• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2014.10a
• /
• pp.247-251
• /
• 2014

As smart education increases the demand for new teaching-learning methods, teacher training colleges need to systematize smart education teaching-learning methods for pre-service teachers. This study designed a smart education teaching-learning model, which is applicable to pre-service teachers, by analyzing the smart education teaching-learning types for primary and secondary schools at national and international levels and by analyzing the Creation Teaching Learning Assessment (CTLA) model. The goal of smart education is to reinforce capability of learners. The smart education teaching-learning model designed to help pre-service teachers reinforce their smart literacy is suitable for reinforcing capability of future learners to receive smart education. The smart education teaching-learning model in this study was designed as a 15-week teaching plan applicable to pre-service teachers at teacher training colleges. In the teaching-learning model, problem-based learning (PBL), a situated learning model, and cooperative learning model were applied to weekly instructions. Further research should be conducted to prove its effectiveness in allowing pre-service teachers to reinforce their smart literacy by making gradual improvement in this model and to develop and test smart education teaching-learning models constantly.

• Journal of the Society of Disaster Information
• /
• v.19 no.1
• /
• pp.44-59
• /
• 2023

Purpose: In the event of mass casualties, triage must be done promptly and accurately so that as many patients as possible can be recovered and returned to the battlefield. However, medical personnel have received many tasks with less manpower, and the battlefield for classifying patients is too complex and uncertain. Therefore, we studied an artificial intelligence model that can assist and replace medical personnel on the battlefield. Method: The triage model is presented using reinforcement learning, a field of artificial intelligence. The learning of the model is conducted to find a policy that allows as many patients as possible to be treated, taking into account the condition of randomly set patients and the medical capability of the military hospital. Result: Whether the reinforcement learning model progressed well was confirmed through statistical graphs such as cumulative reward values. In addition, it was confirmed through the number of survivors whether the triage of the learned model was accurate. As a result of comparing the performance with the rule-based model, the reinforcement learning model was able to rescue 10% more patients than the rule-based model. Conclusion: Through this study, it was found that the triage model using reinforcement learning can be used as an alternative to assisting and replacing triage decision-making of medical personnel in the case of mass casualties.

• Computers and Concrete
• /
• v.32 no.3
• /
• pp.327-337
• /
• 2023

Concrete is the most widely used building material, with various types including high- and ultra-high-strength, reinforced, normal, and lightweight concretes. However, accurately predicting concrete properties is challenging due to the geotechnical design code's requirement for specific characteristics. To overcome this issue, researchers have turned to new technologies like machine learning to develop proper methodologies for concrete specification. In this study, we propose a highly accurate deep learning-based predictive model to investigate the compressive strength (UCS) of lightweight concrete with natural aggregates (pumice). Our model was implemented on a database containing 249 experimental records and revealed that water, cement, water-cement ratio, fine-coarse aggregate, aggregate substitution rate, fine aggregate replacement, and superplasticizer are the most influential covariates on UCS. To validate our model, we trained and tested it on random subsets of the database, and its performance was evaluated using a confusion matrix and receiver operating characteristic (ROC) overall accuracy. The proposed model was compared with widely known machine learning methods such as MLP, SVM, and DT classifiers to assess its capability. In addition, the model was tested on 25 laboratory UCS tests to evaluate its predictability. Our findings showed that the proposed model achieved the highest accuracy (accuracy=0.97, precision=0.97) and the lowest error rate with a high learning rate (R2=0.914), as confirmed by ROC (AUC=0.971), which is higher than other classifiers. Therefore, the proposed method demonstrates a high level of performance and capability for UCS predictions.

• Journal of Korea Multimedia Society
• /
• v.21 no.2
• /
• pp.98-107
• /
• 2018

In this paper, we propose a method to classify the skeletal maturity with a small amount of hand wrist X-ray image using deep learning-based meta-learning. General deep-learning techniques require large amounts of data, but in many cases, these data sets are not available for practical application. Lack of learning data is usually solved through transfer learning using pre-trained models with large data sets. However, transfer learning performance may be degraded due to over fitting for unknown new task with small data, which results in poor generalization capability. In addition, medical images require high cost resources such as a professional manpower and mcuh time to obtain labeled data. Therefore, in this paper, we use meta-learning that can classify using only a small amount of new data by pre-trained models trained with various learning tasks. First, we train the meta-model by using a separate data set composed of various learning tasks. The network learns to classify the bone maturity using the bone maturity data composed of the radiographs of the wrist. Then, we compare the results of the classification using the conventional learning algorithm with the results of the meta learning by the same number of learning data sets.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.215-218
• /
• 1997

