• Journal of Wetlands Research
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• v.26 no.2
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• pp.147-159
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• 2024

Lack of streamflow observations makes model calibration difficult and limits model performance improvement. Satellite-based remote sensing products offer a new alternative as they can be actively utilized to obtain hydrological data. Recently, several studies have shown that artificial intelligence-based solutions are more appropriate than traditional conceptual and physical models. In this study, a data-driven approach combining various recurrent neural networks and decision tree-based algorithms is proposed, and the utilization of satellite remote sensing information for AI training is investigated. The satellite imagery used in this study is from MODIS and SMAP. The proposed approach is validated using publicly available data from 25 watersheds. Inspired by the traditional regionalization approach, a strategy is adopted to learn one data-driven model by integrating data from all basins, and the potential of the proposed approach is evaluated by using a leave-one-out cross-validation regionalization setting to predict streamflow from different basins with one model. The GRU + Light GBM model was found to be a suitable model combination for target basins and showed good streamflow prediction performance in ungauged basins (The average model efficiency coefficient for predicting daily streamflow in 25 ungauged basins is 0.7187) except for the period when streamflow is very small. The influence of satellite remote sensing information was found to be up to 10%, with the additional application of satellite information having a greater impact on streamflow prediction during low or dry seasons than during wet or normal seasons.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.9-18
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• 2024

The importance of active sonar systems is emerging due to the quietness of underwater targets and the increase in ambient noise due to the increase in maritime traffic. However, the low signal-to-noise ratio of the echo signal due to multipath propagation of the signal, various clutter, ambient noise and reverberation makes it difficult to identify underwater targets using active sonar. Attempts have been made to apply data-based methods such as machine learning or deep learning to improve the performance of underwater target recognition systems, but it is difficult to collect enough data for training due to the nature of sonar datasets. Methods based on mathematical modeling have been mainly used to compensate for insufficient active sonar data. However, methodologies based on mathematical modeling have limitations in accurately simulating complex underwater phenomena. Therefore, in this paper, we propose a sonar signal synthesis method based on a deep neural network. In order to apply the neural network model to the field of sonar signal synthesis, the proposed method appropriately corrects the attention-based encoder and decoder to the sonar signal, which is the main module of the Tacotron model mainly used in the field of speech synthesis. It is possible to synthesize a signal more similar to the actual signal by training the proposed model using the dataset collected by arranging a simulated target in an actual marine environment. In order to verify the performance of the proposed method, Perceptual evaluation of audio quality test was conducted and within score difference -2.3 was shown compared to actual signal in a total of four different environments. These results prove that the active sonar signal generated by the proposed method approximates the actual signal.

• Trans-
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• v.16
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• pp.1-31
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• 2024

The advent of digital media has facilitated easy access for adolescents to environments conducive to the purchase of narcotics. In particular, there's an increasing trend in the purchase and consumption of narcotics mediated through Social Network Services (SNS) and messenger services. Adolescents, sensitive to such environments, are at risk of experiencing neurological and mental health issues due to narcotic addiction, increasing their exposure to criminal activities, hence necessitating national-level management and support. Consequently, the quest for sustainable treatment methods for adolescents exposed to narcotics emerges as a critical challenge. In the context of high relapse rates in narcotic addiction, the necessity for cost-effective and user-friendly treatment programs is emphasized. This study conducts a literature review aimed at utilizing digital platforms to create an environment where adolescents can voluntarily participate, focusing on the development of therapeutic content through art. Specifically, it reviews societal perceptions and treatment statuses of adolescent drug addiction, analyzes the impact of narcotic addiction on adolescent brain activity and cognitive function degradation, and explores approaches for developing digital therapeutics to promote the rehabilitation of the addicted brain through analysis of precedential case studies. Moreover, the study investigates the benefits that the integration of digital therapeutic approaches and art therapy can provide in the treatment process and proposes the possibility of enhancing therapeutic effects through various treatment programs such as drama therapy, music therapy, and art therapy. The application of art therapy methods is anticipated to offer positive effects in terms of tool expansion, diversification of expression, data acquisition, and motivation. Through such approaches, an enhancement in the effectiveness of treatments for adolescent narcotic addiction is anticipated. Overall, this study undertakes foundational research for the development of digital therapeutics and related applications, offering economically viable and sustainable treatment options in consideration of the societal context of adolescent narcotic addiction.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.151-164
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• 2024

