• Journal of Communications and Networks
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• 제11권6호
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• pp.544-555
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• 2009

This work continues a trend of developments aimed at exploiting the physical layer of the open systems interconnection (OSI) model to enhance wireless network security. The goal is to augment activity occurring across other OSI layers and provide improved safeguards against unauthorized access. Relative to intrusion detection and anti-spoofing, this paper provides details for a proof-of-concept investigation involving "air monitor" applications where physical equipment constraints are not overly restrictive. In this case, RF fingerprinting is emerging as a viable security measure for providing device-specific identification (manufacturer, model, and/or serial number). RF fingerprint features can be extracted from various regions of collected bursts, the detection of which has been extensively researched. Given reliable burst detection, the near-term challenge is to find robust fingerprint features to improve device distinguishability. This is addressed here using wavelet domain (WD) RF fingerprinting based on dual-tree complex wavelet transform (DT-$\mathbb{C}WT$) features extracted from the non-transient preamble response of OFDM-based 802.11a signals. Intra-manufacturer classification performance is evaluated using four like-model Cisco devices with dissimilar serial numbers. WD fingerprinting effectiveness is demonstrated using Fisher-based multiple discriminant analysis (MDA) with maximum likelihood (ML) classification. The effects of varying channel SNR, burst detection error and dissimilar SNRs for MDA/ML training and classification are considered. Relative to time domain (TD) RF fingerprinting, WD fingerprinting with DT-$\mathbb{C}WT$ features emerged as the superior alternative for all scenarios at SNRs below 20 dB while achieving performance gains of up to 8 dB at 80% classification accuracy.

• 농촌계획
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• 제18권4호
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• pp.117-127
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• 2012

The demand of rural tourism industry has increased among the urbanites in South Korea, in due to the increase of leisure activity and the emergence of ageing society. Rural amenity resources are gaining various interests, in the value creation and promotion of tourism. In this study, the propensities of city dwellers were separated by life-style classification, and each affinity to the rural amenity resources was examined in accordance with the separation. A questionnaire survey of urbanites in the southern area of Gyeonggi-do, the most populous province in South Korea, was conducted to analyze the preference of city dwellers about rural amenity resource and life-style of themselves. For statistical verification, $IBM^{(R)}$ $SPSS^{(R)}$ Statistics 20 software was used for frequency, reliability, factor and multiple regression analysis of this research. The results of the statistical analyses found a noticeable characteristic in life-style classification. The affinities of urbanites can be classified into four congregations of life-style factors in this statistical model. Each congregation of the factors was named as 'Self-development-oriented', 'Leisure-oriented', 'Achievement-oriented', and 'Culture-oriented' life-style, to represent the characteristics for convenience' sake. Among these styles, only 'Self-development-oriented' and 'Achievement-oriented' showed the positive correlation with rural amenity resources in the multiple regression analysis. In addition, the rural amenity resources were also analyzed in accordance with the life-styles classification of urbanites. City dwellers showed the highest interest to the 'natural resource management facility resource' in natural resources, the 'traditional heritage resource' in cultural resources, and the 'community resource' in social resources. Meanwhile, they showed less interest to 'agricultural and scenery resources' in natural resources, 'specialty production resource' in cultural resources, and 'cooperative farming' in social resources. These characteristics can be constructed as meaning that the urbanites who concern self-development and achievement of their lives have high interest in rural amenity resources, and the main interest of them is not 'return-to-the-farm'(歸農) but 'return-to-the-home'(歸村).

• The Korean Journal of Pain
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• 제32권2호
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• pp.120-128
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• 2019

Background: We aimed to explore the American College of Rheumatology (ACR) 1990 and 2011 fibromyalgia (FM) classification criteria's items and the components of Fibromyalgia Impact Questionnaire (FIQ) to identify features best discriminating FM features. Finally, we developed a combined FM diagnostic (C-FM) model using the FM's key features. Methods: The means and frequency on tender points (TPs), ACR 2011 components and FIQ items were calculated in the FM and non-FM (osteoarthritis [OA] and non-OA) patients. Then, two-step multiple logistic regression analysis was performed to order these variables according to their maximal statistical contribution in predicting group membership. Partial correlations assessed their unique contribution, and two-group discriminant analysis provided a classification table. Using receiver operator characteristic analyses, we determined the sensitivity and specificity of the final model. Results: A total of 172 patients with FM, 75 with OA and 21 with periarthritis or regional pain syndromes were enrolled. Two steps multiple logistic regression analysis identified 8 key features of FM which accounted for 64.8% of variance associated with FM group membership: lateral epicondyle TP with variance percentages (36.9%), neck pain (14.5%), fatigue (4.7%), insomnia (3%), upper back pain (2.2%), shoulder pain (1.5%), gluteal TP (1.2%), and FIQ fatigue (0.9%). The C-FM model demonstrated a 91.4% correct classification rate, 91.9% for sensitivity and 91.7% for specificity. Conclusions: The C-FM model can accurately detect FM patients among other pain disorders. Re-inclusion of TPs along with saving of FM main symptoms in the C-FM model is a unique feature of this model.

