• The Journal of the Acoustical Society of Korea
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• v.40 no.5
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• pp.439-451
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• 2021

We propose a deep learning-based beamformer combined with spectral subtraction for continuous speech recognition operating in noisy environments. Conventional beamforming systems were mostly evaluated by using pre-segmented audio signals which were typically generated by mixing speech and noise continuously on a computer. However, since speech utterances are sparsely uttered along the time axis in real environments, conventional beamforming systems degrade in case when noise-only signals without speech are input. To alleviate this drawback, we combine online beamforming algorithm and spectral subtraction. We construct a Continuous Speech Enhancement (CSE) evaluation set to evaluate the online beamforming algorithm in noisy environments. The evaluation set is built by mixing sparsely-occurring speech utterances of the CHiME3 evaluation set and continuously-played CHiME3 background noise and background music of MUSDB. Using a Kaldi-based toolkit and Google web speech recognizer as a speech recognition back-end, we confirm that the proposed online beamforming algorithm with spectral subtraction shows better performance than the baseline online algorithm.

• Journal of Information Technology Services
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• v.22 no.5
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• pp.99-108
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• 2023

As the computerization of hospitals becomes more advanced, security issues regarding data generated from various medical devices within hospitals are gradually increasing. For example, because hospital data contains a variety of personal information, attempts to attack it have been continuously made. In order to safely protect data from external attacks, each hospital has formed an internal team to continuously monitor whether the computer network is safely protected. However, there are limits to how humans can monitor attacks that occur on networks within hospitals in real time. Recently, artificial intelligence models have shown excellent performance in detecting outliers. In this paper, an experiment was conducted to verify how well an artificial intelligence model classifies normal and abnormal data in network traffic data generated from medical devices. There are several models used for outlier detection, but among them, Random Forest and Tabnet were used. Tabnet is a deep learning algorithm related to receive and classify structured data. Two algorithms were trained using open traffic network data, and the classification accuracy of the model was measured using test data. As a result, the random forest algorithm showed a classification accuracy of 93%, and Tapnet showed a classification accuracy of 99%. Therefore, it is expected that most outliers that may occur in a hospital network can be detected using an excellent algorithm such as Tabnet.

• 한국정보교육학회:학술대회논문집
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• 2021.08a
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• pp.239-245
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• 2021

In order to analyze the educational effect of learning mentoring conducted for 4 weeks by the Korea Educational Broadcasting System(EBS) for elementary school students, the changes in self-directed learning skills and emotional stability were analyzed through pre-test and post-test for 27 students who participated in the EBS learning mentoring. As a result, it was found that students' self-directed learning ability and emotional stability were both improved. In addition, the students showed high satisfaction with the mentor who guided their learning and taught them. Therefore, in order to reduce the learning gap of underprivileged students in the distance learning situation, the EBS learning mentoring project should be continuously promoted, and the mentoring period and the number of students and teachers participating in mentoring should be significantly increased.

• Journal of Power Electronics
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• v.14 no.5
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• pp.1008-1027
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• 2014

An electric scooter with a V-belt continuously variable transmission (CVT) driven by a permanent magnet synchronous motor (PMSM) has a lot of nonlinear and time-varying characteristics, and accurate dynamic models are difficult to establish for linear controller designs. A PMSM servo-drive electric scooter controlled by a novel hybrid modified recurrent Legendre neural network (NN) control system is proposed to solve difficulties of linear controllers under the occurrence of nonlinear load disturbances and parameters variations. Firstly, the system structure of a V-belt CVT driven electric scooter using a PMSM servo drive is established. Secondly, the novel hybrid modified recurrent Legendre NN control system, which consists of an inspector control, a modified recurrent Legendre NN control with an adaptation law, and a recouped control with an estimation law, is proposed to improve its performance. Moreover, the on-line parameter tuning method of the modified recurrent Legendre NN is derived according to the Lyapunov stability theorem and the gradient descent method. Furthermore, two optimal learning rates for the modified recurrent Legendre NN are derived to speed up the parameter convergence. Finally, comparative studies are carried out to show the effectiveness of the proposed control scheme through experimental results.

• 대한공업교육학회지
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• v.34 no.2
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• pp.286-303
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• 2009

The purpose of this study is to investigate Chinese e-Learning development strategies based on the analysis of Korean Industry-Academia e-Learning cooperation. Literature review and interview with experts about the subject regarding this study were the main research methods for the study. The results of the study are as follows 1. Chinese government should amend and complement the e-Learning policy of the chinese enterprise continuously through the Korean government's experience about the e-Learning policy such as enterprise e-Learning system, standardization of LMS, and quality assurance. 2. Korean government's policy about e-Learning has emphasized on practical development of e-Learning contents and LMS. And we have found they have good effects from the policy in terms of human resource development. However, Chinese government generally has emphasized on external extension of the scale and administration.nte, Chinese government should follow the practical policy in the e-Learning development as Korean government has done. 3. The way of Individualized learning, cooperative learning, team work, WBI, standardized LMS, and Blended Learning in Korea could be well adapted in Chinese e-Learning.

