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8th International Conference on Education and Multimedia Technology, ICEMT 2024

作者： Anotaipaiboon, Weerachai Department of Electrical and Computer Engineering Faculty of Engineering Thammasat School of Engineering Thammasat University Thailand

ISBN: (纸本)9798400717611

The growing demand for online programming education underscores the necessity for engaging and efficient learning tools. While interactive exercises enhance student engagement and outcomes, creating them presents challenges for educators who teach programming. This paper introduces an authoring framework tailored to address these challenges and simplify the creation of interactive programming exercises. Our framework utilizes the user-friendly Markdown format in combination with a unique syntax extension. This simplified extension, known for its succinctness and ease of use, enables educators to create programming questions with ease. They can produce questions containing pre-set solutions and various test cases all in a single file. Additionally, instructors can customize learning experiences by regulating the complexity level, allowing students to fill in designated code sections that correspond to specific learning goals. Moreover, our system simplifies the assessment process by offering an automated grading system that derives solutions directly from the exercise content. The framework features a dedicated Python parser that transforms plain text questions into interactive HTML exercises. Students can then attempt these exercises and receive instant feedback through automated grading. This novel approach promises to revolutionize online programming education by simplifying content creation, fostering student engagement, and streamlining assessment, ultimately benefiting educators and learners alike. © 2024 Copyright held by the owner/author(s).

关键词： Students

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Job failure prediction in Hadoop based on log file analysis

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International Journal of computers and Applications 2022年第3期44卷 260-269页

作者： Shirzad, Ehsan Saadatfar, Hamid Faculty of Electrical and Computer Engineering University of Birjand Birjand Iran Department of Computer Engineering Faculty of Electrical and Computer Engineering University of Birjand Birjand Iran

Hadoop is a popular framework based on MapReduce programming model to allow for distributed processing of large datasets across clusters with various number of computer nodes. Just like any dynamic computational environment, Hadoop has some problems and one of which is unsuccessful execution of MapReduce jobs. Job failures can cause significant resource wasting, performance deterioration, and user dissatisfaction. Therefore, a proactive and predictive management approach could be very useful in Hadoop systems. In this paper, we try to predict the futurity of MapReduce jobs in OpenCloud Hadoop cluster by using its log files. OpenCloud is a research cluster managed by CMU’s Parallel Data Lab which uses Hadoop to process big data. We first tried to study the log files and analyze the relationship between the jobs, resources, and workload characteristics and the failures in order to discover the effective features for the prediction process. After recognizing the job failure patterns, some popular machine learning algorithms are deployed to predict the success/failure status of the jobs before they start to execute. Eventually, we compared the learning methods and showed that the C5.0 algorithm had the best results with an accuracy of 91.37%, a recall of 74.43%, and a precision of 80.31%. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

关键词： MapReduce

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Novel Symmetric/Asymmetric Generalized Multilevel Inverter Based on T-Type With Reduced Switch Count 16

Novel Symmetric/Asymmetric Generalized Multilevel Inverter B...

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16th Annual Power Electronics, Drive Systems, and Technologies Conference, PEDSTC 2025

作者： Seifi, Ali Nadermohammadi, Ali Hosseini, Seyed-Hossein Hagh, Mehrdad Tarafdar Hosseinpour, Majid University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran University of Mohaghegh Ardabili Department of Electrical and Computer Engineering Ardabil Iran

ISBN: (纸本)9798331533946

A sinusoidal voltage with excellent quality and total harmonic distortion (THD) is produced using multi-level inverters (MLI). The idea of MLI was introduced by some traditional designs, but their drawbacks - such as the quantity of DC sources and switching components and their low THD - made the inverter more expensive and larger. With fewer switches, the suggested MLI can get around these restrictions. The suggested structure contains less switching devices, drivers, and total standing voltage (TSV) as compared to MLI architectures. The T-Type inverter has been enhanced in the suggested construction. An enhanced H -bridge and two T-type inverters are combined in the suggested configuration. Two symmetrical diodes make up the T-type inverter that makes up the suggested configuration. To prove the superiority of the suggested structure, we compare it with different structures. Lastly, we use nearest level modulation (NLM) to show the simulation results under different loads. © 2025 IEEE.

