• 한국가족관계학회지
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• 제22권4호
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• pp.3-29
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• 2018

Objectives: This study is designed to explore and understand what professional identity and professional development are like in family counselors. Method: This study to achieve its purpose, collected data through in-depth interview with fifteen(15) family counselors through grounded theory approach. Results: Major research findings can be summarized as follows. 176 concepts were drawn through open coding, again classified into 47 categories and finally into 18 subcategories. Through selective coding, 'growing as an expert in family counseling through continuous exertions for growth' appeared as core category. Through this process, the types of professional development in family counselors were classified into job pursuing type, self achieving type, self understanding type, and volunteering type. Through process analysis, family counselors' professional development could be divided into three steps with the course of time: step of immersion, self-understanding and acceptance, and integration. Based on this, the hypothetical relations in four areas: personal area, family area, interpersonal area, and vocational area were summarized in statements. Conclusion: This study is significant in that it attempted to establish a theory to explain the professional identity, development and influence factors shown in family counselors. It also provides those who hope to grow as expert in family counseling with long-term visions and implications for family counselor training and supervision. In this study, the suggestions on the tasks to check and solve the factors for improving and supporting the foundations of family counselors' professional identities by highlighting the family counselors own identify that is different from other counselors are expected to be used as primary data for preparing laws and regulations related to family counseling in the future.

• Advances in nano research
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• 제13권2호
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• pp.187-197
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• 2022

The synthesis of organic and hybrid organic-inorganic perovskites directly from solution improves the cost- and energy-efficiency of processing. To date, numerous research efforts have been devoted to investigating the influence of the various solvent parameters for the synthesis of lead halide perovskites, focused on the effects of different single solvents on the efficiency of the resulting perovskites. In this work, we investigated the effect of solvent blends for the first time on the structure and phase of perovskites produced via the Lewis base vapor diffusion method to develop a new synthetic approach for water-stable CsPbBr₃ particles with nanometer-sized dimensions. Solvent blends prepared with DMF and water-miscible solvents with different Gutmann's donor numbers (D_N) affect the Pb ions differently, resulting in a variety of lead bromide species with various colors. The use of a DMF/isopropanol solvent mixture was found to induce the formation of the Ruddlesden-Popper perovskite based on lead bromide. This perovskite undergoes a blue color shift in the solvated state owing to the separation of nanoplatelets. In contrast, the replacement of isopropanol with DMSO, which has a high DN, induces the formation of spherical CsPbBr₃ perovskite nanoparticles that exhibit green emission. Finally, the integration of acetone in the solvent system leads to the formation of lead bromide complexes with a yellow-orange color and the perovskite CsPbBr₃.

• 한국전자통신학회논문지
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• 제18권4호
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• pp.641-648
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• 2023

최근 4차 산업 혁명 중에서 인공지능의 급성장은 반도체의 성능 향상 및 회로의 집적을 기반으로 진보하였다. 전자기기 및 장비의 내부에서 연산을 돕는 트랜지스터는 고도화 및 소형화 되어 가며 발열의 제어 및 방열의 효율 개선이 새로운 성능의 지표로 대두되었다. DUT(Device Under Test) Shell은 트랜지스터의 검수를 위하여 정격 전류를 인가한 후, 임의의 발열 지점에서 전원을 차단한 상태에서, 방열을 통하여 트랜지스터의 내구도를 평가하여 불량 트랜지스터를 검출하는 장비이다. DUT Shell은 장비 내부의 방열 구조에 따라 동시에 더 많은 트랜지스터를 테스트할 수 있기 때문에 방열 효율은 불량 트랜지스터 검출 효율과 직접적인 관계를 갖는다. 이에 본 논문에서는 DUT Shell의 방열 최적화를 위하여 배치구조의 다양한 방법을 제안하고 전산유체역학을 이용하여 최적의 DUT Shell의 다양한 변형과 열 해석을 제안하였다.

• 정보교육학회논문지
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• 제26권5호
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• pp.353-360
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• 2022

본 연구는 인간 지능의 가장 중요한 요소인 스토리텔링을 기반으로 한 인공지능 학습을 돕기 위해 구안된 인공지능 스토리텔링 교육 프로그램이 학습 몰입도에 미치는 영향을 검증하는 데 목적이 있다. 이를 위해 16차시 분량의 인공지능 교육 프로그램을 설계하여 개발하고, 초등학교 5~6학년군 영재 학생 19명을 대상으로 8주에 걸쳐 적용하였다. 인공지능 스토리텔링 교육 프로그램은 차시별 교수·학습과정안과 이야기책의 형태로 개발되었다. 인공지능 스토리텔링 교육 프로그램 적용 결과, 도전과 능력의 조화, 행위와 의식의 통합, 명확한 목표, 구체적인 피드백, 과제에 대한 집중, 통제감, 자의식의 상실, 시간 감각의 왜곡, 자기 목적적 경험 등 학습 몰입도의 9가지 모든 하위 요소에서 평균 점수에 유의미한 향상이 나타났다. 즉, 인공지능 스토리텔링 교육 프로그램은 학습 몰입도 향상에 효과적임을 확인할 수 있었다.

