• The Journal of the Acoustical Society of Korea
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• v.37 no.6
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• pp.451-456
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• 2018

UAComm (Underwater Acoustic Communication) is an active research area, and many experiment has been performed to develop UAComm system. In this paper, we investigate the possibility of modifying and applying VirTEX (Virtual Time series EXperiment) to medium long range MIMO (Multiple-Input Multiple-Output) UAComm of about 20 km range for the analysis and performance prediction of UAComm system. Since VirTEX is a time-domain simulator, the generated time series can be used in HILS (Hardware In the Loop Simulation) to develop UAComm system. The developed package is verified through comparing with the sea-going FAF05 (Focused Acoustic Field 2005) experimental data. The developed simulator can be used to predict the performance of UAComm system, and even replace the expensive sea-going experiment.

• Information Systems Review
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• v.25 no.2
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• pp.57-76
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• 2023

This study classifies the success of creators and YouTubers who have created channels on YouTube recently, which is the most influential digital platform. Based on the actual information disclosure of YouTubers who are in the field of science and technology category, video upload cycle, video length, number of selectable multilingual subtitles, and information from other social network channels that are being operated, the success of YouTubers using machine learning was classified and analyzed, which is the closest to the YouTube revenue structure. Our findings showed that neural network algorithm provided the best performance to predict the success or failure of YouTubers. In addition, our five factors contributed to improve the performance of the classification. This study has implications in suggesting various approaches to new individual entrepreneurs who want to start YouTube, influencers who are currently operating YouTube, and companies who want to utilize these digital platforms. We discuss the future direction of utilizing digital platforms.

• Journal of Broadcast Engineering
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• v.9 no.1
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• pp.17-31
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• 2004

This paper proposes an algorithm for the robust transmission of video in error Prone environment using multiple description codingby optimal split of DCT coefficients and rate-distortionoptimization framework. In MDC, a source signal is split Into several coded streams, which is called descriptions, and each description is transmitted to the decoder through different channel. Between descriptions, structured correlations are introduced at the encoder, and the decoder exploits this correlation to reconstruct the original signal even if some descriptions are missing. It has been shown that the MDC is more resilient than the singe description coding(SDC) against severe packet loss ratecondition. But the excessive redundancy in MDC, i.e., the correlation between the descriptions, degrades the RD performance under low PLR condition. To overcome this Problem of MDC, we propose a hybrid MDC method that controls the SDC/MDC switching according to channel condition. For example, the SDC is used for coding efficiency at low PLR condition and the MDC is used for the error resilience at high PLR condition. To control the SDC/MDC switching in the optimal way, RD optimization framework are used. Lagrange optimization technique minimizes the RD-based cost function, D+M, where R is the actually coded bit rate and D is the estimated distortion. The recursive optimal pet-pixel estimatetechnique is adopted to estimate accurate the decoder distortion. Experimental results show that the proposed optimal split of DCT coefficients and SD/MD switching algorithm is more effective than the conventional MU algorithms in low PLR conditions as well as In high PLR condition.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.809-825
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• 2017

The number of TV entertainment shows is increasing. Competition among programs in the entertainment market is intensifying since cable channels air many entertainment TV shows. There is now a need for research on program ratings and the number of episodes. This study presents predictive models for entertainment TV show ratings and number of episodes. We use various data mining techniques such as linear regression, logistic regression, LASSO, random forests, gradient boosting, and support vector machine. The analysis results show that the average program ratings before the first broadcast is affected by broadcasting company, average ratings of the previous season, starting year and number of articles. The average program ratings after the first broadcast is influenced by the rating of the first broadcast, broadcasting company and program type. We also found that the predicted average ratings, starting year, type and broadcasting company are important variables in predicting of the number of episodes.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.117-124
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• 2015

Recently an opportunistic incremental relaying (OIR) system has been studied for improving the performance degradation in fading channel. However there are few studies on power control in the system, and the studies are assumed perfect knowledge of the all channels at transmitters. The assumption that the source know all channel information is difficult in practical channels. Therefore, in this paper we assume that the source knows partial channel information and propose a modified truncated channel inversion (TCI) power control scheme for the OIR system. We derive the channel capacity of the proposed system and perform Monte Carlo simulation. It is noticed that the proposed OIR system has better capacity than that of the power controlled system with direct path only, and the capacity increases with the number of relays. The power controlled OIR system gained more capacity of 29.7%, 32.7%, and 33.5% than that of the system with direct path only for the number of relays of 1, 3, and 5, respectively. The results from this paper can be applied to the estimation of a theoretical capacity for the currently operating cellular systems when they adopt the IOR system.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.3A
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• pp.232-239
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• 2011

