• Journal of radiological science and technology
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• v.41 no.3
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• pp.255-260
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• 2018

Most patients and parents and guardians display frequent anxiety due to radiation exposure during outpatient, ward, and pediatric general radiographic examinations. This is a behavior that perceives only the harmfulness of radiation. For the recognition of medical radiation, we conduct surveys on outpatients, inpatients, and pediatric parents and guardians to identify their awareness, and then use the radiation dose promotional materials After providing accurate information on the use of radiation, the outpatient, inpatient, and pediatric parents and guardians were asked to explain the change in awareness. The questionnaire items were classified into five categories: repetitive radiation awareness for diagnosis, awareness of exposure dose, availability of exposure information, awareness of radiation risk, and awareness of health problems caused by radiation. There was a statistically significant difference in the items of recognition result of medical radiation, although there was a slight difference in the individual items in the pre and post-recognition results of providing information about the radiologists of the protector and the outpatient(p<0.05). Therefore, through the installation of these promotional materials, we will improve our awareness of medical radiation safety during general radiography surveillance in the Department of Radiology to provide better quality medical information and medical services, thereby contributing to strengthening the competitiveness of the hospital.

• Journal of Korean Academy of Nursing Administration
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• v.16 no.3
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• pp.306-313
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• 2010

Purpose: This study was conducted to examine the factors associated with work related injuries of nurses in small and medium sized hospitals. Method: A cross-sectional survey was conducted with nurses in eight hospitals from October 2007 to January 2008. A questionnaire was designed to collect information on nurses' work related injuries, and individual and job related characteristics. The response rate was 65.1%. Data from 294 nurses were analyzed. Multiple logistic regression analysis was performed to determine factors associated with work related injuries. Result: Of the 294 nurses, 19.1% (n=56) responded as having at least one injury during their job performance. The logistic regression analysis revealed that the significant factors influencing work related injuries were job satisfaction, stress recognition, and hospital's location. Nurses with a higher job satisfaction were less likely to experience work related injuries (OR=0.58). Nurses with a higher stress recognition (OR=2.57) and those working at hospitals in metropolitan cities (OR=3.28) were more likely to experience work related injuries. Conclusions: The result of this study indicated that a substantial proportion of nurses in small and medium sized hospitals had experienced injuries related to nursing job. Interventions to prevent work related injuries among nurses should take into account the job satisfaction, stress recognition, and hospital characteristics.

• Journal of Korean Elementary Science Education
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• v.36 no.1
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• pp.61-72
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• 2017

The purpose of this study was to analyze elementary school teachers' implementation for the emphasis on the revision, major contents, teaching and learning method, and evaluation in the 2009 revised national science curriculum. To fulfill the purpose of this study we carried out a survey with 222 elementary school teachers. Main findings of this research were as follows: First, they highly agreed to the increase in class hours and the application of subject classroom, but they had a lower recognition for setting the subject groups. Second, the degree of necessity in discussion and STS was relatively high, but the degree of implementation in science writing, discussion and STEAM was low. Third, in teaching and learning method, they showed a high performance for mutual cooperation, student-led activities and communication, but, a low implementation for open inquiry and instruction considering individual differences. Fourth, in the evaluation of science learning, they showed a high implementation for evaluation based on achievement standards and one based on the understanding and application of basic concepts, but, a low implementation for the development of common evaluation tools. Fifth, it seemed that their recognition for amount, level and interest of science contents and inquiry activities was appropriate and positive.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.10
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• pp.129-136
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• 2015

We propose a hybrid face recognition to deal with illumination variation. For this, we extract discriminant features by using the different illumination invariant feature extraction methods. In order to utilize both advantages of each method, we evaluate the discriminant power of each feature by using the discriminant distance and then construct a composite feature with only the features that contain a large amount of discriminative information. The experimental results for the Multi-PIE, Yale B, AR and yale databases show that the proposed method outperforms an individual illumination invariant feature extraction method for all the databases.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.37-44
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• 2003

In this paper, we investigated the performance of both DNA coding method and Genetic Algorithm(GA) in numeric pattern (from 0 to 9) recognition. The performance of the DNA coding method is compared to the that of the GA. GA searches effectively an optimal solution via the artificial evolution of individual group of binary string using binary coding, while DNA coding method uses four-type bases denoted by Adenine(A), Cytosine(C), Guanine(G) and Thymine(T). To compare the performance of both method, the same genetic operators(crossover and mutation) are applied and the probabilities of crossover and mutation are set the same values. The results show that the DNA coding method has better performance over GA. The reasons for this outstanding performance are multiple candidate solution presentation in one string and variable solution string length.