• 한국재료학회지
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• 제33권5호
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• pp.175-188
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• 2023

Water electrolysis holds great potential as a method for producing renewable hydrogen fuel at large-scale, and to replace the fossil fuels responsible for greenhouse gases emissions and global climate change. To reduce the cost of hydrogen and make it competitive against fossil fuels, the efficiency of green hydrogen production should be maximized. This requires superior electrocatalysts to reduce the reaction energy barriers. The development of catalytic materials has mostly relied on empirical, trial-and-error methods because of the complicated, multidimensional, and dynamic nature of catalysis, requiring significant time and effort to find optimized multicomponent catalysts under a variety of reaction conditions. The ultimate goal for all researchers in the materials science and engineering field is the rational and efficient design of materials with desired performance. Discovering and understanding new catalysts with desired properties is at the heart of materials science research. This process can benefit from machine learning (ML), given the complex nature of catalytic reactions and vast range of candidate materials. This review summarizes recent achievements in catalysts discovery for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). The basic concepts of ML algorithms and practical guides for materials scientists are also demonstrated. The challenges and strategies of applying ML are discussed, which should be collaboratively addressed by materials scientists and ML communities. The ultimate integration of ML in catalyst development is expected to accelerate the design, discovery, optimization, and interpretation of superior electrocatalysts, to realize a carbon-free ecosystem based on green hydrogen.

• 대한중풍순환신경학회지
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• 제12권1호
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• pp.1-7
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• 2011

Objectives : The purpose is to discuss the clinical applications of Orungsan(Goreisan: 五苓散) as an alternative management for increased intracranial pressure in the field of neurosurgery in Japan. Methods and Results : Attention has focused on Kampo medicine(traditional Japanese medicine) for some cerebral disease including chronic subdural hematoma(CSDH) and cerebral infarction in Japan. Orungsan and one of its classes, Sirungtang(Saireto: 柴苓湯) are well known their effects on brain edema. After some studies of Orungsan has the anti-edemic effects by the inhibition of aquaporin, this herbal medicine has been used widely in the neurosurgery field in Japan. It is high time to think about where we are and we go ahead for the progress and the integration in medicine. We have reviewed the studies using Orungsan or Sirungtang, that was reported at the 20th annual meeting of 'the Japan society for Kampo medicine and neurological surgery' was held on November 5, 2011 in Tokyo. Fifteen studies related with Orungsan or Sirungtang were reported among all 32 studies at the meeting. Orungsan in ten, and Sirungtang in five among 14 studies contained specific clinical case. In the aspects of disease, thirteen papers were related with SDH, including CSDH(11), SSDH(1), aneurism clipping for SDH prevention(1), and one was acute cerebral infarction and one was multiple metastatic brain tumor. In the report style, case control study was 7(mostly retrospective), and the case report was 8. Conclusions : Orungsan may be plausible to be an alternative method to reduce brain edema after SDH and other brain injury in the field of neurosurgery.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.329-329
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• 2022

Accurate characterization of terrestrial precipitation variation from high spatial resolution satellite sensors is beneficial for urban hydrology and microscale agriculture modeling, as well as natural disasters (e.g., urban flooding) early warning. However, the widely-used top-down approach for precipitation retrieval from microwave satellites is limited in several hydrological and agricultural applications due to their coarse spatial resolution. In this research, we aim to apply a novel bottom-up method, the parameterized SM2RAIN, where precipitation can be estimated from soil moisture signals based on an inversion of water balance model, to generate high spatial resolution terrestrial precipitation estimates at 0.01º grid (roughly 1-km) from the C-band SAR Sentinel-1. This product was then tested against a common reanalysis-based precipitation data and a domestic rain gauge network from the Korean Meteorological Administration (KMA) over central South Korea, since a clear difference between climatic types (coasts and mainlands) and land covers (croplands and mixed forests) was reported in this area. The results showed that seasonal precipitation variability strongly affected the SM2RAIN performances, and the product derived from separated parameters (rainy and non-rainy seasons) outperformed that estimated considering the entire year. In addition, the product retrieved over the mainland mixed forest region showed slightly superior performance compared to that over the coastal cropland region, suggesting that the 6-day time resolution of S1 data is suitable for capturing the stable precipitation pattern in mainland mixed forests rather than the highly variable precipitation pattern in coastal croplands. Future studies suggest comparing this product to the traditional top-down products, as well as evaluating their integration for enhancing high spatial resolution precipitation over entire South Korea.

