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Design of 60-GHz Back-to-back Differential Patch Antenna on Silicon Substrate
심동하,김덕기,Juhyeong Seo,Seungmin Ryu,Sangyoon Lee,JaeHyun Noh,Byeongju Kang,Donghyuk Jung,김사라은경 한국반도체디스플레이기술학회 2023 반도체디스플레이기술학회지 Vol.22 No.4
This paper presents a novel design of a differential patch antenna for 60-GHz millimeter-wave applications. The design process of the back-to-back (BTB) patch antenna is based on the conventional single-patch antenna. The initial design of the BTB patch antenna (Type-I) has a patch size of 0.66 × 0.98 mm² and a substrate size of 0.99 × 1.48 mm². It has a gain of 1.83 dBi and an efficiency of 94.4% with an omni-directional radiation pattern. A 0.4 mm-thick high-resistivity silicon (HRS) is employed for the substrate of the BTB patch antenna. The proposed antenna is further analyzed to investigate the effect of substrate size and resistivity. As the substrate resistivity decreases, the gain and efficiency degrade due to the substrate loss. As the substrate (HRS) size decreases approaching the patch size, the resonant frequency increases with a higher gain and efficiency. The BTB patch antenna has optimal performances when the substrate size matches the patch size on the HRS substrate (Type-II). The antenna is redesigned to have a patch size of 0.81 × 1.18 mm2 on the HRS substrate in the same size. It has an efficiency of 94.9% and a gain of 1.97 dBi at the resonant frequency of 60 GHz with an omni-directional radiation pattern. Compared to the initial design of the BTB patch antenna (Type-I), the optimal BTB patch antenna (Type-II) has a slightly higher efficiency and gain with a considerable reduction in antenna area by 34.8%.
스탠딩 데스크 사용의 효과: 인지적 주의분산과 육체적 부하
강상현(Sanghyeon Kang),이주형(Juhyeong Lee),진상은(Sangeun Jin) 대한인간공학회 2019 대한인간공학회 학술대회논문집 Vol.2019 No.10
Objective: The goal of current study was to investigate the effects of sit-stand desks on physical workload and cognitive performance under dual task paradigm. Background: Six of seven previous studies suggested that there is no difference in cognitive performance between the sitting desk and standing desk, but they only tested cognitive abilities without any primary task that may not tap into enough cognitive resource. Method: Total 12 participants visited two times for testing both sitting and standing workstations, and asked to play the Tetris game (primary task) for 40 minutes. The experimental protocol consisted of four 10 min sessions, and each session had level of difficulty in primary task including two easy levels and two difficult levels. The dependent variables included three cognitive tests (secondary task), kinematic and kinetic measures, captured about every 5 min. Results: Head flexion angle and lumbar flexion angle were significantly better in standing (more neutral) than sitting. In addition, the movement of center of pressure (CoP) was significantly bigger in standing than sitting, the but complexity of movement captured by the sample entropy was significantly smaller in the standing desk suggesting that the cognitive load paid for standing is greater. In line with this, cognitive performance showed a significant difference according to the workstation condition, but there was no interaction effect. Specifically, cognitive performance decreased under the standing desk as compared to the sitting desk. These findings provide an evidence that the standing posture leads to increase in cognitive distraction because of having a higher degree of freedom in body segments and consequently bigger cognitive load. Conclusion: While using a standing desk the physical workloads can be reduced, but the cognitive load and distraction could be increased.
Optimal Design of Convolutional Neural Network for EEG-based Authentication
HyeonBin Lee,Gwangho Kim,JuHyeong Kim,YoungShin Kang,Cheolsoo Park 대한전자공학회 2021 IEIE Transactions on Smart Processing & Computing Vol.10 No.3
An electroencephalogram (EEG) is an electrical recording from the scalp when neurons in the brain are active. EEG signals have been studied for authentication because they are difficult to falsify and can distinguish individuals. On the other hand, EEG is nonstationary, and its patterns vary slightly. The authentication model was trained day-to-day to overcome the nonstationarity of EEG. EEG signals were measured on two-channel frontal electrodes for five days from 10 subjects in their resting states. Convolutional neural networks were designed for an EEG-based authentication system, and the model was optimized using a Bayesian optimization method. The proposed neural network model was trained with the EEG data from the first to the fourth day and tested using the fifth-day data, which yielded a mean accuracy of 93.23%, precision of 71.31%, and recall of 57.65%. The incremental learning of the EEG signals day-to-day improves the authentication performance, including various EEG patterns in the model.
조승현(Seunghyun Jo),강민규(Mingyu Kang),김고은(Goeun Kim),반주형(Juhyeong Ban),이지현(Jihyeon Lee),강태원(Taewon Kang) 한국정보기술학회 2021 Proceedings of KIIT Conference Vol.2021 No.11
이 논문은 한 달 후의 전국 주택 매매가 증감예측을 다룬다. 전국의 주택 매매 가격에 영향을 주는 데이터를 수집하여 파이썬의 사이킷런 라이브러리와 판타스 라이브러리를 통해 분석한다. 이를 이용해 로지스틱 회귀(Logistic regression) 알고리즘과 랜덤 포레스트(Random forest) 알고리즘을 이용하여, 한 달 후의 전국 주택 매매가 증감을 예측하였다. 실험 결과 로지스틱 회귀 알고리즘을 통해 도출한 auc는 약 50%로 분류기 성능이 좋지 않고, 랜덤 포레스트 알고리즘을 통해 최종 도출한 결과 auc는 약 73.62%, 적중률은 약 92.94%이다. This paper deals with the prediction of the increase and decrease of the national housing sales price after one month. It collects data that affects house sales prices across the country and analyzes it through Python"s scikit-learn library and Phantas library. Using this, a logistic regression algorithm and a random forest algorithm were used to predict the increase or decrease in the nationwide housing sales price after one month. As a result of the experiment, the auc derived through the logistic regression algorithm is about 50%, and the classifier performance is not good. As a result of the random forest algorithm, the auc is about 73.62% and the accuracy is about 92.94%.