• 방송공학회논문지
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• 제25권1호
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• pp.1-12
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• 2020

의료분야에서 질환별 유병률 차이로 인한 데이터 수적 불균형은 흔하게 발생되는 문제로 인공지능 학습 성능을 저하시켜 개발의 어려움을 초래한다. 최근 이러한 데이터 수적 불균형문제를 해결하기 위한 한 방법으로 적대적 생성 신경망(GAN) 기술이 도입되었고 다양한 분야에 성공적으로 적용되어왔다. 그러나 수적 불균형에 의해 저하된 성능 문제를 해결하는데 있어서 기존 연구들의 영상 해상도가 아직 충분하지 않고 영상 내 구조가 전역적으로 일관성 있게 모델링 되지 않아 좋은 결과를 얻기 어렵다. 본 논문에서는, 흉부 X선 영상 데이터의 수적 불균형문제를 해결하기 위하여 고해상도 영상을 생성할 수 있는 주목 메커니즘 기반 멀티 스케일 조건부 적대적 생성 네트워크를 제안한다. 해당 네트워크는 질환제어 조건변수에 의해 하나의 네트워크만으로 다양한 질환 영상을 생성할 수 있어 각 클래스별로 학습을 하는 비효율성을 줄였고, 자기 주목 메커니즘을 통해 영상 내 장거리 종속성 문제를 해결하였다.

• 융합정보논문지
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• 제10권11호
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• pp.131-140
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• 2020

창업의지 결정요인에 관한 연구에서 창업효능감과 자기효능감은 많은 관심을 받아왔다. 그러나, 마케팅과 기업가 정신은 새로운 기업 설립 과정에서 중요한 역할을 하고 있지만 마케팅 효능감은 학자들의 관심도 대비 학술적 연구가 부족하여 본격적인 연구물 축적이 미약하다. 더욱이, 마케팅 효능감이 어떠한 과정을 통해서 창업의지에 영향을 미치는 지에 대한 구체적 메커니즘을 규명한 연구는 드물다. 이에 본 연구는 예비 창업자의 마케팅 효능감이 창업의지에 어떠한 영향을 미치는지 알아보고, 나아가 기업가 정신 연구에서 중요한 변수로 주목받아온 회복탄력성이 이들 간에 매개역할을 하는지를 실증적인 분석을 통해 알아보았다. 이 연구의 모집단은 국내 대학생 예비 창업자들로 315명에게 설문조사를 실시하였다. 연구 분석 결과 마케팅 효능감과 회복탄력성은 창업의지에 유의한 영향을 미치는 것으로 나타났고, 마케팅 효능감과 창업의지간에 회복탄력성의 매개효과가 입증되었다. 본 연구 결과는 창업의지를 높이기 위한 이론적·실천적 시사점을 제시한다.

• 디지털산업정보학회논문지
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• 제13권1호
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• pp.135-145
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• 2017

Distribution of online media sharply accelerated in the modern society, leading to robust increase in the time where individuals spend their leisure time on the Internet. One of the most popular entertainments among this is the video game industry, which gradually came to possess the characteristics like violence, cruelty, gambling, and sexuality, in order to gain attention. This study aims to investigate the addiction characteristics of users of domestic online games and to verify the causal relationship of these factors on the factors that affect impulsive crimes through Self-control. For practical verification of this study, survey was conducted for students of W High School in Incheon. This study is meaningful in that it prepared a theoretical foundation in the process of inferring criminal intent for impulsive crimes based on the effects of online games on impulsiveness, aggression, and addiction. It is also significant in that it presented a new mechanism that ego control, which occurred in the process of presenting a new variable of negative effect of online game on the criminal intent of impulsive crime, affects impulsive crime.

• 인지과학
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• 제24권4호
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• pp.323-338
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• 2013

원인과 결과 간의 연결 강도에 관한 추정인 인과 추론에는 다수의 처리기제가 다른 처리 시점에 관여하며 따라서 이들 각각에 민감하게 작용하는 개인차 변인은 상이할 가능성이 크다. 특히, 조건부화와 절감 현상은 하나의 결과에 다수의 잠재원인이 존재할 경우 특정 원인과 결과 간의 인과 강도를 추정할 때 일어나는 주요현상에 해당한다. 본 연구에서는 맥락 민감도와 관련된 개인차 변인인 자기 해석을 조작하여 조건부화와 절감 현상에 어떠한 영향을 미치는가를 알아보았다. 그 결과 독립적 자기 조건과 상호의존적 자기 조건 간에는 조건부화 정도의 차이가 발견되지 않았다. 그러나 상호의존적 자기 조건이 더 높은 절감 현상을 보였으며 이는 조건부화와 절감 현상이 각기 다른 처리 기제임을 시사한다고 볼 수 있다.

• 한국연소학회:학술대회논문집
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• 한국연소학회 2002년도 제25회 KOSCI SYMPOSIUM 논문집
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• pp.97-104
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• 2002

The conventional regenerative system has a high thermal efficiency as well as energy saving using the high preheated combustion air. in spite of these advantages, it can not avoid high nitric oxide emissions. Recently, flameless combustion has received much attention to solve these problems. In this research, numerical analysis is performed for flow-combustion phenomena in the self regenerative burner. In this analysis we used Fluent 6.0 code. the that is developed for commercial use, Methane gas is used as a fuel and two-step reaction model for methane and Zeldovich mechanism for NO generation are used. the velocity of the preheated combustion air is used as a parameter and we analyze the characteristics of flow-field, temperature distributions and NO emissions. Due to the increased recirculation rate, the maximum temperature of flame is significantly increased and NOx emissions is reduced

• 스마트미디어저널
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• 제13권4호
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• pp.9-15
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• 2024

Tree-based algorithms have been the dominant methods used build a prediction model for tabular data. This also includes personal credit data. However, they are limited to compatibility with categorical and numerical data only, and also do not capture information of the relationship between other features. In this work, we proposed an ensemble model using the Transformer architecture that includes text features and harness the self-attention mechanism to tackle the feature relationships limitation. We describe a text formatter module, that converts the original tabular data into sentence data that is fed into FinBERT along with other text features. Furthermore, we employed FT-Transformer that train with the original tabular data. We evaluate this multi-modal approach with two popular tree-based algorithms known as, Random Forest and Extreme Gradient Boosting, XGBoost and TabTransformer. Our proposed method shows superior Default Recall, F1 score and AUC results across two public data sets. Our results are significant for financial institutions to reduce the risk of financial loss regarding defaulters.

