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압전나노소재 기반의 플렉서블 에너지 하베스팅 소자 연구동향
박귀일,Park, Kwi-Il 한국분말야금학회 2018 한국분말재료학회지 (KPMI) Vol.25 No.3
Recent developments in the field of energy harvesting technology that convert ambient energy resources into electricity enable the use of self-powered energy systems in wearable and portable electronic devices without the need for additional external power sources. In particular, piezoelectric-effect-based flexible energy harvesters have drawn much attention because they can guarantee power generation from ubiquitous mechanical and vibrational movements. In response to demand for sustainable, permanent, and remote use of real-life personal electronics, many research groups have investigated flexible piezoelectric energy harvesters (f-PEHs) that employ nanoscaled piezoelectric materials such as nanowires, nanoparticles, nanofibers, and nanotubes. In those attempts, they have proven the feasibility of energy harvesting from tiny periodic mechanical deformations and energy utilization of f-PEH in commercial electronic devices. This review paper provides a brief overview of f-PEH devices based on piezoelectric nanomaterials and summarizes the development history, output performance, and applications.
전기영동법에 의한 폴리이미드 고분자 박막의 제작 특성에 관한 연구
박귀만,강명식,김종석,박강식 大田産業大學校 1993 한밭대학교 논문집 Vol.10 No.2
비수용성 에멀젼으로 부터 전기영동법을 이용하여 폴리이미드 박막을 동과 알루미늄도체 위에 증착하였다. 필름의 성장율은 전기영동증착시의 인가전압과 시간을 조절하였다. 비수용성 에멀젼으로 부터 전기영동증착에 의해 얻어진 증착 필름은 전극을 통하여 흐른 전하의 량에 비례하였으며 필름의 표면은 거의 기공이 없는 양호한 상태를 나타냈다. An experimental study was carried out to investigate the process of electrodepositing a polyimide film from nonaqueous emulsion onto metal substrate, copper, aluminum. The rate of film growth is controlled by the applied voltage and time. From the results, yeild is proportion to the electrical charge flow through the electrode the polyimide film obtained by electrophoretic deposition from nonaqueous emulsion was shown good furface morphology without pore.
박귀만(Gwi-Man Bak),오세랑(Se-Rang Oh),박근호(Geun-Ho Park),배영철(Young-Chul Bae) 한국전자통신학회 2021 한국전자통신학회 논문지 Vol.16 No.6
본 논문은 학문적인 이해를 기반을 둔 예측을 수행하기 위해 FDNN(: Flood drought index neural network) 알고리즘을 제시한다. 데이터에 의존한 예측이 아닌 학문적인 이해를 기반을 둔 예측을 딥러닝에 적용하기 위해, 알고리즘을 수리, 수문학을 기반으로 구성하였다. 강수량의 입력으로 하천의 유량을 예측하는 모델을 구성하여 K-교차검증을 통해 모델의 성능을 측정한다. 제시한 알고리즘의 성능을 증명하기 위해 시계열 예측에서 가장 많이 사용되는 LSTM(: Long short term memory) 알고리즘의 예측 성능과 비교하여 제시한 알고리즘의 우수성을 나타낸다. In this paper, we present FDNN algorithm to perform prediction based on academic understanding. In order to apply prediction based on academic understanding rather than data-dependent prediction to deep learning, we constructed algorithm based on mathematical and hydrology. We construct a model that predicts flow rate of a river as an input of precipitation, and measure the model s performance through K-fold cross validation.
박미은,박귀서 한국 정신보건 사회사업학회 1999 정신보건과 사회사업 Vol.8 No.-
Case management has been embraced by numerous human services systems in recent years because it holds great promise for bridging gaps in services, preventing fragmentation and duplication, and promoting continuity of care. In the long run, case management is also expected to contributes the mental patients to return to the community. In order to provide an effective service for the mental patients, it is necessary for mental health social workers to apply case management providing comprehensive services linked and coordinated with community resources. In this study case management is defined as an integrated or coordinated practice system of direct services and indirect services for the mental patients who have various psychiatric and social needs. Under this context, the purpose of this study is to identify the utilization of community resources to be performed through mental health social workers' case management practice and also to propose the task and plan for improvement of resource mobilization for rehabilitation of the discharged mental patients in fuctional and structual aspects. The effective strategies for utilizing mental health resources may be as follows: 1) The degree of performance of resource utilization by case manager is needed to expend to the level of formal and regular-based activity. 2) along with accommodating in time and effort expended in supporting resource mobilization, information-networking and PR sources are required. 3) continuous supervision or education is required for the case manager for improving professional skills related with assessing and connecting resources.
자료기반 학습 LSTM 알고리즘을 이용한 지하수위 예측
박귀만(Gwi-Man Bak),윤호열(Ho-Yeol Yoon),배영철(Young-Chul Bae) 한국지능시스템학회 2020 한국지능시스템학회논문지 Vol.30 No.2
지진 발생 전·후에 지하수위는 급격하게 변화되는 것으로 알려지고 있어 지하수위 예측을 통하여 지진을 예측하는데 이용한다. 지하수위를 강수로 예측한 선행 연구가 있지만 강우에 의한 지하수위 변화가 뚜렷한 지역을 예측하기 때문에 한계가 있다. 본 논문은 LSTM 알고리즘을 이용하여 지진 예측을 위한 지하수위를 예측하는 알고리즘을 제시한다. 본 논문에서는 밀양시에 설치된 한국농어촌공사 농촌지하수관측망의 지하수수위 데이터와 기상청의 강수량, 기온 데이터를 사용하였다. 보다 쉬운 학습을 위해 데이터를 표준화하였고 데이터를 10시간씩 블록으로 만들었다. 예측 정확도를 측정하기 위해 train 95%, test 5%로 각각 지정하여 훈련 데이터로 알고리즘을 훈련시킨 뒤 예측을 하고, RMSE, CORR, MAPE로 계산된 오차척도를 사용하여 예측 정확도를 측정한다. Before and after the earthquake, the underground water level is rapidly changing and it is used to predict earthquakes. This paper presents an algorithm for predicting underground water level for earthquake prediction using LSTM algorithm. Although there is a prior study predicting groundwater levels as precipitation, it is limited because it predicts areas where changes in groundwater levels are evident due to rainfall. For this purpose, this paper used precipitation and temperature acquired from National Weather Service and data of underground water level from Rural Groundwater Observation Network of Korea Rural Community Corporation which is installed in Miryang city, Gyeongnam. We standardized the data to learn easier and, we also make data with 10 hour block. In order to measure accuracy of prediction, we assign 95%, 5% for train data and test data. Respectively, we trained with train data and then we tried prediction, we also measured accuracy of prediction using error criteria by RMSE, CORR and MAPE.