• 한국항해항만학회지
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• 제35권8호
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• pp.677-682
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• 2011

마산항은 경남의 대표항만으로서 현재 개발계획이 진행되고 있다. 그러나, 재도약을 위한 개발계획이 진행되고 있음에도 불구하고 현재의 경쟁력과 거기에 따른 발전방향에 관한 연구가 매우 부족한 실정이다. 따라서, 본 연구는 이러한 점에 주목하여 마산항의 경쟁력을 분석하는 것을 목적으로 수행되었다. 연구의 목적을 달성하기 위해, 우선 실증적 차원에서 중소형 항만의 경쟁력 평가를 위한 평가모형을 수립하고, 엄밀한 경쟁력 평가를 위해 선행연구로부터 상대적 가중치 및 상호중복성을 고려한 경쟁력 지수체계를 도입하였다. 실제 마산항을 이용하는 이용주체들로부터 획득한 자료를 분석한 결과, 마산항은 현재 63점 정도의 수준에 머물러 있는 것으로 파악되었다.

• Geomechanics and Engineering
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• 제27권5호
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• pp.511-525
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• 2021

In the design of underground excavation, the shear strength (SS) is a key characteristic. It describes the way the rock material resists the shear stress-induced deformations. In general, the measurement of the parameters related to rock shear strength is done through laboratory experiments, which are costly, damaging, and time-consuming. Add to this the difficulty of preparing core samples of acceptable quality, particularly in case of highly weathered and fractured rock. This study applies rock index test to the indirect measurement of the SS parameters of shale. For this aim, two efficient artificial intelligence methods, namely (1) adaptive neuro-fuzzy inference system (ANFIS) implemented by subtractive clustering method (SCM) and (2) support vector regression (SVR) optimized by Harmony Search (HS) algorithm, are proposed. Note that, it is the first work that predicts the SS parameters of shale through ANFIS-SCM and SVR-HS hybrid models. In modeling processes of ANFIS-SCM and SVR-HS, the results obtained from the rock index tests were set as inputs, while the SS parameters were set as outputs. By reviewing the obtained results, it was found that both ANFIS-SCM and SVR-HS models can provide acceptable predictions for interlocking and friction angle parameters, however, ANFIS-SCM showed a better generalization capability.

• 지능정보연구
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• 제17권3호
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• pp.187-201
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• 2011

방향성(Direction)과 변동성(Volatility)에 대한 분석은 증권투자를 위한 시장분석의 기초가 된다. 변동성분석이 옵션 투자에서 중요하다면 주식이나 주가지수선물투자는 방향성분석에 의하여 투자성과가 결정된다. 기존의 금융분석에서 기계학습을 이용한 방향성에 대한 연구는 주가나 투자위험의 예측을 중심으로 이루어졌으며, 최근에 와서야 실전투자를 위한 매매시스템(trading system) 개발에 대한 연구가 이루어지고 있다. 인공지능형 주가예측모형에서는 ANN(artificial neural networks), fuzzy system, SVM(Support Vector Machine) 등의 기법이 주로 활용되고 있다. 본 연구에서는 방향성매매를 위한 지능형 기계학습방법 중에서도 패턴인식에서 좋은 성과를 보이고 있는 은닉마코프 모형(Hidden Markov Model)을 이용한다. 실무적으로는 방향성 예측을 위해 주로 주가의 추세분석(Trend Analysis)을 활용한다. 다양한 기술적 지표를 이용한 추세분석에 기반한 시스템트레이딩(System Trading) 기법은 실전투자에서 점차 확대추세에 있다. 본 연구에서는 시스템트레이딩 기법 중 실무에서 많이 이용되는 이동평균교차전략(moving average cross)에 연속 은닉마코프모형을 적용한 지능형 매매시스템을 제안하고, 실제 주가자료를 이용한 시뮬레이션 결과를 제시한다. 세계적 선물시장으로 성장한 KOSPI200 선물시장에서 제안된 매매시스템의 장기간의 투자성과를 분석하기 위하여 지난 21년 동안의 KOSPI200 주가지수자료를 실증 분석하였다. 분석결과는 KOSPI200 주가지수선물의 방향성매매에서 제안된 CHMM기반 지능형 매매시스템이 실전에서 일반적으로 활용되는 시스템트레이딩 기법의 투자성과를 개선할 수 있음을 보여주었다.

• 감성과학
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• 제12권1호
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• pp.77-86
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• 2009

자동차는 첨단공업 기술이 고도로 집적되어 있는 인간-기계 시스템(man machine system)이다. 자동차에 대한 새로운 감성요구를 실현하기 위해서는 인체와 오랜 시간 접촉해 있는 시트 표피재의 분석이 반드시 필요하다. 본 연구에서는 자동차 시트 표피재의 역학적 특성과 감성을 고려한 고급감을 예측하여 고감성 내장 표피재 개발에 기여하고자 한다. 감성용어는 Softness(유연한), Elasticity(탱글탱글한), Volume(풍성한), Stickiness (끈끈한)를 설정하였으며, 이와 대응하는 표피재의 역학적 특성 치를 측정하였다. 피혁의 특성평가에 의한 결과로 resilience, bending moment, thickness와 friction 값을 얻을 수 있었으며, 이러한 역학적 특성 치를 softness, elasticity, volume, stickiness 값으로 변화하기 위해 fuzzy logic을 사용하였다. 또한 Fuzzy logic의 결과인 Softness, Elasticity, Volume, Stickiness 값으로 피혁의 고급감을 예측하기 위한 신경망 모델(Neural network)을 구성하였다. 즉, 자동차 표피재 중 피혁의 4가지 물리량으로 인간의 감성인 표피재의 고급감을 예측하여 고감성 자동차 시트 표피재의 개발을 위한 예측 모델의 가능성을 평가하였다.

