• Industrial Engineering and Management Systems
• /
• v.8 no.1
• /
• pp.22-28
• /
• 2009

We consider facility layout problems, where mn facility units are assigned into mn cells. These cells are arranged into a rectangular pattern with m rows and n columns. In order to solve this cell type facility layout problem, many approximation algorithms with improved local search methods were studied because it was quite difficult to find exact optimum of such problem in case of large size problem. In this paper, new algorithms based on Simulated Annealing (SA) method with two neighborhood generation methods are proposed. The new neighborhood generation method adopts the exchanging operation of facility units in accordance with adjacent preference. For evaluating the performance of the neighborhood generation method, three algorithms, previous SA algorithm with random 2-opt neighborhood generation method, the SA-based algorithm with the new neighborhood generation method (SA1) and the SA-based algorithm with probabilistic selection of random 2-opt and the new neighborhood generation method (SA2), are developed and compared by experiment of solving same example problem. In case of numeric examples with problem type 1 (the optimum layout is given), SA1 algorithm could find excellent layout than other algorithms. However, in case of problem type 2 (random-prepared and optimum-unknown problem), SA2 was excellent more than other algorithms.

• MALSORI
• /
• no.67
• /
• pp.79-102
• /
• 2008

In this paper, we investigate the relation between Bayes factor and likelihood ratio test (LRT) approaches and apply the neighborhood information of Bayes factor to building an alternate hypothesis model of the LRT system. To consider the neighborhood approaches, we contemplate a distance measure between models and algorithms to be applied. We also evaluate several methods to improve performance of utterance verification using neighborhood information. Among these methods, the system which adopts anti-models built by collecting mixtures of neighborhood models obtains maximum error rate reduction of 17% compared to the baseline, linear and weighted combination of neighborhood models.

• Journal of Korea Multimedia Society
• /
• v.18 no.6
• /
• pp.691-700
• /
• 2015

Here, we present a new framework for histogram equalization in which both local and global contrasts are enhanced using neighborhood metrics. When checking neighborhood information, filters can simultaneously improve image quality. Filters are chosen depending on image properties, such as noise removal and smoothing. Our experimental results confirmed that this does not increase the computational cost because the filtering process is done by our proposed arrangement of making the histogram while checking neighborhood metrics simultaneously. If the two methods, i.e., histogram equalization and filtering, are performed sequentially, the first method uses the original image data and next method uses the data altered by the first. With combined histogram equalization and filtering, the original data can be used for both methods. The proposed method is fully automated and any spatial neighborhood filter type and size can be used. Our experiments confirmed that the proposed method is more effective than other similar techniques reported previously.

• Journal of Korea Society of Industrial Information Systems
• /
• v.21 no.6
• /
• pp.13-21
• /
• 2016

Feature selection is the one of important issue in the field of data mining and machine learning. It is the technique to find a subset of features which provides the best classification performance, from the source data. We propose a feature subset selection method using the neighborhood rough set model based on information granularity. To demonstrate the effectiveness of proposed method, it was applied to select the useful features associated with breast tumor diagnosis of 298 shape features extracted from 5,252 breast ultrasound images, which include 2,745 benign and 2,507 malignant cases. Experimental results showed that 19 diagnostic features were strong predictors of breast cancer diagnosis and then average classification accuracy was 97.6%.

• Proceedings of the Korean Information Science Society Conference
• /
• 2007.10c
• /
• pp.428-432
• /
• 2007

We present a novel method for determining k nearest neighbors, which accurately recognizes the underlying clusters in a data set. To this end, we introduce the "tiling neighborhood" which is constructed by tiling a number of small local circles rather than a single circle, as existing neighborhood schemes do. Then we formulate the problem of determining the tiling neighborhood as a minimax optimization, leading to an efficient message passing algorithm. For several real data sets, our method outperformed the k-nearest neighbor method. The results suggest that our method can be an alternative to existing for general classification tasks, especially for data sets which have many missing values.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.16 no.3
• /
• pp.533-540
• /
• 2021

To evaluate the performance of a system, one-dimensional 3-neighborhood cellular automata(CA) based pseudo-random generators are widely used in many fields. Although two-dimensional CA and one-dimensional 5-neighborhood CA have been applied for more effective key sequence generation, designing symmetric one-dimensional 5-neighborhood CA corresponding to a given primitive polynomial is a very challenging problem. To solve this problem, studies on one-dimensional 5-neighborhood CA synthesis, such as synthesis method using recurrence relation of characteristic polynomials and synthesis method using Krylov matrix, were conducted. However, there was still a problem with solving nonlinear equations. To solve this problem, a symmetric one-dimensional 5-neighborhood CA synthesis method using a transition matrix of 90/150 CA and a block matrix has recently been proposed. In this paper, we detail the theoretical process of the proposed algorithm and use it to obtain symmetric one-dimensional 5-neighborhood CA corresponding to high-order primitive polynomials.

