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13th International Conference on Information and Communication Technology Convergence, ICTC 2022

作者： Do, Quang Tuan Hua, Duc Thien Tran, Anh Tien Cho, Sungrae School of Computer Science and Engineering Chung-Ang University Seoul Korea Republic of

ISBN: (数字)9781665499392

ISBN: (纸本)9781665499392

Recently, unmanned aerial vehicles (UAVs) have been widely used in wireless communication. They can serve as aerial base stations for ground users (GUs). However, they cannot work for long because of the limited energy pool, and thus can only cover a limited area of services. In this paper, we investigate a wireless communication system enabled by multiple unmanned aerial vehicles (UAVs) for downlink communication, where energy consumption is taken into consideration. Our goal is to maximize the UAV's service time and downlink throughput by using a deep-reinforcement learning method called deep-deterministic policy gradient (DDPG). Simulation results are illustrated to demonstrate the performance of the proposed method. © 2022 IEEE.

关键词： Energy efficiency

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Multidomain Adaptive Semantic Communications

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IEEE Journal on Selected Areas in Communications 2025年

作者： Won, Dongwook Do, Quang Tuan Win, Thwe Thwe Lee, Donghyun Oh, Junsuk Cho, Sungrae Chung-Ang University School of Computer Science and Engineering Seoul06974 Korea Republic of

The domain adaptation issues in semantic communications become critical when transmitter and receiver operate across different multiple domains or when input data during inference have different distributional characteristics than the data used to train semantic encoders and decoders. In this paper, we introduce the Multidomain Adaptive Deep Semantic Communication (MA-DeepSC) framework, designed to enhance semantic communications across multiple domains. Our framework consists of two core components: the Multidomain Adaptive Semantic Coding Network (MASCN), inherently designed to adapt semantic encoding and decoding across multiple domains, and the multidomain data adaptation network (MDAN), which transforms actual observable data into the data on which the system was initially trained, thus obviating the need for retraining the existing pre-trained semantic coding network. We validate our approach through experiments on digit datasets and CelebA, observing significant outperformance over existing techniques. In addition, we analyze the strategic benefits and drawbacks of both MASC and MDAN, assessing their applicability under various scenarios. The source code for MA-DeepSC is available at https://***/wongdongwook/JSAC-MA-DeepSC. © 1983-2012 IEEE.

关键词： Decoding

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Comprehensive Vulnerability Detection and Malware Infection Testing Strategies for IoT Devices

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IEEE Internet of Things Journal 2025年第12期12卷 20556-20571页

作者： Liang, Bo-Hao Hwang, Ren-Hung Lin, Jia-You Chen, Hsiao-Hwa National Chung Cheng University Department of Computer Science and Information Engineering Taiwan National Yang Ming Chiao Tung University College of Artificial Intelligence Taiwan National Cheng Kung University Department of Engineering Science Taiwan

With the increasing prevalence of IoT devices, security vulnerabilities and malware infections have emerged as significant risks. To address these challenges, advanced vulnerability detection tools are essential for enhancing IoT security assessments. In this study, we analyzed common vulnerabilities and evolving attack methodologies to develop improved detection techniques. Our research focuses on two key areas: comprehensive vulnerability detection and malware infection testing strategies. Through on-site testing and detailed analysis, we identified prevalent security flaws in IoT devices and developed a suite of tools tailored for detecting these vulnerabilities. Additionally, we discovered that some devices exhibit inherent immunity to specific malware strains, emphasizing the need for novel malware infection detection strategies. Real-world evaluations uncovered previously unknown vulnerabilities and weaknesses, revealed widespread susceptibility to DoS attacks, and demonstrated that not all devices are vulnerable to malware infections. These findings confirm the effectiveness of our approach in identifying risks and enhancing IoT security. © 2014 IEEE.

关键词： Internet of Things Security Malware Object recognition Protocols Codes Testing Passwords Botnet Vectors

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Memetic algorithm-based optimization of hybrid forecasting systems for multivariate time series

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Neural Computing and Applications 2025年第17期37卷 10689-10706页

作者： Padilha, Guilherme Afonso Galindo Jung, Jason J. de Mattos Neto, Paulo S. G. Department of Computer Engineering Chung-Ang University 84 Heukseok-ro Seoul06974 Korea Republic of Centro de Informática Universidade Federal de Pernambuco Pernambuco Recife50740-560 Brazil

