• The Journal of Korea Robotics Society
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• v.6 no.3
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• pp.292-300
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• 2011

Global positioning system (GPS) is widely used to measure the position of a vehicle. However, the accuracy of the GPS can be severely affected by surrounding environmental conditions. To deal with this problem, the GPS and odometry data can be combined using an extended Kalman filter. For stable navigation of an outdoor mobile robot using the GPS, this paper proposes two methods to evaluate the reliability of the GPS data. The first method is to calculate the standard deviation of the GPS data and reflect it to deal with the uncertainty of the GPS data. The second method is to match the GPS data to the traversability map which can be obtained by classifying outdoor terrain data. By matching of the GPS data with the traversability map, we can determine whether to use the GPS data or not. The experimental results show that the proposed methods can enhance the performance of the GPS-based outdoor localization.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1999.10c
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• pp.30-40
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• 1999

Regulated Deficit Irrigation (RDI) is one of the most important measures for the watcr-saving and high yield of crops. RDI is based on the crop and water relations. The theories of RDI were analyzed using the experiment data in Sha.anxi. and Gansu Province. There are several problems of RDI in practical use, which include: the uncertainty of cropwwater relations, the proper growth stages and water deficit d$\xi$gree ofRDI applied, and the requirements ofRDI to irrigation system and irrigation tecbniques.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.3 no.1
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• pp.44-51
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• 2003

Knowledge acquisition is a bottleneck in knowledge-based system implementation. Decision tree induction is a useful machine learning approach for extracting classification knowledge from a set of training examples. Many real-world data contain fuzziness due to observation error, uncertainty, subjective judgement, and so on. To cope with this problem of real-world data, there have been some works on fuzzy classification rule learning. This paper makes a survey for the kinds of fuzzy classification rules. In addition, it presents a fuzzy classification rule learning method based on decision tree induction, and shows some experiment results for the method.

• Journal of the Society of Naval Architects of Korea
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• v.42 no.6 s.144
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• pp.710-716
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• 2005

The objective of the present study is to design a hull form of a 9.77 ton class trimaran for use as a Pleasure boat around the bay of Gyounggy. The boat will be made of fiber reinforced plastics and equipped with a 650 hp diesel engine with a conventional water jet propulsion system and the maximum speed be 25 knots after fully loaded. In the present study, the optimal configuration such as relative location of outriggers of the 9.77 ton class trimaran is selected and the resistance characteristics are carefully studied through a series of model tests. The general arrangement of the boat are also considered in the final decision of the hull form.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.8
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• pp.798-804
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• 2006

To overcome the limited relative uncertainty and work range of the existing planar stage and the bulk structure of the contact-less motor for rotation, the novel operating principle to realize the precise rotation is suggested. It uses the two-axial vector forces, normal force and thrust force, of three-induction type of axial motors located $120^{\circ}$ apart, resulting in the contact-free rotation of the mover. Firstly in this paper, the magnetic forces across the air gap are modeled and simulated under the various conditions. It clarifies the feasible range of the derived solution. And the algorithm compensating the strong cross couple between the forces and the control inputs; generally AC magnitude and slip frequency, is given to realize the independent control of six axes. Finally, for the successfully implemented system, the round test and the micro step test results are given.

• Journal of the Korean Data and Information Science Society
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• v.10 no.2
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• pp.289-298
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• 1999

We consider a minimax approach to make a design robust to many types or uncertainty arising in reality when dealing with non-normal linear models. We try to build a design to protect against the worst case, i.e. to improve the "efficiency" of the worst situation that can happen. In this paper, we especially deal with the generalized linear model. It is a known fact that the generalized linear model is a universal approach, an extension of the normal linear regression model to cover other distributions. Therefore, the optimal design for the generalized linear model has very similar properties as the normal linear model except that it has some special characteristics. Uncertainties regarding the unknown parameters, link function, and the model structure are discussed. We show that the suggested approach is proven to be highly efficient and useful in practice. In the meantime, a computer algorithm is discussed and a conclusion follows.

• Journal of Radiation Protection and Research
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• v.15 no.2
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• pp.101-112
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• 1990

For the performance assessment of the radioactive waste disposal system (repository), a biosphere model is suggested. This biosphere model is intended to calculate the annual doses to man caused by the contaminated river water for eight pathways and four radionuclides. This model can also be applied to assess the radiological effects of contaminated well water. To account for the uncertainties on the model parameter values, parameter distributions are assigned to these model parameters. Then, Monte Carlo simulation method with Latin Hypercube sampling technique is used. Also, sensitivity analysis is performed by using the Spearman rank correlation coefficients. It is found that these methods are a very useful tool to treat uncertainties and sensitivities on the model parameter values and to analyze the biosphere model. A conversion factor is proposed to calculate the annual dose rate to humans arising from a unit radionuclide concentration in river water. This conversion factor allows for the substitution of the biosphere model in a probabilistic performance assessment computer code by one single variable.

• Journal of Information Technology Applications and Management
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• v.22 no.4
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• pp.127-158
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• 2015

This study analyzes the factors affecting user resistance to ERP in Vietnam, including the factors related to the cultural values of the users, which is rarely dealt in the previous ERP research. A research model is developed based on Klaus and Blanton [2010] and Hofstede [2011], consisting of the independent variables ('cultural value', 'system', 'organization', and 'process' related variables), a dependent variable ('user resistance to ERP') and a moderating variable ('self efficacy'). Major results of study include (1) users with high degree of uncertainty avoidance and femininity regard ERP as potential threat to their job and are likely to resist to ERP; (2) By training the users with high level of femininity to enhance their self efficacy, the degree of resistance to ERP can be reduced; (3) For ERP to be utilized successfully, systems should be developed in such a way in which working with ERP is not regarded as complex and difficult; and (4) communication and training play an important role in reducing the resistance of users.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.4
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• pp.254-259
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• 2008

Uncertainty of result of context awareness always exists in any context-awareness computing. This falling-off in accuracy of context awareness result is mostly caused by the imperfectness and incompleteness of sensed data, because of this reasons, we must improve the accuracy of context awareness. In this article, we propose a novel approach to model the uncertain context by using ontology and context reasoning method based on Bayesian Network. Our context aware processing is divided into two parts; context modeling and context reasoning. The context modeling is based on ontology for facilitating knowledge reuse and sharing. The ontology facilitates the share and reuse of information over similar domains of not only the logical knowledge but also the uncertain knowledge. Also the ontology can be used to structure learning for Bayesian network. The context reasoning is based on Bayesian Networks for probabilistic inference to solve the uncertain reasoning in context-aware processing problem in a flexible and adaptive situation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.1
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• pp.41-48
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• 2014

This paper analyzes the Monte Carlo localization (MCL) method, which estimates the pose of an indoor mobile robot. A mobile robot must know where it is to navigate in an indoor environment. The MCL technique is one of the most influential and popular techniques for estimation of robot position and orientation using a particle filter. For the analysis, we perform experiments in an indoor environment with a differential drive robot and ultrasonic range sensor system. The analysis uses MATLAB for implementation of the MCL and investigates the effects of the control parameters on the MCL performance. The control parameters are the uncertainty of the motion model of the mobile robot and the noise level of the measurement model of the range sensor.