• 한국정보시스템학회지:정보시스템연구
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• 제21권1호
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• pp.93-116
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• 2012

Internet users' desire to share shopping experiences has resulted in the appearance of the paradigm shift called social commerce, which combines social media with commerce. However, while foreign market realizes social commerce in a various way, current domestic social commerce market still seems to be highly filled with social shopping sites offering 50 percent discount, thus causing concerns about market distortion from a cutthroat competition. In order to overcome the limitations of social commerce remaining in the early stage, and to enhance the communication among consumers, this study conducted an empirical study on the social-commerce business models design based on context. The results of the study brought the imaginary, optimal combination model by conjoint analysis, using the segmented group through cluster analysis. To overcome the uniformity of social commerce and to enhance communication among consumers, the context-based optimal combination model should be proposed as the alternative social commerce business model in the domestic market which remains as a simple group-purchase function. Furthermore, this study will be used as an informative reference designing customer oriented service model for social commerce business.

• Nuclear Engineering and Technology
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• 제13권3호
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• pp.153-161
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• 1981

고리 원자력 1호기에서 일어날 수 있는 가상사고에 의한 동특성 현상이 점근사 원자로 모델에 의한 중성자 및 온도 방정식을 사용하여 해석되었다. 일반적으로 수치해석 결과는 사고해석에 있어서 확실한 동특성 시간전이 현상을 예견하기 위채서는 보다 정밀한 계산모델을 사용해야 된다는 것을 지시한다. 전출력 상태에서 RCCA 인출에 따르는 출력반응의 경우는 점근사 원자로 모델이 고리 1호기의 최종 안정성 분석 보고서의 해석결과와 우수한 일치를 보여줬다.

• Journal of the Korean Data and Information Science Society
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• 제25권5호
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• pp.1069-1077
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• 2014

클럽발은 발이 안쪽으로 굽어있는 상태로 태어나는 선천적인 발 기형의 일종이다. 본 연구에서는 한 쪽 클럽발 환자들의 수술 후 시간에 따른 양 쪽 발의 상대적인 차이 커브들을 군집분석 하려고 한다. 관측값들이 일정하지 않은 (irregular) 시점에서 희박하게 (sparsely) 관측되어서 일반적인 함수적 군집모형을 사용할 수 없어 James와 Sugar (2003) 가 제안한 희박한 자료의 함수적 군집 모형 (functional clustering model)을 이용하여 모수들을 추정하였다. 그리고 Sugar와 James (2003)의 왜곡함수 (distortion function)를 이용하여 군집의 수를 결정하여 군집분석하여 두 개의 군집을 발견하였다.

• 대한산업공학회지
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• 제39권6호
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• pp.577-586
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• 2013

In a shipyard, it is hard to predict block movement due to the uncertainty caused during the long period of shipbuilding operations. For this reason, block movement is rarely scheduled, while main operations such as assembly, outfitting and painting are scheduled properly. Nonetheless, the high operating costs of block movement compel task managers to attempt its management. To resolve this dilemma, this paper proposes a new block movement analysis framework consisting of the following operations: understanding the entire process, log clustering to obtain manageable processes, discovering the process model and detecting exceptional processes. The proposed framework applies fuzzy mining and trace clustering among the process mining technologies to find main process and define process models easily. We also propose additional methodologies including adjustment of the semantic expression level for process instances to obtain an interpretable process model, definition of each cluster's process model, detection of exceptional processes, and others. The effectiveness of the proposed framework was verified in a case study using real-world event logs generated from the Block Process Monitoring System (BPMS).

• Communications for Statistical Applications and Methods
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• 제27권4호
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• pp.487-500
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• 2020

Insurers face increasing demands for cyber liability; entailed in part by a variety of new forms of risk of data breaches. As data breach occurrences develop, our understanding of the volatility in data breach counts has also become important as well as its expected occurrences. Volatility clustering, the tendency of large changes in a random variable to cluster together in time, are frequently observed in many financial asset prices, asset returns, and it is questioned whether the volatility of data breach occurrences are also clustered in time. We now present volatility analysis based on INGARCH models, i.e., integer-valued generalized autoregressive conditional heteroskedasticity time series model for frequency counts due to data breaches. Using the INGARCH(1, 1) model with data breach samples, we show evidence of temporal volatility clustering for data breaches. In addition, we present that the firms' volatilities are correlated between some they belong to and that such a clustering effect remains even after excluding the effect of financial covariates such as the VIX and the stock return of S&P500 that have their own volatility clustering.

• International Journal of Computer Science & Network Security
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• 제21권8호
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• pp.87-96
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• 2021

The demand for e-learning through video lectures is rapidly increasing due to its diverse advantages over the traditional learning methods. This led to massive volumes of web-based lecture videos. Indexing and retrieval of a lecture video or a lecture video topic has thus proved to be an exceptionally challenging problem. Many techniques listed by literature were either visual or audio based, but not both. Since the effects of both the visual and audio components are equally important for the content-based indexing and retrieval, the current work is focused on both these components. A framework for automatic topic-based indexing and search depending on the innate content of the lecture videos is presented. The text from the slides is extracted using the proposed Merged Bounding Box (MBB) text detector. The audio component text extraction is done using Google Speech Recognition (GSR) technology. This hybrid approach generates the indexing keywords from the merged transcripts of both the video and audio component extractors. The search within the indexed documents is optimized based on the Naïve Bayes (NB) Classification and K-Means Clustering models. This optimized search retrieves results by searching only the relevant document cluster in the predefined categories and not the whole lecture video corpus. The work is carried out on the dataset generated by assigning categories to the lecture video transcripts gathered from e-learning portals. The performance of search is assessed based on the accuracy and time taken. Further the improved accuracy of the proposed indexing technique is compared with the accepted chain indexing technique.

