• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권4호
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• pp.1535-1554
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• 2016

This paper proposes a novel channel assignment scheme called multi-cluster based dynamic channel assignment (MC-DCA) to improve system performance for the downlink of dense femtocell networks (DFNs) based on orthogonal frequency division multiple access (OFDMA) and frequency division duplexing (FDD). In order to dynamically assign channels for femtocell access points (FAPs), the MC-DCA scheme uses a heuristic method that consists of two steps: one is a multiple cluster assignment step to group FAPs using graph coloring algorithm with some extensions, while the other is a dynamic subchannel assignment step to allocate subchannels for maximizing the system capacity. Through simulations, we first find optimum parameters of the multiple FAP clustering to maximize the system capacity and then evaluate system performance in terms of the mean FAP capacity, unsatisfied femtocell user equipment (FUE) probability, and mean FAP power consumption for data transmission based on a given FUE traffic load. As a result, the MC-DCA scheme outperforms other schemes in two different DFN environments for commercial and office buildings.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권6호
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• pp.2255-2281
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• 2021

Channel characteristic-based physical layer authentication is one potential identity authentication scheme in wireless communication, such as used in a fog computing environment. While existing channel characteristic-based physical layer authentication schemes may be efficient when deployed in the conventional wireless network environment, they may be less efficient and practical for the industrial wireless communication environment due to the varying requirements. We observe that this is a topic that is understudied, and therefore in this paper, we review the constructions and performance of several commonly used test statistics and analyze their performance in typical industrial wireless networks using simulation experiments. The findings from the simulations show a number of limitations in existing channel characteristic-based physical layer authentication schemes. Therefore, we believe that it is a good idea to combine machine learning and multiple test statistics for identity authentication in future industrial wireless network deployment. Four machine learning methods prove that the scheme significantly improves the authentication accuracy and solves the challenge of choosing a threshold.

• 응용통계연구
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• 제34권2호
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• pp.167-175
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• 2021

준지도학습(semi-supervised learning)은 목표값이 있는 데이터와 없는 데이터를 모두 이용하는 학습방법이다. 준지도학습에서 최근에 많은 관심을 받는 일치성규칙(consistency regularization)과 데이터 증대를 이용한 준지도학습(unsupervised data augmentation; UDA)은 목표값이 없는 데이터를 증대하여 학습에 이용한다. 그리고 성능 향상을 위해 훈련신호강화(training signal annealing; TSA)와 신뢰기반 마스킹(confidence based masking)을 이용한다. 본 연구에서는 UDA에서 사용하는 KL-정보량(Kullback-Leibler divergence)과 TSA 대신 JS-정보량(Jensen-Shanon divergene)과 역-TSA를 사용하고 신뢰기반 마스킹을 제거하는 방법을 제안한다. 실험을 통해 제안된 방법의 성능이 더 우수함을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권1호
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• pp.323-342
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• 2021

Personal health records (PHRs) is an electronic medical system that enables patients to acquire, manage and share their health data. Nevertheless, data confidentiality and user privacy in PHRs have not been handled completely. As a fine-grained access control over health data, ciphertext-policy attribute-based encryption (CP-ABE) has an ability to guarantee data confidentiality. However, existing CP-ABE solutions for PHRs are facing some new challenges in access control, such as policy privacy disclosure and dynamic policy update. In terms of addressing these problems, we propose a privacy protection and dynamic share system (PPADS) based on CP-ABE for PHRs, which supports full policy hiding and flexible access control. In the system, attribute information of access policy is fully hidden by attribute bloom filter. Moreover, data user produces a transforming key for the PHRs Cloud to change access policy dynamically. Furthermore, relied on security analysis, PPADS is selectively secure under standard model. Finally, the performance comparisons and simulation results demonstrate that PPADS is suitable for PHRs.

• 한국수학교육학회지시리즈D:수학교육연구
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• 제25권4호
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• pp.267-284
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• 2022

In response to the current educational situation of students' heavy workload, the author constructs the precision-targeted classroom based on Precision Teaching (PT), Network Pharmacology, and Treatment Based on Syndrome Differentiation. The precision-targeted classroom can solve the current problems of PT and the phenomenon of the heavy academic burden on students, achieve the reduction of the burden and increase the efficiency of education. The precision-targeted classroom includes five key points: targeted goals, childlike thinking, precise intervention, intelligent homework, and stereoscopic evaluation, and the implementation process of the precision-targeted classroom is built from three aspects: before, during and after class. In addition, the author applied it to the actual mathematics classroom to test its teaching effect, and the experimental results showed that: the precision-targeted classroom significantly improved students' academic performance and thinking level; considerably improved students' classroom learning status, and facilitated teaching personalization and realized homework quantity control and quality improvement.

