• Journal of the Korean Society of School Health
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• v.36 no.3
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• pp.113-125
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• 2023

Purpose: The purpose of this study is to develop a model for predicting mental health risk among adolescents based on health behavior information by employing a hybrid machine learning method. Methods: The study analyzed data of 51,850 domestic middle and high school students from 2022 Youth Health Behavior Survey conducted by the Korea Disease Control and Prevention Agency. Firstly, mental health risk levels (stress perception, suicidal thoughts, suicide attempts, suicide plans, experiences of sadness and despair, loneliness, and generalized anxiety disorder) were classified using the k-mean unsupervised learning technique. Secondly, demographic factors (family economic status, gender, age), academic performance, physical health (body mass index, moderate-intensity exercise, subjective health perception, oral health perception), daily life habits (sleep time, wake-up time, smartphone use time, difficulty recovering from fatigue), eating habits (consumption of high-caffeine drinks, sweet drinks, late-night snacks), violence victimization, and deviance (drinking, smoking experience) data were input to develop a random forest model predicting mental health risk, using logistic and XGBoosting. The model and its prediction performance were compared. Results: First, the subjects were classified into two mental health groups using k-mean unsupervised learning, with the high mental health risk group constituting 26.45% of the total sample (13,712 adolescents). This mental health risk group included most of the adolescents who had made suicide plans (95.1%) or attempted suicide (96.7%). Second, the predictive performance of the random forest model for classifying mental health risk groups significantly outperformed that of the reference model (AUC=.94). Predictors of high importance were 'difficulty recovering from daytime fatigue' and 'subjective health perception'. Conclusion: Based on an understanding of adolescent health behavior information, it is possible to predict the mental health risk levels of adolescents and make interventions in advance.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.5 s.305
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• pp.29-40
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• 2005

According as base of digital hologram has been magnified, discussion of compression technology is expected as a international standard which defines the compression technique of 3D image and video has been progressed in form of 3DAV which is a part of MPEG. As we can identify in case of 3DAV, the coding technique has high possibility to be formed into the hybrid type which is a merged, refined, or mixid with the various previous technique. Therefore, we wish to present the relationship between various image/video coding techniques and digital hologram In this paper, we propose an efficient coding method of digital hologram using standard compression tools for video and image. At first, we convert fringe patterns into video data using a principle of CGH(Computer Generated Hologram), and then encode it. In this research, we propose a compression algorithm is made up of various method such as pre-processing for transform, local segmentation with global information of object image, frequency transform for coding, scanning to make fringe to video stream, classification of coefficients, and hybrid video coding. Finally the proposed hybrid compression algorithm is all of these methods. The tool for still image coding is JPEG2000, and the toots for video coding include various international compression algorithm such as MPEG-2, MPEG-4, and H.264 and various lossless compression algorithm. The proposed algorithm illustrated that it have better properties for reconstruction than the previous researches on far greater compression rate above from four times to eight times as much. Therefore we expect that the proposed technique for digital hologram coding is to be a good preceding research.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.79-99
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• 2015

