• International Journal of Highway Engineering
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• v.17 no.5
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• pp.57-66
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• 2015

PURPOSES: Nighttime driving is very different from daytime driving because drivers must obtain nighttime sight-distances based on road lights and headlights. Unfortunately, nighttime driving conditions in Korea are far from ideal due to poor lighting and an insufficient number of road lights and inadequate operation and maintenance of delineators. This study is conducted to develop new standards for nighttime road visibility based on experiments of driver perception for nighttime visibility conditions. METHODS : In the study, perception level and satisfaction of nighttime visibility were investigated. A total of 60 drivers participated, including 34 older drivers and 31 young drivers. To evaluate driver perceptions of nighttime road visibility, fuzzy set theory was used because the conventional analysis methods for driver perception are limited in effectiveness for considering the characteristics of perception which are subjective and vague, and are generally expressed in terms of linguistic terminologies rather than numerical parameters. RESULTS : This study found that levels of nighttime visibility, as perceived by drivers, are remarkably similar to their satisfactions in different nighttime driving conditions with a log-function relationship. Older drivers evaluated unambiguously degree of nighttime visibility but evaluations by young drivers regarding it were unclear. CONCLUSIONS : A minimum value of brightness on roads was established as YUX 30, based on final analyzed results. In other words, road lights should be installed and operated to obtain more than YUX 30 brightness for the safety and comfort of nighttime driving.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.5
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• pp.183-188
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• 2013

A variety of cluster analysis techniques prerequisite to cluster objects having similar characteristics in data mining. But the clustering of those algorithms have lots of difficulties in dealing with categorical data within the databases. The imprecise handling of uncertainty within categorical data in the clustering process stems from the only algebraic logic of rough set, resulting in the degradation of stability and effectiveness. This paper proposes a information-theoretic rough entropy(RE) by taking into account the dependency of attributes and proposes a technique called min-mean-mean roughness(MMMR) for selecting clustering attribute. We analyze and compare the performance of the proposed technique with K-means, fuzzy techniques and other standard deviation roughness methods based on ZOO dataset. The results verify the better performance of the proposed approach.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.5
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• pp.467-472
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• 2002

In this paper, we propose a Fuzzy Cellular Neural Network(FCNN) that is based on a-Least Trimmed Square Hausdorff distance(a-LTSHD) which applies Hausdorff distance(HD) to the FCNN structure in order to remove the impulse noise of images effectively and also improve the speed of operation. FCNN incorporates Fuzzy set theory to Cellular Neural Network(CNN) structure and HD is used as a scale which computes the distance between set or two pixels in binary images without confrontation of the feature object. This method has been widely used with the adjustment of the object. For performance evaluation, our proposed method is analyzed in comparison with the conventional FCNN, with the Opening-Closing(OC) method, and the LTSHD based FCNN by using Mean Square Error(MSE) and Signal to Noise Ratio(SNR). As a result, the performance of our proposed network structure is found to be superior to the other algorithms in the removal of impulse noise.

• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.2
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• pp.126-131
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• 2017

In this paper, we propose a method to extract meaningful motion among various kinds of hand gestures on giving commands to robots using hand gestures. On giving a command to the robot, the hand gestures of people can be divided into a preparation one, a main one, and a finishing one. The main motion is a meaningful one for transmitting a command to the robot in this process, and the other operation is a meaningless auxiliary operation to do the main motion. Therefore, it is necessary to extract only the main motion from the continuous hand gestures. In addition, people can move their hands unconsciously. These actions must also be judged by the robot with meaningless ones. In this study, we extract human skeleton data from a depth image obtained by using a Kinect v2 sensor and extract location data of hands data from them. By using the Kalman filter, we track the location of the hand and distinguish whether hand motion is meaningful or meaningless to recognize the hand gesture by using the hidden markov model.

• Journal of the Korea Institute of Building Construction
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• v.16 no.4
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• pp.331-339
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• 2016

According to high-rise, complexation, and enlargement of buildings, various construction methods are being developed, and the significance of construction method selection about main work types has emerged as a major interest. However, it has been pointed out that hand-on workers cannot consider project characteristics carefully, and they lack an objective standard or reference for main construction method selection. Hence, the selection is being made depending on hand-on workers' experience and intuition. To solve this problem, various studies have proceeded for construction method selection of main work types using Artificial Intelligence like Fuzzy, AHP and Case-based reasoning. It is difficult to apply many different kinds of construction method selection to every main work type with consideration for characteristics of work types and condition of a construction site when selecting construction method in the field. Accordingly, this study proposed the decision-making model which can apply to fields easily. Using matrix analysis and liner transformation, this study verified consistency of study models applied in the process of soil retaining selection with a case study.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.5
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• pp.341-346
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• 2013

