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Prediction of Electrical Characteristics of a-IGZO TFT Based on Transfer Learning-Based Variational Autoencoder

Prediction of Electrical Characteristics of a-IGZO TFT Based...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Bea, Khean-Thye Wei-Hsuan Lin, Shih-Shin Hu Lin, Da-Zheng Hu, Hsin-Hui Chen, Xiu-Zhi Lin, Ting-Ru Chen, Yen-Lin Chen, Kun-Ming Cheng, Wai Khuen National Taipei University of Technology Dep. of Electronic Engineering Taipei106 Taiwan National Taipei University of Technology Dep. of Computer Science and Information Engineering Taipei106 Taiwan Taiwan Semiconductor Research Institute Hsinchu30078 Taiwan Universiti Tunku Abdul Rahman Faculty of Information and Communication Technology Kampar31900 Malaysia

ISBN: (纸本)9798350337020

In this study, we proposed a transfer-learning based variational autoencoder model for predicting the electrical characteristics in the parameter tuning process of a-IGZO TFT structure design. The result achieve a high R2 score of 0.9704 with a low-computing-power hardware-friendly method that reduced time consumption significantly compared to prior approaches. The findings have practical implications for mitigating the time-consuming nature of TCAD simulations, and the method can expand to various types of input data while ensuring high performance and generalization. We demonstrated significant improvement in generalization and accuracy through a k-fold validation. © 2023 IEEE.

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An Improved Real-Time Implementation of Adaptive Neuro-Fuzzy Controller

An Improved Real-Time Implementation of Adaptive Neuro-Fuzzy...

引用

Iranian Conference on Electrical engineering (ICEE)

作者： Iman Gholizadeh Haniye Raziyan Reza Javidan Electrical & Computer Engineering Dep Isfahan University of Technology Isfahan Iran Electrical Engineering Dep Sheikh Bahaei University Isfahan Iran Computer Engineering and IT Dep Shiraz University of Technology Shiraz Iran

An adaptive neural network-based control method is highly effective for controlling complex systems with various operating situations since it can adapt and train its control strategy to compensate for changes in the system's behavior. The backpropagation algorithm is utilized to optimize the neural controller values and reach the desired system reference. Compared to other neural control methods, the proposed neuro-fuzzy controller's learning algorithm has a quick response time, making it suitable for a wide range of real-world applications that require ANNs. Fuzzy rules can be established to improve the adaptability and performance of a neural controller by optimizing its decisions and taking into account various factors such as data and desired outcomes. Furthermore, these fuzzy rules can help determine the optimal momentum and learning rate values for a given problem. The proposed method has been executed using LabViewprogram and validated in practical through a research lab actuation control structure, demonstrating that optimizing the neuro-fuzzy controller with optimal parameters of the neural network model leads to improved control and faster response times in nonlinear controller systems.

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Forecasting of Monkeypox Cases Using Optimized SARIMAX Based Model 3

Forecasting of Monkeypox Cases Using Optimized SARIMAX Based...

引用

3rd IEEE International Conference on Electronic engineering, ICEEM 2023

作者： Elshabrawy, Mahmoud Eid, Marwa M. Abdelhamid, Abdelaziz A. El-Kenawy, El-Sayed M. Ibrahim, Abdelhameed Delta Higher Institute of Engineering and Technology Dept. of Communications & Electronics Mansoura Egypt Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt Ain Shams University Faculty of Computer and Information Sciences Dep. of Computer Science Cairo Egypt University Mansoura Faculty of Engineering Computer Engineering and Control Systems Dept. Egypt

ISBN: (纸本)9798350323511

This study presents a dipper-throated-based ant colony optimization (DTACO) with the Seasonal Auto-Regressive Integrated Moving Average with eXogenous factor (SARIMAX) model (DTACO+SARIMAX) to forecast monkeypox cases. The work optimizes the SARIMAX model using grid search cross-validation and fine-tunes its hyperparameters using DTACO to improve prediction accuracy. The suggested model's consistency and accuracy are considerable compared to previous studies. Comparisons with state-of-the-art models validate the proposed model's predictions. DTACO+SARIMAX can be used to control disease and monitor monkeypox. Healthcare organizations and governments can better manage and track the pandemic's course by offering accurate predictions, reducing public panic, and enabling effective pandemic planning. The Analysis of Variance (ANOVA) and Wilcoxon signed-rank tests are conducted on the proposed DTACO-SARIMAX model and compared models. © 2023 IEEE.

关键词： Ant colony optimization

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Lightweight SRAM-PUF Identity Authentication for Edge Devices

Lightweight SRAM-PUF Identity Authentication for Edge Device...

引用

Iranian Conference on Electrical engineering (ICEE)

作者： Alireza Shafiei Mehrnaz Monajati Dep. of Electrical and Computer Engineering Graduate University of Advanced Technology Kerman Iran

ISBN: (数字)9798350376340

ISBN: (纸本)9798350376357

The expansive and intricate nature of the Internet of Things (IoT) network necessitates intelligent and real-time computation. Edge computing, as an innovative concept and trend within the IoT landscape, addresses this demand by leveraging advanced technologies to overcome challenges such as timely responsiveness, enhanced security, and reduced power consumption. Among the technologies available for securing edge devices, Physical Unclonable Functions (PUFs) stand out. This paper introduces an SRAM-PUF that explores the utilization of adiabatic logic with carbon nanotube field-effect transistors (CNTFETs) to design lightweight edge devices capable of addressing the aforementioned challenges. The proposed computing platform holds promise for enhancing security and energy efficiency in IoT applications.

