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International Conference on Electronics, Information and Communications (ICEIC)

作者： Joonwoo Lee Scott Uk-Jin Lee Department of Applied Artificial Intelligence Major in Bio-Artificial Intelligence Hanyang University Ansan Korea Department of Computer Science and Engineering Hanyang University Ansan Korea

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

Autonomous driving systems demand extensive testing to guarantee safety and reliability. However, real-world testing is often costly and limited in variety. This paper investigates the use of diffusion models to generate synthetic driving images as a means of augmenting test datasets for autonomous driving systems. Leveraging their capability to produce diverse and highly realistic images, diffusion models can simulate various driving conditions, including rare or challenging scenarios that are difficult to replicate. By incorporating these synthetic images into the testing pipeline, self-driving systems can be assessed across a wider range of conditions, enhancing their robustness and safety.

关键词： Deep learning Technological innovation Text to image Transforms Diffusion models Data models Robustness Safety Autonomous vehicles Testing

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OSA-CCNN: Obstructive Sleep Apnea Detection Based on a Composite Deep Convolution Neural Network Model using Single-Lead ECG signal

OSA-CCNN: Obstructive Sleep Apnea Detection Based on a Compo...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Zhou, Yu He, Yinxian Kang, Kyungtae Hanyang University Major in Bio Artificial Intelligence Dept. of Computer Science and Engineering Ansan Korea Republic of Hanyang University Dept. of Artificial Intelligence Ansan Korea Republic of

ISBN: (纸本)9781665468190

Obstructive sleep apnea (OSA) is a common sleeping issue that makes it difficult to breathe while you sleep and is linked to a number of other disorders, including cardiovascular conditions, such as hypertension and coronary heart disease. Nocturnal polysomnography (PSG) is one of the clinical diagnostic criteria for OSA, which is a painful and expensive form of diagnosis as it requires manual interpretation by experts and takes a lot of time. ECG-based techniques for diagnosing OSA have been introduced to alleviate these problems, but the most of solutions that have been put up thus far rely on feature engineering, which requires substantial specialist knowledge and expertise. In this study, we present a novel approach for classifying OSA based on a single-lead ECG signal conversion and a composite deep convolutional neural network model. The ECG signal is transformed into scalogram images with heart rate variability (HRV) characteristics and Gramian Angular Field (GAF) matrix images with temporal characteristics, incorporating the temporal properties of the ECG, to create the hybrid image dataset. The composite model contains three sub-convolutional neural networks, two of which utilize fine-tuned AlexNet and ResNet models, the third is a convolutional neural network with five residual blocks that are evaluated by a voting mechanism. The PhysioNet Apnea-ECG database was used to train and evaluate the proposed model. The results show that the proposed classifier achieved 90.93% accuracy, 83.86% sensitivity, 95.29% specificity, and 0.89 AUC on hybrid image datasets. © 2022 IEEE.

关键词： Electrocardiograms

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Brain tumor detection with integrating traditional and computational intelligence approaches across diverse imaging modalities - Challenges and future directions

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computers in Biology and Medicine 2024年 175卷 108412-108412页

作者： Batool, Amreen Byun, Yung-Cheol Department of Electronic Engineering Institute of Information Science & Technology Jeju National University Jeju63243 Korea Republic of Department of Computer Engineering Major of Electronic Engineering Jeju National University Institute of Information Science Technology Jeju63243 Korea Republic of

Brain tumor segmentation and classification play a crucial role in the diagnosis and treatment planning of brain tumors. Accurate and efficient methods for identifying tumor regions and classifying different tumor types are essential for guiding medical interventions. This study comprehensively reviews brain tumor segmentation and classification techniques, exploring various approaches based on image processing, machine learning, and deep learning. Furthermore, our study aims to review existing methodologies, discuss their advantages and limitations, and highlight recent advancements in this field. The impact of existing segmentation and classification techniques for automated brain tumor detection is also critically examined using various open-source datasets of Magnetic Resonance Images (MRI) of different modalities. Moreover, our proposed study highlights the challenges related to segmentation and classification techniques and datasets having various MRI modalities to enable researchers to develop innovative and robust solutions for automated brain tumor detection. The results of this study contribute to the development of automated and robust solutions for analyzing brain tumors, ultimately aiding medical professionals in making informed decisions and providing better patient care. © 2024 Elsevier Ltd

关键词： Magnetic resonance

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Corrections to 'Improved Renyi Entropy Benchmark for Performance Assessment of Common Cascade Control System'

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IEEE Access 2023年 11卷 61273-61273页

作者： Zhang, Qian Wang, Ya-Gang Lee, Fei-Fei Chen, Qiu Sun, Zhen University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Kogakuin University Major of Electrical Engineering and Electronics Graduate School of Engineering Tokyo163-8677 Japan

In the above article [1], the affiliation of the coauthor Qiu Chen is currently listed incorrectly. His correct affiliation should be stated as 'major of Electrical engineering and Electronics, Graduate School of engineering, Kogakuin University, Tokyo, Japan.' Furthermore, it is necessary to rectify the biographical details of the coauthor Qiu Chen as follows: © 2013 IEEE.

