• Journal of the military operations research society of Korea
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• v.36 no.2
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• pp.1-10
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• 2010

Learning rate is generally applied to estimate an appropriate production labor cost. Learning effect is obtained from repetitive work during the production period under 3 assumptions ; homogeneous production, same producer, quantity measure in continuous unit. However, production breaks occur frequently in Korean defense industry environment because of budget constraint and annual requirements. In this case previous learning effect can not be applied due to learning loss. This paper proposed the application of learning rate when a production break occurs in Korea defense industry. To obtain a learning loss, we surveyed various learning loss factors for different production breaks(6, 12, 18 months) from 4 defense industry companies. Then, we estimate the first unit labor hours in re-production phase after production break using Anderlohr method and Retrograde method with the result of the survey. This work is the first attempt to show a method which defines and evaluates the learning loss factors in Korean defense industry environment.

• Journal of Engineering Education Research
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• v.26 no.6
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• pp.51-61
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• 2023

In this study, the effects of pre-learning attitude on learning participation and academic achievement was analyzed when applying the flipped learning methodology to engineering subject education. The modified PARTN teaching and learning model was applied, and pre-class survey, assessment on learning in pre-class, and post-class survey were conducted to analyze the effectiveness of flipped learning. The results were analyzed for 24 students who took the applied thermodynamics lecture. They were asked to take the course with the videos provided in the pre-class stage, and a pre-learning assessment was conducted to measure the completeness and understanding of the learning. As a result of the study, it was found that students with relatively excellent learning ability had excellent pre-learning evaluation results and excellent final academic achievement. In addition, the lower the pre-learning completion rate within the pre-learning period or the higher the learning rate using mobile devices, the more difficult it was to faithfully complete pre-learning, leading to poor pre-learning evaluation results. Meanwhile, the survey revealed that conducting pre-learning assessments were helpful in encouraging individual learning. In addition, cases reflecting pre-learning evaluation results to course grades showed higher pre-learning evaluation results than cases not reflecting pre-learning evaluation results to course grades, and in flipped learning classes, pre-learning evaluations act as a factor that promotes pre-class learning.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.35-41
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• 2018

The Q-learning algorithm based on reinforcement learning is useful for learning the goal for one behavior at a time, using a combination of discrete states and actions. In order to learn multiple actions, applying a behavior-based architecture and using an appropriate behavior adjustment method can make a robot perform fast and reliable actions. Q-learning is a popular reinforcement learning method, and is used much for robot learning for its characteristics which are simple, convergent and little affected by the training environment (off-policy). In this paper, Q-learning algorithm is applied to a lamp robot to learn multiple behaviors (human recognition, desk object recognition). As the learning rate of Q-learning may affect the performance of the robot at the learning stage of multiple behaviors, we present the optimal multiple behaviors learning model by changing learning rate.

• Education of Primary School Mathematics
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• v.15 no.2
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• pp.159-170
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• 2012

Many obstacles have been found in the learning of ratio and rate. The types of epistemological obstacles concern 'terms', 'calculations' and 'symbols'. It is important to identify the epistemological obstacles that students must overcome to understand the learning of ratio and rate. In this respect, the present study attempts to figure out what types of epistemological obstacles emerge in the area of learning ratio and rate and where these obstacles are generated from and to search for the teaching implications to correct them. The research questions were to analyze this concepts as follow; A. How do elementary students show the epistemological obstacles in ratio and rate? B. What is the reason for epistemological obstacles of elementary students in the learning of ratio and rate? C. What are the teaching implications to correct epistemological obstacles of elementary students in the learning of ratio and rate? In order to analyze the epistemological obstacles of elementary students in the learning of ratio and rate, the present study was conducted in five different elementary schools in Seoul. The test was administered to 138 fifth grade students who learned ratio and rate. The test was performed three times during six weeks. In case of necessity, additional interviews were carried out for thorough examination. The final results of the study are summarized as follows. The epistemological obstacles in the learning of ratio and rate can be categorized into three types. The first type concerns 'terms'. The reason is that realistic context is not sufficient, a definition is too formal. The second type of epistemological obstacle concerns 'calculations'. This second obstacle is caused by the lack of multiplication thought in mathematical problems. As a result of this study, the following conclusions have been made. The epistemological obstacles cannot be helped. They are part of the natural learning process. It is necessary to understand the reasons and search for the teaching implications. Every teacher must try to develop the teaching method.

