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检索条件"任意字段=Student Conference on Research and Development, SCOReD 2003"

共 565 条记录，以下是131-140 订阅

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Enhancing Solar Power Generation with a Bifacial Dual-Axis Parabolic Dish Tracking Prototype

Enhancing Solar Power Generation with a Bifacial Dual-Axis P...

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student conference on research and development (scored)

作者： Sabir Ali Kalhoro Asif. A. Rahimoon Mohd Noor Abdullah Halyani Mohd Yassim Asif Raza Shafique A. Soomro Deptartment of Electrical Engineering University of Colorado United States Faculty of Electrical and Electronic Engineering (FKEE) Universiti Tun Hussein Onn Malaysia (UTHM) Parit Raja 86000 Batu Pahat Johor Malaysia Faculty of Electrical Technology and Engineering Universiti Teknikal Malaysia Melaka(UTeM) Hang Tuah Jaya 76100 Durian Tunggal Malaysia School of Automation Engineering University of Electronics Science and Technology of China (UESTC) Sichuan China Faculty of Engineering Science and Technology (FEST) Indus University Karachi Pakistan

ISBN: (数字)9798331510077

ISBN: (纸本)9798331510084

Photovoltaic (PV) modules often suffer from defects like glass breakage, soiling, discoloration, hotspots, reducing their power generation efficiency. Fixed-angle PV systems are increasingly being replaced by single axis tracking system (STs) and dual axis tracking system (DTs) mode of operation to enhance generation. However, STs improve performance by following daily solar motions but struggle with seasonal adjustments. While DTs overcome this limitation by enabling precise two-axis tracking, capturing optimal solar irradiance. The bifacial configured PV modules further boost generation efficiency by utilizing radiation from both sides, offering flexibility in orientation and reducing tracking limitations. The primary objective of this study is to enhance power generation from the bifacial PV module. Hence, this study is proposing a cost-effective bifacial dual-axis parabolic dish tracker with a closed-loop design. The system employs Light Dependent Resistor (LDR) sensors and a Raspberry Pi controller to achieve precise dual-axis tracking assembled on stainless frame. The size of parabolic dish is comprised of aluminum foil polished around $4 \times 4 \mathrm{ft}$ area. The proposed study was tested over three days in November along with an albedo of approximately 0.39 value. The obtained result shows that generated cumulative power in watt (W) was recorded as $402.47 \mathrm{~W}, 418.06 \mathrm{~W}$ and 427.30 W respectively. Thus, results demonstrate the combined potential of bifacial PV modules and DTs scheme has efficiently improved the power generation.

关键词： Solar irradiance Resistors Renewable energy sources Tracking Prototypes Sensor systems Sensors Solar radiation research and development Testing

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The Application of Clustering Algorithm in College student Information Management

The Application of Clustering Algorithm in College Student I...

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Data Science and Network Security (ICDSNS), International conference on

作者： Hui Ma Jilin Agricultural Science and Technology University Jilin China

To solve the problem that the traditional students' information processing ability is limited with the massive increase of students' information, the application of clustering algorithm in the student information management system of colleges and universities is proposed. In this research, taking the application of clustering algorithm in three aspects of student comprehensive evaluation, student performance evaluation, and classroom teaching evaluation as examples, the clustering algorithm that is competent for student information data processing is selected. The experimental results show that there are 9 students in the second category of good, among which the student number 084210123 scored 70 points for data structure, which is lower than the lowest score of 73 in the third category of medium. This result is equivalent to the passing category of the fourth category, and it may be more appropriate to classify it as a passing category. The 10 teachers are divided into 3 categories, namely {2, 4, 6, 7, 10}, {5, 8} and {1, 3, 9}, and their grades are B, C, A. The experimental results are consistent with the division of the given data. It is concluded that the clustering algorithm has a good development space in the field of student information management.

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Effects of Corrosion Factors on Sulphur Corrosion Activity in Transformer Insulating Oil Using Two-Level Factorial Design

Effects of Corrosion Factors on Sulphur Corrosion Activity i...