A dynamic back-propagation neural network is addressed for adaptive neural control system to approximate non-linear control system rather than static networks. It has the capability to represent the approximation of nonlinear system without mathematical analysis and to carry out the on-line learning algorithm for real time application. The simulated results show fast tracking capability and adaptive response by using dynamic back-propagation neurons.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.146.2-146
• /
• 2001

Recently, a need of non-industrial robot, such as service, medical, entertainment and house-keeping robot, has been increased. Therefore, the capability of robot which can do intelligent behavior like interaction with men and its environment become more prominent than the capability of executing simple repetitive task. To implement an intelligent robot which provides intelligent behavior, an effective system architecture including perception, learning, reasoning and action part is necessary. Control architectures for intelligent robot can be divided into two different classes. One is deliberative type controller which is applicate to high level intelligence like environment ...

• Proceedings of the KIEE Conference
• /
• 2002.07d
• /
• pp.2143-2145
• /
• 2002

This paper proposes the control problem of an inverted pendulum system based on adaptive fuzzy sliding mode. The universal approximating capability, learning ability, adaptation capability and disturbance rejection are collected in one control strategy. The proposed scheme does not require an accurate dynamic model and the joint acceleration measurement, yet it guarantees asymptotic trajectory tracking. Experimental results perform with an inverted pendulum to show the effectiveness of the approach.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.32B no.11
• /
• pp.1527-1535
• /
• 1995

This paper introduces an isolated word recognition system realized on a neurocomputer called E-MIND II, which is a 2-D torus wavefront array processor consisting of 256 DNP IIs. The DNP II is an all digital VLSI unit processor for the EMIND II featuring the emulation capability of more than thousands of neurons, the 40 MHz clock speed, and the on-chip learning. Built by these PEs in 2-D toroidal mesh architecture, the E- MIND II can be accelerated over 2 Gcps computation speed. In this light, the advantages of the E-MIND II in its capability of computing speed, scalability, computer interface, and learning are especially suitable for real time application such as speech recognition. We show how to map a TDNN structure on this array and how to code the learning and recognition algorithms for a user independent isolated word recognition. Through hardware simulation, we show that recognition rate of this system is about 97% for 30 command words for a robot control.

• The Journal of Economics, Marketing and Management
• /
• v.8 no.1
• /
• pp.36-49
• /
• 2020

Purpose - This study investigates factors that affected the utilization of trade support policies and further analyzed how the utilization of trade support policies affected export performance. Research design, data, and methodology - With a sample of 223 small and medium-sized export firms from South Korea, this study examines the determinants of the utilization level of trade support system such as export market orientation, learning orientation, network capability and environmental uncertainty by regression analysis. Results - Export market orientation have a positive effect on the utilization of the trade support system and there is positive relationship between learning orientation and the utilization of trade support system. And network capabilities have had a positive impact on the utilization of the trade support system but there is no relationship between environmental uncertainty and the utilization of trade support system. The utilization of the trade support system had a positive effect on export performance. Conclusions - The internal and external factors of the organization have affected small and medium-sized export firms use of trade support systems. The utilization of trade support system can enhance positive export performance by providing valuable information and resource to external knowledge and also to complementary resources from the external partners.

• International journal of advanced smart convergence
• /
• v.8 no.4
• /
• pp.75-81
• /
• 2019

Though recent advancement of deep learning methods have provided satisfactory results from large data domain, somehow yield poor performance on few-shot classification tasks. In order to train a model with strong performance, i.e. deep convolutional neural network, it depends heavily on huge dataset and the labeled classes of the dataset can be extremely humongous. The cost of human annotation and scarcity of the data among the classes have drastically limited the capability of current image classification model. On the contrary, humans are excellent in terms of learning or recognizing new unseen classes with merely small set of labeled examples. Few-shot learning aims to train a classification model with limited labeled samples to recognize new classes that have neverseen during training process. In this paper, we increase the backbone depth of the embedding network in orderto learn the variation between the intra-class. By increasing the network depth of the embedding module, we are able to achieve competitive performance due to the minimized intra-class variation.