• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.10a
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• pp.80-84
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• 2002

In this study, researchers developed the est algorithm for artificial defects in the semic packages and performed to it by pattern recogn technology. For this purpose, this algorithm was I that researcher made software with matlab. The so consists of some procedures including ultrasonic acquistion, equalization filtering, self-organizing backpropagation neural network. self-organizing ma backpropagation neural network are belong to metho neural networks. And the pattern recognition tech has applied to classify three kinds of detective pa semiconductor packages. that is, crack, delaminat normal. According to the results, it was found estimative algorithm was provided the recognition r 75.7%( for crack) and 83.4%( for delamination) 87.2 % ( for normal).

• Journal of Korean Classical Literature and Education
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• no.15
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• pp.245-264
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• 2008

This study considers Huewa悔窩 Anjung-gwan安重觀's self-consciousness and the recognition of the world. Anjung-gwan resents that fact that Qing淸 rules over China. He insists that Chosun朝鮮 must remain faithful to Ming明. But Chosun served Qing in those days. He holds strongly to his belief until his death. So he chooses living in retirement in his life. In Anjung-gwan's poems, we can see that a certain circle of Chosun Confucianists believe in Sojunghwa小中華, which is small-Sinocentrism. In the first half of the eighteenth-century, some Chosun Confucianists feel sad about the situation that stops them from realizing their ideals. But they take pride in natural beauty and configuration of Chosun. And they pay attention to the life of Chosun masses. They recognize Chosun, which is Hwa華, has to keep self-respect to the last.

• Journal of the Korean School Mathematics Society
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• v.19 no.1
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• pp.83-102
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• 2016

In this study we explored the effect of reading in mathematics classroom on five mathematical affective characteristics of middle school students. 100 2nd male middle schoolers' were participated in this study and five affective characteristics - interests, self-confidence, recognition of mathematics value, self-regulation, and mathematics anxiety- were investigated. According to the results, reading in mathematics class had an overall positive effect. Especially the characteristics interests and self-confidence of students' were improved. And for the low level students all characteristics were improved. And based on the result of pre and post test, and interview with 6 students, we suggest that desirable reading in mathematics classroom.

• Journal of Korean Academy of Nursing
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• v.30 no.5
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• pp.1170-1180
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• 2000

This study was a quasi-experimental study to confirm the effects of a memory training program using efficacy sources. The purpose was to develop an effective memory training program for elderly people and to identify the effects of the memory training program. This study was carried out between February 24 and July 18, 1999 and the subjects of the study were 102 elderly people who were participants at a welfare institute in Seoul. The experimental group (51) and the control group (51) were assigned by means of participation order. The control group was matched to the experimental group and was selected considering age, sex, and religion. The experimental group participated in the memory training program. The memory training program was based on the literature of Fogler & Stern (1994), Wang & Lee (1990), Lee (1991) and Lee (1993). The memory training program was given twice a week for two weeks with each program lasting two hours. Task centered memory self-efficacy was measured using the Memory Self-Efficacy Scale developed by Berry & Dennehey (1989) and Meta Memory was measured by the MIA developed by Dixon et al. (1988) Memory performance was measured by the word list developed by Cho Sung Won (1995) and the face recognition task (Face Recognition Task developed for this study). Data were analyzed by SPSS PC and the results are described below. 1. The experimental group which participated in the Memory Training Program showed higher task centered memory self-efficacy scores as compared to the control group (t=4.354, P=.0001). 2. The experimental group which participated in the Memory Training Program showed higher metamemory scores as compared to the control group (t=4.733, P=.0001). 3. The experimental group which participated in the Memory Training Program showed higher memory performance scores as compared to the control group (t=7.500, P=.0001). The memory performance involved an immediate word recall task, a delayed word recall task, a word recognition task, and the face recognition task. 4. In the experimental group, there was significant correlation between the task centered memory self-efficacy scores and the metamemory scores (r=.382, P=.006), but the correlation between the task centered memory self-efficacy scores and the memory performance scores and between the metamemory scores and the memory performance scores were not significant. The results showed that task centered memory self-efficacy, meta memory and memory performance improved following the Memory Training Program including the memory process, changes in memory with aging, and appropriate use of memory strategies. Memory Training Program is an effective nursing intervention for improving memory in elderly people and, also, in people with complaints of memory loss.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.1
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• pp.52-60
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• 2002

According as many people use a computer newly, damage of computer virus and hacking is rapidly increasing by the crucial users. A computer virus is one of program in computer and has abilities of self reproduction and destruction like a virus of biology. And hacking is to rob a person's data in a intruded computer and to delete data in a Person s computer from the outside. To block hacking that is intrusion of a person's computer and the computer virus that destroys data, a study for intrusion detection of system and virus detection using a biological immune system is in progress. In this paper, we make a model of positive and negative selection for self recognition which have a similar function like T-cytotoxic cell that plays an important role in biological immune system. We embody a self-nonself distinction algorithm in computer, which is an important part when we detect an infected data by computer virus and a modified data by intrusion from the outside. And we showed the validity and effectiveness of the proposed self recognition algorithm by computer simulation about various infected data obtained from the cell change and string change in the self file.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.1
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• pp.49-54
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• 2006

