• Journal of information and communication convergence engineering
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• v.14 no.1
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• pp.45-50
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• 2016

Gait-based classification has gained much interest as a possible authentication method because it incorporate an intrinsic personal signature that is difficult to mimic. The study investigates machine learning techniques to mitigate the natural variations in gait among different subjects. We incorporated several machine learning algorithms into this study using the data mining package called Waikato Environment for Knowledge Analysis (WEKA). WEKA's convenient interface enabled us to apply various sets of machine learning algorithms to understand whether each algorithm can capture certain distinctive gait features. First, we defined 24 gait features by analyzing three-axis acceleration data, and then selectively used them for distinguishing subjects 10 years of age or younger from those aged 20 to 40. We also applied a machine learning voting scheme to improve the accuracy of the classification. The classification accuracy of the proposed system was about 81% on average.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.399-401
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• 2003

This paper presents a geostatistical contextual classifier for the classification of remote sensing data. To obtain accurate spatial/contextual information, a simple indicator kriging algorithm with local means that allows one to estimate the probability of occurrence of certain classes on the basis of surrounding pixel information is applied. To illustrate the proposed scheme, supervised classification of multi-sensor remote sensing data is carried out. Analysis of the results indicates that the proposed method improved the classification accuracy, compared to the method based on the spectral information only.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.382-387
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• 2002

In this paper, we propose a geostatistical approach incorporated to the Bayesian data fusion technique for supervised classification of multi-sensor remote sensing data. Traditional spectral based classification cannot account for the spatial information and may result in unrealistic classification results. To obtain accurate spatial/contextual information, the indicator kriging that allows one to estimate the probability of occurrence of classes on the basis of surrounding observations is incorporated into the Bayesian framework. This approach has its merit incorporating both the spectral information and spatial information and improves the confidence level in the final data fusion task. To illustrate the proposed scheme, supervised classification of multi-sensor test remote sensing data set was carried out.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.6
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• pp.1027-1035
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• 1994

This paper aims to make an artificial arm control strategy. For this, we propose a new feature extraction method and design artificial neural network for the functional classification of myoelectric signal(MES). We first transform the two channel myoelectric signals (MES) for biceps and triceps into frequency domain using fast Fourier transform (FFT). And features were obtained by comparing the magnitudes of ensemble spectrum data and used as inputs to the three-layer neural network for the learning. By changing the number of units in hidden layer of neural network we observed the improvement of classification performance. To observe the effeciency of the proposed scheme we performed experiments for classification of six arm functions to the three subjects. And we obtained on average 94[%] the ratio of classification.

• Korean Journal of Remote Sensing
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• v.28 no.3
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• pp.329-336
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• 2012

This paper give further assessment on the original DoP-CPD classification scheme. This paper provides some additional comparative study on the DoP-CPD with H/A/alpha classifier in terms of multi look effects and classification performances. The statistics and multi looking effects of the DoP and CPD were analyzed with measured polarimetric SAR data. DoP-CPD is less sensitive to the number of averaging pixels than the entropy-alpha technique. A DoP-CPD diagram with appropriate boundaries between six different classes was then developed based on the data analysis. A polarimetric SAR image DoP-CPD classification technique is verified with C-band polarimetric RADARSAT-2 images.

• The Journal of Society for e-Business Studies
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• v.8 no.4
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• pp.151-166
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• 2003

The Topic Maps paradigm allows people and organizations to integrate and merge heterogeneous products classification systems such as UNSPSC and HS. Merging their product ontologies could combine information about classification scheme for products. We analyzed two product classification schemes for UML modeling and developed an integrated TM for watches . Examples in XTM syntax show how UNSPSC and HS can be integrated by merging their ontology.

• Journal of KIISE:Information Networking
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• v.28 no.2
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• pp.251-261
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• 2001

The traffic on the Internet has been growing exponentially for some time. This growth is beginning to stress the current-day routers. However, switching technology offers much higher performance. So the label switching network which combines IP routing with switching technology, is emerged. EspeciaJJy in the data driven label switching, flow classification and cache table management are needed. Flow classification is to classify packets into switching and non-switching packets, and cache table management is to maintain the cache table which contains information for flow classification and label switching. However, the cache table management affects the performance of label switching network considerably as well as flowclassification because the bigger cache table makes more packet switched and maintains setup cost lower, but cache is restricted by local router resources. For that reason, there is need to study the cache replacement scheme for the efficient cache table management with the Internet traffic characterized by user. So in this paper, we propose several cache replacement schemes for label switching network. First, without the limitation at switching capacity in the router. we introduce FIFO(First In First Out). LFC(Least Flow Count), LRU(Least Recently Used! scheme and propose priority LRU, weighted priority LRU scheme. Second, with the limitation at switching capacity in the router, we introduce LFC-LFC, LFC-LRU, LRU-LFC, LRU-LRU scheme and propose LRU-weighted LRU scheme. Without limitation, weighted priority LRU scheme and with limitation, LRU-weighted LRU scheme showed best performance in this paper.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.1
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• pp.27-38
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• 2017

Packet classification is one of the essential functionalities of Internet routers in providing quality of service. Since the arrival rate of input packets can be tens-of-millions per second, wire-speed packet classification has become one of the most challenging tasks. While traditional packet classification only reports a single matching result, new network applications require multiple matching results. Ternary content-addressable memory (TCAM) has been adopted to solve the multi-match classification problem due to its ability to perform fast parallel matching. However, TCAM has a fundamental issue: high power dissipation. Since TCAM is designed for a single match, the applicability of TCAM to multi-match classification is limited. In this paper, we propose a cost- and energy-efficient multi-match classification architecture that combines TCAM with a tuple space search algorithm. The proposed solution uses two small TCAM modules and requires a single-cycle TCAM lookup, two SRAM accesses, and several Bloom filter query cycles for multi-match classifications.

• Journal of the Korean Society for Library and Information Science
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• v.47 no.1
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• pp.57-79
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• 2013

The aim of this study is to reveal the categorizational and classificatory features of the subject-based classification (SBC) as a subject organization system. For this purpose, 12 SBC schemes of public libraries were selected and a comparative analysis was made between the traditional classification system, such as DDC and SBC in terms of the categorizational aspects, and canons for the classification. As a result, there were significant and considerable differences between the two types of classifications. This study concluded that SBC cannot be clearly explained and understood without a consideration of its essential and distinctive characteristics as a classification scheme.

• Journal of Korean Library and Information Science Society
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• v.21
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• pp.459-483
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• 1994

The purpose of the study is to design and implement a prototype expert system for library classification in the literature field of the DDC 20. The system was largely consisted of a knowledge base, an inference engine, a knowledge acquisition facility, an explanation facility and an user interface facility. The knowledge base was represented by inference rules and frames. The name file for authors and titles was designed separately. The forward chaining technique was chosen for the inference engine and the menu-driven dialog technique was also taken for the user interface. The conclusions of the study can be summarized as follows: 1) The difficulty of document classification work is due to the complex and stringent classification rules. Such problems can be considerably alleviated by using the present system. 2) Even the novice with a knowledge about the DDC 20 can easily access the system. And also librarian other than the professional classifier can easily be accustomed to the classification work. 3) The system can be used as an online classification scheme. 4) By adding any local language other than English or Hangeul on the menu screen, the language problem relating classification can be overcome. 5) The system can be employed as the intensification tool for the education of classification as well as library automation.