cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14457/tu.the.2023.541
title: Automatic localization of retinal neovascularization in optical coherence tomography angiography images using feature maps analysis

description: Retinal neovascularization (RNV) is a pathological condition characterized by the abnormal growth of new blood vessels within the retina. Detecting RNV can be achieved through optical coherence tomography angiography (OCTA), an advanced imaging technology capable of visualizing blood vessels under retinal layers. Identifying RNVs within OCTA images is particularly challenging due to varying patterns, unfixed sizes, and being on a vascular network background. Due to domain newness and significant challenges, there are a few studied on methods to identify the RNV location in OCTA. In this thesis, we studied how to localize the RNV location from features maps: vessel density, bifurcation points, and average vessel thinness per area. We considered regions of interest (ROI) by thresholding a weighted linear combination of those feature maps. We selected the final RNV localization from the maximum value of the combined map within the most considerable ROI. We compared this approach with variants of three used feature sets and two combination methods. The weighted linear combination of all feature maps achieved the highest localization accuracy of 69.05% on the 43 OCTA images with RNV. It demonstrates superior performance to other variants.
DOI: 10.14457/tu.the.2023.542
title: Tiny-ML based activity recognition combined with indoor positioning using ultra-wideband sensors for elderly care

description: Elderly care systems play a crucial role in ensuring the well-being of older adults, as nurses cannot constantly monitor them manually. Elderly activity monitoring systems are therefore invaluable, especially in indoor environments where precise activity recognition at low power and cost is essential. Tiny Machine Learning (TinyML) technology, which enables intelligent low-power Microcontroller Units (MCUs), significantly enhances activity recognition, contributing to more efficient elderly care systems. Combining Indoor Positioning Systems (IPS) with TinyML-based activity recognition can create a highly accurate and cost-effective elderly care system. This study focuses on improving the precision of Ultra-Wideband (UWB) technology for indoor positioning and emphasizes the importance of TinyML in elderly care applications, exploring the integration of IPS with TinyML for comprehensive activity monitoring. The findings indicate substantial advancements and provide valuable insights for future developments. The indoor positioning system was evaluated in three scenarios: Line-of-Sight (LoS), Obstructed-Line-of-Sight (OLoS), and a real-world simulation where the tag was concealed in an individual's pocket, achieving average positioning errors of 17.21 cm, 48.27 cm, and 46.17 cm, respectively. The highest accuracy was observed under LoS conditions, while challenges arose in OLoS and pocket-carrying scenarios due to electromagnetic wave propagation through obstructions. Despite these challenges, the system demonstrated satisfactory accuracy for indoor tracking, indicating its potential for practical deployment. The TinyML model for activity recognition achieved an exceptional accuracy of 97.22\% on the testing dataset, supported by a robust confusion matrix and F1 Score, underscoring its reliability in real-world applications where accurate activity recognition is crucial. The integrated system, combining IPS and activity recognition, was tested across 47 activities over 11 rounds, achieving an overall accuracy of 91.48\%. The system excelled in identifying activities involving continuous and minimal movement, making it advantageous for elderly activity monitoring and healthcare settings. While challenges were noted in scenarios requiring differentiation within shorter observation windows, the system's overall performance confirmed its suitability for practical implementation in real-world environments.
DOI: 10.14457/tu.the.2023.543
title: CFD analysis on hydro-thermal behavior of shell and tube waste heat boilers

