• Journal of Information Technology Applications and Management
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• v.29 no.3
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• pp.43-55
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• 2022

In this study, with the goal of developing a deep learning-based product recommendation model for effective matching of influencers and products, a deep learning model with a collaborative filtering model combined with generalized matrix decomposition(GMF), a collaborative filtering model based on multi-layer perceptron (MLP), and neural collaborative filtering and generalized matrix Factorization (NeuMF), a hybrid model combining GMP and MLP was developed and tested. In particular, we utilize one-class problem free boosting (OCF-B) method to solve the one-class problem that occurs when training is performed only on positive cases using implicit feedback in the deep learning-based collaborative filtering recommendation model. In relation to model selection based on overall experimental results, the MLP model showed highest performance with weighted average precision, weighted average recall, and f1 score were 0.85 in the model (n=3,000, term=15). This study is meaningful in practice as it attempted to commercialize a deep learning-based recommendation system where influencer's promotion data is being accumulated, pactical personalized recommendation service is not yet commercially applied yet.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.12
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• pp.461-468
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• 2022

Development of the big data industry, the cases of providing data utilization services on digital platforms are increasing. In this regard, research in data-related fields is being conducted to apply the FAIR principle that can be applied to the assessment of (meta)data quality, service, and function to data quality evaluation. Especially, the European Open Data Portal applies an assessment model based on FAIR principles. Based on this, a data maturity assessment is conducted and the results are disclosed in reports every year. However, public data portals do not conduct data maturity evaluations based on metadata. In this paper, we propose and evaluate a new model for data maturity evaluation on a big data platform built for multiple domestic public data portals and data transactions, FAIR principles used for data maturity evaluation in Europe's open data portals. The proposed maturity evaluation model is a model that evaluates the quality of public data portal datasets.

• Journal of the Korean Data and Information Science Society
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• v.18 no.1
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• pp.97-105
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• 2007

We consider a nonparametric additive risk model that is based on splines. This model consists of both purely and smoothly nonparametric components. As an estimation method of this model, we use the weighted least square estimation by Huller and Mckeague (1991). We provide an illustrative example as well as a simulation study that compares the performance of our method with the ordinary least square method.

• Journal of Korean Society for Quality Management
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• v.29 no.1
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• pp.11-23
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• 2001

This paper is concerned with suggesting a Bayesian method for variable selection in multinomial logit model. It is based upon an optimal rule suggested by use of Bayes rule which minimizes a risk induced by selecting the multinomial logit model. The rule is to find a subset of variables that maximizes the marginal likelihood of the model. We also propose a Laplace-Metropolis algorithm intended to suggest a simple method forestimating the marginal likelihood of the model. Based upon two examples, artificial data and empirical data examples, the Bayesian method is illustrated and its efficiency is examined.

• Journal of the Korean Data and Information Science Society
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• v.10 no.2
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• pp.465-472
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• 1999

We considered a bootstrap method for constructing confidenc intervals for a one parameter model using multinomial sampling. The convergence rates or the proposed bootstrap method are calculated for model-based maximum likelihood estimators(MLE) using multinomial sampling. Monte Carlo simulation was used to compare the performance of bootstrap methods with normal approximations in terms of the average coverage probability criterion.

• Journal of Korea Multimedia Society
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• v.22 no.7
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• pp.749-758
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• 2019

This paper proposes a data augmentation algorithm to improve the performance of DNN(Deep Neural Network) based speech enhancement. Many deep learning models are exploring algorithms to maximize the performance in limited amount of data. The most commonly used algorithm is the data augmentation which is the technique artificially increases the amount of data. For the effective data augmentation algorithm, we used a formant enhancement method that assign the different weights to the formant frequencies. The DNN model which is trained using the proposed data augmentation algorithm was evaluated in various noise environments. The speech enhancement performance of the DNN model with the proposed data augmentation algorithm was compared with the algorithms which are the DNN model with the conventional data augmentation and without the data augmentation. As a result, the proposed data augmentation algorithm showed the higher speech enhancement performance than the other algorithms.

• Korean Journal of Remote Sensing
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• v.31 no.4
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• pp.331-336
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• 2015

In this study, a numerical model developed for sunshine duration based on GIS data was used. This model considers blocking caused by topography and buildings and it is properly applicable to evaluation of sunshine duration environment in urban areas. The model reasonably well predicted the solar altitude and azimuth angels, compared to those provided by Korea Astronomy and Space Science Institute (KASI). The developed model was applied to evaluation of sunshine duration environment around the Seonleung Park located near a building-congested area in Seoul. The model well reproduced shadow caused by buildings and/or topography in the numerical domain at 09:00 on August 1, 2015. In addition, the model was applied to finding a suitable measurement sites for pyrheliometer around the Seonleung Park. The model was also usefully applied to finding a suitable site for pyrheliometer in an urban area.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.2
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• pp.54-63
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• 2016

As technology has developed and cost for data processing has reduced, big data market has grown bigger. Developed countries such as the United States have constantly invested in big data industry and achieved some remarkable results like improving advertisement effects and getting patents for customer service. Every company aims to achieve long-term survival and profit maximization, but it needs to establish a good strategy, considering current industrial conditions so that it can accomplish its goal in big data industry. However, since domestic big data industry is at its initial stage, local companies lack systematic method to establish competitive strategy. Therefore, this research aims to help local companies diagnose their big data capabilities through a reference model and big data capability assessment system. Big data reference model consists of five maturity levels such as Ad hoc, Repeatable, Defined, Managed and Optimizing and five key dimensions such as Organization, Resources, Infrastructure, People, and Analytics. Big data assessment system is planned based on the reference model's key factors. In the Organization area, there are 4 key diagnosis factors, big data leadership, big data strategy, analytical culture and data governance. In Resource area, there are 3 factors, data management, data integrity and data security/privacy. In Infrastructure area, there are 2 factors, big data platform and data management technology. In People area, there are 3 factors, training, big data skills and business-IT alignment. In Analytics area, there are 2 factors, data analysis and data visualization. These reference model and assessment system would be a useful guideline for local companies.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.233-238
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• 2000

CNC machine tool is assembled by central processor, PLC(Programmable Logic Controller), and actuator. The sequential control of machine generally controlled by a PLC. The main fault occured at PLC in 3 control parts. In LC faults, operational fault is charged over 70%. This paper describes diagnosis model and data processing for remote monitoring and diagnosis system in machine tools with open architecture controller. Two diagnostic models based on the ladder diagram. Logical Diagnosis Model(LDM), Sequential Diagnosis Model(SDM), are proposed. Data processing structure is proposed ST(Structured Text) based on IEC1131-3. The faults from CNC are received message form open architecture controller and faults from PLC are gathered by sequential data.. To do this, CNC and PLC's logical and sequential data is constructed database.

• Architectural research
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• v.1 no.1
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• pp.23-30
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• 1999

This research investigates a system for house design automation. The system is based on an object-oriented building data model, aiming to support the house design process conducted by non-expert users. Its object model, with simple yet powerful user interfaces, enables a CAD system to handle a complicated building system with much ease. Hence, the model dramatically simplifies the design process beyond just the automatic document generation. In this paper, we discuss the aspects of the building data model, introduce critical concepts such as grid objects and structured floor plan, and present a prototype system called GPLAN. The system is implemented in the framework of our building data model, and it provides a host of intelligent features that have been proved useful for house design automation.