• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.323-338
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• 2023

TV advertising with deep analysis of watching pattern of audiences is important to set-top box audience targeting. Applying session-based recommendation model(SBR) to internet commercial, or recommendation based on searching history of user showed its effectiveness in previous studies, but applying SBR to the TV advertising was difficult in South Korea due to data unavailabilities. Also, traditional SBR has limitations for dealing with user preferences, especially in data with user identification information. To tackle with these problems, we first obtain set-top box data from three major broadcasting companies in South Korea(SKB, KT, LGU+) through collaboration with Korea Broadcast Advertising Corporation(KOBACO), and this data contains of watching sequence of 4,847 anonymized users for 6 month respectively. Second, we develop personalized session-based recommendation model to deal with hierarchical data of user-session-item. Experiments conducted on set-top box audience dataset and two other public dataset for validation. In result, our proposed model outperformed baseline model in some criteria.

• The Journal of Bigdata
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• v.8 no.2
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• pp.149-163
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• 2023

Due to the continuous growth of the E-commerce market, the volume of orders that fulfillment centers have to process has increased, and various customer requirements have increased the complexity of order processing. Along with this trend, the operational efficiency of fulfillment centers due to increased labor costs is becoming more important from a corporate management perspective. Using historical performance data as training data, this study focused on real-time box recommendations applicable to packaging areas during fulfillment center shipping. Four types of data, such as product information, order information, packaging information, and delivery information, were applied to the machine learning model through pre-processing and feature-engineering processes. As an input vector, three characteristics were used as product specification information: width, length, and height, the characteristics of the input vector were extracted through a feature engineering process that converts product information from real numbers to an integer system for each section. As a result of comparing the performance of each model, it was confirmed that when the Gradient Boosting model was applied, the prediction was performed with the highest accuracy at 95.2% when the product specification information was converted into integers in 21 sections. This study proposes a machine learning model as a way to reduce the increase in costs and inefficiency of box packaging time caused by incorrect box selection in the fulfillment center, and also proposes a feature engineering method to effectively extract the characteristics of product specification information.

• Journal of Broadcast Engineering
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• v.24 no.6
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• pp.1013-1023
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• 2019

In content consumption environment with various live TV channels, VoD contents and web contents, recommendation service is now a necessity, not an option. Currently, various kinds of recommendation services are provided in the OTT service or the IPTV service, such as recommending popular contents or recommending related contents which similar to the content watched by the user. However, in the case of a content viewing environment through TV or IPTV which shares one TV and a TV set-top box, it is difficult to recommend proper content to a specific user because one or more usage histories are accumulated in one subscription information. To solve this problem, this paper interprets the concept of family as {user, time}, extends the existing recommendation relationship defined as {user, content} to {user, time, content} and proposes a method based on deep learning algorithm. Through the proposed method, we evaluate the recommendation performance qualitatively and quantitatively, and verify that our proposed model is improved in recommendation accuracy compared with the conventional method.

• Journal of the Korean Society of Radiology
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• v.17 no.1
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• pp.131-139
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• 2023

In this study, we are purposed to find the educational effect of practical training using a 3D printed structure-based blind box on multidimensional radiographic image interpretation. The subjects were 83 (male: 49, female: 34) 2nd year radiological science students who participated in the digital medical imaging practice that was conducted for 3 years from 2020 to 2022. The learning method used 3D printing technology to print out the inside structure of the blind box designed by itself. After taking X-rays 3 times (x, y, z axis), the structure images in the blind box were analyzed for each small group. We made the 3D structure that was self-made with clay based on our 2D radiographic images. After taking X-rays of the 3D structure, it was compared whether it matches the structural image of the blind box. The educational effect for the practical training surveyed class faithfulness, radiographic image interpretation ability (attenuation concept, contrast concept, windowing concept, 3-dimensional reading ability), class satisfaction (interest, external recommendation, immersion) on a 5-point Likert scale as an anonymous student self-writing method. As a result, all evaluation items had high positive effects without significant differences between males and females. Practical education using blind boxes is a meaningful example of radiology education technology using 3D printing technology, and it is expected to be used as content to improve students' problem-solving skills and increase satisfaction with major subjects.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1994.10a
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• pp.183-190
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• 1994

The paper provides a review of the analysis methods currently being used to perform seismic analysis of free standing high density spent fuel storage racks for PWR. On the basis of the analysis techniques obtained by KAERI from the design experience of Yonggwang unit 3&4 and Ulchin unit 3&4, the analysis procedure and modeling methods are discussed. The analysis of free standing fuel racks requires consideration of complex phenomena such as hydrodynamic coupling, impact through gap between fuel assembly and poison box and racks, frictional effect, rigid body sliding and tipping and etc. The present modeling of these factors is reviewed in comparison with the recommendation of regulatory group. Further improvement of analysis method and the current issues for the development are discussed.

