• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.193-196
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• 2008

Z. Cao had proposed NFRM(new fuzzy reasoning method) which infers in detail using relation matrix. In spite of the small inference rules, it shows good performance than mamdani's fuzzy inference method. In this paper, we propose 2. Cao's fuzzy inference method using learning ability witch is used a gradient descent method in order to improve the performances. Because it is difficult to determine the relation matrix elements by trial and error method which is needed many hours and effort. Simulation results are applied linear and nonlinear system show that the proposed inference method has good performances.

• English Language & Literature Teaching
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• v.12 no.1
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• pp.159-181
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• 2006

The purpose of this paper is to examine the effectiveness of presentation-based instruction and to suggest a model of instruction targeted to the Korean intermediate level students learning English as a foreign language (EFL). To achieve this objective, the author examined how the acquisition of practical English through oral presentation would enhance the students' learning motivation, language abilities, and communicative competence in concrete situations. It was confirmed that the trained leader and systematic teaching and learning are needed to maximize the effects of presentation-based instruction. In doing so, the author compared and analyzed the collected data in order to support the validity of this teaching method. It was further pointed out that the teacher should have a close look at the roles of the presenter and learner in an effort to work out the usefulness of such an instruction model. The method of presentation in classroom settings would be a practical mode to attain the essential purpose of EFL teaching particularly to get over the drawbacks of Korean students' communicative competence. As a result, it would be an effective teaching method to meet the nation's long-standing demands for EFL education.

• Journal of Korean Public Health Nursing
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• v.7 no.2
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• pp.67-76
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• 1993

This study was conducted to investigate the nurses' recognition extent of nursing power and related factors. It was performed in a private university hospital using questionaire papers from Jul. 11 to Jul. 20, 1993. The collected data were in number of 209, and they were handled and analyzed by computer program (SPSS). Through above process, we got meaningful results as follow. 1. The factor concerned with whether the nursing department show its independent power or not was 4.1 on an average. Consequently above results showed that the independent power roles as a important factor. The factor concerned with study and research was 3.9 on an average. So that the extension of learning was also a comparatively important nursing power. But ensuring good material environment was merely 1.6 on an average. So it was understood as less important nursing power. 2. Between the factor concerned with enforcement of independence of nursing department and the factor concerned with extension of learning, there was positive correlation of 0.32 Pearson's Correlation Coefficent. It can be interpreted as the effort of extension of learning go side by side with the showing of independent power of nursing department. As a result the power of nursing become strong. And between the factor concerned with enforcenment of independence of nursing department and the ensuring o(good material environment, there was negative correlation of -0.28 Pearson's Correaltion Coefficint. It can be interpreted as the stronger the independent power of nursing, the more overlooked the recognized of material environment of nurses.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2002.11a
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• pp.171-177
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• 2002

E-Learning is a modem main teaching method starting from the concept of remote education. This research is aimed for proposing cyber education library system, and designing a cyber education component that becomes a basis for e-Learning system. Cyber education library is a storage system of cyber lectures that can supply high quality data to the needed developers. Cyber education component consists of 5 categories and those are text, voice, image, animation, and flash. By using this system, the developers can save the necessary time and effort in education development. This system also helps students. The students can access various lecture data on a given subject and select the best fit for them.

• International Journal of Advanced Culture Technology
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• v.7 no.4
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• pp.289-294
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• 2019

As social network service platforms grow and one-person media market expands, people upload their own photos and/or videos through multiple open platforms. However, it can be illegal to upload the digital contents containing the faces of others on the public sites without their permission. Therefore, many people are spending much time and effort in editing such digital contents so that the faces of others should not be exposed to the public. In this paper, we propose an automatic face hiding system called 'autoblur', which detects all the unregistered faces and mosaic them automatically. The system has been implemented using the GitHub MIT open-source 'Face Recognition' which is based on deep learning technology. In this system, two dozens of face images of the user are taken from different angles to register his/her own face. Once the face of the user is learned and registered, the system detects all the other faces for the given photo or video and then blurs them out. Our experiments show that it produces quick and correct results for the sample photos.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.12 no.9
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• pp.1591-1598
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• 2008

