• The Journal of Engineering Geology
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• v.24 no.4
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• pp.525-534
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• 2014

Environmental problems typically occurring in abandoned mine lands (AML) include: contaminated and acidic surface water and groundwater; stockpiled waste rock and mill tailings; and ground subsidences due to mining operations. This study examines the effectiveness of various geophysical techniques for mapping potential hazard and contaminated zones. Four AML sites with sedimentation contamination problems, acid mine drainage (AMD) channels, ground subsidence, manmade liner leakage, and buried mine tailings, were selected to examine the applicability of various geophysical methods to the identification of the different types of mine hazards. Geophysical results were correlated to borehole data (core samples, well logs, tomographic profiles, etc.) and water sample data (pH, electrical conductivity (EC), and heavy metal contents). Zones of low electrical resistivity (ER) corresponded to areas contaminated by heavy metals, especially contamination by Cu, Pb, and Zn. The main pathways of AMD leachate were successfully mapped using ER methods (low anomaly peaks), self-potential (SP) curves (negative peaks), and ground penetrating radar (GPR) at shallow penetration depths. Mine cavities were well located based on composite interpretations of ER, seismic tomography, and well-log records; mine cavity locations were also observed in drill core data and using borehole image processing systems (BIPS). Damaged zones in buried manmade liners (used to block descending leachate) were precisely detected by ER mapping, and buried rock waste and tailings piles were characterized by low-velocity zones in seismic refraction data and high-resistivity zones in the ER data.

• The Korean Journal of Applied Statistics
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• v.22 no.3
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• pp.585-594
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• 2009

Researchers of microarray experiment transpose processed images of raw data to possible data of statistical analysis: it is preprocessing. Preprocessing of microarray has image filtering, imputation and normalization. There have been studied about several different methods of normalization and imputation, but there was not further study on the order of the procedures. We have no further study about which things put first on our procedure between normalization and imputation. This study is about the identification of differentially expressed genes(DEG) on the order of the preprocessing steps using two-dye cDNA microarray in colon cancer and gastric cancer. That is, we check for compare which combination of imputation and normalization steps can detect the DEG. We used imputation methods(K-nearly neighbor, Baysian principle comparison analysis) and normalization methods(global, within-print tip group, variance stabilization). Therefore, preprocessing steps have 12 methods. We identified concordance measure of DEG using the datasets to which the 12 different preprocessing orders were applied. When we applied preprocessing using variance stabilization of normalization method, there was a little variance in a sensitive way for detecting DEG.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.3
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• pp.380-389
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• 2007

In Korea, undoubtedly, the cancer is one of the most common reasons of death, and hepatoma is the second highest fatal cancer regardless of the gender only next to the stomach cancer In the middle and prime-aged between 40 and 60 years, the incidence of hepatoma is the highest in the world, and the death rate due to hepatoma is the highest among OECD countries. In this paper, we propose a novel method for automatic identification of hepatoma from a contrast enhanced CT images, which is used in an expert system that helps medical specialists. First, consecutive $40{\sim}50$ contrail enhanced CT images are photographed by every 5mm from the upper part of the chest, and using position information on the rib, we classify the internal area including only internal organs and the external one that consists of the rib, subcutaneous fat layers, and the background from the CT images. Then, the region of the liver is extracted from the classified internal area by using information on the intensity, the distribution of brightness, and using the regions extracted from consecutive images, we restore information on the 5 mm space occurred between the consecutive two slides tty applying a shape-based interpolation method. Lastly, using the characteristics such as the brightness and the morphology, we are able to extract the regions of hepatoma. The expert system based on our method is sufficiently competitive when it is compared with the diagnoses by specialists in the diagnostic radiology.

• Journal of dental hygiene science
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• v.4 no.2
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• pp.49-53
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• 2004

The purpose of this study is to show dental hygienist's stable and comfortable attire during dental paitients treatment, and to enhance dental hygienist's image. 486 adult patients who visited local dental clinics or centers in three cities of Jeonbuk province, Jeonju, Iksan and Gunsan were surveyed by self developed questionnaire. The collected data were analyzed with frequency, percentage and chi-square test (${\chi}^2$-test), using SPSS program. The results of this study are as follows: 1. Respondents preferred two-piece skirt (39.1%), the face with whole make-up (45.9%). There were significant differences in gender (p<0.01). 2. 36.2% of respondents preferred pink-colored uniforms. 3. Respondents preferred sandal (44.2%), there were significant differences in age, gender (p<0.01, p<0.05). 4. In terms of indication or identification for dental hygienists, 60.7% of respondents preferred nameplate. There were significant differences in age (p<0.05). 5. 75.9% of respondents thought that favorite uniform and costumes that patients prefer could help them feel easy.

