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박진욱,홍정표,Park, Jinuk,Hong, Jungpyo 한국정보통신학회 2022 한국정보통신학회논문지 Vol.26 No.5
In this paper, a determinant-based two-channel noise reduction method which utilizes speech presence probability (SPP) is proposed. The proposed method improves noise reduction performance from the conventional determinant-based two-channel noise reduction method in [7] by applying SPP to the Wiener filter gain. Consequently, the proposed method adaptively controls the amount of noise reduction depending on the SPP. For performance evaluation, the segmental signal-to-noise ratio (SNR), the perceptual evaluation of speech quality, the short time objective intelligibility, and the log spectral distance were measured in the simulated noisy environments considered various types of noise, reverberation, SNR, and the direction and number of noise sources. The experimental results presented that determinant-based methods outperform phase difference-based methods in most cases. In particular, the proposed method achieved the best noise reduction performance maintaining minimum speech distortion.
박진욱(Jinuk Park),이우람(Uram Lee),이재성(Jaesung Lee) 한국자동차공학회 2022 한국자동차공학회 부문종합 학술대회 Vol.2022 No.6
In a fuel cell system, air containing moisture. This air can freeze inside the Air-cut off valve. This makes it difficult to start the system in cold areas. In this paper, we propose a method of crushing ice using vibration.
젖은 노면에서 타이어마모를 고려한 ESC 강인성 분석에 관한 연구
박진욱(Jinuk Park),조태근(Taekeun Cho),권재준(Jaejoon Kwon),홍태욱(Teawook Hong),박기홍(Kihong Park) 한국자동차공학회 2011 한국자동차공학회 학술대회 및 전시회 Vol.2011 No.11
In most research and development level for vehicle stability improvement, ESC performance evaluation has been conducted assuming a steady road friction. However, under a wet road condition, the tire performance differs greatly depending on the tread wear, vehicle velocity and water depth. For that reason, this research developed a tire force gain based on tread wear under a wet road condition. A vehicle stability test has been conducted on a HILS system, which includes a CarSim vehicle model with the tire force gain developed in the research and a commercial ESC. Through the HILS, a comparative analysis of existing constant road friction coefficient and the newly developed tire force gain has been carried out, focused on their different influences on the ESC performance.
인공 신경망 모형을 이용한 한국프로야구 관중 수요 예측
박진욱 ( Jinuk Park ),박상현 ( Sanghyun Park ) 한국정보처리학회 2017 한국정보처리학회 학술대회논문집 Vol.24 No.1
본 연구는 기존의 수요 예측 등의 시계열 분석에서 주로 사용되는 ARIMA 모형의 어려움을 극복하고자 인공신경망(Artificial Neural Network) 모형을 이용하여 한국 프로 야구 관중 수를 예측하였다. 인공신경망의 가장 기본적인 종류인 전방향 신경망(Feedforward Neural Network)의 초모수(Hyperparameter) 선정에 그리드 탐색(Grid Search)을 적용하여 최적의 모형을 찾고자 하였다. 훈련 자료로는 2015년 3월부터 8월까지의 일별 KBO 관중 수 자료를 대상으로 하였고, 예측력 검증을 위해 2015년 9월 관중 수를 예측하여 실제 관측값과 비교하였다. 그 결과, 그리드 탐색법에서 최적 모형이라고 판단한 모형의 예측력은, 평균 절대 백분율 오차(MAPE) 기준으로 평균 27.14% 였다. 또한, 앙상블 기법에서 착안하여 오차율이 낮은 모형 5개의 예측값 평균의 MAPE는 평균 28.58% 였다. 이는 다중회귀와 비교해보았을 때, 평균적으로 각각 14%, 13.6% 높은 예측력을 보이고 있다.
객체 인식 모델을 활용한 적재 불량 화물차 탐지 시스템
정우진,박진욱,박용주,Jung, Woojin,Park, Jinuk,Park, Yongju 한국정보통신학회 2022 한국정보통신학회논문지 Vol.26 No.12
Recently, the increasing number of overloaded vehicles on the road poses a risk to traffic safety, such as falling objects, road damage, and chain collisions due to the abnormal weight distribution, and can cause great damage once an accident occurs. therefore we propose to build an object detection-based AI model to identify overloaded vehicles that cause such social problems. In addition, we present a simple yet effective method to construct an object detection model for the large-scale vehicle images. In particular, we utilize the large-scale of vehicle image sets provided by open AI-Hub, which include the overloaded vehicles. We inspected the specific features of sizes of vehicles and types of image sources, and pre-processed these images to train a deep learning-based object detection model. Also, we propose an integrated system for tracking the detected vehicles. Finally, we demonstrated that the detection performance of the overloaded vehicle was improved by about 23% compared to the one using raw data.
