• The Journal of the Korea Contents Association
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• v.15 no.3
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• pp.146-159
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• 2015

The purpose of this study was to investigate the influential factors on the job mobility types and patterns of baby boomers over the past 15 years. Sequential analysis was conducted to analyze the data that were derived from Wave 1~15(1998~2013) of Korean Labor and Income Panel Study(KLIPS). In particular, we analyzed the job mobility types and frequency, conversion analysis, probability, and significance rate. The results were as follows: firstly, proportional rates of male baby boomer workers such as technical, functional, and agriculture workers were higher than those of professionals and semi-professionals, whereas in case of female baby boomer workers, proportional rate of office workers were lower than those of service and sales workers. It was showed that functional and labor workers significantly higher than others. We found that after retiring, they left their job to search for farming job or choose to work in secondary labor markets. We suggested that the retirement support system and management system should be designed and conducted in a good manner.

• Journal of Digital Convergence
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• v.14 no.9
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• pp.19-29
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• 2016

This study analyzes life experiences and prospects of poverty/welfare exit of the poor with work ability utilizing Mixed methodology. Based on Sequential Exploratory Design, it qualitatively analyzes 3 waves of qualitative panel data linked to Korea Welfare Panel Study(KWPS) and presents life changes of 14 poor in the context of their prospects of welfare/poverty exit. Then it proposes hypotheses on the role of education, household economy expectation, self-esteem in the prospects of poverty/welfare exit following the sequential exploratory design to quantitatively test qualitative findings utilizing KWPS(7th). The outcomes of the Structural Equation Model(SEM) suggest that household economy expectation plays mediating role between education and the prospects of welfare/poverty exit. This implies that anti-poverty policy needs to consider a psychological approach to enhance household economy expectations of the poor as well as other material support.

• KNOM Review
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• v.22 no.1
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• pp.42-51
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• 2019

Recently, the amount of internet traffic is increasing due to the increase in speed and capacity of the network environment, and protocol data is increasing due to mobile, IoT, application, and malicious behavior. Most of these private protocols are unknown in structure. For efficient network management and security, analysis of the structure of private protocols must be performed. Many protocol reverse engineering methodologies have been proposed for this purpose, but there are disadvantages to applying them. In this paper, we propose a methodology for inferring a detailed protocol structure based on network trace analysis by hierarchically combining CSP (Contiguous Sequential Pattern) and SP (Sequential Pattern) Algorithm. The proposed methodology is designed and implemented in a way that improves the preceeding study, A²PRE, We describe performance index for comparing methodologies and demonstrate the superiority of the proposed methodology through the example of HTTP, DNS protocol.

• Genomics & Informatics
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• v.18 no.1
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• pp.7.1-7.7
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• 2020

In this paper, we present few technical notes about the distance distribution paradigm for Mosaab-metric using 1, 2, and 3 grams feature extraction techniques to analyze composite data points in high dimensional feature spaces. This technical analysis will help the specialist in bioinformatics and biotechnology to deeply explore the biodiversity of influenza virus genome as a composite data point. Various technical examples are presented in this paper, in addition, the integrated statistical learning pipeline to process segmented genomes of influenza virus is illustrated as sequential-parallel computational pipeline.

• Journal of Internet Computing and Services
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• v.12 no.4
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• pp.35-43
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• 2011

Cognitive Radio (CR) ad hoc network is highly considered as one of promising future ad hoc networks, which enables opportunistic access to under-utilized licensed spectrum. Similarly to other CR networks, the spectrum sensing is a prerequisite in CR ad hoc network. Collaborative spectrum sensing can help increasing sensing performance. For such an infrastructureless network, however the coordination for the sensing collaboration is really complicated due to the lack of a central controller. In this paper, we propose a novel collaborative spectrum sensing scheme in which the final decision is made by the node with the highest data reliability based on a sequential Dempster Shafer theory. The collaboration of sensing data is also executed by the proposed contention-aware reporting mechanism which utilizes the sensing data reliability order for broadcasting spectrum sensing result. The proposed method reduces the collecting time and the overhead of the control channel due to the efficiency of the ordered sequential combination while keeping the same sensing performance in comparison with the conventional cooperative centralized spectrum sensing scheme.