关键词： Cascade control systems

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Design and development of a Synergistic Educational Tool with Hiligaynon-English Recurrent Neural Network Model 12

Design and development of a Synergistic Educational Tool wit...

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2023 12th International Conference on Information and Electronics engineering, ICIEE 2023

作者： Siosan, Rosie Jane P. De Castro, Joel T. Byun, Y.C. School of Information and Communications Technology West Visayas State University-Pototan Campus Philippines College of Information and Communications Technology West Visayas State University-Main Campus Philippines Department of Computer Engineering Major of Electronic Engineering Jeju National University Institute of Information Science Technology Jeju63243 Korea Republic of

This project aimed to develop a mobile app tool to teach the Hiligaynon language to elementary pupils, designed with Machine Learning features using the Hiligaynon-Filipino-English Translation RNN Model. The app included games as a pedagogical tool, background music, and incentives to engage learners. The RNN model was developed using Data Science Methodology, while Rapid Agile Development Methodology was used to develop the mobile app for Android operating systems. To assess the app's effectiveness, elementary teachers and IT professionals evaluated its compliance with standards for acceptability. The app was found to be very acceptable, recommended for use in a gamified classroom setting and by parents. Overall, this project promotes technology in language learning to make it more accessible and enjoyable for elementary pupils. © Published under licence by IOP Publishing Ltd.

关键词： Recurrent neural networks

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Group-based Random Access with Age of Information Minimization in 6G umMTC

Group-based Random Access with Age of Information Minimizati...

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Jiseung Youn Joohan Park Seyoung Ahn Soohyeong Kim Sunghyun Cho Dept. of Computer Science & Engineering Major in Bio-Artificial Intelligence Hanyang University Ansan Republic of Korea Dept. of Computer Science & Engineering Hanyang University Ansan Republic of Korea

6G has defined ultra-massive machine type communication (umMTC) as a key scenario in response to the advancement of IoT technologies. The presence of a large number of machine-type communication devices (MTCDs) in umMTC calls for the research of efficient random access (RA) schemes. To address the problem of high collision rates in dense networks, a group-based RA approach has been introduced. In the group-based RA approach, we design an optimal congestion control method to minimize the Age of Information (AoI). Since AoI and collision rate are in a trade-off relationship, we aim to maximize the successful RA rate by iteratively searching for the optimal congestion control factor. Simulation results represent that the proposed scheme achieves a 10% collision rate and an average AoI of less than 100 age, enabling the 90% successful RA rate in 100,000 MTCDs deployment scenario.

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Meta-Explore: Exploratory Hierarchical Vision-and-Language Navigation Using Scene Object Spectrum Grounding

Meta-Explore: Exploratory Hierarchical Vision-and-Language N...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Minyoung Hwang Jaeyeon Jeong Minsoo Kim Yoonseon Oh Songhwai Oh Electrical and Computer Engineering and ASRI Seoul National University Interdisciplinary Major in Artificial Intelligence Seoul National University Department of Electronic Engineering Hanyang University

The main challenge in vision-and-language navigation (VLN) is how to understand natural-language instructions in an unseen environment. The main limitation of conventional VLN algorithms is that if an action is mistaken, the agent fails to follow the instructions or explores unnecessary regions, leading the agent to an irrecoverable path. To tackle this problem, we propose Meta-Explore, a hierarchical navigation method deploying an exploitation policy to correct misled recent actions. We show that an exploitation policy, which moves the agent toward a well-chosen local goal among unvisited but observable states, outperforms a method which moves the agent to a previously visited state. We also highlight the demand for imagining regretful explorations with semantically meaningful clues. The key to our approach is understanding the object placements around the agent in spectral-domain. Specifically, we present a novel visual representation, called scene object spectrum (SOS), which performs category-wise 2D Fourier transform of detected objects. Combining exploitation policy and SOS features, the agent can correct its path by choosing a promising local goal. We evaluate our method in three VLN benchmarks: R2R, SOON, and REVERIE. Meta-Explore outper-forms other baselines and shows significant generalization performance. In addition, local goal search using the proposed spectral-domain SOS features significantly improves the success rate by 17.1% and SPL by 20. 6% against the state-of-the-art method of the SOON benchmark. Project page: https://***/projects/Meta-Explore/***

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Revisiting the Coverage Boundary of Multi-CPU Cell-Free Massive MIMO: CPU Cooperation Aspect

Revisiting the Coverage Boundary of Multi-CPU Cell-Free Mass...

