• Industrial Engineering and Management Systems
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• 제9권2호
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• pp.88-96
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• 2010

This paper presents a two-phase quantitative framework to aid the decision making process for effective selection of an efficient freight logistics hub from 8 alternatives in Thailand on the North-South economic corridor. Phase 1 employs both multiple regression and Pearson Feature selection to find the important criteria, as defined by logistics hub score, and to reduce number of criteria by eliminating the less important criteria. The result of Pearson Feature selection indicated that only 5 of 15 criteria affected the logistics hub score. Moreover, Genetic Algorithm (GA) was constructed from original 15 criteria data set to find the relationship between logistics criteria and freight logistics hub score. As a result, the statistical tools are provided the same 5 important criteria, affecting logistics hub score from GA, and data mining tool. Phase 2 performs the fuzzy stochastic AHP analysis with the five important criteria. This approach could help to gain insight into how the imprecision in judgment ratios may affect their alternatives toward the best solution and how the best alternative may be identified with certain confidence. The main objective of the paper is to find the best alternative for selecting freight logistics hub under proper criteria. The experimental results show that by using this approach, Chiang Mai province is the best place with the confidence interval 95%.

• 한국생산제조학회지
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• 제19권1호
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• pp.145-155
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• 2010

This paper presents a new approach to obstacle avoidance for mobile robot in unknown or partially unknown environments. The method combines two navigation subsystems: low level and high level. The low level subsystem takes part in the control of linear, angular velocities using a multivariable PI controller, and the nonlinear position control. The high level subsystem uses ultrasonic and IR sensors to detect the unknown obstacle include static and dynamic obstacle. This approach provides both obstacle avoidance and target-following behaviors and uses only the local information for decision making for the next action. Also, we propose a new algorithm for the identification and solution of the local minima situation during the robot's traversal using the set of fuzzy rules. The system has been successfully demonstrated by simulations and experiments.

• Asian Journal of Atmospheric Environment
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• 제12권3호
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• pp.215-231
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• 2018

In order to understand the variation of air quality and the concentration of atmospheric particulates in Chengdu Second Ring Road renovation project, this paper starts to investigate the surrounding residents' opinions on the influenced environment and their daily lives via questionnaires. Then the study numerically simulates the change rule of atmospheric particulates in terms of time and space by using the Gaussian dispersion-deposition model and the compartment model. The optimized scientific scheme is selected by the improved fuzzy analytical hierarchy process(FAHP) to help decision making for the future urban reconstructions. Finally, the reduced emissions of atmospheric particulates are measured when the improvement scheme is provided. According to the study, it can be concluded that the concentration of atmospheric particulates increases rapidly in central Chengdu city during the renovation project, which results in worsening air quality in Chengdu during March 2012 to March 2013. Taking related measures on energy saving and emission reduction can effectively reduce the concentration of atmospheric particulates and promote economic, environmental and social coordination.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제16권1호
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• pp.44-51
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• 2016

Recently, artificial intelligence has reached the level of top information technologies that will have significant influence over many aspects of our future lifestyles. In particular, in the fields of machine learning technologies for classification and decision-making, there have been a lot of research efforts for solving estimation and control problems that appear in the various kinds of portfolio management problems via data-driven approaches. Note that these modern data-driven approaches, which try to find solutions to the problems based on relevant empirical data rather than mathematical analyses, are useful particularly in practical application domains. In this paper, we consider some applications of modern data-driven machine learning methods for portfolio management problems. More precisely, we apply a simplified version of the sparse Gaussian process (GP) classification method for classifying users' sensitivity with respect to financial risk, and then present two portfolio management issues in which the GP application results can be useful. Experimental results show that the GP applications work well in handling simulated data sets.

• 한국농업기계학회:학술대회논문집
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• 한국농업기계학회 1996년도 International Conference on Agricultural Machinery Engineering Proceedings
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• pp.773-779
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• 1996

Dried oak mushroom have complex and various visual features. Grading and sorting of dried oak mushrooms has been done by the human expert. Though actions involved in human grading looked simple, a decision making underneath the simple action comes from the result of the complex neural processing of the visual image. Through processing details involved in human visual recognition has not been fully investigated yet, it might say human can recognize objects via one of three ways such as extracting specific features or just image itself without extracting those features or in a combined manner. In most cases, extracting some special quantitative features from the camera image requires complex algorithms and processing of the gray level image requires the heavy computing load. This fact can be worse especially in dealing with nonuniform, irregular and fuzzy shaped agricultural products, resulting in poor performance because of the sensitiveness to the crisp criteria or specific ules set up by algorithms. Also restriction of the real time processing often forces to use binary segmentation but in that case some important information of the object can be lost. In this paper, the neuro net based real time recognition algorithm was proposed without extracting any visual feature but using only the directly captured raw gray images. Specially formated adaptable size of grids was proposed for the network input. The compensation of illumination was also done to accomodate the variable lighting environment. The proposed grading scheme showed very successful results.

