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Water Rights Ownership In Sultanate Of Oman: A Case Study On The Aflāj Institution In Northern Oman
(2024-03)
Ali, Al-marshoudi Ahmed Salim Khalfan
Using the ancient aflāj (singular falāj) system in Sultanate of Oman as a case study, this thesis investigates the Islamic water rights ownership and institution. This was conducted by analyzing the relationship of the physical water extraction variation among the three aflāj types in Oman and the development of water right types and the Islamic equitable principles/institutions. In literature many principles concerning water management and administration have been discussed. The most used one, in most Islamic studies, is how to manage water (as a natural gift) in relation with what Allah almighty given natural resource (also known as ecosystem) and human interaction. However, the focus of this thesis to fulfil the gap in our knowledge in how to provide a legal recognition of water rights ownership and institution. In fact, a guiding tool using Oakerson operational rule/institution which is considered as soft constrain for two main reasons: 1) decision making made operative only through human knowledge, choice, and action 2) rules found in decision making exist entirely in ream language (whether written or unwritten). It is these written and unwritten rules that have been applied in describing aflāj historical institutional (which researcher called Islamic equitable principles) and confirms with revealed information found within aflāj communities in Oman. In addition, Islamic equitable principles have been used to avoid confusion with another Islamic term known as uşūl al-fiĝh or jurisprudences.
Lived Experiences Of Left-behind Children In Henan Province, China
(2024-08)
Lei, Daxia
Since China’s reform and opening up, there has been a significant migration of surplus labourers from rural to urban areas. As a result, a growing population of children has been left behind in rural settings. These children face educational and psychological challenges that have emerged as important social issues. This study aims to explore the main challenges faced by these left-behind children, examine their coping strategies, and identify their specific needs. Additionally, the study seeks to develop an intervention model to improve their well-being. Conducted in Zhoukou City, Henan Province, the research focused on the care services provided for left-behind children. This study utilizes qualitative research methodology and employs the in-depth interview technique to gather data. Twenty-five (25) left-behind children were interviewed to understand their challenges, coping strategies, and specific needs. The study found disparities in living conditions and familial environments between left-behind children and those who were not left behind, contributing to the health vulnerabilities of left-behind children. Key findings include reduced interaction with parents, difficulties in forming interpersonal relationships, academic performance challenges, and the need for life skills acquisition. Left-behind children employ various coping mechanisms, such as recreational activities, emotional recalibration, seeking support, and taking proactive initiatives. Additionally, the study identified the needs of left-behind children, including the implementation of social work service programs, family care and support, educational engagement, community development and services, as well as national financial and policy support.
Fault detection of electrical motor based on thermal imaging and machine learning
(2023-08)
Yap, Jun Hong
Faults could occur on electrical motor due to various reasons, and an early motor fault detection system helps prevent interruption in service and financial losses. However, the current practice of manual fault inspection and preventive maintenance is time consuming, and it may not be effective. Thus, motor fault diagnosis using thermal imaging technique has been on the rise in recent years. To further improve the effectiveness and to automate fault detection using thermal imaging, artificial intelligence (AI) can be employed. Hence, in this project, an electrical motor fault detection system based on thermal imaging and machine learning (ML) technique was developed. Transfer learning (TL) approach using pre-trained convolutional neural networks (CNNs) was used. The CNN was trained to learn the features extracted from the thermal images of a faulty and a healthy motor and use them to diagnose the condition of the motor. Various hyperparameters were configured for network training to obtain the best results. Furthermore, performance analysis was conducted and discussed to evaluate the credibility and reliability of the trained network. A Graphical User Interface (GUI) was then created to ease the user in using the proposed fault detection system by just supplying the thermal images of a test motor to the GUI application for fault diagnosis. The evaluation results showed that GoogLeNet gives the best detection performance with both the mini-batch and the validation accuracy achieving a 100%, and both the losses were low as well, at 0.0015 and 0.0001 respectively. Thus, the final trained network based on GoogLeNet was used in the GUI for the implementation of the proposed motor fault detection system. In conclusion, the aim for implementing a fault detection system and GUI, through the use of thermal images and machine learning was achieved.
Explainable artificial intelligence for signature verification system
(2023-10)
Lee, Sze Yuan
In recent years, the use of personal identity, such as signatures, as a means of authentication has gained significant attention. There are some concerns arise due to the potential for signature forgery and leading to the development of signature verification systems to determine the authenticity of signatures. The lack of understanding behind the AI and DL can erode trust in the tools as incorrect or biased decisions made. The application of Explainable Artificial Intelligence (XAI) methods in signature verification systems can address these concerns by providing insights into the decision-making process and enhancing the trustworthiness and reliability of the system. This research aims to explore and evaluate various explanation models to improve the interpretability and performance of signature verification systems. Furthermore, this research seeks to identify the specific aspects that users and developers focus on when considering explanations generated by these models. Moreover, this research aims to develop a new explanation model by combining the strengths of two widely used methods, LIME and Grad-CAM. The experiment is conducted through MATLAB using package known as Deep Learning Toolbox. The explanation evaluates through the respond of 18 respondents in four aspects, understandability, interpretability, accuracy and usefulness. The survey is also used to identify the evaluation aspect that are focused by users and developers. In addition, a new explanation model is developed through the combination of “scoremap” of LIME and Grad-CAM. Preliminary findings indicate that the Grad-CAM method demonstrates better performance from the user's perspective, while developers tend to prefer the LIME method. By leveraging the strengths of both approaches, the new explanation model achieves an impressive increase in understandability, interpretability, accuracy, and usefulness.
Evaluation of isolated type multilevel inverter with different DC source selection scheme
(2023-07)
Ooi, Wei Tao
Multilevel inverters (MLI) play a major role in various power applications in converting direct current to alternating current for a power system. The ideal output voltage waveform should be a perfect sine wave. In practical, a real inverter will produce output voltage waveform which contains harmonics that are not acceptable for high power applications. Harmonics cause current and voltage waveforms to be distorted, resulting in power system degradation. To date, reducing the total harmonics distortion (THD) is always the main objective to conduct the research regarding the multilevel inverters, especially when the inverters are applied in renewable energy application. Therefore, the simulation testing of this project will focus on the total harmonic distortion value of the system. The proposed multilevel inverter in this research is asymmetrical multilevel inverter with trinary sequence. The targeted number of output levels are 5-L, 7-L and 9-L. This research is aimed to analyze the proposed topologies from the aspects of inverter power ratio, total harmonic distortion level and power contribution of DC voltage source in different DC source scheme. The switching signals are generated using a low frequency modulation technique where the switching angles are pre-calculated using derived mathematical equations. To evaluate the performance of the topology, linear load tests are conducted to ensure proper operation of the proposed topology in reference to theoretical analysis with MATLAB Simulink software. Based on simulation result, the THD values of the 5-L waveforms are the highest, followed by the 7-L waveforms and the lowest THD are measured on the 9-L waveforms. Under RL loads, the current waveforms show much lower THD readings since they are filtered by the inductive loads. The RL filtering effect causes the current waveforms to lost the stepped pattern which explains their smoother waveforms. The results show the high power ratio is averagely achieving 95% and above, indicating the reliability of the system. From DC source power contribution aspect, it is concluded that high power source is required to produce higher number of levels in MLI inverter. In short, the research outcome shows the topology with different DC source configuration is capable of generating high number of output levels with low number of total components. The individual current harmonics are also in accordance to the IEC/EN 61000-3-2 standard. Hence, this topology has great potential to be developed for real applications since the production cost is expected to be lower than the other topologies.