description: Shell-and-tube waste heat boilers (STWHBs) are essential in the recovery of waste heat from high-temperature industrial processes. Kettle-type STWHBs are known to experi-ence failures due to hold-up of steam, a significant concern in the industry. Multiphase CFD simulations are used to study the effect of tube cross-section and tube layout on the steam hold-up of an industrial-scale STWHB. Different design specifications are simulated includ-ing tube cross-section, tube layout, heat flux, and pitch to predict steam hold-up performance and analyze thermo-hydrodynamic phenomena within the tube bundle. Ellipse, an alterna-tive tube cross-section with the same cross-sectional area as in circular one, is introduced in the application of STWHBs due to its ease of manufacturing to determine its impact on steam hold-up performance. Simulation results show that, for similar tube bundle shape and size, 45° rotated square tube bundle layouts perform better, with respect to steam hold-up, than the square and triangle layouts for a range of heat flux duties. Elliptical cross-section tube proves better performance in steam hold-up with 45° rotated square tube bundle layouts.But, the steam hold-up performance of elliptical cross-section tube deteriorate with square tube bundle layouts and that with triangle layout remains unchanged. It is found that steam hold-up increases linearly with increasing heat flux and decreasing tube pitch, for the conditions simulated. The turbulent kinetic energy of the bubbly flow is greatly influenced by increasing phase fraction. Visualizations of the flow of water and steam within and around the tube bundle show that there is a vertical flow deviation for both staggered tube patterns (rotated square and triangle) which has profound influence on the steam hold-up within tube bundle for each tube cross-section. These results motivate further multiphase CFD-basedstudy of the thermo-hydrodynamics of STWHBs for improved understanding, retrofit and new designs.
DOI: 10.14457/tu.the.2023.544
title: A dynamic route-planning system for multi-vehicle distribution

description: Motivation- Nowadays, technology has become increasingly integrated into our daily lives, with advancements such as Artificial Intelligence (AI), Machine Learning, Automated Robotics, and Big Data Analysis. These innovations offer solutions to overcome human limitations, particularly in areas such as autonomous distribution systems for multiple vehicles, the more intelligent of route planning system in autonomous vehicles can potentially lead to reduced operating costs through decreased manpower. Problem statement- This research is specifically focused on route planning of automated vehicles for distribution systems. Most previous studies in the path-planning field have primarily concentrated on path-planning algorithms, emphasizing the movement between two points—origin and destination also in the Vehicle Route Problem (VRP) field which cannot cooperate in a dynamic environment. Therefore, this research aims to introduce a novel approach that addresses the complexities of route planning for multi-vehicle distribution, involving multiple stops, and can be applied to a dynamic environment. The primary objective of this research is to develop a route-planning system that optimizes for the shortest distance, optimal, considers real-time obstacle avoidance, and can adapt to dynamic environments. The vehicles under consideration in this study are Automated Guided Vehicles (AGVs) and Unmanned Aerial Vehicles (UAVs), both of which can play crucial roles in modern logistics. Methodology- To achieve this, two key components are integrated into a single system. The first component involves path planning algorithms for vehicles with obstacle avoidance functionality. The second component is a dynamic route-planning optimization model tailored for multi-vehicle distribution scenarios that uses the concept of Vehicle Route Problem (VRP). This innovative approach is implemented using a combination of EXCEL, CPLEX, and MATLAB programs. Moreover, this research also simulates the dynamic route-planning for a multi-vehicle distribution system in the Gazebo integrated with the Simulink model to ensure the ability of dynamic obstacle avoidance. The significance of this research lies in its contribution to a novel approach for efficiently managing route planning, which is a critical element within logistics management. Result- By addressing the challenges associated with multi-stop distribution systems and dynamic environments, this research successfully combines the path planning algorithm with multi-vehicle route optimization to enhance the overall efficiency and effectiveness of automated vehicle-based logistics. The system optimizes the shortest path while considering the real-time obstacle avoidance which can apply to real-world applications. Originality- This research introduces a new approach to overcoming limitations in traditional path planning and VRP paving the way for future research direction in automated distribution systems that can significantly impact the logistics sector as human consumption continues to rise.
DOI: 10.14457/tu.the.2023.545
title: Smart trading: leveraging machine learnings to improve profitability of technical trading rules in the Thailand SET100 index

