• 인터넷정보학회논문지
• /
• 제21권1호
• /
• pp.33-43
• /
• 2020

Retrieving a 3D model from a 3D database and augmenting the retrieved model in the Augmented Reality system simultaneously became an issue in developing the plausible AR environments in a convenient fashion. It is considered that the sketch-based 3D object retrieval is an intuitive way for searching 3D objects based on human-drawn sketches as query. In this paper, we propose a novel deep learning based approach of retrieving a sketch-based 3D object as for an Augmented Reality Model. For this work, we introduce a new method which uses Sketch CNN, Wasserstein CNN and Wasserstein center loss for retrieving a sketch-based 3D object. Especially, Wasserstein center loss is used for learning the center of each object category and reducing the Wasserstein distance between center and features of the same category. The proposed 3D object retrieval and augmentation consist of three major steps as follows. Firstly, Wasserstein CNN extracts 2D images taken from various directions of 3D object using CNN, and extracts features of 3D data by computing the Wasserstein barycenters of features of each image. Secondly, the features of the sketch are extracted using a separate Sketch CNN. Finally, we adopt sketch-based object matching method to localize the natural marker of the images to register a 3D virtual object in AR system. Using the detected marker, the retrieved 3D virtual object is augmented in AR system automatically. By the experiments, we prove that the proposed method is efficiency for retrieving and augmenting objects.

• Clinical Psychopharmacology and Neuroscience
• /
• 제16권4호
• /
• pp.449-460
• /
• 2018

Objective: Prior functional magnetic resonance imaging (fMRI) work has revealed that children/adolescents with disruptive behavior disorders (DBDs) show dysfunctional reward/non-reward processing of non-social reinforcements in the context of instrumental learning tasks. Neural responsiveness to social reinforcements during instrumental learning, despite the importance of this for socialization, has not yet been previously investigated. Methods: Twenty-nine healthy children/adolescents and 19 children/adolescents with DBDs performed the fMRI social/non-social reinforcement learning task. Participants responded to random fractal image stimuli and received social and non-social rewards/non-rewards according to their accuracy. Results: Children/adolescents with DBDs showed significantly reduced responses within the caudate and posterior cingulate cortex (PCC) to non-social (financial) rewards and social non-rewards (the distress of others). Connectivity analyses revealed that children/adolescents with DBDs have decreased positive functional connectivity between the ventral striatum (VST) and the ventromedial prefrontal cortex (vmPFC) seeds and the lateral frontal cortex in response to reward relative to non-reward, irrespective of its sociality. In addition, they showed decreased positive connectivity between the vmPFC seed and the amygdala in response to non-reward relative to reward. Conclusion: These data indicate compromised reinforcement processing of both non-social rewards and social non-rewards in children/adolescents with DBDs within core regions for instrumental learning and reinforcement-based decision-making (caudate and PCC). In addition, children/adolescents with DBDs show dysfunctional interactions between the VST, vmPFC, and lateral frontal cortex in response to rewarded instrumental actions potentially reflecting disruptions in attention to rewarded stimuli.

• 대한원격탐사학회지
• /
• 제38권6_2호
• /
• pp.1653-1661
• /
• 2022

본 연구에서는 천리안위성 2A호 1일 평균 표층수온영상을 대상으로 합성곱신경망(convolution neural network, CNN) 딥러닝 기법을 적용하여 냉수대 발생 여부를 분류하는 연구를 수행하였다. 이를 위하여, 2019년부터 2022년까지 1,155장의 영상을 사용하였으며, 국립수산과학원 제공 냉수대 발생 주의보 및 경보자료로부터 냉수대 발생 영상과 그 외 영상으로 분류하여 학습을 수행하였다. 학습 결과로 82.5%의 probability of detection (POD)와 54.4%의 false alarm ratio (FAR) 지수를 획득하였다. 오분류 분석을 통해 냉수대 분류에 실패한 경우의 대부분은 구름의 영향 때문이며, 비냉수대를 오분류한 경우의 대부분은 실제 영상에 냉수대가 존재함을 확인하였다.

• 한국결정성장학회지
• /
• 제31권6호
• /
• pp.258-263
• /
• 2021

ZnO 바리스터는 다결정구조를 가지는 반도체 소자로 결정립과 입계의 미세구조 제어를 통해 비선형적인 전류/전압 특성을 가지기 때문에 서지(surge)전압으로부터 회로를 보호하는 역할을 한다. 이러한 ZnO 바리스터에서 원하는 전기적 물성을 얻기 위해서는 소결 공정에서 미세구조의 제어가 중요하다. 따라서 소결 공정에서 중요한 변수들과 소결체의 전기적 물성인 유전율로 구성된 데이터셋을 정의한 후 실험계획법 기반으로 데이터를 수집했다. 수집된 실험데이터셋을 기계학습 알고리즘에 학습하여 메타모델을 개발했고, 개발된 메타모델에 수치기반 최적화 알고리즘인 HMA(Hybrid Metaheuristic Algorithm)를 적용하여 최대 유전율을 가질 수 있는 공정조건을 도출했다. 이러한 메타모델 기반의 최적화를 다변수 시스템인 세라믹공정에 적용한다면 최소한의 실험만으로 최적 공정조건 탐색이 가능할 것으로 판단된다.