• Spatial Information Research
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• v.23 no.3
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• pp.123-132
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• 2015

Recently both the importance and the interests of field-experience learning have kept increasing since the Education of Ministry encourages students to take a field-experience learning with the self-driven and creative education as the main issue in the 7th revised education curriculum in 2009. As the importance of field-experience learning goes up continuously, not only the release of its related mobile applications is increasing, but also a variety of researches for supporting the field-experience learning are ongoing. In this research we perform two things. First, we define the basic concept of authoring tool for which support the field-experience learning based on its characteristics and user requirements analysis. We confirmed that the authoring tool for the field-experience learning has to 1) support all activities that happen in pre-field, field-experience and post-field phases, 2) be possible to write the location-based field work, 3) have the authoring function which reflects the characteristics of curriculum such as history, science and geography, etc. that the field learning can be realized, 4) be designed as the structure that the results of inquiring activity after the field experience activity can be reused. Second, we create a conceptual design after confirming the authoring tool.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.7
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• pp.1491-1497
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• 2003

The improvement of abilities of utilizing information as well as the education of information is coming to the fore as a really significant matter for students living in knowledge and information-oriented society of the 21st century. In this respect, this study aims to develop a teaching and learning model for technical high school students who need mon desire and comprehensive faculty of their learning and to testify its efficiency by relating their abilities of using in with their curriculum. We propose a teaching and learning model, LT&T, which lets the students have academic interests and perform continuously learning activities by helping students themselves to find their achievements of learning and their learning ability. The LT&T teaching and learning model, proposed in this paper, is developed by combining LT and Tournaments, and it enhances individual several intellectual powers, and also activates the academic interests to increase the effects of learning. After we had a class using the LT and the LT&T, we conducted a sample survey of which results show that learning effects of LT&T is better than that of LT by students of more 80%.

• Journal of Information Technology Services
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• v.18 no.2
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• pp.143-159
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• 2019

Machine learning (ML) is a method of fitting given data to a mathematical model to derive insights or to predict. In the age of big data, where the amount of available data increases exponentially due to the development of information technology and smart devices, ML shows high prediction performance due to pattern detection without bias. The feature engineering that generates the features that can explain the problem to be solved in the ML process has a great influence on the performance and its importance is continuously emphasized. Despite this importance, however, it is still considered a difficult task as it requires a thorough understanding of the domain characteristics as well as an understanding of source data and the iterative procedure. Therefore, we propose methods to apply deep learning for solving the complexity and difficulty of feature extraction and improving the performance of ML model. Unlike other techniques, the most common reason for the superior performance of deep learning techniques in complex unstructured data processing is that it is possible to extract features from the source data itself. In order to apply these advantages to the business problems, we propose deep learning based methods that can automatically extract features from transaction data or directly predict and classify target variables. In particular, we applied techniques that show high performance in existing text processing based on the structural similarity between transaction data and text data. And we also verified the suitability of each method according to the characteristics of transaction data. Through our study, it is possible not only to search for the possibility of automated feature extraction but also to obtain a benchmark model that shows a certain level of performance before performing the feature extraction task by a human. In addition, it is expected that it will be able to provide guidelines for choosing a suitable deep learning model based on the business problem and the data characteristics.

• The Journal of Bigdata
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• v.8 no.2
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• pp.15-25
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• 2023

Extreme learning machines (ELMs) are a major analytical method in various prediction fields. ELMs can accurately predict even if the data contains noise or is nonlinear by learning the complex patterns of time series data through optimal learning. This study presents the recent trends of machine learning models that are mainly studied as tools for analyzing online time series data, along with the application characteristics using existing algorithms. In order to efficiently learn large-scale online data that is continuously and explosively generated, it is necessary to have a learning technology that can perform well even in properties that can evolve in various ways. Therefore, this study examines a comprehensive overview of the latest machine learning models applied to big data in the field of time series prediction, discusses the general characteristics of the latest models that learn online data, which is one of the major challenges of machine learning for big data, and how efficiently they can learn and use online time series data for prediction, and proposes alternatives.

• Journal of Korean Society for Quality Management
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• v.47 no.4
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• pp.783-793
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• 2019

Purpose: The purpose of this study is to apply the machine and deep learning methodology on error terms which are continuously auto-generated on the sensors with specific time period and prove the improvement effects of power generator prediction diagnosis system by comparing detection ability. Methods: The SVM(Support Vector Machine) and MLP(Multi Layer Perception) learning procedures were applied for predicting the target values and sequentially producing the error terms for confirming the detection improvement effects of suggested application. For checking the effectiveness of suggested procedures, several detection methodologies such as Cusum and EWMA were used for the comparison. Results: The statistical analysis result shows that without noticing the sequential trivial changes on current diagnosis system, suggested approach based on the error term diagnosis is sensing the changes in the very early stages. Conclusion: Using pattern of error terms as a diagnosis tool for the safety control process with SVM and MLP learning procedure, unusual symptoms could be detected earlier than current prediction system. By combining the suggested error term management methodology with current process seems to be meaningful for sustainable safety condition by early detecting the symptoms.