• 한국컴퓨터정보학회논문지
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• 제18권9호
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• pp.43-51
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• 2013

신뢰성 있는 부정맥 진단을 위해서는 리듬 구간 및 심박 단위의 종합적인 분석을 통하여 심전도 신호에 대한 분류 결과가 제시되어야 한다. 본 논문에서는 심전도 신호의 특징점에 기반하여 규칙기반 분류를 이용한 일정 구간의 리듬 분석을 수행하고 SVM기반 분류를 이용한 심박 단위의 리듬분석을 첨가하였다. 규칙기반 분류에서는 리듬 구간의 특징에 대하여 임상 자료로부터 도출된 규칙 베이스를 이용하여 리듬 유형을 분류하도록 하며, SVM기반 분류에서는 심박 단위의 특징에 대하여 미리 학습된 다중 SVM 분류기를 이용하여 단조 리듬 및 주요 비정상 심박을 분류하도록 한다. MIT-BIH 부정맥 데이터베이스를 이용한 실험을 통하여 11가지 리듬 유형에 대하여 규칙기반 방법만을 적용하였을 경우 68.52%, 규칙기반과 SVM기반의 융합 방법을 적용하였을 경우 87.04%의 분류 성능을 각각 보였다. SVM기반 방법으로 단조 리듬과 배열 리듬에 대한 오분류 개선을 통하여 분류 성능에서 19% 정도가 향상됨을 확인하였다.

• 대한원격탐사학회지
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• 제27권6호
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• pp.637-651
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• 2011

This study presents a rule-based urban image classification method for time series analysis of changes in the vicinity of Asan-si and Cheonan-si in Chungcheongnam-do, using Landsat satellite images (1991-2006). The area has been highly developed through the relocation of industrial facilities, land development, construction of a high-speed railroad, and an extension of the subway. To determine the yearly changing pattern of the urban area, eleven classes were made depending on the trend of development. An algorithm was generalized for the rules to be applied as an unsupervised classification, without the need of training area. The analysis results show that the urban zone of the research area has increased by about 1.53 times, and each correlation graph confirmed the distribution of the Built Up Index (BUI) values for each class. To evaluate the rule-based classification, coverage and accuracy were assessed. When Optimal allowable factor=0.36, the coverage of the rule was 98.4%, and for the test using ground data from 1991 to 2006, overall accuracy was 99.49%. It was confirmed that the method suggested to determine the maximum allowable factor correlates to the accuracy test results using ground data. Among the multiple images, available data was used as best as possible and classification accuracy could be improved since optimal classification to suit objectives was possible. The rule-based urban image classification method is expected to be applied to time series image analyses such as thematic mapping for urban development, urban development, and monitoring of environmental changes.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2010년도 춘계학술대회
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• pp.191-192
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• 2010

본 연구는 부산광역시를 대상으로 작성한 500m메시 별 건물용도별 면적 구성에 근거한 유형화에 대한 연구이다. 본 연구에서는 3289개의 유효한 메시를 대상으로 7개 용도군에 대한 데이터 베이스를 구축하였고, 그중 주거용, 상업용, 교육용 공간이 전체 공간의 92.4%를 차지하고 있으며, 용적율 50% 이상인 메시는 325개로 전체의 약 10% 수준이었다. 다양한 변수를 단순화하기 위해 주성분분석을 수행하였고 그 결과를 이용하여 클러스터분석을 수행하여 모든 메시를 5개 유형으로 분류하였다.

