• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.475-475
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• 2021

전 세계적으로 기후변화에 따른 예측하기 어려운 국지성호우의 발생빈도가 증가하고 있으며, 수반되는 돌발홍수를 사전에 대비하기 위해 하천운영의 중요성이 강조되고 있다. 일반적인 하천 시설물 점검 및 보수 계획 수립 시, 시설물의 중요도나 하천의 현황 보다는 행정적인 편의성을 고려하여 구간에 따른 육안 점검 등을 통해 일률적으로 수행되고 있다. 그러나 현재 우리나라의 하천 시설물 중 약 40% 이상이 준공연수가 30년을 초과한 것으로 파악되고 있으며, 노후화된 하천 시설물의 정확한 상태평가에 따른 보수보강 계획수립이 필요한 실정이다. 이를 위해서는 하천 시설물 관리에 있어 다양한 문제점과 관련된 자료를 수집하여 DB화하고, 모델링 및 정밀검사 등을 통해 다양한 각도에서 분석되어야 한다. 본 연구에서는 시설물통합정보관리시스템(Facilty Management System; FMS)에서 제공하는 하천 시설물 관리대장 중 테스트베드에 위치한 수문의 개별부재에 대한 상태 평가지수를 활용하여 시설물의 생애주기를 통합적으로 고려할 수 있는 성능평가모델을 제안하였다. 본 연구에서 제안된 성능평가모델은 하천 시설물의 합리적인 관리체계를 통해 분석된 결과로부터 시설관리자가 하천 관리 및 계획수립에 있어 의사결정을 할 수 있도록 정보를 제공하는 지원 도구로 활용 가능할 것으로 판단된다.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.05a
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• pp.300-303
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• 2008

IT 관련 프로젝트를 진행 시 프로젝트 진행 도중 예상치 못한 많은 문제에 봉착하게 된다. 본 논문에서는 IT 프로젝트 대부분이 도입, 사용하는 요구사항분석-설계-개발-테스트-배포-유지보수단계 중 개발단계 및 유지보수 단계에서 발생할 수 있는 문제점에 초점을 맞추었다. PMG 란 Project Management Negotiator 의 약자로서 개발 시 고객의 요구사항이 변경되면 고객과 개발자 간의 개발에 소요되는 난이도, 일정, 비용에 따른 관점이 다를 수 있으므로, 이러한 차이점을 서로 이해 할 수 있는 범위 내에서 감소시킬 수 있는 모델을 제시한다. 또한 제시한 모델에 근거한 매핑테이블과 프로세스를 개발하고 실제 pilot 시스템을 구축하여 효용성을 검증한다. 제시한 프로젝트 관리 협상 모델을 통하여 일정에 차질 없는 프로젝트가 수행 가능하며 비용절감 및 고객만족 효과를 거둘 수 있다. 결과적으로 일정, 비용 측면 및 고객 만족 세 가지 측면에서 기존의 프로젝트 개발 방법론보다 더 나은 일정준수, 효율성, 정확성 등의 정량적, 정성적 만족을 확보할 수 있다.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.1
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• pp.52-57
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• 2007

This paper presents the design of intelligent moving machine control system using a real time speech recognition. The proposed moving machine control system is composed of four separated module, which are main control module, speech recognition module, servo motor driving module and sensor module. In main control module with microprocessor(80C196KC), one part of the artificial intelligences, fuzzy logic, was applied to the proposed intelligent control system. In order to improve the non-linear characteristic which depend on an user's weight and variable environment, encoder attached to the servo motors was used for feedback control. The proposed system is tested using 9 words lot control of the mobile robot, and the performance of a mobile robot using voice and joystick command is also evaluated.