In water treatment plants supplying potable water, the management of chlorine concentration in water treatment processes involving pre-chlorination or intermediate chlorination requires process control. To address this, research has been conducted on water quality prediction techniques utilizing AI technology. This study developed an AI-based predictive model for automating the process control of chlorine disinfection, targeting the prediction of residual chlorine concentration downstream of sedimentation basins in water treatment processes. The AI-based model, which learns from past water quality observation data to predict future water quality, offers a simpler and more efficient approach compared to complex physicochemical and biological water quality models. The model was tested by predicting the residual chlorine concentration downstream of the sedimentation basins at Plant, using multiple regression models and AI-based models like Random Forest and LSTM, and the results were compared. For optimal prediction of residual chlorine concentration, the input-output structure of the AI model included the residual chlorine concentration upstream of the sedimentation basin, turbidity, pH, water temperature, electrical conductivity, inflow of raw water, alkalinity, NH₃, etc. as independent variables, and the desired residual chlorine concentration of the effluent from the sedimentation basin as the dependent variable. The independent variables were selected from observable data at the water treatment plant, which are influential on the residual chlorine concentration downstream of the sedimentation basin. The analysis showed that, for Plant, the model based on Random Forest had the lowest error compared to multiple regression models, neural network models, model trees, and other Random Forest models. The optimal predicted residual chlorine concentration downstream of the sedimentation basin presented in this study is expected to enable real-time control of chlorine dosing in previous treatment stages, thereby enhancing water treatment efficiency and reducing chemical costs.

• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.187-201
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• 2024

Osteoporosis is a major health issue globally, often remaining undetected until a fracture occurs. To facilitate early detection, deep learning (DL) models were developed to classify osteoporosis using abdominal computed tomography (CT) scans. This study was conducted using retrospectively collected data from 3,012 contrast-enhanced abdominal CT scans. The DL models developed in this study were constructed for using image data, demographic/clinical information, and multi-modality data, respectively. Patients were categorized into the normal, osteopenia, and osteoporosis groups based on their T-scores, obtained from dual-energy X-ray absorptiometry, into normal, osteopenia, and osteoporosis groups. The models showed high accuracy and effectiveness, with the combined data model performing the best, achieving an area under the receiver operating characteristic curve of 0.94 and an accuracy of 0.80. The image-based model also performed well, while the demographic data model had lower accuracy and effectiveness. In addition, the DL model was interpreted by gradient-weighted class activation mapping (Grad-CAM) to highlight clinically relevant features in the images, revealing the femoral neck as a common site for fractures. The study shows that DL can accurately identify osteoporosis stages from clinical data, indicating the potential of abdominal CT scans in early osteoporosis detection and reducing fracture risks with prompt treatment.

• Journal of Nutrition and Health
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• v.24 no.3
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• pp.230-242
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• 1991

