• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.456-457
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• 2021

Recently, in order to more accurately provide indoor location-based services, technologies using Wi-Fi fingerprints and deep learning are being studied. Among the deep learning models, an RNN model that can store information from the past can store continuous movements in indoor positioning, thereby reducing positioning errors. When using an RNN model for indoor positioning, the collected training data must be continuous sequential data. However, the Wi-Fi fingerprint data collected to determine specific location information cannot be used as training data for an RNN model because only RSSI for a specific location is recorded. This paper proposes a region clustering technique for sequential input data generation of RNN models based on Wi-Fi fingerprint data.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.1
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• pp.1-8
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• 2004

According to the development of the Internet and the pervasion of Data Base, it is not easy to search for necessary information from the huge amounts of data. In order to do efficient analysis of a large amounts of data, this paper proposes a method for pattern classification based on the effective strategy for dimension reduction for narrowing down the whole data to what users wants to search for. To analyze data effectively, Radial Basis Function Networks based on VC-dimension of Support Vector Machine, a model of statistical teaming, is proposed in this paper. The model of Radial Basis Function Networks currently used performed the preprocessing of Perceptron model whereas the model proposed in this paper, performing independent analysis on VD-dimension, classifies each datum putting precise labels on it. The comparison and estimation of various models by using Machine Learning Data shows that the model proposed in this paper proves to be more efficient than various sorts of algorithm previously used.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.1
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• pp.213-231
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• 2023

The purpose of this study is to develop and present a model that can evaluate the data maturity of library. To achieve this goal, library data maturity model can be applied to library was designed by analyzing previous studies related to data maturity. As a result of this study, proposed data maturity model consisting of 19 evaluation factors in 5 areas was designed, and the maturity level was set to 5 levels. In the future, it will be possible to measure the data maturity of libraries participating in the library big data project using the data maturity evaluation model, and it can be expected that in the long term, it will be possible to present a direction for data-based library operation and data utilization development.

• Transactions of Materials Processing
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• v.31 no.4
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• pp.229-239
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• 2022

Convolution Neural Network(CNN) is a class of deep learning algorithms and can be used for image analysis. In particular, it has excellent performance in finding the pattern of images. Therefore, CNN is commonly applied for recognizing, learning and classifying images. In this study, the surface defect classification performance of Al 6061 extruded material using CNN-based algorithms were compared and evaluated. First, the data collection criteria were suggested and a total of 2,024 datasets were prepared. And they were randomly classified into 1,417 learning data and 607 evaluation data. After that, the size and quality of the training data set were improved using data augmentation techniques to increase the performance of deep learning. The CNN-based algorithms used in this study were VGGNet-16, VGGNet-19, ResNet-50 and DenseNet-121. The evaluation of the defect classification performance was made by comparing the accuracy, loss, and learning speed using verification data. The DenseNet-121 algorithm showed better performance than other algorithms with an accuracy of 99.13% and a loss value of 0.037. This was due to the structural characteristics of the DenseNet model, and the information loss was reduced by acquiring information from all previous layers for image identification in this algorithm. Based on the above results, the possibility of machine vision application of CNN-based model for the surface defect classification of Al extruded materials was also discussed.

• Korean Management Science Review
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• v.18 no.1
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• pp.135-145
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• 2001

In recent environment of dynamic management, there is growing recognition that information and knowledge management systems are essential for efficient/effective decision making by CEO. To cope with this situation, we suggest the Data-Miming scheme as a key component of integrated information and knowledge management system. The proposed system measures business performance by considering both VA(Value-Added), which represents stakeholder’s point of view and EVA (Economic Value-Added), which represents shareholder’s point of view. To mine the new information & Knowledge discovery, we applied the improved genetic algorithms that consider predictability, understandability (lucidity) and reasonability factors simultaneously, we use a linear combination model for GAs learning structure. Although this model’s predictability will be more decreased than non-linear model, this model can increase the knowledge’s understandability that is meaning of induced values. Moreover, we introduce a random variable scheme based on normal distribution for initial chromosomes in GAs, so we can expect to increase the knowledge’s reasonability that is degree of expert’s acceptability. the random variable scheme based on normal distribution uses statistical correlation/determination coefficient that is calculated with training data. To demonstrate the performance of the system, we conducted a case study using financial data of Korean automobile industry over 16 years from 1981 to 1996, which is taken from database of KISFAS (Korea Investors Services Financial Analysis System).