• Journal of Preventive Medicine and Public Health
• /
• v.42 no.4
• /
• pp.215-222
• /
• 2009

Objectives : Vigorous physical activity is a well-known method to promote people s health. This research aims to investigate whether perceived neighborhood characteristics affect vigorous physical activity among adult Seoul residents (aged 19 to 64). Methods : Utilizing the 2005 Seoul Citizens Health Indicators Survey data, this study estimates the probability of vigorous physical activity. Particular attention is given to the effects of three perceived neighborhood characteristics satisfaction with relationship to the neighborhood, satisfaction with park and recreational facilities, and satisfaction with public security). Logistic regression models are analyzed separately by gender for the parameter estimation. Results : Vigorous physical activity is positively associated with three perceived neighborhood characteristics for women, while neither significant nor substantive association is found for men. Conclusions : As vigorous physical activity among Seoul citizens is differentially affected by perceived neighborhood characteristics and by gender, a different approach will be needed to increase vigorous physical activity of men and women in Seoul.

• Spatial Information Research
• /
• v.10 no.2
• /
• pp.215-232
• /
• 2002

Variables representing neighborhood quality should be included in hedonic price models to control lfor the influences of negative or positive externalities from the quality of neighborhood on urban housing prices. This study proposes a GIS-based method to effectively measure the neighborhood quality variable when data on the neighborhood quality are aggregated by census sub area. This study also tests the superiority of the proposed neighborhood quality variable created by intensive use of GIS operations to a neighborhood variable not based on GIS operations in explaining the housing price variations by using Seoul＇s apartment sales data. The results from this study show that the neighborhood quality variable based on GIS-based operations shows better performance in explaining the urban housing price variations in Seoul＇s housing market. The implication from the results is that the potentials of GIS-based spatial operations in creating neighborhood quality variables should be well acknowledged by the researchers in the area of urban housing market study and GIS-based spatial operations should be more actively applied to generate better neighborhood quality variables for hedonic price models.

• KIEAE Journal
• /
• v.13 no.3
• /
• pp.21-26
• /
• 2013

Recently, as the concern over environmental problem has been serious all over the world, carbon has become a key issue to mitigate the problem. In the field of Architecture, various sustainable assessment systems and LCCO2 assessment strategies have been developed, guiding low carbon building while planning. However, assessment system for low carbon neighborhood, which can be more efficient strategy, is still in the stage of research. In this paper, as a research for low carbon assessment system and planning guideline for neighborhood, scope meeting domestic condition has been set and sustainable assessment system for neighborhood, which is similar to low carbon concept, have been compared. LEED-Neighborhood Development, BREEAM-Communities, CASBEE-Urban Development, and GBCC-Apartment Houses were used for comparison. As a result, it is found that those systems has many differences in judging system, aggregating method, scope of evaluation, and relations with a building assessment system. The concern about carbon is not insufficient yet. Finally, assessment system for low carbon neighborhood, which is not only for establishing the plan, but also for giving guideline at the very first state of the plan, should be developed as fast as passible. With the result of this paper, establishment of assessment criteria and the way to measure quantitative $CO_2$ should be studied forward.

• Journal of Korea Multimedia Society
• /
• v.11 no.6
• /
• pp.737-745
• /
• 2008

In this paper, a novel neighborhood metric of histogram equalization (HE) algorithm for contrast enhancement is presented. We present a refinement of HE using neighborhood metrics with a general framework which orders pixels based on a sequence of sorting functions which uses both global and local information to remap the image greylevels. We tested a novel sorting key with the suggestion of using the original image greylevel as the primary key and a novel neighborhood distinction metric as the secondary key, and compared HE using proposed distinction metric and other HE methods such as global histogram equalization (GHE), HE using voting metric and HE using contrast difference metric. We found that our method can preserve advantages of other metrics, while reducing drawbacks of them and avoiding undesirable over-enhancement that can occur with local histogram equalization (LHE) and other methods.