• The Korean Journal of Health Service Management
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• v.7 no.4
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• pp.131-141
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• 2013

The purpose of this study was to investigate the long-term care hospital nurse's recognition and performance level of hospital infection control. The subjects of the study were 147 long-term care hospital nurses. The period of data collection was from April 1 to 30, 2013. The data were analyzed by SPSS 19.0 program. The result are as followed; First, the total average scores of the recognition and performance by long-term care hospital nurses of hospital infection control were $4.64{\pm}0.32$ and $4.21{\pm}0.23$. Second, recognition of hospital infection control was significantly different according to position and hospital infection control education experience. Performance of hospital infection control was significantly different according to education level and hospital infection control education experience. Third, there was a positive correlation between the degree of recognition and performance of hospital infection control. Therefore, it is suggested to apply the concrete education program to enhance the recognition in order to improve the performance of hospital infection control of the Long-term hospital nurses.

• Journal of Advanced Information Technology and Convergence
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• v.10 no.2
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• pp.139-151
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• 2020

Entity name recognition is a part of information extraction that extracts entity names from documents and classifies the types of extracted entity names. Entity name recognition technologies are widely used in natural language processing, such as information retrieval, machine translation, and query response systems. Various deep learning-based models exist to improve entity name recognition performance, but studies that compared and analyzed these models on Korean data are insufficient. In this paper, we compare and analyze the performance of CRF, LSTM-CRF, BiLSTM-CRF, and BERT, which are actively used to identify entity names using Korean data. Also, we compare and evaluate whether embedding models, which are variously used in recent natural language processing tasks, can affect the entity name recognition model's performance improvement. As a result of experiments on patent data and Korean corpus, it was confirmed that the BiLSTM-CRF using FastText method showed the highest performance.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.4
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• pp.169-178
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• 2017

Recently, as the interest rate and necessity for security is growing, the demands for a visitor recognition system are being increased. In order to recognize a visitor in visitor recognition systems, the various biometric methods are used. In this paper, we propose a visitor recognition system based on face recognition. The visitor recognition system improves the face recognition performance by integrating several open APIs as a single algorithm and by performing the ensemble of the recognition results. For the performance evaluation, we collected the face data for about five months and measured the performance of the visitor recognition system. As the results of the performance measurement, the visitor recognition system shows a higher face recognition rate than using a single face recognition API, meeting the requirements on performance.

• Women's Health Nursing
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• v.8 no.4
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• pp.506-520
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• 2002

The purpose of this study is to research the degree of recognition & performance of traditional postpartal care for postpartal women and to provide the basic data for improvement of service in a postpartum care center. The respondents of this study were 100 women of 6 postpartum care centers within a C province from Oct. 20 to Dec. 10, 2000. The instruments of measure were used for collecting data on the degree of recognition & performance of traditional postpartal care developed by the researcher. Data analysis consisted of frequency, percentage, mean, standard deviation, paired t-test, t-test, ANOVA which are calculated by Scheffe test and Cronbach's alpha which is used as a reliance level by using a SPSS-PC+. The results of the study were as follows:1. The average score for the degree of recognition of traditional postpartal care(Sanhujori) for postpartal women was $3.09{\pm}.31$, and they recognized that it was important. The methods which were ranked were as follows; Protecting the body from a harmful state, invigorating the body by the argumentation of heat and avoidance of cold, handling with whole heart, and keeping clean, resting without working, eating well. 2. The average score for the degree of performance of traditional postpartal care (Sanhujori) for postpartal women was $2.81{\pm}.31$, and they performed that it was important, too. The methods which were ranked were as follows; Protecting the body from a harmful state, invigorating the body by the augumentation of heat and avoidance of cold, eating well, handling with whole heart, and keeping clean, resting without working. 3. There were significant differences statistically (paired-t=-8.39, p=.000) of the degree of recognition & performance of traditional postpartal care(Sanhujori) for the postpartal women. The degree of recognition was higher than the degree of performance. So, the recognition of traditional postpartal care (Sanhujori) was higher than the performance of it. 4. There were no statistical differences of the degree of recognition & performance of traditional postpartal care(Sanhujori) among the postpartal women's age, religion, job, educational background, delivery frequency, delivery method or the sex of baby. So, the Characteristics of the respondents were not influenced as far as the degree of recognition & performance of traditional postpartal care(Sanhujori). 5. There were significant differences statistically of the degree of performance of traditional postpartal care(Sanhujori) among the 5 postpartum care centers except 1 postpartum care center(p<.01). So, the recognition of traditional postpartal care(Sanhujori) was higher than the performance of traditional postpartal care(Sanhujori) in the 5 postpartum care centers. But there was performed as good as recognition in only 1 postpartum care center.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.1
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• pp.101-107
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• 2011

The background noises and distortions by channel are major factors that disturb the practical use of speech recognition. Usually, noise reduce the performance of speech recognition system DSR(Distributed Speech Recognition) based speech recognition also bas difficulty of improving performance for this reason. Therefore, to improve DSR-based speech recognition under noisy environment, this paper proposes a method which detects accurate speech region to extract accurate features. The proposed method distinguish speech and noise by using entropy and detection of spectral energy of speech. The speech detection by the spectral energy of speech shows good performance under relatively high SNR(SNR 15dB). But when the noise environment varies, the threshold between speech and noise also varies, and speech detection performance reduces under low SNR(SNR 0dB) environment. The proposed method uses the spectral entropy and harmonics of speech for better speech detection. Also, the performance of AFE is increased by precise speech detections. According to the result of experiment, the proposed method shows better recognition performance under noise environment.

