• The Journal of Korean Academic Society of Nursing Education
• /
• v.24 no.2
• /
• pp.160-167
• /
• 2018

Purpose: This study was conducted to identify the influence of clinical practicum-related stress, and awareness, and self-efficacy on strength on health promotion behaviors among nursing students. Methods: The subjects were 274 students in nursing college who had experience in a clinical nursing practicum. Data were collected from self-reported questionnaires and were analyzed by independent t-test, ANOVA, Pearson correlation coefficient, and multiple regression analyses. Results: Multiple regression analyses showed that strength self-efficacy and self-awareness on strength significantly affected overall health promotion behaviors. Regarding subscales of health promotion behaviors, self-awareness on strength significantly affected health responsibility and stress management in health promotion behavior when controlling for sociodemographic characteristics, while the subscales of clinical practicum-related stress did not affect health promotion behavior. Conclusion: The recognition of an individual's strength and self-efficacy might be a factor in improving health promotion behaviors among nursing college students, although they suffer from stress during clinical nursing practicum.

• Korean Journal of Adult Nursing
• /
• v.12 no.1
• /
• pp.17-29
• /
• 2000

The purpose of this study is to find out the effects of depression on older adults' metamemory and memory performances. The subjects of the study consisted of 103 older adults over the age of 60 who are living in Kangwon Province. Some data were collected by means of the interview method, using questionnaires for metamemory (MIA questionnaire by Hultsch, et al., 1988), and depression(GDS by Yesavage and Sheikl, 1986). Other data were collected by a testing method on the memory performance, such as the immediate word recall task, the delayed word recall task, the word recognition task(Elderly Verbal Learning Test by Kyung Mi Choi, 1998), and the face recognition task(Face Recognition Task tool developed by this study). The results of this study were as follows: 1) The average point of depressed older persons' metamemory is 3.2 on a 5 point scale and was significantly lower than nondepressed older persons' point of 3.6. Looking into each sub-concept of metamemory, depressed persons' points are higher in terms of task(4.1), but are lower in terms of change(2.3), locus(2.6), and strategy(2.9) in comparison with nondepressed persons' points. 2) Depressed older persons' memory performances are all significantly lower than nondepressed person's, especially in terms of face recognition task(t=7.26, p<.0082) and word recognition task(t=6.58, p<.01). 3) In both depressed and nondepressed persons, metamemory has a close correlation with all memory tasks. In particular, depressed older persons' correlation is higher across the board, especially in memory self-efficacy of metamemory(r=.36 - .49) in comparison with nondepressed persons. 4) According to the results of analysis on the relations between metamemory and memory performances of each memory task using canonical analysis, in the case of depressed older persons, strategy, locus, capability and task have high correlation with word recognition task and delayed word recall task. Also in the case of nondepressed persons, achievement, strategy, change and locus variable have high correlation with face recognition task and immediate word recall task. As mentioned above, depression variables have a negative effect on older persons' metamemory and memory performance. In conclusion, when we care for depressed older persons with less memory ability, we have to consider the outcomes of this study are relevant. In addition, it is necessary to develop nursing intervention in order to prevent memory loss and improve memory performance in depressed older persons.

• Journal of Korean Institute of Industrial Engineers
• /
• v.28 no.2
• /
• pp.162-170
• /
• 2002

Detecting straight lines in an image is frequently required for various machine vision applications such as restoring CAD drawings from scanned images and object recognition. The standard Hough transform has been dominantly used to that purpose. However, massive storage requirement and low precision in estimating line parameters due to the quantization of parameter space are the major drawbacks of the Hough transform technique. In this paper, to overcome the drawbacks, an iterative algorithm based on a self-organizing map is presented. The self-organizing map can be adaptively learned such that image points are clustered by prominent lines. Through the procedure of the algorithm, a set of lines are sequentially detected one at a time. The algorithm can produce highly precised estimates of line parameters using very small amount of storage memory. Computational results for synthetically generated images are given. The promise of the algorithm is also demonstrated with its application to two natural images of inserts.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.21 no.48
• /
• pp.323-331
• /
• 1998

This study aims at the introduction to the recall system and its defence strategies for the automobile industry according to the rights and interests protection for consumers, the trade pressure solution of the United States and government's will for the self-certification. Therefore, I make the following proposal in view of the low technological level of Korea's automobile industry, the imperfect means of recall system, the insufficiency of the social recognition and the worst management condition under the IMF system : First, introduction to three steps for self-certification in case of changing the present pype-approval system into the self-certification and its overall operation after 2003. Second, the defence plan of the automobile industry after analyzing the problems coming from the domestic automobile industry.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.15 no.3
• /
• pp.134-141
• /
• 2011