• Journal of the Korean Vacuum Society
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• v.17 no.4
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• pp.311-316
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• 2008

Since the presence of the chemical impurities and defect at surfaces and interfaces greatly influence the properties of various semiconductor devices, an unambiguous chemical characterization of the metal and semiconductor surfaces become more important in the view of the miniaturization of the devices toward nano scale. Among the various conventional surface characterization tools, Electron-induced Auger Electron Spectroscopy (EAES), X-ray Photoelectron Spectroscopy (XPS) and Secondary Electron Ion Mass Spectroscopy (SIMS) are being used for the identification of the surface chemical impurities. Recently, a novel surface characterizaion technique, Positron-annihilation induced Auger Electron Spectroscopy (PAES) is introduced to provide a unique method for the analysis of the elemental composition of the top-most atomic layer. In PAES, monoenergetic positron of a few eV are implanted to the surface under study and these positrons become thermalized near the surface. A fraction of the thermalized positron trapped at the surface state annihilate with the neighboring core-level electrons, creating core-hole excitations, which initiate the Auger process with the emission of Auger electrons almost simultaneously with the emission of annihilating gamma-rays. The energy of electrons is generally determined by employing ExB energy selector, which shows a poor resolution of $6{\sim}10eV$. In this paper, time-of-flight system is employed to measure the electrons energy with an enhanced energy resolution. The experimental result is compared with simulation results in the case of both linear (with retarding tube) and reflected TOF systems.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.64 no.4
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• pp.373-383
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• 2019

The objectives of this study were to compare the protein expression patterns and degrees and identify the protein function of disomic addition lines (DAs) in Leymus racemosus, in order to improve the quality of wheat. Upon SDS-PAGE, L. racemosus showed two major protein bands whereas Chinese Spring (CS) had four major protein bands of high molecular weight. The DA(s) generally showed a similar protein expression pattern to that of CS, because 42 chromosomes were from CS and two chromosomes were from L. racemosus. However, only the L.r[J] line showed two protein bands of between 15 and 20 kDa, like L. racemosus. Image analysis based on 2-DE revealed that L.r[F] had the most upregulated protein spots, whereas L.r[N] had the least upregulated protein spots. For L.r[I], the frequency of the downregulated protein spots was higher than that of the upregulated ones. Using MALDI-TOF MS, the protein function was identified for each protein spot on the 2-DE polyacrylamide gel. The protein spots were classified into 11 groups according to protein function. Among the 11 groups, most protein spots of the DA(s) were identified as proteins related to metabolism. Additionally, unique protein spots of the DA(s) were related to abiotic stressors such as cold and heat. Those proteins are useful for improving wheat quality with resistance against abiotic stressors.

• Journal of Distribution Science
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• v.12 no.8
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• pp.17-29
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• 2014

Purpose - The use of corporate social activities to implement the concept of corporate social responsibility enhances brand equity and attitude, and strengthens economic competitiveness. In areas such as mobile communications, companies take the responsibility of protecting customers and enhance the quality of the mobile communication service, helping to make an effort to obey the regulations of the public trade order and fair trade agreement, enabling a healthy society through communication with elderly living alone or youths without parents, and enhancing marketing strategies. Research design, data, and methodology - To test the hypothesis, a survey was conducted. The surveyed population includes people who use the big three mobile communication services. The survey was conducted from October 4th to October 14th, 2013. A total of 500 survey questionnaires were circulated and 483 were collected; out of these, 32 were excluded due to missing or incomprehensible information. The data was analyzed with SPSS 18.0 via frequency analysis, trust analysis, search factor analysis, relationship analysis, confirmation factor analysis using AMOS 18.0, and structural equation model analysis. Results - Research on corporate social responsibility has been frequently conducted recently. Companies are perceived as social constituents satisfying the social desires of people in addition to customer needs. Further, companies are returning profits to society to satisfy community needs, because there is greater emphasis on the social responsibilities of companies. Companies' social responsibilities should include marketing strategies and the identification of customer needs. This study shows that social service activities influence brand value, which influences customer attitudes; therefore, social service activities indirectly influence customer attitudes. In order to increase customers' purchasing intention, it is essential to improve brand image via social services and provide a distinctive quality of service. Conclusions - This research has used the purposive selection method in the empirical analysis to identify the effect of social services on brand value and customer attitude. Therefore, this study revealed that businesses, whose ultimate objective is to improve customers' purchasing intention, should promote their brand equity through corporate social responsibility activities and offer a distinct service quality. Limitations in the progress of research were found and future indications to overcome these limitations are suggested as follows. First, survey responders had a limited understanding of social responsibilities; therefore, this concept needs to be explained to people first. Second, the research was done on people who live in Daejeon; thus, it is not representative of the entire country. The research has to be repeated with people in other cities. Third, there is a limitation in the study because the purposive selection method was used on Daejeon customers. In the future, a more precise selection of the population is needed. Fourth, Daejeon has unique geographical and size characteristics. Thus, customers in Seoul and other areas may display different characteristics and research on them may reveal different findings. Therefore, again, this study has to be repeated in other areas.

