cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14457/tu.the.2021.564
title: Design and development of an IoT system for air-conditioning control in smart living

description: A split-type air conditioner is installed in many households and buildings to produce a comfortable environment. The air conditioner is still mostly operated with occupant’s configurations through the remote control. However, it may unexpectedly affect the occupant’s health because of the static configurations. Furthermore, improper configurations can reduce the energy-efficient of the air conditioner usage. Integration of sensing system and a split air conditioner (AC) gives better occupants’ comfort control in a built environment. In order to reach optimal control from the air-conditioning system, various parameters such as temperature, humidity, and wind speed are used to indicate thermal comfort of the occupants. This thesis then designs and develops two thermal-comfort controllers using an occupancy-detection system for the split AC. The occupancy detection system uses a camera sensor to receive scenes of an indoor environment and apply an image processing-based algorithm, to detect the occupant’s position in the room. The proposed controllers, which are conditional control and fuzzy logic control approaches, aim to reach and maintain a thermally comfortable environment by controlling direction and speed of airflow based on the obtained occupant’s position in the room. The experimental results showed that the conditional control and the fuzzy logic control can achieve the comfortable environment for 91.55% and 100 % of the total experimental time, respectively.
DOI: 10.14457/tu.the.2021.565
title: Analysis of visitor data prediction of National Science Museum Thailand (NSM)

description: At present, many museums have begun to use data analysis for assisting management. For example, data prediction has been used to predict the number of visitor and help museum curators planning how to improve their service. In this thesis, we investigated and analyzed the factors that affect the number of visitors by using data correlation to select the factors. We proposed museum visitor number prediction method based on the Random Forest (RF) model and the XGBoost model using historical data from 2016 to 2020 from three museums under the supervision of the National Science Museum Thailand. Since all selected museums locate in the same area, to improve the prediction performance, our purposed method combines the data from the three museums. More specifically, the result of one museum is used to help predict the visitor number of the other museum. Originally, the RF and XGBoost models have average accuracy at 56.99% and 58.08%, respectively. We had proposed four methods in this thesis all of which have accuracy more than the original. Regarding to our proposed method, the highest average accuracy was 85.14 and 83.13% from proposed method D. We also observed that the XGBoost model consumed less computational time. In addition, the RF model performed better than the XGBoost model with 97.85% accuracy after reducing the visitor number classes from five to three.
DOI: 10.14457/tu.the.2021.566
title: Development of non-water-stressed baseline for realtime crop water stress monitoring

description: In this work, we are interested in determining and using crop water status and quantify crop’s stress, an indicator called crop water stress index (CWSI) which is empirical approach and was developed by Idso et al. Our experiment has been conducted with the purpose of estimating CWSI of a durian tree around durian orchard under project called “IRRIGATION 4.0”, which is located at Chanthaburi, Thailand. Because real-time recorded dataset was scattered due to ever-changing environmental conditions. We find the correlation between stem water potential and CWSI of the durian tree from mid-November 2020 to mid-February 2021 to evaluate accuracy of calculated CWSI. From our experiment, the correlation coefficient was found to be -0.085 on average. Therefore, we propose a data cleansing framework that includes time, irradiance, wind speed, air relative humidity as parameters, and implemented long short-term memory (LSTM) to filter the dataset for estimating non-water-stressed baseline. The results show that this framework can produce correlation coefficient between stem water potential and CWSI to be equal to -0.671 on average, a much higher correlation compared to -0.085 when not using our framework.
DOI: 10.14457/tu.the.2021.567
title: An autonomous framework for real-time wrong-way driving vehicle detection from CCTV

description: In 2018, around 22,000 people were killed in road accidents in Thailand and more than 80% of road accidents involved motorcycles. There are several reasons for traffic death and one of them is motorcycles driving in the wrong direction. In our research, we proposed an autonomous framework, WrongWay-LVDC, that can detect vehicles that drive in the wrong direction from CCTV videos. The framework is composed of other additional features that allow the detection process to run smoothly and automatically. The features that are included in the framework are road lane detecting, correct driving direction validating, detecting the wrong-way driving vehicles, and evidence image capturing. In total, we have proposed three algorithms and a feature for our main contributions. The three algorithms are, first, the Road Lane Boundary Detection based on the CCTV camera angle (RLB-CCTV) algorithm. Second, is the Majority-Based Correct Direction Detection (MBCDD) algorithm. Third, is the Distance-based Direction Detection (DBDD) algorithm. The additional feature we developed is the Inside Boundary Image (IBI) capturing feature that captures the clearest shot of the wrong-way driving vehicle. Each algorithm and feature will be explained in terms of its methods, result, and discussion in this thesis. As a result, the framework can execute continuously and output a report for vehicles’ driving behaviors in each area. The accuracy of our approach is 95.23% as we tested with several CCTV videos using the DBDD algorithm. Moreover, the framework can be implemented on edge devices with real-time speed where it is proved that the framework is light-weighted and consume low resource while executing.
DOI: 10.14457/tu.the.2021.568
title: Fundus image transformation to indocyanine green angiography using generative adversarial networks

