• International journal of advanced smart convergence
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• v.1 no.2
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• pp.30-33
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• 2012

This paper propose a new design of fuzzy neural networks based on hard partition to generate the rules of the networks. For this we use hard c-means (HCM) clustering algorithm. The premise part of the rules of the proposed networks is realized with the aid of the hard partition of input space generated by HCM clustering algorithm. The consequence part of the rule is represented by polynomial functions. And the coefficients of the polynomial functions are learned by BP algorithm. The number of the hard partition of input space equals the number of clusters and the individual partitioned spaces indicate the rules of the networks. Due to these characteristics, we may alleviate the problem of the curse of dimensionality. The proposed networks are evaluated with the use of numerical experimentation.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.7 no.4
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• pp.151-158
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• 2014

In this paper, we introduce an inference system using hard scatter partition method and model the nonlinear process. To do this, we use the hard scatter partition method that partition the input space in the scatter form with the value of the membership degree of 0 or 1. The proposed method is implemented by C-Means clustering algorithm. and is used for the initial center values by means of binary split. by applying the LBG algorithm to compensate for shortcomings in the sensitive initial center value. Hard-scatter-partitioned input space forms the rules in the rule-based system modeling. The premise parameters of the rules are determined by membership matrix by means of C-Means clustering algorithm. The consequence part of the rules is expressed in the form of polynomial functions and the coefficient parameters of each rule are determined by the standard least-squares method. The data widely used in nonlinear process is used to model the nonlinear process and evaluate the characteristics of nonlinear process.

• Journal of Computing Science and Engineering
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• v.9 no.2
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• pp.51-72
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• 2015

Time predictability is crucial in hard real-time and safety-critical systems. Cache memories, while useful for improving the average-case memory performance, are not time predictable, especially when they are shared in multicore processors. To achieve time predictability while minimizing the impact on performance, this paper explores several time-predictable scratch-pad memory (SPM) based architectures for multicore processors. To support these architectures, we propose the dynamic memory objects allocation based partition, the static allocation based partition, and the static allocation based priority L2 SPM strategy to retain the characteristic of time predictability while attempting to maximize the performance and energy efficiency. The SPM based multicore architectural design and the related allocation methods thus form a comprehensive solution to hard real-time multicore based computing. Our experimental results indicate the strengths and weaknesses of each proposed architecture and the allocation method, which offers interesting on-chip memory design options to enable multicore platforms for hard real-time systems.

• IE interfaces
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• v.21 no.1
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• pp.1-7
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• 2008

In this paper we study the Minimum Edge Addition Problem(MEAP) to decrease the diameter of a graph. MEAP can be used for improving the serviceability of telecommunication networks with a minimum investment. MEAP is an NP-hard optimization problem. We present two mathematical programming models : One is a multi-commodity flow formulation and the other is a path partition formulation. We propose a branch-and-price algorithm to solve the path partition formulation to the optimality. We develop a polynomial time column generation sub-routine conserving the mathematical structure of a sub problem for the path partition formulation. Computational experiments show that the path partition formulation is better than the multi-commodity flow formulation. The branch-and-price algorithm can find the optimal solutions for the immediate size graphs within reasonable time.

• Macromolecular Research
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• v.15 no.7
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• pp.682-687
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• 2007

The equation of state developed herein is predicated on a hard-sphere reference with perturbations introduced via a potential function to account for electrostatic forces and for attraction between protein particles. During this process, the generalized Lennard-Jones (GLJ) pair potential function is employed. The GLJ pair potential function is employed to represent the protein-protein interaction in two-protein systems. Via the use of the relation between the equation of state and the chemical potential, the phase behavior in the aqueous two-protein system can be estimated. The partition coefficients can be obtained via these processes. The calculated values of the coefficients agree fairly well with the experimental data in the given pH and ionic strength range, with no additional adjustable model parameters.

