• Journal of Families and Better Life
• /
• v.14 no.3
• /
• pp.211-222
• /
• 1996

The purpose of this study is to classify the expenditure behaviors of housewives into some types and to identify the characteristics of the types focucing on diverse expenditure behaviors of urban households. In this study it is assumed that the expenditure behaviors are classified by there factors-(1)the orientation of money saving (2)the orientation of time saving and (3)the orientation of others. The present study suggests following three questions. (1). Can the expenditure behaviors of urban housewives be classified according to the orientation of money saving the orientation of others? (2) What distictions exist among the types? (3) Which variables are useful in classifying the expenditure behaviors? For empirical analysis the data of the study was collected from 650 housewives living in Seoul. The statistical methods adopted for data analysis are frequency percentage mean Pearson's correlation coefficient factor analysis cluster analysis one way ANOVA Duncun's multiple ran e test and discriminant analysis. As the major findings 4 types were extracted, According to the level of each dimensions the names for the each type were given as "the type of attaching importance to money saving" "the type of attaching importance to time and appearance" "the type of attaching importance to money saving and time" "the type of attaching importance to money saving and time" "the type of attaching importance to money saving and time" "the type of attaching importance to money saving and appearance" In "the type of attaching importance to money saving" the significant portion of housewives have high school degrees and compared with other types this type includes more husbands having sales and service job 55% of housewives of "The type of attaching importance to time and appearance" have graduate or higher degrees. The significant part of earned incomes range from 3 million won to 5 million won. The rate of housewives employed in the professional job is higher than other types. In "The type of attaching importance to money saving and time" the rate of the employment of housewives in this type is the highest among the types. In "The type of attaching importance to money saving and appearance" the significant portion of housewives have graduate degrees. In the jobs of he spouses the management job is major. The consciousness of belonging to the middle class is higher than other types. In this type the level of education is high but that of income is not. The result of the discriminant analysis says that the earned income and the consciousness of belonging to a calss are the most critical variables to classify the expenditure behaviors into 4 type The accuray of the classification of the discrimination equation composed of these variables is 47,5% The accuracy is improved by 10%.

• Biomedical Science Letters
• /
• v.25 no.4
• /
• pp.426-430
• /
• 2019

Currently, molecular diagnostic assays based on nucleic acid amplification tests have been shown to effectively detect mycobacterial infections in various types of specimen, however, variable sensitivity was shown in FFPE samples according to the kind of commercial kit used. The present study therefore used automated PCR-reverse blot hybridization assay (REBA) system, REBA Myco-ID HybREAD 480^®, for the rapid identification of Mycobacterium species in various types of human tissue and compared the conventional one-tube nested-PCR assay for detecting Mycobacterium tuberculosis (MTB). In conventional nested-PCR tests, 25 samples (48%) were MTB positive and 27 samples (52%) were negative. In contrast, when conducted PCR-REBA assay, 11 samples (21%) were MTB positive, 20 samples (39%) were NTM positive, 8 samples (15%) were MTB-NTM double positive, and 13 samples (25%) were negative. To determine the accuracy and reliability of the two molecular diagnostic tests, the one-tube nested-PCR and PCR-REBA assays, were compared with histopathological diagnosis in discordant samples. When conducted nested-PCR assay, 10 samples (59%) were MTB positive and seven samples (41%) were negative. In contrast, when conducted PCR-REBA test, three samples (17%) were MTB positive, 10 samples (59%) were NTM positive and four samples (24%) were negative. In conclusion, the automated PCR-REBA system proved useful to identify Mycobacterium species more rapidly and with higher sensitivity and specificity than the conventional molecular assay, one-tube nested-PCR; it might therefore be the most suitable tool for identifying Mycobacterium species in various types of human tissue for precise and accurate diagnosis of mycobacterial infection.

• Korean Journal of Digital Imaging in Medicine
• /
• v.10 no.1
• /
• pp.45-50
• /
• 2008

