• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5379-5388
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• 2012

In fuzzy modeling for nonlinear process, the fuzzy rules are typically formed by selection of the input variables, the number of space division and membership functions. The Generation of fuzzy rules for nonlinear processes have the problem that the number of fuzzy rules exponentially increases. To solve this problem, complex nonlinear process can be modeled by generating the fuzzy rules by means of fuzzy division of input space. Therefore, in this paper, rules of non-fuzzy inference systems are generated by partitioning the input space in the scatter form using HCM clustering algorithm. The premise parameters of the rules are determined by membership matrix by means of HCM clustering algorithm. The consequence part of the rules is represented in the form of polynomial functions and the consequence parameters of each rule are identified by the standard least-squares method. And lastly, we evaluate the performance and the nonlinear characteristics using the data widely used in nonlinear process. Through this experiment, we showed that high-dimensional nonlinear systems can be modeled by a very small number of rules.

• Journal of Korean Society of Transportation
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• v.20 no.1
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• pp.65-76
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• 2002

This study is realized using fuzzy evaluation and AHP(the analytic hierarchy process) for the optimum search of traffic route and estimated by the quantitative analysis in the vague subjective judgement. It is different from classical route search and noticed thinking method of human. Appraisal element, weight, appraisal value of route is extracted from basic of the opinion gathering fur the driving expert and example of route model was used for the finding of practice utility. Model assessment was performed attribute membership function making of estimate element, estimate value setting, weight define by the AHP, non additive presentation of weight according to $\lambda$-fuzzy measure, Choquet fuzzy integral.

• Journal of the Korea Society of Computer and Information
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• v.10 no.6 s.38
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• pp.117-126
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• 2005

In this paper, it was suggested method to recognize the motion of a person(lying, sitting, walking, running) using fuzzy inference and wireless biologic signal processing system. These are to Perceive the motion of the person. Furthermore, the information of motion is indispensable parameter for Context Awareness (CA). In the present study, ADXL 202JE accelerometer sensor was used to measure for checking the continuance motion, biological quantify of motion, and motion pattern of a Person. The measured data was transmitted to CA server by Radio Frequency(RF). From the present result, we confirmed that it is difficult to decide the motion of walking and running with only the magnitude of the Longitudinal Accelerometer Average Value(LAAV) and moreover the covariance of LAAV in any block is very useful for CA of walking and running.

• Journal of Intelligence and Information Systems
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• v.13 no.3
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• pp.141-160
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• 2007

User interface has been regarded as a crucial issue to increase the acceptance of mobile services. In special, even though to what extent the machine as speaker communicates with human as listener in a timely and polite manner is important, fundamental studies to come up with these issues have been very rare. Hence, the purpose of this paper is to propose a methodology of estimating politeness level in a certain context-aware setting and then to design a context-aware system for polite mobile interface. We will focus on Korean language for the polite level estimation simply because the polite interface would highly depend on cultural and linguistic characteristics. Nested Minkowski aggregation model, which amends Minkowski aggregation model, is adopted as a privacy-preserving similarity evaluation for case retrieval under distributed computing environment such as ubiquitous computing environment. To show the feasibility of the methodology proposed in this paper, simulation-based experiment with drama cases has performed to show the performance of the methodology proposed in this paper.

• Journal of the Korea Computer Industry Society
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• v.4 no.12
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• pp.965-974
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• 2003

Today's network consists of a large number of routers and servers running a variety of applications. In this paper, we have designed and constructed the general simulation environment of network security model composed of multiple IDSs agent and a firewall agent which coordinate by CNP (Contract Net Protocol). The CNP, the methodology for efficient integration of computer systems on heterogeneous environment such as distributed systems, is essentially a collection of agents, which cooperate to resolve a problem. Command console in the CNP is a manager who controls the execution of agents or a contractee, who performs intrusion detection. In the knowledge-based network security model, each model of simulation environment is hierarchically designed by DEVS (Discrete Event system Specification) formalism. The purpose of this simulation is the application of rete pattern-matching algorithm speeding up the inference cycle phases of the intrusion detection expert system. we evaluate the characteristics and performance of CNP architecture with rete pattern-matching algorithm.

