• International journal of advanced smart convergence
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• v.10 no.4
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• pp.198-205
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• 2021

With the development of blockchain technology, the scope of blockchain applications has expanded rapidly. Blockchain decentralization allows transaction participants to make transparent and safe transactions without a third trust agency. A distributed ledger-based system enables transparent and trusted business for anonymous users. For this reason, many companies apply blockchain to various fields such as logistics, electronic voting, and real estate. Despite this interest, there are still not enough case studies confirming the potential of blockchain as a concrete business model. Therefore, it is necessary to study how blockchain technology can change the existing business model and connect it to a new business model. In this paper, we propose blockchain-based business models and workflow types in various fields such as healthcare, logistics, and energy. We also present application cases. We expect to help companies apply blockchain to their business.

• Journal of the Korea Society of Computer and Information
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• v.24 no.9
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• pp.69-75
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• 2019

We propose a method to analyze user requirements to design a virtual reality-based risk management system. This paper presents surveys, interviews, prototype evaluation methods, and implementation process. Architectural heritage is easily exposed to natural and artificial dangers caused by various material combinations and structural features. So, risk management of cultural heritage plays a key role in preserving and managing cultural heritage. However, risk management has been carried out through empirical methods using distributed data. This study analyzes user requirements for designing functions and interfaces of VR-based risk management system and evaluates prototypes to overcome the above problems. As a result, most heritage managers wanted a system function to support risk analysis and response. They also found that they prefer 2D information such as existing drawings and photos rather than 3D information. The results of the user requirements analysis derived from this study will be used to create risk management applications.

• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.93-96
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• 2021

The desire for a technology that can mechanically acquire 2D images starting with the manual method of drawing has been making possible a wide range of modern image-based technologies and applications over a period. Moreover, this trend of the utilization of image-related technology as well as image-based information is likely to continue. Naturally, as like other technology areas, the function that humans produce and utilize by using images needs to be automated by using computing-based technologies. Surprisingly, technology using images in the future will be able to discover knowledge that humans have never known before through the information-related process that enables new perception, far beyond the scope of use that human has used before. Regarding this trend, the manipulation and configuration of massively distributed image database system is strongly demanded. In this paper, we discuss image identifier production methods based on the utilization of the image hashing technique which especially puts emphasis over an entropy operator.

• Annual Conference of KIPS
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• 2013.11a
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• pp.966-969
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• 2013

최근 새로운 스마트 기기의 등장과 활용으로 분산 컴퓨팅 산업이 발전하고 있다. 이런 환경 속에서 각 단말기기간 또는 시스템간의 어플리케이션간 연동 또한 그 규모가 커지고 있다. 연동하는 시스템들의 상호작용을 검사하기 위해서는 기존의 단일시뮬레이션 기법으로는 모델-모델 연동시뮬레이션, 코드-코드 연동시뮬레이션은 가능하지만 모델-코드 연동시뮬레이션 기법이 불가능하다. 또한 일반적으로 모델 시뮬레이션 후 코드 시뮬레이션이 이루어지는데 모든 모델이 코드로 완전히 구현되기 전에는 시뮬레이션이 불가능하다. 본 논문에서는 앞서 언급한 어려움들을 해결하기 위해 시뮬레이터 합성기와 코드 어댑터를 사용하여 모델 및 소스코드의 연동시뮬레이션이 가능한 시뮬레이션 구조를 제안한다. 또한 모델과 코드가 분산하여 존재하므로 시스템의 점진적인 개발이 가능하다.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.37-50
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• 2021

Query applications based on nested data, the most commonly used form of data representation on the web, especially precise query, is becoming more extensively used. MapReduce, a distributed architecture with parallel computing power, provides a good solution for big data processing. However, in practical application, query requests are usually concurrent, which causes bottlenecks in server processing. To solve this problem, this paper first combines a column storage structure and an inverted index to build index for nested data on MapReduce. On this basis, this paper puts forward an optimization strategy which combines query execution service tree and frequent sub-query trajectory to reduce the response time of frequent queries and further improve the efficiency of multi-user concurrent queries on large scale nested data. Experiments show that this method greatly improves the efficiency of nested data query.