The recent fingerprint recognition system has unstable factors, such as copy of fingerprint patterns and hacking of fingerprint feature point, which mali cause significant system error. Thus, in this research, we used the fingerprint as the main recognition device and then implemented the multi-biometric recognition system in serial using the speech recognition which has been widely used recently. As a multi-biometric recognition system, once the speech is successfully recognized, the fingerprint recognition process is run. In addition, speaker-dependent DTW(Dynamic Time Warping) algorithm is used among existing speech recognition algorithms (VQ, DTW, HMM, NN) for effective real-time process while KSOM (Kohonen Self-Organizing feature Map) algorithm, which is the artificial intelligence method, is applied for the fingerprint recognition system because of its calculation amount. The experiment of multi-biometric recognition system implemented in this research showed 2 to $7\%$ lower FRR (False Rejection Ratio) than single recognition systems using each fingerprints or voice, but zero FAR (False Acceptance Ratio), which is the most important factor in the recognition system. Moreover, there is almost no difference in the recognition time(average 1.5 seconds) comparing with other existing single biometric recognition systems; therefore, it is proved that the multi-biometric recognition system implemented is more efficient security system than single recognition systems based on various experiments.

• Journal of the Korean Society of School Health
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• v.22 no.1
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• pp.17-32
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• 2009

Purpose: This study was performed to investigate the attitude and recognition on how to describe the elderly of elementary school students. Methods: The subject of this study was total 806 students of 4, 5, 6 grade at 2 elementary schools in Gyunggi-do. The data were collected through self-reporting questionnaires for a month. Results: First, the score of attitude about the elderly was 107.8 and image score was the highest. Second, there were significant differences in the attitude about the elderly according to grade, birth order of siblings, domestic atmosphere, and economic status, domestic education on respect about the elderly, and education about the elderly at school. Third, there were significant differences in the attitude about the elderly according to parent-grandparent relationship, health and economic status of grandparents, meeting frequency with grandparents. Fourth, the score of word recognition about the elderly was 43.3 and social score was the highest. fifth, there were significant differences in recognition on how to describe the elderly according to grade, birth order of siblings of students and parents, domestic atmosphere, and economic status, domestic education on respect about the elderly. Sixth, there were significant differences in recognition on how to describe according to parent-grandparent relationship, health status and economic status of grandparents, meeting frequency with grandparents. Lastly, The attitude and recognition about the elderly showed significant positive relationship. Conclusion: We should provide qualitative education programs to improve the attitude and recognition about the elderly of elementary school students.

• Health Policy and Management
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• v.20 no.3
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• pp.157-172
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• 2010

This study was conducted to evaluate the effects of health-behavioral change for the elderly group after community based education of chronic diseases management. We measured self recognition of health status, medication administration of hypertension and diabetes, regular check for blood pressure and blood sugar level, recognition of body indicators (weight, hight, blood pressure, blood sugar etc), knowledge level for chronic diseases management and smoking and alcohol habitation before and after education of chronic diseases management for participants. The subjects of this study consist of 432 people with community-dwelling Seoul citizen being active churches. Education programs designed essential parts of fundamental chronic diseases management, physical exercises for health promotion, diet and nutrition etc. All data collection completed for 5 months from Aug. 2008 to Dec. 2008 by trained surveyors via interview survey. The data obtained were analyzed using descriptive statistics, Wilcoxon Singed Rank test, McNemar test and Paired t-test. The results showed that self recognition of health status, knowledge level for chronic diseases management, recognition of body indicators were statistically significantly increased after the education of chronic diseases management. Also, blood pressure were statistically significantly decreased in elderly with hypertension and blood sugar were statistically significantly decreased in elderly of high-risk group. Based on these results, it was suggested that preventive education policy of chronic diseases management should be considered with priority coming true for successful aging society.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1494-1507
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• 2018

Intelligent human identification using face information has been the research hotspot ranging from Internet of Things (IoT) application, intelligent self-service bank, intelligent surveillance to public safety and intelligent access control. Since 2D face images are usually captured from a long distance in an unconstrained environment, to fully exploit this advantage and make human recognition appropriate for wider intelligent applications with higher security and convenience, the key difficulties here include gray scale change caused by illumination variance, occlusion caused by glasses, hair or scarf, self-occlusion and deformation caused by pose or expression variation. To conquer these, many solutions have been proposed. However, most of them only improve recognition performance under one influence factor, which still cannot meet the real face recognition scenario. In this paper we propose a multi-scale parallel convolutional neural network architecture to extract deep robust facial features with high discriminative ability. Abundant experiments are conducted on CMU-PIE, extended FERET and AR database. And the experiment results show that the proposed algorithm exhibits excellent discriminative ability compared with other existing algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1833-1848
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• 2022

Named entity recognition (NER) is an important basic task in the field of Natural Language Processing (NLP). Recently deep learning approaches by extracting word segmentation or character features have been proved to be effective for Chinese Named Entity Recognition (CNER). However, since this method of extracting features only focuses on extracting some of the features, it lacks textual information mining from multiple perspectives and dimensions, resulting in the model not being able to fully capture semantic features. To tackle this problem, we propose a novel Multi-view Semantic Feature Fusion Model (MSFM). The proposed model mainly consists of two core components, that is, Multi-view Semantic Feature Fusion Embedding Module (MFEM) and Multi-head Self-Attention Mechanism Module (MSAM). Specifically, the MFEM extracts character features, word boundary features, radical features, and pinyin features of Chinese characters. The acquired font shape, font sound, and font meaning features are fused to enhance the semantic information of Chinese characters with different granularities. Moreover, the MSAM is used to capture the dependencies between characters in a multi-dimensional subspace to better understand the semantic features of the context. Extensive experimental results on four benchmark datasets show that our method improves the overall performance of the CNER model.