• Journal of Korea Multimedia Society
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• v.23 no.1
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• pp.50-57
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• 2020

The work that detection and elimination to disease and pest have important in agricultural field because it is directly related to the production of the crops, early detection and treatment of the disease insects. Image classification technology based on traditional computer vision have not been applied in part such as disease and pest because that is falling a accuracy to extraction and classification of feature. In this paper, we proposed model that determine to disease and pest of shiitake based on deep-CNN which have high image recognition performance than exist study. For performance evaluation, we compare evaluation with Alexnet to a proposed deep learning evaluation model. We were compared a proposed model with test data and extend test data. The result, we were confirmed that the proposed model had high performance than Alexnet which approximately 48% and 72% such as test data, approximately 62% and 81% such as extend test data.

• Journal of Distribution Science
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• v.17 no.12
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• pp.5-12
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• 2019

Purpose - In this paper, we examine the effect of accrual earnings management and real earnings management on the corporate value of retail corporations. Research design, data, and Methodology - The sample cover firms whose settlement is December among retail companies listed on the Korea Stock Exchange's securities market and KOSDAQ market from 2001 to 2016. Of these, the targets were companies with operating profit and equity capital of zero or higher and with sales data. The secondary data was collected through KIS-VALUE data base. The Jones model and the modified Jones model were used for the calculating the accrual-based earnings management and the real earnings management. Result - According to the empirical results, the relationship between accrual earnings management, real earnings management and firm value is positively significant in the retail industry as in manufacturing industry. These results are also significant when controlling the size, profitability, investment, debt ratio, dividend, and growth potential of a company. Conclusions - The characteristics of the distribution business can be identified and the influence of the various kinds of earnings management, which is being researched around the manufacturing industry, can be studied in the distribution industry to give practical implications to investors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.504-519
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• 2023

Heart disease is becoming the top reason of death all around the world. Diagnosing cardiac illness is a difficult endeavor that necessitates both expertise and extensive knowledge. Machine learning (ML) is becoming gradually more important in the medical field. Most of the works have concentrated on the prediction of cardiac disease, however the precision of the results is minimal, and data integrity is uncertain. To solve these difficulties, this research creates an Integrated Accurate-Secure Heart Disease Prediction (IAS) Model based on Deep Convolutional Neural Networks. Heart-related medical data is collected and pre-processed. Secondly, feature extraction is processed with two factors, from signals and acquired data, which are further trained for classification. The Deep Convolutional Neural Networks (DCNN) is used to categorize received sensor data as normal or abnormal. Furthermore, the results are safeguarded by implementing an integrity validation mechanism based on the hash algorithm. The system's performance is evaluated by comparing the proposed to existing models. The results explain that the proposed model-based cardiac disease diagnosis model surpasses previous techniques. The proposed method demonstrates that it attains accuracy of 98.5 % for the maximum amount of records, which is higher than available classifiers.

• Journal of Korean Society of Water and Wastewater
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• v.38 no.1
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• pp.1-15
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• 2024

Occurrence of process environment changes, such as influent load variances and process condition changes, can reduce treatment efficiency, increasing effluent water quality. In order to prevent exceeding effluent standards, it is necessary to manage effluent water quality based on process operation data including influent and process condition before exceeding occur. Accordingly, the development of the effluent water quality prediction system and the application of technology to wastewater treatment processes are getting attention. Therefore, in this study, through the multi-channel measuring instruments in the bio-reactor and smart multi-item water quality sensors (location in bio-reactor influent/effluent) were installed in The Seonam water recycling center #2 treatment plant series 3, it was collected water quality data centering around COD, T-N. Using the collected data, the artificial intelligence-based effluent quality prediction model was developed, and relative errors were compared with effluent TMS measurement data. Through relative error comparison, the applicability of the artificial intelligence-based effluent water quality prediction model in wastewater treatment process was reviewed.

• Journal of the Korea Society of Computer and Information
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• v.21 no.3
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• pp.97-104
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• 2016

In this paper, we propose spatial information based simulator for user experience optimization and minimize real space complexity. We focus on developing simulator how to design virtual space model and to implement virtual character using real space data. Especially, we use expanded events-driven inference model for SVM based on machine learning. Our simulator is capable of feature selection by k-fold cross validation method for optimization of data learning. This strategy efficiently throughput of executing inference of user behavior feature by virtual space model. Thus, we aim to develop the user experience optimization system for people to facilitate mapping as the first step toward to daily life data inference. Methodologically, we focus on user behavior and space modeling for implement virtual space.