• Journal of Information Processing Systems
• /
• v.15 no.2
• /
• pp.359-373
• /
• 2019

For clustering large-scale data, which cannot be loaded into memory entirely, incremental clustering algorithms are very popular. Usually, these algorithms only concern the within-cluster compactness and ignore the between-cluster separation. In this paper, we propose two incremental fuzzy compactness and separation (FCS) clustering algorithms, Single-Pass FCS (SPFCS) and Online FCS (OFCS), based on a fuzzy scatter matrix. Firstly, we introduce two incremental clustering methods called single-pass and online fuzzy C-means algorithms. Then, we combine these two methods separately with the weighted fuzzy C-means algorithm, so that they can be applied to the FCS algorithm. Afterwards, we optimize the within-cluster matrix and betweencluster matrix simultaneously to obtain the minimum within-cluster distance and maximum between-cluster distance. Finally, large-scale datasets can be well clustered within limited memory. We implemented experiments on some artificial datasets and real datasets separately. And experimental results show that, compared with SPFCM and OFCM, our SPFCS and OFCS are more robust to the value of fuzzy index m and noise.

• International journal of advanced smart convergence
• /
• v.9 no.4
• /
• pp.42-51
• /
• 2020

In this paper, the authors propose the pseudo complex correlation coefficient (PCCC) of the two complex random variables (RV), because the four real correlation coefficients (RCC) of the corresponding four real RVs cannot be obtained only from the complex correlation coefficient (CCC) of given two complex RV. Such observation is motivated by the general statement; "The complex jointly-Gaussian random M-vector cannot be completely described by the complex covariance matrix, even though the real Gaussian random 2M-vector can be completely descried by the real covariance matrix. Therefore, in order to describe completely the complex jointly-Gaussian random M-vector, we need an additional matrix, namely the complex pseudo-covariance matrix, along with the complex covariance matrix." Then, we apply PCCC to correlated information sources (CIS) for non-orthogonal multiple access (NOMA) in 5G system, and investigate impact of the proposed PCCC on the achievable data rate of the stronger channel user in the conventional successive interference cancellation (SIC) NOMA with CIS. It is shown that for the given same CCC, the achievable data rates with the different PCCC are different, because the corresponding RCC are different. We also show that as the absolute value of the same CCC increases, the impact of the different PCCC becomes more significant.

• Asia pacific journal of information systems
• /
• v.34 no.1
• /
• pp.49-90
• /
• 2024

Intradisciplinary research refers to research that integrates ideas often associated with different research domains in a discipline. Such cross-fertilization leverages abundant diversity present in the IS discipline to tackle increasingly complex IS problems and grand challenges. Despite its importance and recent attention, a concerted, sustained effort toward intradisciplinary research is lagging. A fundamental issue we see is a lack of an elaborate IS research map that effectively shows similarities and differences among research domains and demonstrates types of ideas that may travel and integrate into different domains. We thus aim to propose an elaborate IS research map and compile research elements that can be tried and combined across research domains. To do so, we utilize two IS classics (i.e., IT views and IS phenomena), identify their complementarity, and interweave the two disparate ways of portraying the IS research field. The resultant view-phenomenon (VP) matrix specifies research domains based on two consistent, comprehensive criteria and helps researchers discern similarities and differences among research domains more effectively. The VP matrix also sheds light on a variety of research elements that can flow across research domains. The VP matrix along with the research elements together facilitate intradisciplinary efforts and, more broadly, help the IS discipline to prosper. The VP matrix is particularly helpful for doctoral students and young scholars.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.46 no.1
• /
• pp.34-42
• /
• 2009

In this paper, new bounds for the solution of the unified Lyapunov matrix equation for decentralized singularly perturbed systemare obtained, and some of the existing works using deficient assumptions are also generalized.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.5
• /
• pp.2171-2185
• /
• 2020

In order to judge that whether the vehicles in different images which are captured by surveillance cameras represent the same vehicle or not, we proposed a novel vehicle face recognition algorithm based on improved Nonnegative Matrix Factorization (NMF), different from traditional vehicle recognition algorithms, there are fewer effective features in vehicle face image than in whole vehicle image in general, which brings certain difficulty to recognition. The innovations mainly include the following two aspects: 1) we proposed a novel idea that the vehicle type can be determined by a few key regions of the vehicle face such as logo, grille and so on; 2) Through adding weight, sparseness and classification property constraints to the NMF model, we can acquire the effective feature bases that represent the key regions of vehicle face image. Experimental results show that the proposed algorithm not only achieve a high correct recognition rate, but also has a strong robustness to some non-cooperative factors such as illumination variation.