关键词： Electrical engineering Power demand Physical unclonable function Market research Real-time systems Energy efficiency CNTFETs

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The Correlation Between Architecture Smells, Package Stability and Reusability: A Case Study 4

The Correlation Between Architecture Smells, Package Stabili...

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4th IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2024

作者： Benidris, Mrwan Elfallah, Ehab A. Omar Cheema, Marwa Aslam Alsma, Mohammed Salah Addin Kadiki, Tareq Ziad Elbarsha, Abdelfattah University of Benghazi Faculty of Information Technology Dep. of Computer Science Libya Libyan International Medical University Faculty of Information Technology Dept. of Software Engineering Libya University of Benghazi Faculty of Public Health Health Informatics Department Libya

ISBN: (纸本)9798350372632

the design decisions made in the architecture of a software system are essential to its maintainability, and thus its quality is of great importance. Architecture smells (ASs) can be used to identify any quality issues within the system. To further explore the relationships between ASs, stability, and reusability, Spearman correlation coefficients will be calculated. This paper seeks to examine the connection between architecture smells, software stability, and reusability. To do this, we utilized a dataset created by other researchers and conducted an empirical study of the associations between five types of architecture smells and software stability and reusability. This research is necessary as there is a lack of in-depth research on architecture smells and their relationship to software stability and reusability. We found a relationship between the presence of Feature Concentration in package and the stability and reusability of the package. Also, package's stability is affected by both Cyclic dependency and Unstable dependency. A slight positive correlation has been found between God Class and stability, as well as a very weak negative correlation between Unstable dependency and reusability. Finally, we found a slight negative correlation between package stability and reusability. © 2024 IEEE.

关键词： Reusability

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Improving Distribution Systems Maintenance Management, by Predicting Reliability Indices Using Fully Connected Neural Networks 12

Improving Distribution Systems Maintenance Management, by Pr...

引用

12th Smart Grid Conference, SGC 2022

作者： Tananaeifar, Mohammad-Hossein Alimadaday, Mahdis Ghadamgahi, SeyedMohammad-Mahdi Ghafourian-Nasiri, Mohammad Fereidunian, Alireza Amirkabir University of Technology Construction Engineering and Management Dep. Tehran Iran Tarbiat Modares University Industrial Engineering Dep. Tehran Iran Kharazmi University Computer Engineering Dep. Tehran Iran K. N. Toosi University of Technology Electrical Engineering Faculty Tehran Iran

ISBN: (纸本)9798350333961

Due to the increasing amount of industrial data worldwide, deep learning solutions have become extensively popular for preventive maintenance programs. Preventive maintenance reduces the costs of equipment failures, thus crafting detailed preventive maintenance programs are very effective. Neural networks are intelligent computing techniques inspired by biological neurons. Neural networks are one of the most common and practical machine learning algorithms that are utilized in many industrial applications. In this research, a fully connected neural network is developed to predict the systems reliability indices, i.e. the number of failures occurring in feeders and the amount of energy not served (ENS) in electricity distribution feeders. As a result, a data-driven improvement is provided for the preventive maintenance program. © 2022 IEEE.

关键词： Neural networks

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A Proposed Approach for Risk Planning and Management 25

A Proposed Approach for Risk Planning and Management

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25th International Arab Conference on Information technology, ACIT 2024

作者： Al Refai, Mohammed N. Al-Smadi, Yazan Al-Momani, Adai Jamhawi, Zeyad M. Otoom, Ahmed Ali Atoum, Issa Yaseen, Ahid Ababneh, Jafar Kanan, Mohammad Zarqa University Software Engineering Dep. Zarqa Jordan Zarqa University Department of Computer Science Zarqa Jordan Applied Science Private University Department of Computer Science Amman Jordan Applied Science Private University Department of Cyber Security Amman Jordan Philadelphia University Faculty of Information Technology Software Engineering Department Jordan Zarqa University Department of Cyber Security Zarqa Jordan University of Business and Technelogy Dep. of Industry Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Project Risk management is the process of identifying, evaluating, avoiding, or reducing risks. Where there is no software project without risks existence are natural in the context of project planning and management. This study focuses on proposing an enhanced risk management approach where testing phases were added to make the risk management process more effective by proposing solutions to many mistakes and challenges that occur through dealing with risks. The proposed approach focuses on dealing with risks that cannot be easily defined, more accurate risk evaluation, planning, and managing risk coordinated with a contingency plan. © 2024 IEEE.

