• ETRI Journal
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• v.45 no.6
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• pp.1007-1021
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• 2023

Stroke is the leading cause of permanent disability in adults, and it can cause permanent brain damage. According to the World Health Organization, 795 000 Americans experience a new or recurrent stroke each year. Early detection of medical disorders, for example, strokes, can minimize the disabling effects. Thus, in this paper, we consider various risk factors that contribute to the occurrence of stoke and machine learning algorithms, for example, the decision tree, random forest, and naive Bayes algorithms, on patient characteristics survey data to achieve high prediction accuracy. We also consider the semisupervised self-training technique to predict the risk of stroke. We then consider the near-miss undersampling technique, which can select only instances in larger classes with the smaller class instances. Experimental results demonstrate that the proposed method obtains an accuracy of approximately 98.83% at low cost, which is significantly higher and more reliable compared with the compared techniques.

• Geomechanics and Engineering
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• v.37 no.4
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• pp.307-321
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• 2024

This paper delves into the critical assessment of predicting sidewall displacement in underground caverns through the application of nine distinct machine learning techniques. The accurate prediction of sidewall displacement is essential for ensuring the structural safety and stability of underground caverns, which are prone to various geological challenges. The dataset utilized in this study comprises a total of 310 data points, each containing 13 relevant parameters extracted from 10 underground cavern projects located in Iran and other regions. To facilitate a comprehensive evaluation, the dataset is evenly divided into training and testing subset. The study employs a diverse array of machine learning models, including recurrent neural network, back-propagation neural network, K-nearest neighbors, normalized and ordinary radial basis function, support vector machine, weight estimation, feed-forward stepwise regression, and fuzzy inference system. These models are leveraged to develop predictive models that can accurately forecast sidewall displacement in underground caverns. The training phase involves utilizing 80% of the dataset (248 data points) to train the models, while the remaining 20% (62 data points) are used for testing and validation purposes. The findings of the study highlight the back-propagation neural network (BPNN) model as the most effective in providing accurate predictions. The BPNN model demonstrates a remarkably high correlation coefficient (R2 = 0.99) and a low error rate (RMSE = 4.27E-05), indicating its superior performance in predicting sidewall displacement in underground caverns. This research contributes valuable insights into the application of machine learning techniques for enhancing the safety and stability of underground structures.

• International journal of advanced smart convergence
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• v.6 no.2
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• pp.9-15
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• 2017

According to a recent survey, most elementary and secondary school students nationwide are stressed out by their academic records. Furthermore most of high school students in Korea have to study under the great duress. Some of them who can't overcome the academic stress finalize their life by suiciding. A study has found that it is one of the leading causes of stimulating the thought of committing suicide in Korean high school students. So it is necessary to reduce the high school student's suicide rate. Main content of this research is to implement a personalized music recommendation system. Music therapy can help the student deal with the stress, anxiety and depression problems. Proposed system works as a therapist. The music choice and duration of the music is adjusted based on the student's current emotion recognized automatically from EEG. If the happy emotion is not induced by the current music, the system would automatically switch to another one until he or she feel happy. Proposed system is personalized brain music treatment that is making a brain training application running on smart phone or pad. That overcomes the critical problems of time and space constraints of existing brain training program. By using this brain training program, student can manage the stress easily without the help of expert.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.171-178
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• 2015

In these days, the practical training education is getting highlighted in IT curriculum. This study is for the Cloud computing based Virtual Desktop Service Plan of IT education and its efficient operation and management plan. With the implementation of a virtual lab environment system, the training environment which is customized by the curriculum is able to be provided. Also in the case of the limited system, the curriculum is able to be provided for each subject in advance. Therefore if the Cloud Training (or Practicing) Portal system for the multiple cyber universities is implemented according to this study's estimated scale and operation managing policies, the virtual training education service system could be provided in more efficient and more effective ways.

• The Journal of the Korea Contents Association
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• v.13 no.12
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• pp.530-539
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• 2013

It is needed to provide a supplicated and standardized training program for hospital coordinators to boost their competencies as professionals. The purpose of this study was to propose a standardized training program for hospital coordinators especially trained at private institutions. Using DACUM method, with 11 professionals, we first developed competencies required for hospital coordinators. They were service mind, attitude, MOT(Moment of Truth) & phone-call etiquette, communication skill, customer behavior, basic medical terminology, insurance, computer skills, etc. Finally, we proposed a standardized training program for hospital coordinators which covers 16 subject areas.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.325-332
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• 2014

It is generally accepted that classification accuracy is affected by the number of learning documents, but there are few studies that show how this influences automatic text classification. This study is focused on evaluating the deviation-based classification model which is developed recently for genre-based classification and comparing it to other classification algorithms with the changing number of training documents. Experiment results show that the deviation-based classification model performs with a superior accuracy of 0.8 from categorizing 7 genres with only 21 training documents. This exceeds the accuracy of Bayesian and SVM. The Deviation-based classification model obtains strong feature selection capability even with small number of training documents because it learns subject information within genre while other methods use different learning process.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.52-59
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• 2014

