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International Conference on Advanced Mechatronic Systems (ICAMechS)

作者： Zizhen An Mingcong Deng Department of Electrical Engineering and Computer Science Graduate School of Engineering Tokyo University of Agriculture and Technology Tokyo Japan

ISBN: (数字)9798350366204

ISBN: (纸本)9798350366211

In this note, a new structure of Right Coprime Factorization (RCF) for nonlinear systems with uncertainty has been proposed based on a time-varying Bezout identity. This is inspired from the concept of dilation from homogeneous theory. However, this time-varying dilation in right coprime factorized uncertain nonlinear systems is not limited to homogeneous plants but is integrated into Bezout identity so as to ensure the bounded input bounded output (BIBO) stability of uncertain systems. Additionally, simulation results of two numerical examples are exhibited to validate the proposition.

关键词： Uncertain systems Uncertainty Robust stability Simulation Stability analysis Circuit stability Proposals Nonlinear systems Time-varying systems Numerical stability

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Farsi Optical Character Recognition Using a Transformer-based Model 13

Farsi Optical Character Recognition Using a Transformer-base...

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13th International Conference on computer and Knowledge engineering, ICCKE 2023

作者： Asadi-Zeydabadi, Fatemeh Shabaninia, Elham Nezamabadi-Pour, Hossein Shojaee, Melika Shahid Bahonar University of Kerman Department of Electrical Engineering Kerman Iran Graduate University of Advanced Technology Faculty of Sciences and Modern Technologies Department of Applied Mathematics Kerman Iran Shahid Bahonar University of Kerman Department of Computer Engineering Kerman Iran

ISBN: (纸本)9798350330151

Optical Character Recognition (OCR) techniques have made significant advances in recent years using new approaches such as Transformers for Latin languages. However, research on low-resource languages like Farsi is still relatively scarce. One of the reasons for this is the complex nature of Farsi script, which poses unique challenges for OCR. Farsi OCR is essential for various applications, such as document management, digital archiving, and automated data entry. In the context of this investigation, we presents a deep neural network that utilizes transformer architecture for the recognition of Farsi words, yielding promising results. Additionally, we assess the effectiveness of our method through a comparison with state-of-the-art techniques on two datasets namely, Shotor and Sadri, achieving accuracy rates of 99.75% and 99.23%, respectively. Our results outperform previous methods and highlight the potential of transformer-based approaches for Farsi OCR. © 2023 IEEE.

关键词： Optical character recognition

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Unsupervised Cross-Domain Adaptation through Mutual Mean Learning and GANs for Person Re-identification 3

Unsupervised Cross-Domain Adaptation through Mutual Mean Lea...

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3rd IEEE International Conference on Artificial Intelligence, ICAI 2023

作者： Yao, Leethar Lin, Bo-Yu Ul Haq, Qazi Mazhar Ul Islam, Ihtesham National Taipei University of Technology Department of Electrical Engineering Taipei Taiwan Graduate Institute of Automation Technology National Taipei University of Technology Taipei Taiwan National University of Sciences and Technology Department of Computer Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350322125

Unsupervised cross-domain adaptation is a challenging task for person re-identification due to the unavailability of target domain labels. Among existing methods, pseudo-Iabels-based methods have considerable performance but most of them use target domain data without labels which are challenging difficult for the target model to learn enough features. In this paper, we use generative based models that generate more target data. In cooperation with the generative model, a mutual learning model is used to transfer knowledge of one model to another model that ultimately improves overall model performance. Ex-tensive experiments are performed on Duke and Market datasets that significantly achieve improved performance in comparison to state-of-the-art methods. © 2023 IEEE.

关键词： Generative adversarial networks

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Development of Buffer Zone into Living Lab-Garuda Smart City Framework: Systematic Review 10

Development of Buffer Zone into Living Lab-Garuda Smart City...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Emmanuel, Gunadi Supangkat, Suhono Harso Gaol, Ford Lumban Ranti, Benny Bina Nusantara University Doctor of Computer Science Jakarta Indonesia Univ. Katolik Musi Charitas Fakultas Sains Dan Teknologi Palembang Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung Indonesia Bina Nusantara University Binus Graduate Program-Doctor of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350339543

The phenomenon of urbanization in Indonesia is inevitable. The new residential and economic centers in suburban areas is also a problem in city development. The gradual planning and development of smart cities in a living lab require consideration of the right location for a living lab. This study wants to show that suburban areas as city buffer zones can become living laboratories for smart city development. The diversity of situations in cities and regencies across Indonesia, the potential for resources, and the problems faced are the challenges of developing a living lab-Garuda Smart City Framework. This research uses the method of reviewing the literature of research publications for the last five years (2019-2023) to obtain information on Smart City development in Indonesia. We collected selected articles from databases in Google Scholar, IEEE, and Scopus using the Publish and Perish 8. The search keywords used were garuda AND Smart city AND Framework. The findings show the potential and dynamics of buffer zones to become appropriate living laboratories. Smart city regional planning can more holistically involve neighborhoods and address urban issues. © 2023 IEEE.