• 한국도서관정보학회지
• /
• 제38권4호
• /
• pp.187-213
• /
• 2007

본 연구는 평생교육과 연계한 공공도서관의 정보활용 교육 방안을 제시하기 위하여 지방평생교육센터로 지정된 공공도서관에서 수행하고 있는 평생교육 프로그램을 조사하여 e-learning과 오프라인 프로그램으로 나누어 분석하였으며 정보활용교육이 활발한 공공도서 관의 정보활용교육의 실제 사례를 수집하여 분석하였다.

• 한국산업정보학회:학술대회논문집
• /
• 한국산업정보학회 2006년도 춘계 국제학술대회 논문집
• /
• pp.241-248
• /
• 2006

Web is greatly contributing in providing a variety of information. Especially, as media for the purpose of development and education of human resources, the role of web is important. Furthermore, E-Learning through web plays an important role for each enterprise and an educational institution. Also, above all, fast and various searches are required in order to manage and search a great number of educational contents in web. Therefore, most of present information is composed in HTML, so there are lots of restrictions. As a solution to such restriction, XML a standard of Web document, and its various search functions is being extended and studied variously. This paper proposes a search system able to search XML in E-Learning or var ious contents of non-XML using mobile device PDA.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1995년도 하계학술대회 논문집 B
• /
• pp.804-806
• /
• 1995

This paper discusses Self-organized Distributed Networks(SODN) as identifier of nonlinear dynamical systems. The structure of system identification employs series-parallel model. The identification procedure is based on a discrete-time formulation. The learning with the proposed SODN is fast and precise. Such properties arc caused from the local learning mechanism. Each local networks learns only data in a subregion. Large number of memory requirements and low generalization capability for the untrained region, which are drawbacks of conventional local network learning, are overcomed in the SODN. Through extensive simulation, SODN is shown to be effective for identification of nonlinear dynamical systems.

• Clinical Endoscopy
• /
• 제51권6호
• /
• pp.547-551
• /
• 2018

Unlike wired endoscopy, capsule endoscopy requires additional time for a clinical specialist to review the operation and examine the lesions. To reduce the tedious review time and increase the accuracy of medical examinations, various approaches have been reported based on artificial intelligence for computer-aided diagnosis. Recently, deep learning-based approaches have been applied to many possible areas, showing greatly improved performance, especially for image-based recognition and classification. By reviewing recent deep learning-based approaches for clinical applications, we present the current status and future direction of artificial intelligence for capsule endoscopy.

• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2021년도 추계학술발표대회
• /
• pp.458-461
• /
• 2021

유동인구 예측은 상권의 특성에 따른 점포의 입지 선정 및 고객 맞춤형 마케팅 등 민간 분야에서부터 교통망 등 사회 간접 자본 설계를 위한 공공 분야에 이르기까지 다양한 목적으로 연구되어 왔으며, 최근에는 Covid-19 의 확산에 따라 그 중요도가 더욱 높아지고 있다. 보다 정교한 예측을 위해서는 전체적인 유동 인구 뿐만 아니라 특성 별로 세분화된 하위 그룹에 대해서도 정확한 예측이 요구되나, 기존의 예측 모델들은 이러한 데이터의 계층 구조를 고려하지 않았다. 본 연구에서는 세분화된 하위 그룹 별 유동인구의 예측 정확도를 높이기 위해 전체 유동인구의 패턴을 동시에 활용하는 Global-Local 구조 기반의 Deep Learning 유동인구 분석 모델을 제안한다. 실험 결과 단일 시계열 데이터만을 사용하는 경우 대비 5.4%~52.6%의 예측 오류 감소 효과가 있음을 확인하였다.

• Architectural research
• /
• 제26권1호
• /
• pp.1-12
• /
• 2024

This study introduces a deep reinforcement learning (DRL)-based methodology for optimizing apartment space arrangements, addressing the limitations of human capability in evaluating all potential spatial configurations. Leveraging computational power, the methodology facilitates the autonomous exploration and evaluation of innovative layout options, considering architectural principles, legal standards, and client re-quirements. Through comprehensive simulation tests across various apartment types, the research demonstrates the DRL approach's effec-tiveness in generating efficient spatial arrangements that align with current design trends and meet predefined performance objectives. The comparative analysis of AI-generated layouts with those designed by professionals validates the methodology's applicability and potential in enhancing architectural design practices by offering novel, optimized spatial configuration solutions.