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ConvLSTM을 사용한 토마토 생산량 및 성장량 예측 모델에 관한 연구
홍성은(Seongeun Hong),박태주(Taeju Park),방준일(Junil Bang),김화종(Hwajong Kim) 한국정보기술학회 2020 한국정보기술학회논문지 Vol.18 No.1
The most important technology is the accurate prediction model of the growth and production of smart farms. However, domestic research is largely based on annual and monthly production forecast studies. Predictive model studies using farm unit data are insufficient, and studies for output forecasting (assumption) are being conducted to derive statistical models, not data-based ones. Therefore, the researcher developed a data-based growth and production prediction model using data from smart farm environments. In the study, multi linear regression, random forest and deep learning algorithm (ConvLSTM) were compared, and the ConvLSTM model, which applied deep learning technique, had the highest R² score for individual and average farmers. The R² score for the production forecast model was 0.981, and the R² for the growth forecast was 0.805.
시간 정보를 활용한 Time Aligned-LSTM 사람 행동 예측 연구
홍성은(Seongeun Hong),방준일(Junil Bang),김용진(Youngjin Kim),김화종(Hwajong Kim) 한국정보기술학회 2022 한국정보기술학회논문지 Vol.20 No.10
Recently, as IoT devices are widely spread, many sensors exist and measure various information. the house occupies a large part of a humans life, and various sensors can be installed, making it easy to collect various information. By analyzing the users current location, device usage information, and time information, the users activity, patterns, and habits can be found, and activity prediction enables various services. Users Behavior Prediction In previous studies, the time the behavior occurred is very important, but this information was not used for model training. In this study, we propose a users behavior prediction model that uses occurrence time information in addition to sensor data in a smart home environment. The accuracy of the proposed model was 1.2~5.7% higher than that of Bi-LSTM as a result of using the model input of occurrence time and evaluating model performance in multiple data sets.
클래스 불균형 문제에 연합학습 적용을 위한 최적화 기법 연구
이현수(Hyeonsu Lee),홍성은(Seongeun Hong),방준일(Junil Bang),김화종(Hwajong Kim) 한국정보기술학회 2021 한국정보기술학회논문지 Vol.19 No.1
Recently, as highly advanced personal identification technology has made it easier to identify individuals, various measures are required to guarantee the rights of information subjects in the information society. Federated learning is a machine learning approach proposed by these needs, a specific approach to educating machine learning algorithms while keeping the data private. In this paper, in order to identify problems that may arise when applying federated learning to the medical industry, which is sensitive to privacy issues, a retinal patient data set, was disproportionately distributed like the environment in which the actual medical institution holds the data. As a result of experiments applying various learning optimization techniques to class imbalance problems that occur here, F1 score 0.96 was achieved in experiments with under sampling and TopkAvg techniques, and the learning time was also shortened.
데이터 임베딩을 활용한 사용자 플레이리스트 기반 음악 추천에 관한 연구
이현수(Hyeonsu Lee),홍성은(Seongeun Hong),방준일(Junil Bang),김화종(Hwajong Kim) 한국정보기술학회 2020 한국정보기술학회논문지 Vol.18 No.9
Recently, the online recommendation system, which is attracting attention, analyzes many variables such as user behavior pattern, item characteristics, and additional variables to recommend items that users want. In this paper, we propose a new method to recommend each item through data embedding and clustering using various catalog formats in the Melon music data set. The proposed method of recommending music based on user playlist using data embedding is used for learning by converting information about songs such as tags, genres, detailed genres, and singer names into a sentence form combined a list of words. The comparison performance evaluation and Item2Vec method of the proposed method are performed based on the similarity of embedded songs by embedding songs in multidimensional vector space through SGNS. As a result, the proposed method improved the recommended performance with an average nDCG 0.2996, compared to the average nDCG 0.1850 of Item2Vec.
관절 데이터 기반 동작 인식 모델 연합학습 프레임워크 연구
방준일(Junil Bang),홍성은(Seongeun Hong),전석환(Sukhwan Jeon),이주원(Joowon Lee),김화종(Hwajong Kim) 한국정보기술학회 2023 한국정보기술학회논문지 Vol.21 No.3
This study corresponds to the implementation of federated learning among the systems that help caregivers taking care of many patients in a nursing hospital by photographing a nursing hospital patient with a bedside imaging device and building a motion recognition model with this image. De-identified and lightweight ETRI-Activity3D joint data was used for federated learning of the graph-based motion recognition deep learning model, and lightweight STGCN(Spatio-Temporal Graph Convolutional Networks) based motion recognition model was used for federated learning of time-series graphs. model was modified. Federated learning was implemented based on the open source Flower. The global model collected by the aggregation algorithm in the federated learning client showed better Accuracy than the model using only locally owned original data. Compared to the centralized model performed with the same physical and temporal resources, about 98% of performance was achieved.
극단적 에너지절감형 초절전 미래 반도체소자 기술 개발 연구
조가람(Karam Cho),고은아(Eunah Ko),김소영(Soyeong Kim),박윤희(Yunhee Park),홍성은(Seongeun Hong),임지현(Jihyun Lim),이유진(Yujin Lee),황윤정(Yun Jeong Hwang),신창환(Changhwan Shin) 대한전자공학회 2016 대한전자공학회 학술대회 Vol.2016 No.11
A negative capacitor is fabricated using poly(vinylidene fluoride-trifluoroethylene) copolymer and connected in series to an a-IZO TFT (amorphous-InZnO thin film transistor). It is experimentally demonstrated that the negative capacitance of the negative capacitor can achieve super steep switching in the a-IZO TFT (i.e., a subthreshold slope from 342 mV/decade to 221 mV/decade (forward voltage sweep) and 209 mV/decade (reverse voltage sweep) at room-temperature.