• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.29B no.9
• /
• pp.68-75
• /
• 1992

In this paper the back-propagation algorithm through omitting redundant learning has been proposed to improve learning speed. The proposed algorithm has been applied to XOR, Parity check and pattern recognition of hand-written numbers. The decrease of the number of patterns to be learned has been confirmed as learning proceeds even in early learning stage. The learning speed in pattern recognition of hand-written numbers is improved more than 2 times in various cases of hidden neuron numbers. It is observed that the improvement of learning speed becomes better as the number of patterns and the number of hidden numbers increase. The recognition rate of the proposed algorithm is nearly the same as that conventional method.

• The Journal of the Convergence on Culture Technology
• /
• v.9 no.2
• /
• pp.199-206
• /
• 2023

This study attempted to examine the relationship between college students' participation in the learning community according to the characteristics of learners. To this end, the learning community divided the subject-linked learning community into a foundation learning community and an advanced learning community. Learner characteristics were classified by gender, grade, and major. A cross-analysis was conducted to examine the difference between participation in the foundation learning community and the advanced learning community according to the learner characteristics. The results are as follows. First, the participation of female students in the foundation learning community and the advanced learning community was higher than that of male students, but it was not statistically significant. Second, it was found that there was a significant difference in participation in the learning community according to the grade. In the case of the foundation learning community, the participation rate of the first and second year students was relatively high, and in the case of the advanced learning community, the ratio of the third and fourth year students was relatively high. Third, as a result of examining the differences by major, it was found that the participation rate of health and welfare universities was high in both the foundation learning community and the advanced learning community. Based on these results, discussions and suggestions are presented.

• Proceedings of the Korea Contents Association Conference
• /
• 2006.11a
• /
• pp.792-795
• /
• 2006

In this paper, we examined the influence for a new learning method using on-line e-learning education model in the field of music appreciation. We applied this loaming method to the field of motive induction, the expectation of learning effect and the increase learning will through mesuring the student's pulse change in the learning class. We analysed the influence of interesting rate and emotional stability in the field of music appreciation.

• Electronics and Telecommunications Trends
• /
• v.34 no.1
• /
• pp.98-110
• /
• 2019

Machine learning has been applied to medical imaging and has shown an excellent recognition rate. Recently, there has been much interest in preventive medicine. If data are accessible, machine learning packages can be used easily in digital healthcare fields. However, it is necessary to prepare the data in advance, and model evaluation and tuning are required to construct a reliable model. On average, these processes take more than 80% of the total effort required. In this study, we describe the basic concepts of machine learning, pre-processing and visualization of datasets, feature engineering for reliable models, model evaluation and tuning, and the latest trends in popular machine learning frameworks. Finally, we survey a explainable machine learning analysis tool and will discuss the future direction of machine learning.

• The Journal of Korea Robotics Society
• /
• v.19 no.1
• /
• pp.92-97
• /
• 2024

This paper provides a study of distributional perspective on reinforcement learning for application in mobile robot navigation. Mapless navigation algorithms based on deep reinforcement learning are proven to promising performance and high applicability. The trial-and-error simulations in virtual environments are encouraged to implement autonomous navigation due to expensive real-life interactions. Nevertheless, applying the deep reinforcement learning model in real tasks is challenging due to dissimilar data collection between virtual simulation and the physical world, leading to high-risk manners and high collision rate. In this paper, we present distributional reinforcement learning architecture for mapless navigation of mobile robot that adapt the uncertainty of environmental change. The experimental results indicate the superior performance of distributional soft actor critic compared to conventional methods.

• International Journal of Knowledge Content Development & Technology
• /
• v.5 no.2
• /
• pp.103-122
• /
• 2015

This study examined student satisfaction with e-learning experiences in order to determine which factors had the greatest impact on reports of satisfaction among students at Konkuk University. We surveyed 4,889 students enrolled in e-learning courses and analyzed 830 completed questionnaires to identify factors that influence student satisfaction with e-learning. Results showed significant correlations between system factors and satisfaction ($R^2=0.577$; p = 0.000). The system factor with the greatest impact on satisfaction was course attendance rate (0.224; p = 0.000).

• The Journal of Korea Robotics Society
• /
• v.15 no.4
• /
• pp.398-403
• /
• 2020

Robot's throwing control is difficult to accurately calculate because of air resistance and rotational inertia, etc. This complexity can be solved by using machine learning. Reinforcement learning using reward function puts limit on adapting to new environment for robots. Therefore, this paper applied deep reinforcement learning using neural network without reward function. Throwing is evaluated as a success or failure. AI network learns by taking the target position and control policy as input and yielding the evaluation as output. Then, the task is carried out by predicting the success probability according to the target location and control policy and searching the policy with the highest probability. Repeating this task can result in performance improvements as data accumulates. And this model can even predict tasks that were not previously attempted which means it is an universally applicable learning model for any new environment. According to the data results from 520 experiments, this learning model guarantees 75% success rate.

