• Journal of dental hygiene science
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• v.15 no.5
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• pp.560-568
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• 2015

The aim of this study was to provide basic data that could give a positive effect for both new dental hygienists and dental institutions by identifying the impact of one the job training on the organizational socialization, targeting 162 new dental hygienists who currently worked in dental clinic. The data were analyzed using the chi-squire, independent-samples t-test, hierarchical regression analysis. It was shown that the characteristics and contents of a duration of inservice training for new dental hygienists, difficulty, satisfaction, and details of on-the-job training affected organizational socialization by 26.1%. The factor that had the greatest impact on the organizational socialization was dental hygiene service training, followed by guidance of hospital service regulations. This study was significant in that it applied the concept of organizational socialization to the dental hygienists. The future study on the more effective and systematic training program will be needed to help new dental hygienists be socialized more effectively in the new organization.

• Journal of Broadcast Engineering
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• v.23 no.5
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• pp.642-655
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• 2018

Occupancy sensors installed in buildings and households turn off the light if the space is vacant. Currently PIR(pyroelectric infra-red) motion sensors have been utilized. Recently, the researches using camera sensors have been carried out in order to overcome the demerit of PIR that cannot detect stationary people. The detection of moving and stationary people is a main functionality of the occupancy sensors. In this paper, we propose an on-line human occupancy tracking method using convolutional neural network (CNN) and self-organizing map. It is well known that a large number of training samples are needed to train the model offline. To solve this problem, we use an untrained model and update the model by collecting training samples online directly from the test sequences. Using videos capurted from an overhead camera, experiments have validated that the proposed method effectively tracks human.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.10
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• pp.3378-3393
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• 2014

We have witnessed the rapid development of information technology in recent years. One of the key phenomena is the fast, near-exponential increase of data. Consequently, most of the traditional data classification methods fail to meet the dynamic and real-time demands of today's data processing and analyzing needs--especially for continuous data streams. This paper proposes an improved incremental learning algorithm for a large-scale data stream, which is based on SVM (Support Vector Machine) and is named DS-IILS. The DS-IILS takes the load condition of the entire system and the node performance into consideration to improve efficiency. The threshold of the distance to the optimal separating hyperplane is given in the DS-IILS algorithm. The samples of the history sample set and the incremental sample set that are within the scope of the threshold are all reserved. These reserved samples are treated as the training sample set. To design a more accurate classifier, the effects of the data volumes of the history sample set and the incremental sample set are handled by weighted processing. Finally, the algorithm is implemented in a cloud computing system and is applied to study user behaviors. The results of the experiment are provided and compared with other incremental learning algorithms. The results show that the DS-IILS can improve training efficiency and guarantee relatively high classification accuracy at the same time, which is consistent with the theoretical analysis.

• Journal of The Korean Astronomical Society
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• v.52 no.6
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• pp.217-225
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• 2019

We apply a modified Convolutional Neural Network (CNN) model in conjunction with transfer learning to predict whether an active region (AR) would produce a ≥C-class or ≥M-class flare within the next 24 hours. We collect line-of-sight magnetogram samples of ARs provided by the SHARP from May 2010 to September 2018, which is a new data product from the HMI onboard the SDO. Based on these AR samples, we adopt the approach of shuffle-and-split cross-validation (CV) to build a database that includes 10 separate data sets. Each of the 10 data sets is segregated by NOAA AR number into a training and a testing data set. After training, validating, and testing our model, we compare the results with previous studies using predictive performance metrics, with a focus on the true skill statistic (TSS). The main results from this study are summarized as follows. First, to the best of our knowledge, this is the first time that the CNN model with transfer learning is used in solar physics to make binary class predictions for both ≥C-class and ≥M-class flares, without manually engineered features extracted from the observational data. Second, our model achieves relatively high scores of TSS = 0.640±0.075 and TSS = 0.526±0.052 for ≥M-class prediction and ≥C-class prediction, respectively, which is comparable to that of previous models. Third, our model also obtains quite good scores in five other metrics for both ≥C-class and ≥M-class flare prediction. Our results demonstrate that our modified CNN model with transfer learning is an effective method for flare forecasting with reasonable prediction performance.

