도로 위 노면전차 트램를 포함한 다양한 이동수단 흐름을 원활하게 유지하기 위해서는 효율적인 교통 신호 제어가 필요하다. 검증 되지 않은 기술의 현장 평가는 교통안전 측면에서 위험하기에 대부분 가상환경을 통해 적용 기술검증을 선행한다. 본 연구는 다양한 교통신호 제어 알고리즘을 센터 수준에서 적용하는 가상 실험환경 마련을 위한 기능적 요구사항을 정의한다. 기능적 요구사항으로 가 상환경 센터 기반으로 실험을 시작하거나 중지하는 기능, 교통량 등 입력값을 입력하는 기능 등의 기본적 요구사항을 도출하였다. 이 렇게 정의된 기능적 요구사항은 향후 트램 등 다양한 교통수단을 대상으로 하는 가상환경 센터 구축 과정에 효율적으로 참조될 수 있 을 것으로 기대된다.

Most of real-world decision-making processes are used to optimize problems with many objectives of conflicting. Since the betterment of some objectives requires the sacrifice of other objectives, different objectives may not be optimized simultaneously. Consequently, Pareto solution can be considered as candidates of a solution with respect to a multi-objective optimization (MOP). Such problem involves two main procedures: finding Pareto solutions and choosing one solution among them. So-called multi-objective genetic algorithms have been proved to be effective for finding many Pareto solutions. In this study, we suggest a fitness evaluation method based on the achievement level up to the target value to improve the solution search performance by the multi-objective genetic algorithm. Using numerical examples and benchmark problems, we compare the proposed method, which considers the achievement level, with conventional Pareto ranking methods. Based on the comparison, it is verified that the proposed method can generate a highly convergent and diverse solution set. Most of the existing multi-objective genetic algorithms mainly focus on finding solutions, however the ultimate aim of MOP is not to find the entire set of Pareto solutions, but to choose one solution among many obtained solutions. We further propose an interactive decision-making process based on a visualized trade-off analysis that incorporates the satisfaction of the decision maker. The findings of the study will serve as a reference to build a multi-objective decision-making support system.

PURPOSES : In a previous study, an error was detected in data pertaining to the direction and velocity of a roller. Hence, in this study, the correlation between these two variables and acceleration data is analyzed. Relevant algorithms are developed by adding variables to existing algorithms. METHODS : A tachometer and GPS are used to acquire the velocity, compaction direction of rollers, and number of compactions. Subsequently, data input to an accelerometer are compared and analyzed. RESULTS : Based on FFT analysis, it is discovered that the data are inaccurate when a forward reverse is performed. Using the GPS, the velocity of the roller is differentiated based on the number of pledges, and then added as a variable to the algorithm. Subsequently, it is evaluated and analyzed only with data during forward movement based on changes in the latitude and longitude. CONCLUSIONS : It is discovered that the acceleration data values from both the left and right rollers differ owing to their weight difference, as indicated by the asphalt gradient. Data changes based on asphalt gradients are analyzed using gyro sensors. If the correlation between the two sets of data is high, then the algorithm is advanced by introducing a cross spectrum after calibrating the acceleration value based on the gradient.

In weapon assignment studies to defend against threats such as ballistic missiles and long range artillery, threat assessment was partially lacking in analysis of various threat attributes, and considering the threat characteristics of warheads, which are difficult to judge in the early flight stages, it is very important to apply more reliable optimal solutions than approximate solution using LP model, Meta heuristics Genetic Algorithm, Tabu search and Particle swarm optimization etc. Our studies suggest Generic Rule based threat evaluation and weapon assignment algorithm in the basis of various attributes of threats. First job of studies analyzes information on Various attributes such as the type of target, Flight trajectory and flight time, range and intercept altitude of the intercept system, etc. Second job of studies propose Rule based threat evaluation and weapon assignment algorithm were applied to obtain a more reliable solution by reflection the importance of the interception system. It analyzes ballistic missiles and long-range artillery was assigned to multiple intercept system by real time threat assessment reflecting various threat information. The results of this study are provided reliable solution for Weapon Assignment problem as well as considered to be applicable to establishing a missile and long range artillery defense system.

PURPOSES : The driver's ability to make a commitment has resulted in excessive force and a lack of commitment. To solve this problem, we are developing an algorithm that analyzes resolution in real-time by introducing IoT and informs drivers of the completion of compaction. METHODS : Real-time compaction was analyzed by installing accelerometers on the rollers. To evaluate the algorithms, we conducted an apparent density test. RESULTS : The algorithm data and apparent density test data showed similar trends. This means that the proposed algorithms are sufficiently reliable. However, a lack of data samples and the fact that only data prior to completion of the commitment were analyzed may indicate a lack of reliability. CONCLUSIONS : In subsequent studies, the number of samples will be increased and the data after completion of the commitment analyzed to increase reliability. Introducing a tachometer will prevent the TVL from falling sharply when the direction of the rollers' progress changes. In addition, it is also planned to upgrade the algorithms by researching cases in which the algorithms did not produce satisfactory results owing to problems such as temperature and speed.

