• KIPS Transactions on Software and Data Engineering
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• v.2 no.6
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• pp.383-394
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• 2013

As a way of augmenting constrained resources of mobile devices such as CPU and memory, many works on mobile cloud computing (MCC), where mobile devices utilize remote resources of cloud services or PCs, have been proposed. Typically, in MCC, many nodes with different operating systems and platform and diverse mobile applications or services are located, and a central manager autonomously performs several management tasks to maintain a consistent level of MCC overall quality. However, as there are a larger number of nodes, mobile applications, and services subscribed by the mobile applications and their interactions are extremely increased, a traditional management method of MCC reveals a fundamental problem of degrading its overall performance due to overloaded management tasks to the central manager, i.e. a bottle neck phenomenon. Therefore, in this paper, we propose a clustering-based optimization method to solve performance-related problems on large-scaled MCC and to stabilize its overall quality. With our proposed method, we can ensure to minimize the management overloads and stabilize the quality of MCC in an active and autonomous way.

• Journal of the Korean Data and Information Science Society
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• v.25 no.2
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• pp.349-356
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• 2014

In baseball, sabermetric batting statistics are used to compare an offensive performance of players. There exist dozens of sabermetric statistics, but baseball fans don't like the complexity of an abundance of measures. This paper provides a batting grade index (BGI) using principal component based on eight batting statistics. These are OPS, ISO, SECA, TA, RC, RC/27, wOBA and XR. We show that how standardized batting statistics are aggregated and weighted to arrive at a single composite measure of BGI. Also our result allows for segmentation of players into groups using the K-means clustering algorithm.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.11
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• pp.1796-1801
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• 2001

For micro-machines, very few design methodologies based on optimization hale been developed so far. To overcome the difficulties of design optimization of micro-machines, the search method for multi-dimensional design window (DW)s is proposed. The proposed method is defined as areas of satisfactory design solutions in a design parameter space, using both continuous evolutionary algorithms (CEA) and the modified K-means clustering algorithm . To demonstrate practical performance of the proposed method, it was applied to an optimal shape design of micro electrostatic actuator of optical memory. The shape design problem has 5 design parameters and 5 objective functions, and finally shows 4 specific design shapes and design characters based on the proposed DWs.

• The Journal of Bigdata
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• v.5 no.1
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• pp.155-180
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• 2020

It takes the most important role the problem of assigining vehicles and desigining optimal routes for each vehicle in order to enhance the logistics service level. While solving the problem, various cost factors such as number of vehicles, the capacity of vehicles, total travelling distance, should be considered at the same time. Although most of logistics service providers introduced the Transportation Management System (TMS), the system has the limitation which can not consider the practical constraints. In order to make the solution of TMS applicable, it is required experts revised the solution of TMS based on their own experience and intuition. In this research, different from previous research which have focused on minimizing the total cost, it has been proposed the methodology which can enhance the efficiency and fairness of asset utilization, simultaneously. First of all, it has been adopted the Cluster-First Route-Second (CFRS) approach. Based on the location of customers, we have grouped customers as clusters by using four different clustering algorithm such as K-Means, K-Medoids, DBSCAN, Model-based clustering and a procedural approach, Fisher & Jaikumar algorithm. After getting the result of clustering, it has been developed the optiamal vehicle routes within clusters. Based on the result of numerical experiments, it can be said that the propsed approach based on CFRS may guarantee the better performance in terms of total travelling time and distance. At the same time, the variance of travelling distance and number of visiting customers among vehicles, it can be concluded that the proposed approach can guarantee the better performance of assigning tasks in terms of fairness.

• Journal of IKEEE
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• v.22 no.3
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• pp.658-663
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• 2018

In this paper, we consider node deployment algorithms for the sequence-based localization (SBL) which is recently employed for in-door positioning systems, Whereas previous node selection or deployment algorithms seek to place nodes at centrold of the region where more targets are likely to be found, we observe that the boundaries dividing such regions can be good locations for the nodes in SBL systems. Motivated by this observation, we propose an efficient node deployment algorithm that determines the boundaries by using the well-known K-means algorithm and find the potential node locations based on the bi-section method for low-complexity design. We demonstrate through experiments that the proposed algorithm achieves significant localization performance over random node allocation with a substantially reduced complexity as compared with a full search.