• 천문학회지
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• 제51권6호
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• pp.185-195
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• 2018

Galaxy clusters are known to host many active galaxies (AGNs) with radio jets, which could expand to form radio bubbles with relativistic electrons in the intracluster medium (ICM). It has been suggested that fossil relativistic electrons contained in remnant bubbles from extinct radio galaxies can be re-accelerated to radio-emitting energies by merger-driven shocks via diffusive shock acceleration (DSA), leading to the birth of radio relics detected in clusters. In this study we assume that such bubble consist primarily of thermal gas entrained from the surrounding medium and dynamically-insignificant amounts of relativistic electrons. We also consider several realistic models for magnetic fields in the cluster outskirts, including the ICM field that scales with the gas density as $B_{ICM}{\infty}n^{0.5}_{ICM}$. Then we perform time-dependent DSA simulations of a spherical shock that runs into a lower-density but higher-temperature bubble with the ratio $n_b/n_{ICM}{\approx}T_{ICM}/T_b{\approx}0.5$. We find that inside the bubble the shock speed increases by about 20 %, but the Mach number decreases by about 15% in the case under consideration. In this re-acceleration model, the observed properties of a radio relic such as radio flux, spectral index, and integrated spectrum would be governed mainly by the presence of seed relativistic electrons and the magnetic field profile as well as shock dynamics. Thus it is crucial to understand how fossil electrons are deposited by AGNs in the ICM and how the downstream magnetic field evolves behind the shock in detailed modeling of radio relics.

• 한국멀티미디어학회논문지
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• 제22권4호
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• pp.480-490
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• 2019

Nowadays, Electronic Medical Record (EMR) has just implemented at few hospitals for Outpatient Department (OPD). OPD is the diversified data, it includes demographic and diseases of patient, so it need to be clustered in order to explore the hidden rules and the relationship of data types of patient's information. In this paper, we propose a novel approach for unsupervised clustering of patient's demographic and diseases in OPD. Firstly, we collect data from a hospital at OPD. Then, we preprocess and transform data by using powerful techniques such as standardization, label encoder, and categorical encoder. After obtaining transformed data, we use some strong experiments, techniques, and evaluation to select the best number of clusters and best clustering algorithm. In addition, we use some tests and measurements to analyze and evaluate cluster tendency, models, and algorithms. Finally, we obtain the results to analyze and discover new knowledge, meanings, and rules. Clusters that are found out in this research provide knowledge to medical managers and doctors. From these information, they can improve the patient management methods, patient arrangement methods, and doctor's ability. In addition, it is a reference for medical data scientist to mine OPD dataset.

• 산업경영시스템학회지
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• 제41권4호
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• pp.101-110
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• 2018

This study aims to objectively measure the efficiency of nanotechnology R&D programs by systematically evaluating the inputs and outputs of nanotechnology R&D activities and to find implications for improving the efficiency of nanotechnology R&D programs. Data on input factors such as R&D investment, R&D manpower, R&D period, and output factors such as paper, patent, and commercialization for R&D projects which started from 2008 or afterwards and ended by 2011 are gathered through National Science and Technology Knowledge Information Service, which are used for efficiency evaluation. In this study, we analyzed R&D efficiency in detailed technology units in depth. The process taken in this study is as follows. First, the basic statistics of input and output factors to compare and analyze R&D investment, R&D manpower, R&D period, paper, patent, and commercialization status by technology unit are analyzed. Next, DEA models are utilized to derive the overall efficiency, pure technology efficiency, and scale efficiency by conducting the efficiency evaluation for each technology unit, from which implications for strategic budget allocation are derived. In addition, partial efficiency evaluation is conducted to identify advantages and disadvantages of each technology unit. In turn, cluster analysis is performed to identify similar technology units, from which implications for efficiency improvement are derived.

• 한국멀티미디어학회논문지
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• 제22권6호
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• pp.676-683
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• 2019

Wireless Sensor Networks (WSNs) consist of multiple tiny and power constrained sensors which use radio frequencies to carry out sensing in a designated sensor area. To effectively design and implement reliable WSN, it is critical to consider models, protocols, and algorithms that can optimize energy consumption of all the sensor nodes with optimal amount of packet delivery. It has been observed that deploying a single sink node comes with numerous challenges especially in a situation with high node density and congestion. Sensor nodes close to a single sink node receive more transmission traffic load compared to other sensors, thus causing quick depletion of energy which finally leads to an energy hole and sink hole problems. In this paper, we proposed the use of multiple energy efficient sink nodes with brute force technique under optimized parameters to improve on the number of packets delivered within a given time. Simulation results not only depict that, deploying N sink nodes in a sensor area has a maximum limit to offer a justifiable improvement in terms of packet delivery ratio but also offers a reduction in End to End delay and reliability in case of failure of a single sink node, and an improvement in the network lifetime rather than deploying a single static sink node.