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student conference on research and development (scored)

作者： Nur Farhana Mohd Azlan Sharin Ab Ghani Mohd Shahril Ahmad Khiar Electrical Asset Condition Monitoring Research Group (e-AMCM) Faculty of Electrical Technology & Engineering Universiti Teknikal Malaysia Melaka Melaka Malaysia

ISBN: (数字)9798350318821

ISBN: (纸本)9798350318838

The issue of sulphur corrosion in transformer insulating oils has been a significant problem for several decades, primarily due to the increasing incidence of power transformer failures. The longevity of power transformers may be reduced due to their reliance on the oil-paper insulation, which can deteriorate due to sulphur corrosion. The formation and deposition of copper sulphide can lead to transformer failures, which is of great concern to manufacturers and operators of power transformers. Nevertheless, there is a lack of extensive studies that systematically investigate the effects of corrosion-related variables on both the dissolution of copper in transformer insulating oil and the accumulation of copper sulphide on the surface of the solid insulation, including kraft paper. This study is carried out to investigate the effects of three factors (oil temperature, elemental sulphur concentration, and ageing period) on the sulphur corrosion activity of transformer insulating oil by using two-level factorial design. The results indicate that oil temperature is the factor that significantly influences the surface resistivity of the kraft paper, with a percentage contribution of 26.09%. A regression model is also developed to predict the surface resistivity of kraft paper as a function of the oil temperature, elemental sulphur concentration, ageing period, and the interactions between these factors, which will be beneficial to users investigating sulphur corrosion in transformer insulating oils.

关键词： Temperature measurement Oils Corrosion Aging Oil insulation Conductivity Predictive models

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A Comparative Analysis of Machine Learning Techniques for Heart Disease Prediction

A Comparative Analysis of Machine Learning Techniques for He...

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student conference on research and development (scored)

作者： Alawi Alqushaibi Mohammed Gamal Ragab Ebrahim Hamid Sumiea Safwan Mahmood Al-Selwi Mohd Hilmi Hasan Said Jadid Abdulkadir Hitham Alhussian Mahmoud Mustafa Al-Asbahi Computer & Information Sciences Universiti Teknologi PETRONAS Perak Malaysia Centre for Research in Data Science Universiti Teknologi PETRONAS Perak Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Malaysia

ISBN: (数字)9798350318821

ISBN: (纸本)9798350318838

Detection of heart disease is critical, as it is a common and serious health condition. Predicting its occurrence is vital in the healthcare domain, and machine learning techniques are increasingly being utilized to accurately identify patients at risk. To evaluate how well machine learning models can effectively predict heart disease, we used a pre-existing dataset from the UCI repository’s Cleveland database. Though the dataset originally contained 303 records and 76 attributes, we followed the literature by narrowing the focus to 14 relevant attributes in order to improve the precision of various algorithmic approaches. Our study employed five machine learning classifiers, namely KNearest Neighbors (KNN), CatBoost Classifier (CBC), Decision Tree Classifier (DT), XGBClassifier (XGBC), and Support Vector Classifier (SVM). We evaluated their performance using different metrics like accuracy, precision, AUC, and F1 score on the heart disease dataset. We found that the CatBoost Classifier had the best performance, achieving high accuracy and AUC with 0.88% and 0.94% values, respectively. This suggests that machine learning algorithms could aid in identifying heart disease at an early stage and have significant implications for improving patient outcomes in healthcare.

关键词： Heart Accuracy Machine learning algorithms Machine learning Predictive models Prediction algorithms Vectors

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development of E-Module Assisted by Kvisoft Flipbook Maker on Work And Energy Materials for Senior High School student Class X

Development of E-Module Assisted by Kvisoft Flipbook Maker o...

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Universitas-Riau International conference on Education Technology (URICET) - Educational Technology for a Better Learning Quality

作者： Islami, Nur Arif, Tia Fitriani Nasir, Muhammad Pd, Azhar S. M. T. Fakhruddin, Fakhruddin Irawan, Dedi Univ Riau Phys Educ Pekanbaru Indonesia

ISBN: (纸本)9781665420969

This research is to develop an electronic module (e-module) for teaching materials using Kvisoft Flipbook Maker software in the work and energy topic for the high school students class X. This e-module was developed as an idea for the solution of difficulties in learning of physics. This research used the research and development (R&D) and employed the ADDIE instructional model which is consisted of five stages, analysis, design, development, and implementation and evaluation stages. To make sure this e-module is good for the student, the e-module was assessed by three validators. The validation data was scored using the validity index of the Aiken's V formula. After validation process, the results showed that the e-module met the valid criteria. It valid in the aspect of content feasibility, learning design aspects, linguistic aspects, aspects of program operation ease, aspects of consistency and animation aspect with Aiken's V validity index of 0.87. Finally, it can be said that the e-module that was developed using Kvisoft Flipbook Maker on work and energy materials for class X high school students can be used in the real teaching for the class X the high school students.