The prediction of bankruptcy has been extensively studied in the accounting and finance field. It can have an important impact on lending decisions and the profitability of financial institutions in terms of risk management. Many researchers have focused on constructing a more robust bankruptcy prediction model. Early studies primarily used statistical techniques such as multiple discriminant analysis (MDA) and logit analysis for bankruptcy prediction. However, many studies have demonstrated that artificial intelligence (AI) approaches, such as artificial neural networks (ANN), decision trees, case-based reasoning (CBR), and support vector machine (SVM), have been outperforming statistical techniques since 1990s for business classification problems because statistical methods have some rigid assumptions in their application. In previous studies on corporate bankruptcy, many researchers have focused on developing a bankruptcy prediction model using financial ratios. However, there are few studies that suggest the specific types of bankruptcy. Previous bankruptcy prediction models have generally been interested in predicting whether or not firms will become bankrupt. Most of the studies on bankruptcy types have focused on reviewing the previous literature or performing a case study. Thus, this study develops a model using data mining techniques for predicting the specific types of bankruptcy as well as the occurrence of bankruptcy in Korean small- and medium-sized construction firms in terms of profitability, stability, and activity index. Thus, firms will be able to prevent it from occurring in advance. We propose a hybrid approach using two artificial neural networks (ANNs) for the prediction of bankruptcy types. The first is a back-propagation neural network (BPN) model using supervised learning for bankruptcy prediction and the second is a self-organizing map (SOM) model using unsupervised learning to classify bankruptcy data into several types. Based on the constructed model, we predict the bankruptcy of companies by applying the BPN model to a validation set that was not utilized in the development of the model. This allows for identifying the specific types of bankruptcy by using bankruptcy data predicted by the BPN model. We calculated the average of selected input variables through statistical test for each cluster to interpret characteristics of the derived clusters in the SOM model. Each cluster represents bankruptcy type classified through data of bankruptcy firms, and input variables indicate financial ratios in interpreting the meaning of each cluster. The experimental result shows that each of five bankruptcy types has different characteristics according to financial ratios. Type 1 (severe bankruptcy) has inferior financial statements except for EBITDA (earnings before interest, taxes, depreciation, and amortization) to sales based on the clustering results. Type 2 (lack of stability) has a low quick ratio, low stockholder's equity to total assets, and high total borrowings to total assets. Type 3 (lack of activity) has a slightly low total asset turnover and fixed asset turnover. Type 4 (lack of profitability) has low retained earnings to total assets and EBITDA to sales which represent the indices of profitability. Type 5 (recoverable bankruptcy) includes firms that have a relatively good financial condition as compared to other bankruptcy types even though they are bankrupt. Based on the findings, researchers and practitioners engaged in the credit evaluation field can obtain more useful information about the types of corporate bankruptcy. In this paper, we utilized the financial ratios of firms to classify bankruptcy types. It is important to select the input variables that correctly predict bankruptcy and meaningfully classify the type of bankruptcy. In a further study, we will include non-financial factors such as size, industry, and age of the firms. Thus, we can obtain realistic clustering results for bankruptcy types by combining qualitative factors and reflecting the domain knowledge of experts.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.28 no.1
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• pp.25-44
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• 2017

This study was to suggest display of RDA resource type in OPAC efficiently. Literature reviews and users test and preference survey were used as research methods. The 4 ways for the display of RDA resource type were suggested. First, GMD and the resource type code(bcode2) invented by library itself as well as leader/06, 007, and 008 field should be used for converting AACR2 resource type to RDA resource type in the bibliographic records. Second, RDA resource type vocabularies applicable to Korean cataloging environment should be designed and described in 33X subfield ${\blacktriangledown}9$ and detailed resource terms described in 34X should be also expressed in OPAC. Third, two option is suggested as content type and carrier type display separately and as content type and carrier type combination. Fourth, 336, 338 filed, leader/07 bibliographic level, 008/30-31 Literary text for sound recordings, 34X field were useful to develop user centered resource type icon. This study would be able to increase the utilization of RDA resource types and help the users to understand the RDA resource type in OPAC.

• The Journal of the Korea Contents Association
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• v.4 no.4
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• pp.45-51
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• 2004

The number of development in educational software is currently decreasing despite rapid improvement in computer technology. This is partly due to the fact that the current software relies on the conventional hypermedia method or on the hierarchical method, both of which have inherent limitations. To compensate their limitations while keeping their merits, a hybrid method called the structured hypermedia link method has recently been proposed in the literature. It has been found that the structured hypermedia link method is far superior than the conventional methods in terms of educational effectiveness because it can help a learner to search various data by navigating between the study topics on his own while maintaining his level of cognitively overloaded stress to the minimum. The clinical microbiology laboratories were chosen as the candidate site for this study because its educational system has not been standardized and has been relying heavily on the personal experience. In this study, the hypermedia link method was applied to the development of an education system for the image analysis in clinical microbiology laboratories. For this purpose, a web-based computer aided instruction(CAI) program was designed to systematically organize the Gram stain method based on the standardized image data. In the future, CAI program must be intended to educate for beginner and developed to accept for variable knowledge. And it will be useful program for technicians in case of applying various examinations based Gram stain method of this study.

• Journal of Korea Multimedia Society
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• v.8 no.11
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• pp.1449-1461
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• 2005

In this paper, we propose a vehicle recognition system based on the classification of vehicle identification mark and recognition of vehicle license plate. In the proposed algorithm, From the input vehicle image, we first simulate preprocessing procedures such as noise reduction, thinning etc., and detect vehicle identification mark and license plate region using the frequency distribution of intensity variation. And then, we classify extracted vehicle candidate region into identification mark, character and number of vehicle by using structural feature informations of vehicle. Lastly, we recognize vehicle informations with recognition of identification mark, character and number of vehicle using hybrid and vertical/horizontal pattern vector method. In the proposed algorithm, we used three properties of vehicle informations such as Independency property, discriminance property and frequency distribution of intensity variation property. In the vehicle images, identification mark is generally independent of the types of vehicle and vehicle identification mark. And also, the license plate region between character and background as well as horizontal/vertical intensity variations are more noticeable than other regions. To show the efficiency of the propofed algorithm, we tested it on 350 vehicle images and found that the propofed method shows good Performance regardless of irregular environment conditions as well as noise, size, and location of vehicles.