Recently, MR Cholangiography used mainly bu controlling of patient's breathing. There is breathing hold techniques to get images within shopt time and gating technique adjusted to respiration cycle for high resolution image. In this study, the aim of this experiment is to know on clinical usefulness compared with PACE and RTG thchniques. This study's period is from 2006 in November to 2007 in January. A total of 21 patients investigated at MAGNETOM Sonata 1.5T (SIEMENS Erlangen) with use of 12ch body coil. MR acquisition protocol used 3D turbo spin echo coronal sequence. Scan parameters applied to potimal setting in use as gating techniques, respectively. Analysis of consuming timing evaluated with rapidness. As analysis of quantity, the common bile duct, gall bladder measured in signal intensities, then these data were calculated by signal to noise ratio and contrast to noise ratio. Qualitative analysis, experienced 2radiologists and 3 RTs were evaluated into 3groups about artifact, accuracy of lesions, sharpness of the common bile duct or gall bladder. As a result of analysis, when compared to PACE, consuming time of the RTG took less than PACE, On both CNRs and SNRs, PACE technique was slightly high values than RTG(p<0.05). Qualitative analysis' results, discrimination of lesions in the common bile duct, gall bladder get a significance level in both RTG and PACE techniques but presence's artifact of breathing and pulsation highly demonstrate in PACE techniques. In conclusion, both PACE and RTG methods at MRCP provided prominently clinical information for the common bile duct, gall bladder. If machines have not limitation with performance, induction of breathing holding also will help getting diagnistic quality.

• Animal Bioscience
• /
• v.34 no.1
• /
• pp.26-35
• /
• 2021

Objective: This study was conducted to assess the level of pregnancy-associated glycoprotein (PAG) in whole and skim milk samples, and its suitability for early pregnancy diagnosis in goats. Methods: A two-step sandwich enzyme-linked immunosorbent assay (ELISA) system for estimation of milk PAG was developed and validated, which employed caprine-PAG specific polyclonal antisera. Whole and skim milk samples (n = 210 each) from fifteen multiparous goats were collected on alternate days from d 10 to d 30, and thereafter weekly till d 51 post-mating. PAG levels in milk samples were estimated by ELISA and the pregnancies were confirmed at d40 post-mating by transrectal ultrasonography (TRUS). Results: The level of PAG in whole and skim milk samples of both pregnant and nonpregnant goats remained below the threshold values until d 24 after mating. Thereafter, PAG concentration in whole and skim milk increased steadily in pregnant goats, whereas it continued below the threshold in non-pregnant does. The PAG profiles in whole and skim milk of pregnant goats were almost similar and exhibited strong positive relationship (r = 0.891; p<0.001). Day 26 post-mating was identified as the first time-point for significantly (p<0.05) higher milk PAG concentration in pregnant goats than to non-pregnant goats. When compared to TRUS examination for pregnancy diagnosis, the accuracy and specificity of PAG ELISA using whole and skim milk samples were 94.5% and 95.4%; and 95.3% and 100%, respectively. The high values of area-under-curve (0.904 [whole milk] and 0.922 [skim milk]), demonstrate outstanding discrimination ability of the milk assays. Among the sampling dates chosen, d 37 post-mating was identified as the best suitable time point for collection of milk samples to detect pregnancy in goats. Conclusion: The PAG concentration in whole and skim milk of goats collected between days 26 and 51 post-breeding can be used for the accurate prediction of pregnancy and may be useful for assisting management decisions in goat flocks.

• The Journal of Society for e-Business Studies
• /
• v.26 no.2
• /
• pp.19-43
• /
• 2021

The prediction of box office performance in performing arts institutions is an important issue in the performing arts industry and institutions. For this, traditional prediction methodology and data mining methodology using standardized data such as cast members, performance venues, and ticket prices have been proposed. However, although it is evident that audiences tend to seek out their intentions by the performance guide poster, few attempts were made to predict box office performance by analyzing poster images. Hence, the purpose of this study is to propose a deep learning application method that can predict box office success through performance-related poster images. Prediction was performed using deep learning algorithms such as Pure CNN, VGG-16, Inception-v3, and ResNet50 using poster images published on the KOPIS as learning data set. In addition, an ensemble with traditional regression analysis methodology was also attempted. As a result, it showed high discrimination performance exceeding 85% of box office prediction accuracy. This study is the first attempt to predict box office success using image data in the performing arts field, and the method proposed in this study can be applied to the areas of poster-based advertisements such as institutional promotions and corporate product advertisements.

• Journal of Bio-Environment Control
• /
• v.30 no.1
• /
• pp.19-26
• /
• 2021

In this study, analysis was performed to identify three greenhouses located in the same area using principal component analysis (PCA) and linear discrimination analysis (LDA). The environmental data in the greenhouse were from 3 farms in the same area, and the values collected at 1 hour intervals for a total of 4 weeks from April 1 to April 28 were used. Before analyzing the data, it was pre-processed to normalize the data, and the analysis was performed by dividing it into 80% of the training data and 20% of the test data. As a result of PCA and LDA analysis, it was found that PCA classification accuracy was 57.51% and LDA classification was 67.06%, indicating that it can be classified by greenhouse. Based on the farmhouse data classified in advance, the data of the new environment can be classified into specific groups to determine the tendency of the data. Such data is judged to be a way to increase the utilization of data by facilitating identification.

