• 한국정밀공학회지
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• 제21권8호
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• pp.112-119
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• 2004

This paper develops a computer model of a cabover type large truck for estimating the effects of the mounted method of frame on handling performance. The computer model considers two mounted methods of frame; flange mounted and web mounted. Frame is modeled by finite elements using MSC/NASTRAN in order to consider the flexibility of frame. The reliability of the developed computer model is verified by comparing the actual vehicle test results with the simulation results. The actual vehicle test is performed in a double lane change course, and lateral acceleration, yaw rate, and roll angle are measured. To estimate the effects of the mounted method of frame on handling performance, simulations are performed with the flange mounted and web mounted frame. Simulation results show that the web mounted frame＇s variations of roll angle, lateral acceleration, and yaw rate are larger than the flange mounted frame＇s variations, especially in the high test velocity and the second part of the double lane course. Also, simulation results show that the web mounted frame＇s tendencies of roll angle, lateral acceleration, and yaw rate advance the flange mounted frame＇s tendencies, especially in the high test velocity and the second part of the double lane course.

• 컴퓨터교육학회논문지
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• 제7권1호
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• pp.15-25
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• 2004

본 논문에서는 제7차 교육과정에 따른 고등학교 '정보사회와 컴퓨터' 교과의 평가도구를 제시한다. 제시된 평가도구는 제7차 교육과정의 목표와 '정보사회와 컴퓨터' 교과의 특성을 모두 반영할 수 있도록 개발하였고 현장 교사들이 구체적으로 활용할 수 있도록 하였으며, 궁극적으로 교육과정의 질을 향상시키도록 하였다. 이를 위하여 먼저 분석된 교과내용을 영역별로 분류하고 성취기준과 평가영역, 평가기준을 개발하였다. 또한 개발된 평가 모델 기반의 예시 평가도구를 제시함으로써 학교현장에서 교육목표가 구현된 평가 활동이 이루어지게 하였다. 현장 전문가의 타당성 검증을 통하여 제시된 평가 도구의 현장 활용도를 높이고자 하였다.

• 융합보안논문지
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• 제8권1호
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• pp.91-100
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• 2008

The previous works in sensor networks security have focused on the aspect of confidentiality, authentication and integrity based on cryptographic primitives. There has been no prior work to assess the survivability in systematic way. Accordingly, this paper presents a survivability model of wireless sensor networks using software rejuvenation for dual adaptive cluster head. The survivability model has state transition to reflect status of real wireless sensor networks. In this paper, we only focus on a survivability model which is capable of describing cluster head compromise in the networks and able to switch over the redundant cluster head in order to increase the survivability of that cluster. Second, this paper presents how to enhance the survivability of sensor networks using software rejuvenation methodology for dual cluster head in wireless sensor network. We model and analyze each cluster as a stochastic process based on Semi Markov Process (SMP) and Discrete Time Markov Chain (DTMC). The proof of example scenarios and numerical analysis shows the feasibility of our approach.

• Journal of the Korean Data and Information Science Society
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• 제24권4호
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• pp.755-762
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• 2013

본 논문은 NHPP 소프트웨어 신뢰성모형에서 모수추정과 고장시간에 대한 예측을 다루고자 한다. 소프트웨어 신뢰성모형 Goel-Okumoto모형에서 평균값 함수에 대한 최우추정과 경험적 사전분포를 가정한 공액사전분포에서 베이지안 추정을 다루었다. 실제 자료에서 두 가지 추정법에 의한 모수 추정값을 제공하였으며, 모형의 적합성을 판정하고, 고장수에 대한 예측값을 비교하였다.

• 전기학회논문지
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• 제66권2호
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• pp.305-314
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• 2017

Most of the existing researches on systemwide optimization of generator maintenance scheduling do not consider the equivalent operating hours(EOHs) mainly due to the difficulties of calculating the EOHs of the CCGTs in the large scale system. In order to estimate the EOHs not only the operating hours but also the number of start-up/shutdown during the planning period should be estimated, which requires the mathematical model to incorporate the economic dispatch model and unit commitment model. The model is inherently modelled as a large scale mixed-integer nonlinear programming problem and the computation time increases exponentially and intractable as the system size grows. To make the problem tractable, this paper proposes an EOH calculation based on demand grouping by K-means clustering algorithm. Network congestion is also considered in order to improve the accuracy of EOH calculation. This proposed method is applied to the actual Korean electricity market and compared to other existing methods.