• Journal of the Korean Mathematical Society
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• v.58 no.1
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• pp.237-264
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• 2021

In this work the stationary bootstrap of Politis and Romano [27] is applied to the empirical distribution function of stationary and associated random variables. A weak convergence theorem for the stationary bootstrap empirical processes of associated sequences is established with its limiting to a Gaussian process almost surely, conditionally on the stationary observations. The weak convergence result is proved by means of a random central limit theorem on geometrically distributed random block size of the stationary bootstrap procedure. As its statistical applications, stationary bootstrap quantiles and stationary bootstrap mean residual life process are discussed. Our results extend the existing ones of Peligrad [25] who dealt with the weak convergence of non-random blockwise empirical processes of associated sequences as well as of Shao and Yu [35] who obtained the weak convergence of the mean residual life process in reliability theory as an application of the association.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.374-386
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• 2022

Nowadays, research in deep learning leveraged automated computing and networking paradigm evidenced rapid contributions in terms of Software Defined Networking (SDN) and its diverse security applications while handling cybercrimes. SDN plays a vital role in sniffing information related to network usage in large-scale data centers that simultaneously support an improved algorithm design for automated detection of network intrusions. Despite its security protocols, SDN is considered contradictory towards DDoS attacks (Distributed Denial of Service). Several research studies developed machine learning-based network intrusion detection systems addressing detection and mitigation of DDoS attacks in SDN-based networks due to dynamic changes in various features and behavioral patterns. Addressing this problem, this research study focuses on effectively designing a multistage hybrid and intelligent deep learning classifier based on modified deep forest classification to detect DDoS attacks in SDN networks. Experimental results depict that the performance accuracy of the proposed classifier is improved when evaluated with standard parameters.

• Communications for Statistical Applications and Methods
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• v.29 no.1
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• pp.41-51
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• 2022

We forecast the US oil consumption level taking advantage of google trends. The google trends are the search volumes of the specific search terms that people search on google. We focus on whether proper selection of google trend terms leads to an improvement in forecast performance for oil consumption. As the forecast models, we consider the least absolute shrinkage and selection operator (LASSO) regression and the structured regularization method for large vector autoregressive (VAR-L) model of Nicholson et al. (2017), which select automatically the google trend terms and the lags of the predictors. An out-of-sample forecast comparison reveals that reducing the high dimensional google trend data set to a low-dimensional data set by the LASSO and the VAR-L models produces better forecast performance for oil consumption compared to the frequently-used forecast models such as the autoregressive model, the autoregressive distributed lag model and the vector error correction model.

• International Journal of Computer Science & Network Security
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• v.22 no.5
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• pp.250-254
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• 2022

This study touched on the limitations of educational technologies in gifted students' schools depending on the learners' viewpoints. The descriptive approach was used, and the tool was represented in a questionnaire distributed to a sample of 196 gifted secondary school students in Jeddah. Results showed moderate obstacles to educational technologies in gifted students' schools. The general mean of the responses of the study sample was 2.76. based on the findings, the author suggested some recommendations to reduce the difficulties that gifted students face in using educational technologies, as well as provide gifted students with electronic applications in order to their development, and especially the development of school buildings for gifted students with modern devices to help them facilitate the use of technology.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.139-146
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• 2022

Wireless access technologies are emerging to enable high data rates for mobile users and novel applications that encompass both human and machine-type interactions. An essential approach to meet the rising demands on network capacity and offer high coverage for wireless users on upcoming fifth generation (5G) networks is heterogeneous networks (HetNets), which are generated by combining the installation of macro cells with a large number of densely distributed small cells Deployment in 5G architecture has several issues because to the rising complexity of network topology in 5G HetNets with many distinct base station types. Aside from the numerous benefits that dense small cell deployment delivers, it also introduces key mobility management issues such as frequent handover (HO), failures, delays and pingpong HO. This article investigates 5G HetNet mobility management in terms of radio resource control. This article also discusses the key challenges for 5G mobility management.