• Proceedings of the Korean Institute of Interior Design Conference
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• 1999.04a
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• pp.27-30
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• 1999

Telecommuting becomes a new form of work according to the development of computer and telecommunication technology. Therefore, the purpose of this study is to provide basic data for the interior space planning of telecommuting centers by means of surveying office workers' opinions. The major findings of this research are as follows. The opinion about whether to use telecommuting center if provided is showed highly positively. In the matter of space type of telecommuting center, 'mixing type I (open plan office but division with high partition)' is the most preferred, the next is 'closed type'. The most preferred type of workstation is 'individual space type ', the next if 'X type' and the third is 'link type'. referred partition height is '1m300-1,500mm'.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.2
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• pp.95-101
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• 2005

In this paper, we proposed a learning algorithm for the neuro-fuzzy modeling using a learning rule to adapt clustering. The proposed algorithm includes the data partition, assigning the rule into the process of partition, and optimizing the parameters using predetermined threshold value in self-constructing algorithm. In order to improve the clustering, the learning method of neuro-fuzzy model is extended and the learning scheme has been modified such that the learning of overall model is extended based on the error-derivative learning. The effect of the proposed method is presented using simulation compare with previous ones.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1724-1727
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• 2002

Asynchronous circuits have the potential to solve the problems related to parameter variations such as gate delays in deep sub-micron technologies. However, current CAD tools for large-scale asyn-chronous circuits partition specification irrelevantly, because these tools cannot control the granularity of circuit decomposition. In this paper we propose a hierarchical Control/Data Flow Graph (CDFG) containing nodes that are flexibly partitioned or merged into other nodes. We show a partitioning algorithm for such CDFGs to generate handleable Signal Transition Graphs (STGs) for asynchronous synthesis tools. The algorithm a1lows designers to assign the maximum number of signals of partitioned nodes considering of timality. From an experiment, this algorithm can flexibly partition and result in more compact asynchronous controllers.

• Computers and Concrete
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• v.4 no.1
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• pp.67-82
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• 2007

The discrete crack-concept is applied to study the 3D propagation of tensile-dominated failure in plain concrete. To this end the Partition of Unity Finite Element Method (PUFEM) is utilized and the strong discontinuity approach is followed. A consistent linearized implementation of the PUFEM is combined with a predictor-corrector algorithm to track the crack path, which leads to a robust numerical description of concrete cracking. The proposed concept is applied to study concrete failure during the PCT3D test and the predicted numerical results are compared to experimental data. The proposed numerical concept provides a clear interface for constitutive models and allows an investigation of their impact on concrete cracking under 3D conditions, which is of significant scientific interests to interpret results from 3D experiments.

• Bulletin of the Korean Chemical Society
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• v.6 no.6
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• pp.337-343
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• 1985

A statistical mechanical approach to elucidate the solvent effects on the high polymer solutions has been carried out on the basis of the simple model of liquids improved by Pak. In our works, the partition function of the polymer solutions is formulated by the lattice model and our simple treatment of liquid structures. For the ideal polymer solutions proposed by Flory, thermodynamic functions of the polymer solutions are obtained and equations of mixing properties and partial molar quantities are derived from the presented partition function of the polymer solutions. Partial molar quantities are calculated for the rubber solutions in carbon disulfide, benzene and carbon tetrachloride. Comparisons have been made between our equations and those of Flory's original paper for partial molar properties of the rubber-benzene system. Comparing the experimental data of the osmotic pressure of polystyrene-cyclohexane system with our calculated values and those of Flory's, our values fit to the agreeable degrees better than those of Flory's.

