseases Detection
(2023-01-01)
Chugh, Manop
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Anantavrasilp, Isara
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Thiemjarus, Surapa
Timely detection of cardiovascular diseases (CVDs) is crucial to reducing mortality rates. Recent advances in artificial intelligence (AI) and machine learning (ML) models for CVD detection often suffer from low model performance and hence lower accuracy and practicality of early CVD detection. In this study, we propose a novel hybrid ensemble learning framework that combines multiple ML algorithms and a fuzzy expert system to improve CVD diagnosis and prediction accuracy. We evaluate our proposed method on two standard datasets, namely the UCI Cleveland and Framingham, and compare it with four popular ensemble algorithms, namely Random Forest, Gradient Boosting, eXtreme Gradient Boosting, and Adaptive Boosting. Our results demonstrate that the proposed ensemble learning framework achieves higher accuracies of 91.2% (UCI Cleveland) and 91.7% (Framingham), surpassing existing algorithms by 3.3% and 8.8%, respectively.
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Development of locally designed smart bike-sharing system on Thai campus: Lessons learnt of a pilot-scale study
(2020-08-31)
Pakdeewanich, Chitsanu
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Tiyarattanachai, Ronnachai
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Anantavrasilp, Isara
Campus-wide bike-sharing program is one of the solutions for achieving sustainability in higher education. It also contributes to the UN's Sustainable Development Goals (SDGs). Smart bike-sharing systems have been implemented in some Thai cities and Thai universities since 2012. The systems were provided by foreign companies and developers. A locally designed smart bike-sharing system have been developed and pilot-scale tested at a Thai university. Many problems were found throughout the test-period, while design changes and application modifications were conducted to address the issues. The changes were done to make the system appropriate for the context of Thai university and users' demands. This paper discusses the development process and lessons learnt from this locally designed system. The results can be used by developers and software designers of bike-sharing industry to provide better design and implementation for Thailand market.
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Locally Designed Campus Smart Bike Sharing System: Lessons Learned and Design Optimization for Thailand
(2020-04-01)
Pakdeewanich, Chitsanu
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Tiyarattanachai, Ronnachai
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Anantavrasilp, Isara
Bike-sharing systems have been widely implemented in around 700 cities worldwide since the 2000s. The quick expansion is due to the growing concerns over environmental impacts and climate change problems. Bicycles are deemed to be a promising mode of transportation for achieving urban sustainability and sustainability in higher education. Sustainable transportation is an important factor in achieving the UN's Sustainable Development Goals. In Thailand, a bike-sharing service was first launched in Bangkok in 2012. However, bike-sharing in Thailand heavily relied on systems designed and operated by international companies. Many systems have not been very successful and some were discontinued. We discuss lessons learned from a locally designed bike-sharing system and its optimization for a Thai university. A pilot-scale of public bicycles was launched and over 24,000 trips were observed in six months. Most trips were 0-10 minutes and peak hours were in the morning, which means most students picked up the public bike on the last-mile based on study timetable. From heat maps of bicycle usage, nearly educational buildings and connecting transit points had the highest departure and arrival rates.
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Development of locally designed smart bike-sharing system on Thai campus: Lessons learnt of a pilot-scale study
(2020-01-01)
Pakdeewanich, Chitsanu
;
Tiyarattanachai, Ronnachai
;
Anantavrasilp, Isara
Campus-wide bike-sharing program is one of the solutions for achieving sustainability in higher education. It also contributes to the UN's Sustainable Development Goals (SDGs). Smart bike-sharing systems have been implemented in some Thai cities and Thai universities since 2012. The systems were provided by foreign companies and developers. A locally designed smart bike-sharing system have been developed and pilot-scale tested at a Thai university. Many problems were found throughout the test-period, while design changes and application modifications were conducted to address the issues. The changes were done to make the system appropriate for the context of Thai university and users' demands. This paper discusses the development process and lessons learnt from this locally designed system. The results can be used by developers and software designers of bike-sharing industry to provide better design and implementation for Thailand market.
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Robust image encryption method with cipher stream chaining process
(2019-02-01)
Tep, Sovan
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Anantavrasilp, Isara
A new image encryption algorithm that uses one dimensional logistic map combined with perceptron model is proposed. The algorithm uses logistic map to produce pseudo random sequences, which are used sequences of keys to specify the weights of the perceptron. The, perceptron is used to encrypt the pixels of the image. The approach is also equipped with the novel Cipher Stream Chaining Process (CSCP), making it highly sensitive to given image. Our work is evaluated against histogram analysis, information entropy, key sensitivity analysis. Experiment results show that, the cipher image does not give out any information on the plain image and the algorithm is highly sensitive to plain image and key.
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The effect of sizes of the feature sets on intrusion detection performances
(2017-12-28)
Kuy, Yoekleng
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Anantavrasilp, Isara
Adaptive Intrusion Detection System (IDS) is a class of IDS that uses observed flows behaviors to detect malicious activities - usually with the aids of machine learning techniques. Most researches in this field focus on which features to be used or which classification methods to be employed. However, none have studied the impact of number of opted features on the accuracies of the anomaly detection or the smallest set of features that should be employed. This paper attempts to address these issues. We have applied feature selection algorithm, ReliefF [1] on NSL-KDD dataset [2] to select 10 most discriminative features out of 41 features. Then several machine learning algorithms are employed to classify normal and anomaly flows (both binary and multiple classes) using different set of features, each with different sizes. Experiment results show that >95% accuracies can be achieved with only 4-5 features and accuracy does not improve significantly after 6-7 features. We have also compared our results with other works and show that our work yields better results using the lower or the same number of features.
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Analysis of optimal sensor positions for activity classification and application on a different data collection scenario
(2017-04-05)
Pannurat, Natthapon
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Thiemjarus, Surapa
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Nantajeewarawat, Ekawit
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Anantavrasilp, Isara
This paper focuses on optimal sensor positioning for monitoring activities of daily living and investigates different combinations of features and models on different sensor positions, i.e., the side of the waist, front of the waist, chest, thigh, head, upper arm, wrist, and ankle. Nineteen features are extracted, and the feature importance is measured by using the Relief-F feature selection algorithm. Eight classification algorithms are evaluated on a dataset collected from young subjects and a dataset collected from elderly subjects, with two different experimental settings. To deal with different sampling rates, signals with a high data rate are down-sampled and a transformation matrix is used for aligning signals to the same coordinate system. The thigh, chest, side of the waist, and front of the waist are the best four sensor positions for the first dataset (young subjects), with average accuracy values greater than 96%. The best model obtained from the first dataset for the side of the waist is validated on the second dataset (elderly subjects). The most appropriate number of features for each sensor position is reported. The results provide a reference for building activity recognition models for different sensor positions, as well as for data acquired from different hardware platforms and subject groups.

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