• 한국산학기술학회:학술대회논문집
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• 한국산학기술학회 2003년도 Proceeding
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• pp.223-230
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• 2003

Feature-based similarity retrieval has become an important research issue in multimedia database systems. The features of multimedia data are useful for discriminating between multimedia objects (e 'g', documents, images, video, music score, etc.). For example, images are represented by their color histograms, texture vectors, and shape descriptors, and are usually high-dimensional data. The performance of conventional multidimensional data structures(e'g', R- Tree family, K-D-B tree, grid file, TV-tree) tends to deteriorate as the number of dimensions of feature vectors increases. The R＊-tree is the most successful variant of the R-tree. In this paper, we propose a SOM-based R＊-tree as a new indexing method for high-dimensional feature vectors.The SOM-based R＊-tree combines SOM and R＊-tree to achieve search performance more scalable to high dimensionalities. Self-Organizing Maps (SOMs) provide mapping from high-dimensional feature vectors onto a two dimensional space. The mapping preserves the topology of the feature vectors. The map is called a topological of the feature map, and preserves the mutual relationship (similarity) in the feature spaces of input data, clustering mutually similar feature vectors in neighboring nodes. Each node of the topological feature map holds a codebook vector. A best-matching-image-list. (BMIL) holds similar images that are closest to each codebook vector. In a topological feature map, there are empty nodes in which no image is classified. When we build an R＊-tree, we use codebook vectors of topological feature map which eliminates the empty nodes that cause unnecessary disk access and degrade retrieval performance. We experimentally compare the retrieval time cost of a SOM-based R＊-tree with that of an SOM and an R＊-tree using color feature vectors extracted from 40, 000 images. The result show that the SOM-based R＊-tree outperforms both the SOM and R＊-tree due to the reduction of the number of nodes required to build R＊-tree and retrieval time cost.

• 품질경영학회지
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• 제35권4호
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• pp.147-158
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• 2007

Owing to the rapid growth in automated manufacturing systems, screening inspection becomes an attractive practice for removing nonconforming items, and it has been suggested that inspection will essentially become an inherent part of modem manufacturing processes. In this paper, we propose rectifying screening inspections which allow quality practitioners to use performance and surrogate variables interchangeably in real-time applications. Two screening inspections are considered; a statistically-based screening inspection to reduce the current proportion of nonconforming items to a specified AOQ(average outgoing quality) after screening, and an economically-based screening inspection where the tolerance limit is determined so that the expected total cost is minimized. It is assumed that the performance variable and the surrogate variable are jointly normally distributed. For two screening inspections, methods of finding the optimal solutions are presented and numerical examples are also given.

• Communications for Statistical Applications and Methods
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• 제4권1호
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• pp.101-109
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• 1997

For the detection on variance changes in the nonstationary time series with a unit root two types of test statistics are proposed, of which one is based on the cumulative sum of squares and the other is based on the likelihood ratio test. The properties of the cusum type test statistic are derived and the performance of two tests in small samples are compared through Monte Carlo study. It is ovserved that the test based on the cumulative sum of squares can detect a samll change in the variance faster than the one based on the likelihood ratio.

• 한국임상보건과학회지
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• 제3권4호
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• pp.476-489
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• 2015

Purpose. The purpose of this study was to investigate the evidence-based pain management knowledge, performance and Evidence-Based Practice(EBP) attitude of nurses at small-medium sized hospitals. Methods. The sample of this study were 214 nurses. Data were analyzed by descriptive statistics, t-test, ANOVA, Pearson's correlation and stepwise multiple regression using SPSS/Win 21.0 Results. Evidence-based pain management knowledge score averaged $3.95{\pm}0.43$, with significant differences depending on clinical experiences. Performance score averaged $3.77{\pm}0.51$. EBP attitude score averaged $3.21{\pm}0.57$, and significant differences were evident depending on hospital division and clinical setting. Evidence-based pain management knowledge and EBP attitude were significant predictors of evidence-based pain management performance. Conclusions. This result indicates that education and training programs to facilitate evidence-based pain management knowledge, performance, and EBP attitude are needed among nurses.

• 한국컴퓨터정보학회논문지
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• 제19권3호
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• pp.45-54
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• 2014

본 논문에서는 기존 기법들의 제한점을 개선하기 위해 휴리스틱 규칙 및 기계학습 분석 결과를 이용한 두 단계의 P2P 트래픽 분류 기법을 제안한다. 첫 번째 단계는 패킷 레벨의 시그니처 기반 분류기이고, 두 번째 단계는 플로우 레벨에서 수행되는 패턴 휴리스틱 규칙 및 통계 기반 분류기이다. 제안된 패턴 휴리스틱 규칙은 분류의 정확도를 높이고 통계 기반 분류기가 처리할 트래픽의 양을 줄일 수 있다. 다양한 의사 결정 트리 알고리즘의 분석을 기반으로 통계 기반 분류기는 가장 효율적인 REPTree로 구현하고, 앙상블 알고리즘을 통해 통계 기반 분류기의 성능을 개선한다. 실제 환경의 데이터 집합을 이용한 검증 분석을 통해, 본 제안 기법이 기존 기법에 비해 높은 정확도와 낮은 과부하를 제공함을 제시한다.