• 대한의용생체공학회:의공학회지
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• 제45권2호
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• pp.95-100
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• 2024

Vitiligo is a condition characterized by the destruction or dysfunction of melanin-producing cells in the skin, resulting in a loss of skin pigmentation. Facial vitiligo, specifically affecting the face, significantly impacts patients' appearance, thereby diminishing their quality of life. Evaluating the efficacy of facial vitiligo treatment typically relies on subjective assessments, such as the Facial Vitiligo Area Scoring Index (F-VASI), which can be time-consuming and subjective due to its reliance on clinical observations like lesion shape and distribution. Various machine learning and deep learning methods have been proposed for segmenting vitiligo areas in facial images, showing promising results. However, these methods often struggle to accurately segment vitiligo lesions irregularly distributed across the face. Therefore, our study introduces a framework aimed at improving the segmentation of vitiligo lesions on the face and providing an evaluation of vitiligo lesions. Our framework for facial vitiligo segmentation and lesion evaluation consists of three main steps. Firstly, we perform face detection to minimize background areas and identify the face area of interest using high-quality ultraviolet photographs. Secondly, we extract facial area masks and vitiligo lesion masks using a semantic segmentation network-based approach with the generated dataset. Thirdly, we automatically calculate the vitiligo area relative to the facial area. We evaluated the performance of facial and vitiligo lesion segmentation using an independent test dataset that was not included in the training and validation, showing excellent results. The framework proposed in this study can serve as a useful tool for evaluating the diagnosis and treatment efficacy of vitiligo.

• International Journal of Computer Science & Network Security
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• 제21권4호
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• pp.145-166
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• 2021

M-learning is one of the most important modern learning environments in developed countries, especially in the context of the COVID-19 pandemic. According to the Ministry of Education policies in Saudi Arabia, gender segregation in education reflects the country's religious values, which are a part of the national policy. Thus, it will help many in the target audience to accept online learning more easily in Saudi society. The literature review indicates the importance to use the UTAUT conceptual framework to study the level of acceptance through adding a new construct to the model which is Mobile Application Quality. The study focuses on the end user's requirements to use M-learning applications. It is conducted with a qualitative method to find out the students' and companies' opinions who working in the M-learning field to determine the requirements for the development of M-learning applications that are compatible with the aspirations of conservative societies.

• 한국지능정보시스템학회:학술대회논문집
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• 한국지능정보시스템학회 1999년도 추계학술대회-지능형 정보기술과 미래조직 Information Technology and Future Organization
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• pp.111-120
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• 1999

Although the concept of viewing knowledge as a critical resource has been widely accepted in prior studies, it is not fully understood how to acquire available knowledge in order to improve organizational effectiveness. However, it si sure that organizational knowledge management should pursuit the achievement of the business goal by delivering relevant and useful information to the right person at the right time. Group Support System (GSS) can play an important role to transfer scatter information into meaningful business knowledge for supporting strategic corporate decision-making. This study proposes a fuzzy GSS framework for acquiring workgroup knowledge from individual memory and aggregating workgroup knowledge to organizational knowledge. This study also proposes an architecture to support the fuzzy GSS framework. The architecture consists of user agents, information management agents, and a fuzzy model manager. To illustrate how the fuzzy GSS framework can be used to support the whole process of organization knowledge acquisition, an Internet-based GSS was developed and applied in a marketing decision process. It showed that the framework was effective for acquiring organizational knowledge.

• 한국IT서비스학회지
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• 제13권1호
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• pp.197-207
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• 2014

With the recent advance in smartphones, users are allowed to use mobile applications anytime anywhere, and change their way to interact with smart environment and people. As a result, the need for developing context-aware applications on smartphones has a great attention from users and developers. This paper proposes a context-aware framework for supporting UI/UX of smartphones. The proposed framework collects a wide range of sensory data from smartphones and allows developers to analyze and model context models for their desired apps. In addition, it also supports real-time inference within the apps to make them to adapt to context. In order to show effectiveness of the proposed framework, we introduce two smartphone apps: context-aware home screen and automatic detection of smartphone problem use. Therefore, we expect that the proposed framework will help developers easily implement their apps with respect to context-awareness.

• 한국수학교육학회지시리즈D:수학교육연구
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• 제25권4호
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• pp.285-309
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• 2022

A large body of previous studies investigated mathematical tasks by analyzing the design process prior to lessons or textbooks. While researchers have revealed the significant roles of mathematical tasks within written curricular, there has been a call for studies about how mathematical tasks are implemented or what is experienced and learned by students as enacted curriculum. This article proposes a mathematical task analytic framework based on a holistic definition of tasks encompassing both written tasks and the process of task enactment. We synthesized the features of the mathematical tasks and developed a task analytic framework with multiple dimensions: breadth, depth, bridging, openness, and interaction. We also applied the scoring rubric to analyze three multiplication tasks to illustrate the framework by its five dimensions. We illustrate how a series of tasks are analyzed through the framework when students are engaged in multiplicative thinking. The framework can provide important information about the qualities of planned tasks for mathematics instruction (proactive) and the qualities of implemented tasks during instruction (reactive). This framework will be beneficial for curriculum designers to design rich tasks with more careful consideration of how each feature of the tasks would be attained and for teachers to transform mathematical tasks with the provision of meaningful learning activities into implementation.

