• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.519-522
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• 2017

In this paper, we propose an algorithm for an alternative method using the clustering algorithm in a system that needs classification to extract individual user context information. In the conventional user classification system, the user has to input his own information. In this paper, we will research and develop a system applying a clustering algorithm which can extract user 's perceived information applying the improved algorithm for user management base. Generally, the algorithm that distinguishes users with the same data makes sure that recorded information matches the newly entered information, and then responds accordingly. However, it is troublesome to manually input information of the new user. Therefore, in this paper, we propose a method to distinguish users by using the clustering algorithm based on the analyzed data from the working memory in the accumulated system without directly inputting the user information. The study shows that the management method applied to the applied algorithm is more adaptive in environments where the number of people is different from that of the existing system (as a subjective observer test method).

• Journal of Korea Port Economic Association
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• v.31 no.1
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• pp.51-68
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• 2015

Recently, it has been emphasized that 'marine special economic zone' need to be designated and developed. Therefore, the purpose of this paper is to analyse on successful growth factors of 'marine special economic zone'. This study deals with the terms of three connection success factors which are a firm's subjective factors, a industrial environment factors, and a governmental policy factors. According to analysis results of the three successful growth factors, a firm's subjective factors(4.11 score) are scored at the most ones of the three successful growth factors, to be compared with a industrial environment factors(3.89 score). with a government policy factors(3.72 score). Therefore, first of all, it is important to enhance competitiveness of 'marine special economic zone' through as follows, a firm's subjective factors : (1) to procure concentrated market strategy and real market capacity, (2) to promote customer service, (3) to procure speedy satisfaction of customer needs and confidence, (4) to enhance competitiveness through standing in a trio of connection growth model. And, the next, we have to enhance competitiveness of 'marine special economic zone' through considering a industrial environment factors, that is, sustainable growth of marine industry, clustering of marine industry, expansion of infrastructure, etc., and a government policy factors, that is, leading law improvement and policy of leading 'marine special economic zone' designation and development, etc.,

• Tunnel and Underground Space
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• v.15 no.1 s.54
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• pp.47-54
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• 2005

One of the standard procedures of discontinuity survey is the joint set identification from the population of field orientation data. Discontinuity set identification is fundamental to rock engineering tasks such as rock mass classification, discrete element analysis, key block analysis. and discrete fracture network modeling. Conventionally, manual method using contour plot had been widely used for this task, but this method has some short-comings such as yielding subjective identification results, manual operations, and so on. In this study, the method of discontinuity set identification using genetic algorithm was introduced, but slightly modified to handle the orientation data. Finally, based on the genetic algorithm, we developed a FORTRAN program, Genetic Algorithm based Clustering(GAC) and applied it to two different discontinuity data sets. Genetic Algorithm based Clustering(GAC) was proved to be a fast and efficient method for the discontinuity set identification task. In addition, fitness function based on variance showed more efficient performance in finding the optimal number of clusters when compared with Davis - Bouldin index.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.288-307
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• 2016

Image distortions are typically characterized by degradations of structures. Dictionaries learned from natural images can capture the underlying structures in images, which are important for image quality assessment (IQA). This paper presents a general-purpose no-reference image quality metric using a GRadient-Induced Dictionary (GRID). A dictionary is first constructed based on gradients of natural images using K-means clustering. Then image features are extracted using the dictionary based on Euclidean-norm coding and max-pooling. A distortion classification model and several distortion-specific quality regression models are trained using the support vector machine (SVM) by combining image features with distortion types and subjective scores, respectively. To evaluate the quality of a test image, the distortion classification model is used to determine the probabilities that the image belongs to different kinds of distortions, while the regression models are used to predict the corresponding distortion-specific quality scores. Finally, an overall quality score is computed as the probability-weighted distortion-specific quality scores. The proposed metric can evaluate image quality accurately and efficiently using a small dictionary. The performance of the proposed method is verified on public image quality databases. Experimental results demonstrate that the proposed metric can generate quality scores highly consistent with human perception, and it outperforms the state-of-the-arts.

• Journal of the Ergonomics Society of Korea
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• v.28 no.4
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• pp.147-154
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• 2009

The tactical computer is currently being developed and installed in armored vehicles and tanks for reinforcement. With the tactical computer, Korea Army will be able to grasp the deployment status of our forces, enemy, and obstacles under varying situations. Furthermore, it makes the exchange of command and tactical intelligence possible. Recent studies showed that the task performance is greatly affected by the user interface. The U.S. Army is now conducting user-centered evaluation tests based on C2 (Command & Control) to develop tactical intelligence machinery and tools. This study aims to classify and regroup subordinate menu functions according to the user-centered task performance for the Korea Army's tactical computer. Also, the research suggests an ergonomically sound layout and size of main touch buttons by considering human factors guidelines for button design. To achieve this goal, eight hierarchical subordinate menu functions are initially drawn through clustering analysis and then each group of menu functions was renamed. Based on the suggested menu structure, new location and size of the buttons were tested in terms of response time, number of error, and subjective preference by comparing them to existing ones. The result showed that the best performance was obtained when the number of buttons or functions was eight to conduct tactical missions. Also, the improved button size and location were suggested through the experiment. It was found in addition that the location and size of the buttons had interactions regarding the user's preference.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1618-1624
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• 2020

