• Journal of the Korea Computer Graphics Society
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• v.27 no.5
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• pp.55-61
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• 2021

The character motion control based on physics simulation using reinforcement learning continue to being carried out. In order to solve a problem using reinforcement learning, the network structure, hyperparameter, state, action and reward must be properly set according to the problem. In many studies, various combinations of states, action and rewards have been defined and successfully applied to problems. Since there are various combinations in defining state, action and reward, many studies are conducted to analyze the effect of each element to find the optimal combination that improves learning performance. In this work, we analyzed the effect on reinforcement learning performance according to the state representation, which has not been so far. First we defined three coordinate systems: root attached frame, root aligned frame, and projected aligned frame. and then we analyze the effect of state representation by three coordinate systems on reinforcement learning. Second, we analyzed how it affects learning performance when various combinations of joint positions and angles for state.

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.183-191
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• 2020

Today, taking photos using smartphones has become an essential element of modern people. According to these social changes, modern people need a larger storage capacity, and the number of unnecessary photos has increased. To support the storage, cloud-based photo storage services from various platforms have appeared, and many people are using the services. As the number of photos increases, it is difficult for users to find the photos they want, and it takes a lot of time to organize. In this paper, we propose a cloud-based photo management service that facilitates photo management by classifying photos and recommending unnecessary photos using deep learning. The service provides the function of tagging photos by identifying what the subject is, the function of checking for wrongly taken photos, and the function of recommending similar photos. By using the proposed service, users can easily manage photos and use storage capacity efficiently.

• Journal of Engineering Education Research
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• v.17 no.6
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• pp.62-68
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• 2014

The purpose of this work is to provide systematic lecture materials for instructers who search for the effective teaching of applied mechanics of materials course with respect to lecture contents, teaching methods, and itemized course evaluations according to each class learning objective. For this. the evolution of teaching contents since 2010 until 2014 are briefly depicted and then most recent course learning objectives, lecture contents, and evaluation schemes are presented in detail. The results of this study may be used as base line data for the lecturers who teach similar courses and for the evaluation of program outcomes in ABEEK scheme through course-embedded assessment.

• 한국디지털정책학회:학술대회논문집
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• 2005.11a
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• pp.581-593
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• 2005

최근 기술 중 국방, 교육, 오락 등 다양한 산업 분야의 핵심 기술로 부상하고 있는 "모델링 및 시뮬레이션 (M&S: Modeling & Simulation)"에 관한 기술 연구와 어플리케이션 개발이 활발하게 이루어지고 있다. 특히 e-Learning 산업분야와 관련하여 가상현실 또는 가상 시뮬레이션 기술로 대표되는 가상 시뮬레이터 교육 구현 기술에 관하여 집중적으로 조명되고 있다. 그러나 이를 만족시킬 수 있는 가상 시뮬레이션 기술에 공통으로 적용할 수 있는 표준 기술은 현재 매우 부족한 상태이다. 따라서 본 논문에서는 e-Learning 분야의 학습효과를 증대하기 위한 목적으로 가상 시뮬레이션 데이터 요소, 즉 이와 관련된 기재 사항들의 기재 방식에 관한 표준 기술을 정의하고, 이를 표준 요소들로 제안한다.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.967-985
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• 2019

Bug report processing is a key element of bug fixing in modern software maintenance. Bug reports are not processed immediately after submission and involve several processes such as bug report deduplication and bug report triage before bug fixing is initiated; however, this method of bug fixing is very inefficient because all these processes are performed manually. Software engineers have persistently highlighted the need to automate these processes, and as a result, many automation techniques have been proposed for bug report processing; however, the accuracy of the existing methods is not satisfactory. Therefore, this study focuses on surveying to improve the accuracy of existing techniques for bug report processing. Reviews of each method proposed in this study consist of a description, used techniques, experiments, and comparison results. The results of this study indicate that research in the field of bug deduplication still lacks and therefore requires numerous studies that integrate clustering and natural language processing. This study further indicates that although all studies in the field of triage are based on machine learning, results of studies on deep learning are still insufficient.

• Annual Conference on Human and Language Technology
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• 2020.10a
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• pp.427-432
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• 2020

딥러닝(Deep-learning) 기반의 자연어 이해(Natural Language Understanding) 기술들은 최근에 상당한 성과를 성취했다. 하지만 딥러닝 기반의 자연어 이해 기술들은 내적인 동작들과 결정에 대한 근거를 설명하기 어렵다. 본 논문에서는 벡터를 그래프로 변환함으로써 신경망의 내적인 의미 표현들을 설명할 수 있도록 한다. 먼저 인간과 기계 모두가 이해 가능한 표현방법의 하나로 그래프를 주요 표현방법으로 선택하였다. 또한 그래프의 구성요소인 노드(Node) 및 엣지(Edge)의 결정을 위한 Element-Importance Inverse-Semantic-Importance(EI-ISI) 점수와 Element-Element-Correlation(EEC) 점수를 심층신경망의 훈련방법 중 하나인 드랍아웃(Dropout)을 통해 계산하는 방법을 제안한다. 다양한 실험들을 통해, 본 연구에서 제안한 벡터-그래프(Vector2graph) 변환 프레임워크가 성공적으로 벡터의 의미정보를 유지하면서도, 설명 가능한 그래프를 생성함을 보인다. 더불어, 그래프 기반의 새로운 시각화 방법을 소개한다.

