• 한국산업정보학회:학술대회논문집
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• 한국산업정보학회 2002년도 춘계학술대회 논문집
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• pp.367-371
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• 2002

I construct a chaotically driven contraction system having some analogy with the information transfer mechanism in the brain system especially from CA1 cell to CA3 cell known from the empirical result. And I consider the properties of the response system on a state space according to the external input into the drive neuron by observing the fractal hierarchical structure. Then I induce the relation between the information about state transition of the chaotic time series and the spatial information on a fractal attractor to confirm the possibility of encoding of time series data to spatial information.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권12호
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• pp.4531-4544
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• 2021

Because the traditional network information security vulnerability risk assessment method does not set the weight, it is easy for security personnel to fail to evaluate the value of information security vulnerability risk according to the calculation value of network centrality, resulting in poor evaluation effect. Therefore, based on the network security data element feature system, this study designed a quantitative assessment method of network information security vulnerability detection risk under single transmission state. In the case of single transmission state, the multi-dimensional analysis of network information security vulnerability is carried out by using the analysis model. On this basis, the weight is set, and the intrinsic attribute value of information security vulnerability is quantified by using the qualitative method. In order to comprehensively evaluate information security vulnerability, the efficacy coefficient method is used to transform information security vulnerability associated risk, and the information security vulnerability risk value is obtained, so as to realize the quantitative evaluation of network information security vulnerability detection under single transmission state. The calculated values of network centrality of the traditional method and the proposed method are tested respectively, and the evaluation of the two methods is evaluated according to the calculated results. The experimental results show that the proposed method can be used to calculate the network centrality value in the complex information security vulnerability space network, and the output evaluation result has a high signal-to-noise ratio, and the evaluation effect is obviously better than the traditional method.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 추계종합학술대회 논문집
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• pp.392-395
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• 1999

The state assignment for a finite state machine greatly affects the delay, area, and testabilities of the sequential circuits. In order to minimize the dependencies among state variables, therefore possibly to reduce the length and number of feedback cycles, a new state assignment technique based on m-block partition is introduced in this paper. After the completion of state assignment and logic synthesis, partial scan design is performed to choose minimal number of scan flip-flops. Experiment shows drastic improvement in testabilities while preserving low area and delay overhead.

• 전자공학회논문지C
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• 제34C권12호
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• pp.1-8
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• 1997

In this paper, a new method of state code assignment for reduction of switching activities of state transition in asynchronous circuits is proposed. The algorithm is based on a on-hot code and modifies it to reduce switching activities. To estimate switching activities as a cost functions we introduce state transition probability (STP). AS a results, the proposed algorithm has an advantage of 60% over with the conventional code assignment in terms of switching and code length of state assignment.

• 한국항공운항학회지
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• 제24권1호
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• pp.33-40
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• 2016

ROK issued its first type certificate to the KC-100 airplane, Part 23 normal category, and become the State of Design (SoD) in 2013. Before this, ROK aviation regulations were focused on the operation and continued airworthiness of aircraft registered and operated in ROK that were designed and manufactured in another contracting state. Therefore the implementation of reporting system were restricted to gather the failure and service difficulty reports from the owners or operators and transmit the information to the State of Design and/or the manufacture relating to the type certificated aircraft. However, ROK, to fulfill the accountability of the State of Design, has to ensure there is a system to address the information received from the State of Registry on failure, malfunctions, defects and other occurrences that might cause adverse effects on the continuing airworthiness of the korean type certificated aircraft. This paper presents an overview of ICAO requirements for the State of Design, and current implementation of reporting system of USA and Japan and discusses the current status and further considerations on the rule-making for the malfunction, defect and failure reporting system applicable to the korean indigenous aircraft.

• Journal of Information Processing Systems
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• 제16권5호
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• pp.1223-1230
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• 2020

Recently, extensive studies have been conducted to apply deep learning to reinforcement learning to solve the state-space problem. If the state-space problem was solved, reinforcement learning would become applicable in various fields. For example, users can utilize dance-tutorial systems to learn how to dance by watching and imitating a virtual instructor. The instructor can perform the optimal dance to the music, to which reinforcement learning is applied. In this study, we propose a method of reinforcement learning in which the action space is dynamically adjusted. Because actions that are not performed or are unlikely to be optimal are not learned, and the state space is not allocated, the learning time can be shortened, and the state space can be reduced. In an experiment, the proposed method shows results similar to those of traditional Q-learning even when the state space of the proposed method is reduced to approximately 0.33% of that of Q-learning. Consequently, the proposed method reduces the cost and time required for learning. Traditional Q-learning requires 6 million state spaces for learning 100,000 times. In contrast, the proposed method requires only 20,000 state spaces. A higher winning rate can be achieved in a shorter period of time by retrieving 20,000 state spaces instead of 6 million.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2019년도 춘계학술대회
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• pp.283-284
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• 2019

