• Journal of Advanced Marine Engineering and Technology
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• 제24권4호
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• pp.502-508
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• 2000

The amount of container freight is continuously increasing recently, but freight congestion problem frequently occurs at ports due to low efficiency of container crane in transportation and cargo handling. In this paper, a method for designing a fuzzy controller of the container crane system is presented. In this scheme a mathematical model for the system is obtained in state space representation. The response of the proposed fuzzy controller is compared with that of the optimal controller at the same condition. Through the simulation results, the performance of the fuzzy controller was observed better than that of optimal controller in respect of reference change, disturbances and parameter change. The fuzzy controller was also more stable and robust than the optimal controller.

• 한국항해항만학회지
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• 제27권4호
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• pp.383-388
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• 2003

일반적으로 거의 모든 항만들은 출입구가 하나뿐인 폐쇄형태로 설계되어 있다. 선박이 대형화됨에 따라서 선박의 조종곤란성, 선회수역의 크기, 추가적인 예선사용료 등의 문제가 발생한다. 이러한 문제를 해결하기 위해서 폐쇄형태의 항만에서 선박의 출입구가 각각 존재하는 일방통항형 항만형태로의 발상을 전환할 필요가 있다. 따라서 이와 같은 일방통항형 항만의 장점을 선박운항자의 조종부하경감 관점에서 검토하기 위해서는 정량적이고 시스템적인 분석절차가 필요하다. 본 연구에서는 각각 다른 세가지 출항방식에 대한 조선자의 선호도를 퍼지적분 모델을 이용하여 평가하였다.

• International Journal of Computer Science & Network Security
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• 제21권12호
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• pp.257-263
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• 2021

Though many have studied choosing one of the alternative ways to reach a destination, the factors such as average road speed, distance, and number of traffic signals, traffic congestion, safety, and services still presents an indisputable challenge. This paper proposes two approaches: Appropriate membership function and ambiguous rule-based approach. It aims to tackle the route choice problem faced by almost all drivers in any city. It indirectly helps in tackling the problem of traffic congestion. The proposed approach considers the preference of each driver which is determined in a flexible way like a human and stored in the driver profile. These preferences relate to the criteria for evaluating each candidate route, considering the average speed, distance, safety, and services available. An illustrative case study demonstrates the added value of the proposed approach compared to some other approaches.

• 대한교통학회:학술대회논문집
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• 대한교통학회 1995년도 제28회 학술발표회
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• pp.77-107
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• 1995

This paper documents the development of a fuzzy logic based incident detection model for urban diamond interchanges. Research in incident detection for intersections and arterials is at a very initial stage. Existing algorithms are still far from being robust in dealing with the difficulties related with data availability and the multi-dimensional nature of the incident detection problem. The purpose of this study is to develop a new real-time incident detection model for urban diamond interchanges. The development of the algorithm is based on fuzzy logic. The incident detection model developed through this research is capable of detecting lane¬blocking incidents when their effects are manifested by certain patterns of deterioration in traffic conditions and, thereby, adjustments in signal control strategies are required. The model overcomes the boundary condition problem inherent in conventional threshold-based concepts. The model captures system-wide incident effects utilizing multiple measures for more accurate and reliable detection, and serves as a component module of a real-time traffic adaptive diamond interchange control system. The model is designed to be readily scalable and expandable for larger systems of arterial streets. The prototype incident detection model was applied to an actual diamond interchange to investigate its performance. A simulation study was performed to evaluate the model's performance in terms of detection rate, false alarm rate, and mean time to detect. The model's performance was encouraging, and the fuzzy logic based approach to incident detection is promising.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제9권3호
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• pp.166-171
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• 2009

In recent years, video detection systems have been implemented in various infrastructures such as airport, public transportation, power generation system, water dam and so on. Recognizing moving objects in video sequence is an important problem in computer vision, with applications in several fields, such as video surveillance and target tracking. Segmentation and tracking of multiple vehicles in crowded situations is made difficult by inter-object occlusion. In the system described in this paper, the mean shift algorithm is firstly used to filter and segment a color vehicle image in order to get candidate regions. These candidate regions are then analyzed and classified in order to decide whether a candidate region contains a license plate or not. And then some characters in the license plate is recognized by using the fuzzy ARTMAP neural network, which is a relatively new architecture of the neural network family and has the capability to learn incrementally unlike the conventional BP network. We finally design a license plate recognition system using the mean shift algorithm and fuzzy ARTMAP neural network and show its performance via some computer simulations.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1996년도 Proceedings of the Korea Automatic Control Conference, 11th (KACC); Pohang, Korea; 24-26 Oct. 1996
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• pp.32-35
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• 1996

Traffic signal cycle optimization is one of the most efficient ways for reducing fuel consumption and improving vehicle waiting time of highsaturated traffic conditions. But most research focused on lowsaturated traffic conditions. Only a few studies have researched traffic control for highsaturated traffic conditions. In this paper reviews the problem of conventional traffic signal system and creates optimal traffic cycle of at the bottom traffic intersection using 27 fuzzy rules. Moreover, to prevent spillback, it can adapt control even though upper traffic intersection has a different saturation rate, road length, road slope and road width.