PURPOSES: The intensiveness of highway management has increased owing to the growth in the number of vehicles and the rapid climate change. The disadvantages produced by these factors can affect management time and cost. Serious traffic accidents and traffic jam may be experienced when snow fall accumulates on highway surfaces and the friction between tires and pavements is lower than that in the general state, in a non-management condition. Such conditions need intensive management. In this regard, one of the spread methods used for the melting material is pre-wetted salt (PWS), which is the frequently used method in South Korea. In the PWS method, the solid material with CaCl2 is mixed with water in 30% concentration and then finally mixed with NaCl before application to pavements. The chloride-type melting material not only is cheaper, but also has a high melting property than the others. It can shorten the pavement or structure life by deterioration and corrosion. This melting material can affect the flora near the highways; hence, an eco-friendly de-icing agent must be utilized considering the environmental effect. METHODS : The Kalman filter algorithm (KFA) was utilized herein to develop optimization models using the performed test data. The KFA, which was developed from recursive filter algorithms, such as the low- and high-pass filters, applies a weighting filter to the Kalman filter. The algorithm has the property of utilizing the filter and updated estimations. In this regard, melting tests were performed for the real applicative utilization of de-icing agents. The KFA was also applied to reduce the error rates and optimize the relationships between the test data and the predictions. RESULTS: Comparing the measurements performed, the error was reduced by 1.69 g when the KFA was applied. Moreover, the error can be optimized to approximately 91.4% compared to the test errors. The prediction data had over 85% tendency in the test measurement, showing that the KFA application can reduce the error and increase the tendency. By comparison, the agent with CaCl2 showed the best ice melting performance within 10 min without surface temperature. However, the PWS with a 25% concentration indicated the best water melting performance from start to end of the test time, implying that this is a powerful agent in terms of performance. CONCLUSIONS : The melting test is an artificial test method; therefore, it can generate a huge error from the test. The error and the tendency can be controlled by tracking the measurement error and the white noise matrix using the KFA. A further research will be performed to track the measurement error and the white noise matrix. Other optimization methods will also be applied to reduce the experimental error.

Recently, blockchain technology has been recognized as one of the most important issues for the 4th Industrial Revolution which can be represented by Artificial Intelligence and Internet of Things. Cryptocurrency, named Bitcoin, was the first successful implementation of blockchain, and it triggered the emergence of various cryptocurrencies. In addition, blockchain technology has been applied to various applications such as finance, healthcare, manufacturing, logistics as well as public services. Distributed consensus algorithm is an essential component in blockchain, and it enables all nodes belonging to blockchain network to make an agreement, which means all nodes have the same information. For example, Bitcoin uses a consensus algorithm called Proof-of-Work (PoW) that gives possession of block generation based on the computational volume committed by nodes. However, energy consumption for block generation in PoW has drastically increased due to the growth of computational performance to prove the possession of block. Although many other distributed consensus algorithms including Proof-of-Stake are suggested, they have their own advantages and limitations, and new research works should be proposed to overcome these limitations. For doing this, above all things, we need to establish an evaluation method existing distributed consensus algorithms. Based on this motivation, in this work, we suggest and analyze assessment items by classifying them as efficiency and safety perspectives for investigating existing distributed consensus algorithms. Furthermore, we suggest new assessment criteria and their implementation methods, which can be used for a baseline for improving performance of existing distributed consensus algorithms and designing new consensus algorithm in future.

Distributed genetic algorithm (DGA), also known as island model or coarse-grained model, is a kind of parallel genetic algorithm, in which a population is partitioned into several sub-populations and each of them evolves with its own genetic operators to maintain diversity of individuals. It is known that DGA is superior to conventional genetic algorithm with a single population in terms of solution quality and computation time. Several researches have been conducted to evaluate effects of parameters on GAs, but there is no research work yet that deals with structure of DGA. In this study, we tried to evaluate performance of various genetic algorithms (GAs) for the famous symmetric traveling salesman problems. The considered GAs include a conventional serial GA (SGA) with IGX (Improved Greedy Crossover) and several DGAs with various combinations of crossover operators such as OX (Order Crossover), DPX (Distance Preserving Crossover), GX (Greedy Crossover), and IGX. Two distinct immigration policies, conventional noncompetitive policy and newly proposed competitive policy are also considered. To compare performance of GAs clearly, a series of analysis of variance (ANOVA) is conducted for several scenarios. The experimental results and ANOVAs show that DGAs outperform SGA in terms of computation time, while the solution quality is statistically the same. The most effective crossover operators are revealed as IGX and DPX, especially IGX is outstanding to improve solution quality regardless of type of GAs. In the perspective of immigration policy, the proposed competitive policy is slightly superior to the conventional policy when the problem size is large.

