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성산업의 연장으로서의 리얼돌 산업과 사용자 커뮤니티의 남성동성사회성
한준희(Han, Junhee) 한국여성학회 2023 한국여성학 Vol.39 No.4
Sex doll production process and online male communities show a male desire for “controllable women” observed in the South Korean sex industry. This article examines the South Korean sex doll (also referred to as RealDoll) research positions the realdoll industry and its relationship to the sex industry. For this study, Korean sex doll manufacturers, experience room operators, and individuals who have owned sex dolls between 2019 and 2022 were interviewed. Additionally, two online RealDoll forums were observed. First, the Korean RealDoll industry seeks to depoliticize criticism on sex dolls by dissociating it from the sex industry. However, the sector selectively associates with the sex doll market, indicating that the rhetoric around RealDolls is inherently political. Second, the RealDoll production process and the operation of the sex doll experience rooms adopt the Korean sex industry’s image of ‘intimate’ women. The image of a ‘controllable woman’ is materialized through the use of TPE, silicone, or the experience space. Third, online communities and forums serve as a central hub that attracts players within the Korean sex doll industry, and are also reproduced through the male homosocial dynamics among members. Online communities facilitate relationships among individuals involved in the industry, allowing those who own sex dolls to reinforce their sense of masculinity create connections between industry participants through dolls, and users reaffirm their masculine ego through produced images and reaffirm a male-homogeneous society.
부분 해석 결합 모델을 이용한 커넥터의 효율적 해석 방법
한준희(Junhee Han),우기룡(Giryoung Woo),최재우(Jaewoo Choi),나완수(Wansoo Nah) 한국전자파학회 2021 한국전자파학회논문지 Vol.32 No.3
본 논문에서는 커넥터의 설계를 효율적으로 수행하기 위한 EM 시뮬레이션 및 분석 방법을 제안한다. 높은 동작 주파수를 갖는 시스템에서 사용되는 커넥터는 다양한 매질로 구성되며, 내부 선로 또한 복잡한 구조를 가지고 있기 때문에 선로의 불연속 부분의 각각을 임피던스 정합하기가 매우 어렵다. 따라서 설계 단계에서 이러한 임피던스 불연속을 예측 및 수정하는 과정은 필수이다. 또한 전체 시스템에 장착된 커넥터의 성능 저하 및 기판 사이의 전자기적 결합 현상을 사전에 파악하기 위해 EM 시뮬레이션을 통해서 특성을 예측하는 과정을 필수적으로 수행하게 되며, 따라서 시뮬레이션의 정확도 및 소요시간은 커넥터의 설계단계에서 중요하게 고려되어야 할 문제이다. 본 논문은 효율적인 시뮬레이션을 위하여 부분 해석 결합 모델을 제안하며, 해당 방법을 통해서 구한 S-파라미터를 이용해서 과도상태 시뮬레이션을 수행하고, TDR 임피던스를 추출하였다. 부분 해석은 특히 PCB 사이의 EM 커플링을 고려하여 커넥터와 주변 시스템으로 나누었으며, 각각 8×8 S-파라미터와 16×16 S-파라미터를 추출하였고, 측정을 위한 SMA 커넥터부의 2×2 S-파라미터를 포함한다. 끝으로 전체 전자기장 해석 모델, 부분 해석 결합 모델, 그리고 실측 데이터 사이의 결과, 비교를 통해서 본 논문에서 제시한 부분 해석 결합 모델이 유효함을 보였다. This paper presents a method for the efficient simulation and analysis of connector designs. Connectors used in a high-frequency system comprise various materials. Matching the impedance of the discontinuous part of the internal lines is difficult, owing to their complex structures. Therefore, it is essential to predict and modify the impedance discontinuity at the design stage. Moreover, the process of predicting the characteristics through electromagnetic (EM) simulations is essential to detect the deterioration in the performance of the connector installed in the system and the electromagnetic coupling between the boards. Therefore, the accuracy and time required for the simulations are important factors to be considered at the design stage of the connector. This paper proposes an assembled EM simulation method, which observes the scattering (S) parameter and time domain reflectometer impedance by performing transient analysis. To reduce the difference from the full EM simulation, the number of parts to be analyzed should be minimized. Thus, the full model is divided into connectors and peripheral systems to extract 8×8, 16×16, and 2×2 S-parameters of the Sub-Miniature version A connector, which is used for measurement. By comparing the results of the full EM simulation, the assembled EM simulation, and the measurement, the assembled EM simulation method proposed in this paper is validated.
MC Dropout을 활용한 CNN 기반 악기 소리 분류의 성능 향상과 Out-of-Distribution 탐지
현준희(Junhee Hyeon),임채진(Chaejin Lim),한동일(Dongil Han) 대한전자공학회 2023 대한전자공학회 학술대회 Vol.2023 No.6
Convolutional neural networks (CNNs) are widely used in various fields, such as classification, object detection, segmentation, generation, natural language processing, and speech processing. Although CNNs exhibit strong performance on the trained data, they tend to fail on unseen data, leading to unexpected results. Therefore, it is essential to develop and research exception handling methods. In this study, we apply MC-dropout to the CNN model to handle exceptions and compare the performance with the model without MC-dropout. We evaluated the performance using a dataset consisting of instrument sounds, and different sounds. Image classification using CNNs is a wellknown method, but instrument sounds are represented as frequencies rather than images. Therefore, we convert sound into frequency to perform Image classification. We evaluated the ability to handle out-of-distribution data when MC-dropout is applied and examine its impact on the models performance. This study provides insights into improving the performance of instrument sound classification.