• Journal of Broadcast Engineering
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• v.27 no.5
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• pp.751-761
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• 2022

With the development of digital graphics technology, the metaverse has become a significant trend in the content market. The demand for technology that generates high-quality 3D (dimension) models is rapidly increasing. Accordingly, various technical attempts are being made to create high-quality 3D virtual humans represented by digital humans. 3D volumetric capture is spotlighted as a technology that can create a 3D manikin faster and more precisely than the existing 3D model creation method. In this study, we try to analyze 3D high-precision facial production technology based on practical cases of the difficulties in content production and technologies applied in volumetric 3D and 4D model creation. Based on the actual model implementation case through 3D volumetric capture, we considered techniques for 3D virtual human face production and producted a new metahuman using a graphics pipeline for an efficient human facial generation.

• Information Systems Review
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• v.25 no.2
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• pp.31-56
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• 2023

As the COVID-19 pandemic is prolonged, non-contact work has increased, as well as the demand for automation of simple and repetitive questions and tasks with success of using them. Therefore, companies are attempting to expand the area of automated business and apply various technologies such as AI to complex and various business processes of E2E to provide automation of all business. However, the extension to Intelligent Process Automation (IPA) is still in its beginning stage so that it is difficult to find practical use cases and related solutions. In this aspect, it is safe to say that there is insufficient evidence for companies which have various and complex enterprise processes to make a decision about the adoption. In this study, to solve this problem, a Hyper Automation Platform (HAP) that consists of RPA, Chatbot, and AI technology was proposed. Moreover, an implementation method that can bring intelligent process automation using HAP, and practical use-cases were provided so that it makes it possible to review the implementation of the HAP objectively and comprehensively. This study is meaningful and valuable to check the feasibility of the Hyper Automation concept and to actively utilize HAP.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.2
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• pp.281-288
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• 2024

The sixth generation(6G) wireless communication technology is advancing toward ultra-high speed, ultra-high bandwidth, and hyper-connectivity. With the development of communication technologies, the formation of a hyper-connected society is rapidly accelerating, expanding from the IoT(Internet of Things) to the IoE(Internet of Everything). However, at the same time, security threats targeting IoT devices have become widespread, and there are concerns about security incidents such as unauthorized access and information leakage. As a result, the need for security-enhancing solutions is increasing. In this paper, we implement an autoencoder-based anomaly detection model utilizing real-time collected network traffics in respond to IoT security threats. Considering the difficulty of capturing IoT device traffic data for each attack in real IoT environments, we use an unsupervised learning-based autoencoder and implement 6 different autoencoder models based on the use of noise in the training data and the dimensions of the latent space. By comparing the model performance through experiments, we provide a performance evaluation of the anomaly detection model for detecting abnormal network traffic.

• Journal of the Korean Data and Information Science Society
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• v.28 no.5
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• pp.971-980
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• 2017

The varying coefficient regression model has gained lots of attention since it is capable to model dynamic changes of regression coefficients in many regression problems of science. In this paper we propose a varying coefficient regression model that effectively considers the errors on both input and response variables, which utilizes the kernel method in estimating the varying coefficient which is the unknown nonlinear function of smoothing variables. We provide a generalized cross validation method for choosing the hyper-parameters which affect the performance of the proposed model. The proposed method is evaluated through numerical studies.

• ETRI Journal
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• v.39 no.1
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• pp.41-50
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• 2017

In connection-oriented data transport services, data loss can occur when a service experiences a problem in its end-to-end path. To resolve the problem promptly, the data transport systems providing the service must quickly modify their internal configurations, which are distributed among different locations within each system. The configurations are modified through a series of problem (event) handling procedures, which are carried out by multiple control processors in the system. This paper proposes a provisioning-to-signaling method for inter-control-processor messaging to improve the time efficiency of event processing. This method simplifies the sharing of the runtime event, and minimizes the time variability caused by the amount of event data, which results in a decrease in the latency time and an increase in the time determinacy when processing global events. The proposed method was tested for an event that required 4,000 internal path changes, and was found to lessen the latency time of global event processing by about 50% compared with the time required for general methods to do the same; in addition, it reduced the impact of the event data on the event processing time to about 30%.

