• Journal of Electrical Engineering and Technology
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• v.12 no.1
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• pp.363-372
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• 2017

This paper presents a novel face recognition algorithm based on the deep convolution neural network and key point detection jointed local binary pattern methodology to enhance the accuracy of face recognition. We firstly propose the modified face key feature point location detection method to enhance the traditional localization algorithm to better pre-process the original face images. We put forward the grey information and the color information with combination of a composite model of local information. Then, we optimize the multi-layer network structure deep learning algorithm using the Fisher criterion as reference to adjust the network structure more accurately. Furthermore, we modify the local binary pattern texture description operator and combine it with the neural network to overcome drawbacks that deep neural network could not learn to face image and the local characteristics. Simulation results demonstrate that the proposed algorithm obtains stronger robustness and feasibility compared with the other state-of-the-art algorithms. The proposed algorithm also provides the novel paradigm for the application of deep learning in the field of face recognition which sets the milestone for further research.

• Journal of the East Asian Society of Dietary Life
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• v.21 no.1
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• pp.108-117
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• 2011

The purpose of this study was to analyze the association between educational environment (physical environment of cooking institutes and curriculum) and satisfaction with learning of students at local cooking institutes. Self-administered questionnaires were distributed to 300 student enrolled at cooking institutes located in Pohang and Gyeongju, and a total of 265 were usable. Collected data were statistically analyzed using SPSS 12.0 by frequency, factor, reliability, t-test and Duncan's multiple range test. The results can be summarized as follows. Most of the subjects were enrolled at cooking institutes to learn Korean and Western cuisine. There were significant differences in learning according to institution, facility, method and instructor. There were also significant differences in learning according to gender, age, education, and attended classes. Based on the results, the physical environment of cooking institutes contributed to learning in the students, but the operation system also needs to be improved. However, study was limited in sample size and area, the results can-not be generalized.

• Journal of Korea Multimedia Society
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• v.6 no.7
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• pp.1312-1320
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• 2003

In this paper, we consider a multilayer neural network, with a single hidden layer. Error backpropagation learning method used widely in multilayer neural networks has a possibility of local minima due to the inadequate weights and the insufficient number of hidden nodes. So we propose a fuzzy supervised learning algorithm by using self-generation that self-generates hidden nodes by the compound fuzzy single layer perceptron and modified ART1. From the input layer to hidden layer, a modified ART1 is used to produce nodes. And winner take-all method is adopted to the connection weight adaptation, so that a stored pattern for some pattern gets updated. The proposed method has applied to the student identification card images. In simulation results, the proposed method reduces a possibility of local minima and improves learning speed and paralysis than the conventional error backpropagation learning algorithm.

• The Journal of Information Technology
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• v.4 no.1
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• pp.71-79
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• 2001

This paper presents an adaptive back propagation algorithm that update the learning parameter by the generated error, adaptively and configuration of the range for the initial connecting weight according to the different maximum target value from minimum target value. This algorithm is expected to escaping from the local minimum and make the best environment for the convergence. On the simulation tested this algorithm on three learning pattern. The first was 3-parity problem learning, the second was $7{\times}5$ dot alphabetic font learning and the third was handwritten primitive strokes learning. In three examples, the probability of becoming trapped in local minimum was reduce. Furthermore, in the alphabetic font and handwritten primitive strokes learning, the neural network enhanced to loaming efficient about 27%~57.2% for the standard back propagation(SBP).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.9
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• pp.1541-1550
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• 2016

In this paper, we introduce a novel learning methodology of fuzzy clustering-based neural network pattern classifier. Fuzzy clustering-based neural network pattern classifier depicts the patterns of given classes using fuzzy rules and categorizes the patterns on unseen data through fuzzy rules. Least squares estimator(LSE) or weighted least squares estimator(WLSE) is typically used in order to estimate the coefficients of polynomial function, but this study proposes a novel coefficient estimate method which includes advantages of the existing methods. The premise part of fuzzy rule depicts input space as "If" clause of fuzzy rule through fuzzy c-means(FCM) clustering, while the consequent part of fuzzy rule denotes output space through polynomial function such as linear, quadratic and their coefficients are estimated by the proposed local least squares estimator(LLSE)-based learning. In order to evaluate the performance of the proposed pattern classifier, the variety of machine learning data sets are exploited in experiments and through the comparative analysis of performance, it provides that the proposed LLSE-based learning method is preferable when compared with the other learning methods conventionally used in previous literature.

