• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2004년도 하계학술대회 논문집 Vol.5 No.1
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• pp.559-562
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• 2004

The $high-T_c$ Superconducting(HTS) antenna which consists of "H" type resonator has the benefits for the miniaturization of antenna in comparison with the microstrip antenna of the similar dimension. To fabricate the "H" type antenna HTS $YBa_2Cu_3O_{7-x}$(YBCO) thin films were deposited on MgO substrates using rf-magnetron sputtering. Standard etching processes were performed for the patterning of the "H" type antenna. For comparison between normal conducting antennas and superconducting antennas, the gold antennas with the same dimension were also fabricated. An aperture coupling was used for impedance matching between $50\Omega$ feed line and HTS radiating patch. The diverse experimental results were reported in terms of the resonant frequency, the return loss and the characteristics impedance. The "H" type superconducting antenna showed the performance of 1.36 in SWR, 24 % in efficiency, and 14.6 dB in the return loss superior to the normal conducting counterpart.

• 한국항행학회논문지
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• 제8권2호
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• pp.91-97
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• 2004

본 논문은 수직 구조와 고성능 특성을 가진 InGaAsP 다중양자우물(MQW; Multiple Quantum Well) 비대칭 페브리페롯 변조기(AFPM; Asymmetric Fabry-Perot Modulators)에 관한 연구결과로써 광대역 동작특성과 저가격이 요구되는 피코셀과 같은 차세대 광대역 무선통신 시스템에 응용 가능성을 제안하고자 한다. 이 AFPM은 <-2V 동작전압과 광섬유와 간단히 결합되어 결합손실이 3dB 내외인 장점과 -3dB 주파수응답 특성이 10GHz인 특성을 보이므로 광대역 및 다중 무선서비스가 요구되는 시스템에 적용 가능할 것으로 기대된다. 이를 위한 간단한 링크실험 결과 92dB/Hz의 SFDR(Spurious Free Dynamic Range)과 약 40dB의 IMD(Inter-Modulation Distortion)의 결과를 보였다.

• International Journal of Computer Science & Network Security
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• 제21권9호
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• pp.212-222
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• 2021

Multiple waves of COVID-19 highlighted one crucial aspect of this pandemic worldwide that factors affecting the spread of COVID-19 infection are evolving based on various regional and local practices and events. The introduction of vaccines since early 2021 is expected to significantly control and reduce the cases. However, virus mutations and its new variant has challenged these expectations. Several countries, which contained the COVID-19 pandemic successfully in the first wave, failed to repeat the same in the second and third waves. This work focuses on COVID-19 pandemic control and management in Saudi Arabia. This work aims to predict new cases using deep learning using various important factors. The proposed method is called Deep Learning and Dynamic Weighing-based (DLDW) COVID-19 cases prediction method. Special consideration has been given to the evolving factors that are responsible for recent surges in the pandemic. For this purpose, two weights are assigned to data instance which are based on feature importance and dynamic weight-based time. Older data is given fewer weights and vice-versa. Feature selection identifies the factors affecting the rate of new cases evolved over the period. The DLDW method produced 80.39% prediction accuracy, 6.54%, 9.15%, and 7.19% higher than the three other classifiers, Deep learning (DL), Random Forest (RF), and Gradient Boosting Machine (GBM). Further in Saudi Arabia, our study implicitly concluded that lockdowns, vaccination, and self-aware restricted mobility of residents are effective tools in controlling and managing the COVID-19 pandemic.

• 정보보호학회논문지
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• 제18권2호
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• pp.117-128
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• 2008

RFID 시스템은 동시에 여러 개의 개체를 인식할 수 있는 장점을 바탕으로 기존의 광학 바코드 시스템을 대체할 새로운 기술로 주목받고 있다. 하지만 RFID 시스템을 이루는 태그와 리더 사이의 통신은 RF 신호를 이용하기 때문에 공격자에게 쉽게 노출될 수 있고, 그로 인해 여러 가지 보안위협이 존재한다. 이런 위협을 해결하기 위해서 많은 연구 활동이 있었다. 본 논문에서는 기존 연구를 기반으로 RFID 인증 프로토콜을 동기 필요의 유무에 따라 상태기반과 비 상태기반 인증 모델로 나누어 설명한다. 그리고 Bloom filter를 이용해서 백엔드 데이터베이스에서의 인증 시간을 단축하는 방법을 제안한다. 이는 상태기반과 비 상태기반 모두에 적용가능하며, RFID 인증에서 발생할 수 있는 태그 검색 문제를 해결하는 새로운 접근이다.

• International Journal of Computer Science & Network Security
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• 제23권2호
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• pp.13-30
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• 2023

Since the COVID-19 vaccine became available, people have been sharing their opinions on social media about getting vaccinated, causing discussions of the vaccine to trend on Twitter alongside certain events, making the website a rich data source. This paper explores people's perceptions regarding the COVID-19 vaccine during certain events and how these events influenced public opinion about the vaccine. The data consisted of tweets sent during seven important events that were gathered within 14 days of the first announcement of each event. These data represent people's reactions to these events without including irrelevant tweets. The study targeted tweets sent in Arabic from users located in Saudi Arabia. The data were classified as positive, negative, or neutral in tone. Four classifiers were used-support vector machine (SVM), naïve Bayes (NB), logistic regression (LOGR), and random forest (RF)-in addition to a deep learning model using BiLSTM. The results showed that the SVM achieved the highest accuracy, at 91%. Overall perceptions about the COVID-19 vaccine were 54% negative, 36% neutral, and 10% positive.