• Proceedings of the Korean Society of Computer Information Conference
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• 2010.07a
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• pp.429-432
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• 2010

그래프 분할 문제는 각각의 가중치가 주어진 에지와 노드를 정해진 목적에 맞게 몇 개의 그룹으로 분할하는 문제이다. 이 문제는 휴리스틱 방법으로 해결되어져 왔으나, NP-hard 문제로 인한 지역 최적해에 빠지기 쉬운 단점을 갖는다. 유전자 알고리즘이 해결 방법으로 제시되고 있는 가운데 단순 유전자 알고리즘에서 초기의 모집단 메모리(population memory)를 이용하여 적은 크기의 모집단을 생성하고 외부메모리에 최적해들을 저장하고 있어 GA의 효율성을 높이며, 다수의 지역 최적해에 빠지지 않게 하며 수렴 속도를 향상시키는 마이크로 유전자 알고리즘을 적용한다. ${\mu}$-GA를 통해 본 논문에서는 클러스터들의 가중치를 비교적 동일하게 하는 GPP를 해결하고자 한다.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.2
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• pp.225-234
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• 2018

In this paper, we consider a problem of partitioning a search area into smaller rectangular regions, so that multiple platforms can conduct search operations independently without requiring unnecessary coordination among themselves. The search area consists of cells where each cell has some prior information regarding the probability of target existence. The detection probability in particular cell is evaluated by multiplying the observation probability of the platform and the target existence probability in that cell. The total detection probability within the search area is defined as the cumulative detection probability for each cell. However, since this search area partitioning problem is NP-Hard, we decompose the problem into three sequential phases to solve this computationally intractable problem. Additionally, we discuss a special case of this problem, which can provide an optimal analytic solution. We also examine the performance of the proposed approach by comparing our results with the optimal analytic solution.

• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.48-55
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• 2011

In this paper, we analyze the input-output characteristics of fuzzy inference systems according to the division of entire input spaces and the fuzzy reasoning methods to identify the fuzzy model for nonlinear process. And fuzzy model is expressed by identifying the structure and parameters of the system by means of input variables, fuzzy partition of input spaces, and consequence polynomial functions. In the premise part of the rules Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the hard clusters are used for identification of fuzzy model and membership function is used as a series of triangular membership function. In the consequence part of the rules fuzzy reasoning is conducted by two types of inferences. The identification of the consequence parameters, namely polynomial coefficients, of the rules are carried out by the standard least square method. And lastly, we use gas furnace process which is widely used in nonlinear process and we evaluate the performance for this nonlinear process.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.757-759
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• 2003

암의 치료법은 같은 종류의 암이라 해도 그 하부 클래스에 따라 매우 다르기 때문에 암의 클래스를 예측하는 것은 그 정확한 치료를 위하여 매우 중요하다. 유전자 발현 데이터를 이용한 암의 분류에 있어 기존의 연구들은 각 데이터를 하나의 클러스터에 소속시키는 하드 분할(hard partition)에 의한 분할 방식을 사용하는 하드 클러스터링을 사용하였다. 하지만 일반적으로 유전자 발현 암 데이터와 같은 실세계의 데이터는 쉽게 나뉘어지기 힘들거나 클러스터 간의 경계가 분명하지 않기 때문에 하드 클러스터링 기법은 주어진 데이터의 성질을 손실시킬 수 있는데 반해, 퍼지 클러스터링 기법은 각 데이터가 소속 정도에 따라 여러 개의 클러스터에 속할 수 있도록 분할하기 때문에 이러한 손실을 최소화할 수 있다. 따라서 본 논문에서는 퍼지 클러스터링의 대표적인 방법인 fuzzy c-means 클러스터링을 적용하여 암의 클래스를 예측하고, 다양한 하드 클러스터링 방법과 비교함으로써 퍼지 클러스터링의 성능을 검증하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2005.07a
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• pp.892-894
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• 2005

Graph partitioning provides an important tool for data clustering, but is an NP-hard combinatorial optimization problem. Spectral clustering where the clustering is performed by the eigen-decomposition of an affinity matrix [1,2]. This is a popular way of solving the graph partitioning problem. On the other hand, semidefinite relaxation, is an alternative way of relaxing combinatorial optimization. issuing to a convex optimization[4]. In this paper we present a semidefinite programming (SDP) approach to graph equi-partitioning for clustering and then we use eigen-decomposition to obtain an optimal partition set. Therefore, the method is referred to as semidefinite spectral clustering (SSC). Numerical experiments with several artificial and real data sets, demonstrate the useful behavior of our SSC. compared to existing spectral clustering methods.