• The Journal of the Convergence on Culture Technology
• /
• v.8 no.6
• /
• pp.921-926
• /
• 2022

Sugar solutions (5%, 10%, 15% and 20%) were tested by seven sensors of Astree E-Tongue for selecting a sensor for sweetness. NMS sensor was chosen as a sensor for sweetness among two sensors (PKS and NMS sensors selected in first stage) by considering precision, linearity and accuracy. Sugar, fructose, glucose and xylitol (5%, 10% and 15%) were tested by E-tongue. The principal component analysis (PCA) result by E-Tongue with seven sensors at 5% concentration level of four sweetners was not satisfactory (Discrimination index was -0.1). On the other hand, the relative NMS sensor response values were derived as 1.08 (fructose), 0.99 (glucose) and 1.00 (xylitol) comparing to sugar. Only the E-Tongue relative glucose response 0.99 was different from 0.5~0.75 of the relative sweetness range reported as the human sensory test results. Considering the excellent precision (%RSD, 1.53~3.64%) of E-Tongue using NMS single sensor for three types of sweeteners compared to sugar in the concentration range of 5% to 15%, replacing sensory test of sweetened beverages by E-Tongue might be possible for new product development and quality control.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.23 no.4
• /
• pp.41-46
• /
• 2023

Human urine is a process of excreting waste products in the blood, and it is easy to collect and contains various substances. Urinalysis is used to check for diseases, health conditions, and urinary tract infections. There are three methods of urinalysis: physical property test, chemical test, and microscopic test, and chemical test results can be easily confirmed using urine test strips. A variety of items can be tested on the urine test strip, through which various diseases can be identified. Recently, with the spread of smart phones, research on reading urine test strips using smart phones is being conducted. There is a method of detecting and reading the color change of a urine test strip using a smartphone. This method uses the RGB values and the color difference formula to discriminate. However, there is a problem in that accuracy is lowered due to various environmental factors. This paper applies a deep learning model to solve this problem. In particular, color discrimination of a urine test strip is improved in a smartphone using a lightweight CNN (Convolutional Neural Networks) model. CNN is a useful model for image recognition and pattern finding, and a lightweight version is also available. Through this, it is possible to operate a deep learning model on a smartphone and extract accurate urine test results. Urine test strips were taken in various environments to prepare deep learning model training images, and a urine test service application was designed using MobileNet V3.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.8
• /
• pp.21-30
• /
• 2023

The study proposes a model that utilizes Python-based deep learning text classification techniques to detect the legality of illegal financial advertising posts on the internet. These posts aim to promote unlawful financial activities, including the trading of bank accounts, credit card fraud, cashing out through mobile payments, and the sale of personal credit information. Despite the efforts of financial regulatory authorities, the prevalence of illegal financial activities persists. By applying this proposed model, the intention is to aid in identifying and detecting illicit content in internet-based illegal financial advertisining, thus contributing to the ongoing efforts to combat such activities. The study utilizes convolutional neural networks(CNN) and recurrent neural networks(RNN, LSTM, GRU), which are commonly used text classification techniques. The raw data for the model is based on manually confirmed regulatory judgments. By adjusting the hyperparameters of the Korean natural language processing and deep learning models, the study has achieved an optimized model with the best performance. This research holds significant meaning as it presents a deep learning model for discerning internet illegal financial advertising, which has not been previously explored. Additionally, with an accuracy range of 91.3% to 93.4% in a deep learning model, there is a hopeful anticipation for the practical application of this model in the task of detecting illicit financial advertisements, ultimately contributing to the eradication of such unlawful financial advertisements.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.10
• /
• pp.27-35
• /
• 2023

In this paper, we propose a process of increasing productivity by applying a deep learning-based defect detection and classification system to the prepreg fiber manufacturing process, which is in high demand in the field of producing composite materials. In order to apply it to toe prepreg manufacturing equipment that requires a solution due to the occurrence of a large amount of defects in various conditions, the optimal environment was first established by selecting cameras and lights necessary for defect detection and classification model production. In addition, data necessary for the production of multiple classification models were collected and labeled according to normal and defective conditions. The multi-classification model is made based on CNN and applies pre-learning models such as VGGNet, MobileNet, ResNet, etc. to compare performance and identify improvement directions with accuracy and loss graphs. Data augmentation and dropout techniques were applied to identify and improve overfitting problems as major problems. In order to evaluate the performance of the model, a performance evaluation was conducted using the confusion matrix as a performance indicator, and the performance of more than 99% was confirmed. In addition, it checks the classification results for images acquired in real time by applying them to the actual process to check whether the discrimination values are accurately derived.