In recent decades, wind speed’s growing use in electricity generation has posed challenges due to its intermittent and fluctuating nature, hindering its reliable incorporation into the grid. To address this, accurate forecasting of wind energy production is essential. This paper presents the Memetic Hybrid Forecasting System (MHFS), a hybrid system that combines linear statistical and deep learning (DL) models employing a novel memetic algorithm (MA). The MHFS is divided into two phases: (i) linear modeling of the wind speed and (ii) generation of the final forecast using a DL model optimized by the MA. The MA searches for the best combination of lags of the time series, residual series, and exogenous variables with the linear forecast aiming to enhance the accuracy. Two versions of the MHFS are evaluated by combining Seasonal Autoregressive Integrated Moving Average (SARIMA) and SARIMA with eXogenous factors (SARIMAX) with long short-term memory (LSTM) neural network in three wind speed datasets. Results consistently demonstrate superior performance of MHFS over single and hybrid models (linear and nonlinear combinations) in the literature, assessed by mean squared error (MSE) and mean absolute percentage error (MAPE). The MHFS version using SARIMA and LSTM attained an overall MAPE superior to single models, linear and nonlinear combinations of 21.02%, 21.48%, and 18.76%, respectively. The version that employs SARIMAX and LSTM reached an improvement regarding MAPE of 21.38%, 13.35%, and 8.68%, respectively. The proposal presents an innovative combination of statistical and DL models using an MA that refines feature selection, improving forecasting accuracy. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Wind forecasting

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Collaborative Intelligence Framework with Skip-Decoder for Balanced Edge Power and Cloud Cost in Object Detection

Collaborative Intelligence Framework with Skip-Decoder for B...

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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Im, Jongbeom Lee, Munkyu Hong, Cheol-Ho Hwang, Jaehyun Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of Chung-Ang University Department of Intelligent Semiconductor Engineering Seoul Korea Republic of

ISBN: (纸本)9798331530839

This study proposes a cost-effective distributed inference framework for real-time object detection in edge environments. Due to the resource constraints of edge devices, Col-laborative Intelligence (CI) has become increasingly important in enabling efficient distributed inference between cloud and edge. This study leverages the YOLOv7 model to analyze inference latency between edge devices and the server, and presents strategies to address issues related to the power consumption of edge devices and the costs associated with cloud usage. Additionally, a feature map recovery technique called Skip-decoder is introduced to reduce network latency. This novel CI model architecture aims to optimize the use of edge resources, thereby reducing cloud expenses and enhancing real-time object detection performance. © 2024 IEEE.

关键词： Collaborative learning feature reconstructor model optimization object detection

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Sliced Wasserstein adversarial training for improving adversarial robustness

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Journal of Ambient Intelligence and Humanized Computing 2024年第8期15卷 3229-3242页

作者： Lee, Woojin Lee, Sungyoon Kim, Hoki Lee, Jaewook School of AI Convergence Dongguk University-Seoul Seoul Korea Republic of Department of Computer Science Hanyang University Seoul Korea Republic of Department of Industrial Security Chung-Ang University Seoul Korea Republic of Department of Industrial Engineering Seoul National University Seoul Korea Republic of

Recently, deep-learning-based models have achieved impressive performance on tasks that were previously considered to be extremely challenging. However, recent works have shown that various deep learning models are susceptible to adversarial data samples. In this paper, we propose the sliced Wasserstein adversarial training method to encourage the logit distributions of clean and adversarial data to be similar to each other. We capture the dissimilarity between two distributions using the Wasserstein metric and then align distributions using an end-to-end training process. We present the theoretical background of the motivation for our study by providing generalization error bounds for adversarial data samples. We performed experiments on three standard datasets and the results demonstrate that our method is more robust against white box attacks compared to previous methods. © The Author(s) 2024.

关键词： Deep learning

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Copper telluride with manipulated carrier concentrations for high-performance solid-state thermoelectrics

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Journal of Materials science & Technology 2022年第34期129卷 190-195页

作者： Hyun Ju Dabin Park Minsu Kim Jooheon Kim G.W.Woodruff School of Mechanical Engineering Georgia Institute of TechnologyAtlantaGA 30332USA School of Chemical Engineering&Materials Science Chung-Ang UniversitySeoul 06974Republic of Korea Department of Intelligent Energy and Industry Graduate SchoolChung-Ang UniversitySeoul 06974Republic of Korea Department of Advance Materials Engineering Chung-Ang UniversityAnseong 17546Republic of Korea

This study proposed a strategy for effectively diminishing the carrier concentration in Cu_(2)Te by introducing graphene sheets,Based on thermoelectric property measurements and single parabolic band modeling,the incorporated graphene effectively reduced the carrier concentration,not only enhancing the thermoelectric performance of the Cu_(2)Te/graphene composite but also substantially improving its figure of merit up to ~1.47 at 1000 K,which is 268% higher than that of pristine Cu_(2)Te,This study gives an insight into the control of carrier concentration and thermoelectric properties in Cu_(2)Te,and it could be extended to other copper chalcogenides for excellent thermoelectrics.