• 대한공업교육학회지
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• v.42 no.1
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• pp.106-124
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• 2017

This study used the secondary Delphi method for experts, in order to propse a proper formation plan for the goal and curriculum of elementary and secondary invention/intellection property education. Its results are as following; First, the key objective of invention/intellectual property education for each school level is evaluated as appropriate. With regard to the key objective, elementary schools are aiming at 'fostering awareness and attitude for invention'(M=4.5), middle schools, 'understanding of invention process and method'(M=4.2), general high schools, 'application and evaluation of invention method'(M=4.1), and specialized high schools, 'understanding and application of Employee Invention'(M=4.6). The objective and goal of education for each school level are also evaluated as appropriate. Second, although the proper formation plans for a key learning element of elementary and secondary invention/intellectual property education were almost identical to an actual formation of preceding literature, overall change is required for the formation balance of each learning element, according to the objective and goal of school-leveled invention/intellectual property education. An appropriate formation shall be focusing on basic learning elements (A, B, C, D, E, and F) for elementary and middle schools(73.2%, 65.1%), lowering somewhat the former elements and increasing expanded learning elements for high schools(51.0%), which are connected to the invention, course(H), and patent application(K). Third, elementary and secondary invention/intellectual property education system should be oriented to its objective and goal. In order to reach this, an appropriate formation plan should be made for each school level, based on the principle of Tyler's learning organization, such as continuity, sequence and integration, which are key learning element. Specialized high schools, in particular, need to be differentiated from general ones, as well as elementary and middle schools. Additionally, for understanding and applying an employee invention, invention/intellectual property education system needs to be established in the phase of secondary occupational education.

• Journal of Electrical Engineering and Technology
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• v.11 no.1
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• pp.29-37
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• 2016

Fast and accurate fault diagnosis of the position sensor is of great significance to ensure the reliability as well as sensor fault tolerant operation of the Switched Reluctance Wind Generator (SRWG). This paper presents a fault diagnostic scheme for a SRWG based on the residual between the estimated rotor position and the actual output of the position sensor. Extreme Learning Machine (ELM), which could build a nonlinear mapping among flux linkage, current and rotor position, is utilized to design an assembled estimator for the rotor position detection. The data for building the ELM based assembled position estimator is derived from the magnetization curves which are obtained from Finite Element Analysis (FEA) of an SRWG with the structure of 8 stator poles and 6 rotor poles. The effectiveness and accuracy of the proposed fault diagnosis method are verified by simulation at various operating conditions. The results provide a feasible theoretical and technical basis for the effective condition monitoring and predictive maintenance of SRWG.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.4
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• pp.81-90
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• 2017

This paper presents a smart seat for correction of driver posture while driving. We introduce good postures with seat height, seat angle, head height, back of knees, distances of foot pedals, tilt of seat, etc. There have been some studies on correction of good posture while driving, effects of driving environment on driver's posture, sitting strategies based on seating pressure distribution, estimation of driver's standard postures, and others. However, there are a few studies on guide of good postures while driving for problem of driver's posture using machine leaning. Therefore, we suggest a smart seat for correction of driver's posture based on machine leaning, 1) developed the system to get postures by 10 piezoelectric effect element, 2) collect piezoelectric values from 37 drivers and 28 types of cars, 3) suggest 4 types of good postures while driving, 4) analyze test postures by kNN. As the results, we can guide good postures for bad or problems of postures while driving.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.1
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• pp.151-160
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• 2024

Artificial Neural Networks that enabled Artificial Intelligence are being used in many fields. However, the application to mechanical structures has several problems and research is incomplete. One of the problems is that it is difficult to secure a large amount of data necessary for learning Artificial Neural Networks. In particular, it is important to detect and recognize external forces and forces for safety working and accident prevention of mechanical structures. This study examined the possibility by applying the Current Neural Network of Artificial Neural Networks to detect and recognize the load on the machine. Tens of thousands of data are required for general learning of Recurrent Neural Networks, and to secure large amounts of data, this paper derives load data from ANSYS structural analysis results and applies a stacked auto-encoder technique to secure the amount of data that can be learned. The usefulness of Stacked Auto-Encoder data was examined by comparing Stacked Auto-Encoder data and ANSYS data. In addition, in order to improve the accuracy of detection and recognition of load data with a Recurrent Neural Network, the optimal conditions are proposed by investigating the effects of related functions.