• Proceedings of the IEEK Conference
• /
• 2008.06a
• /
• pp.671-672
• /
• 2008

K-means is a popular one in clustering algorithms, and it minimizes the mutual euclidean distance among the sample points. But K-means has some demerits, such as depending on initial condition, unsupervised learning and local optimum. However mahalanobis distancecan deal this case well. In this paper, the author proposed a new clustering algorithm, named exponential probability clustering, which applied Mahalanobis distance into K-means clustering. This new clustering does possess not only the probability interpretation, but also clustering merits. Finally, the simulation results also demonstrate its good performance compared to K-means algorithm.

• Proceedings of the IEEK Conference
• /
• 2002.06c
• /
• pp.99-102
• /
• 2002

K-means clustering algorithm is a very popular clustering technique, which is used in the field of information retrieval. In this paper, We deal with the problem of K-means Algorithm from the view of creating the centroids and suggest a method reflecting document feature and considering the context of each document to determine the new centroids during the process of forming new centroids. For experiment, We used the automatic document summarizer to summarize the Reuter21578 newslire test dataset and achieved 20% improved results to the recall metrics.

• Journal of the Korean Statistical Society
• /
• v.19 no.1
• /
• pp.80-87
• /
• 1990

In reliability and life testing the mean residual life (MRL) of an item plays a significant role. While there has been a great deal of discussion on the theoretical aspects of the MRL, good estimators of MRL have been difficult to obtain. In this paper we propose a new estimator of the MRL of items at a given age, which is especially good for a small sample. The new estimator compares favorably with the empirical MRL estimator for small samples.

• Earthquakes and Structures
• /
• v.18 no.6
• /
• pp.719-730
• /
• 2020

Recently, population based optimization algorithms are developed to deal with a variety of optimization problems. In this paper, the salp swarm algorithm (SSA) is dramatically enhanced and a new algorithm is named Enhanced Salp Swarm Algorithm (ESSA) which is effectively utilized in optimization problems. To generate the ESSA, an opposition-based learning and merit function methods are added to standard SSA to enhance both exploration and exploitation abilities. To have a clear judgment about the performance of the ESSA, firstly, it is employed to solve some mathematical benchmark test functions. Next, it is exploited to deal with engineering problems such as optimally designing the benchmark buildings equipped with multiple tuned mass damper (MTMD) under earthquake excitation. By comparing the obtained results with those obtained from other algorithms, it can be concluded that the proposed new ESSA algorithm not only provides very competitive results, but also it can be successfully applied to the optimal design of the MTMD.

• Smart Structures and Systems
• /
• v.16 no.2
• /
• pp.329-345
• /
• 2015

As commonly known, sensor errors and faulty signals may potentially lead structures in vibration to catastrophic failures. This paper presents a new approach to deal with sensor errors/faults in vibration control of structures by using the Fault detection and isolation (FDI) technique. To demonstrate the effectiveness of the approach, a space truss structure with semi-active devices such as Magneto-Rheological (MR) damper is used as an example. To address the problem, a Linear Matrix Inequality (LMI) based fixed-order $H_{\infty}$ FDI filter is introduced and designed. Modeling errors are treated as uncertainties in the FDI filter design to verify the robustness of the proposed FDI filter. Furthermore, an innovative Fuzzy Fault Tolerant Controller (FFTC) has been developed for this space truss structure model to preserve the pre-specified performance in the presence of sensor errors or faults. Simulation results have demonstrated that the proposed FDI filter is capable of detecting and isolating sensor errors/faults and actuator faults e.g., accelerometers and MR dampers, and the proposed FFTC can maintain the structural vibration suppression in faulty conditions.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.766-768
• /
• 2003

Selectivity estimation for spatial query is curial in Spatial Database Management Systems(SDBMS). Many works have been performed to estimate accurate selectivity. Although they deal with some problems such as false-count, multi-count arising from properties of spatial dataset, they can not get such effects in little memory space.* Therefore, we need to compress spatial dataset into little memory. In this paper, we propose a new technique called MW Histogram which is able to compress summary data and get reasonable results. Our method is based on two techniques:(a)MinSkew partitioning algorithm which deal with skewed spatial datasets. efficiently (b) Wavelet transformation which compression effect is proven. We evaluate our method via real datasets. The experimental result shows that the MW Histogram has the ability of providing estimates with low relative error and retaining the similar estimates even if memory space is small.

• Journal of the Chungcheong Mathematical Society
• /
• v.19 no.4
• /
• pp.417-426
• /
• 2006

In this paper, we are to construct a new fibred Riemannian space with almost complex structure from the lift of an almost contact structures of the base space and that of each fibre. Moreover, we deal with the fibred Riemannian space with various Kaehlerian structure.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.13 no.4
• /
• pp.307-314
• /
• 2009

In this work, we introduce a new quatnary approximating subdivision scheme for curve and deal with its analysis (convergence and regularity) using Laurent polynomials method. We also discuss various properties, such as approximation order and support of basic limit function.

• 제어로봇시스템학회:학술대회논문집
• /
• 1994.10a
• /
• pp.293-299
• /
• 1994

A new approach to deal with the model matching problem for square plants is suggested. Admissibility conditions of the model matching error are derived in terms of state-space parameters and the derived formulas are exploited to obtain the solution to the model matching problem in H$_{2}$ norm.

• Proceedings of the Korea Institute of Convergence Signal Processing
• /
• 2000.08a
• /
• pp.129-132
• /
• 2000

We propose a new method to deal with the optimized auto-tuning for the PID controller which is used to the process -control in various fields. First of all, in this method, initial values of DC motor are determined by the Ziegler-Nichols method. Finally, after studying the parameters of PID controller by input vector of multiple regression analysis, when we give new K, L, T values to multiple regression model, the optimized parameters of PID controller is found by multiple regression analysis program.