• Journal of Korea Water Resources Association
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• v.56 no.8
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• pp.471-484
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• 2023

Deep learning models based on generative adversarial neural networks are specialized in generating new information based on learned information. The deep generative models (DGMR) model developed by Google DeepMind is an generative adversarial neural network model that generates predictive radar images by learning complex patterns and relationships in large-scale radar image data. In this study, the DGMR model was trained using radar rainfall observation data from the Ministry of Environment, and rainfall prediction was performed using an generative adversarial neural network for a heavy rainfall case in August 2021, and the accuracy was compared with existing prediction techniques. The DGMR generally resembled the observed rainfall in terms of rainfall distribution in the first 60 minutes, but tended to predict a continuous development of rainfall in cases where strong rainfall occurred over the entire area. Statistical evaluation also showed that the DGMR method is an effective rainfall prediction method compared to other methods, with a critical success index of 0.57 to 0.79 and a mean absolute error of 0.57 to 1.36 mm in 1 hour advance prediction. However, the lack of diversity in the generated results sometimes reduces the prediction accuracy, so it is necessary to improve the diversity and to supplement it with rainfall data predicted by a physics-based numerical forecast model to improve the accuracy of the forecast for more than 2 hours in advance.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.7 no.2
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• pp.190-202
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• 1996

This study assessed psychiatrically referred 5-to 13-year-old children who presented inattention or hyperactivity as chief complaints. Demographic characteristics, primary diagnosis, and comorbid psychiatric conditions of them were identified, and they were assessed using questionnaires and neuropsychological tests. Primary diagnoses included ADHD, anxiety disorder, mental retardation, depression, oppositional defiant disorder, developmental language disorder and others. functional enuresis, conduct disorder, and developmental language disorder were among the secondarily diagnosed disorders. In patients diagnosed as ADHD, overall comorbidity rate was 55.3%. The disorders that frequently co-occured with ADHD were specific developmental disorder, conduct disorder, oppositional defiant disorder, anxiety disorder and other. ADHD groups with or without comorbidity differed in performance IQ and CPT scores. ADHD group differed from externalizing disorders group in the information subscore of IQ, MFFT, and CPT scores, and differed in teachers rating scales, the uncommunication factor of CBCL, and CPT card error compared with internalizing disorders group. The authors concluded that inattentive or hyperactive children should be assessed using various instruments to differentiate other disorders and to identify possible presence of comorbid conditions.

• Clinical and Experimental Pediatrics
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• v.48 no.10
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• pp.1132-1138
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• 2005

Purpose : The mucopolysaccharidoses (MPSs) are a heterogeneous group of lysosomal storage disorders. They are caused by a deficiency of the enzymes involved in the degradation of glycosaminoglycans. Early recognition is important because recombinant enzyme replacement therapy is now available for MPS. We studied the clinical characteristics of 80 MPS children with the object of determining the epidemiological, clinical and radiological features in Korean MPS children. Methods : Diagnosis of MPS was confirmed by skin fibroblast enzyme analysis in 80 patients between February 1995 and December 2004. Charts were retrospectively reviewed for clinical and radiological findings, as well as for intelligence and speech evaluations. Results : Hunter syndrome (MPS type II) was the most prevalent type, appearing in 51/80 cases (64 %), followed by Sanfilippo syndrome (MPS III-18%), Hurler syndrome (MPS I-15%), and Morquio syndrome (MPS IV-4%). The average age at diagnosis was 5.5 years (range 1 to 20), and the male-to-female ratio was 4.7 : 1. Typical radiographic changes were observed in 45/54 cases (83%). Mitral regurgitation was the most common cardiac defect. Moderate to profound mental retardation and hearing loss were present in 14/35 cases (56%) and 33/38 cases (82%), respectively. Four MPS II patients had bone marrow transplantation, with mixed outcomes. Five MPS I patients are currently on enzyme replacement therapy. Conclusion : Our study showed a high proportion of MPS II cases (64%), which may represent population variability. By studying the clinical features of these patients, we hope to alert pediatricians of the warning signs of MPS.

• Therapeutic Science for Rehabilitation
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• v.12 no.4
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• pp.67-80
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• 2023

Objective : This study aimed to investigate the current status of physical therapy in children and adolescents. Methods : Sixty questionnaires from physical therapists treating children and adolescents with disabilities were analyzed. The questionnaire consisted of questions on physical therapy, participants, satisfaction, and the assessment of pediatric and adolescent physical therapy. Descriptive statistics and frequencies were used to investigate the current status, participants, and satisfaction. Differences between physical therapy participation difficulty, importance-ability of major aspects of pediatric and adolescent physical therapy, and therapy goal frequency were analyzed using paired T-test. Results : 11 to 15 cases (66.7%) and one-on-one treatment (95.0%) were performed independently (95.0%). The main ages of the subjects were preschool and school, the diagnoses were brain lesions and developmental delay, and treatment was conducted for up to 20 years or older. Satisfaction with pediatric and adolescent physical therapy was high (70.0%), as was the intensity of work (71.7%). Neurodevelopmental therapy, gait training, and goal-directed rehabilitation were the main treatments, and Gross Motor Function Measures of 88 and 66, respectively, were used. Respondents said that current fee system is inadequate (66.1%) and appropriate fee system is needed. Conclusion : This study extensively investigated the content of and factors related to pediatric and adolescent physical therapy. Based on the current situation, efforts to improve the expertise and continuity of pediatric and adolescent physical therapists and apply the latest treatment techniques are required.

