• The Korean Journal of Applied Statistics
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• v.32 no.6
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• pp.795-835
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• 2019

Big data has been generated in various fields. Many companies have now tried to make profits by building a system capable of analyzing big data based on artificial intelligence (AI) techniques. Integrating AI technology has made analyzing and utilizing vast amounts of data increasingly valuable. In particular, demand forecasting with maximum accuracy is critical to government and business management in various fields such as finance, procurement, production and marketing. In this case, it is important to apply an appropriate model that considers the demand pattern for each field. It is possible to analyze complex patterns of real data that can also be enlarged by a traditional time series model or regression model. However, choosing the right model among the various models is difficult without prior knowledge. Many studies based on AI techniques such as machine learning and deep learning have been proven to overcome these problems. In addition, demand forecasting through the analysis of stereotyped data and unstructured data of images or texts has also shown high accuracy. This paper introduces important areas where demand forecasts are relatively active as well as introduces machine learning and deep learning techniques that consider the characteristics of each field.

• Journal of KIISE
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• v.44 no.9
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• pp.893-907
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• 2017

In safety-critical systems, software defects may have serious consequences. Therefore, defects should be considered during the requirements specification process, which is the first step of a software development lifecycle. Stakeholder requirements that are usually written in natural language are difficult to derived, and there may also be defects due to ambiguity and inaccuracy. To address these issues, we propose a requirement specification method using a standardized Boilerplate and a GSN Model. The Boilerplate is a semi-standard language that follows a predefined format. Due to its ability to provide a consistent representation of the requirements, boilerplate helps stakeholders avoid ambiguities about what they mean and to define the exact meaning of the requirement. Meanwhile, GSN is recognized notation to prepare a Safety Case to prove to authorities that a system is safe. It can be expressed as a functional goal, e.g., Safety Evidence, etc. The proposed study allows an analyst to easily identify a fault from the early stage of the software development lifecycle. The Boilerplate and GSN Model are designed to specify the requirements of safety critical systems and to prove safety conformity through a connection with Safety Evidence. In addition, the proposed approach is also useful to develop secure software by correcting deficiencies in the requirements found during this process.

• Journal of KIISE:Databases
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• v.34 no.6
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• pp.503-517
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• 2007

In this paper we address the MBR-safe property of Piecewise Aggregation Approximation(PAA), and propose an of efficient subsequence matching method based on the MBR-safe PAA. A transformation is said to be MBR-safe if a low-dimensional MBR to which a high- dimensional MBR is transformed by the transformation contains every individual low-dimensional sequence to which a high-dimensional sequence is transformed. Using an MBR-safe transformation we can reduce the number of lower-dimensional transformations required in similar sequence matching, since it transforms a high-dimensional MBR itself to a low-dimensional MBR directly. Furthermore, PAA is known as an excellent lower-dimensional transformation single its computation is very simple, and its performance is superior to other transformations. Thus, to integrate these advantages of PAA and MBR-safeness, we first formally confirm the MBR-safe property of PAA, and then improve subsequence matching performance using the MBR-safe PAA. Contributions of the paper can be summarized as follows. First, we propose a PAA-based MBR-safe transformation, called mbrPAA, and formally prove the MBR-safeness of mbrPAA. Second, we propose an mbrPAA-based subsequence matching method, and formally prove its correctness of the proposed method. Third, we present the notion of entry reuse property, and by using the property, we propose an efficient method of constructing high-dimensional MBRs in subsequence matching. Fourth, we show the superiority of mbrPAA through extensive experiments. Experimental results show that, compared with the previous approach, our mbrPAA is 24.2 times faster in the low-dimensional MBR construction and improves subsequence matching performance by up to 65.9%.

• Journal of KIISE:Information Networking
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• v.32 no.4
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• pp.525-534
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• 2005

This paper is concerned with the query slipping and its prevention for trajectory-based matchmaking service in wireless sensor networks. The problem happens when a query propagating along a subscribe trajectory moves through a publish trajectory without obtaining desired information, even though two trajectories intersect geometrically. There follows resubmission of the query or initiation of another subscribe trajectory Thus, query slipping results in considerable time delay and in the worst, looping in the trajectory or query flooding the network. We address the problem formally and suggest a solution. First, the area where nodes are distributed is logically partitioned into smaller grids, and a grid-based multicast next-hop selection algorithm is proposed. Our algorithm not only attempts to make the trajectory straight but also considers the nodal density of recipient nodes and the seamless grid-by-grid multicast. We prove that the publishing and subscribing using the algorithm eventually eliminate the possibility of the slipping. It toms out that our algorithm dissipates significantly less power of neighbor nodes, compared to the non grid-based method, as greedy forwarding, and the fixed- sized grid approach, as GAF (Geographical Adaptive Fidelity)

• Korean Journal of Remote Sensing
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• v.33 no.5_1
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• pp.495-506
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• 2017

This study was carried out to effectively perform relative color correction for high-resolution aerial ortho image. For this study, relative geometrical error between adjacent images was analyzed. The block sum method is proposed to reduce the relative geometrical error. We used the regression coefficients determined based on the block sum size to perform the color correction. As a result, it was confirmed that the relative color correction was visually performed well. Quantitative analysis was performed through histogram similarity analysis. It is proved that block sum method is useful for relative color correction. Particularly, the block sum size was very important to correct color based on the amount of relative geometrical error.

