• Journal of Information Processing Systems
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• 제9권4호
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• pp.633-650
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• 2013

All the imputation techniques proposed so far in literature for data imputation are offline techniques as they require a number of iterations to learn the characteristics of data during training and they also consume a lot of computational time. Hence, these techniques are not suitable for applications that require the imputation to be performed on demand and near real-time. The paper proposes a computational intelligence based architecture for online data imputation and extended versions of an existing offline data imputation method as well. The proposed online imputation technique has 2 stages. In stage 1, Evolving Clustering Method (ECM) is used to replace the missing values with cluster centers, as part of the local learning strategy. Stage 2 refines the resultant approximate values using a General Regression Neural Network (GRNN) as part of the global approximation strategy. We also propose extended versions of an existing offline imputation technique. The offline imputation techniques employ K-Means or K-Medoids and Multi Layer Perceptron (MLP)or GRNN in Stage-1and Stage-2respectively. Several experiments were conducted on 8benchmark datasets and 4 bank related datasets to assess the effectiveness of the proposed online and offline imputation techniques. In terms of Mean Absolute Percentage Error (MAPE), the results indicate that the difference between the proposed best offline imputation method viz., K-Medoids+GRNN and the proposed online imputation method viz., ECM+GRNN is statistically insignificant at a 1% level of significance. Consequently, the proposed online technique, being less expensive and faster, can be employed for imputation instead of the existing and proposed offline imputation techniques. This is the significant outcome of the study. Furthermore, GRNN in stage-2 uniformly reduced MAPE values in both offline and online imputation methods on all datasets.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1990년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 26-27 Oct. 1990
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• pp.47-51
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• 1990

In the system of contemporary factory, the frequent generation of robot program reduces the efficiency of robot working. In this study, the SIOPS (Soldering and Insertion Offline Programming System) that automatically generates the robot program is presented. The system can change the parameter about soldering and insertion interactively and generate the robot tool path.

• 항공우주산업기술동향
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• 제8권2호
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• pp.15-23
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• 2010

본 논문에서는 최근에 개발된 위성 데이터 후처리 시스템의 동향에 대해 기술 하고자 한다. 위성 데이터 후처리 시스템은 위성의 텔레메트리를 분석하고 위성의 상태를 파악하는데 사용되는 시스템으로 빠른 처리속도, 데이터 도시화, 사용의 용이성, 그리고 범용성의 요구사항을 갖는다. 본 논문에서는 이러한 요구사항을 만족시키기 위해 여러 위성 데이터 후처리 시스템이 어떻게 설계되고 구현되었는지를 살펴본다.

• 한국게임학회 논문지
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• 제10권5호
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• pp.87-94
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• 2010

본 탐색적 연구에서는 MMORPG 이용자들의 게임 내 리더십 관련 경험과 오프라인 리더십의 관계를 온라인 서베이(N=664)를 통해 살펴보고자 하였다. 상관관계 분석 결과, MMORPG 이용자들의 게임 내 리더십 관련 경험, 연령, 게임이용시간과 오프라인 리더십 간에 정적 상관관계가 존재함을 확인하였다. 이중에서 특히, 게임 내에서 리더십을 증진할 수 있는 관련 활동들을 많이 경험할수록, 오프라인에서의 리더십 점수가 높아지는 경향을 확인할 수 있었다. 온라인 공간에서의 MMORPG 이용이 오프라인 공간으로 넘어와 긍정적인 영향을 미치게 되는 스필오버효과(spillover effect) 측면에서, 게임 이용의 순기능적 활용 가능성을 결론에 함께 제시하였다.

• 한국경영과학회지
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• 제36권1호
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• pp.1-11
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• 2011

This article examines the impact of electronic commerce on environmental welfare. In particular, we analyze a game model of price competition between offline and online firms when consumption taxes are imposed on both offline and online transactions that produce environmental pollution. We investigate the properties of optimal taxation between offline and online markets and demonstrate that there is an optimal difference between the two taxes, depending upon not only the transaction cost between offline and online consumption, but also the environmental damage cost. We also investigate the effect of tax-free online transactions on tax revenues, and the financial feasibility of the optimal taxation.