关键词： Module Kvisoft Flipbook Maker Work and Energy

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Comparative Study of Recurrent Neural Networks for Electric Vehicle Battery State of Charge Estimation

Comparative Study of Recurrent Neural Networks for Electric ...

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student conference on research and development (scored)

作者： Choong Ting Hui Aaruththiran Manoharan K.M. Begam Denesh Sooriamoorthy Department of Electrical and Electronic Engineering University of Nottingham Malaysia Selangor Malaysia Department of Electrical and Electronic Engineering Intelligent Electric Vehicle System (i-EVeS) Research Group University of Nottingham Malaysia Selangor Malaysia School of Engineering Asia Pacific University Jalan Teknologi 5 Taman Teknologi Malaysia Kuala Lumpur Malaysia

ISBN: (数字)9798350318821

ISBN: (纸本)9798350318838

To slow down global climate change, the automotive industry is shifting their attention gradually from internal combustion engine vehicles to electric vehicles. Lithium ion batteries (LiBs) are used to store energy for the propulsion system of electric vehicles. An accurate state-of-charge (SOC) estimator can provide safe and reliable usage of electric vehicles. From the findings in recent literature, it was found that using recurrent artificial neural networks for SOC estimation produce high estimation accuracy. This work investigates two variants of the recurrent neural network studied extensively in the literature, namely, gated recurrent unit (GRU-RNN) and long short-term memory (LSTM-RNN). LiB discharge data when exposed to FUDS, US06 and BJDST driving profiles at 0°C, 25°C and 45°C from CALCE dataset were used for training and evaluating the performance of the ANNs. LSTM-RNN was found to achieve 0.0117% maximum mean squared error (MSE) and GRU-RNN with 0.00975% maximum MSE. However, when tested with real time battery discharge data, it was found that LSTM-RNN is robust to noise than GRU-RNN, thus ensuring more stability in the SOC estimations.

关键词： Training Recurrent neural networks Accuracy Estimation Electric vehicles Discharges (electric) Real-time systems

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Time Scheduling and Finance Management: University student Survival Kit

Time Scheduling and Finance Management: University Student S...

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18th IEEE student conference on research and development (scored)

作者： Yeo, J. L. JosephNg, P. S. Alezabi, K. A. Eaw, H. C. Phan, K. Y. UCSI Univ ICSDI Kuala Lumpur Malaysia UCSI Univ FBM Kuala Lumpur Malaysia Univ Tunku Abdul FICT Rahman Malaysia

ISBN: (纸本)9781728193175

With plenty of opportunities for new social interactions, events, and other perspectives, it may he a challenge for students to balance both time and money simultaneously. Even though there is a variety of planning and budgeting application in the market to help students in terms of education and personal life, students ought to download multiple applications that only have a particular function. With this multifunctional application - StuLogger, it aims to improve student knowledge and promotes self-reflection, which may encourage students 'perception of their time spent and allow them to track their financial activity efficiently. It allows users to set up income and expense from various options such as food, transportation, hills, and others. Besides that, the app conies with a calendar, notes and reminder to allow users to (organize their daily activities. The study employed a mixed-method approach in which both survey and interview were conducted online by students from private university.

关键词： multifunction application students time planning finance managing money note-taking reminder countdown planner tracker time expenditures financial behaviour

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Explaining Deep Learning Decisions Via Fuzzy Inference System on Medical Images

Explaining Deep Learning Decisions Via Fuzzy Inference Syste...

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student conference on research and development (scored)

作者： Nafeesa Yousuf Murad Mohd Hilmi Hasan Muhammad Hamza Azam Nadia Yousuf Sonia Abdul Khalique Computer & Information Sciences Universiti Teknologi PETRONAS Seri Iskandar Perak Malaysia Centre for Research in Data Science Universiti Teknologi PETRONAS Seri Iskandar Perak Malaysia Computer & Information Sciences Benazir Bhutto Shaheed University Karachi Pakistan