• 한국지진공학회:학술대회논문집
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• 한국지진공학회 2000년도 추계 학술발표회 논문집 Proceedings of EESK Conference-Fall 2000
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• pp.251-260
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• 2000

Seismic performance evaluation systems require rational classification of structure systems, proper evaluation criteria, and their scoring index for synthesis. Current seismic performance systems need expert judgments based on collection of available data, approximate analysis of important items, and various scoring system. This study presents a three-step seismic performance evaluation system for building structures in Korea. Each evaluation step determines the seismic performance and the method depends on the degree of refinement of analysis. The preliminary step evaluation involves the global attributes of structures such as vertical irregularity, asymmetric plan, redundancy, and age of structures. The second step requires an elastic analysis for estimation of forces acting on critical sections and checks the strength and ductility. The final step requires inelastic capacity of structures. Each stephas own evaluation scheme with proper weighing factor dependent on the importance and consequence. This study applies the fuzzy theory to a scoring method that synthesizes the individual quantity to a representative value.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제1권1호
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• pp.29-34
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• 2001

This paper is devoted to investigating direct adaptive neural control of nonlinear systems with uncertain or unknown dynamic models. In the direct adaptive neural networks control area, theoretical issues of the existing backpropagation-based adaptive neural networks control schemes. The major contribution is proposing the variable index control approach, which is of great significance in the control field, and applying it to derive new stable robust adaptive neural network control schemes. This new schemes possess inherent robustness to system model uncertainty, which is not required to satisfy any matching condition. To demonstrate the feasibility of the proposed leaning algorithms and direct adaptive neural networks control schemes, intensive computer simulations were conducted based on the flexible joint robot systems and functions.

• 한국정보과학회논문지:소프트웨어및응용
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• 제26권1호
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• pp.130-130
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• 1999

In this paper, we propose a novel design method for improving performance of existing FCM-type clustering algorithms. First, we define the performance measure which focuses on bothcompactness and separation of clusters. Next, we optimize this measure using evolution program.Especially the proposed method has following merits: ① using evolution program, it solves suchproblems as initialization, number of clusters, and convergence to local optimum ② it reduces searchspace and improves convergence speed of algorithm since it represents chromosome with possiblepotential centers which are selected possible candidates of centers by density measure ③ it improvesperformance of clustering algorithm with the performance index which embedded both compactnessand separation Properties ④ it is robust to noise data since it minimizes its effect on center search.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제4권3호
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• pp.337-340
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• 2004

The clustering problem can be formulated as the problem to find the number of clusters and a partition matrix from a given data set using the iterative or non-iterative algorithms. The author proposes a nearest neighbor and validity-based clustering algorithm where each data point in the data set is linked with the nearest neighbor data point to form initial clusters and then a cluster in the initial clusters is linked with the nearest neighbor cluster to form a new cluster. The linking between clusters is continued until no more linking is possible. An optimal set of clusters is identified by using the conventional cluster validity index. Experimental results on well-known data sets are provided to show the effectiveness of the proposed clustering algorithm.

• 산업경영시스템학회지
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• 제14권24호
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• pp.199-206
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• 1991

As the development of modern technology, human works shift whose roll from physical conditions to the system monitoring tasks. In this paper, safety-presentation configuration is discussed instead of well-known fault-warning configuration. Safety-presentation configuration is verified as superior to the fault-warning configuration in hazard prevention. The estimation of system states involves the decision making environments which lack of required in formations and most of all the informations are not precise too. And the limitation of human information processing show doubtful results. So the estimation of system states is regardes as fuzzy number, and its operation produces the parameter that explain the discriminability(d), decision criterion ($\beta$) of system operator's behaviors. These two values served as performance indices. Especially the $\beta$ is a good milestone of the operator's altitude degree of caution.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1254-1257
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• 1993

Presented in this paper is a newly developed motion planning method of an autonomous mobile robot(MAR) which can be applied to flexible manufacturing systems(FMS). The mobile robot is designed for transporting tools and workpieces between a set-up station and machines according to production schedules of the whole FMS. The proposed method is implemented based on an earlier developed real-time obstacle avoidance method which employs Kohonen network for pattern classification of sonar readings and fuzzy logic for local path planning. Particulary, a novel obstacle avoidance method for moving objects using a collision index, collision possibility measure, is described. Our method has been tested on the SNU mobile robot. The experimental results show that the robot successfully navigates to its target while avoiding moving objects.