• Journal of the Korea Society of Computer and Information
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• v.24 no.11
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• pp.51-59
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• 2019

Due to the exponential growth of access information on the web, the need for predicting web users' next access has increased. Various models such as markov models, deep neural networks, support vector machines, and fuzzy inference models were proposed to handle web access prediction. For deep learning based on neural network models, training time on large-scale web usage data is very huge. To address this problem, deep neural network models are trained on cluster of computers in parallel. In this paper, we investigated impact of several important spark parameters related to data partitions, shuffling, compression, and locality (basic spark parameters) for training Multi-Layer Perceptron model on Spark standalone cluster. Then based on the investigation, we tuned basic spark parameters for training Multi-Layer Perceptron model and used it for tuning Spark when training Multi-Layer Perceptron model for web access prediction. Through experiments, we showed the accuracy of web access prediction based on our proposed web access prediction model. In addition, we also showed performance improvement in training time based on our spark basic parameters tuning for training Multi-Layer Perceptron model over default spark parameters configuration.

• Journal of the Korean Society of Systems Engineering
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• v.4 no.2
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• pp.27-33
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• 2008

As a part of development of Korean K2 main battle tank, embedded training computer to be operated in the main equipment, which makes it possible to train without a special-purposed training simulator, was adopted for tank combat training. The category of embedded training of Korean K2 main battle tank includes driving training, gunnery training, single tank combat training, platoon level combat training, and command and platoon leaders combat training. For realization unit level tank embedded training system, the virtual reality was utilized for real time image rendering, and network based real time communication system of K2 tank was utilized for sharing status information between tanks. As a result, it is possible to train themselves on their own tank for enhancing the operational skills and harmonized task with members.

• Journal of Korea Multimedia Society
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• v.17 no.12
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• pp.1446-1452
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• 2014

Existing LDA uses the transform matrix that maximizes distance between classes. So we have to convert from an image to one-dimensional vector as training vector. However, in 2D-LDA, we can directly use two-dimensional image itself as training matrix, so that the classification performance can be enhanced about 20% comparing LDA, since the training matrix preserves the spatial information of two-dimensional image. However 2D-LDA uses same calculation schema for transformation matrix and therefore both LDA and 2D-LDA has the heteroscedastic problem which means that the class classification cannot obtain beneficial information of spatial distances of class clusters since LDA uses only data correlation-based covariance matrix of the training data without any reference to distances between classes. In this paper, we propose a new method to apply training matrix of 2D-LDA by using WPS-LDA idea that calculates the reciprocal of distance between classes and apply this weight to between class scatter matrix. The experimental result shows that the discriminating power of proposed 2D-LDA with weighted between class scatter has been improved up to 2% than original 2D-LDA. This method has good performance, especially when the distance between two classes is very close and the dimension of projection axis is low.

• Journal of the Korea Society of Computer and Information
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• v.23 no.4
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• pp.147-153
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• 2018

In this paper, we propose an efficient WBC 14-Diff classification which performs using the WBC-ResNet-152, a type of CNN model. The main point of view is to use Super-pixel for the segmentation of the image of WBC, and to use ResNet for the classification of WBC. A total of 136,164 blood image samples (224x224) were grouped for image segmentation, training, training verification, and final test performance analysis. Image segmentation using super-pixels have different number of images for each classes, so weighted average was applied and therefore image segmentation error was low at 7.23%. Using the training data-set for training 50 times, and using soft-max classifier, TPR average of 80.3% for the training set of 8,827 images was achieved. Based on this, using verification data-set of 21,437 images, 14-Diff classification TPR average of normal WBCs were at 93.4% and TPR average of abnormal WBCs were at 83.3%. The result and methodology of this research demonstrates the usefulness of artificial intelligence technology in the blood cell image classification field. WBC-ResNet-152 based morphology approach is shown to be meaningful and worthwhile method. And based on stored medical data, in-depth diagnosis and early detection of curable diseases is expected to improve the quality of treatment.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.153-159
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• 2022