This research was conducted on the 10 take-out boxed-lunches commercially prepared in the department stores. chain stores. and the public railroad trains in Korea. Sampling was conducted from February 1990 to March 1990. Nutritive values and sodium contents of the 10 boxed-lunch samples are summarized as follows : 1) The average weight(percentage) of the cooked rice and the side dishes were 304.6g(49.4) and 312.4(506%), respectively. The weight of these samples were significantly heavier than that of Japanese style boxed-lunches. 2) The average number of the side dishes was 12. The average numbers of food items classified by the five food groups were 6.1 in protein food group, 0.3 in calcium food group. 6.0 in vitamin and mineral food group. 1.5 in carbohydrate food group, and 1.5 in oil and fat food group. 3) They contained on the average 840.7kcal of energy, 38.9g of protein, 22.7g of fat, 120.4g of carbohydrate. 300.8mg of calcium. 410.8mg of phosphours, 6.61 mg of iron. 219.8 R.E. of vitamin A, 0.46mg of thiamin, 0.67mg of riboflavin, 10.5mg of niacin, 27.5mg of ascorbic acid. Thus. except vitamin t the content of all the nutrients were higher than the value of 1/3 of the RDA for adults. 4) The high priced group(group 2) had more protein, calcuim. iron and niacin contents than the cheaper group(group 1). Probably, it's because the group 2 had more animal foods than the group 1. 5) The average energy content per unit price(100 won) was 37.3kcal and the average protein content per unit price(100 won) was 1.64g. Korena style boxed-lunches had higher energy and protein contents per unit price than Japanese style, and the group 1 higher than the group 2. 6) The average energy Proportions of Protein, carbohydrate. and fat were 18.3%, 57.4%, and 24.3%, respectively. These proportions are good enough. 7) Frequency of cooking methods for the side dishes were found in the decreasing order : pan-frying, frying, braising, seasoning, kimchi, grilling, pickling, stir-frying, steaming and fermenting. Generally simple cooking methods were used, thus the menus were lack or varieties. 8) Frequency of colors for the side dishes were found in the decreasing order : red, brown. yellow, green, black, white. Too much red pepper was used. 9) The average capacity of the containers for the staples and the side dishes were 468.1ml and 590.6ml, respectively. And the containers could not keep the food items well seperated. 10) The average contensts of sodium and salt were 2.287mg and 5.76g, in the range of 1, 398mg to 3, 489mg and 3.53g to 8.80g, respectively. These are much higher values than the recommended amount of salt.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.11 no.2
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• pp.209-220
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• 2000

The purpose of this study is to find out the characteristics of depressive episode about major depression and bipolar disorder in child and adolescent. The subjects of this study were 34 major depression patients and 17 bipolar disorder patients hospitalized at child and adolescent psychiatry in OO university children's hospital from 1st March 1993 to 31st October 1999. The method of this study is to review socio-demographic characteristics, diagnostic classification, chief problems and symptoms at admission, frequency of symptoms, maternal pregnancy problem history, childhood developmental history, coexisting psychiatric disorders, family psychopathology and family history and therapeutic response through their chart. 1) The ratio of male was higher than that of female in major depressive disorder while they are similar in manic episode, bipolar disorder. 2) Average onset age of bipolar disorder was 14 years 1 month and it was 12 years 8 months in the case of major depression As a result, average onset age of major depression is lower than that of bipolar disorder. 3) The patients complained of vegetative symptoms than somatic symptoms in both bipolar disorder and depressive disorder. Also, the cases of major depression developed more suicide idea symptom while the case of bipolar disorder developed more aggressive symptoms. In the respect of psychotic symptoms, delusion was more frequently shown in major depression, but halucination was more often shown in bipolar disorder. 4) Anxiety disorder coexisted most frequently in two groups. And there coexisted symptoms such as somartoform disorder, mental retardation and personality disorder in both cases. 5) The influence of family loading was remarkable in both cases. Above all, the development of major depression had to do with child abuse history and inappropriate care of family. It is apparent that there are distinctive differences between major depression and bipolar disorder in child and adolescent through the study, just as in adult cases. Therefore the differences of clinical characteristics between two disorders is founded in coexisting disorders and clinical symptoms including onset age, somatic symptoms and vegetative symptoms.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.111-131
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• 2015

