cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14457/tu.the.2022.784
title: Application of mixed integer linear programming in optimization of sequence-dependent fluid packaging production scheduling

description: In the manufacturing process of packaging fluid, there are processes in between the assigned producing jobs called ‘changeover’ in which the machine or the production line must have an adjustment to prepare for the upcoming job. Therefore, scheduling requires optimization to minimize the total production time from the changeover of the sequence scheduled. The process is normally executed by a planner with effort spent to find the optimal solution with the lowest makespan as fast as possible. This research considers the job scheduling problem with sequence-dependent changeover times on a single machine to minimize the makespan of the jobs assigned. Different changeover types with varied required times will be assumed. To provide an effort improvement tool, a mixed integer linear programming (MILP) is developed to compute the optimal solution and compared with the human-executed heuristic proposed to find the solution with a better computational time in exchange for a non-optimized solution. Up to 71 jobs, the developed MILP with a tolerance 0.5% takes approximately 60 minutes to find the solution. In comparison, the heuristic cannot reduce the computational time until scheduling 71 jobs and can reduce the computational time by 87.5%. However, compared to the MILP with a tolerance of 5%, it can reduce the computational time and the MILP with a tolerance of 0.5% and can improve the results compared to the heuristic at any number of jobs.
DOI: 10.14457/tu.the.2022.785
title: An (R, Q) inventory policy for medicine under limited storage with a backlog

description: In hospitals have various sensitive and necessary items such as medicine, medical supplies and so on, the focus of this study is on multiple medicine items whose demand is determined from actual data in the central pharmacy room. There are issues on inventory stock management, which includes medicine supplies, due to the fact that each patient's medication is highly specific and needs special storage conditions. The quantities of medicine are depending on patient needs to ensure the orderly that have sufficient for the requirement, the stock management should be placed the order reach to the inventory level. This study focuses on comparing the effectiveness of sharing information and not sharing information in a central stock room to determine the optimal solution for minimizing total annual cost considers the (R, Q) inventory policy uncertain demand, a constant lead time, limited storage capacity with a backlog. The evaluation of performance is conducted using Microsoft Excel and Python through a spreadsheet simulation. To determine the best approach, the cyclic coordinate method with the golden section search is used to find the reorder point (R) and order quantity (Q) that result in the minimum total annual cost which are consist of the total annual cost include ordering cost, inventory holding cost, backlog, and over storage cost. The analysis is based on actual demand data collected from the pharmacy. This study's simulation optimization method can assist the central stockroom in managing the supply of medications while minimizing the total annual cost. The results are effective and realistic.
DOI: 10.14457/tu.the.2022.786
title: The Analysis of Explainable AI via Notion of Congruence

description: Explainable AI has become a popular topic which draw attentions of many researchers, and since the inception of this field, the most popular method of model explanation is undoubtedly using Decision Tree to approximate black box model, due to its straightforwardness and ease of explanation. However, Decision Tree is single dimensional model without any nuance in the decision making which may be intrinsic to the structural properties of the black box model itself. With this, we propose using a structured framework in the form of Probabilistic Assumption-based Argumentation (PABA) framework in place of Decision Tree which allows for more complex relationship between arguments within the model, and demonstrate an application using PABA framework as an alternative to Decision Tree by establishing “notion of congruence” between an example black box model and its structured argumentation framework (PABA) predictor counterpart, that is, some measurable similarity between both models along with establishing methodology to evaluate their relative faithfulness. In order to accomplish that, we partially translated both models to PABA framework first, then extract the explanations from both models using an adaptation of two existing techniques (Anchor and LORE), which is then successfully compared using established notion of congruence to get a numerical measurement of faithfulness. By proving that this is possible, our study suggests that improvement on the framework level of machine explanation problem is a possibility.
DOI: 10.14457/tu.the.2022.788
title: Development of supported non-noble metal catalysts for conversion of bio-lipids into biofuels/biochemicals

description: H-ZSM-5-supported cobalt is widely used as a catalyst for hydrodeoxygenation (HDO) and hydroisomerization (HI) of fatty acid methyl esters (FAMEs). Nevertheless, ZSM-5 has a high proportion of micropores and a long diffusion path that might cause pore blockage in fatty acid-related processes. To overcome this, modifications of its structure into sheet-like material have been investigated using these steps (i) Ureamodification for sheet-like structure (ii) Secondary growth for fabricated nanosheet by acid/base treatment and seeding methods. The result showed that the ZSM-5 structure was developed into a b-axis oriented structure which provides a sheet-like structure with a b-axis length of 590 nm after modification with the mole of urea at 160 and Si/Al at 100. The catalyst performance on FAMEs conversion was 97.72%. Furthermore, a shorter b-axis length of 330 nm was obtained by implementing the 0.5 M of the HCl etching and secondary growth approach. The catalyst exhibited a 99.69% FAMEs conversion, 89.38% liquid yield, and product selectivity towards 35.78% n-alkanes, 37.47% iso-alkanes, and 19.71% of jet fuel.
DOI: 10.14457/tu.the.2022.789
title: Highly-efficient heterogeneous catalysts for hydroisomerization of hexadecane