关键词： Project management

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Multi-party secure communication using blockchain over 5G

Multi-party secure communication using blockchain over 5G

引用

International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Archana, K. Kareem, Z.H. Al-Farhani, Liwa H. Bagyalakshmi, K. Jenvi, Ignatia K. Majella Kumar, Ashok Department of Computer Engineering Pimpri Chinchwad College of Engineering and Research Maharashtra Pune India Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq System Analysis Control and Information Processing dep Academy of Engineering RUDN University Moscow Russia Sri Ranganathar Institute of Engineering and Technology Coimbatore India Mathematics Department Saveetha School of Engineering SIMATS Saveetha Nagar Chennai India Department of Computer Science Banasthali Vidyapith Rajasthan Banasthali India

ISBN: (纸本)9781032669663

One of the most critical problems facing the blockchain technology industry right now is how to protect the privacy of users’ data on the blockchain in a way that is both effective and cheap. Based on the Pedersen commitment and the Schnorr protocol, this study comes up with a secure multi-party computing protocol (BPLSM). By making the structure of the protocol and doing formal proof calculations, it has been shown that the protocol can be used in the blockchain network to combine private messages for efficient signing while keeping people’s identities secret. Furthermore, by looking at the nature and security of the protocol, it is also possible to find that the BPLSM protocol on the blockchain has a low cost of computing and a high level of information secrecy. Furthermore, it was found that the BPLSM protocol takes less time to check than the current mainstream BLS signature in a simple multi-party transaction with a fixed number of participants. © 2024 The Author(s).

关键词： Blockchain

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Comparison of Milk Quality Grades Using the Logistic Model Tree Algorithm

Comparison of Milk Quality Grades Using the Logistic Model T...

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Cybernetics and Innovations (ICCI), International Conference on

作者： Phanintorn Suaprae Thanakrit Janchidfah Brijesh Bhatt Dep. Of Business Information Technology Management Phibulsongkram Rajabhat University Phitsanulok Thailand Dep. Of Digital Media Technology Muban Chombueng Rajabhat University Ratchaburi Thailand Dep. Of Computer Engineering Faculty of Technology Dharmsinh Desai University India

ISBN: (数字)9798331533267

ISBN: (纸本)9798331533274

This study aims to track and categorize milk quality grades using the Logistic Model Tree (LMT) algorithm, based on the analysis of 1,059 milk samples. The focus of the research is to evaluate key factors such as pH value, taste, smell, fat content, concentration, and color, which play a crucial role in determining the overall quality of milk. The goal is to create an efficient and standardized process for sorting cow's milk, which can save significant time and resources in the dairy industry. To identify the most suitable algorithm for this task, the research team initially tested 12 different algorithms, assessing their performance in predicting milk quality. Among these, the Logistic Model Tree (LMT) emerged as the most effective model, achieving an impressive accuracy rate of 99.76%. The LMT algorithm, known for combining the advantages of decision trees and logistic regression, demonstrated its ability to classify milk quality grades with high precision. The results of this study indicate that the LMT algorithm can be a valuable tool for automating the milk quality grading process. By leveraging key attributes such as pH, fat content, and color, it is possible to efficiently sort and grade milk, reducing the need for manual inspection and improving overall productivity in the dairy industry. This method offers a significant improvement over traditional quality assessment techniques and has the potential to be widely adopted for milk sorting and quality control applications.

关键词： Analytical models Dairy products Accuracy Standardization Prediction algorithms Data models Classification algorithms Quality assessment Logistics Sorting

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Deep Learning technology Used High Dynamic Range Image Reconstruction And Detection

Deep Learning Technology Used High Dynamic Range Image Recon...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Bheemalingaiah, M. Srinivasan, R. Nathan, V. Vijey Kolluri, Raju Gupta, Amit J.B Institute of Engineering and Technology Department of CSE Telangana Hyderabad India School of Computer Science and Engineering Galgotia University Department of CSE U.P India SRM TRP Engineering College Department of CSE Tamilnadu Tiruchirappalli India Narasaraopet Engineering College Department of ECE Andhra Pradesh Narasaraopet India J.B Institute of Engineering and Technology Dep. of AI & ML Telangana Hyderabad India

ISBN: (纸本)9798350378092

The suggested remedy for saturated pixels in digital camera photos introduces a brand-new learning-based method for reconstructing images with high dynamic range (HDR). While current techniques concentrate on increasing brightness range, they frequently fall short in producing realistic textures, leading to errors in places that are too saturated. Our method reconstructs an HDR image by using aesthetically beautiful saturated pixels from a low dynamic range (LDR) image. We provide a feature masking strategy to address problems discovered during training, when saturated and well-exposed pixels got comparable convolutional filters, resulting in uncertainty and checkerboard and halo aberrations. This method improves the overall quality of the reconstruction by reducing the contribution of features from saturated regions. Furthermore, in order to guarantee visually pleasing textures in the reconstructed HDR photos, we adjust the perceptual loss function based on *** use a two-step procedure in order to properly train our system. We first train the system for picture in painting on a sizable dataset, and then we modify it for HDR reconstruction. Our method performs exceptionally well even with a small number of HDR photos because it uses an advanced sampling strategy to choose hard training patches from a wide range of settings. Convolutional neural networks, feature masking, perceptual loss, and high dynamic range imagery are important components of our methodology. © 2024 IEEE.

关键词： Convolutional neural networks

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