• The Korean Journal of Emergency Medical Services
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• v.28 no.1
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• pp.47-62
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• 2024

Purpose: This study aimed to provide basic data for infection control education plans based on infection control awareness and performance of paramedic students during clinical field training. Methods: Data were collected from paramedic students with experience in clinical field training. The data collection period was from May 4, 2023, to June 4, 2023, and 132 copies of the collected survey were analyzed using the SPSS27.0 program. Results: Infection control awareness and performance were 4.80±0.24 points and 4.49±0.55 points out of 5, respectively. The infection control awareness of the participants according to clinical field training-related characteristics differed significantly in university education before clinical field training (t=2.100, p=.038). In addition, there were significant differences in performance in the number of clinical field training sessions (F=9.149, p=.000), hospital education before clinical field training (t=5.365, p=.000), and hospital education during clinical field training (t=3.094, p=.002). Conclusion: Before clinical field training, schools should provide infection control education that combines theory and practice suitable for hospital practice so that students can complete the infection control education organized by the hospital. Furthermore, if a university develops infection control in the clinical field training guidelines, it will have a positive impact on students' infection control performance through prior education.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.637-639
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• 2003

As the number of spectral bands of high spectral resolution data increases, the capability to detect more detailed classes should also increase, and the classification accuracy should increase as well. Often, it is impossible to access enough training pixels for supervise classification. For this reason, the performance of traditional classification methods isn't useful. In this paper, we propose a new model for classification that operates based on decision fusion. In this classifier, learning is performed at two steps. In first step, only training samples are used and in second step, this classifier utilizes semilabeled samples in addition to original training samples. At the beginning of this method, spectral bands are categorized in several small groups. Information of each group is used as a new source and classified. Each of this primary classifier has special characteristics and discriminates the spectral space particularly. With using of the benefits of all primary classifiers, it is made sure that the results of the fused local decisions are accurate enough. In decision fusion center, some rules are used to determine the final class of pixels. This method is applied to real remote sensing data. Results show classification performance is improved, and this method may solve the limitation of training samples in the high dimensional data and the Hughes phenomenon may be mitigated.

• Journal of the Korea Society of Computer and Information
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• v.23 no.4
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• pp.147-153
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• 2018

In this paper, we propose an efficient WBC 14-Diff classification which performs using the WBC-ResNet-152, a type of CNN model. The main point of view is to use Super-pixel for the segmentation of the image of WBC, and to use ResNet for the classification of WBC. A total of 136,164 blood image samples (224x224) were grouped for image segmentation, training, training verification, and final test performance analysis. Image segmentation using super-pixels have different number of images for each classes, so weighted average was applied and therefore image segmentation error was low at 7.23%. Using the training data-set for training 50 times, and using soft-max classifier, TPR average of 80.3% for the training set of 8,827 images was achieved. Based on this, using verification data-set of 21,437 images, 14-Diff classification TPR average of normal WBCs were at 93.4% and TPR average of abnormal WBCs were at 83.3%. The result and methodology of this research demonstrates the usefulness of artificial intelligence technology in the blood cell image classification field. WBC-ResNet-152 based morphology approach is shown to be meaningful and worthwhile method. And based on stored medical data, in-depth diagnosis and early detection of curable diseases is expected to improve the quality of treatment.

• Structural Engineering and Mechanics
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• v.90 no.2
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• pp.189-208
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• 2024

Artificial intelligence is one of the efficient methods that can be developed to simulate nonlinear behavior and predict the response of building structures. In this regard, an adaptive method based on optimization algorithms is used to train the TSK model of the fuzzy inference system to estimate the seismic behavior of building structures based on analytical data. The optimization algorithm is implemented to determine the parameters of the TSK model based on the minimization of prediction error for the training data set. The adaptive training is designed on the feedback of the results of previous time steps, in which three training cases of 2, 5, and 10 previous time steps were used. The training data is collected from the results of nonlinear time history analysis under 100 ground motion records with different seismic properties. Also, 10 records were used to test the inference system. The performance of the proposed inference system is evaluated on two 3 and 20-story models of nonlinear steel moment frame. The results show that the inference system of the TSK model by combining the optimization method is an efficient computational method for predicting the response of nonlinear structures. Meanwhile, the multi-vers optimization (MVO) algorithm is more accurate in determining the optimal parameters of the TSK model. Also, the accuracy of the results increases significantly with increasing the number of previous steps.

• Journal of IKEEE
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• v.27 no.1
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• pp.38-44
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• 2023

Performance of a STAP(space-time adaptive processing) algorithm highly depends on how closely the estimated covariance matrix(CM) resembles the actual CM by the interference in CUT(cell under test). A STAP has 2 dimensional data structure determined by the number of array elements and the number of transmitting pulses and both numbers are generally not small. Thus, to meet the degree of freedom(DOF) of the CM, a huge amount of training data is required. This paper presents an algorithm to generate virtual training data from small received data, via converting them into the data in spatial frequency-Doppler plane. We theoretically derive where the clutter exist in the plane and present the procedure to implement the proposed algorithm. Finally, with the simulated scenario of small received data, we show the proposed algorithm can improve STAP performance.