• Proceedings of the Korea Contents Association Conference
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• 2006.05a
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• pp.231-234
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• 2006

Semantic web ontologies can be viewed as logic-based domain-oriented contents which allow distributed and heterogeneous information to be semantically integrated, automatically circulated, and enable implicit knowledge to be reasoned. This paper describes the 'Science and Technology Research Area' ontology which is being developed by the Korea Institute of Science and Technology Information (KISTI). This ontology was defined to assist actual researchers and project planners to grasp the researchers community from a variety of viewpoints. We describe classes and properties as ontology components and exemplify the representation of real instances in the ontology. In order to represent the identities of real world instances within the ontology, the above ontology employs both class-dependent URI assignment schemes and the identity resolution methods.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.1
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• pp.35-45
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• 2007

The log data collected from mobile devices contains diverse meaningful and practical personal information. However, this information is usually ignored because of its limitation of memory capacity, computation power and analysis. We propose a novel method that detects landmarks of meaningful information for users by analyzing the log data in distributed modules to overcome the problems of mobile environment. The proposed method adopts Bayesian probabilistic approach to enhance the inference accuracy under the uncertain environments. The new cooperative modularization technique divides Bayesian network into modules to compute efficiently with limited resources. Experiments with artificial data and real data indicate that the result with artificial data is amount to about 84% precision rate and about 76% recall rate, and that including partial matching with real data is about 89% hitting rate.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.5 no.4
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• pp.224-231
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• 2012

In this paper, we introduce a fuzzy inference systems based on fuzzy c-means clustering algorithm for fuzzy modeling of nonlinear process. Typically, the generation of fuzzy rules for nonlinear processes have the problem that the number of fuzzy rules exponentially increases. To solve this problem, the fuzzy rules of fuzzy model are generated by partitioning the input space in the scatter form using FCM clustering algorithm. The premise parameters of the fuzzy rules are determined by membership matrix by means of FCM clustering algorithm. The consequence part of the rules is expressed in the form of polynomial functions and the coefficient parameters of each rule are determined by the standard least-squares method. And lastly, we evaluate the performance and the nonlinear characteristics using the data widely used in nonlinear process.

• Korean Journal of Applied Biomechanics
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• v.18 no.2
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• pp.49-58
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• 2008

The purpose of this study was to identify effects of walking speed and a cognitive task during treadmill walking on gait variability. Experiments consisted of 5 different walking speeds(80%, 90%, 100%, 110% and 120% of preferred walking speed) with/without a cognitive task. 3D motion analysis system was used to measure subject's kinematic data. Temporal/spatial variables were selected for this study; stride time, stance time, swing time, step time, double support time, stride length, step length and step width. Two parameters were used to compare stride-to-stride variability with/without cognitive task. One is the coefficient of variance which is used to describe the amount of variability. The other is the detrended fluctuation analysis which is used to infer self-similarity from fluctuation of aspects. Results showed that cognitive task may influence stride-to-stride variability during treadmill walking. Further study is necessary to clarify this result.

• The Korean Journal of Applied Statistics
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• v.33 no.5
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• pp.527-540
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• 2020

While analyzing data, researching outliers, which are out of the main tendency, is as important as researching data that follow the general tendency. In this study we discuss the influence function for outlier discrimination. We derive sample influence functions of sample mean, sample variance, and sample standard deviation, which were not directly derived in previous research. The results enable us to mathematically examine the relationship between the empirical influence function and sample influence function. We can also consider a method to approximate the sample influence function by the empirical influence function. Also, the validity of the relationship between the approximated sample influence function and the empirical influence function is also verified by the simulation of random sampled data in normal distribution. As the result of a simulation, both the relationship between the two influence functions, sample and empirical, and the method of approximating the sample influence function through the emperical influence function were verified. This research has significance in proposing a method that reduces errors in the approximation of the empirical influence function and in proposing an effective and practical method that proceeds from previous research that approximates the sample influence function directly through empirical influence function by constant revision.