• 시스템엔지니어링학술지
• /
• 제15권2호
• /
• pp.72-78
• /
• 2019

Pathology is the motor that drives healthcare to understand diseases. The way pathologists diagnose diseases, which involves manual observation of images under a microscope has been used for the last 150 years, it's time to change. This paper is specifically based on tumor detection using deep learning techniques. Pathologist examine the specimen slides from the specific portion of body (e-g liver, breast, prostate region) and then examine it under the microscope to identify the effected cells among all the normal cells. This process is time consuming and not sufficiently accurate. So, there is a need of a system that can detect tumor automatically in less time. Solution to this problem is computational pathology: an approach to examine tissue data obtained through whole slide imaging using modern image analysis algorithms and to analyze clinically relevant information from these data. Artificial Intelligence models like machine learning and deep learning are used at the molecular levels to generate diagnostic inferences and predictions; and presents this clinically actionable knowledge to pathologist through dynamic and integrated reports. Which enables physicians, laboratory personnel, and other health care system to make the best possible medical decisions. I will discuss the techniques for the automated tumor detection system within the new discipline of computational pathology, which will be useful for the future practice of pathology and, more broadly, medical practice in general.

• 한국컴퓨터정보학회논문지
• /
• 제29권2호
• /
• pp.21-30
• /
• 2024

본 연구는 폭증하는 디지털 비즈니스의 수요 증가를 감당하기 위하여 AI를 활용한 새로운 제작 방법을 모색하는데 목적이 있다. 이에 딥러닝과 빅데이터를 기반으로 실제 웹페이지 생성 가능 시스템을 구축하고자 하였다. 첫째, 이커머스 웹사이트 기능을 바탕으로 분류체계를 수립하였다. 둘째, 웹페이지 구성요소의 유형을 체계적으로 분류하였다. 셋째, 딥러닝이 적용가능한 웹페이지 자동생성시스템 전체를 설계하였다. 실제 데이터를 학습하여 구현된 딥러닝 모델이 기존 웹사이트를 분석하고 자동생성되도록 재설계 함으로써, 산업에서 바로 사용가능한 방안을 제안했다. 나아가 체계가 부족했던 웹사이트 레이아웃 및 특징에 대한 분류체계를 수립했다는 측면에서 의의가 있다. 이는 향후 생성형 AI 기반의 웹사이트 연구 및 산업 분야에 크게 기여할 수 있을 것이다.

• 한국수학교육학회지시리즈E:수학교육논문집
• /
• 제29권4호
• /
• pp.661-685
• /
• 2015

본 연구는 수학교사의 전문성을 신장시킬 수 있는 구체적인 가능성을 탐색하는 연구로, 다항식의 해법에 대한 수학교사의 대수 내용지식을 선정하고, 선정된 내용지식을 바탕으로 수학교사의 자립연수를 위한 학습 자료를 개발하였다. 개발된 학습 자료는 수학교사들에게 제공되었으며, 학습 자료가 자립연수에서 활용 가능한지, 수학교사들이 이해 가능한지 등에 대해 검사지로 조사하였고, 연수 방법 및 내용에 대해서도 설문을 하였다. 교사들의 대답을 분석한 결과, 개발된 학습 자료는 자립연수의 활용 가능성, 교사들의 이해 가능성, 연수 방법에 대해 긍정적인 결과를 얻었다.

• 한국멀티미디어학회논문지
• /
• 제21권2호
• /
• pp.188-198
• /
• 2018

With recent development of high-speed wide-area wireless networks and wide spread of highperformance wireless devices, the demand on seamless video streaming services in Long Term Evolution (LTE) network environments is ever increasing. To meet the demand and provide enhanced Quality of Experience (QoE) with mobile users, the Dynamic Adaptive Streaming over HTTP (DASH) has been actively studied to achieve QoE enhanced video streaming service in dynamic network environments. However, the existing DASH algorithm to select the quality of requesting video segments is based on a procedural algorithm so that it reveals a limitation to adapt its performance to dynamic network situations. To overcome this limitation this paper proposes a novel quality selection mechanism based on a Deep Q-Network (DQN) model, the DQN-based DASH ABR($DQN_{ABR}$) mechanism. The $DQN_{ABR}$ mechanism replaces the existing DASH ABR algorithm with an intelligent deep learning model which optimizes service quality to mobile users through reinforcement learning. Compared to the existing approaches, the experimental analysis shows that the proposed solution outperforms in terms of adapting to dynamic wireless network situations and improving QoE experience of end users.