• Journal of Information Processing Systems
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• v.14 no.1
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• pp.205-217
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• 2018

Deep learning using convolutional neural networks (CNNs) is being studied in various fields of image recognition and these studies show excellent performance. In this paper, we compare the performance of CNN architectures, KCR-AlexNet and KCR-GoogLeNet. The experimental data used in this paper is obtained from PHD08, a large-scale Korean character database. It has 2,187 samples of each Korean character with 2,350 Korean character classes for a total of 5,139,450 data samples. In the training results, KCR-AlexNet showed an accuracy of over 98% for the top-1 test and KCR-GoogLeNet showed an accuracy of over 99% for the top-1 test after the final training iteration. We made an additional Korean character dataset with fonts that were not in PHD08 to compare the classification success rate with commercial optical character recognition (OCR) programs and ensure the objectivity of the experiment. While the commercial OCR programs showed 66.95% to 83.16% classification success rates, KCR-AlexNet and KCR-GoogLeNet showed average classification success rates of 90.12% and 89.14%, respectively, which are higher than the commercial OCR programs' rates. Considering the time factor, KCR-AlexNet was faster than KCR-GoogLeNet when they were trained using PHD08; otherwise, KCR-GoogLeNet had a faster classification speed.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.9-17
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• 2024

It is widely recognized that for 3D human pose estimation (HPE), dataset acquisition is expensive and the effectiveness of augmentation techniques of conventional visual recognition tasks is limited. We address these difficulties by presenting a simple but effective method that augments input images in terms of viewpoints when training a 3D human pose estimation (HPE) model. Our intuition is that meaningful variants of the input images for HPE could be obtained by viewing a human instance in the images from an arbitrary viewpoint different from that in the original images. The core idea is to synthesize new images that have self-occlusion and thus are difficult to predict at different viewpoints even with the same pose of the original example. We incorporate this idea into the training procedure of the 3D HPE model as an augmentation stage of the input samples. We show that a strategy for augmenting the synthesized example should be carefully designed in terms of the frequency of performing the augmentation and the selection of viewpoints for synthesizing the samples. To this end, we propose a new metric to measure the prediction difficulty of input images for 3D HPE in terms of the distance between corresponding keypoints on both sides of a human body. Extensive exploration of the space of augmentation probability choices and example selection according to the proposed distance metric leads to a performance gain of up to 6.2% on Human3.6M, the well-known pose estimation dataset.

• Journal of the Korean Association of Geographic Information Studies
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• v.5 no.1
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• pp.27-37
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• 2002

The classification accuracy of land cover has been considered as one of the major issues to estimate pollution loads generated from diffuse landuse patterns in a watershed. This research aimed to assess the effects of the acquisition methods and sampling size of training reference data on the classification accuracy of land cover using an imagery acquired by optical sensor(OPS) on JERS-1. Two kinds of data acquisition methods were considered to prepare training data. The first was to assign a certain land cover type to a specific pixel based on the researchers subjective discriminating capacity about current land use and the second was attributed to an aerial photograph incorporated with digital maps with GIS. Three different sizes of samples, 0.3%, 0.5%, and 1.0% of all pixels, were applied to examine the consistency of the classified land cover with the training data of corresponding pixels. Maximum likelihood scheme was applied to classify the land use patterns of JERS-1 imagery. Classification run applying an aerial photograph achieved 18 % higher consistency with the training data than the run applying the researchers subjective discriminating capacity. Regarding the sample size, it was proposed that the size of training area should be selected at least over 1% of all of the pixels in the study area in order to obtain the accuracy with 95% for JERS-1 satellite imagery on a typical small-to-medium-size urbanized area.