关键词： Smart city

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A New Adiabatic Logic Circuit for RF Energy Harvesting

A New Adiabatic Logic Circuit for RF Energy Harvesting

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International Technical Conference o Circuits/Systems, computers and Communications (ITC-CSCC)

作者： Bendito Freitas Ribeiro Yasuhiro Takahashi Graduate School of Engineering Gifu University Japan Department of Electrical Electronic and Computer Engineering Gifu University Japan

ISBN: (数字)9798350379051

ISBN: (纸本)9798350379068

Wireless-powered energy harvesting has been employed in various Internet of Things (IoT) devices. Optimizing low power consumption and ensuring the security of confidential information have become critical points. In this paper, the proposed circuit presents a stable power consumption and supply current in various input transition logics. The current passes in each input transition logic at the function block have been uniformized to reduce the current and energy fluctuation. The different power clock frequencies and various load capacitances have been evaluated. The current and energy fluctuation has been reduced significantly from the conventional adiabatic logic circuit. The bipolar sinusoidal from an RF signal can be converted to unipolar by a diode-connected PMOS transistor for supplying energy to an adiabatic circuit.

关键词： Radio frequency Fluctuations Power demand Logic circuits RF signals Current supplies Internet of Things

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Prediction of DHF (Dengue Hemorrhagic Fever) Severity Using Random Forest, KNN, Decision Tree and Naïve Bayes 7

Prediction of DHF (Dengue Hemorrhagic Fever) Severity Using ...

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7th IEEE International Conference on Information Technology and Digital Applications, ICITDA 2022

作者： Pohan, Husni Iskandar Suparta, Wayan Heryadi, Yaya Wibowo, Antoni Lukas Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Institut Teknologi Nasional Yogyakarta Department of Electrical Engineering Yogyakarta55281 Indonesia Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta12930 Indonesia

ISBN: (数字)9781665461368

ISBN: (纸本)9781665461368

Dengue Hemorrhagic Fever is an acute viral infectious disease caused by the dengue virus. Transmitted through the bite of Aedes Mosquitoes and divided into 4 severity. Severity 1 and 2 are characterized by a decrease in platelet levels below normal and there is a change in hemoconcentration, while Severity 3 and 4 are included in Dengue Shock Syndrome (DSS). The purpose of the study was to classify the severity of dengue, based on age, gender, platelets, body temperature, bleeding, and also doctor's recommendation. The advancement of information technology is the reason for the development of medical science, especially with artificial intelligence. Abundant medical data becomes a source that can be processed in improving the quality of medical information. The method chosen is Random Forest, kNN, Decision Tree and Naïve Bayes. The dataset used is the medical records of all DHF patients for 30 months in the period Jan 2019 to June 2021, at the Nadhifa Al Ghiffari Clinic in Bojongsoang District, Bandung Regency. © 2022 IEEE.

关键词： Decision trees

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Stunting Classification in Toddlers: Implementation and Evaluation of the Decision Tree Algorithm

Stunting Classification in Toddlers: Implementation and Eval...

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2024 International Conference on Information Technology and Computing, ICITCOM 2024

作者： Alita, Debby Ahmad, Imam Jafar Rumandan, Rhaishudin Erkamim, Moh Widyasmoro Universitas Teknokrat Indonesia Faculty of Engineering and Computer Science Bandarlampung Indonesia Institut Agama Islam Negeri Ambon Islamic Education Management Study Program Ambon Indonesia Universitas Tunas Pembangunan Smart City Information Systems Study Program Surakarta Indonesia Universitas Muhammadiyah Yogyakarta Faculty of Engineering Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350379839

Stunting in toddlers is a chronic nutritional issue that affects the physical and cognitive development of children, with serious long-term consequences such as reduced cognitive function and an increased risk of chronic diseases in adulthood. Therefore, early identification and prevention efforts for stunting are crucial. Classifying toddlers into categories of at-risk for stunting or not is essential to provide timely and appropriate interventions. This study employs data mining techniques using the decision tree algorithm to expedite the stunting detection process and improve the accuracy of nutritional status classification in children. The results indicate that the constructed decision tree model can classify children's nutritional status with an accuracy of 83.26%. The decision tree achieves high accuracy in classifying stunting in toddlers due to its ability to handle complex data and identify significant patterns within the data. © 2024 IEEE.