• The KIPS Transactions:PartD
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• v.15D no.2
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• pp.155-162
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• 2008

Biological sequences such as DNA sequences and amino acid sequences typically contain a large number of items. They have contiguous sequences that ordinarily consist of hundreds of frequent items. In biological sequences analysis(BSA), a frequent contiguous sequence search is one of the most important operations. Many studies have been done for mining sequential patterns efficiently. Most of the existing methods for mining sequential patterns are based on the Apriori algorithm. In particular, the prefixSpan algorithm is one of the most efficient sequential pattern mining schemes based on the Apriori algorithm. However, since the algorithm expands the sequential patterns from frequent patterns with length-1, it is not suitable for biological dataset with long frequent contiguous sequences. In recent years, the MacosVSpan algorithm was proposed based on the idea of the prefixSpan algorithm to significantly reduce its recursive process. However, the algorithm is still inefficient for mining frequent contiguous sequences from long biological data sequences. In this paper, we propose an efficient method to mine maximal frequent contiguous sequences in large biological data sequences by constructing the spanning tree with the fixed length. To verify the superiority of the proposed method, we perform experiments in various environments. As the result, the experiments show that the proposed method is much more efficient than MacosVSpan in terms of retrieval performance.

• Journal of the Korea Society of Computer and Information
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• v.14 no.9
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• pp.95-104
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• 2009

This paper proposes the noise robust algorithm to detect the starting point of music. Detection of starting point of music is necessary to solve computational-waste problem and retrieval-comparison problem with inconsistent input data in music content based retrieval system. In particular, such detection is even more necessary in time sequential retrieval method that compares data in the sequential order of time in contents based music retrieval system. Whereas it has the long point that the retrieval is fast since it executes simple comparison in the order of time, time sequential retrieval method has the short point that data starting time to be compared should be the same. However, digitalized music cannot guarantee the equity of starting time by bit rate conversion. Therefore, this paper ensured that recognition rate shall not decrease even while executing high speed retrieval by applying time sequential retrieval method through detection of music starting point in the pre-processing stage of retrieval. Starting point detection used minimum wave model that can detect effective sound, and for strength against noise, the noises existing in mute sound were swapped. The proposed algorithm was confirmed to produce about 38% more excellent performance than the results to which starting point detection was not applied, and was verified for the strength against noise.

• The KIPS Transactions:PartD
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• v.9D no.5
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• pp.779-788
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• 2002

There are many technically difficult issues in searching multimedia data such as image, video and audio because they are massive and more complex than simple text-based data. As a method of searching multimedia data, a similarity retrieval has been studied to retrieve automatically basic features of multimedia data and to make a search among data with retrieved features because exact match is not adaptable to a matrix of features of multimedia. In this paper, data clustering and its indexing are proposed as a speedy similarity-retrieval method of multimedia data. This approach clusters similar images on adjacent disk cylinders and then builds Indexes to access the clusters. To minimize the search cost, the hashing is adapted to index cluster. In addition, to reduce I/O time, the proposed searching takes just one I/O to look up the location of the cluster containing similar object and one sequential file I/O to read in this cluster. The proposed schema solves the problem of multi-dimension by using clustering and its indexing and has higher search efficiency than the content-based image retrieval that uses only clustering or indexing structure.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.105-110
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• 2009

Conventional TV viewing environment had provided limited numbers of channels and contents so that accessibility of contents was made user's manual change of TV channels and by manual selection of TV program contents. However, with advent of IPTV and various contents and channels available to users’ terminals, excessive numbers of TV contents become available to users’ terminals, thus leading to totally different TV viewing environments. In this TV environment, users are required to make much effort to choose their preferred TV channels or program contents, which becomes much cumbersome to the users. Therefore, in this paper, we will propose TV contents schedule recommendation by making reasoning on users’ TV viewing patterns from TV viewing history data using sequential pattern mining so that so that it increases accessibility of users to many TV program contents which may be or may not be aware of the users.

• The Journal of the Korea Contents Association
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• v.10 no.7
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• pp.10-22
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• 2010

This study proposes a new method of multiple homographies estimation between two images. With a large proportion of outliers, RANSAC is a general and very successful robust parameter estimator. However it is limited by the assumption that a single model acounts for all of the data inliers. Therefore, it has been suggested to sequentially apply RANSAC to estimate multiple 2D projective transformations. In this case, because outliers stay in the correspondence data set through the estimation process sequentially, it tends to progress slowly for all models. And, it is difficult to parallelize the sequential process due to the estimation order by the number of inliers for each model. We introduce a guided sequential RANSAC algorithm, using the local model instances that have been obtained from RANSAC procedure, which is able to reduce the number of random samples and deal simultaneously with multiple models.