description: The Thai stock market, represented by the SET100 index, has recently faced a downturn, prompting traders to seek innovative strategies to navigate these challenging conditions. This study explores the potential of machine learning-enhanced trading strategies to improve profitability, with a particular focus on sector-specific approaches.We analyzed a comprehensive dataset of 41 high-liquidity, large-cap SET100 stocks from January 2016 to December 2023. To identify optimal entry and exit points for stock transactions, we integrate technical indicators, specifically the Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD), to generate trading signals. These signals are then analyzed using Random Forest (RF) and Artificial Neural Network (ANN) machine learning models.Our analysis reveals that the effectiveness of machine learning integration with technical indicators varies significantly across different market sectors. In the FOOD and HELTH sectors, the RF-RSI strategy yielded substantial return improvements compared to using RSI alone. Additionally, the ANN-RSI strategy demonstrated the ability to potentially avoid losses in the TRANS sector compared to relying on RSI alone.Integrating machine learning with MACD produced mixed results. Both ANN-MACD and RF-MACD strategies reduced losses in the HELTH sector, suggesting potential benefits in mitigating downward trends. However, in some sectors, using MACD alone might be sufficient based on the specific dataset used. This highlights the need to consider the historical performance of technical indicators within each sector when determining if machine learning integration can offer additional value.Our research offers valuable insights for investors seeking to optimize profits in challenging market conditions. The findings demonstrate the potential for sector-specific, machine learning-enhanced trading strategies to improve profitability. Investors can leverage these insights by carefully selecting machine learning models and technical indicators that align with the characteristics of their targeted sectors.
DOI: 10.14457/tu.the.2023.546
title: Personal identification based on fusion of iris and periocular information using convolutional neural networks

description: Iris recognition, a pivotal component in computer vision, serves as a vital tool for verification and identification in security and forensic science domains. Despite itswidespread application, current iris recognition systems face significant performance limitations, especially under adverse conditions like suboptimal lighting, partial iris visibility, eye misalignment, and scenarios involving greater distances. These challenges often impede the effectiveness of traditional iris recognition methods. However, the emergence of advanced neural network-based image recognition technologies, particularly convolutional neural networks (CNNs), has opened new avenues for addressing these limitations. CNNs excel in extracting intricate features from images, thereby enhancing recognition performance to levels that either match or exceed those of conventional iris recognition techniques. In our research, we introduce an innovative methodology that integrates traditionaliris recognition with periocular information through the application of CNNs. This fusion aims to overcome the aforementioned challenges effectively. To validate our approach, we employed three distinct datasets: CASIA-Iris-Thousand, CASIA-Iris-Lamp, and UBIPr. The results from these evaluations demonstrate a significant improvement in accuracy and reliability over standalone iris recognition systems. Furthermore, our paper delves into the practical implications of deploying such an advanced system in real-world scenarios. We explore the potential applications and benefits of this enhanced iris recognition technology, underscoring its suitability for highsecurity environments where accuracy and reliability are paramount. This research not only contributes to the field of computer vision but also sets a new benchmark for future developments in biometric security systems.
DOI: 10.14457/tu.the.2023.547
title: A computer vision-based system for cattle behavior monitoring and automated estrus detection

description: The profitability of cattle farming is predominantly dependent on effective reproductive outcomes, particularly high pregnancy rates, which are typically facilitated through artificial insemination. Nevertheless, the precise detection of estrus behavior presents a considerable challenge, significantly influencing the success of insemination procedures and the associated costs of farm operations. This thesis aims to revolutionize cattle reproduction management by implementing Computer Vision (CV) technology to monitor and interpret cow behavior for optimal insemination timing. Through remote camera surveillance and the development of specialized models, including cow detection, keypoint localization, and behavior classification, the project seeks to create a comprehensive system for real-time monitoring and decision support. The knowledge gained from this endeavor extends beyond technological advancements, offering insights into cattle behavior, ethical research practices, and the potential for transformative impacts on farming practices. Utilizing the YOLOv8 model, the research successfully developed a computer vision system that demonstrated high precision and recall rates in detecting and localizing cattle. The system was trained and validated on a dataset featuring diverse behaviors captured in farm environments. However, the dataset was noted as insufficient for optimal model training, highlighting the need for a more expansive collection of annotated data. A behavior classification model was also developed, achieving an overall accuracy of 64\%. This model incorporated features critical for understanding inter-cow relationships but was limited by the current capabilities in cow identification technology, representing a significant area for future research.The expert system built on these models supports farmers by reducing the reliance on manual observation and increasing breeding process efficiency. However, challenges in scaling these technologies to real-world applications were identified, particularly in terms of technological integration and operational adaptability. Future work will focus on expanding the dataset, enhancing model accuracy through advanced identification features, and refining integration with farm management systems. This thesis contributes to agricultural technology by demonstrating the potential of integrating computer vision and machine learning into effective farm management practices.
DOI: 10.14457/tu.the.2023.548
title: Environmental impact assessment of a domestic wastewater treatment plant in Bangkok, Thailand