• ETRI Journal
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• 제45권1호
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• pp.105-118
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• 2023

We tested the feasibility of automated discrimination of patients with panic disorder (PD) from healthy controls (HCs) based on multimodal physiological responses using machine learning. Electrocardiogram (ECG), electrodermal activity (EDA), respiration (RESP), and peripheral temperature (PT) of the participants were measured during three experimental phases: rest, stress, and recovery. Eleven physiological features were extracted from each phase and used as input data. Logistic regression (LoR), k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) algorithms were implemented with nested cross-validation. Linear regression analysis showed that ECG and PT features obtained in the stress and recovery phases were significant predictors of PD. We achieved the highest accuracy (75.61%) with MLP using all 33 features. With the exception of MLP, applying the significant predictors led to a higher accuracy than using 24 ECG features. These results suggest that combining multimodal physiological signals measured during various states of autonomic arousal has the potential to differentiate patients with PD from HCs.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 추계학술발표대회
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• pp.185-188
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• 2021

본 논문에서는 기술의 빠른 발전에도 불구하고 줄어들지 않는 화재 사고, 그 중에서도 많은 인명피해를 내는 실내 화재 사고에 대하여 기존 실내 화재 알림 시스템의 한계점인 알림의 양치기 소년화로 인한 안전 불감증 증가와 알림의 사각지대 문제를 해결하고자 새로운 대안 시스템을 설계 및 구현하고, 실험 검증을 진행하였다. 위 두 가지 문제점을 해결하기 위해, 본 연구에서는 스마트폰이 매우 대중적으로 보급되어 있다는 점을 기반으로 IoT, 엣지 컴퓨팅, 비콘 등을 응용한다. 비콘 신호를 broadcasting 하는 엣지 노드의 신호 범위 내에 진입하면 사용자 정보를 수집하여 대상 건물에 출입한 대상을 특정한다. 말단 센서 노드와 엣지 노드 간의 무선 RF 통신으로 화재를 모니터링하며 화재가 발생했을 시 특정된 대상들에게만 스마트폰 어플의 푸시 알림으로 화재 발생 상황을 전송하는 시스템을 설계 및 구현하였다. 시스템 성능 평가를 위해 동국대학교 건물 내에서 수평, 수직으로 이동하며 실험을 진행하였고, 그 결과를 통해 대안 시스템의 성능과 한계를 분석하여 이를 실내 공간에 적용하기 위한 적합성을 평가하였다.

• 전기전자학회논문지
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• 제27권4호
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• pp.411-417
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• 2023

무인 항공기(UAV) 지원 중계는 분포가 쉽고 통신 채널이 양호하며, 이동성이 뛰어나 기존 지상 중계에 비해 높은 무선 연결성을 가질 수 있다. 본 논문에서는 무선 주파수(RF) 대역을 활용하여 소스로부터 에너지를 수확하고 테라헤르츠(THz) 대역을 활용하여 2차 송신기와 해당 수신기 간에 정보를 전송할 수 있는 UAV 지원 중계 네트워크를 설계한다. 그 후, 릴레이 채널 용량을 최대화하는 UAV의 최적 위치를 결정하기 위한 최적화 문제의 해를 유도하고, 유도된 해를 활용하여 두 가지 UAV 궤적(직선 궤적과 타원 궤적)을 설계하는 알고리즘을 제안한다. 시뮬레이션 결과, 제안된 알고리즘은 UAV 직선 궤도가 수확된 에너지 및 채널 용량 측면에서 더 나은 시스템 성능을 제공할 수 있음을 보여준다.

• International Journal of Computer Science & Network Security
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• 제24권4호
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• pp.179-191
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• 2024

With the advancement of modern technology, cyber-attacks are always rising. Specialized defense systems are needed to protect organizations against these threats. Malicious behavior in the network is discovered using security tools like intrusion detection systems (IDS), firewall, antimalware systems, security information and event management (SIEM). It aids in defending businesses from attacks. Delivering advance threat feeds for precise attack detection in intrusion detection systems is the role of cyber-threat intelligence (CTI) in the study is being presented. In this proposed work CTI feeds are utilized in the detection of assaults accurately in intrusion detection system. The ultimate objective is to identify the attacker behind the attack. Several data sets had been analyzed for attack detection. With the proposed study the ability to identify network attacks has improved by using machine learning algorithms. The proposed model provides 98% accuracy, 97% precision, and 96% recall respectively.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.196-202
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• 2024

Currently, the second most devastating form of cancer in people, particularly in women, is Breast Cancer (BC). In the healthcare industry, Machine Learning (ML) is commonly employed in fatal disease prediction. Due to breast cancer's favorable prognosis at an early stage, a model is created to utilize the Dataset on Wisconsin Diagnostic Breast Cancer (WDBC). Conversely, this model's overarching axiom is to compare the effectiveness of five well-known ML classifiers, including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), and Naive Bayes (NB) with the conventional method. To counterbalance the effect with conventional methods, the overarching tactic we utilized was hyperparameter tuning utilizing the grid search method, which improved accuracy, secondary precision, third recall, and finally the F1 score. In this study hyperparameter tuning model, the rate of accuracy increased from 94.15% to 98.83% whereas the accuracy of the conventional method increased from 93.56% to 97.08%. According to this investigation, KNN outperformed all other classifiers in terms of accuracy, achieving a score of 98.83%. In conclusion, our study shows that KNN works well with the hyper-tuning method. These analyses show that this study prediction approach is useful in prognosticating women with breast cancer with a viable performance and more accurate findings when compared to the conventional approach.