• Phonetics and Speech Sciences
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• v.7 no.2
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• pp.63-73
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• 2015

This study investigated longitudinally early vocalization and phonological developments of typically developing children. Ten typically developing children participated in the study from 9 months to 18 months of age. Spontaneous utterance samples were collected at 9, 12, 15, 18 months of age and phonetically transcribed and analyzed. Utterance samples were classified into 5 levels using Stark Assessment of Early Vocal Development-Revised(SAEVD-R). The data analysis focused on 4 and 5 levels of vocalizations classified by SAEVD-R and word productions. The percentage of each vocalization level, vocalization length, syllable structures, and consonant inventory were obtained. The results showed that the percentages of level 4 and 5 vocalizations and word significantly increased with age and the production of syllable structures containing consonants significantly increased around 12 and 15 months of age. On average, the children produced 4 types of syllable structure and 5.4 consonants at 9 months and they produced 5 types of syllable structure and 9.8 consonants at 18 months. The phonological development patterns in this study were consistent with those analyzed from children's meaningful utterances in previous studies. The results support the perspective on the continuity between babbling and early speech. This study has clinical implications in early identification and speech-language intervention for young children with speech delays or at risk.

• Proceedings of the Korean Society of Plant Biotechnology Conference
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• 2005.11a
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• pp.423-423
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• 2005

• Journal of Korean Neurosurgical Society
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• v.67 no.3
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• pp.308-314
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• 2024

Vein of Galen aneurysmal malformation is one of important pediatric arteriovenous shunt diseases, especially among neonates and infants. Here, early history of the disease identification, basic pathoanatomy with a focus on the embryonic median prosencephalic vein, classification and differential diagnoses, and recent genetic studies are reviewed.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.5
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• pp.49-54
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• 2010

The wooden cultural assets have the characteristics such as the fast spread of flame and leading to total destruction. Therefore, there is a need for a system for early countermeasure of recognized problem, along with the technological response for accurately recognizing the situation, for the prevention and early suppression of fire. To utilize such technology for detecting the situation through the latest ubiquitous technology and for a quick response to suppress fire, the ubiquitous sensor network (USN) technology, flame detector, image sensor, USN-based cultural asset disaster prevention management application case and malfunction identification system realization were examined in this study and the study result was presented focusing on the flame detector malfunction identification system for the ubiquitous-type cultural asset disaster prevention system.

• Journal of Microbiology and Biotechnology
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• v.31 no.11
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• pp.1481-1489
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• 2021

Early and accurate detection of pathogens is important to improve clinical outcomes of bloodstream infections (BSI), especially in the case of drug-resistant pathogens. In this study, we aimed to develop a culture-independent digital PCR (dPCR) system for multiplex detection of major sepsis-causing gram-negative pathogens and antimicrobial resistance genes using plasma DNA from BSI patients. Our duplex dPCR system successfully detected nine targets (five bacteria-specific targets and four antimicrobial resistance genes) through five reactions within 3 hours. The minimum detection limit was 50 ag of bacterial DNA, suggesting that 1 CFU/ml of bacteria in the blood can be detected. To validate the clinical applicability, cell-free DNA samples from febrile patients were tested with our system and confirmed high consistency with conventional blood culture. This system can support early identification of some drug-resistant gram-negative pathogens, which can help improving treatment outcomes of BSI.

• Fashion & Textile Research Journal
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• v.15 no.1
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• pp.12-21
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• 2013

This study examines the effectiveness of brand awareness, brand image and brand identification on brand loyalty for the Nike brand. The subjects of this study were 336 elementary school boys. The statistical methods used for this study were factor analysis, Cronbach's alpha analysis, confirmatory factor analysis and path analysis with SPSS 19.0 and AMOS 19.0. The results of this study were as follows. First, brand awareness had a direct effect on brand image and brand loyalty. Brand awareness also had an indirect effect on brand loyalty. Second, brand image directly or indirectly influenced brand loyalty as well as directly influenced brand identification. Third, brand identification directly influenced brand loyalty. The $5^{th}$ grade group and $6^{th}$ grade group had different paths; however, the most powerful path was the same as brand awareness to brand image. The results of this study will help fashion companies understand the importance of new consumer groups in their early teens or elementary school.

