• Korea Journal of Hospital Management
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• v.1 no.1
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• pp.37-55
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• 1996

This study attempts to analyze the factors affecting the intention of transferring to another hospitals among radiology technologists. 344 cases were reviewed in 5 university hospitals and 1 general hospital. Self-administered questionaire were given to study the socioeconomic characteristics, working conditions, job satisfaction level, and the reasons for transfer among the technologists. The major findings were as follows : 1. Job position and hospital characteristics had a statistically significant relationship with the intention of transferring to another hospital. 2. Those who were not satisfied with their salaries and promotional opportunity showed a higher tendency towards to transfer. 3. Those who were less satisfied with the opportunity for developing the personal ability and had the negative attitude on their job showed a higher tendency to transfer. 4. Those who did not sustain good relationship with their superiors and co-workers scored high on the tendency to transfer. 5. In the result of mutiple regression, recognition of radiation hazard, job satisfaction, satisfaction with salary levels, job attitude were significantly related to transfer. The above indicate that besides economic incentives, job satisfaction and organizational culture to promote their ability and form a good relationship with organization members were very important to decrease the intention of transfer. Since these results represent only 6 hospitals from a limited area, more hospitals nationwide, especially small and medium-sized institutions where there is a high turnover rate of employment, need to be examined in order to investigate the various factors that affect the intention of transferring.

• Tuberculosis and Respiratory Diseases
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• v.69 no.5
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• pp.331-336
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• 2010

Background: Recently, the rate of infections with non-tuberculous mycobacteria (NTM) has been increasing in Korea. Precise identification of NTM is critical to determination of the pathogen and to target treatment of NTM patients. Methods: Sixty-eight unclassified mycobacteria isolates by rpoB PCR-RFLP assay (PRA) collected in 2008 were analyzed by National Center for Biotechnology Information (NCBI) Basic Local Alignment Search Tool (BLAST) search after sequencing of 16S rRNA, hsp65, rpoB genes. Results: Nineteen strains of 68 isolates were specified as species after sequencing analysis of 3 gene types. We found 3 M. lentifulavum, 5 M. arupense, 4 M. triviale, 4 M. parascrofulaceum, and one M. obuense. One M. tuberculosis and another M. peregrinum were mutated at the Msp I recognition site needed for rpoB PRA. The remaining 49 isolates did not coincide with identical species at the 3 kinds genes. Conclusion: Sequencing analysis of 16S rRNA, hsp65, rpoB was useful for identification of NTM unclassified by rpoB PRA.

• Journal of the Korea Computer Graphics Society
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• v.25 no.5
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• pp.11-20
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• 2019

With the development of deep learning technology, research is being actively carried out on user-friendly interfaces that are suitable for use in virtual reality or augmented reality applications. To support the interface using the user's hands, this paper proposes a deep learning-based fingertip detection method to enable the tracking of fingertip coordinates to select virtual objects, or to write or draw in the air. After cutting the approximate part of the corresponding fingertip object from the input image with the Grad-CAM, and perform the convolution neural network with Atrous Convolution for the cut image to detect fingertip location. This method is simpler and easier to implement than existing object detection algorithms without requiring a pre-processing for annotating objects. To verify this method we implemented an air writing application and showed that the recognition rate of 81% and the speed of 76 ms were able to write smoothly without delay in the air, making it possible to utilize the application in real time.

• Journal of the Korea Convergence Society
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• v.11 no.2
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• pp.17-22
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• 2020

In this paper, we measured the user's EEG signal and classified the EEG signal using the Support Vector Machine algorithm and measured the accuracy of the signal. An experiment was conducted to measure the user's EEG signals by separating men and women, and a single channel EEG device was used for EEG signal measurements. The results of measuring users' EEG signals using EEG devices were analyzed using R. In addition, data in the study was predicted using a 80:20 ratio between training data and test data by applying a combination of specific vectors with the highest classifying performance of the SVM, and thus the predicted accuracy of 93.2% of the recognition rate. This paper suggested that the user's EEG signal could be recognized at about 93.2 percent, and that it can be performed only by simple linear classification of the SVM algorithm, which can be used variously for biometrics using EEG signals.