description: A colored fundus image is an initial method for examining eye diseases. Due to its limits of visualization, diseases such as wet age-related macular degeneration (wet-AMD) and polypoidal choroidal vasculopathy (PCV) on a fundus image are difficult to distinguish using only a fundus image. An indocyanine green angiography (ICGA) image has been introduced to handle such limitations, but the acquiring process is invasive, complicated, and painful to a patient. To avoid unnecessary pain for non-PCV patients, this work presents a framework to generate an ICGA from an input fundus image. Given pair-patches of high-resolution fundus and ICGA images, they are pre-processed and trained using generative adversarial networks (GAN) to construct an image generator model. The generated patches are combined and post-processed to obtain the generated ICGA image. To assess the generated ICGA images, Peak signal-to-noise ratio (PSNR) and Fréchet inception distance (FID) are used to measure the similarity to the original fundus and the ICGA images. The results show that the generated ICGA is similar to ICGA than the original fundus and the signs of polyp, which are crucial for the circulatory system diagnosis, are exhibited. In addition, EfficientNetB5 model is built to evaluate the classification performance of the generated images for the PCV and wet-AMD. The result shows that sensitivity and specificity of our generated ICGA is greater than the original fundus.
DOI: 10.14457/tu.the.2021.569
title: Application potential of expansive concrete to slabs on piles

description: Cracking in concrete structures occurs when the shrinkage stress exceeds the tensile capacity, which is called drying shrinkage cracking and is commonly found in reinforced concrete structures. A solution to this problem is to apply expansive concrete. This independent study is aimed at surveying the overviews of the application of expansive concrete and expansive additive and its mechanisms to prevent restrained shrinkage cracking by introducing expansion in the expansion concrete. Some related existing standards are compared. Examples of construction projects of slabs on piles are surveyed and some typical characteristics such as slab thickness and reinforcement ratio are summarized.
DOI: 10.14457/tu.the.2021.570
title: Effective logistic of e-commerce in Thailand

description: The goal of this thesis report is to study the importance of logistics service quality for e-commerce business: an application of Analytic Hierarchy Process (AHP). The study was qualitative research and the in-depth interview was employed for data collection that expertise and continuous growth 6 people and the result data were analyzed through content analysis and interpretation. In order to study the relationship between the elements of the logistics problem and the logistics strategy employed to develop service values and meet the happiness of targeted consumers, a mix of business strategies in the product distribution companies that sell items were done.
DOI: 10.14457/tu.the.2021.571
title: Apply reinforcement learning as model selection to predict power consumption

description: Electricity forecasting is critical to a country's economy, particularly in developing countries. Accuracy in power forecasting is essential to support growth in electricity demand, provide adequate power planning, and prevent power system outages. However, the accuracy of available forecasting methods greatly depends on the characteristics of the data and the nature of the problem. As a result, determining which forecasting approach is optimal for a given scenario is challenging. This study presents a novel approach using Double Deep Q-network (Double DQN) as a model selection to specify the most accurate forecasting model for each time point. The monthly peak electricity consumption in Thailand from 2003 to 2017 provided by the Electricity Generating Authority of Thailand (EGAT) is used to illustrate and verify the model. The proposed model selection using Double DQN surpasses all the individual models in terms of prediction accuracy calculated by mean squared error (MSE) and provides a method to leverage the strength of each forecasting model.
DOI: 10.14457/tu.the.2021.572
title: Optimal design of distribution network: case study of home decoration materials store

description: First of all, I would like to acknowledge my deepest thanks and sincere gratitude to my Independence Study advisor, Assoc. Prof. Dr. Pisal Yenrade for great academic advices, inspiring guidance, useful critiques, and enthusiastic encourage me throughout this project. Furthermore, I would like to thank to one of the committee members, Asst. Prof. Dr. Jirachai Buddhakulsomsiri who provides useful comments and recommendations to improve in independent study during presentation. Nevertheless, my completion of this Independent Study could not have been accomplished without support from the seniors at my intern. Finally, special thanks to my caring family who always support and encourage me when the time get tough. My heartfelt thanks.
DOI: 10.14457/tu.the.2021.573
title: Mapping poverty in Thailand using machine learning, satellite imagery, and crowd-sourced geospatial information