암반지반에서 말뚝으로 보강된 풍력발전 기초의 말뚝 근입깊이에 따른 수평저항력 거동
강기천,김동주,박진욱,어현준,박혜정,김지성,Kang, Gichun,Kim, Dongju,Park, Jinuk,Euo, Hyunjun,Park, Hyejeong,Kim, Jiseong 한국지반신소재학회 2022 한국지반신소재학회 논문집 Vol.21 No.2
This study conducted to obtain the lateral resistance of a wind power foundation reinforced with piles through an model experiment. In particular, the lateral resistance of the foundation was compared with the existing gravity-type wind power foundation by integrating the pile, the wind power generator foundation, and the rocky ground. In addition, changes in the lateral resistance and bending moment of the pile were analyzed by embeded depths of the pile. As a result, it was found that the lateral resistance increased with the depth of embedment of the piles. In particular, the pile's resistance increase ratio was 2.11 times greater in the case where the pile embedded up to the rock layer than the case where the pile was embedded into the riprap. It was found that the location of the maximum bending moment occurred at the interface between the wind turbine foundation and the riprap layer when the pile embeded to the rock layer. Through this, as the lateral resistance of the wind power foundation reinforced with piles is greater than that of the existing gravity-type wind power foundation, it is understood that it can be a more advantageous construction method in terms of safety.
기업 직무 정보를 활용한 OOPP(Optimized Online Portfolio Platform)설계
정보근(Bogeun Jung),박진욱(Jinuk Park),이병관(ByungKwan Lee) 한국정보전자통신기술학회 2018 한국정보전자통신기술학회논문지 Vol.11 No.5
본 논문에서는 직무별로 취업에 필요한 역량을 나타내고, 구직자가 온라인상에서 포트폴리오를 효율적으로 작성하고 관리하는 OOPP(Optimized Online Portfolio Platform)를 제안한다. 제안하는 OOPP는 세 가지 모듈로 구성된다. 첫째, JDCM(Job Data Collection Module)은 직업정보 사이트의 구인 광고들을 수집하여 스프레드시트에 저장한다. 둘째, CSM(Competency Statistical Medel)은 수집한 구인 광고들을 텍스트 마이닝하여 직무별로 요구되는 핵심역량을 분류한다. 셋째, OBBM(Optimize Browser Behavior Module)은 브라우저의 처리속도를 개선하여 사용자가 데이터를 빠르게 조회할 수 있게 한다. OBBM은 검색엔진의 연산을 최적화하는 PSES(Parallel Search Engine Sub-Module)과 이미지 텍스트 등의 로드를 최적화하는 OILS(Optimized Image Loading Sub-Module)로 구성된다. 제안하는 OOPP의 성능분석 결과 CSM로 분석된 데이터의 정확도는 최대 100%, 최소 99.4%로 실제 광고와 분석된 데이터의 차이가 거의 발생하지 않았으며, OBBM을 이용한 브라우저 최적화를 실행하면, 작업시간이 약 68.37%가 감소한다. 결과적으로 OOPP는 직현재 직업정보 사이트의 구인 광고를 정확하게 분석하여 사용자가 분석한 결과를 웹페이지에서 신속하게 조회할 수 있다 This paper proposes the OOPP(Optimized Online Portfolio Platform) design for the job seekers to search for the job competency necessary for employment and to write and manage portfolio online efficiently. The OOPP consists of three modules. First, JDCM(Job Data Collection Module) stores the help-wanted advertisements of job information sites in a spreadsheet. Second, CSM(Competency Statistical Model) classifies core competencies for each job by text-mining the collected help-wanted ads. Third, OBBM(Optimize Browser Behavior Module) makes users to look up data rapidly by improving the processing speed of a browser. In addition, The OBBM consists of the PSES(Parallel Search Engine Sub-Module) optimizing the computation of a Search Engine and the OILS(Optimized Image Loading Sub-Module) optimizing the loading of image text, etc. The performance analysis of the CSM shows that there is little difference in accuracy between the CSM and the actual advertisement because its data accuracy is 99.4~100%. If Browser optimization is done by using the OBBM, working time is reduced by about 68.37%. Therefore, the OOPP makes users look up the analyzed result in the web page rapidly by analyzing the help-wanted ads. of job information sites accurately.