• Geophysics and Geophysical Exploration
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• v.26 no.2
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• pp.62-76
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• 2023

Recently, machine learning (ML) techniques have been actively applied for seismic trace interpolation. However, because most research is based on training-inference strategies that treat missing trace gather data as a 2D image with a blank area, a sufficient number of fully sampled data are required for training. This study proposes trace interpolation using ML, which uses only irregularly sampled field data, both in training and inference, by modifying the training-inference strategies of trace-based interpolation techniques. In this study, we describe a method for constructing networks that vary depending on the maximum number of consecutive gaps in seismic field data and the training method. To verify the applicability of the proposed method to field data, we applied our method to time-migrated seismic data acquired from the Vincent oilfield in the Exmouth Sub-basin area of Western Australia and compared the results with those of the conventional trace interpolation method. Both methods showed high interpolation performance, as confirmed by quantitative indicators, and the interpolation performance was uniformly good at all frequencies.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.6-10
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• 2007

Classification of hyperspectral images is challenging. A very high dimensional input space requires an exponentially large amount of data to adequately and reliably represent the classes in that space. In other words in order to obtain statistically reliable classification results, the number of necessary training samples increases exponentially as the number of spectral bands increases. However, in many situations, acquisition of the large number of training samples for these high-dimensional datasets may not be so easy. This problem can be overcome by using multiple classifiers. In this paper we compared the effectiveness of two approaches for creating multiple classifiers, feature selection and feature extraction. The methods are based on generating multiple feature subsets by running feature selection or feature extraction algorithm several times, each time for discrimination of one of the classes from the rest. A maximum likelihood classifier is applied on each of the obtained feature subsets and finally a combination scheme was used to combine the outputs of individual classifiers. Experimental results show the effectiveness of feature extraction algorithm for generating multiple classifiers.

• Journal of Korean society of Dental Hygiene
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• v.24 no.1
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• pp.69-77
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• 2024

Objectives: The purpose of this study was to explore the general public's perception and status of oral muscle strength training, to develop age-appropriate educational media and training methods, and to promote the need for oral muscle strength training. Methods: Data were collected from 15 individuals across different age groups (young, middle-aged, and elderly) from December 2022 to February 2023 through focus group interviews, and they were conducted twice for each group in a face-to-face manner. Results: Four key categories were identified: lack of information, effectiveness of training, need for promotion, and factors necessary for implementation. The following themes emerged: lack of information, need for training, age-specific characteristics, need for repetition, age at which training is needed, lack of promotion, need for promotion, number of practitioners, willingness to practice, and appropriate media for training. Conclusions: Awareness of oral muscle strength training was found to be very low, and it is necessary to improve awareness through continuous information and appropriate education on its need among the public. Additionally, quality content or media that can be easily applied for effective training should be developed, and personnel who can perform training efficiently should be trained.

• Journal of Ocean Engineering and Technology
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• v.28 no.6
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• pp.552-559
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• 2014

In general, the number of underwater transient signals is very limited for research on automatic recognition. Data-dependent feature extraction is one of the most effective methods in this case. Therefore, we suggest WPCC (Wavelet packet ceptsral coefficient) as a feature extraction method. A wavelet packet best tree for each data set is formed using an entropy-based cost function. Then, every terminal node of the best trees is counted to build a common wavelet best tree. It corresponds to flexible and non-uniform filter bank reflecting characteristics for the data set. A GMM (Gaussian mixture model) is used to classify five classes of underwater transient data sets. The error rate of the WPCC is compared using MFCC (Mel-frequency ceptsral coefficients). The error rates of WPCC-db20, db40, and MFCC are 0.4%, 0%, and 0.4%, respectively, when the training data consist of six out of the nine pieces of data in each class. However, WPCC-db20 and db40 show rates of 2.98% and 1.20%, respectively, while MFCC shows a rate of 7.14% when the training data consists of only three pieces. This shows that WPCC is less sensitive to the number of training data pieces than MFCC. Thus, it could be a more appropriate method for underwater transient recognition. These results may be helpful to develop an automatic recognition system for an underwater transient signal.

• The Journal of Korean Physical Therapy
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• v.10 no.1
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• pp.173-179
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• 1998

To overcome obstacles and to attain the goals of practical training for the students in the department of physical therapy it is necessary to effective an effective system for practical training. And to develop a good System it is essential to monitor the response of the students in practical training of physical therapy and to gather fundamental data. The author made up a questionnaire to the students in the department of physical therapy after the end of practical training course. The questionnaire consisted of 13 questionnaire for the general characterisitcs of the students, 19 questions for the degree of satisfaction (4 for the environment of practical training, 4 for the environment of practical training, 4 for the content 3 for the time assignment and 4 for the evaluation) and one open question. As results the students were satisfied with the practicality and fresh experience. But they were relatively unsatisfied with the environment of the practical training and the number and sincerity of the trainers. Especially they were dissatisfied with the correspondence with the objectives of education, conection with the lecture, communication with the trainers, time assignment and evaluation. As a Part of efforts to formulate an effective system fer practical training it is necessary to estabilish concrete goals and detail check lists to guide both trainers and students.