• 한국수학교육학회지시리즈A:수학교육
• /
• 제57권4호
• /
• pp.329-352
• /
• 2018

This study analyzed each process of demand analysis(A), design(D), development(D), implementation(I) and evaluation(E) of the program to support mathematics learning of students with under-achievement of math in high school. To analyze the demand, a survey was conducted on 235 high school math teachers and 334 high school students who were under-achieved in mathematics. To design and develope the program, this study linked middle school math to high school math so that the students with poor math learning could easily participate in mathematics learning. The programs developed in this study were implemented in three high schools, where separate classes were organized and run for students with poor math learning. The evaluation of the programs developed in this study was done in two ways. One was a quantitative evaluation conducted by five experts, and the other was a qualitative evaluation conducted through interviews with teachers and students participating in the program. This study found that students with poor mathematics learning were more motivated to learn, started to do mathematics, and encouraged to be confident when using learning materials that included easy problems and detailed solutions that they could solve themselves. From these results, the following three implications can be derived in developing a program to support students who are experiencing poor mathematics learning in high school. First, we should develop learning materials that link middle school mathematics to high school mathematics so that students can supplement middle school mathematics related to high school mathematics. Second, we need to develop learning materials that include detailed solutions to basic examples and include homogeneous problems that can be solved while looking at the basic example's solution process. Third, we should avoid the challenge of asking students who are under-achieving to respond too openly.

• Smart Structures and Systems
• /
• 제29권1호
• /
• pp.195-206
• /
• 2022

Crack detection is essential for inspection of existing structures and crack segmentation based on deep learning is a significant solution. However, datasets are usually one of the key issues. When building a new dataset for deep learning, laborious and time-consuming annotation of a large number of crack images is an obstacle. The aim of this study is to develop an approach that can automatically select a small portion of the most informative crack images from a large pool in order to annotate them, not to label all crack images. An active learning method with difficulty learning mechanism for crack segmentation tasks is proposed. Experiments are carried out on a crack image dataset of a steel box girder, which contains 500 images of 320×320 size for training, 100 for validation, and 190 for testing. In active learning experiments, the 500 images for training are acted as unlabeled image. The acquisition function in our method is compared with traditional acquisition functions, i.e., Query-By-Committee (QBC), Entropy, and Core-set. Further, comparisons are made on four common segmentation networks: U-Net, DeepLabV3, Feature Pyramid Network (FPN), and PSPNet. The results show that when training occurs with 200 (40%) of the most informative crack images that are selected by our method, the four segmentation networks can achieve 92%-95% of the obtained performance when training takes place with 500 (100%) crack images. The acquisition function in our method shows more accurate measurements of informativeness for unlabeled crack images compared to the four traditional acquisition functions at most active learning stages. Our method can select the most informative images for annotation from many unlabeled crack images automatically and accurately. Additionally, the dataset built after selecting 40% of all crack images can support crack segmentation networks that perform more than 92% when all the images are used.