There are only a handful number of research conducted on pattern analysis of corporate distress as compared with research for bankruptcy prediction. The few that exists mainly focus on audited firms because financial data collection is easier for these firms. But in reality, corporate financial distress is a far more common and critical phenomenon for non-audited firms which are mainly comprised of small and medium sized firms. The purpose of this paper is to classify non-audited firms under distress according to their financial ratio using data mining; Self-Organizing Map (SOM). SOM is a type of artificial neural network that is trained using unsupervised learning to produce a lower dimensional discretized representation of the input space of the training samples, called a map. SOM is different from other artificial neural networks as it applies competitive learning as opposed to error-correction learning such as backpropagation with gradient descent, and in the sense that it uses a neighborhood function to preserve the topological properties of the input space. It is one of the popular and successful clustering algorithm. In this study, we classify types of financial distress firms, specially, non-audited firms. In the empirical test, we collect 10 financial ratios of 100 non-audited firms under distress in 2004 for the previous two years (2002 and 2003). Using these financial ratios and the SOM algorithm, five distinct patterns were distinguished. In pattern 1, financial distress was very serious in almost all financial ratios. 12% of the firms are included in these patterns. In pattern 2, financial distress was weak in almost financial ratios. 14% of the firms are included in pattern 2. In pattern 3, growth ratio was the worst among all patterns. It is speculated that the firms of this pattern may be under distress due to severe competition in their industries. Approximately 30% of the firms fell into this group. In pattern 4, the growth ratio was higher than any other pattern but the cash ratio and profitability ratio were not at the level of the growth ratio. It is concluded that the firms of this pattern were under distress in pursuit of expanding their business. About 25% of the firms were in this pattern. Last, pattern 5 encompassed very solvent firms. Perhaps firms of this pattern were distressed due to a bad short-term strategic decision or due to problems with the enterpriser of the firms. Approximately 18% of the firms were under this pattern. This study has the academic and empirical contribution. In the perspectives of the academic contribution, non-audited companies that tend to be easily bankrupt and have the unstructured or easily manipulated financial data are classified by the data mining technology (Self-Organizing Map) rather than big sized audited firms that have the well prepared and reliable financial data. In the perspectives of the empirical one, even though the financial data of the non-audited firms are conducted to analyze, it is useful for find out the first order symptom of financial distress, which makes us to forecast the prediction of bankruptcy of the firms and to manage the early warning and alert signal. These are the academic and empirical contribution of this study. The limitation of this research is to analyze only 100 corporates due to the difficulty of collecting the financial data of the non-audited firms, which make us to be hard to proceed to the analysis by the category or size difference. Also, non-financial qualitative data is crucial for the analysis of bankruptcy. Thus, the non-financial qualitative factor is taken into account for the next study. This study sheds some light on the non-audited small and medium sized firms' distress prediction in the future.

• Journal of the korean academy of Pediatric Dentistry
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• v.26 no.2
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• pp.399-415
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• 1999