• Journal of Korea Entertainment Industry Association
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• v.14 no.4
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• pp.223-235
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• 2020

The purpose of this study is to verify the effect of golf exercise through rehabilitation training for middle-aged women and to suggest the right golf activities. To achieve the purpose of this study, the subjects were 40-50 year old middle-aged women in Jinju, Gyeongnam Province in February 2020. The subjects of this study were 8 women who were controlled by the subjects who needed to be corrected in golf swing orbit. For the accurate measurement test, the program was conducted for 10 days after explaining the purpose and utilization plan of the study. The data collected by testing level of physical strength and distance before and after the experiment were finally analyzed and used. The statistical processing of the collected data was conducted using SPSS win18.0 program, and the statistical techniques were calculated by means of frequency analysis, average(M) and standard deviation(sd), and t-test, one-way ANOVA and multiple regression analysis were conducted. The results of this study through these methods and procedures are as follows. First, rehabilitation training of general characteristics showed a high difference in golf exercise. Second, there was a high difference in the level of rehabilitation training and physical fitness in swing orbit and distance. Third, rehabilitation training and physical fitness level had a high effect on swing orbit and distance.

• Journal of Pharmaceutical Investigation
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• v.3 no.4
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• pp.5-15
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• 1973

An investigation was carried out on a basis of the bacteriological examination with a view to detecting the degree of bacterial contamination for the 77 samples collected from the locally-sold liquid specialties. It's test period was 50 days from July 10 to August 30, 1971. Specially, the survey has put emphasis on the population of general bacteria and the identification of coli-form group, staphylococcus species, streptococcus species, bacillus species, fungi, and yeast species from liquid samples. The results obtained are summarized as follows; (1) For the 77 samples tested, the contamination of general bacteria was found out as minimun 0, i,e., maximum, $12{\times}10^4$ and the total average $45{\times}10^2$ per milliliter. (2) Although streptococcus species could not be detected with the samples, the contamination of the coli-form and staphylococcus species means the strong suggestion of the possibility of pathogenic bacterial contamination. (3) Specially, the products which stay in the neutral pH range and use suspending agents need to care for the microbial contamination in the manufacturing crocess. (4) It is thought necessary to perform the microbiological quality control in the liquid preparations only at least. (5) As the microbial contamination degree in the liquid decreases according to the elapse of time, the microbiological quality control will have to be carried out immediately after the completion of the manufacturing process in order to know the accurate degree. (6) The author thinks that the main reason of the microbial contamination in the liquid is the contamination during the manufacturing process. (7) For the purpose of prevention of the microbial contamination in liquid, therefore, it is more important to make efforts for the rationalization of manufacturing process, the improvement of equipment and environment, the specific training of workers for hygienic knowledges, etc. rather than the use of preservatives for the preparations.

• Journal of radiological science and technology
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• v.43 no.6
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• pp.503-509
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• 2020

This study aims to develop a patient-specific radiation exposure dose prediction model based on anthropometric data that can be easily measurable during CT examination, and to be used as basic data for DRL setting and radiation dose management system in the future. In addition, among the machine learning algorithms, the most suitable model for predicting exposure doses is presented. The data used in this study were chest CT scan data, and a data set was constructed based on the data including the patient's anthropometric data. In the pre-processing and sample selection of the data, out of the total number of samples of 250 samples, only chest CT scans were performed without using a contrast agent, and 110 samples including height and weight variables were extracted. Of the 110 samples extracted, 66% was used as a training set, and the remaining 44% were used as a test set for verification. The exposure dose was predicted through random forest, linear regression analysis, and SVM algorithm using Orange version 3.26.0, an open software as a machine learning algorithm. Results Algorithm model prediction accuracy was R^2 0.840 for random forest, R^2 0.969 for linear regression analysis, and R^2 0.189 for SVM. As a result of verifying the prediction rate of the algorithm model, the random forest is the highest with R^2 0.986 of the random forest, R^2 0.973 of the linear regression analysis, and R^2 of 0.204 of the SVM, indicating that the model has the best predictive power.