• Advances in aircraft and spacecraft science
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• 제10권5호
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• pp.393-418
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• 2023

Drones are increasingly used in logistics delivery due to their low cost, high-speed and straight-line flight. Considering the small cargo capacity, limited endurance and other factors, this paper optimized the pickup and delivery vehicle routing problem with time windows in the mode of "truck+drone". A mixed integer programming model with the objective of minimizing transportation cost was proposed and an improved adaptive large neighborhood search algorithm is designed to solve the problem. In this algorithm, the performance of the algorithm is improved by designing various efficient destroy operators and repair operators based on the characteristics of the model and introducing a simulated annealing strategy to avoid falling into local optimum solutions. The effectiveness of the model and the algorithm is verified through the numerical experiments, and the impact of the "truck+drone" on the route cost is analyzed, the result of this study provides a decision basis for the route planning of "truck+drone" mode delivery.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권7호
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• pp.2399-2413
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• 2021

A recommendation system is an information filter tool, which uses the ratings and reviews of users to generate a personalized recommendation service for users. However, the cold-start problem of users and items is still a major research hotspot on service recommendations. To address this challenge, this paper proposes a high-efficient hybrid recommendation system based on Fuzzy C-Means (FCM) clustering and supervised learning models. The proposed recommendation method includes two aspects: on the one hand, FCM clustering technique has been applied to the item-based collaborative filtering framework to solve the cold start problem; on the other hand, the content information is integrated into the collaborative filtering. The algorithm constructs the user and item membership degree feature vector, and adopts the data representation form of the scoring matrix to the supervised learning algorithm, as well as by combining the subjective membership degree feature vector and the objective membership degree feature vector in a linear combination, the prediction accuracy is significantly improved on the public datasets with different sparsity. The efficiency of the proposed system is illustrated by conducting several experiments on MovieLens dataset.

• 해양환경안전학회:학술대회논문집
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• 해양환경안전학회 2007년도 춘계학술발표회
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• pp.133-139
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• 2007

본 연구는 해상교통정보시스템의 정보제공에 대한 구조를 퍼지구조모델법을 이용하여 분석하였다. 일반적으로 퍼지구조모델법에서는 임계치와 파라메타의 선정의 어려움으로 인해 시스템의 전체적인 구조를 파악하기가 어려웠다. 따라서 본 연구에서는 계층구조 그래프와 분할도를 이용하여 해상교통정보시스템의 정보제공에 대한 전체적인 구조를 분석할 수 있었다. 또한 몇가지의 예를 통해 모델의 해석과 실제 문제점을 제시하고 분석하였다.

• 해양환경안전학회:학술대회논문집
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• 해양환경안전학회 2006년도 추계학술발표회
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• pp.253-258
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• 2006

본 연구는 우리나라 RCC/RSC의 효율성을 2개의 입력변수와 5개의 출력변수를 자료를 토대로 퍼지 DEA를 이용하여 측정 평가하였다. 특히, 본 연구에서는 해양사고 발생자료 뿐만 아니라 환경스트레스치에 의한 잠재적 해양사고 발생가능 자료도 고려하여 운영효율성을 분석하였다. 한편, 현실자료에는 수많은 애매성이 존재하기 때문에 본 연구에서는 DEA법에 퍼지이론을 이용한 퍼지 DEA 법을 이용하였다. 이 퍼지 DEA법은 ${\alpha}-cut$ 을 기반으로 한 크리습 선형계획문제로 변환된 것으로 입/출력 변수를 삼각형 퍼지수로 하여 해를 산출하는 방법을 제안하였고, 제안된 퍼지 DEA를 이용하여 퍼지 RCC/RSC의 순위를 결정하였다. ${\alpha}-cut$를 0.5 로 하여 효율성을 산출한 결과 YS, BS, MP, TS, JJ, PH, US, IC, SC, DH, GS, TA, WD RCC/RSC 순으로 효율성 이 높게 나타났다. 따라서 효율성이 비교적 낮게 평가된TA, WD RCC/RSC는 효율성이 높은 준거집단을 참조하여 벤치마킹을 해야할 필요가 있다.