From bacteria to mammalian cells, one of the most important mediators of intracellular signal transduction mechanisms which regulate a variety of intracellular processes is free calcium. In salivary acinar cells, elevation of intracellular calcium concentration ($[Ca^{2+}]_i$) is essential for the salivary secretion induced by parasympathetic stimulation. However, in addition to $[Ca^{2+}]_i$, gap junctions which couple individual cells electrically and chemically have also been reported to regulate enzyme secretion in pancreatic acinar cells. Since the plasma membrane of salivary acinar cells has a high density of gap junctions, and these cells are electrically and chemically coupled with each other, gap junctions may modulate the secretory function of salivary glands. In this respect, I planned to investigate the role of gap junctions in the modulation of salivary secretion and $[Ca^{2+}]_i$, using mandibular salivary glands of rats. In order to measure the salivary flow rate, fluid was collected from the cannulated duct of the isolated perfused rat mandibular glands at 2 min intervals. $[Ca^{2+}]_i$, was measured from the cells loaded with fura-2 by spectrofluorometry. The results obtained were as follows: 1. CCh-induced salivary secretion was reversibly inhibited by 1 mM octanol, a gap junction blocker. 2. CCh-induced increase in $[Ca^{2+}]_i$, was also reversed by the application of 1 mM octanol. 3. Octanol did not block the initial increase in $[Ca^{2+}]_i$ caused by CCh, which suggested that the reduction of $[Ca^{2+}]_i$, caused by gap junction blockade was not resulted from the inhibition of $Ca^{2+}$ release from intracellular $Ca^{2+}$ stores. 4. Addition of octanol during stimulation with $1{\mu}M$ thapsigargin, a potent microsomal ATPase inhibitor, reduced $[Ca^{2+}]_i$, to the basal level. This suggested that inhibition of gap junction permeability closed plasma membrane $Ca^{2+}$ channels. 5. 2,5-di-tert-butyl-1,4-benzohydroquinone (TBQ) generated $[Ca^{2+}]_i$ oscillations resulting from periodic influx of $Ca^{2+}$ via plasma membrane. The TBQ-induced $[Ca^{2+}]_i$ oscillations were stopped by the application of 1mM octanol which implicated that gap junctions modulate the permeability of plasma membrane $Ca^{2+}$ channels. 6. Glycyrrhetinic acid, another well known gap junction blocker, also inhibited CCh-induced salivary secretion from rat mandibular glands. These results suggested that gap junctions play an important role in the modulation of fluid secretion from the rat mandibular glands and this was probably due to the inhibition of $Ca^{2+}$ influx through the plasma membrane $Ca^{2+}$ channels.

• Journal of the Korean Society of Food Science and Nutrition
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• v.37 no.12
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• pp.1589-1598
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• 2008

The objective of this study was to investigate the effect of obesity on health by analyzing the factors which are related to obesity through the questionnaires on the dietary pattern, nutrient intake and physical measurements. The subjects, 419 college women aged 20 to 24 year-old, were randomly selected from Seoul and data were collected during March to May, 2008 and classified as under underweight, normal weight, and obesity groups according to BMI. However, weight, skeletal muscle mass, body fat mass, fat free mass, percentage of body fat, and waist to hip ratio showed significant differences among all the groups (p<0.05). In the obese group, 77.8% ate fat-rich foods such as galbi and samgyopsal more than two times per week, 66.7% ate vegetables other than kimchi (p<0.05) as compared to the underweight and normal groups by mini dietary assessment (p<0.05). The cholesterol intakes of the underweight, normal weight, and obese groups were $164.67{\pm}114.52mg/dL$, $143.31{\pm}99.58mg/dL$, and $121.92{\pm}54.91mg/dL$, respectively, and the obese group had a significantly lower intake than the other groups (p<0.05). The serum total cholesterol levels of the underweight, normal, and obese groups were $177.04{\pm}26.36mg/dL$, $189.46{\pm}29.05mg/dL$, and $170.00{\pm}12.75mg/dL$, respectively, and the obese group showed lower total cholesterol level than the other groups (p<0.05). The triacylglycerol level of the obese group ($132.00{\pm}64.60mg/dL$) was significantly higher than the other two groups (p<0.05). The HDL-cholesterol levels of the underweight, normal weight, and obese groups were $51.92{\pm}9.39mg/dL$, $59.20{\pm}13.53mg/dL$, and $43.00{\pm}8.98mg/dL$, respectively, showing that the obese subjects had significantly lower HDL-cholesterol levels as compared to the subjects in the other groups (p<0.05). The HDL-C/LDL-C ratios of the underweight ($0.52{\pm}0.45$) and normal weight ($0.59{\pm}0.23$) groups were higher than the ratio of the obese group ($0.41{\pm}0.06$). Total cholesterol were positively correlated with LDL-cholesterol (r=0.768, p<0.01), but triacylglycerol were adversely correlated with HDL-cholesterol. In conclusion, our results show that college-aged women in Seoul should be encouraged to amend their overall dietary habits, make a dietary plan that fits their individual needs, and maintain an effective exercise schedule.