• Journal of Information Processing Systems
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• 제17권3호
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• pp.441-452
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• 2021

Federated learning provides an efficient integrated model for distributed data, allowing the local training of different data. Meanwhile, the goal of multi-task learning is to simultaneously establish models for multiple related tasks, and to obtain the underlying main structure. However, traditional federated multi-task learning models not only have strict requirements for the data distribution, but also demand large amounts of calculation and have slow convergence, which hindered their promotion in many fields. In our work, we apply the rank constraint on weight vectors of the multi-task learning model to adaptively adjust the task's similarity learning, according to the distribution of federal node data. The proposed model has a general framework for solving optimal solutions, which can be used to deal with various data types. Experiments show that our model has achieved the best results in different dataset. Notably, our model can still obtain stable results in datasets with large distribution differences. In addition, compared with traditional federated multi-task learning models, our algorithm is able to converge on a local optimal solution within limited training iterations.

• 한국데이타베이스학회:학술대회논문집
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• 한국데이타베이스학회 1997년도 International Conference MULTIMEDIA DATABASES on INTERNET
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• pp.215-269
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• 1997

Outline $\textbullet$ Introduction $\textbullet$ Multimedia - Types of Data - Motivation - Key issue - Hardware Products - Application Areas $\textbullet$ Agents - Rationale for Agents - Sedentary vs. Mobile - Functional Categories - Application Areas $\textbullet$ Data Mining - 2-D Framework for Data Mining Tools - Classification of Tool - Application Areas - Learning Methodologies ＊ Case Based Reasoning ＊ Neural Networks ＊ Statistical Learning： Orthogonal Arrays ＊ Multi-strategy Learning $\textbullet$ Case Study - Finbot $\textbullet$ Conclusion

• Composites Research
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• 제36권3호
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• pp.205-210
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• 2023

본 연구에서는 폼 구조의 효율적인 유효 기계적 물성 및 열전도율 예측을 위한 균질화 데이터 기반 전이학습 프레임워크를 개발하였다. Eshelby 텐서 기반의 평균장 균질화(Mean-field homogenization, MFH)는 타원체 형태의 공동을 포함하는 다공성 구조의 물성을 효율적으로 예측할 수 있지만, 셀룰러(cellular) 폼 구조의 물성은 정확하게 예측하기 어렵다. 한편, 유한요소 균질화(Finite element homogenization, FEH)는 정확성은 높지만 상대적으로 높은 해석 시간을 동반한다. 본 논문에서는 평균장 균질화와 유한요소 균질화의 장점을 결합한 데이터 기반 전이학습 프레임워크(Framework)를 제안하였다. 구체적으로, 대량의 평균장 균질화 데이터를 도출하여 사전학습 모델(Pre-trained model)을 구축하고, 상대적으로 소량의 유한요소 균질화 데이터를 이용하여 미세 조정(Fine-tuning) 하였다. 제안된 프레임워크를 검증하기 위한 수치 예제를 수행하였으며, 해석 정확도를 확인하였다. 본 연구의 결과는 다양한 폼 구조를 가진 재료의 해석에 적용할 수 있을 것으로 기대한다.

• 한국컴퓨터그래픽스학회논문지
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• 제27권3호
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• pp.13-23
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• 2021

본 논문에서는 단일 강체 모델(single rigid body)의 무게 중심(center of mass) 좌표계와 발의 위치를 활용하여 캐릭터의 동작을 생성하는 프레임워크를 제안한다. 이 프레임워크를 사용하면 기존의 전신 동작(full body)에 대한 정보를 사용할 때 보다 입력 상태 벡터(input state)의 차원을 줄임으로써 강화 학습의 속도를 개선할 수 있다. 또한 기존의 방법보다 학습 속도를 약 2 시간(약 68% 감소) 감소시켰음에도 기존의 방법 대비 최대 7.5배(약 1500 N)의 외력을 더 견딜 수 있는 더욱 견고한(robust) 모션을 생성할 수 있다. 본 논문에서는 이를 위해 무게 중심의 다음 좌표계를 구하기 위해 중심 역학(centroidal dynamics)을 활용하였고, 이에 필요한 매개 변수(parameter)들과 다음 발의 위치와 접촉력 계산에 필요한 매개 변수들을 구하는 정책(policy)의 학습을 심층 강화 학습(deep reinforcement learning)을 사용하여 구현하였다.

• 한국가정과교육학회지
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• 제31권2호
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• pp.113-135
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• 2019

본 연구는 OECD Education 2030 프로젝트에서 제시하고 있는 핵심역량의 반영 특성을 2015 개정 가정과 교육과정에서 분석하고자 하였으며 연구결과는 다음과 같다. 첫째, 핵심역량 범주에서 일반적인 특징은 기능, 태도, 가치 영역은 46.5%로, 학습 개념 프레임워크 영역은 17%, 역량 개발 사이클 영역은 24.2%, 복합 역량 영역은 12.5%로 나타났다. 전체적으로 교육과정 항목에서 성취기준(59%), 성격(16.1%), 교수·학습 평가 방향(9.4%), 내용체계(8%), 목표(7.6%) 순으로 반영되었으며 학습 개념 프레임워크의 역량은 성취기준에 가장 많이 반영되었다. 둘째, 핵심역량 항목은 중학교 교육과정에서 행동, 문제해결, 의사소통, 존중, 창의적 사고, 갈등해결, 공감, 비판적 사고, 자기관리, 학생 주체성 순으로 나타났다. 고등학교 교육과정에서 행동, 공감, 문제해결, 예측, 글로벌 역량, 자기관리, 학생 주체성, 지속가능 발전을 위한 리터러시, 반성, 비판적 사고 순으로 나타났다. 셋째, 열지도를 통해 3, 4단계에 해당되는 역량의 반영 정도가 높게 나타나 핵심역량의 효과적인 실천을 계획하고 지원할 필요가 있었다. 본 연구를 통해 미래를 위한 학습 안내자의 역할로 OECD에서 강조하는 핵심역량과 가정교과 역량 간의 상호관련성을 파악하고 실천 교과로서 개인의 총체적인 역량 함양을 도울 수 있어야 할 것이다.