• Wind and Structures
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• v.31 no.3
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• pp.209-216
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• 2020

Aiming at the wind-resistant design of a sea-crossing arch bridge, the static aerodynamic coefficients of its girder (composed of stretches of π-shaped cross-section and box cross-section) were studied by using computational fluid dynamics (CFD) numerical simulation and wind tunnel test. Based on the comparison between numerical simulation, wind tunnel test and specification recommendation, a combined calculation method for the horizontal force coefficient of intermediate and small span bridges is proposed. The results show that the two-dimensional CFD numerical simulations of the individual cross sections are sufficient to meet the accuracy requirements of engineering practice.

• Journal of information and communication convergence engineering
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• v.17 no.1
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• pp.41-48
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• 2019

In recent years, sentiment analysis research has become popular. The research results of sentiment analysis have achieved remarkable results in practical applications, such as in Amazon's book recommendation system and the North American movie box office evaluation system. Analyzing big data based on user preferences and evaluations and recommending hot-selling books and hot-rated movies to users in a targeted manner greatly improve book sales and attendance rate in movies [1, 2]. However, traditional machine learning-based sentiment analysis methods such as the Classification and Regression Tree (CART), Support Vector Machine (SVM), and k-nearest neighbor classification (kNN) had performed poorly in accuracy. In this paper, an improved kNN classification method is proposed. Through the improved method and normalizing of data, the purpose of improving accuracy is achieved. Subsequently, the three classification algorithms and the improved algorithm were compared based on experimental data. Experiments show that the improved method performs best in the kNN classification method, with an accuracy rate of 11.5% and a precision rate of 20.3%.

• Smart Media Journal
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• v.12 no.11
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• pp.125-133
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• 2023

Traditional attire has assumed a pivotal role within the contemporary fashion industry. The objective of this study is to construct a computer vision model tailored to the recognition of traditional costumes originating from five distinct countries, namely India, Korea, Japan, Tanzania, and Vietnam. Leveraging a dataset comprising 1,608 images, we proceeded to train the cutting-edge computer vision model YOLOv8. The model yielded an impressive overall mean average precision (MAP) of 96%. Notably, the Indian sari exhibited a remarkable MAP of 99%, the Tanzanian kitenge 98%, the Japanese kimono 92%, the Korean hanbok 89%, and the Vietnamese ao dai 83%. Furthermore, the model demonstrated a commendable overall box precision score of 94.7% and a recall rate of 84.3%. Within the realm of the fashion industry, this model possesses considerable utility for trend projection and the facilitation of personalized recommendation systems.

• The Journal of the Korea Contents Association
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• v.20 no.8
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• pp.653-665
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• 2020

As the use of recommended agents has become more active, the "Explain Facilities" is drawing attention as a way to solve the black-box problem that could not explain internal logic to users. This study wants to look at how the description Method and Detail affects to user experience. The Explanation method was divided into 'why the agent did a particular action' and 'why not do a particular action' and the detail condition were divided into 'high or low'. Studies have found that 'why method' have a positive effect on users' transparency, trust, satisfaction, and behavioral intention to use, and 'high detail condition' higher the user' Psychological reactance. In addition, it was found that the explanation methods and detail influenced the 'Explanation' perception through interaction and tended to affect satisfaction and intention to adopt recommendation. This study suggested that careful attention is needed to determine the method and detail of the Explanation facilities in the context of the recommended agent, based on the research findings that it affects the user experience through the interaction of the method and detail.

• Structural Engineering and Mechanics
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• v.63 no.5
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• pp.647-657
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• 2017

To achieve this goal, two four-span concrete box-girder bridges with typical configurations of California highway bridges are selected as representative bridges: an integral abutment bridge and a seat-type abutment bridge. A detailed numerical model of the representative bridges is created in OpenSees to perform dynamic analyses. To examine the effect of earthquake incidence angle on the fragility of skewed bridges, the representative bridge models are modified with different skew angles. Dynamic analyses for all bridge models are performed for all earthquake incidence angles examined. Simulated results are used to develop demand models and component and system fragility curves for the skewed bridges. The fragility characteristics are compared with regard to earthquake incidence angle. The results suggest that the earthquake incidence angle more significantly affects the seismic demand and fragilities of the integral abutment bridge than the skewed abutment bridge. Finally, a recommendation to account for the randomness due to the ground motion directionality in the fragility assessment is made in the absence of the predetermined earthquake incidence angle.