description: In this study, acidic heterogeneous catalysts will be synthesized with different Si/Al ratios, framework structures (3D vs. 2D structure) to improve the iso-alkane selectivity in hydroisomerization (HI) of n-hexadecane (C16). The synthesized materials will be characterized by X-ray diffraction (XRD), N2 adsorption-desorption, NH3-temperature-programm desorption (NH3-TPD), Scanning electron microscopy (SEM), pyridine diffuse reflectance infrared Fourier transform (Py-DRIFT), and energy-dispersive X-ray fluorescence (EDXRF). The synthesized materials will be loaded with Ni and then tested for catalytic performance in hexadecane’s HI reaction in a continuous fixed-bed reactor.
DOI: 10.14457/tu.the.2022.791
title: Developing health-related products to enhance quality of life for senior adults and working women

description: With the elderly population increasing, there is a growing need for products that can aid their daily activities, such as grocery shopping. To address this issue, this study applies the product design and development method to create a grocery shopping cart that can improve the shopping experience for senior adults. Through a survey, customer feedback was collected to identify the problems and requirements of senior adults during grocery shopping. The results were then used to create a new design by combining the existing product with functional components like a smartphone holder, fan/cooler, lift-up plate, sensor, and light. Several methods, including Quality Function Deployment and Bartlett's Test, were employed to ensure the usefulness and quality of the new design. Simulated force and material analyses showed that HDPE and stainless steel were suitable for the container part (basket) and foot supporter, respectively. The outcome of this study can be used as a guideline for mass production of the designed grocery shopping cart for senior adults, which can enhance their quality of life by providing a more comfortable and convenient shopping experience. Further revisions and improvements may be made based on feedback from users. Proper accomplishment of design under constraints can be facilitated with the assistance of product design and development (PDD). The development of a durable, flexible, and accessible briefcase-style bag for work-and-travel activities is presented in this study. The target customers are set as women who require specific details that lead to the comprehension of hidden issues by the designers. Key considerations for the bag's design include providing ample space and a strong body to store many items while preserving them in good condition. A solid-based baggage cover was applied to provide a shock-absorption structure. The bag’s size must not exceed 40cm x 20cm x 25cm. The weight of the bag and its contents was set at 4 kg, which is the main parameter for Finite Element Analysis (FEA). To ensure safety, force distribution around the bag was simulated. When a 1000 N external force or impact force was applied to the front/back area of the bag for a relatively short period, the developed bag remained usable without any cracks found. The main body of the bag is made of polycarbonate (PC) - Acrylonitrile Butadiene Styrene or ABS plastic material, which is durable and strong. The analyzed results can serve as key guidelines for manufacturers or customers to assist work-and-travel activities with happiness. Designing a bag under constraints requires careful consideration of various design elements and materials. With the utilization of product design and development (PDD), a bag that satisfies the unique needs of the target customers can be developed, providing a durable and flexible solution for work-and-travel activities.
DOI: 10.14457/tu.the.2022.792
title: Machine scheduling approach for the sustainable development of a residential-industrial area

description: Rapid urbanization brings many sustainable development challenges, including the impacts of urban factories. While contributing to the improved resource efficiency and growth of the cities, urban factories are often associated with negative impacts on the health and well-being of urban residents. Well-designed engineering controls can be implemented to make the factories more environmentally and socially acceptable. However, administrative controls such as production scheduling can support the already invested efforts to be more effective. In this study, a multi-objective machine scheduling approach is proposed to promote the sustainable coexistence of factories and cities. The scheduling aims to reduce cost, energy consumption, and noise disturbance impacts on the residents, simultaneously satisfying all three sustainability aspects. Our approach is twofold: Firstly, a multi-objective mixed-integer programming model is formulated to ensure energy-efficient and just-in-time scheduling solutions. The electricity cost and total weighted earliness and tardiness penalty are simultaneously minimized. Secondly, a multi-criteria fuzzy decision-making technique is used to select solutions with satisfactory non-processing energy consumption and noise disturbance levels. A benchmark data set of Fisher and Thompson ft06 problem instances are used to demonstrate the application of the model. The analysis results show that the proposed machine scheduling approach can be used to reach low noise disturbance solutions that are also satisfactory on other criteria. The proposed approach is the first attempt to consider all the sustainability dimensions and the sustainable development of a residential-industrial area.
DOI: 10.14457/tu.the.2022.793
title: Classification of lime defects with convolutional neural network (CNN)