• The Journal of Information Technology
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• v.5 no.2
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• pp.55-67
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• 2002

This paper presents an adaptive back propagation algorithm that update the learning parameter by the generated error, adaptively and varies the number of hidden layer node. This algorithm is expected to escaping from the local minimum and make the best environment for convergence to be change the number of hidden layer node. On the simulation tested this algorithm on two learning pattern. One was exclusive-OR learning and the other was $7{\times}5$ dot alphabetic font learning. In both examples, the probability of becoming trapped in local minimum was reduce. Furthermore, in alphabetic font learning, the neural network enhanced to learning efficient about 41.56%~58.28% for the conventional back propagation. and HNAD(Hidden Node Adding and Deleting) algorithm.

• Information Systems Review
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• v.25 no.3
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• pp.27-41
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• 2023

Unlike the previous works focusing on the state-of-the-art methodologies to improve the performance of machine learning models, this study improves the 'quality' of training data used in machine learning. We propose a method to enhance the quality of training data through the processing of 'local grammar,' frequently used in corpus analysis. We collected a vast amount of unstructured corporate review text data posted by employees working in the top 100 companies in Korea. After improving the data quality using the local grammar process, we confirmed that the classification model with local grammar outperformed the model without it in terms of classification performance. We defined five factors of work engagement as classification categories, and analyzed how the pattern of reviews changed before and after the COVID-19 pandemic. Through this study, we provide evidence that shows the value of the local grammar-based automatic identification and classification of employee experiences, and offer some clues for significant organizational cultural phenomena.

• Journal of The Korean Association of Information Education
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• v.26 no.5
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• pp.327-340
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• 2022

This study applied effective playful learning to increase the learning motivation of non-CS major students to help them achieve learning and to successfully operate online SW liberal arts classes for 560 students. As a result of analyzing the students' reflection journals, most of the students accepted the 'white radish' of dialect names as fun playful learning in the process of learning local variables and global variables. And they were surprised and amazed at discovering unexpected contents in our SW class. It was found that they experienced delight in learning, learning-flow, confidence, and intrinsic motivation. In the final term exam at the end of the semester, it was confirmed that the correct rate of 92% for questions related to local and global variables was higher than the average rate of other questions' correctness of 67.1%.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.75-82
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• 2018

Recently, emerging countries have been paying attention to Korean economic development policy, trying to adopt the Korean regional innovation model. Korea is also interested in exporting its regional innovation model and enhancing economic cooperation with those countries. This paper aims to analyze the capacity-building programs of the Korean regional innovation model for emerging countries and suggests policies for it. For this purpose, the local innovators' participation patterns in the process of collaborative learning/networking/interaction are investigated with a focused group-interview method. From an analysis of the programs supported by Korean organizations, this study finds that the correlation coefficient between the training time of capacity building and the participation rate of local members' collaborative learning is very high (0.975). Since the correlation coefficient between the participation rates of collaborative learning and networking is relatively low (0.667), a policy to link local collaborative learning to networking should be provided. As the correlation coefficient between the participation rates of networking and interaction is high (0.950), networking is a key to regional innovation. This study recommends activity programs to promote networking among local innovators, rather than training and consulting programs. As introduced in the Chungnam Techno Park case, this study suggests that the capacity-building program should include programs to initiate a collaborative learning network, to create a local-demand, regional innovation model, and to operate the regional innovation platform, which should be done by local innovators in the emerging countries.

• Journal of the Korea institute for structural maintenance and inspection
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• v.24 no.6
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• pp.206-216
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• 2020

As the number of aging railway bridges in Korea increases, maintenance costs due to aging are increasing and continuous management is becoming more important. However, while the number of old facilities to be managed increases, there is a shortage of professional personnel capable of inspecting and diagnosing these old facilities. To solve these problems, this study presents an improved model that can detect Local damage to structures using machine learning techniques of AI technology. To construct a damage detection machine learning model, an analysis model of the bridge was set by referring to the design drawing of a non-ballasted plate-girder railroad bridge. Static strain data according to the damage scenario was extracted with the analysis model, and the Local damage index based on the reliability of the bridge was presented using statistical techniques. Damage was performed in a three-step process of identifying the damage existence, the damage location, and the damage severity. In the estimation of the damage severity, a linear regression model was additionally considered to detect random damage. Finally, the random damage location was estimated and verified using a machine learning-based damage detection classification learning model and a regression model.