• Resources Recycling
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• v.24 no.3
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• pp.44-50
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• 2015

Recycling technologies would be required in consideration of increasing demand in lithium ion batteries (LIBs). In this study, the leaching behavior of Ni, Co and Mn is investigated with ammoniacal medium for spent cathode active materials, which are separated from a commercial LIB pack in hybrid electric vehicles. The leaching behavior of each metal is analyzed in the presence of reducing agent and pH buffering agent. The existence of reducing agent is necessary to increase the leaching efficiency of Ni and Co. The leaching of Mn is insignificant even with the existence of reducing agent in contrast to Ni and Co. The most conspicuous difference between acid and ammoniacal leaching would be the selective leaching behavior between Ni/Co and Mn. The ammoniacal leaching can reduce the cost of basic reagent that makes the pH of leachate higher for the precipitation of leached metals in the acid leaching.

• Polymer(Korea)
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• v.37 no.3
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• pp.373-378
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• 2013

The interfacial adhesion strength is very important in $SiO_x$ deposited PC film for the barrier enhanced polycarbonate (PC) flexible substrate. In this study, PC films were treated by undercoating, UV/$O_3$ and low temperature plasma and then the effect of physical and chemical surface modifications on the interfacial adhesion strength between PC film and $SiO_x$ barrier layer were studied. It was found that untreated PC film shows significantly low interfacial adhesion strength due to the smooth surface and low surface free energy of PC. Low temperature plasma treatments resulted in the increase of both surface roughness and surface free energy due to etching and the appearance of polar molecules on the PC surface. However, UV/$O_3$ treatment only shows the increase of surface free energy by developed polar molecules on the surface. These surface modifications caused the enhancement of surface interfacial strength between PC film and $SiO_x$ barrier. In the case of undercoating, it was found that the increase of surface interfacial strength was achieved by adhesion between various acrylic acid on acrylate coated surface and $SiO_x$ without increase of polar surface energy. In addition, the barrier property is also improved by organic-inorganic hybrid multilayer structure.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.42 no.3 s.333
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• pp.25-34
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• 2005

In this paper, we analyzed and measured the electrical crosstalk characteristics of a triplex transceiver module for ethernet Passive optical networks(EPONS). And we improved the electrical crosstalk levels using Dummy ground lines with signal lines. The triplex transceiver module consists of a laser diode as a transmitter, a digital photodetector as a digital data receiver, and a analog photodetector as a community antenna television signal receiver. And there are integrated on silicon substrate. The digital receiver and analog receiver sensitivity have to meet -24 dBm at $BER=10^{-l2}$ and -7.7 dBm at 44 dB SNR. And the electrical crosstalk levels have to maintain less than -86 dB from DC to 3 GHz. From analysis and measurement results, the proposed silicon substrate structure that contains the Dummy ground line with $100\;{\mu}m$ space from signal lines and separates 4 mm among devices respectively, is satisfied the electrical crosstalk level compared to simple structure. This proposed structure can be easily implemented with design convenience and greatly reduced the silicon substrate size about $50\%$.

• Korean Journal of Social Welfare
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• v.67 no.2
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• pp.137-159
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• 2015

This longitudinal study examined the influence of substandard housing conditions and housing affordability on physical and mental health. Using data from the Korea Welfare Panel Study, this study followed 8,583 adults who continued to participate in the survey from 2009 to 2013. Multivariate analyses involved linear and logistic regression models with the hybrid method that incorporates both fixed and random effects. Results show that substandard housing conditions and excess housing cost burden had significant adverse effects on adults' mental health (e.g., depressive symptoms). About one fourth of the entire sample and one third of those in poverty reported having lived in substandard housing conditions. Additionally, nearly one fourth of those in poverty reported having experienced excess housing cost burden, which is 4 times greater than that of the entire sample. Our findings show that a substantial proportion of individuals, particularly among the poor, have a difficulty in accessing to decent, affordable housing, and that housing assistance may have additional benefits of improving the mental health of individuals with housing issues.