There have been increased economic and societal demands to continuously monitor the integrity and long-term deterioration of civil infrastructures to ensure their safety and adequate performance throughout their life span. However, it is very difficult to continuously monitor the structural condition of the pipeline structures because those are placed underground and connected each other complexly, although pipeline structures are core underground infrastructures which transport primary sources. Moreover, damage can occur at several scales from micro-cracking to buckling or loose bolts in the pipeline structures. In this study, guided wave measurement can be achieved with a self-sensing circuit using a piezoelectric active sensor. In this self sensing system, a specific frequency-induced structural wavelet response is obtained from the self-sensed guided wave measurement. To classify the multiple types of structural damage, supervised learning-based statistical pattern recognition was implemented using the damage indices extracted from the guided wave features. Different types of structural damage artificially inflicted on a pipeline system were investigated to verify the effectiveness of the proposed SHM approach.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.11 no.7
• /
• pp.1380-1386
• /
• 2007

This paper proposed an automatic recognition system of shipping container identifiers using fuzzy-based noise removal method and ART2-based self-organizing supervised learning algorithm. Generally, identifiers of a shipping container have a feature that the color of characters is blacker white. Considering such a feature, in a container image, all areas excepting areas with black or white colors are regarded as noises, and areas of identifiers and noises are discriminated by using a fuzzy-based noise detection method. Areas of identifiers are extracted by applying the edge detection by Sobel masking operation and the vertical and horizontal block extraction in turn to the noise-removed image. Extracted areas are binarized by using the iteration binarization algorithm, and individual identifiers are extracted by applying 8-directional contour tacking method. This paper proposed an ART2-based self-organizing supervised learning algorithm for the identifier recognition, which improves the performance of learning by applying generalized delta learning and Delta-bar-Delta algorithm. Experiments using real images of shipping containers showed that the proposed identifier extraction method and the ART2-based self-organizing supervised learning algorithm are more improved compared with the methods previously proposed.

• The Journal of the Convergence on Culture Technology
• /
• v.7 no.3
• /
• pp.71-78
• /
• 2021

The purpose of this study was to investigate the effect of good death recognition, self-esteem, attitude toward withdrawal of life-sustaining treatmenton the consciousness of biomedical ethics in nursing students. Data were collected from 154 nursing students in B city and analyzed by t-test, ANOVA, Pearson correlation coefficient, and multiple regression using SPSS/WIN 22.0. The degree of consciousness of biomedical ethics in nursing students was 2.87±0.26. There were significant differences in consciousness of biomedical ethics with respect to religion(t=-2.90, p=.004). There was positive correlation between consciousness of biomedical ethics and good death recognition(r=.27, p=.001), self-esteem(r=.36, p<.001), negative correlation between consciousness of biomedical ethics and attitude toward withdrawal of life-sustaining treatment(r=-.29, p<.001). The factors affecting consciousness of biomedical ethics of the study subjects were good death recognition(β=.26, p<.001), self-esteem(β=.29, p<.001), attitude toward withdrawal of life-sustaining treatment(β=-.30, p<.001), religion(β=-.20, p=.004), with an explanatory power of 28.7%. Through this research requires the fellow study to determine the factors affecting consciousness of biomedical ethics of nursing students.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.9
• /
• pp.3924-3940
• /
• 2020

Dynamic hand gesture recognition(d-HGR) plays an important role in human-computer interaction(HCI) system. With the growth of hand-pose estimation as well as 3D depth sensors, depth, and the hand-skeleton dataset is proposed to bring much research in depth and 3D hand skeleton approaches. However, it is still a challenging problem due to the low resolution, higher complexity, and self-occlusion. In this paper, we propose a hand-shape feature extraction(HSFE) network to produce robust hand-shapes. We build a hand-shape model, and hand-skeleton based on LSTM to exploit the temporal information from hand-shape and motion changes. Fusion between two models brings the best accuracy in dynamic hand gesture (DHG) dataset.

• Journal of Korea Multimedia Society
• /
• v.19 no.5
• /
• pp.826-836
• /
• 2016

Mastering a musical instrument for an unskilled beginning learner is not an easy task. It requires playing every note correctly and maintaining the tempo accurately. Any music comes in two forms, a music score and it rendition into an audio music. The proposed method of assisting beginning music players in both aspects employs two popular pattern recognition methods for audio-visual analysis; they are support vector machine (SVM) for music score recognition and hidden Markov model (HMM) for audio music performance tracking. With proper synchronization of the two results, the proposed music learning assistant system can give useful feedback to self-training beginners.

• International Journal of Ocean System Engineering
• /
• v.2 no.1
• /
• pp.57-61
• /
• 2012

In this paper we use the Kohonen neural network based Self Organizing Map (SOM) algorithm for Urdu Character Recognition. Kohenen NN have more efficient in terms of performance as compare to other approaches. Classification is used to recognize hand written Urdu character. The number of possible unknown character is reducing by pre-classification with respect to subset of the total character set. So the proposed algorithm is attempt to group similar character. Members of pre-classified group are further analyzed using a statistical classifier for final recognition. A recognition rate of around 79.9% was achieved for the first choice and more than 98.5% for the top three choices. The result of this paper shows that the proposed Kohonen SOM algorithm yields promising output and feasible with other existing techniques.