• 한국컴퓨터정보학회:학술대회논문집
• /
• 한국컴퓨터정보학회 2014년도 제49차 동계학술대회논문집 22권1호
• /
• pp.413-414
• /
• 2014

최근 스마트기기 보급의 확산과 이러닝 환경의 고도화로 이러닝 산업의 패러디임이 변화하면서 스마트 디바이스를 통한 학습형태의 스마트러닝이 주목받고 있다. 이처럼 스마트러닝이 부각되면서 기존의 이러닝 콘텐츠를 스마트 디바이스 환경에서도 제공 받고자 하는 사용자들의 요구가 증가하고 있지만 현재 기존 PC기반으로 구현된 이러닝 콘텐츠를 스마트기기에 적용하는데 있어 다양한 문제가 발생하고 있다. 특히 가장 근본적인 문제는 표준에 기인한다고 할 수 있으며 이를 해결하기 위해서는 콘텐츠에 대한 표준화가 필수적인 요소로 작용한다. 이에 본 논문에서는 최근 국내에서 전자책 및 디지털교과서 개발에 표준으로 자리잡고 있는 EPUB 3.0을 준용하여 기존 이러닝 콘텐츠의 데이터 포맷을 변경하고 표준화된 형태로 다양한 스마트 디바이스에 적용이 가능한 디지털교과서 솔루션을 설계하였다.

• 대한건축학회논문집:구조계
• /
• 제35권2호
• /
• pp.45-52
• /
• 2019

BEMS(Building Energy Management Systems) have been applied to office buildings and collect relevant building energy data, e.g. temperatures, mass flow rates and energy consumptions of building mechanical systems and indoor spaces. The aforementioned measured data can be beneficially utilized for developing data-driven machine learning models which can be then used as part of MPC(Model Predictive Control) and/or optimal control strategies. In this study, the authors developed ANN(Artificial Neural Network) models of an AHU (Air Handling Unit) and a chiller for a real-life office building using BEMS data. Based on the ANN models, the authors developed optimal control strategies, e.g. daily operation schedule with regard to optimal start and stop of the AHU and the chiller (500 RT). It was found that due to the optimal start and stop of the AHU and the chiller, 4.5% and 16.4% of operation hours of the AHU and the chiller could be saved, compared to an existing operation.

• 한국수학교육학회지시리즈D:수학교육연구
• /
• 제26권3호
• /
• pp.167-202
• /
• 2023

A central problem of mathematics teaching worldwide is probably the insufficient adaptive handling of tasks-especially in computational practice phases and modeling tasks. All students in a classroom must often work on the same tasks. In the process, the high-achieving students are often underchallenged, and the low-achieving ones are overchallenged. This publication uses different modeling of the tennis serve as an example to show a possible solution to the problem and develops and discusses one adaptive task each for middle school, high school, and college using three mathematical models of the tennis serve each time. From model to model within the task, the complexity of the modeling increases, the mathematical or physical demands on the students increase, and the new modeling leads to more realistic results. The proposed models offer the possibility to address heterogeneous learning groups by their arrangement in the surface structure of the so-called parallel adaptive task and to stimulate adaptive mathematics teaching on the instructional topic of mathematical modeling. Models A through C are suitable for middle school instruction, models C through E for high school, and models E through G for college. The models are classified in the specific modeling cycle and its extension by a digital tool model, and individual modeling steps are explained. The advantages of the presented models regarding teaching and learning mathematical modeling are elaborated. In addition, we report our first teaching experiences with the developed parallel adaptive tasks.

• 한국경영과학회지
• /
• 제23권4호
• /
• pp.141-149
• /
• 1998

One of the difficulties in using the current Boolean-based information retrieval systems is that it is hard for a user, especially a novice, to formulate an effective Boolean query. One solution to this problem is to let the system formulate a query for a user from his relevance feedback documents in this research, an intelligent query reformulation mechanism based on ID3 is proposed and the sensitivity of its retrieval effectiveness, i.e., recall, precision, and E-measure, to various input settings is analyzed. The parameters in the input settings is the number of relevant documents. Experiments conducted on the test set of Medlars revealed that the effectiveness of the proposed system is in fact sensitive to the number of the initial relevant documents. The case with two or more initial relevant documents outperformed the case with one initial relevant document with statistical significances. It is our conclusion that formulation of an effective query in the proposed system requires at least two relevant documents in its initial input set.