关键词： Thermoelectric Copper telluride Graphene Carrier concentration Single parabolic band model

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PageRank-Guided Random Walks for Improved Graph Representations

PageRank-Guided Random Walks for Improved Graph Representati...

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Yejong Ryu Tae Young Choe Taeyoon Park Mucheol Kim Department of Computer Science and Engineering Chung-Ang University Yongin City Republic of Korea Department of Computer Science and Engineering Chung-Ang University Seoul Republic of Korea Department of Physics Chung-Ang University Seoul Korea School of Computer Science & Engineering Chung-Ang University Seoul Republic of Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

Graph embedding efficiently maps complex graph features within a low-dimensional vector space, facilitating a variety of downstream network prediction tasks. Recent advancements often leverage random walk-based methods due to their proficiency in capturing the structural information of graphs. However, these methods tend to exhibit an inherent bias toward high-centrality nodes, which frequently serve as pivotal bridges in network topology. Such biases disproportionately influence the learning process and can skew embeddings. This leads to inaccurate predictions, particularly for nodes with sparse connections. This paper proposes a novel approach that utilizes the PageRank algorithm to quantify node importance and identify imbalances. By integrating these insights, we aim to mitigate centrality bias in graph embeddings, thus enhancing the representational fairness and predictive accuracy of the model.

关键词： Measurement Bridges Accuracy Network topology Predictive models Prediction algorithms Vectors Topology Information and communication technology Convergence

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A Smart Heart Disease Diagnostic System Using Deep Vanilla LSTM

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computers, Materials & Continua 2023年第10期77卷 1251-1279页

作者： Maryam Bukhari Sadaf Yasmin Sheneela Naz Mehr Yahya Durrani Mubashir Javaid Jihoon Moon Seungmin Rho Department of Computer Science COMSATS University IslamabadAttock CampusAttockPakistan Department of Computer Science COMSATS University IslamabadIslamabadPakistan Department of Computer Science UET TaxilaTaxilaPakistan Department of AI and Big Data Soonchunhyang UniversityAsanKorea Department of Industrial Security Chung-Ang UniversitySeoul06974Korea

Effective smart healthcare frameworks contain novel and emerging solutions for remote disease diagnostics,which aid in the prevention of several diseases including heart-related *** this context,regular monitoring of cardiac patients through smart healthcare systems based on Electrocardiogram(ECG)signals has the potential to save many *** existing studies,several heart disease diagnostic systems are proposed by employing different state-of-the-art methods,however,improving such methods is always an intriguing area of ***,in this research,a smart healthcare system is proposed for the diagnosis of heart disease using ECG *** proposed framework extracts both linear and time-series information on the ECG signals and fuses them into a single framework *** linear characteristics of ECG signals are extracted by convolution layers followed by Gaussian Error Linear Units(GeLu)and time series characteristics of ECG beats are extracted by Vanilla Long Short-Term Memory Networks(LSTM).Following on,the feature reduction of linear information is done with the help of ID Generalized Gated Pooling(GGP).In addition,data misbalancing issues are also addressed with the help of the Synthetic Minority Oversampling Technique(SMOTE).The performance assessment of the proposed model is done over the two publicly available datasets named MIT-BIH arrhythmia database(MITDB)and PTB Diagnostic ECG database(PTBDB).The proposed framework achieves an average accuracy performance of 99.14%along with a 95%recall value.

关键词： Smart systems deep learning ECG signals heart disease concurrent learning LSTM generalized gated pooling

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Virtual Private Network (VPN) with Post-Quantum Cryptography (PQC) 15

Virtual Private Network (VPN) with Post-Quantum Cryptography...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Shim, Hyoungsub Kang, Bongho Im, Haejung Jeon, Duhyun Kim, Seok-Min Chung-Ang University Seoul Department of Mechanical Engineering Seoul Korea Republic of Research and Development Center ICTK.LTD. Seoul Korea Republic of Chung-Ang University Department of Computer Scicne and Engineering Department of Mechanical Engineering Seoul Korea Republic of

ISBN: (纸本)9798350364637

This paper introduces qTrustNet VPN, a next-generation Virtual Private Network (VPN) designed to enhance security in the quantum era. With the rapid advancement of quantum computing, traditional encryption methods face obsolescence, necessitating more robust security solutions. qTrustNet VPN addresses this need by integrating the WireGuard protocol, known for its simplicity and high performance, with Post-Quantum Cryptography (PQC) algorithms developed by the National Institute of Standards and Technology (NIST). These PQC algorithms are designed to withstand the decryption capabilities of quantum computers, providing long-term security. © 2024 IEEE.

关键词： Quantum computers

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