• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.1
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• pp.81-85
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• 2014

This paper investigates the challenges and outlook of textbook teaching and learning material industry. 1. The challenges of teaching and learning material industry. First, classification criteria are needed. Second, Evaluation Standard are needed. Third, quality control is needed. Fourth, ready-made products are insufficient. Fifth, economic policy for teaching and learning material industry is required. Sixth, manage system for teaching and learning material is necessary. Seventh, distribution system for teaching and learning material should be reformed. 2. The outlook of teaching and learning material industry. First, accreditation system will be introduced. Second, teaching and learning material industry will develop continually. Third, eco-friendly and sustainable system will be built. Fourth, multimedia industry will be extended. Fifth, managing system to enhance usage rate will be settled.

• Journal of Sensor Science and Technology
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• v.8 no.1
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• pp.45-52
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• 1999

In this raper, an enhanced learning method is proposed for improving the learning speed of the error back propagation learning algorithm. In order to cope with the premature saturation phenomenon at the initial learning stage, a variation scheme of active functions is introduced by using higher order functions, which does not need much increase of computation load. It naturally changes the learning rate of inter-connection weights to a large value as the derivative of sigmoid function abnormally decrease to a small value during the learning epoch. Also, we suggest the hybrid learning method incorporated the proposed method with the momentum training algorithm. Computer simulation results show that the proposed learning algorithm outperforms the conventional methods such as momentum and delta-bar-delta algorithms.

• The Journal of Information Technology
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• v.7 no.3
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• pp.11-18
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• 2004

Learning procedure in the neural network is updating of weights between neurons. Unadequate initial learning coefficient causes excessive iterations of learning process or incorrect learning results and degrades learning efficiency. In this paper, adaptive learning algorithm is proposed to increase the efficient in the learning algorithms of Self-Organization Neural Networks. The algorithm updates the weights adaptively when learning procedure runs. To prove the efficiency the algorithm is experimented to classification of strokes which is the reference handwritten character. The result shows improved classification rate about 1.44~3.65% proposed method compare with Kohonan and Mao's algorithms, in this paper.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.4
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• pp.472-476
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• 2007

Learning rule affects importantly the performance of neural network. This paper proposes a new fuzzy learning rule that uses the learning rate considering the distance between the input vector and the prototypes of classes. When the learning rule updates the prototypes of classes, this consideration reduces the effect of outlier on the prototypes of classes. This comes from making the effect of the input vector, which locates near the decision boundary, larger than an outlier. Therefore, it can prevents an outlier from deteriorating the decision boundary. This new fuzzy learning rule is integrated into IAFC(Integrated Adaptive Fuzzy Clustering) fuzzy neural network. Iris data set is used to compare the performance of the proposed fuzzy neural network with those of other supervised neural networks. The results show that the proposed fuzzy neural network is better than other supervised neural networks.

• ETRI Journal
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• v.44 no.5
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• pp.733-745
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• 2022

In software-defined wireless networking (SDWN), the optimal routing technique is one of the effective solutions to improve its performance. This routing technique is done by many different methods, with the most common using integer linear programming problem (ILP), building optimal routing metrics. These methods often only focus on one routing objective, such as minimizing the packet blocking probability, minimizing end-to-end delay (EED), and maximizing network throughput. It is difficult to consider multiple objectives concurrently in a routing algorithm. In this paper, we investigate the application of machine learning to control routing in the SDWN. An intelligent routing algorithm is then proposed based on the machine learning to improve the network performance. The proposed algorithm can optimize multiple routing objectives. Our idea is to combine supervised learning (SL) and reinforcement learning (RL) methods to discover new routes. The SL is used to predict the performance metrics of the links, including EED quality of transmission (QoT), and packet blocking probability (PBP). The routing is done by the RL method. We use the Q-value in the fundamental equation of the RL to store the PBP, which is used for the aim of route selection. Concurrently, the learning rate coefficient is flexibly changed to determine the constraints of routing during learning. These constraints include QoT and EED. Our performance evaluations based on OMNeT++ have shown that the proposed algorithm has significantly improved the network performance in terms of the QoT, EED, packet delivery ratio, and network throughput compared with other well-known routing algorithms.