• The KIPS Transactions:PartA
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• v.9A no.2
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• pp.147-162
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• 2002

Multi-processor system with fast I/O devices improves processing performance and reduces the bottleneck by I/O concentration. In the system, the Performance influenced by shared memory used for exchanging data between processors varies with configuration and utilization. This paper suggests a prediction model for buffer and shared memory optimization under interrupt recognition method using mailbox. Ethernet (IEEE 802.3) packets are used as the input of system and the amount of utilized memory is measured for different network bandwidth and burstiness. Some empirical studies show that the amount of buffer and shared memory varies with packet concentration rate as well as I/O bandwidth. And the studies also show the correlation between two memories.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.1C
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• pp.65-71
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• 2004

We improved the MLLR speaker adaptation algorithm with reduction of the order of HMM parameters using PCA(Principle Component Analysis) or ICA(Independent Component Analysis). To find a smaller set of variables with less redundancy, we adapt PCA(principal component analysis) and ICA(independent component analysis) that would give as good a representation as possible, minimize the correlations between data elements, and remove the axis with less covariance or higher-order statistical independencies. Ordinary MLLR algorithm needs more than 30 seconds adaptation data to represent higher word recognition rate of SD(Speaker Dependent) models than of SI(Speaker Independent) models, whereas proposed algorithm needs just more than 10 seconds adaptation data. 10 components for ICA and PCA represent similar performance with 36 components for ordinary MLLR framework. So, compared with ordinary MLLR algorithm, the amount of total computation requested in speaker adaptation is reduced by about 1/167 in proposed MLLR algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.3
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• pp.385-396
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• 1993

In this paper, a improved method of digital image analysis required in basic medical science for diagnosis of cells was proposed. The object image was the thyroid gland cell image, and the purpose was automatic discrimination of three classes cells(normal cell, follicular neoplastic cells, and papillary neoplastic cells) by difference of chromatin patterns. To segment the cell nucleus from background, the region segmentation algorithm by edge tracing was proposed. And feature parameter was obtained from discrete Fourier transformation of image. After construct a feature sample group of each cells, experiment of discrimination was executed with any verification cells. As a consequency of using features proposed in this paper, get a better recognition rate(70-90%) than previously reported papers, and this method give shape to get objectivity and fixed quantity in diagnosis of cells, The methods described in this paper be used immediately for discrimination of neoplastic cells.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.4
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• pp.1-9
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• 2007

in this paper, we propose the Adaptive Decision Algorithm for RFID Tag Anti-Collision. We study the RFID Tag anti-collision technique of ALOHA and the anti-collision algorithm of binary search. The existing technique is several problems; the transmitted data rate included of data, the recognition time and energy efficiency. For distinction of all tags, the Adaptive Decision algorithm identify smaller one ,each Tag_ID bit's sum of bit '1'. In other words, Adaptive Decision algorithm had standard of selection by actively, the algorithm can reduce unnecessary number of search even than the exisiting algorithm. The Adaptive Decision algorithm had performance test that criterions were reader's number of repetition and number of transmitted bits for understanding tag. We showed the good performance of Adaptive Decision algorithm better than exisiting algorithm.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.3
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• pp.383-388
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• 2006

SBR is one of the most general sewage/wastewater treatment processes and, particularly, has an advantage in high concentration wastewater treatment like sewage wastewater. A Kernel PCA based fault diagnosis system for biological reaction in full-scale wastewater treatment plant was proposed using only common bio-chemical sensors such as ORP(Oxidation-Reduction Potential) and DO(Dissolved Oxygen). During the SBR operation, the operation status could be divided into normal status and abnormal status such as controller malfunction, influent disturbance and instrumental trouble. For the classification and diagnosis of these statuses, a series of preprocessing, dimension reduction using PCA, LDA, K-PCA and feature reduction was performed. Also, the diagnosis result using differential data was superior to that of raw data, and the fusion data show better results than other data. Also, the results of combination of K-PCA and LDA were better than those of LDA or (PCA+LDA). Finally, the fault recognition rate in case of using only ORP or DO was around maximum 97.03% and the fusion method showed better result of maximum 98.02%.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.12
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• pp.71-83
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• 1996

This paper describes facial component detection and skew correction algorithm for face recognition. We use a priori knowledge and models about isolated regions to detect eye location from the face image captured in natural office environments. The relations between human face components are represented by several rules. We adopt an edge detection algorithm using sobel mask and 8-connected labelling algorith using array pointers. A labeled image has many isolated components. initially, the eye size rules are used. Eye size rules are not affected much by irregular input image conditions. Eye size rules size, and limited in the ratio between gorizontal and vertical sizes. By the eye size rule, 2 ~ 16 candidate eye components can be detected. Next, candidate eye parirs are verified by the information of location and shape, and one eye pair location is decided using face models about eye and eyebrow. Once we extract eye regions, we connect the center points of the two eyes and calculate the angle between them. Then we rotate the face to compensate for the angle so that the two eyes on a horizontal line. We tested 120 input images form 40 people, and achieved 91.7% success rate using eye size rules and face model. The main reasons of the 8.3% failure are due to components adjacent to eyes such as eyebrows. To detect facial components from the failed images, we are developing a mouth region processing module.