• Journal of IKEEE
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• v.6 no.1 s.10
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• pp.10-20
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• 2002

The purpose of this work is to develop a system fer rapid and automatic identification of persons, with high reliability and confidence levels. The iris of the eye is used as an optical fingerprint, having a highly detailed pattern that is unique for each individual and stable over many years. Image analysis algorithms find the iris in a image, and encode its texture into an iris code. Iris texture is extracted from the image at multiple scales of analysis by wavelet transformation. The features of many different parts of the iris are projected onto the space-frequency space. They are used to determine an abstract iris code which is similar to 2D barcode. Pattern recognition is achieved by using modified CPN.

• The Transactions of the Korea Information Processing Society
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• v.6 no.6
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• pp.1686-1692
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• 1999

This paper describes the feature area-based vehicle plate recognition system(VPRS). For the extraction of vehicle plate in a vehicle image, we used the method which extracts vehicle plate area from a s vehicle image using intensity variation. For the extraction of the feature area containing character from the extracted vehicle plate, we used the histogram-based approach and the relative location information of individual characters in the extracted vehicle plate. The extracted feature area is used as the input vector of ART2 neural network. The proposed method simplifies the existing complex preprocessing the solves the problem of distortion and noise in the binarization process. In the difficult cases of character extraction by binarization process of previous method, our method efficiently extracts characters regions and recognizes it.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.7
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• pp.2785-2799
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• 2020

We propose a novel speaker adaptation method using acoustic model clustering. The similarity of different speakers is defined by the cosine distance between their i-vectors (intermediate vectors), and various efficient clustering algorithms are applied to obtain a number of speaker subsets with different characteristics. The speaker-independent model is then retrained with the training data of the individual speaker subsets grouped by the clustering results, and an unknown speech is recognized by the retrained model of the closest cluster. The proposed method is applied to a large-scale speech recognition system implemented by a hybrid hidden Markov model and deep neural network framework. An experiment was conducted to evaluate the word error rates using Resource Management database. When the proposed speaker adaptation method using i-vector based clustering was applied, the performance, as compared to that of the conventional speaker-independent speech recognition model, was improved relatively by as much as 12.2% for the conventional fully neural network, and by as much as 10.5% for the bidirectional long short-term memory.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.360-363
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• 2018

Human activity is influenced by various factors, from individual physical features such as vertebral flexion and pelvic distortion to feelings such as joy, anger, and sadness. However, the nature of these behaviors changes over time, and behavioral characteristics do not change much in the short term. The activity data of a person has a time series characteristic that changes with time and a certain regularity for each action. In this study, we applied LSTM, a kind of cyclic neural network to deal with time - series characteristics, to the technique of recognizing activity type and improved recognition rate of activity type by measuring time and parameter optimization of components of LSTM model.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.1
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• pp.98-103
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• 2000

This paper suggests a method to recognize the various defect patterns of a cold mill strip using a binary decision tree automatically constructed by a genetic algorithm(GA). In classifying complex patterns with high similarity like the defect patterns of a cold mill stirp, the selection of an optimal feature set and an appropriate recognizer is important to achieve high recognition rate. In this paper a GA is used to select a subset of the suitable features at each node in the binary decision tree. The feature subset with maximum fitness is chosen and the patterns are classified into two classes using a linear decision function. This process is repeated at each node until all the patterns are classified into individual classes. In this way, the classifier using the binary decision tree is constructed automatically. After constructing the binary decision tree, the final recognizer is accomplished by having neural network learning sits of standard patterns at each node. In this paper, the classifier using the binary decision tree is applied to the recognition of defect patterns of a cold mill strip, and the experimental results are given to demonstrate the usefulness of the proposed scheme.