• Journal of the military operations research society of Korea
• /
• v.29 no.1
• /
• pp.76-87
• /
• 2003

This paper proposes that we need a simpler method for developing the Website strategy for public institutions. The research objectives are threefold: (1) A model that relates to the monitoring of Website strategy in the public sector is proposed. The model includes basic dimensions and a $2{\times}3$ matrix that is a simplified version of the Mohammed et dl.s Marketspace Matrix. (2) The model is tested empirically with a sample of 56 Websites selected from govemment agencies, military organizations and police stations in Korea. (3) The effect of dimension/factors on the innovation level is tested. A special attention is paid to online attracting that is important for public institutions which usually do not use offline advertising aggressively. Results from regression analyses show that main dimensions (Marketing Drivers and relationship Stages) and three factors (Basic Information, Support Information, and Participation) in the matrix are all significantly influential on the innovation level, but the Attracting is not. Colorful designs and attracting features of a homepage have not necessarily anything to do with innovation. This message can offer a good piece of advice for managers of Websites.

• Journal of the Korean Data and Information Science Society
• /
• v.17 no.3
• /
• pp.953-961
• /
• 2006

Let's say that we are given a k number of random variables following Poisson distribution that are individually dependent and which forms multivariate Poisson distribution. We particularly dealt with a method of creating random numbers that satisfies the covariance matrix, where the elements of covariance matrix are parameters forming a multivariate Poisson distribution. To create such random numbers, we propose a new algorithm based on the method reducing the number of parameter set and deal with its relationship to the Park et al.(1996) algorithm used in creating multivariate Bernoulli random numbers.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• v.1
• /
• pp.133-138
• /
• 2006

Singular value decomposition (SDV) approach is applied to the observability analysis of GPS/INS in this paper. A measure of observability for a subspace is introduced. It indicates the minimum size of perturbation in the information matrix that makes the subspace unobservable. It is shown that the measure has direct connections with observability of systems, error covariance, and singular structure of the information matrix. The observability measure given in this paper is applicable to the multi-input/multi-output time-varying systems. An example on the observability analysis of GPS/INS is given. The measure of observability is confirmed to be less sensitive to system model perturbation. It is also shown that the estimation error for the vertical component of gyro bias can be considered unobservable for small initial error covariance for a constant velocity horizontal motion.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2001.06a
• /
• pp.351-355
• /
• 2001

2차원 바코드는 2차원인 점자식 코드로서 낮은 공간점유, 높은 정보, 다양한 정보처리 기능이 가능한 차세대 라벨링 기법이다. 즉, 2차원(2D) 심볼로지는 양축(X 방향, Y 방향)으로 데이터를 배열시켜 평면화 시킨것으로서 기존의 일차원(1D) 바코드 심볼로지가 가지는 문제점인 데이터 표현의 제한성, 즉 선적용 패키지와 같은 로트 번호, 구매 주문 번호, 수취자, 수랑 기타 정보 등의 다양한 내용을 바코드로 표현하여 대상물에 부착하거나 동반시킴으로써 1750년대 중반에 등장하게 되었고, 현재 많은 부분에서 사용하고 있다. 본 논문에서는 현재 많이 쓰이는 2 차원 바코드 중 하나인 Data Matrix 의 구성과 디코딩 알고리즘을 제안한다. Data Matrix는 데이터를 효율적으로 나타내기 위하며 각 정보의 교환에 따라 다른 인코딩 방식을 사용하고 있다. 디코딩 알고리즘은 그에 따라서 구현되었다.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2007.11a
• /
• pp.489-498
• /
• 2007

For better predictions and classifications in customer recommendation, this study proposes an integrative model that efficiently combines the currently-in-use statistical and artificial intelligence models. In particular, by integrating the models such as Association Rule, Frequency Matrix, and Rule Induction, this study suggests an integrative prediction model. The data set for the tests is collected from a convenience store G, which is the number one in its brand in S. Korea. This data set contains sales information on customer transactions from September 1, 2005 to December 7, 2005. About 1,000 transactions are selected for a specific item. Using this data set, it suggests an integrated model predicting whether a customer buys or not buys a specific product for target marketing strategy. The performance of integrated model is compared with that of other models. The results from the experiments show that the performance of integrated model is superior to that of all other models such as Association Rule, Frequency Matrix, and Rule Induction.