• Journal of The Korean Association of Information Education
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• v.22 no.1
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• pp.151-158
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• 2018

The purpose of this study is to develop a system for predicting students' collaborative problem solving ability based on the ICT factors of PISA 2015 that affect collaborative problem solving ability. The PISA 2015 computer-based collaborative problem-solving capability evaluation included 5,581 students in Korea. As a research method, correlation analysis was used to select meaningful variables. And the collaborative problem solving ability prediction model was created by using the deep learning method. As a result of the model generation, we were able to predict collaborative problem solving ability with about 95% accuracy for the test data set. Based on this model, a collaborative problem solving ability prediction system was designed and implemented. This research is expected to provide a new perspective on applying big data and artificial intelligence in decision making for ICT input and use in education.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.86-99
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• 2021

One of the methods to alleviate traffic congestion is to increase the efficiency of the roads by providing traffic condition information on road user and distributing the traffic. For this, reliability must be guaranteed, and quantitative real-time traffic speed prediction is essential. In this study, and based on analysis of traffic speed related to traffic conditions, historical data correlated with traffic flow were used as input. We developed an LSTM model that predicts speed in response to normal traffic conditions, along with a CNN-LSTM model that predicts speed in response to incidents. Through these models, we try to predict traffic speeds during the hour in five-minute intervals. As a result, predictions had an average error rate of 7.43km/h for normal traffic flows, and an error rate of 7.66km/h for traffic incident flows when there was an incident.

• Journal of Platform Technology
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• v.9 no.3
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• pp.24-35
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• 2021

High-speed military aircraft shall be able to identify and resolve enemy threats or internal component defects with survival equipment and warning systems to minimize casualties. Warning system is divided into visual method with symbolic display and auditory method with communication equipment, which is superior in that they it has a short response time and does not cause pilot confusion by listening to simple messages. Thus, this paper suggested and evaluated effective design methods of voice/tone warning systems for military aircraft based on a life cycle perspective. Since military aircraft is safety-sensitive, priorities and three properties(Inhibitible, Interruptible, and Deactivatable) were applied to each warning to reflect criticality and urgency. As a result, we confirmed that it took 40ms to play the voice warnings, satisfying all requirements through V model-based development and testing, and improving product reliability.

• Journal of Intelligence and Information Systems
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• v.14 no.2
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• pp.21-44
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• 2008

Recently, feasible services in ubiquitous computing environment are commonly based on context -aware computing. With the concept of context-awareness we can imagine more effective way to measure human being's daily stress and provide anti-stress services. Our study introduces logical and methodological approach to manage the stress through the development of stress index. From the practical perspectives, we also designed a business model for u-SMC, which is a profitable organization specialized in providing stress measurement services and personalized anti-stress services by utilizing the stress index model.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 1999.05a
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• pp.97-105
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• 1999

An important task for any software project manager is to be able to predict and control project size. Unfortunately, there is comparatively little work that deals with the problem of building estimation methods for software size in fourth-generation languages systems. In this paper, we propose a new estimation method for estimating for software size based on minimum relative error(MRE) criterion. The characteristic of the proposed method is insensitive to the extreme values of the observed measures which can be obtained early in the development life cycle. In order to verify the performance of the proposed estimation method for software size in terms of both quality of fit and predictive quality, the experiments has been conducted for the dataset I and II, respectively. For the data set I and II, our proposed estimation method was shown to be superior to the traditional method LS and RLS in terms of both the quality of fit and predictive quality when applied to data obtained from actual software development projects.

• Journal of the Korea Society of Computer and Information
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• v.25 no.5
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• pp.11-18
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• 2020

This study proposes a technique for the determination of outlet blockage using deep learning in a conveyor system. The proposed method aims to apply the best model to the actual process, where we train various CNN models for the determination of outlet blockage using images collected by CCTV in an industrial scene. We used the well-known CNN model such as VGGNet, ResNet, DenseNet and NASNet, and used 18,000 images collected by CCTV for model training and performance evaluation. As a experiment result with various models, VGGNet showed the best performance with 99.03% accuracy and 29.05ms processing time, and we confirmed that VGGNet is suitable for the determination of outlet blockage.

• The Journal of the Acoustical Society of Korea
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• v.39 no.1
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• pp.64-68
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• 2020

In a conventional speech recognition system using Gaussian Mixture Model-Hidden Markov Model (GMM-HMM), the cepstral feature normalization method based on pole filtering was effective in improving the performance of recognition of short utterances in noisy environments. In this paper, the usefulness of this method for the state-of-the-art speech recognition system using Deep Neural Network (DNN) is examined. Experimental results on AURORA 2 DB show that the cepstral mean and variance normalization based on pole filtering improves the recognition performance of very short utterances compared to that without pole filtering, especially when there is a large mismatch between the training and test conditions.