본 연구는 전자상거래에서 협력적 필터링 알고리즘을 통한 사용자의 선호도 예측 정확도와 사용자가 평가한 선호도 평가치의 관계를 분석하여 알고리즘의 예측 정확도 에 영향을 미치는 평가치의 통계적 특성에 관하여 연구한다. 협력적 필터링 알고리즘 의 예측 정확도는 상품에 대해 공통의 관심을 갖는 이웃 사용자들의 선정과 이들의 선호도 경향이 중요한 요인이지만 본 연구에서는 선호도 예측을 위한 자신의 선호도 평가치 특성이 알고리즘에 중요한 요인임을 제시한다. 이러한 평가치의 평균, 표준편 차, 왜도, 첨도 등과 같은 통계적 특성이 선호도 예측 정확도와 연관성이 있음을 제시 하여 차후 연구에서 선호도 예측 이전에 사용자의 선호도 예측성과에 대한 사전평가 의 가능성을 제시하고자 한다.

플라스틱 사출 제품은 다양한 가전제품과 하이테크 제품에 널리 사용되고 있다. 그러나 현재의 치열한 경쟁적 비즈니스 환경에서 플라스틱 사출 제품 제조업자들은 고객을 만족시키면서 경쟁력을 얻기 위하여 다른 경쟁자들보다 먼저 새로운 제품을 시장에 출시하고 신제품의 개발기간을 줄이기 위한 노력을 할 여유가 부족하다. 따라서 무한경쟁의 시장에서 살아남기 위해서는 제조업자들은 시장 마켓 점유를 빠르게 올리는 것과 동시에 제품의 가격 경쟁력을 가져야 한다. 특징기반

자동사고검지 알고리즘의 대부분은 사고가 발생했을 때 사고로 검지하지 못하고, 혼잡으로 검지하는 경우가 많다는 문제점을 가지고 있다. 또한 교통정보센터 운영자들은 교통사고검지시스템을 운영하면서 대부분 CCTV 육안감시 또는 운전자들의 신고에 의존하여 사고처리를 하고 있는 실정이다. 그 이유는 현재 운영되고 있는 교통사고검지시스템에서는 실제 사고가 아닌데도 불구하고, 사고라는 오검지 경고가 많이 발생되어 시스템 전체의 신뢰도가 떨어진다는 문제점이 있기 때문이다. 다시 말해 교통사고검지시스템의 알고리즘은 검지율(Detection probability)이 높아야 함과 동시에, 오검지율(False alarm probability)은 낮아야 하고, 정확한 사고지점과 시간을 검지해 낼 수 있어야 한다. 이에 본 연구는 검지율을 높이고 동시에, 오검지율을 낮추는 방법으로 기 개발된 가우시안 혼합모델(Gaussian Mixture Model)과 개별차량 Tracking을 이용하여 개발한 사고검지 알고리즘을 교통정보센터 관리시스템(Center Management System)에 적용하고, 실제 교통상황에서 사고검지율과 오검지의 빈도를 측정하여 그 효과를 검증 및 평가하고자 한다.

양궁의 슈팅과정에 대하여 정신 집중력과 긴장 이완도를 뇌파를 이용하여 평가하였다. 정신 집중과 긴장 이완 수준의 평가는 집중과 명상 알고리즘을 이용하여 수행되었다. 우수, 중급, 그리고 초급 양궁선수들이 야외 양궁장에서 근거리와 장거리 타깃을 대상으로 슈팅 훈련을 할 때 앞이마(Fp1)에 전극을 부착한 헤드밴드 형태의 휴대용 뇌파 시스템으로 뇌파를 기록하였다. 개인별로 기록된 뇌파는 집중과 명상 알고리즘을 이용하여 실시간으로 정신집중과 긴장이완 수준이 계산되었으며, 슈팅 과정에서 정신집중과 긴장이완 수준의 변화 형태가 분석되었다. 개인별로 각각의 슈팅에 대한 정신집중 및 긴장이완 수준 변화는 네 유형으로 분류되었으며, 이 변화 유형은 양궁 선수들의 경기력을 평가하는 신뢰성 있는 지표로 나타났다. 우수 선수의 경우 정신 집중과 긴장이완 수준이 슈팅과정의 진행에 따라 동시에 증가하는 형태로 나타났으며, 중급 선수의 경우 정신 집중도는 증가하는 반면 긴장 이완 수준은 감소하는 결과를 보여 주었다. 실시간으로 제공되는 정신 집중과 긴장 이완 수준의 변화는 양궁 선수들의 경기력 평가뿐만 아니라 훈련 시에 유익한 피드백이 되었다.

본 논문에서는 수정 슬라이딩 모드제어기의 비선형 이력구조물의 지진응답 제어성능이 평가되었다. 수정 슬라이딩모드 제어는 제어력을 계산하기 위해 Lyapunov함수의 목표변화율을 이용하는 기법으로 기존 연구에서는 선형구조물에 대한 성능만이 조사되었다. 그러나 강진시 대부분의 구조물은 비선형 거동을 보인다는 점을 고려할 때 기존 연구의 결과는 실제 적용에 있어 제한점을 가지고 있다. Bouc-Wen 모델을 사용하여 구조물의 비선형 거동을 모델링 하였으며, 이력이선형 단자유도 구조물에 대한 통계해석과 비선형이력 면진구조물에 대한 해석결과는 제안된 수정 슬라이딩모드 제어알고리즘이 기존의 슬라이딩모드 제어기보다 우수한 성능을 가짐을 보여준다.