Z. Cao had proposed NFRM(new fuzzy reasoning method) which infers in detail using relation matrix. In spite of the small inference rules, it shows good performance than mamdani's fuzzy inference method. In this paper, we propose Z. Cao's fuzzy inference method with learning ability which is used a gradient descent method in order to improve the performances. It is hard to determine the relation matrix elements by trial and error method. Because this method is needed many hours and effort. Simulation results are applied nonlinear systems show that the proposed inference method using a gradient descent method has good performances.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.369-376
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• 2021

One of the most active areas of research in the field of affective computing and signal processing is emotion recognition. This paper proposes emotion recognition of low-resource (Sindhi) language. This work's uniqueness is that it examines the emotions of languages for which there is currently no publicly accessible dataset. The proposed effort has provided a dataset named MAVDESS (Mehran Audio-Visual Dataset Mehran Audio-Visual Database of Emotional Speech in Sindhi) for the academic community of a significant Sindhi language that is mainly spoken in Pakistan; however, no generic data for such languages is accessible in machine learning except few. Furthermore, the analysis of various emotions of Sindhi language in MAVDESS has been carried out to annotate the emotions using line features such as pitch, volume, and base, as well as toolkits such as OpenSmile, Scikit-Learn, and some important classification schemes such as LR, SVC, DT, and KNN, which will be further classified and computed to the machine via Python language for training a machine. Meanwhile, the dataset can be accessed in future via https://doi.org/10.5281/zenodo.5213073.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.27 no.3
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• pp.45-53
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• 2019

The purpose of this study is to provide a brief overview of the helicopter pilot training system applied to the Army, and to examine what factors positively affect the successful flight training of helicopter pilots. For this purpose, we analyzed the correlations between various factors such as individual characteristics, selection factors, flight aptitude evaluation, theoretical subjects test, and self-life evaluation. As a result, it was found that only the flight experience was influential on the individual characteristics at the beginning of the training course, and the learning achievement represented by the test of theoretical subjects was positively influenced throughout the flight training course. This reaffirms the fact that an individual's high level of motivation or effort influences his flight training performance. These results are expected to be useful indicators for future development of pilot selection system and pilot training system.

• Smart Media Journal
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• v.8 no.2
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• pp.46-57
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• 2019

In recent research, deep learning-based methods have achieved state-of-the-art performance in various computer vision tasks. However, these methods are commonly supervised, and require huge amounts of annotated data to train. Acquisition of data demands an additional costly effort, particularly for the tasks where it becomes challenging to obtain large amounts of data considering the time constraints and the requirement of professional human diligence. In this paper, we present a data level synthetic sampling solution to learn from small and imbalanced data sets using Generative Adversarial Networks (GANs). The reason for using GANs are the challenges posed in various fields to manage with the small datasets and fluctuating amounts of samples per class. As a result, we present an approach that can improve learning with respect to data distributions, reducing the partiality introduced by class imbalance and hence shifting the classification decision boundary towards more accurate results. Our novel method is demonstrated on a small dataset of 2789 tomato plant disease images, highly corrupted with class imbalance in 9 disease categories. Moreover, we evaluate our results in terms of different metrics and compare the quality of these results for distinct classes.

• Imaging Science in Dentistry
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• v.52 no.3
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• pp.275-281
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• 2022

Purpose: The aim of this study was to assess the performance of a deep learning system for permanent tooth germ detection on pediatric panoramic radiographs. Materials and Methods: In total, 4518 anonymized panoramic radiographs of children between 5 and 13 years of age were collected. YOLOv4, a convolutional neural network (CNN)-based object detection model, was used to automatically detect permanent tooth germs. Panoramic images of children processed in LabelImg were trained and tested in the YOLOv4 algorithm. True-positive, false-positive, and false-negative rates were calculated. A confusion matrix was used to evaluate the performance of the model. Results: The YOLOv4 model, which detected permanent tooth germs on pediatric panoramic radiographs, provided an average precision value of 94.16% and an F1 value of 0.90, indicating a high level of significance. The average YOLOv4 inference time was 90 ms. Conclusion: The detection of permanent tooth germs on pediatric panoramic X-rays using a deep learning-based approach may facilitate the early diagnosis of tooth deficiency or supernumerary teeth and help dental practitioners find more accurate treatment options while saving time and effort