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IEEE International Conference on Communications (ICC)

作者： Soohyeong Kim Seyoung Ahn Joohan Park Jiseung Youn Yongseok Kwon Sunghyun Cho Dept. of Computer Science and Engineering Major in Bio Artificial Intelligence Hanyang University Ansan South Korea Dept. of Computer Science and Engineering Hanyang University Ansan South Korea

Cell-free massive MIMO (CF-mMIMO) provides flattened spectral efficiency throughout the network. Recently, multi-CPU CF-mMIMO (MC-CF-mMIMO) has been considered for addressing a scalability problem. However, MC-CF-mMIMO now suffers from a performance degradation problem in the boundary of the CPU overage, referred the CPU-edge. In this study, we analyze and improve the performance of MC-CF-mMIMO, focusing on the CPU-edge. First, we present a simple CPU cooperation scheme to mitigate the performance degradation problem on the CPU-edge. Then, we provide the closed-form expression of the downlink spectral efficiency according to the precoding schemes. Experimental results indicate that performance without CPU cooperation on the CPU-edge is degraded by 28.66% compared with CPU cooperation.

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Metaphor Detection with Additional Auxiliary Context

Metaphor Detection with Additional Auxiliary Context

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IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Takuya Hayashi Minoru Sasaki Major in Computer and Information Sciences Graduate School of Sci. and Eng. Ibaraki Univ. Hitachi Japan Department of Computer and Information Sciences College of Engineering Ibaraki Univ. Hitachi Japan

ISBN: (数字)9798350377903

ISBN: (纸本)9798350377910

‘Metaphor detection’ refers to the detection of figurative expressions and metaphors in sentences and text data. Metaphor expressions are expressions that cannot be interpreted literally and their meaning needs to be inferred. Traditionally, linguistic features such as word classes have been used as input for specific machine learning models to infer Metaphor content. In previous studies, detection has been performed on a single input sentence, which has the problem that the context before and after the sentence cannot be properly captured. Therefore, in order to perform effective Metaphor detection for sentences with little context information, this study proposes a method to generate and add sentences that complement the context using ChatGPT. Using this method, we compared the predictions made by the Metaphor detection model for both the dataset to which the complementary sentences were added and the original dataset. The results show that adding supplementary context to the dataset improves the accuracy of Metaphor detection.

关键词： Accuracy Machine learning Predictive models Linguistics Chatbots Informatics

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Defense and Recovery Strategies for Flash-Based Storage Under Ransomware Attacks: A Survey

Defense and Recovery Strategies for Flash-Based Storage Unde...

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International Conference on Electronics, Information and Communications (ICEIC)

作者： Junhong Yin Kyungtae Kang Dept. Computer Science and Engineering Major in. Bio Artificial Intelligence Hanyang University Ansan Korea Dept. Artificial Intelligence Hanyang University Ansan Korea

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

As ransomware evolves, traditional OS-based de-tection mechanisms face growing challenges, particularly from advanced attacks exploiting system vulnerabilities and escalating privileges. The continuous evolution of ransomware attacks has driven researchers to explore storage-level defense strategies, focusing on the potential of Solid-State Drives (SSDs) to enhance data protection and recovery. This paper presents a compre-hensive survey of the current state and future developments in SSD firmware-level ransomware detection and recovery, based on literature published between 2015 and 2024. Firmware-level I/O monitoring helps detect ransomware by identifying the typical ‘read-encrypt-overwrite’ pattern. This low-level access offers an advantage in detecting ransomware activity. However, conventional detection methods are often prone to false pos-itives. To enhance detection accuracy, some systems integrate machine learning models within the SSD controllers, analyzing I/O request patterns and entropy values of written data to assess potential threats. The Garbage Collection(Gc)mechanism plays a crucial role in data recovery by retaining older versions of data until reclaimed, which can aid in restoring encrypted content. Similarly, backing up legitimate I/O operations incurs performance overhead. To address this, some systems utilize techniques such as neural networks and remote backups to reduce unnecessary performance degradation. Future research should prioritize developing lightweight solutions tailored for SSDs with limited computational resources. Emphasis should be placed on enhancing the timeliness of real-time detection to effectively respond to the rapidly evolving nature of ransomware attacks.

关键词： Surveys Solid state drives Neural networks Focusing Machine learning Real-time systems Entropy Ransomware Monitoring Faces

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