IEEE 802.11n standard provides a framework for new link adaptation. A station can request that another station provide a Modulation and Coding Scheme (MCS) feedback, to fully exploit channel variations on a link. However, if the time elapsed between MCS feedback request and the data frame transmission using the MCS feedback becomes bigger, the previously received feedback information may be obsolete. In that case, the effectiveness of the feedback-based link adaptation is compromised. If a station can estimate how fast the channel quality to the target station changes, it can improve accuracy of the link adaptation. The contribution of this paper is twofold. First, through a thorough NS-2 simulation, we show how the coherence time affects the performance of the MCS feedback based link adaptation of 802.11n networks. Second, this paper proposes an effective algorithm for coherence time estimation. Using Allan variance information statistic, a station estimates the coherence time of the receiving link. A proposed link adaptation scheme considering the coherence time can provide better performance.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.99-120
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• 2010

Due to the wide spread of customers' frequent access of non face-to-face services, there have been many attempts to improve customer satisfaction using huge amounts of data accumulated throughnon face-to-face channels. Usually, a call center is regarded to be one of the most representative non-faced channels. Therefore, it is important that a call center has enough agents to offer high level customer satisfaction. However, managing too many agents would increase the operational costs of a call center by increasing labor costs. Therefore, predicting and calculating the appropriate size of human resources of a call center is one of the most critical success factors of call center management. For this reason, most call centers are currently establishing a department of WFM(Work Force Management) to estimate the appropriate number of agents and to direct much effort to predict the volume of inbound calls. In real world applications, inbound call prediction is usually performed based on the intuition and experience of a domain expert. In other words, a domain expert usually predicts the volume of calls by calculating the average call of some periods and adjusting the average according tohis/her subjective estimation. However, this kind of approach has radical limitations in that the result of prediction might be strongly affected by the expert's personal experience and competence. It is often the case that a domain expert may predict inbound calls quite differently from anotherif the two experts have mutually different opinions on selecting influential variables and priorities among the variables. Moreover, it is almost impossible to logically clarify the process of expert's subjective prediction. Currently, to overcome the limitations of subjective call prediction, most call centers are adopting a WFMS(Workforce Management System) package in which expert's best practices are systemized. With WFMS, a user can predict the volume of calls by calculating the average call of each day of the week, excluding some eventful days. However, WFMS costs too much capital during the early stage of system establishment. Moreover, it is hard to reflect new information ontothe system when some factors affecting the amount of calls have been changed. In this paper, we attempt to devise a new model for predicting inbound calls that is not only based on theoretical background but also easily applicable to real world applications. Our model was mainly developed by the interactive decision tree technique, one of the most popular techniques in data mining. Therefore, we expect that our model can predict inbound calls automatically based on historical data, and it can utilize expert's domain knowledge during the process of tree construction. To analyze the accuracy of our model, we performed intensive experiments on a real case of one of the largest car insurance companies in Korea. In the case study, the prediction accuracy of the devised two models and traditional WFMS are analyzed with respect to the various error rates allowable. The experiments reveal that our data mining-based two models outperform WFMS in terms of predicting the amount of accident calls and fault calls in most experimental situations examined.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.2 s.302
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• pp.59-68
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• 2005

The amount of digitalized contents has been rapidly increased, but the main distribution channel of them is Internet which is easily accessible. Therefore 'security' necessarily arises as one of the most important issues and the method of protecting contents becomes a major research topic as much as data coding techniques. In recent years, many developers have studied on techniques that allow only authorized person to access contents. Among them the scrambling method is one of well-known security techniques. In this paper, we propose a simple and effective digital video scrambling method which utilizes the intra block properties of a recent video coding technique, H.264. Since intra prediction modes are adopted in H.264 standard, it is easy to scramble a video sequence with modification of the intra prediction modes. In addition to its simplicity, the proposed method does not increase bit rate after scrambling. The inter blocks are also distorted by scrambling intra blocks only. This paper introduces a new digital video scrambling method and verifies its effectiveness through simulation.

• Journal of Broadcast Engineering
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• v.15 no.1
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• pp.112-121
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• 2010

Distributed video coding is a coding paradigm that allows complexity to be shared between encoder and decoder, in contrast with conventional video coding. We propose that complexity balancing method of encoder/decoder by phase motion estimation algorithm. The encoder performs partial motion estimation. The result of the partial motion estimation is transferred to the decoder, and the decoder performs motion estimation within the narrow range. When the encoder can afford some complexity, complexity balancing is possible. The method proposed is able to know relativity between complexity balancing and coding efficiency. The coding efficiency increase rate by the encoder complexity increases is higher than that by the decoder complexity increases. The proposed method can control the complexity and coding efficiency according to devices' resources and channel conditions.

• The Korean Journal of Applied Statistics
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• v.28 no.5
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• pp.933-949
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• 2015

Audience rating competition in the domestic drama market has increased recently due to the introduction of commercial broadcasting and diversification of channels. There is now a need for thorough studies and analysis on audience rating. Especially, a drama rating is an important measure to estimate advertisement costs for producers and advertisers. In this paper, we study the drama rating prediction models using various data mining techniques such as linear regression, LASSO regression, random forest, and gradient boosting. The analysis results show that initial drama ratings are affected by structural elements such as broadcasting station and broadcasting time. Average drama ratings are also influenced by earlier public opinion such as the number of internet searches about the drama.