• Journal of the Korean Wood Science and Technology
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• v.47 no.2
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• pp.229-238
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• 2019

Various wood defects occur during tree growing or wood processing. Thus, to use wood practically, it is necessary to objectively assess their quality based on the usage requirement by accurately classifying their defects. However, manual visual grading and species classification may result in differences due to subjective decisions; therefore, computer-vision-based image analysis is required for the objective evaluation of wood quality and the speeding up of wood production. In this study, the SIFT+k-NN and CNN models were used to implement a model that automatically classifies knots and analyze its accuracy. Toward this end, a total of 1,172 knot images in various shapes from five domestic conifers were used for learning and validation. For the SIFT+k-NN model, SIFT technology was used to extract properties from the knot images and k-NN was used for the classification, resulting in the classification with an accuracy of up to 60.53% when k-index was 17. The CNN model comprised 8 convolution layers and 3 hidden layers, and its maximum accuracy was 88.09% after 1205 epoch, which was higher than that of the SIFT+k-NN model. Moreover, if there is a large difference in the number of images by knot types, the SIFT+k-NN tended to show a learning biased toward the knot type with a higher number of images, whereas the CNN model did not show a drastic bias regardless of the difference in the number of images. Therefore, the CNN model showed better performance in knot classification. It is determined that the wood knot classification by the CNN model will show a sufficient accuracy in its practical applicability.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.10
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• pp.285-290
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• 2021

In the medical field, disease diagnosis and prediction research using artificial intelligence technology is being actively conducted. It is being released as a variety of products for disease diagnosis and prediction, which are most widely used in the application of artificial intelligence technology based on medical images. Artificial intelligence is being applied to diagnose diseases, to classify diseases into benign and malignant, and to separate disease regions for use in identification or reading according to the risk of disease. Recently, in connection with cloud technology, its utility as a service product is increasing. Among the diseases dealt with in this paper, liver disease is a disease with very high risk because it is difficult to diagnose early due to the lack of pain. Artificial intelligence technology was introduced based on medical images as a non-invasive diagnostic method for diagnosing these diseases. We describe the development of a web service to help the most meaningful clinical reading of liver cirrhosis patients. Then, it shows the web service process and shows the operation screen of each process and the final result screen. It is expected that the proposed service will be able to diagnose liver cirrhosis at an early stage and help patients recover through rapid treatment.

• Journal of the Korea Society of Computer and Information
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• v.25 no.12
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• pp.25-34
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• 2020

In this paper, we empirically compare the effectiveness of training models to recognize beauty-related skin disease using supervised deep learning algorithms. Recently, deep learning algorithms are being actively applied for various fields such as industry, education, and medical. For instance, in the medical field, the ability to diagnose cutaneous cancer using deep learning based artificial intelligence has improved to the experts level. However, there are still insufficient cases applied to disease related to skin beauty. This study experimentally compares the effectiveness of identifying beauty-related skin disease by applying deep learning algorithms, considering CNN, ResNet, and SE-ResNet. The experimental results using these training models show that the accuracy of CNN is 71.5% on average, ResNet is 90.6% on average, and SE-ResNet is 95.3% on average. In particular, the SE-ResNet-50 model, which is a SE-ResNet algorithm with 50 hierarchical structures, showed the most effective result for identifying beauty-related skin diseases with an average accuracy of 96.2%. The purpose of this paper is to study effective training and methods of deep learning algorithms in consideration of the identification for beauty-related skin disease. Thus, it will be able to contribute to the development of services used to treat and easy the skin disease.