• 대한임베디드공학회논문지
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• 제9권3호
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• pp.157-163
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• 2014

This paper demonstrates the benefit of using MOOS-IvP in the development of control system for Unmanned Underwater Vehicles(UUV). The demand for autonomy in UUVs has significantly increased due to the complexity in missions to be performed. Furthermore, the increased number of sensors and actuators that are interconnected through a network has introduced a need for a middleware platform for UUVs. In this context, MOOS-IvP, which is an open source software architecture, has been developed by several researchers from MIT, Oxford University, and NUWC. The MOOS software is a communication middleware based on the publish-subscribe architecture allowing each application to communicate through a MOOS database. The IvP Helm, which is one of the MOOS modules, publishes vehicle commands using multi-objective optimization in order to implement autonomous decision making. This paper explores the benefit of MOOS-IvP in the development of control software for UUVs by using a case study with an auto depth control system based on self-organizing fuzzy logic control. The simulation results show that the design and verification of UUV control software based on MOOS-IvP can be carried out quickly and efficiently thanks to the reuse of source codes, modular-based architecture, and the high level of scalability.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제13권4호
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• pp.315-323
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• 2013

Vessel Traffic Service (VTS) is being used at ports and in coastal areas of the world for preventing accidents and improving efficiency of the vessels at sea on the basis of "IMO RESOLUTION A.857 (20) on Guidelines for Vessel Traffic Services". Currently, VTS plays an important role in the prevention of maritime accidents, as ships are required to participate in the system. Ships are diversified and traffic situations in ports and coastal areas have become more complicated than before. The role of VTS operator (VTSO) has been enlarged because of these reasons, and VTSO is required to be clearly aware of maritime situations and take decisions in emergency situations. In this paper, we propose a prediction table to improve the work of VTSO through the Cognitive Work Analysis (CWA), which analyzes the VTS work very systematically. The required data were collected through interviews and observations of 14 VTSOs. The prediction tool supports decision-making in terms of a proactive measure for the prevention of maritime accidents.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.1
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• pp.77-82
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• 2006

Automatic vessel collision-avoidance systems have been studied in the fields of artificial intelligence and navigation for decades. And to facilitate automatic collision-avoidance decision-making in two-vessel-encounter situation, several expert and fuzzy expert systems have been developed. However, none of them can negotiate with each other as seafarers usually do when they intend to make a more economic overall plan of collision avoidance in the COLREGS-COST-HIGH situations where collision avoidance following the International Regulations for Preventing Collisions at Sea(COLREGS) costs too much. Automatic Identification System(AIS) makes data communication between two vessels possible, and negotiation methods can be used to optimize vessel collision avoidance. In this paper, a negotiation framework is put forward to enable vessels to negotiate to optimize collision avoidance in the COLREGS-COST-HIGH situations at open sea. A vessel vector space is defined and therewith a cost model is put forward to evaluate the cost of collision-avoidance actions. Negotiations between a give-way vessel and a stand-on vessel and between two give-way vessels are considered respectively to reach overall low cost agreements. With the framework proposed in this paper, two vessels involved in a COLREGS-COST-HIGH situation can negotiate with each other to get a more economic overall plan of collision avoidance than that suggested by the traditional collision-avoidance expert systems.

• 한국컴퓨터정보학회논문지
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• 제3권2호
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• pp.189-199
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• 1998

본 논문에서는 성공적인 기업활동에 많은 영향을 미치는 정보시스템 계획에 있어주요 관리활동중의 하나인 정보시스템 프로젝트의 선정과정에 대해 DHP(Delphic Hierarchy Process)기법과 FZOGP(Fuzzified Zero-One Goal Programming) 모형을 검토하였다. 정보시스템 선정과정에 적용되는 기존의 AHP에서는 우선순위 결정에 있어 객관적인 측면과 전문가들의 의견이 충분히 반영되지 못하기 때문에 Delphi법을 동시에 고려하는 DHP기법이 필요성이 제시되어지는 것이다. 그러나 이러한 우선순위결정기법은 의사결정과정에 있어서 기업의 자원의 제약과 같은 현실적인 면이 고려되었다고 할 수 없기 때문에 목표계획법의 적용이 검토된다. 또한 다기준 의사결정의 상황에서 목표기준의 모호함이 기존의 목표계획법에서는 반영되지 않기 때문에 이러한 점을 고려하여 퍼지집합을 적용한 모형을 구축하고자 하였다.

• 한국지진공학회논문집
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• 제9권5호
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• pp.41-52
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• 2005

지진응답제어를 위한 효율적 방법으로 제시된 퍼지관리제어기법은 퍼지에 기반한 의사결정과정을 통하여 가변 제어이득행렬을 구현함으로써 하나의 제어이득만으로 표현되는 선형제어기법보다 개선된 제어성능을 발휘할 수 있다. 이 논문에서는 퍼지관리제어기법의 효율성을 하중 및 교량모델의 불확실성에 대한 제어성능의 강인성 측면에서 평가하였다. 강인성 평가에 있어서는 Dyke등이 제시한 벤치마크 교량에 대하여, 최적설계된 LQG기법과 제어성능을 비교하는 방법을 사용하였다. 불확실성을 주는 요인으로는 주파수 특성이 다른 여러 지진가속도의 규모 및 교량의 강성변화를 가정하였다. 최적설계된 LQG 제어기와 제어효과를 비교한 결과, FSC시스템이 지진의 종류와 규모에 따라 보다 작은 전력을 사용하면서도 개선된 제어성능을 발휘하였다. 특히, LQG 제어시스템이 강성변화에 대하여 불안정한 제어성능을 보인 반면, FSC 시스템은 매우 안정적인 응답제어효과를 보이면서도 제어시스템에 소요되는 전력량과 제어장치의 스트로크에 있어서도 큰 변화를 보이지 않음으로써 매우 탁월한 강인성을 보장할 수 있는 것으로 나타났다.