• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.217-218
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• 2023

인공지능은 디지털 대전환 시대의 핵심적인 기술로 사회 전반에 변화를 주도하였다. 우리나라는 인공지능을 이해하고 이를 활용하는 역량을 길러주기 위해 전 국민을 대상으로 교육을 진행하고 있다. 그러나 초등학생 대상 인공지능 교육 프로그램은 체험 및 놀이 실습으로 한정되어 교육적 효과에 한계가 있다. 그러므로 본 연구에서는 생산적 실패를 활용하여 인공지능에 대한 개념적 이해 및 실생활 전이를 촉진하는 교육 프로그램을 개발하였다. 연구 대상은 초등학교 5~6학년이며 2022 개정 교육과정에서 강조하는 자기 주도적 학습 역량과 실생활 연계 교육을 반영하여 설계한 6차시 분량의 프로그램이다. 본 연구에서 개발한 교육 프로그램은 향후 타당성 및 신뢰도 검증을 거쳐 현장에 적용하는 후속 연구로 이어질 것이다.

• Informatization Policy
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• v.23 no.2
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• pp.3-18
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• 2016

Radical paradigm shift is being expected due to the coming of the so-called intelligent society. In the intelligent society, things or machines are able to use intelligence for the first time in history and this will bring about fundamental changes at every corner of human society. This article analyzes views of the world in order to figure out basic directions of this paradigm shift. A dualistic view of world mediated by technology is suggested for a new world view of the intelligent society, based on comparison of a traditional dualistic view of world between human-nature and a tripartite view of world between human-machine-nature. This article summarizes paradigms of the intelligent society into four ; externalization of intelligence, productivity explosion, platform society, and self-organizing society. These new paradigms will provide lots of benefits such as intelligence augmentation, production capacity increase, and self-organizing effect. But at the same time, it will increase risks of system failure because of loss of human control on technologies. In conclusion, it is argued that human choices and efforts will decide the future of the intelligent society becuase the paradigm shift is value neutral in essence.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.7 s.172
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• pp.146-154
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• 2005

Recently, the high-speed and intelligence technology of machine tools are developed for the high efficiency of productivity Under the operating condition from the high-speed of machine tools, the various failure modes can occur in core units of manufacturing system. Therefore it is for the reliability concept of machine tool to be required in a design level. And the above-stated technology must be accommodated in the feeding and spindle subsystem, etc those are the core units of machine tools. In this study, we are developed the test-bed of sliding cover (C-plate) in order to evaluating reliability and estimating failure modes of feeding subsystem under operating conditions. The reliability experiment using the developed test-bed and the additional structural analysis executed on single and double structure. We found out the weak parts of sliding cover and were able to predict a life cycle from the experiment results. In this study, we propose the new C-plate model with double link structure to apply the high-speed machine tool in the fundamental guideline.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.6
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• pp.1-10
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• 2023

The nuclear power plant(NPP)'s Instrumentation and Control(I&C) system periodically conducts integrity checks for the maintenance of self-diagnostic function during normal operation. Additionally, it performs functionality and performance checks during planned preventive maintenance periods. However, there is a need for technological development to diagnose failures and prevent accidents in advance. In this paper, we studied methods for estimating the reliability function by utilizing environmental data and self-diagnostic data of the I&C equipment. To obtain failure data, we assumed probability distributions for component features of the I&C equipment and generated virtual failure data. Using this failure data, we estimated the reliability function using representative artificial intelligence(AI) models used in survival analysis(DeepSurve, DeepHit). And we also estimated the reliability function through the Cox regression model of the traditional semi-parametric method. We confirmed the feasibility through the residual lifetime calculations based on environmental and diagnostic data.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.19-27
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• 2020

In this paper, we propose a machine learning method for diagnosing the failure of a gas pressure regulator. Originally, when implementing a machine learning model for detecting abnormal operation of a facility, it is common to install sensors to collect data. However, failure of a gas pressure regulator can lead to fatal safety problems, so that installing an additional sensor on a gas pressure regulator is not simple. In this paper, we propose various machine learning approach for diagnosing the abnormal operation of a gas pressure regulator with only the flow rate and gas pressure data collected from a gas pressure regulator itself. Since the fault data of a gas pressure regulator is not enough, the model is trained in all classes by applying the over-sampling method. The classification model was implemented using Gradient boosting, 1D Convolutional Neural Networks, and LSTM algorithm, and gradient boosting model showed the best performance among classification models with 99.975% accuracy.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.218-232
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• 2022

Researches are underway to use a radar sensor, a sensor used for object recognition in vehicles, for position estimation. In particular, a method of classifying dynamic and static objects using the Doppler velocity, the output from the radar sensor, and calculating ego-motion using only static objects has been researched recently. Also, for the existing dynamic object classification, several methods using RANSAC or robust filtering has been proposed. Still, a classification method with higher performance is needed due to the nature of the position estimation, in which even a single failure causes large effects. Hence, in this paper, we propose a method to improve the classification performance compared to existing methods through tracking and filtering of dynamic objects. Additionally, the method used a GMPHD filter to maximize tracking performance. In effect, the method showed higher performance in terms of classification accuracy compared to existing methods, and especially shows that the failure of the RANSAC could be prevented.

