• Digital Contents
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• no.6 s.133
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• pp.112-113
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• 2004

• Digital Contents
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• no.6 s.133
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• pp.16-17
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• 2004

• The Optical Journal
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• s.173
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• pp.30-31
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• 2019

• The Optical Journal
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• s.176
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• pp.27-28
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• 2020

• The Optical Journal
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• s.175
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• pp.26-26
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• 2019

• The Optical Journal
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• s.175
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• pp.29-29
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• 2019

• The Optical Journal
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• s.175
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• pp.28-28
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• 2019

• The Journal of the Acoustical Society of Korea
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• v.39 no.6
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• pp.533-540
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• 2020

A total length of sperm whales can be estimated by measuring the Inter-Pulse Interval(IPI) of their clicks composed by multiple pulses. The IPI is caused by the two-way travel time of the sound transmission in the spermaceti within the whale head. Therefore, the IPI can be used to measure the whale's total length based on allometric relationships between head and body length. In this paper, the click signals recorded in the East Sea, Korea in 2017 were analyzed to estimate the size of sperm whales. The size of sperm whales calculated by the relationship between IPI and body length was 9.9 m to 10.9 m, which is corresponding to the size of an adult female or a juvenile male sperm whale. This non-lethal acoustic method has been demonstrated to accurately estimate the sperm whale size, and can provide useful information for domestic sperm whale monitoring.

• The Journal of the Korea Contents Association
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• v.20 no.3
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• pp.254-266
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• 2020

The purpose of this study is to analyze the influence of the type of recommender system on consumer's perceived benefit and privacy risk. The result showed that the perceived usefulness and intension to click was high in the order of Hybrid-filtering, Bestseller, and SNS-based system. Privacy concern was high in order of SNS-based system, Hybrid-filtering, and Bestseller. Moderating effects of perceived personalization on the type of recommender system and perceived usefulness were significant. Finally perceived usefulness had positive effect, and privacy concern had negative effect on consumer's intension to click. This study has significant implications for digital marketing bt comparing consumer responses according to the type of recommended service. The result of this study can be helpful for providing and developing future recommender service.

• The Journal of the Korea Contents Association
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• v.20 no.3
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• pp.267-274
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• 2020

Click-through rate (CTR) prediction is an estimate of the probability that a user will click on a given item and plays an important role in determining strategies for maximizing online ad revenue. Recently, research has been performed to utilize CNN for CTR prediction. Since the CTR data does not have a meaningful order in terms of correlation, the CTR data may be arranged in any order. However, because CNN only learns local information limited by filter size, data arrays can have a significant impact on performance. In this paper, we propose a multi-array CNN model that generates a data array set that can extract all local feature information that CNN can collect, and learns features through individual CNN modules. Experimental results for large data sets show that the proposed model achieves a 22.6% synergy with RI in AUC compared to the existing CNN, and the proposed array generation method achieves 3.87% performance improvement over the random generation method.