• Bulletin of the Korean Chemical Society
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• 제9권4호
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• pp.223-231
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• 1988

Some pattern recognition methods have been used to characterize Korean ancient porcelainsherds using their elemental composition as analyzed by instrumental neutron activation analysis. A combination of analytical data by means of statistical linear discriminant analysis(SLDA) has resulted in removal of redundant variables, optimal linear combination of meaningful variables and formulation of classification rules. The plot in the first-to-second discriminant scores has shown that the three distinct territorial regions exist among porcelainsherds of Kyungki, Chunbuk-Chungnam, and Chunnam, with respective efficiencies of 20/30, 22/27 and 14/15. Similar regions have been found to exist among punchong porcelain and ceradonsherds of Kyungki, Chungnam and Chunbuk, with respective efficiencies of 7/9, 15/16 and 6/6. Classification has been further attempted by statistical isolinear multiple component analysis(SIMCA), using the sample set selected appropriately through SLDA as training set. For this purpose, all analytical data have been used. An agreement has generally been found between two methods, i.e., SLDA and SIMCA.

• 한국정밀공학회지
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• 제14권11호
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• pp.135-146
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• 1997

This paper describes fault diagnosis method in complex system with hierachical structure similar to human body structure. Complex system is divided into unit, item and component. For diagnosing this hierarchical complex system, it is necessary to implement special neural network. Fault diagnosis system can forecast faults in a system and decide from current machine state signal information. Comparing with other diagnosis system for single fault, the developed system deals with multiple fault diagnosis comprising Hierarchical Neural Network(HNN). HNN consists of four level neural network, first level for item fault symptom classification, second level for item fault diagnosis, third level for component symptom classification, forth level for component fault diagnosis. UNIX IPC(Inter Process Communication) is used for implementing HNN wiht multitasking and message transfer between processes in SUN workstation with X-Windows(Motif). We tested HNN at four units, seven items per unit, seven components per item in a complex system. Each one neural newtork operate as a separate process in HNN. The message queue take charge of information exdhange and cooperation between each neural network.

• Communications for Statistical Applications and Methods
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• 제22권6호
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• pp.575-587
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• 2015

The development in data collection techniques results in high dimensional data sets, where discrimination is an important and commonly encountered problem that are crucial to resolve when high dimensional data is heterogeneous (non-common variance covariance structure for classes). An example of this is to classify microbial habitat preferences based on codon/bi-codon usage. Habitat preference is important to study for evolutionary genetic relationships and may help industry produce specific enzymes. Most classification procedures assume homogeneity (common variance covariance structure for all classes), which is not guaranteed in most high dimensional data sets. We have introduced regularized elimination in partial least square coupled with QDA (rePLS-QDA) for the parsimonious variable selection and classification of high dimensional heterogeneous data sets based on recently introduced regularized elimination for variable selection in partial least square (rePLS) and heterogeneous classification procedure quadratic discriminant analysis (QDA). A comparison of proposed and existing methods is conducted over the simulated data set; in addition, the proposed procedure is implemented to classify microbial habitat preferences by their codon/bi-codon usage. Five bacterial habitats (Aquatic, Host Associated, Multiple, Specialized and Terrestrial) are modeled. The classification accuracy of each habitat is satisfactory and ranges from 89.1% to 100% on test data. Interesting codon/bi-codons usage, their mutual interactions influential for respective habitat preference are identified. The proposed method also produced results that concurred with known biological characteristics that will help researchers better understand divergence of species.

• 한국경영과학회지
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• 제26권4호
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• pp.55-70
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• 2001

Artificial neural network(ANN) models have been widely used for the classification problems in business such as bankruptcy prediction, credit evaluation, etc. Although the application of ANN to classification of consumers＇ choice behavior is a promising research area, there have been only a few researches. In general, most of the researches have reported that the classification performance of the ANN models were better than conventional statistical model Because the survey data on consumer behavior may include much noise and missing data, ANN model will be more robust than conventional statistical models welch need various assumptions. The purpose of this paper is to study the potential of the ANN model for forecasting consumers＇ choice behavior based on survey data. The data was collected by questionnaires to the shoppers of department stores and discount stores. Then the correct classification rates of the ANN models for the training and test sample with that of multiple discriminant analysis(MDA) and logistic regression(Logit) model. The performance of the ANN models were betted than the performance of the MDA and Logit model with respect to correct classification rate. By using input variables identified as significant in the stepwise MDA, the performance of the ANN models were improved.