• 한국진공학회:학술대회논문집
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• 한국진공학회 2011년도 제40회 동계학술대회 초록집
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• pp.490-490
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• 2011

Graphene, hexagonal network of carbon atoms forming a one-atom thick planar sheet, has been emerged as a fascinating material for future nanoelectronics. Huge attention has been captured by its extraordinary electronic properties, such as bipolar conductance, half integer quantum Hall effect at room temperature, ballistic transport over ${\sim}0.4{\mu}m$ length and extremely high carrier mobility at room temperature. Several approaches have been developed to produce graphene, such as micromechanical cleavage of highly ordered pyrolytic graphite using adhesive tape, chemical reduction of exfoliated graphite oxide, epitaxial growth of graphene on SiC and single crystalline metal substrate, and chemical vapor deposition (CVD) synthesis. In particular, direct synthesis of graphene using metal catalytic substrate in CVD process provides a new way to large-scale production of graphene film for realization of graphene-based electronics. In this method, metal catalytic substrates including Ni and Cu have been used for CVD synthesis of graphene. There are two proposed mechanism of graphene synthesis: carbon diffusion and precipitation for graphene synthesized on Ni, and surface adsorption for graphene synthesized on Cu, namely, self-limiting growth mechanism, which can be divided by difference of carbon solubility of the metals. Here we present that large area, uniform, and layer controllable graphene synthesized on Cu catalytic substrate is achieved by acetylene-assisted CVD. The number of graphene layer can be simply controlled by adjusting acetylene injection time, verified by Raman spectroscopy. Structural features and full details of mechanism for the growth of layer controllable graphene on Cu were systematically explored by transmission electron microscopy, atomic force microscopy, and secondary ion mass spectroscopy.

• Smart Structures and Systems
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• 제30권6호
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• pp.601-612
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• 2022

The prediction of structural mechanical behaviors is vital important to early perceive the abnormal conditions and avoid the occurrence of disasters. Especially for underground engineering, complex geological conditions make the structure more prone to disasters. Aiming at solving the problems existing in previous studies, such as incomplete consideration factors and can only predict the continuous performance, the deep attention fused temporal convolution network (DATCN) is proposed in this paper to predict the spatial mechanical behaviors of structure, which integrates both the temporal effect and spatial effect and realize the cross-time prediction. The temporal convolution network (TCN) and self-attention mechanism are employed to learn the temporal correlation of each monitoring point and the spatial correlation among different points, respectively. Then, the predicted result obtained from DATCN is compared with that obtained from some classical baselines, including SVR, LR, MLP, and RNNs. Also, the parameters involved in DATCN are discussed to optimize the prediction ability. The prediction result demonstrates that the proposed DATCN model outperforms the state-of-the-art baselines. The prediction accuracy of DATCN model after 24 hours reaches 90 percent. Also, the performance in last 14 hours plays a domain role to predict the short-term behaviors of the structure. As a study case, the proposed model is applied in an underwater shield tunnel to predict the stress variation of concrete segments in space.

• 한국정보통신학회논문지
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• 제26권3호
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• pp.389-395
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• 2022

심층 학습 기술의 발전으로 인해 분류, 객체 검출, 분할과 같은 시각 정보를 이용한 심층 학습이 다양한 분야에서 활용되고 있다. 그 중 자율 주행은 시각 데이터를 잘 활용하는 대표적인 분야 중 하나이다. 본 논문에서는 도로 위의 사람과 운송수단 객체에 대한 개별적인 깊이 값을 예측하는 망을 제안한다. 제안하는 모델은 YOLOv3와 Monodepth를 기반으로 하며, 하드 파라미터 쉐어링을 이용한 인코더와 디코더를 통해 객체 검출과 깊이 추정을 동시에 수행한다. 또한 주의 집중 기법을 사용하여 객체 검출 및 깊이 추정의 정확도를 높이고자 하였다. 깊이 추정은 단안 이미지를 통해 이루어지며, 자가 학습 방법을 통해 학습을 수행하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권3호
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• pp.1189-1204
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• 2018

Detecting and capturing 3D human structures from the intensity-based image sequences is an inherently arguable problem, which attracted attention of several researchers especially in real-time activity recognition (Real-AR). These Real-AR systems have been significantly enhanced by using depth intensity sensors that gives maximum information, in spite of the fact that conventional Real-AR systems are using RGB video sensors. This study proposed a depth-based routine-logging Real-AR system to identify the daily human activity routines and to make these surroundings an intelligent living space. Our real-time routine-logging Real-AR system is categorized into two categories. The data collection with the use of a depth camera, feature extraction based on joint information and training/recognition of each activity. In-addition, the recognition mechanism locates, and pinpoints the learned activities and induces routine-logs. The evaluation applied on the depth datasets (self-annotated and MSRAction3D datasets) demonstrated that proposed system can achieve better recognition rates and robust as compare to state-of-the-art methods. Our Real-AR should be feasibly accessible and permanently used in behavior monitoring applications, humanoid-robot systems and e-medical therapy systems.