• Journal of the Korean Statistical Society
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• v.27 no.4
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• pp.507-514
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• 1998

The minimum distance estimation based on the L$_2$ distance between a model density and a density estimator is studied from M-estimation point of view. We will show that how a model density and a density estimator are incorporated in order to create an M-estimation function. This method enables us to create an M-estimating function reflecting the natures of both an assumed model density and a given set of data. Some new types of M-estimation functions for estimating a location and scale parameters are introduced.

• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.3
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• pp.53-63
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• 2001

An efficient mobility management for mobile stations plays an important role in mobile communication network. This paper studies the mobility management scheme that combines a distance-based registration(DBR) and a selective paging (SP). We introduce an analytical model based on 2-dimensional random walk mobility model and evaluate the performance of the proposed mobility management scheme using the model to determine the optimal size of location area that results in the minimum signaling traffic on radio channels. Numerical results are provided to demonstrate the performance of the proposed mobility management scheme under various circumstances. These results can be used effectively in design and evaluation of registration methods considering the system circumstances.

• Earthquakes and Structures
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• v.22 no.2
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• pp.147-155
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• 2022

Regarding the importance of seismic pounding, the available standards and guidelines specify minimum separation distance between adjacent buildings. However, the rules in this field are generally based on some simple assumptions, and the level of confidence is uncertain. This is attributed to the fact that the relative response of adjacent structures is strongly dependent on the frequency content of the applied records and the Eigen frequencies of the adjacent structures as well. Therefore, this research aims at investigating the separation distance of the buildings through a probabilistic-based algorithm. In order to empower the algorithm, the record-to-record uncertainties, are considered by probabilistic approaches; besides, a wide extent of material nonlinear behaviors can be introduced into the structural model by the implementation of the hysteresis Bouc-Wen model. The algorithm is then simplified by the application of the linearization concept and using the response acceleration spectrum. By implementing the proposed algorithm, the separation distance in a specific probability level can be evaluated without the essential need of performing time-consuming dynamic analyses. Accuracy of the proposed method is evaluated using nonlinear dynamic analyses of adjacent structures.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.1
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• pp.62-68
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• 2016

The fingerprint, which is one of the methods of indoor localization using WiFi, has been frequently studied because of its ability to be implemented via wireless access points. This method has low positioning resolution and high computational complexity compared to other methods, caused by its dependence of reference points in the radio map. In order to compensate for these problems, this paper presents a radio map designed algorithm based on the log-distance path loss model fusing a WiFi and BLE fingerprint. The proposed algorithm designs a radio map with variable values using the log-distance path loss model and reduces distance errors using a median filter. The experimental results of the proposed algorithm, compared with existing fingerprinting methods, show that the accuracy of positioning improved by from 2.747 m to 2.112 m, and the computational complexity reduced by a minimum of 33% according to the access points.

• Communications for Statistical Applications and Methods
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• v.27 no.6
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• pp.589-602
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• 2020

The development of smart grids has enabled the easy collection of a large amount of power data. There are some common patterns that make it useful to cluster power consumption patterns when analyzing s power big data. In this paper, clustering analysis is based on distance functions for time series and clustering algorithms to discover patterns for power consumption data. In clustering, we use 10 distance measures to find the clusters that consider the characteristics of time series data. A simulation study is done to compare the distance measures for clustering. Cluster validity measures are also calculated and compared such as error rate, similarity index, Dunn index and silhouette values. Real power consumption data are used for clustering, with five distance measures whose performances are better than others in the simulation.

• Journal of The Korean Association of Information Education
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• v.9 no.3
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• pp.549-560
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• 2005

Web-based distance teacher training should focus on increasing self-directed problem solving skills of trainers by problem-based instruction(PBL). Performance assessment evaluates achievement of learning goals through the practical performance learning to solve problems in the real situation. This study proposes a web-based performance assessment model for the distance teacher training. It reflects fairness and objectiveness issues of performance assessment which elementary teachers are concerned about through the survey on distance teacher training, and relieves instructors from overload of managing and scoring performance tests. The proposed model provides problem-based learning situations, interactions between individuals and groups, and web-based cooperative evaluation and the peer evaluation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.18 no.33
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• pp.29-37
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• 1995

An Automated Storage/Retrieval Systems(AS/RS) has been gradually emphasized because of the change of production and distribution environment. This paper develops algorithm and Shortest Distance Model that can reduce the traveling time of a stacker crane for efficient operation of AS/RS. In order to reduce the traveling time of a stacker crane, we determine the order processing and then the sequencing of storage/retrieval for each item. Order processing is determined based on the SPT(Shortest Processing Time) concept considering a criterion of retrieval coordinate. The sequencing of storage/retrieval is determined based on the Shortest Distance Model by using a modified SPP(Shortest Path Problem) of network problem. A numerical example is provided to illustrate the developed algorithm and Shortest Distance Model.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.85-87
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• 2000

In pattern classification, the Bhattacharyya distance has been used as a class separability measure and provides useful information for feature selection and extraction. In this paper, we propose a method to predict the classification error for multimodal data based on the Bhattacharyya distance. In our approach, we first approximate the pdf of multimodal distribution with a Gaussian mixture model and find the bhattacharyya distance and classification error. Exprimental results showed that there is a strong relationship between the Bhattacharyya distance and the classification error for multimodal data.

• Journal of Korea Multimedia Society
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• v.14 no.1
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• pp.85-93
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• 2011

Previous existing vocabulary recognition programs calculate general vector values from a database, so they can not process phonemes that form during a search. And because they can not create a model for phoneme data, the accuracy of the Gaussian model can not secure. Therefore, in this paper, we recommend use of the Bhattacharyya distance measurement method based on the features of the phoneme-thus allowing us to improve the recognition rate by picking up accurate phonemes and minimizing recognition of similar and erroneous phonemes. We test the Gaussian model optimization through share continuous probability distribution, and we confirm the heighten recognition rate. The Bhattacharyya distance measurement method suggest in this paper reflect an average 1.9% improvement in performance compare to previous methods, and it has average 2.9% improvement based on reliability in recognition rate.

• Asia pacific journal of information systems
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• v.12 no.3
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• pp.45-70
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• 2002

This paper examines the role of distance learning self-efficacy, belief in one's capabilities of using a system in the accomplishment of web-based distance learning, in predicting user intention to use and performance of distance learning system. It used self-efficacy theory and technology acceptance model(TAM) to build a model that predicts relationships between antecedents to students' distance learning self-efficacy assessments and their behavioral and attitudinal consequences. The model was tested using LISREL analysis on the sample of 250 students who have worked with the Distance Learning System. The results indicated partial support for the conceptual model. In accordance with TAM, perceived usefulness had strong direct effects on intention to use and performance, while perceived ease of use had both direct and indirect effects on intention to use, but not performance. Distance learning self-efficacy had only direct effect on perceived ease of use to use. Computer experience was found to have a strong positive effect on distance learning self-efficacy, and computer anxiety had a negative effect on distance learning self-efficacy. Implications of these findings are discussed for researchers and practitioners.