description: The defection of lime are been easily visible to classify by humans. But it required time-consuming and costly labor for the industry. Using computer vision is one of the approaches for determining the defection of lime since it’s less time-consuming and less expensive. The main purpose is to determine the defections of fresh market lime by utilizing color feature value, computer vision, and machine learning. In this project, using a convolutional neural network (CNN) classify lime into 2 classes; defection (1) and healthy (0) since the CNN model is deep learning and the performance of the result is very high accuracy. The 5 hyperparameters for tuning the CNN model— Epoch, Learning Rate, Decaying Rate, and Momentum—are taken into account and designed using factorial methods to improve the model's performance. For the training, validation, and test models, respectively, the average performance of results by f1-score is 96.30%, 91.79%, and 85.92%.
DOI: 10.14457/tu.the.2022.794
title: Functional layered materials for chemical detection using surface-enhanced Raman spectroscopy

description: Surface-enhanced Raman scattering (SERS) is one of the useful techniques for identifying a specific fingerprint of molecules. Conventional SERS consists of colloid based and thin film-based types. The colloid-based SERS uses a nanoparticle metal colloid to enhance the Raman signal of the target molecules while the thin film SERS uses coating technique to make a cluster of metal nanoparticles on the substrate to enhance the Raman signal. The challenge in developing the thin film-based SERS technique besides the sensitivity is the improvement of the reproducibility and stability of the SERS substrate. In this work, a graphene/Ag-nanoparticle/polymer (G/AgNP/polymer) SERS substrate was successfully fabricated using chemical vapor deposition (CVD), electrodeposition, transferring, and etching techniques. CVD was used for growing a graphene layer on a copper foil substrate. Graphene functions as a protective layer of the SERS substrate and help enhance Raman signal via chemical enhancement. Silver nanoparticles as an enhancement material were coated on graphene by electrodeposition technique. The structure of the silver nanoparticles growth on the graphene/copper substrate can be tuned by varying the electrodeposition condition to obtain proper ‘hot spot’ for Raman amplification. The electrodeposition condition at the current density of 10 uA cm-2 for 3 min generates a rattan ball-like structure of the Ag nanoparticles with the gaps between metal nanoparticles between 30-100 nm. The as-prepared layers were then transferred onto two types of polymer substrates which are polyimide (PI) tape and polydimethylsiloxane (PDMS) substate. The copper layer was etched out using a ferric chloride solution (FeCl3). The developed SERS substrate could detect methyl parathion with the enhancement factor (EF) of the primary peak at 1344 cm-1 of 1.5x104 and 4.7x104 for the PMDS and PI SERS substrates, respectively. Compare between two types of substrates, PDMS SERS substrate shows better enhancement factor, while the uniformity of the prepared PI SERS substrate was better than that of the PDMS SERS substrate. The shelf life of both SERS substrates were longer than 48 days at room temperature, thanks to the protective graphene layer, making the developed SERS substrate practical for field use. The developed technique offers a facile fabrication with low cost and ease of SERS storage without additional packaging process. This polymer-based SERS substrate is a promising platform with potential for use as a screening technique in field tests.
DOI: 10.14457/tu.the.2022.795
title: Measuring efficiency of Thai logistics firms using fuzzy data envelopment analysis

description: Logistics and supply chain is one of the most important industries in Thailand in term of sustainable development, investment and career opportunity. Plus, this field can grow much further. Therefore, data envelopment analysis (DEA) is a well-known technique for performance measurement and optimization by applying the microeconomics with the linear programing. The purpose is to measure the efficiency of 30 big logistic enterprise that can be consider as the leader of logistics and supply chain industry’s leader by using a long-term investor perspective by applying the financial data to indicate how good they are in term of financial management. For more accuracy of investors’ decision, triangular fuzzy logic can help make the decision easier.
DOI: 10.14457/tu.the.2022.796
title: Comparison of RGB-D processing for walkway detection in bionic eye

description: The Bionic Eye aims to restore vision for the blind, but limited hardware often leads to low-resolution images. Image processing is crucial for enhancing these images for the visually impaired. One promising approach is using RGB-D images to improve object detection and create a danger map for safe navigation. This study compares various RGB-D processing methods for generating a danger map in bionic eye walkway navigation. The methods include MSAC and four Convolutional Neural Network (CNN) models with semantic segmentation. Findings show that the Feature concatenated CNN models outperform other methods in terms of both accuracy and F1 score.
DOI: 10.14457/tu.the.2022.797
title: LiDAR for measuring growth of plant in smart farm