ISBN: (数字)9798350318821

ISBN: (纸本)9798350318838

Explaining deep learning models has become crucial in various domains, including medical image processing. Understanding these models’ decision making processes is complicated as deep learning act like a black box when it comes on decision making. This can also help in advancements of medical imaging analysis, by allowing for more accurate diagnostic and treatment decisions and, ultimately, better patient care. In this study, we addressed the requirement for interpretability, by fusing the strength of CNNs with a Takagi-Sugeno-Kang (TSK) fuzzy inference system. We have utilized ResNet50, a transfer learning form of CNN, to classify a batch of 1000 images from dataset of chest cancer into four classes: ’Adenocarcinoma,’ ’Squamous Cell,’ ’Normal,’ and ’Large Cell’. The resolution of each image was varied, which was afterwards rectified in the preprocessing step, and this model attained 94.5 percent accuracy. We tried to improve the transparency and dependability of the model’s decision-making process. In this regard, we applied PCA to minimize the dimensions of predicted probabilities obtained from CNN for looking insight into the CNN model’s decision making process. Fuzzy c-means clustering is used to group the decision depending on the number of fuzzy rules. We extracted important insights from CNN’s predictions and offer a thorough justification of its decision-making process by incorporating the TSK fuzzy inference system.

关键词： Deep learning Accuracy Decision making Transfer learning Predictive models Feature extraction Medical diagnostic imaging

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Exploration Decay Policy (EDP) to Enhanced Exploration-Exploitation Trade-Off in DDPG for Continuous Action Control Optimization

Exploration Decay Policy (EDP) to Enhanced Exploration-Explo...

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student conference on research and development (scored)

作者： Ebrahim Hamid Sumiea Said Jadid AbdulKadir Hitham Alhussian Safwan Mahmood Al-Selwi Mohammed Gamal Ragab Alawi Alqushaibi Computer & Information Sciences Centre for Research in Data Science Universiti Teknologi PETRONAS Seri Iskandar Perak Malaysia

ISBN: (数字)9798350318821

ISBN: (纸本)9798350318838

The optimization of continuous action control tasks is a crucial step in deep reinforcement learning (DRL) applications. The goal is to identify optimal actions through the accumulation of experience in continuous action control tasks. This process can be achieved through DRL, which trains agents to develop a policy that maximizes the cumulative rewards gained from decision-making in dynamic environments. Balancing exploration and exploitation is a crucial challenge in acquiring this policy. To address the exploration-exploitation trade-off, the Exploration Decay Policy (EDP) implements a dynamic exploration noise strategy that adapts to the current training progress, enabling efficient exploration in the initial phases while gradually reducing exploration to focus on exploitation as training progresses. However, the fluctuating training stability across episodes in dynamic environments poses a challenge for exploitation policies to adapt accordingly. In this paper, we propose EDP to address exploration–exploitation trade-off dilemma. The objective is to dynamically modulate the noise scale, gradually decreasing it during periods of high training stability to promote exploration, while reducing it to maintain exploitation during periods of low training stability. The study introduces the EDP-DDPG method, enhancing continuous control tasks in Box2D environments. EDP-DDPG outperforms the standard DDPG by achieving higher rewards and quicker convergence. Its success stems from dynamically adjusting exploration noise every 25 episodes, balancing exploration and exploitation. This adaptive approach, reducing noise by 10% every 25 episodes, evolves from random to strategic limb movements, optimizing policy exploitation and adaptability in dynamic settings.

关键词： Training Correlation Noise Decision making Deep reinforcement learning Stability analysis Task analysis

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UX Law Implementation at Sevima edLink from Design Science research Perspective

UX Law Implementation at Sevima edLink from Design Science R...

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Technology Innovation and Its Applications (ICTIIA), International conference on

作者： Indri Rozas Aris Tjahyanto Departement of Information System UIN Sunan Ampel Surabaya Surabaya Indonesia Departement of Information System Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798350351613

ISBN: (纸本)9798350351620

The ability to provide good usability in a Learning Management System (LMS) is crucial for its success. Usability measures the quality of the user experience when interacting with a product or system. In an LMS, usability is a key requirement because it significantly impacts the effectiveness of student learning. Moodle, one of the most widely used LMS platforms in academic institutions and universities, has been criticized for its low usability. According to an analysis, feature complexity is the primary issue for most users in Indonesia. Despite various challenges facing Educational Technology (EdTech) applications in Indonesia, Sevima edLink has flourished over the past five years, boasting a total of 1.6 million registered users and 35,000 active users per day. This research, therefore, conducted a case study on Sevima edLink to examine its development process and UX evaluation using the design science research method. The results of the usability evaluation, measured through the User Experience Questionnaire (UEQ) framework, showed that the scales for Attractiveness, Efficiency, Perspicuity, Dependability, Stimulation, and Novelty all scored above 60%.

关键词： Technological innovation Learning management systems Design methodology Educational technology User experience Complexity theory Usability

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