• Korean Security Journal
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• no.13
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• pp.207-234
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• 2007

This study examines the roles of government and reasons of defeat on 9.11 terror, iraq war between 2001 and 2003. The administration functions critical role of national security. And punctual, accurate information supply capability helps policymaker's decision-making. Hence, information of punctuality and accuracy should be given to policymakers. And without two above written factors, it will result in failing. Information concoction on policymaker's pressure, biased informant, inaccurate information and lack of assembly means under the extensive organization and technologized spying means, Fail to keep information objectivity, leads to information failure. In the context of a series of facts, we shall cover the position of government and reasons of calamities. Two incidents deem as information failure by national security service, but concoction of Iraqi mass destruction weaponry is believed as bush administration's deception on account of political gains. For fully functional government role, governing body should reinforce all aspects of gathering, analyzing, and making use of information more objectively in the first place. In particular, information concoction involving policymakers post massive stumbling block to organized outcome. The thesis presents a prospective view of government position under the U.S. secret agent over 9.11 terror and Iraq war.

• Structural Engineering and Mechanics
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• v.45 no.3
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• pp.323-336
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• 2013

With the ongoing development in the computer science areas of artificial intelligence and computational intelligence, researchers are able to apply them successfully in the construction industry. Given the complexities indeep beam behaviour and the difficulties in accurate evaluation of its deflection, the current study has employed the Adaptive Network-based Fuzzy Inference System (ANFIS) as one of the modelling tools to predict deflection for high strength self compacting concrete (HSSCC) deep beams. In this study, about 3668measured data on eight HSSCC deep beams are considered. Effective input data and the corresponding deflection as output data were recorded at all loading stages up to failure load for all tested deep beams. The results of ANFIS modelling and the classical linear regression were compared and concluded that the ANFIS results are highly accurate, precise and satisfactory.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.7
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• pp.1585-1609
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• 2013

Over recent years, enormous amounts of research in wireless sensor networks (WSNs) have been conducted, due to its multifarious applications such as in environmental monitoring, object tracking, disaster management, manufacturing, monitoring and control. In some of WSN applications dependent the energy-efficient and link reliability are demanded. Hence, this paper presents a routing protocol that considers these two criteria. We propose a new mechanism called Reliable Routing Scheme for Energy-Balanced (RRSEB) to reduce the packets dropped during the data communications. It is based on Swarm Intelligence (SI) using the Ant Colony Optimization (ACO) method. The RRSEB is a self-adaptive method to ensure the high routing reliability in WSNs, if the failures occur due to the movement of the sensor nodes or sensor node's energy depletion. This is done by introducing a new method to create alternative paths together with the data routing obtained during the path discovery stage. The goal of this operation is to update and offer new routing information in order to construct the multiple paths resulting in an increased reliability of the sensor network. From the simulation, we have seen that the proposed method shows better results in terms of packet delivery ratio and energy efficiency.

• Advances in Computational Design
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• v.5 no.4
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• pp.445-465
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• 2020

This paper presents a study conducted with the aim of developing a model of tendering based on a technique of artificial intelligence by managing and controlling the factors of success or failure of construction projects through the evaluation of the process of invitation to tender. Aiming to solve this problem, analysis of the current environment based on SWOT (Strengths, Weaknesses, Opportunities, and Threats) is first carried out. Analysis was evaluated through a case study of the construction projects in Algeria, to bring about the internal and external factors which affect the process of invitation to tender related to the construction projects. This paper aims to develop a mean to identify threats-opportunities and strength-weaknesses related to the environment of various national construction projects, leading to the decision on whether to continue the project or not. Following a SWOT analysis, novel artificial intelligence models in forecasting the project status are proposed. The basic principal consists in interconnecting the different factors to model this phenomenon. An artificial neural network model is first proposed, followed by a model based on fuzzy logic. A third model resulting from the combination of the two previous ones is developed as a hybrid model. A simulation study is carried out to assess performance of the three models showing that the hybrid model is better suited in forecasting the construction project status than RNN (recurrent neural network) and FL (fuzzy logic) models.