description: Despite being of great importance to the economy and society of Thailand, the agricultural sector is unable to generate as much income as it should. The low average growth rate of agricultural production per labor is the main problem. Due to traditional farming practices, the resulting yields are only as effective as they should be, as it cannot compete with countries that are able to effectively apply digital technology and innovation. This study aims to address this issue by utilizing LiDAR (Light Detection and Ranging) technology and machine learning which used for predicting the accuracy of plant stage prediction to monitor the height and color of plants at three stages which divided into three stages, consisting of stage 1 for plants aged 0-7 days, stage 2 for plants aged 7-14 days, and stage 3 for plants aged 15-45 days to accurately determine their harvesting stage. This concept was demonstrated using LiDAR measurements of lettuces that consist of Green Cos, Green Oak, Butterhead, and Frillice Iceberg lettuces. Point clouds were generated from 3D RGB images and depth information from LiDAR camera (Intel RealSense L515) at three stages of the plant. For machine learning, feature extraction and model training and evaluation were used. Results show height feature prediction is 80% accuracy, RGB image feature prediction is 100% and height with RGB image feature prediction is 90%. This can be further applied to real-world use in smart farming in the future.
DOI: 10.14457/tu.the.2022.800
title: Study on atmospheric corrosion resistance of hot-dip galvanized structural steel in Thailand

description: This thesis investigated the coating thickness loss of a hot dip galvanized steel in Thailand. The hot dip galvanized steels studied were HDG-SS400 (carbon steel SS400 as the base steel) and HDG-SM490A (carbon steel SM490A as the base steel). The atmospheric exposure test was conducted in 19 different locations. The Locations included a variety of land types such as urban, sub-urban industrial, rural and coastal areas. The thickness loss rate was found in the range of 0.2 to 2.1 µm/y. The severity was categorized into C2, C3 and C4 according to the DPT 1333-61. During the test, it was found that the coating thickness loss was strongly correlated to the rainfall. The regression model was made and used to construct a corrosion map. The constructed corrosion map is publicly available in Corrosion Map App (steelcorrosionmap.com). This map provides the estimated service life of a 100 µm hot dip galvanized steel structure.
DOI: 10.14457/tu.the.2022.801
title: Long-term energy demand forecasting in Thailand using ensemble prediction model

description: Predicting the electricity demand of a country will help with design, planning, and future energy management. Instead of creating a forecasting model depending only on historical energy demand and single time-series or machine learning models, sophisticated statistical and intelligent energy prediction models are proposed with related factors to enhance prediction performance and could be useful to energy managers. We have designed and implemented a data-driven method to predict the electricity monthly energy profile for Thailand. Our preliminary work exploits a data set of monthly energy demand from Thailand from 2011 to 2019. Our method combines the most relevant factors forecasted on future value from 2020 to 2022 to obtain a prediction of the long-term future energy demand in the next 3-year period accordingly. This research has proposed to utilize the combination of Machine Learning models (ML models) to optimally forecast the energy demand in Thailand. The various ML models are explored in which the individual and the combination of ML models are each optimized and evaluated for their best achievable performances. Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) are utilized to compare models’ performances. A total of 4 ML models are executed, which include Artificial Neural Network (ANN), Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF) Ensemble, and proposed two heuristic models of Vote Ensemble learning. The results show that, by means of choosing a suitable combination to produce an ensemble model, the Vote Ensemble model (VEM) with a neural net and decision tree algorithms (NND) could perform well over individual learnings with the lowest RMSE for training and testing of 509.68 and 523.49 and the lowest training and testing MAPE of 2.43% and 4.21% accordingly while also using the lowest execution time of 4 minutes and 01 seconds for training and 41 seconds for testing of NND ensemble model. We present empirical evidence that our procedures can be superior in its forecasting performance when compared to other econometric forecasting methods.
DOI: 10.14457/tu.the.2022.802
title: A smart identification system based on computer vision for supporting hotel check-in process

description: Citizen ID card verification is critical across various sectors, including banking, access control, product pickups, and hotel check-ins. However, the traditional manual registration process is time-consuming and error-prone. Moreover, the prevalence of mask usage during the COVID-19 pandemic presents challenges for face recognitionsystems, leading to potential inconveniences and delays. To address these concerns, this study introduces two advanced models: Optical Character Recognition (OCR) and face-mask recognition. The OCR model accurately extracts information from ID cards and automatically populates the registration form, while the face-mask recognition model improves face-recognition accuracy, even with individuals wearing masks.By implementing these models, check-in procedures can be streamlined, aiming toenhance efficiency, data accuracy, and potentially improve customer satisfaction.