• Journal of Satellite, Information and Communications
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• v.12 no.3
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• pp.69-73
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• 2017

In this paper, we developed a real-time image recognition algorithm based on machine learning and tested the performance of the algorithm. The real-time image recognition algorithm recognizes the input image in real-time based on the machine-learned image data. In order to test the performance of the real-time image recognition algorithm, we applied the real-time image recognition algorithm to the autonomous vehicle and showed the performance of the real-time image recognition algorithm through the application of the autonomous vehicle.

• Korean Journal of Occupational Health Nursing
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• v.10 no.1
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• pp.5-23
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• 2001

The purpose of this study was to analyse the level of recognition and performance of clinical nurses about the prevention of nosocomial infection. Subjects of the study were 425 nurses working at two university hospitals. Self report questionnaires were used to measure the level of recognition and performance about the prevention of nosocomial infection. These instruments had five dimensions of the management of nosocomial infection : hand washing, fluid therapy, foley catheterization, respiratory tract, and aseptic articles. Reliability coefficients of these instruments were found Cronbach's ${\alpha}=.94-.95$. Data were collected from August 1 to August 15, 2000. The results of the study were as follows : 1) The mean score of the recognition scores about the management of nosocomial infection was 3.89. 2) The mean score of the performance about the management of nosocomial infection was 3.42. 3) The mean score of the recognition about the management of nosocomial infection was significantly higher than the performance score(t=25.72. p<.001). 4) There was significant difference in the score of the recognition about managment in nosocomial infection according to nurses working unit(p<.001).

• The Journal of Korea Robotics Society
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• v.17 no.3
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• pp.334-338
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• 2022

In visual simultaneous localization and mapping (vSLAM), the correct recognition of a place benefits in relocalization and improved map accuracy. However, its performance is significantly affected by the environmental conditions such as variation in light, viewpoints, seasons, and presence of dynamic objects. This research addresses the problem of feature occlusion caused by interference of dynamic objects leading to the poor performance of visual place recognition algorithm. To overcome the aforementioned problem, this research analyzes the role of scene semantics in correct detection of a place in challenging environments and presents a semantics aided visual place recognition method. Semantics being invariant to viewpoint changes and dynamic environment can improve the overall performance of the place matching method. The proposed method is evaluated on the two benchmark datasets with dynamic environment and seasonal changes. Experimental results show the improved performance of the visual place recognition method for vSLAM.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.4
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• pp.737-742
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• 2013

The surveillance system has been developed to be intelligent which can judge and cope by itself using human recognition technique. The existing face recognition is excellent at a short distance but recognition rate is reduced at a long distance. In this paper, we analyze the performance of face recognition according to interpolation and face recognition algorithm in face recognition using the multiple distance face images to training. we use the nearest neighbor, bilinear, bicubic, Lanczos3 interpolations to interpolate face image and PCA and LDA to face recognition. The experimental results show that LDA-based face recognition with bilinear interpolation provides performance in face recognition.

• Journal of the Optical Society of Korea
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• v.14 no.4
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• pp.388-394
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• 2010

In this paper the object recognition performance of a photon counting integral imaging system is quantitatively compared with that of a conventional gray scale imaging system. For 3D imaging of objects with a small number of photons, the elemental image set of a 3D scene is obtained using the integral imaging set up. We assume that the elemental image detection follows a Poisson distribution. Computational geometrical ray back propagation algorithm and parametric maximum likelihood estimator are applied to the photon counting elemental image set in order to reconstruct the original 3D scene. To evaluate the photon counting object recognition performance, the normalized correlation peaks between the reconstructed 3D scenes are calculated for the varied and fixed total number of photons in the reconstructed sectional image changing the total number of image channels in the integral imaging system. It is quantitatively illustrated that the recognition performance of the photon counting integral imaging system can be similar to that of a conventional gray scale imaging system as the number of image viewing channels in the photon counting integral imaging (PCII) system is increased up to the threshold point. Also, we present experiments to find the threshold point on the total number of image channels in the PCII system which can guarantee a comparable recognition performance with a gray scale imaging system. To the best of our knowledge, this is the first report on comparisons of object recognition performance with 3D photon counting & gray scale images.