In the last decades patient's suffering with Parkinson's disease is increasing at a rapid rate and as per prediction it will grow more rapidly as old age population is increasing at a rapid rate through out the world. As the performance of wearable sensor based approach reached to a new height as well as powerful machine learning technique provides more accurate result these combination has been widely used for assessment of various neurological diseases. ON state is the state where the effect of medicine is present and OFF state the effect of medicine is reduced or not present at all. Classification of ON/OFF state for the Parkinson's disease is important because the patients could injure them self due to freezing of gait and gait related problems in the OFF state. in this paper wearable sensor based approach has been used to collect the data in ON and OFF state and machine learning techniques are used to automate the classification based on the gait pattern. Supervised machine learning techniques able to provide 97.6% accuracy while classifying the ON/OFF state.

• 자동차안전학회지
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• 제14권2호
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• pp.70-74
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• 2022

This paper describes the development of a target vehicle state estimation algorithm using vehicle-to-vehicle (V2V) communication. Perceiving the state of the target vehicle has great importance for successful autonomous driving and has been studied using various sensors and methods for many years. V2V communication has advantage of not being constrained by surrounding circumstances relative to other sensors. In this paper, we adopt the V2V signal for estimating the target vehicle state. Since applying only the V2V signal is improper by its low frequency and latency, the signal is used as additional measured data to improve the estimation accuracy. We estimate the target vehicle state using Extended Kalman filter (EKF); a point mass model was utilized in process update to predict the state of next step. The process update is followed by measurement update when ego vehicle receives V2V information. The proposed study evaluated state estimation by comparing input V2V information in an experiment where the ego vehicle follows the target vehicle behind it.

• 한국정보통신학회:학술대회논문집
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• 한국해양정보통신학회 2011년도 춘계학술대회
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• pp.263-266
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• 2011

자동차 운전자는 자신의 안전을 위해 항상 차량의 상태를 점검하여 차량의 상태를 파악하는 것이 필수이다. 만약 운전자가 차량의 상태를 알고자 한다면 전문 업체에게 의뢰하기 때문에 운전자는 많은 시간과 금전적 비용이 지불되어야 한다. 현재 IT의 발달로 인해 스마트폰의 다양한 기능을 이용하여 차량의 상태를 점검 할 수 있게 되었다. 그렇지만 기존의 스마트폰 자동차 진단 시스템은 자동차의 전문적인 지식이 학습해야 차량 상태를 알 수 있는 상태라 사용자들에게 진단기의 필요성이 부각되지 않는다. 이러한 불편한 점을 반영하여 스마트폰을 이용해 사용자들이 손쉽게 사용할 수 있으면서 자신의 차량 상태를 한 번에 파악할 수 있는 시스템이 요구된다. 본 논문에서는 OBD-II 프로토콜 변환 블루투스 커넥터를 통해 받아오는 OBD-II 정보를 이용해 자동차 운전자에게 필요한 차량 소모품 교체 주기의 점검, 차량 문제점 진단, 그리고 에코 드라이빙 판단 정보를 사용자에게 실시간으로 보여주며 손쉽게 사용할 수 있는 자동차 진단 시스템을 안드로이드 기반 스마트폰에서 구현 하였다.

• 한국정보통신학회논문지
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• 제26권10호
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• pp.1509-1516
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• 2022

본 논문에서는 UML 상태 다이어그램으로부터 코드 구현 모델을 생성하는 방법을 제시한다. 첫째로 상태 다이어그램의 상태를 객체화하고, 상태 디자인 패턴에 기반하여 동작 메커니즘을 구조화한다. 다음으로 이 구조에 기반하여 컨텍스트 클래스와 이의 인터페이스 역할을 하는 추상 상태 클래스, 그리고 하위 구상 클래스의 구현 코드를 생성하는 규칙들을 설정한다. 이들 규칙들은 Java의 언어 구조에 기반해서, 상태 다이어그램의 상태들과 동작들에 대한 코드 구현 모델을 생성하게 된다. 한편, 코드의 자동 생성을 위해서는 코드 모델로부터 코드 생성을 위해 구조화된 정보가 필요하다. 따라서, 코드 생성을 위한 정보를 메타 클래스 모델과 메타 행위 모델 형태로 구조화하여 구축한다. 이 메타 정보들에 기반하여 상태 다이어그램으로부터 Java 프로그래밍 언어로의 자동 코드 생성을 위한 엔진의 구축이 가능하다. 또한 코드 모델 생성 엔진은 독자적으로 또는 UML 도구의 상태 다이어그램 기능을 지원하는 도구에 통합된 모듈로서 사용될 수 있다.