description: Poverty, one of the most major drivers of ill health outcomes internationally, is the primary source of social instability and one of the most significant causes of human potential loss. Humanitarian organizations and policymakers, especially in developing countries, need to map the distribution of poverty to develop targeted programs and assistance. However, acquire socioeconomic data using traditional methods can be costly, time-taking, and labor-intensive. Now, satellite imagery and machine learning can be used as an alternative to map poverty from outer space as a quick, affordable, and scalable methods of giving detailed poverty data. This study aims to investigate poverty related to environmental pollution and NDVI extracted from satellite images, and other geospatial data such as point of interest and road density, a study has not well explored in the previous studies. Google Earth Engine is used to extract satellite images and geospatial data is downloaded from an online repository, OpenStreetMap (OSM) website. This study will compare four different machine learning models (random forest, xgboost, ridge regression, and lasso regression) for poverty prediction at the sub-district level. To evaluate our regression models, we utilize the metric namely coefficient of determination (R2). The result shows that satellite imagery (environmental pollution and NDVI) and geospatial data are potential sources to estimate poverty since they are highly associated with the poverty rate. The random forest model produces the best degree of prediction accuracy among the methods considered in this study with R2 value of 0.83 in the final experiment, due to its capability to handle multicollinearity problem. Finally, we employ feature importance analysis using Shapley Additive Explanations (SHAP) to identify the most influential features to assist decision-makers in gaining a better understanding of poverty. Future research is required to determine if the association shown here is consistent over time and may be utilized to estimate the prevalence of poverty in years without conducting poverty surveys. This paper contributes to academic knowledge as an alternative method and feature for mapping poverty at sub-district level.
DOI: 10.14457/tu.the.2021.574
title: Evaluation of atmospheric exposure test on structural steel coated with different materials in the east of Thailand

description: The environment has a significant impact on the corrosion of steel. Corrosion is unavoidable; however, the rate of corrosion can be minimized by painting and galvanizing. The rate of corrosion depends on the coating and the environmental condition. The purpose of this research is to study and evaluate different types of structural steel coated with different materials in the East of Thailand. The atmospheric corrosion test is conducted for two types of steel, SS400 and SM490 with six different coating systems which are bare steel, hot-dip galvanized steel, hot-dip galvanized with painting (duplex system), common base practice paint steel, well base practice paint steel, and the premium base paint steel and the location of atmosphere corrosion test is conducted in three test site which is at Chachoengsao, Chonburi, and Rayong.Following the ASTM G50 standard, specimens are exposed to the real environment for three months, six months, and one year at the desired location. Weathering station at the test site collects and monitors environmental data such as temperature, rainfall, and relative humidity on a daily basis but the SO2 pollution, and airborne chlorides on a monthly basis. The weathering stations are present only at Station 13 Chachoengsao and Station 14, Chonburi. The sulfur dioxide was collected through a lead dioxide cylinder and the chloride was collected through the dry gauze method. The exposed steel specimen for bare steel and Hot dip galvanized are cleaned and the corrosion products are removed. After analyzing the thickness loss data of the specimens, we are able to predict the thickness loss of the specimens for bare steel and hot-dip galvanized steel and using the environmental parameters and the thickness loss data of one year we are able to predict first-year corrosion based on environmental parameters. The painted specimens are analyzed, and we can evaluate and compare different types of paint-coated specimens using the data. The results would indicate how the environmental parameters affect corrosion rate and able us to compare structural steel with various coatings. The results can be used to choose a coating, predict the steel’s service life, and predict thickness loss.
DOI: 10.14457/tu.the.2021.575
title: Landslide semantic segmentation using satellite imagery

description: This work aims to improve the accuracy of landslide detection and detecting landslide scar with single satellite imagery by using slope factor extraction and normalized difference vegetation index (NDVI) extraction and by combining three semantic segmentation models based on a convolutional neural network with a classification/decision tree. The proposed method used three semantic segmentation models with a decision tree. The first model is trained by a training set of color images.The second model is trained by a training set of slope factor images. The third model is trained by a training set of NDVI images. The slope factor and NDVI are extracted from color images that contain red, green, and blue bands. The results from threemodels are used to generate features for training the classification tree. Evaluation metrics (precision, recall, and F2 score) can be improved by using slope factor and NDVI. In combining three models, the F1 score and F2 score are increased more thanusing a single of color images 16.71% and 24.15%, respectively in Resnet18, and increased16.80% and 26.20% respectively in Resnet50. Moreover, the slope factor detection model and NDVI detection model can support some areas that the color imagedetection model cannot detect.
DOI: 10.14457/tu.the.2021.576
title: Customer behavior classification for service robot