• Journal of Multimedia Information System
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• v.4 no.4
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• pp.233-238
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• 2017

Automatic identification of disease in plants from their leaves is one of the most challenging task to researchers. Diseases among plants degrade their performance and results into a huge reduction of agricultural products. Therefore, early and accurate diagnosis of such disease is of the utmost importance. The advancement in deep Convolutional Neural Network (CNN) has change the way of processing images as compared to traditional image processing techniques. Deep learning architectures are composed of multiple processing layers that learn the representations of data with multiple levels of abstraction. Therefore, proved highly effective in comparison to many state-of-the-art works. In this paper, we present a plant disease identification methodology from their leaves using deep CNNs. For this, we have adopted GoogLeNet that is considered a powerful architecture of deep learning to identify the disease types. Transfer learning has been used to fine tune the pre-trained model. An accuracy of 85.04% has been recorded in the identification of four disease class in Apple plant leaves. Finally, a comparison with other models has been performed to show the effectiveness of the approach.

• Journal of the Korean Operations Research and Management Science Society
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• v.40 no.2
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• pp.31-42
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• 2015

The purpose of airborne radars is to detect and identify approaching targets as early as possible. If the targets are identified as enemies, detection systems must provide defense systems with information of the targets to counter. Though many previous studies based on the detection theory of the target have shown various ways to derive detection probability of each radar, optimal arrangement of radars for effective detection, and determination of the search pattern, they did not reflect the fact that most military radar sites run multiple radars in order to increase the accuracy of identifications by radars. In this paper, we propose a model to analyze the probability of identification generated by the multiple radars using non-homogeneous absorbing markov chains. Our results are expected to help the military commanders counter the enemy targets effectively by using radars in a way to maximize the identification rate of targets.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1115-1126
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• 2021

Plant disease is one of the most irritating problems for agriculture growers. Thus, timely detection of plant diseases is of high importance to practical value, and corresponding measures can be taken at the early stage of plant diseases. Therefore, numerous researchers have made unremitting efforts in plant disease identification. However, this problem was not solved effectively until the development of artificial intelligence and big data technologies, especially the wide application of deep learning models in different fields. Since the symptoms of plant diseases mainly appear visually on leaves, computer vision and machine learning technologies are effective and rapid methods for identifying various kinds of plant diseases. As one of the fruits with the highest nutritional value, apple production directly affects the quality of life, and it is important to prevent disease intrusion in advance for yield and taste. In this study, an improved deep residual network is proposed for apple leaf disease identification in a novel way, a global residual connection is added to the original residual network, and the local residual connection architecture is optimized. Including that 1,977 apple leaf disease images with three categories that are collected in this study, experimental results show that the proposed method has achieved 98.74% top-1 accuracy on the test set, outperforming the existing state-of-the-art models in apple leaf disease identification tasks, and proving the effectiveness of the proposed method.

• Journal of the Korean Home Economics Association
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• v.41 no.5
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• pp.41-58
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• 2003

The purpose of this study is to develop and evaluate a social competence enrichment program for institutionalized children in early childhood. The program was developed to enhance institutionalized children's social competence. The program consists of sixteen 30-minute sessions. 19 institutionalized children (11 boys and 8 girls) participated in this program. The effect of this program was evaluated by institutionalized preschool children's emotional knowledge and interpersonal problem solving strategy. Assessment of emotional knowledge consists of identification and situation task. Interpersonal problem solving strategy includes forceful, prosocial, and manipulative strategy. Results showed that there was a significant experimental effect for emotional knowledge. Experimental children showed higher scores of emotional knowledge than control children. Also, there was a significant experimental effect for prosocial and forceful strategy. Experimental children showed higher ratio of prosocial strategy and lower ratio of forceful strategy than control children. The results of program evaluation revealed that this program was helpful for promoting institutionalized children's social competence.