关键词： Harmonic distortion

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Quantum illumination with high-dimensional Bell states

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Physical Review A 2024年第5期110卷 L050603-L050603页

作者： Armanpreet Pannu Amr S. Helmy Hesham El Gamal The Edward S. Rogers Department of Electrical and Computer Engineering University of Toronto Toronto Canada M5S 3G4 School of Electrical and Information Engineering Faculty of Engineering The University of Sydney Sydney 2008 Australia

We propose a protocol in quantum illumination (QI) leveraging entanglement in discrete-variable states. Our investigation shows that, as M→∞, the M-mode Bell state matches the 6 dB advantage of the two-mode squeezed vacuum in high noise. It also excels in low- and mid-noise conditions, demonstrating that QI's benefits are not restricted to high background noise. Moreover, the protocol benefits from a sequential decision rule, increasing the advantage beyond 6 dB. These findings present an intriguing alternative to continuous-variable states and open different applications for QI using discrete-variable states.

关键词： Quantum sensing

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A deep convolutional neural network for COVID-19 detection using chest X-rays

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Research on Biomedical engineering 2022年第1期38卷 139-148页

作者： Bassi, Pedro R. A. S. Attux, Romis Department of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering University of Campinas Campinas Brazil

Purpose: We present image classifiers based on Dense Convolutional Networks and transfer learning to classify chest X-ray images according to three labels: COVID-19, pneumonia, and normal. Methods: We fine-tuned neural networks pretrained on ImageNet and applied a twice transfer learning approach, using NIH ChestX-ray14 dataset as an intermediate step. We also suggested a novelty called output neuron keeping, which changes the twice transfer learning technique. In order to clarify the modus operandi of the models, we used Layer-wise Relevance Propagation (LRP) to generate heatmaps. Results: We were able to reach test accuracy of 100% on our test dataset. Twice transfer learning and output neuron keeping showed promising results improving performances, mainly in the beginning of the training process. Although LRP revealed that words on the X-rays can influence the networks’ predictions, we discovered this had only a very small effect on accuracy. Conclusion: Although clinical studies and larger datasets are still needed to further ensure good generalization, the state-of-the-art performances we achieved show that, with the help of artificial intelligence, chest X-rays can become a cheap and accurate auxiliary method for COVID-19 diagnosis. Heatmaps generated by LRP improve the interpretability of the deep neural networks and indicate an analytical path for future research on diagnosis. Twice transfer learning with output neuron keeping improved DNN performance. © 2021, Sociedade Brasileira de Engenharia Biomedica.

关键词： COVID-19

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An Ant-colony Based Model for Load Balancing in Fog Environments

Supercomputing Frontiers and Innovations

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Supercomputing Frontiers and Innovations 2023年第1期10卷 4-20页

作者： Mirtaheri, Seyedeh Leili Azari, Mahya Greco, Sergio Arianian, Ehsan Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran Faculty of Mathematics and Computer Science Kharazmi University Tehran Iran Department of Computer Engineering Modeling Electronics and Systems University of Calabria Italy Tehran Iran

Delay-sensitive applications are becoming more and more in demand as a result of the development of information systems and the expansion of communication in cloud computing technologies. Some of these requests will be overlooked in cloud environments due to the communication delay between the processing center and the client’s request. The ‘fog-based computing paradigm’, a novel processing model, can be added to cloud computing to help with the aforementioned issues. The performance of computing systems is always influenced by latency. In this paper, we focus on balancing the load on the fog nodes to lower the latency. It is also a crucial component of fog computing devices. It necessitates the use of load-balancing algorithms to select the optimal hosts, resulting in an even distribution of the load on the available *** provide a load-balancing approach based on the Ant-colony optimization algorithm’s latency rate for responding to tasks. A random data set evaluation of this model reveals shorter response times than those of earlier strategies suggested in this field. © The Authors 2023. This paper is published with open access at ***

关键词： Fog

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An Improved Zeta-Based DC-DC Converter 50

An Improved Zeta-Based DC-DC Converter

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Ghasri, Mohammad Gholizadeh, Hossein Hamzeh, Mohsen Sadeghi, Erfan Saif, Mehrdad School of Electrical and Computer Engineering Faculty of Engineering University of Tehran Tehran Iran Department of Electrical and Computer Engineering University of Windsor WindsorON Canada

ISBN: (纸本)9781665464543

This paper proposes a non-isolated high-gain DC-DC converter capable of delivering a lOx voltage gain with a 50 percent duty cycle. The topology represents an enhanced version of the Zeta converter and aims to address issues such as discontinuous input current and low voltage gain of the conventional Zeta converters, making it suitable for renewable applications. Moreover, it offers a common ground between input source and load. Semiconductor stress is also kept below unity in terms of normalized voltage/current. The proposed converter is analyzed in both ideal and non-ideal modes, with a particular focus on sensitivity analysis regarding voltage gain and efficiency in the latter. Simulation results are provided to validate the theoretical relations expressed. © 2024 IEEE.