Operator training simulator, within a training environment designed to understand the principles and behavior of the railway system with respect to operator's entries and predefined scenario, can provide a very strong benefit in facilitating operators' handling undesired operations. This simulator consists of computer system and applications, and the purpose of applications is to generate the power and voltage and analyze the AC substation and DC railway, respectively. This paper describes a novel approach to the new techniques for AC/DC hybrid simulation for the operator training simulator in the railway system. We first propose the structure the database of railway system. Then, topology processing and power flow using a linked-list method based on the proposed database, full or decoupled newton-rapshon methods are presented. Finally, the interface between the analysis for AC substation using a newton-rapshon method and the analysis for DC railway system using a time-interval power flow method is described. We have verified and tested the developed algorithm through the extensive testing for the proposed test system. To demonstrate the validity of the developed algorithm, comparative simulations between the proposed algorithm and PSS/E for the test system were conducted.

• Journal of Biomedical Engineering Research
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• v.15 no.4
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• pp.465-474
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• 1994

This study is concerned with a method which helps human to generate EMG signals accurately and consistently to make reliable design samples of function discriminator for prosthetic arm control. We intend to ensure a signal accuracy and consistency by training human as a signal generation source. For the purposes, we construct a human training system using a digital computer, which generates visual graphes to compare real target motion trajectory with the desired one, to observe EMG signals and their features. To evaluate the effect which affects a feature variance and a feature separability between motion classes by the human training system, we select 4 features such as integral absolute value, zero crossing counts, AR coefficients and LPC cepstrum coefficients. We perform a experiment four times during 2 months. The experimental results show that the hu- man training system is effective for accurate and consistent EMG signal generation and reduction of a feature variance, but is not correlated for a feature separability, The cepstrum coefficient is the most preferable among the used features for reduction of variance, class separability and robustness to a time varing property of EMG signals.

• Journal of Biosystems Engineering
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• v.40 no.3
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• pp.284-288
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• 2015

Training power tiller operators in safe farming is necessary to avoid farming accidents. With the continuing progress in computational technology, driving simulators have become increasingly popular for conducting such training. Purpose: The objective of this study is to develop mock control devices and data acquisition apparatus for a tiller simulator. Methods: Except for the stand and tail wheel adjusting levers, the mock control devices were developed using a tiller handle assay. The data acquisition apparatus was realized using an embedded data-logging device and LabVIEW, the system design software. Results: The control devices of a real handle assay were successfully mimicked by the mock operator control devices, which used sensors for the relevant measurements. The data from the mock devices were acquired and transmitted to the main computer at intervals of 10 ms via Wi-Fi. Conclusions: The developed mock control devices operate similar to real power tillers and can be utilized in power tiller training simulators.

• Journal of Korean Academy of Nursing
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• v.25 no.4
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• pp.617-632
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• 1995

Nowadays, most big hospitals have a computer system to manage their administration. For maxi mum effectiveness in managing the computer system, an analysis of the variables affecting its implementation is necessary from the beginning. This study was done to analyze the variables influencing the operation of a hospital information system (HIS). The theoretical base for this study considered the combined effects of user expectations of computerization, and computer-anxiety. The relationship between variables in the theoretical base were analyzed and the individual characteristics influencing each variable were also analyzed. This study was done in two steps. First, 344 nurses were given an initial questionnaire developed to evaluate the reliability of the items. Based on the results, a second revised questionnaire was administered to 88 nurses who had been working in the areas where HIS was applied. The results of the first and second steps of the study are as follows 1. The initial study was done with nurses who were trained on the computer system briefly before HIS was implemented. The individual characteristics influencing computer anxiety and expectation regarding computer system usage in that initial study included, length of career, type of degree or certification, previous experiences with a computer, training on a computer, desire for computer training, and level of acceptance of a computerized work environment. But in the second study with nurses working in areas of the hospital where HIS was introduced, the work site was the only influencing characteristics. There-fore, in applying a computer system, overcoming work-environment barriers will be more import-ant than any individual characteristics. 2. The computer anxiety of the nurses in both groups, before and after the computer system ap-plication, was below the average level but the expectation of the effects of computerization was above average. The nurses using the computer program showed an above average level of satis-faction with the computer system itself, and with its effect on their efficiency. Therefore, the ability of nurses operating HIS will be positively. predictive. 3. For the variables included in the theoretical framework of the study, all of the correlational coefficients were statistically significant in the analysis of variation correlation. Therefore, the theoretical base of the study, "expectation in con junction with computer anxiety" can be considered an model which can be evaluated. Accord-ing to our analysis, the higher the level of nurses' motivation to use the computer system and the lower the anxiety about computer usage, the higher the possibility of computer system acceptance by nurses. The results of this study showed that in applying a computer system in the hospital, the main characteristic influencing acceptance was where the individual worked rather than personal characteristics such as length of career, type of degree or certification, and previous experiences with a computer. Therefore, it is suggested that the first step in uncovering and eliminating hindrance factors in ap-plication of a computer system should be an analysis of working conditions in relation to the functional content of the computer system. The suitability of the theoretical model based on the hypothesis ap-plied in this study should be further tested.