description: The Wastewater Treatment Plant in Thailand plays a crucial role in treating raw domestic wastewater. The wastewater treatment plant can also be integrated with the water reclamation system for sustainable reuse. This system integration promotes the circular economy principle and has already been implemented at the Bang Sue wastewater treatment plant. As the demand for treatment continues to increase, the energy requirements associated with these processes become increasingly significant. This study focuses on calculating environmental impacts, transportation, direct air emissions, water pollution, energy, and chemical consumption during the operation phase. In this research, the impact assessment is carried out using ReciPe 2016. The results of the study will be beneficial for government stakeholders in making a decision. To narrow the scope of the research, we specifically analyze the gate-to-gate aspects. The input output inventory data was examined through midpoint and endpoint impact assessments using Open LCA software. This study compared the environmental impact of two scenarios: current practices (Scenario 1) and a projection for 2037 (Scenario 2). The results show a positive environmental shift in Scenario 2. The impact on ecosystem quality decreased from 1.21×10-9 species-years to 1×10-9 species-years. Similarly, the impact on human health improved with a reduction in disability-adjusted life years (DALYs) from 5.77×10-7 to 4.18×10-7. There was a slight decrease in the impact on natural resources, with the cost dropping from $0.01697 (USD 2013) to $0.01692 (USD 2013). This study recommends implementing energy conservation and prioritizing the transition from lignite to renewable energy. A 5% reduction in energy consumption can lead to significant improvements across various environmental metrics, including a 4.74% reduction in impact on total ecosystem quality, a 4.65% reduction in impact on total human health, and a 4.79% reduction in impact on total natural resources.
DOI: 10.14457/tu.the.2023.549
title: Piezo-resistive stress sensor based on MXene for energy harvesting

description: Current piezo-resistive stress sensors are more complex due to their structural designs, materials, fabrication techniques and multiple components such as electrodes, sensing layers and multiple substrates which directly affect the electrical measurements. This complexity of the piezo-resistive sensors led to inconsistent performance, lower sensitivity and higher rate of wear and tear. Due to these challenges, the self-powered energy from the piezo-resistive stress sensors could not be used effectively.The goal of this work is to develop and fabricate a novel piezo-resistive stress sensor based on MXene which could be used in energy harvesting to facilitate gait monitoring as an application of the Tribo-Electric Nano Generator. Utilizing the piezo-resistive effect, the sensors are designed to measure mechanical stress and strain by converting them into electrical signals. High electrical conductivity, mechanical flexibility, and enhanced piezo-resistive response are the excellent properties offered by the MXene material for different sensing applications. The work performs an extensive characterization after fabricating the sensors using a simple and scalable process to ensure its performance in terms of sensitivity, stability, and repeatability. The devices were fabricated to measure mechanical stress, sensitivity, and tribo-electric measurements by converting them into electrical signals which leads to energy harvesting by storing the energy in a capacitor. With high electrical conductivity, mechanical flexibility, and piezo-resistive characteristics, MXene based device offers novel sensing and tribo-electric related applications too. Using pure MXene layer as the core material, this work introduces a pioneering study focused on stress monitoring by piezo-resistive stress sensor and tribo-electric nanogenerator.
DOI: 10.15468/dl.aym3y6
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Dasia subcaerulea (Boulenger, 1891), Dasia subcoerulea (Boulenger, 1891))",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006490-240906103802322/datasets/export for details.
DOI: 10.15468/dl.9efste
title: Occurrence Download

description: A dataset containing 201 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Graptemys gibbonsi Lovich & Mccoy, 1992)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 201 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006491-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x2gugh
title: Occurrence Download

description: A dataset containing 19 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Dopasia gracilis (Gray, 1845)"
 ]
}

 The dataset includes 19 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006492-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yaswna
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Cnemaspis jerdonii (Theobald, 1868))",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006493-240906103802322/datasets/export for details.
DOI: 10.15468/dl.m3tnaw
title: Occurrence Download

description: A dataset containing 108 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Pseudemys alabamensis Baur, 1893)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 108 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006494-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xpy8jx
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dopasia gracilis",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006495-240906103802322/datasets/export for details.
DOI: 10.15468/dl.3wjhnb
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Cnemaspis goaensis Sharma, 1976, Cnemaspis laoensis Grismer, 2010)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 5 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006496-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.