description: Behavior mirrors the unique personality through two factors nature and nurture. The human expression of under control or over control is the human behavior that expresses some destinations. Human behavior looks like the one way to communicate with humans is non-verbal communication. Non-verbal communication is an inborn skill used to survive in the world. This communication is not necessary to use the language to be the intermediate to transfer the data. However, this communication uses an intermediate expression such as body language, facial expression, or hand gesture. A robot is a machine created for many tasks, such as serving food in a restaurant. We can see many types of robots more than in the past. Developer developed the service robot to support or improve human work performance in many fields. Human behavior is essential information, so if the service robot can know the human behavior, it will enhance the performance of responding to a human and include the possibility of the robot. Nowadays, many people work at an office or their home at a desk, especially in the COVID-19 situation. However, if the customers do their work at a desk for a long time, they will have some stress or illness. Therefore, if we have a service robot that can be a friend while a customer is working at a desk, it will help decrease the customers' stress. Many people work with a laptop or computer. If we set the robot in front of the customer, the laptop or computer screen will cover the customer's body. Therefore, we must place the robot at the indented angle at the customer's right or left sides. We use in this research consist of 5 static behaviors: raising hands, lying down, working, thinking, and relaxing.To classify the human behavior while a customer is working at a desk, we have used the method that consists of 3 steps: 1) skeleton feature extraction, 2) data processing, and 3) behavior classification. We have first gotten the human skeleton information on an input RGB image using the OpenPose framework. Then, we have calculated the vectors and angles of the skeleton information to be the input features of a model. Finally, we used the input features from the previous step to classify human behavior using the Deep Learning algorithm.After we have trained and tested many cases, we have found the MLPClassifier of the Scikit-Learn library with hidden layer size is [128] can predict the human behavior with high accuracy faster than other algorithms. Therefore, the MLPClassifier is the best algorithm for human behavior classification of working situations with the F1 score of each behavior above 0.96.
DOI: 10.14457/tu.the.2021.577
title: Environmental and economic assessment of carbon capture and utilization at coal-fired power plant in Thailand

description: Greenhouse gas reduction from large point sources has been of interest for global warming mitigation. Coal-fired power plants with carbon capture and utilization (CCU) technology can minimize carbon emissions. In this study, Life Cycle Assessment (LCA) methodology is applied to evaluate the potential environmental risks of capturing carbon dioxide at the coal-fired power plant in Thailand and utilizing it in alternative methanol (CO2_MeOH) and formic acid (CO2_FA) productions through direct CO2 hydrogenation. The study aims to achieve low carbon growth and sustainable development success in Thailand. According to the analyses, the negative carbon feedstock provides larger decarbonization in the CO2-based productions than the conventional productions (i.e., -862 vs. 712 kgCO2-eq in a ton of MeOH production while 1.96E3 vs. 4.11E3 kgCO2-eq in a ton of FA production). Despite the net negative carbon emissions, CO2_MeOH production can rise 6 out of 8 investigated environmental impact indicators, when 2 out of those would increase in CO2_FA production. The maximum impact scores as a result of the energy demand of the carbon capture can be reduced by integrating the waste heat recovery from the carbon removal process. Consequently, the higher impacts would be 4 out of 8 indicators in the CO2_MeOH production, whereas the CO2_FA production remains the same pattern.Furthermore, the investment costs of CCU projects are estimated on a unit price basis to strengthen an economy with a clean energy transition. The findings revealed that the process with the waste heat integration could decrease not only the related environmental risks but also the investment cost. However, total production costs (1227 $/tCO2_MeOH & 1044 $/tCO2_FA) are still expensive to compete with the conventional production prices (295-368 $/tMeOH & 546 $/tFA) and the current global market prices (750 $/tMeOH & 500-800 $/tFA), that could be lowered if low electricity cost was available. Selling by-product oxygen from CO2_MeOH production and cheaper catalysts from local or global markets can reduce the total production cost.
DOI: 10.14457/tu.the.2021.578
title: Consistent and sustainable supplier evaluation and order allocation: evaluation score based model and multiple objective linear programming model

description: This paper is to develop an integrated approach of supplier evaluation and order allocation to suppliers that suggests the buyer to place more orders to the supplier that has higher evaluation score (consistent order allocation) considering sustainability issues including economic, social, environmental, and disruption of supply chain issues. The proposed approach is handled by an Evaluation Score based Linear Programming(ESLP) Model. Performances of ESLP model is compared with those of Multiple Objective Linear Programming (MOLP) model that does not explicitly consider the evaluation scores of suppliers for order allocation. Experimental results show that ESLP model offers consistent order allocation while MOLP model offers inconsistent order allocation. Moreover, MOLP model has different priorities of suppliers for orderallocation when the customer demands are changed. Inconsistent order allocation makes the purchasing process nontransparent, unexplainable, and susceptible for biased decisions. ESLP and MOLP models generate compromised solutions that are nondominated. They are better and worse for some performances. This paper emphasizes a need of further research that develops consistent order allocation methods.