• The Journal of the Convergence on Culture Technology
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• v.4 no.4
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• pp.173-180
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• 2018

In order to find how their learning satisfaction to impact on continuous learning, this study analyzed 206 participants who are learners under the literacy education from Ulsan city. With the first results, when looking over their learning satisfaction, we found that the level of satisfaction was the highest at the educational environment following teacher quality, learning results and contents sequentially. In general characteristics, the longer is learning attending and the satisfier is education contents and learning satisfaction. With the second results, when we look over how the general characteristics of literacy learners to impact on continuous learning, those who are over 70 years old expressed the higher rate on continuous learning. With the third results, considering the correlation between learning satisfaction and continuous learning, we concluded that the first was education contents, the second was teacher quality, the third was learning results and the last was learning environment. Consequently, we found that for literacy learners, the older and longer attending and the higher satisfaction, in addition, the continuous learning was higher according to needs of everyday life and the education contents impacted on continuous learning.

• Korean Journal of Microbiology
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• v.49 no.1
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• pp.51-57
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• 2013

As facilities and equipments for learning activities in college campuses are handled by mass public, their contact surfaces may function as major routes of cross-infection of microbial pathogens. However, unlike items in cafeteria which is the typical target for campus hygiene, those surfaces are not under regular surveillance or sanitary maintenance. In this study, I made a quantitative comparison of the risk of being exposed to microbial pathogens from use of learning facilities such as classrooms and library to the risk from use of cafeteria, for about 1,500 students in a college. Regarding total coliforms as surrogate model of bacterial pathogens, exposure rates were estimated for each item in learning facilities and cafeterias by devising deterministic exposure algorithms based on bacterial abundance, contract rates and transfer rates. The exposure rate in cafeterias was 1.0 CFU/day while learning facilities imposed the rate of 0.5 CFU/day, which reaches a half of the exposure rate in cafeterias. However, 70% of students were exposed more in learning facilities than cafeteria because individuals had different frequencies in using cafeteria. Based on the results, some human-contact surfaces of learning facilities, including elevator buttons, may require regular sanitary maintenance. An efficient sanitary maintenance considering seasonality in diversity of pathogens involved with cross-infections is suggested besides improvement of personal hygiene among students.

• Smart Media Journal
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• v.11 no.11
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• pp.17-24
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• 2022

With the spread of smart speakers based on voice recognition technology and deep learning technology, not only non-disabled people, but also the blind or physically handicapped can easily control home appliances such as lights and TVs through voice by linking home network services. This has greatly improved the quality of life. However, in the case of speech-impaired people, it is impossible to use the useful services of the smart speaker because they have inaccurate pronunciation due to articulation or speech disorders. In this paper, we propose a personalized voice classification technique for the speech-impaired to use for some of the functions provided by the smart speaker. The goal of this paper is to increase the recognition rate and accuracy of sentences spoken by speech-impaired people even with a small amount of data and a short learning time so that the service provided by the smart speaker can be actually used. In this paper, data augmentation and one cycle learning rate optimization technique were applied while fine-tuning ResNet18 model. Through an experiment, after recording 10 times for each 30 smart speaker commands, and learning within 3 minutes, the speech classification recognition rate was about 95.2%.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.5
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• pp.400-405
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• 2001

This paper addresses an effective approach of training neural networks classifiers for credit card fraud detection. The proposed approach uses evolutionary programming to trails the neural networks classifiers based on maximization of the detection rate of fraudulent usages on some ranges of the rejection rate, loot minimization of mean square error(MSE) that Is a common criterion for neural networks learning. This approach enables us to get classifier of satisfactory performance and to offer a directive method of handling various conditions and performance measures that are required for real fraud detection applications in the classifier training step. The experimental results on "real"credit card transaction data indicate that the proposed classifiers produces classifiers of high quality in terms of a relative profit as well as detection rate and efficiency.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.322-325
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• 2002

We proposed a method to improve KCN's problems. Proposed method adjusts neighborhood and teaming rate by fuzzy logic system. The input of fuzzy logic system used a distance and a change rate of distance. The output was used by site of neighborhood and learning rate. The rule base of fuzzy logic system was taken by using KCN simulation results. We used Anderson's Iris data to illustrate this method, and simulation results showed effect of performance.