• Clinical and Experimental Pediatrics
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• v.52 no.3
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• pp.289-294
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• 2009

Purpose : To assess the usefulness of magnetic resonance imaging (MRI), karyotyping, brainstem auditory evoked potential (BAEP), electroencephalogram (EEG), tandem mass screening test, and newborn metabolic screening test in children with language delay for diagnosing underlying diseases. Methods : From January 2000 to June 2007, a retrospective chart review was performed for 122 children with language delay who visited the Child Neurology Clinic at Yeungnam University Hospital and who underwent neuropsychologic tests and other diagnostic evaluations for underlying diseases. They were grouped into phenomenological diagnostic categories, and test results were analyzed according to the underlying diseases. Results : Of 122 patients, 47 (38.5%) had mental retardation, 40 (32.8%) had developmental language disorders, 23 (18.9 %) had borderline IQ, and 12 (9.8%) had autism spectrum disorder. In 26 (21.3%) cases, the causes or relevant clinical findings to explain language delay were found. Eight (10.4%) of 77 MRIs, 6 (8.0%) of 75 EEGs, and 4 (5%) of 80 BAEPs showed abnormal results. Results directly attributed to diagnosing underlying diseases were 2 hearing defects in BAEPs and 1 bilateral perisylvian cortical dysplasia in MRIs. No abnormal results were found in karyotyping, tandem mass screening tests, and new-born screening tests. Conclusion : Commonly used tests to diagnose the cause of language delay are not very effective and should only be used selectively, according to patient characteristics. However, despite the low diagnostic yields from these tests, because many patients show abnormal results, these tests are useful when conducted in complete evaluation.

• Korean Journal of Environmental Biology
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• v.35 no.1
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• pp.21-36
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• 2017

Phthalates, known as typical endocrine disruptors, are plasticizers used to soften plastics such as polyvinyl chloride (PVC). Because of their material properties, phthalates are used extensively in the production of toys, flooring, wood processing, detergents, and even cosmetics as lubricants and perfume solvents. Due to their endocrine disrupting effect and other adverse health effects published, recently, phthalates have been regulated in many countries. Besides, in an effort to replace phthalates, several chemical plasticizers such as trioctyltrimellitate (TOTM) and dioctylterephthalate (DIOP) have been used instead of the existing harmful phthalates, and novel alternatives are continuously being developed. Nonetheless, phthalates are still being detected in several plastic products, and the safety of alternatives that are considered safe is being questioned. In this review, we describe the adverse health effects of phthalates, their regulation and the current status of their alternatives.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.1 no.1
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• pp.108-116
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• 1990

Child Psychiatric consultations on 92 patients hospitalized at the Seoul National Childrens Hospistal were analyzed retrospectively. The main referral sources were Pedictrics(77.2%), Neurosurgery(8.91%) and Orthopedics(3.96%) Consultation rate was 0.81% for the Hospital, 1.41% for Department of Pediatrics, 3.54% for Neurosurgery and 0.3% for the other department were made for the differential diagnosis raher than for the treatment or intervention. The diagnosis of referred patients were somatoform disorder(25%), organic mental disorder(18.5%), developmental disorder(14.1), conduct disorder(6.5%), anxiety disorder(6.5%).

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.6
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• pp.227-233
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• 2019

Recently, along with the recent development of deep learning technique, neural networks are achieving success in computer vision filed. Convolutional neural network have shown outstanding performance in not only for a simple image classification task, but also for tasks with high difficulty such as object segmentation and detection. However many such deep learning models are based on supervised-learning, which requires more annotation labels than image-level label. Especially image semantic segmentation model requires pixel-level annotations for training, which is very. To solve these problems, this paper proposes a weakly-supervised semantic segmentation method which requires only image level label to train network. Existing weakly-supervised learning methods have limitations in detecting only specific area of object. In this paper, on the other hand, we use multi-classifier deep learning architecture so that our model recognizes more different parts of objects. The proposed method is evaluated using VOC 2012 validation dataset.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.6
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• pp.1379-1386
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• 2005

For efficient welding it is necessary to maintain stability of the welding process and control the shape of the welding bead. The welding quality can be controlled by monitoring important parameters, such as, the Arc Voltage, Welding Current and Welding Speed during the welding process. Welding systems use either a vision sensor or an Arc sensor, both of which are unable to control these parameters directly. Therefore, it is difficult to obtain necessary bead geometry without automatically controlling the welding parameters through the sensors. In this paper we propose a novel approach using fuzzy logic and neural networks for improving welding qualify and maintaining the desired weld bead shape. Through experiments we demonstrate that the proposed system can be used for real welding processes. The results demonstrate that the system can efficiently estimate the weld bead shape and remove the welding detects.

• Information Systems Review
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• v.23 no.4
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• pp.69-86
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• 2021

As artificial intelligence technology advances, personalized recommendation systems using big data have attracted huge attention. In the case of beauty products, product preferences are clearly divided depending on customers' skin types and sensitivity along with individual tastes, so it is necessary to provide customized recommendation services based on accumulated customer data. Therefore, by employing deep learning methods, this study proposes a neural network-based recommendation model utilizing both product search history and context information such as gender, skin types and skin worries of customers. The results show that our model with context information outperforms collaborative filtering-based recommender system models using customer search history.