• International journal of advanced smart convergence
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• v.9 no.1
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• pp.113-120
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• 2020

Recently, the license plate OCR system has been commercialized in a variety of fields and preferred utilizing low-cost embedded systems using only cameras. This system has a high recognition rate of about 98% or more for the environments such as parking lots where non-vehicle is restricted; however, the environments where non-vehicle objects are not restricted, the recognition rate is about 50% to 70%. This low performance is due to the changes in the environment by non-vehicle objects in real-time situations that occur anomaly data which is similar to the license plates. In this paper, we implement the appropriate anomaly detection based on semi-supervised learning for the license plate OCR system in the real-time environment where the appearance of non-vehicle objects is not restricted. In the experiment, we compare systems which anomaly detection is not implemented in the preceding research with the proposed system in this paper. As a result, the systems which anomaly detection is not implemented had a recognition rate of 77%; however, the systems with the semi-supervised learning based on anomaly detection had 88% of recognition rate. Using the techniques of anomaly detection based on the semi-supervised learning was effective in detecting anomaly data and it was helpful to improve the recognition rate of real-time situations.

• Journal of Korea Multimedia Society
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• v.9 no.3
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• pp.369-376
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• 2006

In this paper, we propose an improved fuzzy RBF network which dynamically adjusts the rate of learning by applying the Delta-bar-Delta algorithm in order to improve the learning performance of fuzzy RBF networks. The proposed learning algorithm, which combines the fuzzy C-Means algorithm with the generalized delta learning method, improves its learning performance by dynamically adjusting the rate of learning. The adjustment of the learning rate is achieved by self-generating middle-layered nodes and by applying the Delta-bar-Delta algorithm to the generalized delta learning method for the learning of middle and output layers. To evaluate the learning performance of the proposed RBF network, we used 40 identifiers extracted from a container image as the training data. Our experimental results show that the proposed method consumes less training time and improves the convergence of teaming, compared to the conventional ART2-based RBF network and fuzzy RBF network.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.568-570
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• 2004

The error-backpropagation (EBP) algerithm for training multilayer perceptrons (MLPs) is known to have good features of robustness and economical efficiency. However, the algorithm has difficulty in selecting an optimal constant learning rate and thus results in non-optimal learning speed and inflexible operation for working data. This paper Introduces an elastic learning rate that guarantees convergence of learning and its local realization by online upoate of MLP parameters Into the original EBP algorithm in order to complement the non-optimality. The results of experiments on a speaker verification system with Korean speech database are presented and discussed to demonstrate the performance improvement of the proposed method in terms of learning speed and flexibility fer working data of the original EBP algorithm.

• Journal of Advanced Navigation Technology
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• v.24 no.5
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• pp.430-437
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• 2020

The proposed model implements a model that improves the face prediction rate and recognition rate through learning with an artificial neural network using face detection, landmark and face recognition algorithms. After landmarking in the face images of a specific person, the proposed model use the previously learned Caffe model to extract face detection and embedding vector 128D. The learning is learned by building machine learning algorithms such as support vector machine (SVM) and deep neural network (DNN). Face recognition is tested with a face image different from the learned figure using the learned model. As a result of the experiment, the result of learning with DNN rather than SVM showed better prediction rate and recognition rate. However, when the hidden layer of DNN is increased, the prediction rate increases but the recognition rate decreases. This is judged as overfitting caused by a small number of objects to be recognized. As a result of learning by adding a clear face image to the proposed model, it is confirmed that the result of high prediction rate and recognition rate can be obtained. This research will be able to obtain better recognition and prediction rates through effective deep learning establishment by utilizing more face image data.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.11
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• pp.142-150
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• 1994

This paper deals with fast competitive learning using classified learning rates. The basic idea of the proposed method is to assign a classified learning rate to each weight vector. The weight vector associated with an output node is updated using its own learning rate. Each learning rate is changed only when its corresponding output node wins the competition, and the learning rates of the losing nodes are not changed. The experimental results obtained with image vector quantization show that the proposed method learns more rapidly and yields better quality that conventional competitive learning.

• Educational Technology International
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• v.19 no.1
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• pp.123-151
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• 2018

This study aims to examine the effects of self-directed learning readiness (SDLR) on academic performance and the perceived usefulness for each elements of flipped learning. Based on their SDLR scores, 69 students were assigned to a high SDLR group and a low SDLR group. Academic performance was measured by the completion rate of a pre-class online learning and the final exam score, and perceived usefulness for each element of flipped learning was measured by a survey designed by the researcher. For academic performance, the high SDLR group showed a significantly higher completion rate than the low SDLR group, but no significant difference was observed in their final exam scores. Students in the high SDLR group perceived in-class student-centered activities as more useful than those in the low SDLR group. Additional qualitative analyses indicated that students needed more support from instructors and well-prepared peers. Finally, this study suggested that more examination on the various learning characteristics that may influence the effectiveness of flipped learning should be done.