• 인터넷정보학회논문지
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• 제20권3호
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• pp.85-92
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• 2019

In this paper, we extract the quantitative relation data of activities from the workflow event log file recorded in the XES standard format and connect them to rediscover the workflow process model. Extract the workflow process patterns and proportions with the rediscovered model. There are four types of control-flow elements that should be used to extract workflow process patterns and portions with log files: linear (sequential) routing, disjunctive (selective) routing, conjunctive (parallel) routing, and iterative routing patterns. In this paper, we focus on four of the factors, disjunctive routing, and conjunctive path. A framework implemented by the authors' research group extracts and arranges the activity data from the log and converts the iteration of duplicate relationships into a quantitative value. Also, for accurate analysis, a parallel process is recorded in the log file based on execution time, and algorithms for finding and eliminating information distortion are designed and implemented. With these refined data, we rediscover the workflow process model following the relationship between the activities. This series of experiments are conducted using the Large Bank Transaction Process Model provided by 4TU and visualizes the experiment process and results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권6호
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• pp.1833-1848
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• 2022

Named entity recognition (NER) is an important basic task in the field of Natural Language Processing (NLP). Recently deep learning approaches by extracting word segmentation or character features have been proved to be effective for Chinese Named Entity Recognition (CNER). However, since this method of extracting features only focuses on extracting some of the features, it lacks textual information mining from multiple perspectives and dimensions, resulting in the model not being able to fully capture semantic features. To tackle this problem, we propose a novel Multi-view Semantic Feature Fusion Model (MSFM). The proposed model mainly consists of two core components, that is, Multi-view Semantic Feature Fusion Embedding Module (MFEM) and Multi-head Self-Attention Mechanism Module (MSAM). Specifically, the MFEM extracts character features, word boundary features, radical features, and pinyin features of Chinese characters. The acquired font shape, font sound, and font meaning features are fused to enhance the semantic information of Chinese characters with different granularities. Moreover, the MSAM is used to capture the dependencies between characters in a multi-dimensional subspace to better understand the semantic features of the context. Extensive experimental results on four benchmark datasets show that our method improves the overall performance of the CNER model.

• 한국컴퓨터그래픽스학회논문지
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• 제29권1호
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• pp.23-29
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• 2023

본 논문은 강화학습을 통해 이족보행에 대한 모션 캡처를 통해 참조 모션의 데이터들을 기반으로 근골격 캐릭터의 시뮬레이션을 적은 비용으로 높은 품질의 결과를 얻을 방법을 소개한다. 우리는 참조 모션 데이터를 캐릭터 모델이 수행할 수 있게끔 재설정을 한 후, 강화학습을 통해 해당 모션을 학습하도록 훈련시킨다. 참조 모션 모방과 근육에 대한 최소한의 메타볼릭 에너지를 결합하여 원하는 방향으로 근골격 모델이 이족보행을 수행하게끔 학습한다. 이러한 방법으로 근골격 모델은 기존의 수동으로 설계된 컨트롤러보다 적은 비용으로 학습할 수 있으며 높은 품질의 이족보행을 수행할 수 있게 된다.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.150-154
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• 2024

In recent years, there are extensive researches on the applications of machine learning to the automation and decision support for medical experts during disease detection. However, the performance of machine learning still needs improvement so that machine learning model produces result that is more accurate and reliable for disease detection. Selecting the hyper-parameter that could produce the possible maximum classification accuracy on medical dataset is the most challenging task in developing decision support systems with machine learning algorithms for medical dataset classification. Moreover, selecting the features that best characterizes a disease is another challenge in developing machine-learning model with better classification accuracy. In this study, we have proposed an optimized decision tree model for heart disease classification by using heart disease dataset collected from kaggle data repository. The proposed model is evaluated and experimental test reveals that the performance of decision tree improves when an optimal number of features are used for training. Overall, the accuracy of the proposed decision tree model is 98.2% for heart disease classification.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2006년도 춘계학술발표대회
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• pp.913-916
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• 2006

SNMP는 망 관리 시스템을 구현하는데 있어서 비교적 단순하고 안전한 일대일 통신 방안을 제공하고 있다. 원격 회의 또는 강의, VoIP, 네트워크 게임 등의 다양한 통신 컨텐츠에 대해 인터넷을 통한 이용이 급증하면서 UDP 기반의 SNMP 통신에 있어서도 Congestion Control을 적용하기 위한 방안이 필요하게 되었다. 본 논문에서는 UDP에서 Congestion Control을 이용하는 DCCP를 이용하여 SNMP를 기반의 망 관리 시스템을 구축할 수 있는 구조를 제안한다.