• Journal of the Korean Statistical Society
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• v.15 no.1
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• pp.1-8
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• 1986

The k-medians clustering method is considered to partition observations into k clusters. Consistency and advantage of bootstrap confidence sets of k optimal cluster centers are discussed. The k-medians and k-means clustering methods are compared by using actual data sets.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10c
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• pp.28-32
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• 2006

유비쿼터스(Ubiquitous)환경과 같이 외부로부터 입력되는 데이터가 stream의 형식으로 실시간으로 들어오고, 입력의 끝을 알 수 없는 환경에서는 기존의 join방식으로는 문제를 해결 할 수 없다. 또한 이러한 환경 하에서는 데이터의 크기나 특성이 모두 다르고 네트워크 상태에 따라 입력이 많은 영향을 받게 된다. 이런 stream환경의 join연산을 위하여 double pipelined hash join, Xjoin, Pjoin등 많은 알고리즘이 기존의 연구를 대표하여 왔다. 그 중 Xjoin은 symmetric hash join과 hybrid hash join의 특징들을 이용해서 들어오는 data의 흐름에 따라서 reactive하게 join과정을 조절함으로써 streaming data에 대한 join을 수행한다. 그러나 여러 단계의 수행에 따른 연산의 중복결과를 체크하기 위한 overhead로 인해 성능이 떨어진다. 이 논문에서는 이러한 점을 개선하기 위해서 Xjoin의 수행과정을 수정한 방법을 제시할 것이다. 각 partition마다 구분자만을 추가함으로써 간단하게 중복을 만들어내지 않는 방법을 제안하고 불필요한 연산과 I/O를 줄일 수 있도록 partition선택방법을 추가할 것이다. 이를 통해서 중복된 연산인지 체크하는 과정을 상당히 단순화함으로써 좀 더 좋은 성능을 가지게 될 것이고 또한 timestamp를 저장해야 하는 overhead를 줄여서 전체 연산에 필요한 저장 공간을 절약할 수 있다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.05a
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• pp.109-112
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• 1999

Image segmentation is the critical first step in image information extraction for computer vision systems. Clustering methods have been used extensively in color image segmentation. Most analytic fuzzy clustering approaches are divided from the fuzzy c-means(FCM) algorithm. The FCM algorithm uses fie probabilistic constraint that the memberships of a data point across classes sum to 1. However, the memberships resulting from the FCM do not always correspond to the intuitive concept of degree of belonging or compatibility. Moreover, the FCM algorithm has considerable trouble under noisy environments in the feature space. Recently, a possibilistic approach to clustering(PCM) for solving above problems was proposed. In this paper, we used the PCM for color image segmentation. This approach differs from existing fuzzy clustering methods for color image segmentation in that the resulting partition of the data can be interpreted as a possibilistic partition. So, the problems in the FCM can be solved by the PCM. But, the clustering results by the PCM are not smoothly bounded, and they often have holes. The region growing was used as a postprocessing after smoothing the noise points in the pixel seeds. In our experiments, we illustrate that the PCM us reasonable than the FCM in noisy environments.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.4
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• pp.299-304
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• 2017

A wireless passive sensor network is a network consisting of sink nodes, sensor nodes, and radio frequency (RF) sources, where an RF source transfers energy to sensor nodes by radiating RF waves, and a sensor node transmits data by consuming the received energy. Against theoretical expectations, a wireless passive sensor network suffers from many practical difficulties: scarcity of energy, non-simultaneity of energy reception and data transmission, and inefficiency in allocating time resources. Perceiving such difficulties, we propose a simple contending-type medium access control (MAC) scheme for many sensor nodes to deliver packets to a sink node. Then, we derive an approximate expression for the network-wide throughput attained by the proposed MAC scheme. Also, we present an approximate expression for the optimal partition, which maximizes the saturated network-wide throughput. Numerical examples confirm that each of the approximate expressions yields a highly precise value for network-wide throughput and finds an exactly optimal partition.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.12
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• pp.5464-5484
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• 2016

Few samples face recognition has become a highly challenging task due to the limitation of available labeled samples. As two popular paradigms in face image representation, sparse component analysis is highly robust while parts-based paradigm is particularly flexible. In this paper, we propose a probabilistic generative model to incorporate the strengths of the two paradigms for face representation. This model finds a common spatial partition for given images and simultaneously learns a sparse component analysis model for each part of the partition. The two procedures are built into a probabilistic generative model. Then we derive the score function (i.e. feature mapping) from the generative score space. A similarity measure is defined over the derived score function for few samples face recognition. This model is driven by data and specifically good at representing face images. The derived generative score function and similarity measure encode information hidden in the data distribution. To validate the effectiveness of the proposed method, we perform few samples face recognition on two face datasets. The results show its advantages.