• Geosciences Journal
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• v.22 no.6
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• pp.891-902
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• 2018

Techniques for more reliable detection of 3D subsurface flow paths are highly important for most water-related geotechnical projects. In this case study, a magnetometric resistivity method with a new approach and state-of-the-art technology ("Willowstick survey") was applied to the testbed dam (YD dam) site, and its applicability was validated by geotechnical investigation techniques including borehole drilling and sampling, Lugeon test, flow direction and velocity test, and seismic tomography. In addition to the magnetometric survey, a 3D electrical resistivity survey was performed independently and the results were compared and discussed. The electrical resistivity survey was effective in detecting groundwater levels, but it was limited in mapping leakage paths. On the other hand, the Willowstick magnetometric survey effectively detected geologic weaknesses (e.g., fault fracture) and potential leakage paths of the dam site foundation rocks. The results of this research are expected to be effective for water infrastructures where leakage is an important issue.

• Journal of information and communication convergence engineering
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• v.19 no.3
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• pp.166-174
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• 2021

A large volume of patient data is generated from various devices used in healthcare applications. With increase in the volume of data generated in the healthcare industry, more wellness monitoring is required. A cloud-enabled analysis of healthcare data that predicts patient risk factors is required. Machine learning techniques have been developed to address these medical care problems. A novel technique called the radix-trie-based Tanimoto kernel regressive infomax boost classification (RT-TKRIBC) technique is introduced to analyze the heterogeneous health data in the cloud to predict the health risks and send alerts. The infomax boost ensemble technique improves the prediction accuracy by finding the maximum mutual information, thereby minimizing the mean square error. The performance evaluation of the proposed RT-TKRIBC technique is realized through extensive simulations in the cloud environment, which provides better prediction accuracy and less prediction time than those provided by the state-of-the-art methods.

• International journal of advanced smart convergence
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• v.8 no.3
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• pp.167-176
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• 2019

Building skins are expanding even beyond theirfunctions as a simple boundary between the exterior and interior and into the realm of linguistic functions thanks to the development of media art. LED has been used as material on outer walls following the advancement of building materials, so the outerskins of large buildings are evolving into a messenger of language capable of communication. In big cities, buildings send out video images to enable communication between people and architecture, which plays a huge role in determining the identity of a building beyond simple advertising. Such media fa?ade technologies can be understood based on the concept of outerskin change, which refers to the idea that animals change the colors or textures of their skins to show their various states. In addition, various message delivery functions in human clothes should be included in such a discussion. We need to research on the possibilities of seeing media facades for their information delivery function and expanding them into information delivery between buildings as well as just between buildings and people.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.3
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• pp.1403-1417
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• 2019

With the rapid growth of multimedia data, research on cross-media feature learning has significance in many applications, such as multimedia search and recommendation. Existing methods are sensitive to noise and edge information in multimedia data. In this paper, we propose a semi-supervised method for cross-media feature learning by means of $L_{2,q}$ norm to improve the performance of cross-media retrieval, which is more robust and efficient than the previous ones. In our method, noise and edge information have less effect on the results of cross-media retrieval and the dynamic patch information of multimedia data is employed to increase the accuracy of cross-media retrieval. Our method can reduce the interference of noise and edge information and achieve fast convergence. Extensive experiments on the XMedia dataset illustrate that our method has better performance than the state-of-the-art methods.

• Journal of information and communication convergence engineering
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• v.17 no.1
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• pp.14-20
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• 2019

Currently, microarray gene expression data take advantage of the sufficient classification of cancers, which addresses the problems relating to cancer causes and treatment regimens. However, the sample size of gene expression data is often restricted, because the price of microarray technology on studies in humans is high. We propose enhancing the gene expression classification of support vector machines with generative adversarial networks (GAN-SVMs). A GAN that generates new data from original training datasets was implemented. The GAN was used in conjunction with nonlinear SVMs that efficiently classify gene expression data. Numerical test results on 20 low-sample-size and very high-dimensional microarray gene expression datasets from the Kent Ridge Biomedical and Array Expression repositories indicate that the model is more accurate than state-of-the-art classifying models.

• Journal of Industrial Convergence
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• v.15 no.1
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• pp.53-59
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• 2017

This study conducted a survey of 400 public officials in kyoung ki Do districts from July to September 2016, and used 390 copies for final analysis. The results of this study can be summarized as follows. First, the level of Job competence and service quality of public officias were statistically siqnificant. Second, the esults showed that both Job competence and sevice quality had a statistically significant influential relationship, so as these variables increased, the level of service quality improved. Third, the influential relationship of Job competence on the service quality of public officials was statistically significant and positive. These results provide a theoretical groung for the understanding and integrated approach to the causality of job competence and of service quality.

• Journal of Industrial Convergence
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• v.13 no.4
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• pp.1-10
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• 2015

The purpose of this study is to propose a model built on the theoretical foundation of ecological sensitivity applicable to group and coaching process for organization members in industry management environment. For this, I have looked into theoretical foundation basis-foundation required for the ecological sensitivity process based on ecological discourse, based on which I have proposed a process model. The ecological basis-foundation with regards to ecological sensitivity dealt with in this paper includes 1)Crisis awareness, 2)Acquisition of ecological unconsciousness, 3)Fostering green imagination and 4)Setting targets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4355-4371
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• 2020

Phase retrieval, recovering a signal from phaseless measurements, is generally considered to be an NP-hard problem. This paper adopts an amplitude-based nonconvex optimization cost function to develop a new stochastic gradient algorithm, named stochastic reweighted phase retrieval (SRPR). SRPR is a stochastic gradient iteration algorithm, which runs in two stages: First, we use a truncated sample stochastic variance reduction algorithm to initialize the objective function. The second stage is the gradient refinement stage, which uses continuous updating of the amplitude-based stochastic weighted gradient algorithm to improve the initial estimate. Because of the stochastic method, each iteration of the two stages of SRPR involves only one equation. Therefore, SRPR is simple, scalable, and fast. Compared with the state-of-the-art phase retrieval algorithm, simulation results show that SRPR has a faster convergence speed and fewer magnitude-only measurements required to reconstruct the signal, under the real- or complex- cases.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.453-461
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• 2021

Many deep learning approaches have been studied for image classification in computer vision. However, there are not enough data to generate accurate models in medical fields, and many datasets are not annotated. This study presents a new method that can use both unlabeled and labeled data. The proposed method is applied to classify cervix images into normal versus cancerous, and we demonstrate the results. First, we use a patch self-supervised learning for training the global context of the image using an unlabeled image dataset. Second, we generate a classifier model by using the transferred knowledge from self-supervised learning. We also apply attention learning to capture the local features of the image. The combined method provides better performance than state-of-the-art approaches in accuracy and sensitivity.

• KEPCO Journal on Electric Power and Energy
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• v.6 no.4
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• pp.399-404
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• 2020

There are many attempts to apply AI technology to diagnose facilities or improve the work efficiency of the power industry. The emergence of new machine learning technologies, such as deep learning, is accelerating the digital transformation of the power sector. The problem is that traditional power systems face security risks when adopting state-of-the-art AI systems. This adoption has convergence characteristics and reveals new cybersecurity threats and vulnerabilities to the power system. This paper deals with the security measures and implementations of the power system using machine learning. Through building a commercial facility operations forecasting system using machine learning technology utilizing power big data, this paper identifies and addresses security vulnerabilities that must compensated to protect customer information and power system safety. Furthermore, it provides security guidelines by generalizing security measures to be considered when applying AI.