Intestinal obstruction diagnosis method based on X-ray can affect objective diagnosis because it includes subjective factors of the examiner. Therefore, in this paper, a detection method of Intestinal Obstruction from X-Ray image using Hough transform and PCM is proposed. The proposed method uses Hough transform to detect straight lines from the extracted ROI of the intestinal obstruction X-Ray image and bowel obstruction is extracted by using air fluid level's morphological characteristic detected by the straight lines. Then, ROI is quantized by applying PCM clustering algorithm to the extracted ROI. From the quantized ROI, cluster group that includes bowel obstruction's characteristic is selected and small bowel regions are extracted by using object search from the selected cluster group. The proposed method of using PCM is applied to 30 X-Ray images of intestinal obstruction patients and setting the initial cluster number of PCM to 4 showed excellent performance in detection and the TPR was 81.47%.

• Communications for Statistical Applications and Methods
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• v.30 no.5
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• pp.485-499
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• 2023

This study aimed to predict the number of meals served in a group cafeteria using machine learning methodology. Features of the menu were created through the Word2Vec methodology and clustering, and a stacking ensemble model was constructed using Random Forest, Gradient Boosting, and CatBoost as sub-models. Results showed that CatBoost had the best performance with the ensemble model showing an 8% improvement in performance. The study also found that the date variable had the greatest influence on the number of diners in a cafeteria, followed by menu characteristics and other variables. The implications of the study include the potential for machine learning methodology to improve predictive performance and reduce food waste, as well as the removal of subjective elements in menu classification. Limitations of the research include limited data cases and a weak model structure when new menus or foreign words are not included in the learning data. Future studies should aim to address these limitations.

• Journal of Broadcast Engineering
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• v.22 no.1
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• pp.28-41
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• 2017

In this paper, the fast encoding algorithm in High Efficiency Video Coding (HEVC) encoder was studied. For the encoding efficiency, the current HEVC reference software is divided the input image into Coding Tree Unit (CTU). then, it should be re-divided into CU up to maximum depth in form of quad-tree for RDO (Rate-Distortion Optimization) in encoding precess. But, it is one of the reason why complexity is high in the encoding precess. In this paper, to reduce the high complexity in the encoding process, it proposed the method by determining the maximum depth of the CU using a hierarchical clustering at the pre-processing. The hierarchical clustering results represented an average combination of motion vectors (MV) on neighboring blocks. Experimental results showed that the proposed method could achieve an average of 16% time saving with minimal BD-rate loss at 1080p video resolution. When combined the previous fast algorithm, the proposed method could achieve an average 45.13% time saving with 1.84% BD-rate loss.

• The KIPS Transactions:PartB
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• v.11B no.1
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• pp.21-26
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• 2004

Color image quantization is a process of selecting a set of colors to display an image with some representative colors without noticeable perceived difference. It is very important in many applications to display a true color image in a low cost color monitor or printer. The basic problem is how to display 256 colors or less colors, called color palette, In this paper, we propose improved binary tree vector quantization based on spatial sensitivity which is one of the human visual properties. We combine the weights based on the responsibility of human visual system according to changes of three Primary colors in blocks of images with the process of splitting nodes using eigenvector in binary tree vector quantization. The test results show that the proposed method generates the quantized images with fine color and performs better than the conventional method in terms of clustering the similar regions. Also the proposed method can get the better result in subjective quality test and WSNR.

• Journal of the Korean Society for information Management
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• v.35 no.1
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• pp.231-250
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• 2018

In this paper, we propose a new methodology for extracting and formalizing subjective topics at a specific time using a set of keywords extracted automatically from online news articles. To do this, we first extracted a set of keywords by applying TF-IDF methods selected by a series of comparative experiments on various statistical weighting schemes that can measure the importance of individual words in a large set of texts. In order to effectively calculate the semantic relation between extracted keywords, a set of word embedding vectors was constructed by using about 1,000,000 news articles collected separately. Individual keywords extracted were quantified in the form of numerical vectors and clustered by K-means algorithm. As a result of qualitative in-depth analysis of each keyword cluster finally obtained, we witnessed that most of the clusters were evaluated as appropriate topics with sufficient semantic concentration for us to easily assign labels to them.