关键词： Voltage-source converters

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Solving false active power readings problem for air high voltage lines with MATLAB model simulations 12

Solving false active power readings problem for air high vol...

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12th International Scientific Conference on computer Science, COMSCI 2024

作者： Tsenov, Georgi Technical University of Sofia Faculty of Automation Department Fundamentals of Electrical Engineering Bulgaria

ISBN: (纸本)9798350392166

The Bulgarian electrical distribution company experienced abnormal electric power results, observed while the lines are idle sometimes in 110, 220, and 400 kV high voltage power lines ranging from 50 to 150 km when active power readings were taken. In these particular occurrences, the electricity distribution lines were with one side being voltage supplied, with the other side being open and with no consumer load present, yet the readings on the supplied side indicated that is entering active energy from the other side. This occurrence is impossible physically and necessitates an investigation to determine its cause. To address this a case study in MATLAB was conducted with application of nodal analysis method by modelling all relevant parameters and characteristics and their variations. The findings confirmed that such an abnormal instrument readings for the electricity power can indeed occur under certain conditions, which we validated through simulations on a hardware scale model. © 2024 IEEE.

关键词： Power distribution lines

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A novel IoT-based arrhythmia detection system with ECG signals using a hybrid convolutional neural network and neural architecture search network

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International Journal of Applied Decision Sciences 2024年第5期17卷 636-656页

作者： Şentürk, Ümit Gülen, Ceren Polat, Kemal Department of Computer Engineering Faculty of Engineering Abant Izzet Baysal University Bolu Turkey Department of Computer Engineering Education of Graduate Institute Abant Izzet Baysal University Bolu Turkey Department of Electrical and Electronics Engineering Bolu Abant Izzet Baysal University Bolu Turkey

Electrocardiogram (ECG) signals are the most common tool to evaluate the heart’s function in cardiovascular diagnosis. Irregular heartbeats (arrhythmia) found in the ECG play an essential role in diagnosing cardiovascular diseases (CVD). In this paper, we proposed an arrhythmia classification method with the neural architecture search network (NASNet) model, an optimised version of the convolutional neural networks (2D-CNN) model, which performs very well in visual information analysis classification. We aimed to use the arrhythmia classification problem in IoT and mobile devices with the NASNet model with low parameter numbers and processing capacity without performance loss. 2D input data have been obtained by converting the classified heart rate signals in the datasets into image files. The 2D data obtained have been classified by machine learning, CNN, and NASNet models. As a result of classification, 2D CNN accuracy was 97.51%, and the NASNet model was 96.89% accuracy. As a result of arrhythmia classification, the accuracy rates of the NASNet and the 2D CNN models were close. In conclusion, the proposed IoT-based arrhythmia detection system with ECG signals using a hybrid CNN and NASNet is a promising tool for the early detection of arrhythmias. Furthermore, it could help to reduce the mortality associated with these potentially fatal conditions. © 2024 Inderscience Enterprises Ltd.

关键词： Convolutional neural networks

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Evolutionary Approach to GAN Hyperparameter Tuning: Minimizing Discriminator and Generator Loss Functions 14

Evolutionary Approach to GAN Hyperparameter Tuning: Minimizi...

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14th International Conference on computer and Knowledge engineering, ICCKE 2024

作者： Klidbary, Sajad Haghzad Babaei, Anahita Ghorbani, Ramin Department of Computer Engineering Faculty of Engineering University of Zanjan Zanjan Iran Department of Electrical Engineering Faculty of Engineering University of Zanjan Zanjan Iran

ISBN: (纸本)9798331511272

Image Reconstruction has always been among machine vision’s challenging topics. One of image restoration’s most challenging is to fill the damaged area after deleting, it in a visually acceptable way. The beginning of image reconstruction goes back to the last five decades, but due to the ineffectiveness of the basic methods, new methods have been offered. In the field of image restoration, GAN or Generative Adversarial Networks can be very useful due to the high similarity between the generated data and the training data. In this paper, by presenting an algorithm based on these networks, we try to increase the accuracy of the image restoration process. The GAN's accuracy is related to the correct selection of parameters. Using trial and error methods to find parameters is time-consuming. In this paper, the optimal parameters of the GANs have been used by providing suitable coding for the genetic algorithm. The simulation results represent that the proposed GA has notable performance. The best results give a minimum value of about 0.16 for the discriminator loss function and 0 for the generator loss function. ©2024 IEEE.

关键词： Generative adversarial networks

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