• 한국전자통신학회논문지
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• 제19권2호
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• pp.417-426
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• 2024

일반적으로 머신러닝을 수행하기 위해서는 딥러닝 모델에 대한 사전 지식과 경험이 필요하고, 데이터를 연산하기 위해 고성능 하드웨어와 많은 시간이 필요하게 된다. 이러한 이유로 머신러닝은 임베디드 프로세서에서 실행하기에는 많은 제약이 있다.본 논문에서는 이러한 문제를 해결하기 위해 머신러닝의 과정 중 콘볼루션 연산(Convolution operation)에 유전 알고리즘을 적용하여 선택적 콘볼루션 연산(Selective convolution operation)과 학습 방법을 제안한다. 선택적 콘볼루션 연산에서는 유전 알고리즘에 의해 추출된 픽셀에 대해서만 콘볼루션을 수행하는 방식이다. 이 방식은 유전 알고리즘에서 지정한 비율만큼 픽셀을 선택하여 연산하는 방식으로 연산량을 지정된 비율만큼 줄일 수 있다. 본 논문에서는 유전 알고리즘을 적용한 머신러닝 연산의 심화학습을 진행하여 해당 세대의 적합도가 목표치에 도달하는지 확인하고 기존 방식의 연산량과 비교한다. 적합도가 충분히 수렴할 수 있도록 세대를 반복하여 학습하고, 적합도가 높은 모델을 유전 알고리즘의 교배와 돌연변이를 통해 다음 세대의 연산에 활용한다.

• Computers and Concrete
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• 제33권5호
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• pp.535-544
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• 2024

In the rapidly advancing landscape of computer vision (CV) technology, there is a burgeoning interest in its integration with the construction industry. Camera calibration is the process of deriving intrinsic and extrinsic parameters that affect when the coordinates of the 3D real world are projected onto the 2D plane, where the intrinsic parameters are internal factors of the camera, and extrinsic parameters are external factors such as the position and rotation of the camera. Camera pose estimation or extrinsic calibration, which estimates extrinsic parameters, is essential information for CV application at construction since it can be used for indoor navigation of construction robots and field monitoring by restoring depth information. Traditionally, camera pose estimation methods for cameras relied on target objects such as markers or patterns. However, these methods, which are marker- or pattern-based, are often time-consuming due to the requirement of installing a target object for estimation. As a solution to this challenge, this study introduces a novel framework that facilitates camera pose estimation using standardized materials found commonly in construction sites, such as concrete forms. The proposed framework obtains 3D real-world coordinates by referring to construction materials with certain specifications, extracts the 2D coordinates of the corresponding image plane through keypoint detection, and derives the camera's coordinate through the perspective-n-point (PnP) method which derives the extrinsic parameters by matching 3D and 2D coordinate pairs. This framework presents a substantial advancement as it streamlines the extrinsic calibration process, thereby potentially enhancing the efficiency of CV technology application and data collection at construction sites. This approach holds promise for expediting and optimizing various construction-related tasks by automating and simplifying the calibration procedure.

• 스마트미디어저널
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• 제12권11호
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• pp.36-47
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• 2023

주요 산업현장에서 설비를 제어하는 산업제어시스템(ICS, Industrial Control System)이 네트워크로 다른 시스템과 연결되는 사례가 증가하고 있다. 또한, 이러한 통합과 함께 한 번의 외부 침입이 전체 시스템 마비로 이루어질 수 있는 지능화된 공격의 발달로, 산업제어시스템에 대한 보안에 대한 위험성과 파급력이 증가하고 있어, 사이버 공격에 대한 보호 및 탐지 방안의 연구가 활발하게 진행되고 있으며, 비지도학습 형태의 딥러닝 모델이 많은 성과를 보여 딥러닝을 기반으로 한 이상(Anomaly) 탐지 기술이 많이 도입되고 있다. 어어, 본 연구에서는 딥러닝 모델에 전처리 방법론을 적용하여 시계열 데이터의 이상 탐지성능을 향상시키는 것에 중점을 두어, 그 결과 웨이블릿 변환(WT, Wavelet Transform) 기반 노이즈 제거 방법론이 딥러닝 기반 이상 탐지의 전처리 방법론으로 효과적임을 알 수 있었으며, 특히 센서에 대한 군집화(Clustering)를 통해 센서의 특성을 반영하여 Dual-Tree Complex 웨이블릿 변환을 차등적으로 적용하였을 때 사이버 공격의 탐지성능을 높이는 것에 가장 효과적임을 확인하였다.