关键词： Classification Data Mining Decision Tree Stunting

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Effects of positive and negative group velocity dispersion on the generation of a noise-like pulse and its effectiveness for supercontinuum generation

Effects of positive and negative group velocity dispersion o...

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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Chang, Kuan-Yuan Liu, Jia-Ming Institute of Photonic System National Yang Ming Chiao Tung University Tainan Taiwan Electrical and Computer Engineering Department University of California Los AngelesCA United States Graduate Institute of Optoelectronic Engineering National Chung Hsing University Taichung Taiwan

Generation of noise-like pulses by the intrapulse Raman process alone doesn't enhance the efficiency of supercontinuum generation. Instead, utilization of a fiber with a negative group-velocity dispersion to induc... 详细信息

关键词： Group velocity dispersion

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A solution for resource allocation through complex systems in fog computing for the internet of things

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Computing 2024年第7期106卷 2107-2131页

作者： Faraji, Fatimah Javadpour, Amir Sangaiah, Arun Kumar Zavieh, Hadi Department of Computer Engineering Yazd University Safaeih Yazd100190 Iran Harbin Institute of Technology Shenzhen10587 China ADiT-Lab Electrotechnics and Telecommunications Department ISMAI and INESC TEC Instituto Politécnico de Viana do Castelo ISMAI Porto10587 Portugal International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Yunlin 64002 Taiwan Department of Economic and Statistics Guangzhou Higher Education Mega Center Guangzhou University Guangzhou510006 China Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon

To manage the IoT resources to process the massive amount of collected data in complex systems, Allocation of tasks to nodes to increase user satisfaction has been noticed by researchers. In allocating tasks, the necessity of a powerful auxiliary platform is felt. Cloud and fog networks are two suitable options for performing and completing tasks on the Internet of Things. In the cloud network, due to offloading tasks to remote computing centers, problems such as increasing complete time, increasing traffic in the network, and high energy consumption for the Internet of Things network occur. The fog network solves the problem of completing tasks due to reducing the geographical distance between Internet of Things devices and processor nodes. The devices in the fog network cooperate with the Internet of Things network’s diverse and variable nodes with time computing, storage, and processing resources. In this heterogeneous environment, the non-cooperation and dishonesty of malicious fog nodes through misrepresentation of shared resources and non-completion of tasks can disrupt the performance of the Internet of Things network. As a result, two fundamental challenges in the heterogeneous fog network include providing resources to allocate tasks and recognizing valid fog nodes. This paper focuses on creating a resource provider network to allocate tasks to authentic fog nodes with appropriate resources. When an Internet of Things node faces a shortage of resources and link prediction, it can form a resource supply network with neighboring nodes to solve the two stated challenges. For this purpose, in the first stage, efforts are made to select the most appropriate options among the neighboring nodes based on specific criteria. In the second step, reliable nodes are identified based on a trust mechanism, including resource trust and entity trust. In the last step, tasks are divided among reliable devices with sufficient resources. The evaluation of the proposed method i

关键词： Quality of service

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Multi-task Learning Graph Neural Networks for Cancer Prognosis Prediction with Genomic Data 46

Multi-task Learning Graph Neural Networks for Cancer Prognos...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Lin, Tsung-Wei Arriagada, Sofia Ormazabal Lin, Che Taiwan Taiwan International Graduate Program AIoT Ntu Taiwan Research Center for Information Technology Innovation Academia Sinica Taiwan Department of Electrical Engineering Ntu Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taiwan Smart Medicine and Health Informatics Program Ntu Taiwan

ISBN: (纸本)9798350371499

Providing robust prognosis predictions for cancers with limited data samples remains a challenge for precision oncology. In this study, we propose a novel approach that combines multi-task learning (MTL) and graph neural networks (GNNs) to address this issue. By representing gene-gene interactions as a graph network, our approach leverages multi-task learning to effectively capture the relationships of genes relevant to the oncogenesis and progression of breast, lung, and colon cancer. We demonstrate that our approach improves the cancer prognosis prediction for cancers with fewer samples, such as colon adenocarcinoma, by leveraging the shared gene-gene interactions across different cancer types, obtaining increases in the area under the precision-recall curve (AUPRC) of 24%. Our work contributes to the field of smart healthcare by demonstrating the potential of MTL and GNNs for enhancing cancer prognosis prediction, even with limited data samples. © 2024 IEEE.

关键词： Lung cancer

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