• JSTS:Journal of Semiconductor Technology and Science
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• 제15권5호
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• pp.539-545
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• 2015

Deduplication technique can expand the lifespan and capacity of flash memory-based storage devices by eliminating duplicated write operations. The deduplication techniques can be classified into two approaches, i.e., online and offline approaches. We propose an offline deduplication technique that uses a lightweight hash algorithm, whereas the previous offline technique uses a high-cost hash algorithm. Therefore, the memory space for caching hash values can be reduced, and more pages can be examined for deduplication during short idle intervals. As a result, it can provide shorter write latencies compared to the online approach, and can show low garbage collection costs compared to the previous offline deduplication technique.

• Journal of Information Technology Applications and Management
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• 제24권4호
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• pp.1-12
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• 2017

Item-based collaborative filtering (IBCF) is an important technology that is widely used in recommender system of online shopping malls. It uses historical information to compute item-item similarity and make predictions. However, in offline shopping each customer's purchasing pattern can be occurred continuously and repeatedly due to time and space constraints contrast to online shopping. Those facts can make IBCF to have limitations from being applied to offline shopping malls directly. In order to improve the quality of recommendations made by IBCF in offline shopping mall, we propose an ensemble approach that considers both item-item similarity of IBCF and each customer's purchasing patterns which are modeled by item networks. Our experimental results show that this approach produces recommendation results superior to those of existing works such as pure IBCF or bestseller approaches.

• Journal of Communications and Networks
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• 제7권1호
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• pp.65-75
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• 2005

In this paper, we consider a scenario in which the source has been offered QoS guarantees subject to token-bucket regulation. The rate of the source should be controlled such that it conforms to the token-bucket regulation, and also the distortion obtained is the minimum. We have developed an optimal scheduling algorithm for offline (like pre-recorded video) sources with convex distortion function and which can not tolerate any delay. This optimal offline algorithm has been extended for the real-time online source by predicting the number of packets that the source may send in future. The performance of the online scheduler is not substantially degraded as compared to that of the optimal offline scheduler. A sub-optimal offline algorithm has also been developed to reduce the computational complexity and it is shown to perform very well. We later consider the case where the source can tolerate a fixed amount of delay and derive optimal offline algorithm for such traffic source.

• Asia pacific journal of information systems
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• 제34권1호
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• pp.226-247
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• 2024

Most studies on recommender systems to evaluate recommendation performances focus on offline evaluation methods utilizing past customer transaction records. However, evaluating recommendation performance through real-world stimulation becomes challenging. Moreover, such methods cannot evaluate the duration of the recommendation effect. This study measures the personalized recommendation (stimulus) effect when the product recommendation to customers leads to actual purchases and evaluates the duration of the stimulus personalized recommendation effect leading to purchases. The results revealed a 4.58% improvement in recommendation performance in the online environment compared with that in the offline environment. Furthermore, there is little difference in recommendation performance in offline experiments by period, whereas the recommendation performance declines with time in online experiments.

• 한국의류학회지
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• 제42권5호
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• pp.809-822
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• 2018

Fashion brands communicate with consumers through the use of fashion films that are effective in the delivery of the image. This is because digital media (such as fashion films) are popular and accessible as well as effective in attracting people's attention. The reasons why fashion films have become a new fashion media among many others is becuase they are based on an existing platform. This study will examine offline platforms of fashion film: flagship store, fashion exhibition, and fashion show. The offline platform of a fashion film is a physical space where a fashion film can be exhibited and viewed as a medium that can effectively convey fashion film to consumers. As a research method, the concept of an offline platform of fashion film is established based on a literature review of fashion films and platform that is followed by an empirical case study. The study results show that the offline platform of a fashion film is in the progress of turning into an autotelic space where the consumption of such culture as cinema and theater has become possible.