• Journal of Korean Medical Science
• /
• v.33 no.43
• /
• pp.239.1-239.12
• /
• 2018

Background: We described a novel multi-step retinal fundus image reading system for providing high-quality large data for machine learning algorithms, and assessed the grader variability in the large-scale dataset generated with this system. Methods: A 5-step retinal fundus image reading tool was developed that rates image quality, presence of abnormality, findings with location information, diagnoses, and clinical significance. Each image was evaluated by 3 different graders. Agreements among graders for each decision were evaluated. Results: The 234,242 readings of 79,458 images were collected from 55 licensed ophthalmologists during 6 months. The 34,364 images were graded as abnormal by at-least one rater. Of these, all three raters agreed in 46.6% in abnormality, while 69.9% of the images were rated as abnormal by two or more raters. Agreement rate of at-least two raters on a certain finding was 26.7%-65.2%, and complete agreement rate of all-three raters was 5.7%-43.3%. As for diagnoses, agreement of at-least two raters was 35.6%-65.6%, and complete agreement rate was 11.0%-40.0%. Agreement of findings and diagnoses were higher when restricted to images with prior complete agreement on abnormality. Retinal/glaucoma specialists showed higher agreements on findings and diagnoses of their corresponding subspecialties. Conclusion: This novel reading tool for retinal fundus images generated a large-scale dataset with high level of information, which can be utilized in future development of machine learning-based algorithms for automated identification of abnormal conditions and clinical decision supporting system. These results emphasize the importance of addressing grader variability in algorithm developments.

• Journal of Institute of Control, Robotics and Systems
• /
• v.13 no.4
• /
• pp.279-285
• /
• 2007

The objective of the research is two fold. The first is to design and propose a stable and robust learning control algorithm. The controller is CMAC Learning Controller which consists of a model-based controller, such as LQR or PID, as a reference control and a CMAC. The second objective is to implement a reference control and CMAC at two different sampling rates. Generally, a conventional controller is designed based on a mathematical plant model. However, increasing complexity of the plant and accuracy requirement on mathematical models nearly prohibits the application of the conventional controller design approach. To avoid inherent complexity and unavoidable uncertainty in modeling, biology mimetic methods have been developed. One of such attempts is Cerebellar Model Articulation Computer(CMAC) developed by Albus. CMAC has two main disadvantages. The first disadvantage of CMAC is increasing memory requirement with increasing number of input variables and with increasing accuracy demand. The memory needs can be solved with cheap memories due to recent development of new memory technology. The second disadvantage is a demand for processing powers which could be an obstacle especially when CMAC should be implemented in real-time. To overcome the disadvantages of CMAC, we propose CMAC learning controller with multiple sampling rates. With this approach a conventional controller which is a reference to CMAC at high enough sampling rate but CMAC runs at the processor's unoccupied time. To show efficiency of the proposed method, an inverted pendulum controller is designed and implemented. We also demonstrate it's possibility as an industrial control solution and robustness against a modeling uncertainty.

• Journal of the Architectural Institute of Korea Structure & Construction
• /
• v.36 no.3
• /
• pp.113-120
• /
• 2020

The purpose of this study is to compare the models of Deep Learning-based Convolution Neural Network(CNN) for concrete crack detection. The comparison models are AlexNet, GoogLeNet, VGG16, VGG19, ResNet-18, ResNet-50, ResNet-101, and SqueezeNet which won ImageNet Large Scale Visual Recognition Challenge(ILSVRC). To train, validate and test these models, we constructed 3000 training data and 12000 validation data with 256×256 pixel resolution consisting of cracked and non-cracked images, and constructed 5 test data with 4160×3120 pixel resolution consisting of concrete images with crack. In order to increase the efficiency of the training, transfer learning was performed by taking the weight from the pre-trained network supported by MATLAB. From the trained network, the validation data is classified into crack image and non-crack image, yielding True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN), and 6 performance indicators, False Negative Rate (FNR), False Positive Rate (FPR), Error Rate, Recall, Precision, Accuracy were calculated. The test image was scanned twice with a sliding window of 256×256 pixel resolution to classify the cracks, resulting in a crack map. From the comparison of the performance indicators and the crack map, it was concluded that VGG16 and VGG19 were the most suitable for detecting concrete cracks.