In this paper, we propose a rule extraction method using a modified Fuzzy Min-Max (FMM) neural network. The suggested method supplements the hyperbox definition with a frequency factor of feature values in the learning data set. We have defined a relevance factor between features and pattern classes. The proposed model can solve the ambiguity problem without using the overlapping test process and the contraction process. The hyperbox membership function based on the fuzzy partitions is defined for each dimension of a pattern class. The weight values are trained by the feature range and the frequency of feature values. The excitatory features and the inhibitory features can be classified by the proposed method and they can be used for the rule generation process. From the experiments of sign language recognition, the proposed method is evaluated empirically.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.12
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• pp.2769-2774
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• 2013

The key of USN(Ubiquitous Sensor Network) technology is low power wireless communication technology and proper resource allocation technology for efficient routing. The distinguished resource allocation method is needed for efficient routing in sensor network. To solve this problems, we propose an algorithm that can be adopted in USN with making up for weak points of PQ and WRR in this paper. The proposed algorithm produces the control discipline by the fuzzy theory to dynamically assign the weight of WRR scheduler with checking the Queue status of each class in sensor network. From simulation results, the proposed algorithm improves the packet loss rate of the EF class traffic to 6.5% by comparison with WRR scheduling method and that of the AF4 class traffic to 45% by comparison with PQ scheduling method.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.128-132
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• 2012

물은 생물의 생존을 위해 필수적인 요소로 인류가 시작된 이래로 물을 효율적으로 이용하고 안전하게 관리하기 위한 노력은 계속되어 왔다. 최근 지구 온난화가 주요 원인으로 알려진 국지성 집중호우의 피해는 매우 심각하며, 이로 인해 치수에 대한 중요성은 날로 커지고 있다. 지금까지 사용해 왔던 홍수 예 경보 과정은 특정 지점의 유출량을 예측하기 위해서 강우-유출 모형을 운영하였다. 그러나 물리적 모형의 경우 운영에 필요한 매개변수의 결정과정이 복잡하고, 매개변수 결정을 위해 많은 자료를 필요로 한다. 또한 그 매개변수의 결정과정은 많은 불확실성을 포함하고 있어서 모형의 운영을 위한 전처리과정과 계산과정을 거치는 동안 발생한 오차가 누적되어 결과물 속에는 많은 오차가 포함되어 있다. 본 연구에서는 기존의 홍수 예 경보 시스템의 문제점과 불확실성을 최대한 감소시키고 더 우수한 유출량 예측을 위해 neuro-fuzzy 추론 기법을 이용한 모형인 ANFIS(Adaptive Neuro-Fuzzy Inference System)를 사용하여 하천수위를 예측하였다. ANFIS는 신경회로망과 퍼지이론을 결합한 기법으로 신경회로망의 구조와 학습 능력을 이용하여 제어환경에서 획득한 입 출력 정보로부터 언어변수의 membership 함수와 제어규칙을 제어 대상에 적합하도록 자동으로 조종하는 기법이다. 본 연구에서는 ANFIS를 사용하여 탄천 하류에 위치한 대곡교의 수위를 예측하였다. 분석을 위해 2007년부터 2011년까지의 탄천 유역의 관측 강우자료와 수위 자료 중 강우강도와 지속시간, 강우 형태에 따라 7개의 강우사상을 선정하였다. 학습자료 및 보정자료의 변화에 따른 예측 오차를 비교하여 모형의 적용성과 적정성을 평가하였다. 적용결과 입력자료 구성의 경우 해당 시간의 강우량 및 수위자료와 10분 전 강우자료를 이용한 모델이 가장 우수한 예측을 보였고, 학습자료의 경우 자료의 길이가 길고, 최대홍수량이 큰 경우 가장 우수한 예측 결과를 보였다. 본 연구의 적용결과 가장 우수한 모형의 경우 30분 예측 첨두수위 오차는 0.32%, RMSE는 0.05m 이고 예측시간이 길어짐에 따라 오차가 비선형적으로 증가하는 경향을 보였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.3
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• pp.307-316
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• 2011

The majority of maritime accidents happened by human factor. For that reason, navigation experts want to an intelligent support system for navigation safety, without officer involvement. The expert system which is one of artificial intelligence skills for navigation support is an important tool that a machine can substitute for an expert through the design of a knowledge base and inference engine using the experience or knowledge of an expert. Further, in the real world, a complex situation requires synthetic estimation with the input of experts in various fields for the correct estimation of the situation, not any one expert. In particular, synthetic estimation is more important for navigation situations than in other cases, because of diverse potential threats. This paper presents the method of knowledge fusion pertaining to navigation safety knowledge from various expert systems, using a blackboard system. Then we will show the validity of the method via a design and implementation of test system effort.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.720-724
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• 2009

This thesis analyze structure of consciousness of success factors of company project by applying FSM(Fuzzy Structural Modeling). FSM is a theory that implied fuzzy theory to ISM(Interpretive Structural Modeling) and is known to be more valid in recognizing a complex pluralistic value system and it is also designed to choose structure model that fits reality with when it is changed by parameter p and $\lambda$. It is desirable to conduct conformity assessment to complement even though selected structure model is considered as conformed because structure model is chosen without objective evaluation for conformity. Therefore, this paper present more objective structure model through conformity evaluation using structural equation modeling on success factors to achieve company project obtained by FSM and analyze the consciousness structure according to that structure.