The article discusses the use of smart technologies in the training of future specialists. Today, learning using smart technologies is becoming a new educational standard, where information is presented in a logical sequence, computer training systems have powerful functions for the educational process. The functions of smart technologies are highlighted. It is noted that smart technologies are successfully used in the field of education and professional training. The concept of "smart education" is characterized. Smart education is an educational paradigm that underlies a new type of education system. The implementation of the smart education paradigm is aimed at the process of obtaining competencies and competencies for flexible and adapted interaction with the social, economic and technological environment. Smart education should ensure that the benefits of the global information society can be used to meet educational needs and interests. A special place is occupied by computer-based educational multimedia systems that allow you to deepen your knowledge, reduce the duration of training, and increase the number of students per teacher. The main principles of smart education are highlighted. Improving the efficiency of training in a modern higher education institution is impossible without the introduction of smart technologies in the organization of the educational process.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.06a
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• pp.102-105
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• 2019

In the classical single-image super-resolution (SISR) reconstruction method using convolutional neural networks, the extracted features are not fully utilized, and the training time is too long. Aiming at the above problems, we proposed an improved SISR method based on a residual network. Our proposed method uses a feature fusion technology based on improved residual blocks. The advantage of this method is the ability to fully and effectively utilize the features extracted from the shallow layers. In addition, we can see that the feature fusion can adaptively preserve the information from current and previous residual blocks and stabilize the training for deeper network. And we use the global residual learning to make network training easier. The experimental results show that the proposed method gets better performance than classic reconstruction methods.

• Journal of Internet Computing and Services
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• v.20 no.4
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• pp.47-53
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• 2019

In general, dizziness is caused by various situations, but among them, symptoms due to dysfunction of the motor system belonging to the nervous system are the most severe, accompanied by nausea and vomiting. Treatment of these dizziness includes drug therapy, surgical therapy, and rehabilitation. Drug therapy and surgery are generally performed in vest rehabilitation training, which is a rehabilitation therapy because of the risk of aftereffects. The vestibular rehabilitation training includes eye training, posture stabilization training, and walking training. Among them, walking training is performed in a certain space under the supervision of a doctor or a professional therapist, so that the time and space burden is increased. In order to solve this problem, we implemented gait training contents which can be used for rehabilitation training by using the augmented reality technology. It is expected that it can be utilized as dizziness rehabilitative contents which can be used in medical environment through clinical tests for patients with dizziness.

• Journal of Korea Multimedia Society
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• v.24 no.2
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• pp.328-335
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• 2021

In the era of 'Untact', virtual reality-based job training platforms are actively being used as part of non-face-to-face education for students with developmental disabilities. Because the people with developmental disabilities may lack sufficient cognitive abilities, it is difficult to conduct untact training seamlessly without the help of a third party. Therefore, it is necessary for training programs to identify the right timing to provide help so that the training can be continued. This research analyzed the visual attention of students with developmental disabilities in virtual reality-based job training program in order to determine the point of time when an intervention is required by the trainee. Results showed that students who completed the mission tended to have intense visual attention on a small number of objects for a certain period of time; the visual attention of the students who failed tended to shift erratically among multiple objects.

• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.120-124
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• 2021

Recently, human attribute recognition (HAR) attracts a lot of attention due to its wide application in video surveillance systems. Recent deep-learning-based solutions for HAR require time-consuming training processes. In this paper, we propose a post-processing technique that utilizes the historical video frames to improve prediction results without invoking re-training or modifying existing deep-learning-based classifiers. Experiment results on a large-scale benchmark dataset show the effectiveness of our proposed method.

• Journal of the Korea Society of Computer and Information
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• v.25 no.9
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• pp.71-80
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• 2020

As societies become hyperconnected, we need more cyber security experts. To this end, in this paper, based on the analysis results of the real world cyber attacks and the MITRE ATT&CK framework, we developed CyTEA that can model cyber threats and generate simulated cyber threats in a cyber security training system. In order to confirm whether the simulated cyber threat has the effectiveness of the actual cyber threat level, the simulation level was examined based on procedural, environmental, and consequential similarities. in addition, it was confirmed that the actual defense training using cyber simulation threats is the same as the expected defense training when using real cyber threats in the cyber security training system.