• Journal of KIISE
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• v.41 no.10
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• pp.799-809
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• 2014

In this paper, we address the problem of computing Pearson correlation coefficients and Spearman's rank correlation coefficients in a secure manner while data providers preserve privacy of their own data in distributed environment. For a data mining or data analysis in the distributed environment, data providers(data owners) need to share their original data with each other. However, the original data may often contain very sensitive information, and thus, data providers do not prefer to disclose their original data for preserving privacy. In this paper, we formally define the secure correlation computation, SCC in short, as the problem of computing correlation coefficients in the distributed computing environment while preserving the data privacy (i.e., not disclosing the sensitive data) of multiple data providers. We then present SCC solutions for Pearson and Spearman's correlation coefficients using secure scalar product. We show the correctness and secure property of the proposed solutions by presenting theorems and proving them formally. We also empirically show that the proposed solutions can be used for practical applications in the performance aspect.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.3
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• pp.292-297
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• 2016

This paper presents a navigation problem for an autonomous mobile robot in an unknown environment. The environment contains various types of obstacles and is completely unknown to the robot. Therefore, all of the surrounding information must be detected by the robot's proximity sensors. A navigation method was developed based on a fuzzy inference system to guide the robot to move along a collision-free path and reach the goal position quickly. The obstacles are assumed to be static, and both regular and irregular types of obstacles were investigated. A wall following method is also proposed for a special environment that contains a labyrinth or sharp U-valley obstacles. Simulation results demonstrate that the proposed method has great potential for this navigation problem.

• Journal of Digital Convergence
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• v.12 no.12
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• pp.651-658
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• 2014

The purpose of this study is to see how the sling lumbar stabilization exercise affects muscle body type and subjective pain degree of 17 middle-age men who are diagnosed with sacroiliac joint syndrome and investigate the effectiveness. The subjects of this study are patients who have been diagnosed with sacroiliac joint syndrome through tests like X-ray in S Orthopedics in C City and have had chronic lumbar pain for more than 6 months and the before and after results about subjective pain degree through three dimension Moire Muscle Body Type Tester and subjective pain degree through VAS Pain Awareness after sling lumbar stabilization exercise 3 times a week, 30 minutes a time, total 24 times for 8 weeks have revealed the following: The change of muscle body type has been comprehensively improved in the statistics before and after the exercise and the subjective pain degree has also comprehensively decreased in the statistics. Therefore, sling lumbar stabilization exercise is proved to give positive affect to muscle body type and pain improvement of the patients with sacroiliac joint syndrome.

• Journal of KIISE:Computer Systems and Theory
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• v.27 no.6
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• pp.608-618
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• 2000

We consider the problem of placing resources in a distributed computing system so that certain performance requirements may be met while minimizing the number of required resource copies, irrespective of node or link failures. To meet the requirements for high performance and high availability, minimum number of resource copies should be placed in such a way that each node has at least two copies on the node or its neighbor nodes. This is called the fault-tolerant resource placement problem in this paper. The structure of a parallel or a distributed computing system is represented by a graph. The fault-tolerant placement problem is first transformed into the problem of finding the smallest fault-tolerant dominating set in a graph. The dominating set problem is known to be NP-complete. In this paper, searching for the smallest fault-tolerant dominating set is formulated as a state-space search problem, which is then solved optimally with the well-known A* algorithm. To speed up the search, we derive heuristic information by analyzing the properties of fault-tolerant dominating sets. Some experimental results on various regular and random graphs show that the search time can be reduced dramatically using the heuristic information.

• Journal of the Korea Society of Computer and Information
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• v.25 no.5
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• pp.27-36
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• 2020

In this paper, we propose a method to derive valuable but hidden infromation from the data which is the core foundation in the 4th Industrial Revolution to pursue knowledge-based service fusion. The hyper-connected societies characterized by IoT inevitably produce big data, and with the data in order to derive optimal services for trouble situations it is first processed by discovering valuable information. A data-centric IoT platform is a platform to collect, store, manage, and integrate the data from variable devices, which is actually a type of middleware platforms. Its purpose is to provide suitable solutions for challenged problems after processing and analyzing the data, that depends on efficient and accurate algorithms performing the work of data analysis. To this end, we propose specially designed structures to store IoT data without losing the semantics and provide algorithms to discover the useful information with several definitions and proofs to show the soundness.