• International Journal of Highway Engineering
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• v.15 no.5
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• pp.177-185
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• 2013

PURPOSES : This study attempts to design and establish the road surface condition detection system by using the image processing that is expected to help implement the low-cost and high-efficiency road information detection system by examining technology trends in the field of road surface condition information detection and related case studies. METHODS : Adapted visual information collecting method(setting a stereo camera outside of the vehicle) and visual information algorithm(transform a Wavelet Transform, using the K-means clustering) Experiments and Analysis on Real-road, just as four states(Dry, Wet, Snow, Ice). RESULTS : Test results showed that detection rate of 95% or more was found under the wet road surface, and the detection rate of 85% or more in snowy road surface. However, the low detection rate of 30% was found under the icy road surface. CONCLUSIONS : As a method to improve the detection rate of the mobile road surface condition information detection system developed in this study, more accurate phase analysis in the image processing process was needed. If periodic synchronization through automatic settings of the camera according to weather or ambient light was not made at the time of image acquisition, a significant change in the values of polarization coefficients occurs.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.369-384
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• 2021

In a personalized product recommendation system, when the amount of log data is large or sparse, the accuracy of model recommendation will be greatly affected. To solve this problem, a personalized product recommendation method using deep factorization machine (DeepFM) to analyze user behavior is proposed. Firstly, the K-means clustering algorithm is used to cluster the original log data from the perspective of similarity to reduce the data dimension. Then, through the DeepFM parameter sharing strategy, the relationship between low- and high-order feature combinations is learned from log data, and the click rate prediction model is constructed. Finally, based on the predicted click-through rate, products are recommended to users in sequence and fed back. The area under the curve (AUC) and Logloss of the proposed method are 0.8834 and 0.0253, respectively, on the Criteo dataset, and 0.7836 and 0.0348 on the KDD2012 Cup dataset, respectively. Compared with other newer recommendation methods, the proposed method can achieve better recommendation effect.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.2
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• pp.45-52
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• 2020

Since the opening of the national petition site, it has attracted much attention. In this paper, we perform topic analysis of the national petition site and propose a prediction model for answerable petitions based on deep learning. First, 1,500 petitions are collected, topics are extracted based on the petitions' contents. Main subjects are defined using K-means clustering algorithm, and detailed subjects are defined using topic modeling of petitions belonging to the main subjects. Also, long short-term memory (LSTM) is used for prediction of answerable petitions. Not only title and contents but also categories, length of text, and ratio of part of speech such as noun, adjective, adverb, verb are also used for the proposed model. Our experimental results show that the type 2 model using other features such as ratio of part of speech, length of text, and categories outperforms the type 1 model without other features.

• Journal of the Korea Convergence Society
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• v.12 no.4
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• pp.31-42
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• 2021

The purpose of this study was to explore research topics and examine the trend in COVID19 related research papers. We identified eight topics using latent Dirichlet allocation and found acceptable validity in comparison with the structural topic model. The subtopics have been extracted using k-means clustering and plotted in PCA space. Additionally, we discovered the topics bearing negative tones and warning signs by sentiment analysis. The results flagged up the issues of the topics, Biomedical Related, International Dynamics and Psychological Impact. The findings could serve as a guideline for researchers who explore new research directions and policymakers who need to make decisions about which research projects to support.

• The KIPS Transactions:PartB
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• v.8B no.2
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• pp.144-154
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• 2001

협력적 여과 시스템은 사용자가 검색하고 읽었던 웹문서를 기반으로 사용자 군집을 생성하여 웹문서의 정확한 추천을 가능하게 한다. 이러한 목적으로 설계된 다양한 알고리즘이 있으나 속도가 느리거나 정확도가 낮다는 등의 단점이 있다. 본 논문에서는 이러한 단점을 보완하기 위하여 협력적 여과 시스템을 위한 효과적인 사용자 군집 알고리즘인 CUG알고리즘은 사용자 군집을 생성하기 위해 Apriori 알고리즘, Native Bayes 알고리즘을 이용한다. Apriori 알고리즘은 연관 단어 지식 베이스를 구축하고, Native Bayes 알고리즘은 구축된 연관 단어 지식 베이스에 가중치를 추가하며, 사용자가 검색하여 읽은 웹문서를 클래스별로 분류한다. CUG 알고리즘은 분류된 웹문서를 기반으로 하여 사용자 군집을 만든다. 이러한 방법으로 설계된 CUG 알고리즘은 사용자들이 사용할 문서를 미리 검색하여 저장함에 의해 정보검색의 효율성을 향상시키는데 사용될 수 있다. 본 논문에서 설계한 CUG 알고리즘의 선능을 평가하기 위하여 기존의 K-means 방법과 Gibbs샘플링 방법에 의한 군집과 비교한다.