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음악적 말하기 자극(MUSTIM)을 사용한 음악치료가 비유창성 실어증 환자의 문장 구사력에 미치는 효과
공성현,박정미 한국재활심리학회 2017 재활심리연구 Vol.24 No.2
The purpose of this study was to investigate the effects of a musical speech stimulation (MUSTIM)-based therapy on the sentence fluency of non-fluent aphasics. To investigate, the study administered a session to each of the three non-fluent aphasic participants over a total of 16, 40-minute sessions twice per week. To measure speech fluency, the investigator collected acoustic data using the praat program. Analysis revealed that the number of accumulated syllables produced increased from an average of 7 in the first session to an average of 188 in the last session; success rate of speech with a mean of 84%. Additionally, reaction time decreased from a mean of 2.3476 seconds in the first session to 0.6983 seconds in the final session. Results also revealed that participants were able to speak more syllables at similar speeds leading researchers to believe that a MUSTIM can be useful for increasing sentence fluency of non-fluent aphasics.
Deep Neural Network와 Convolutional Neural Network 모델을 이용한 산사태 취약성 매핑
공성현,백원경,정형섭,Gong, Sung-Hyun,Baek, Won-Kyung,Jung, Hyung-Sup 대한원격탐사학회 2022 大韓遠隔探査學會誌 Vol.38 No.6
Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.
KOMPSAT 정사모자이크 영상으로부터U-Net 모델을 활용한 농촌위해시설 분류
공성현,정형섭,이명진,이광재,오관영,장재영 대한원격탐사학회 2023 大韓遠隔探査學會誌 Vol.39 No.6
Rural areas, which account for about 90% of the country’s land area, are increasing inimportance and value as a space that performs various public functions. However, facilities that adverselyaffect residents’ lives, such as livestock facilities, factories, and solar panels, are being builtindiscriminately near residential areas, damaging the rural environment and landscape and lowering thequality of residents’ lives. In order to prevent disorderly development in rural areas and manage ruralspace in a planned manner, detection and monitoring of hazardous facilities in rural areas is necessary. KOMPSAT ortho-mosaic optical imagery provided by the Korea Aerospace Research Institute in 2020with a spatial resolution of 0.7 meters was used, and AI training data for livestock facilities, factories,and solar panels were produced by hand for training and inference. After training with U-Net, pixelaccuracy of 0.9739 and mean Intersection over Union (mIoU) of 0.7025 were achieved. The results ofthis study can be used for monitoring hazardous facilities in rural areas and are expected to be used asbasis for rural planning. Data can be acquired through satellite imagery, which can be acquired periodically and provideinformation on the entire region. Effective detection is possible by utilizing image-based deep learningtechniques using convolutional neural networks. Therefore, U-Net model, which shows high performancein semantic segmentation, was used to classify potentially hazardous facilities in rural areas. In this study,
KOMPSAT 정사모자이크영상을 활용한 농촌 위해시설 분류를 위한 GeoAI 학습용 데이터셋
공성현,정형섭,이명진,이광재,오관영,장재영 (사)지오에이아이데이터학회 2023 GEO DATA Vol.5 No.4
In South Korea, rural areas have been recognized for their potential as sustainable spaces for the future, but they are currently facing major problems. Unplanned construction of facilities such as factories, livestock facilities, and solar panels near residential areas is destroying the rural environment and deteriorating the quality of life of residents. Detection and monitoring of rural facilities are necessary to prevent disorderly development in rural areas and to manage rural space in a planned manner. In this study, satellite imagery data was utilized to obtain information on rural areas, which is useful for observing large areas and monitoring time series changes compared to field surveys. In this study, KOMPSAT ortho-mosaic optical imagery from 2019 and 2020 were utilized to construct AI training datasets for rural hazardous facilities segmentation for Seosan, Anseong, Naju, and Geochang areas. The dataset can be used in image segmentation models to classify rural facilities and can be used to monitor potentially hazardous facilities in rural areas. It is expected to contribute to solving rural problems by serving as the basis for rural planning.
공성현,서승우,민재홍,이장우,Gong, Seong-Hyeon,Seo, Seung-U,Min, Jae-Hong,Lee, Jang-U 한국전자통신연구원 1989 전자통신동향분석 Vol.4 No.4
전파는 통신, 방송, 고주파 가열 및 세척, 의료진단 및 치료, 원격검침, 방향탐지 등의 다양한 부문에서 널리 활용되고 있다. 전파가 지닌 이러한 경제적 가치에도 불구하고 전파이용자가 부담하는 제비용은 그가 전파이용으로부터 얻는 경제적, 문화적 편익과는 무관하게 책정되어 있다. 따라서 본고에서는 전파자원의 합리적 이용을 위한 방안으로서 전파이용자가 전파관리에 드는 비용을 적절히 부담할 수 있도록 하는 방안에 대해 논의하였다. 이를 위하여 본고에서는 우선 전파가 지닌 개념 및 특성을 검토하고 국내의 다른 부문에 대한 부과사례 및 외국의 전파관련 부과사례 등을 조사분석한 후 전파에 대한 경제적 부과의 타당성 및 부과명목 등을 논의하였다.