• Smart Structures and Systems
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• v.29 no.4
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• pp.599-616
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• 2022

Bridge displacement contains vital information for bridge condition and performance. Due to the limits of direct displacement measurement methods, the indirect displacement reconstruction methods based on the strain or acceleration data are also developed in engineering applications. There are still some deficiencies of the displacement reconstruction methods based on strain or acceleration in practice. This paper proposed a novel method based on long short-term memory (LSTM) networks to reconstruct the bridge dynamic displacements with the strain and acceleration data source. The LSTM networks with three hidden layers are utilized to map the relationships between the measured responses and the bridge displacement. To achieve the data fusion, the input strain and acceleration data need to be preprocessed by normalization and then the corresponding dynamic displacement responses can be reconstructed by the LSTM networks. In the numerical simulation, the errors of the displacement reconstruction are below 9% for different load cases, and the proposed method is robust when the input strain and acceleration data contains additive noise. The hyper-parameter effect is analyzed and the displacement reconstruction accuracies of different machine learning methods are compared. For experimental verification, the errors are below 6% for the simply supported beam and continuous beam cases. Both the numerical and experimental results indicate that the proposed data fusion method can accurately reconstruct the displacement.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.196-202
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• 2024

Currently, the second most devastating form of cancer in people, particularly in women, is Breast Cancer (BC). In the healthcare industry, Machine Learning (ML) is commonly employed in fatal disease prediction. Due to breast cancer's favorable prognosis at an early stage, a model is created to utilize the Dataset on Wisconsin Diagnostic Breast Cancer (WDBC). Conversely, this model's overarching axiom is to compare the effectiveness of five well-known ML classifiers, including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), and Naive Bayes (NB) with the conventional method. To counterbalance the effect with conventional methods, the overarching tactic we utilized was hyperparameter tuning utilizing the grid search method, which improved accuracy, secondary precision, third recall, and finally the F1 score. In this study hyperparameter tuning model, the rate of accuracy increased from 94.15% to 98.83% whereas the accuracy of the conventional method increased from 93.56% to 97.08%. According to this investigation, KNN outperformed all other classifiers in terms of accuracy, achieving a score of 98.83%. In conclusion, our study shows that KNN works well with the hyper-tuning method. These analyses show that this study prediction approach is useful in prognosticating women with breast cancer with a viable performance and more accurate findings when compared to the conventional approach.

• Communications for Statistical Applications and Methods
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• v.25 no.2
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• pp.217-234
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• 2018

Copulas are a tool for constructing multivariate distributions and formalizing the dependence structure between random variables. From copula literature review, there are a few asymmetric copulas available so far while data collected from the real world often exhibit asymmetric nature. This necessitates developing asymmetric copulas. In this study, we discuss a method to construct a new class of bivariate asymmetric copulas based on products of symmetric (sometimes asymmetric) copulas with powered arguments in order to determine if the proposed construction can offer an added value for modeling asymmetric bivariate data. With these newly constructed copulas, we investigate dependence properties and measure of association between random variables. In addition, the test of symmetry of data and the estimation of hyper-parameters by the maximum likelihood method are discussed. With two real example such as car rental data and economic indicators data, we perform the goodness-of-fit test of our proposed asymmetric copulas. For these data, some of the proposed models turned out to be successful whereas the existing copulas were mostly unsuccessful. The method of presented here can be useful in fields such as finance, climate and social science.

• Transactions of Materials Processing
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• v.29 no.2
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• pp.89-96
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• 2020

The finite element method has been widely applied in the sheet metal forming process. However, the finite element method is computationally expensive and time consuming. In order to tackle this problem, surrogate modeling methods have been proposed. An artificial neural network (ANN) is one such surrogate model and has been well studied over the past decades. However, when it comes to ANN with two or more layers, so called deep neural networks (DNN), there is distinct a lack of research. We chose to use DNNs our surrogate model to predict the behavior of sheet metal in the deep drawing process. Thickness variation is selected as an output of the DNN in order to evaluate workpiece feasibility. Input variables of the DNN are radius of die, die corner and blank holder force. Finite element analysis was conducted to obtain data for surrogate model construction and testing. Sampling points were determined by full factorial, latin hyper cube and monte carlo methods. We investigated the performance of the DNN according to its structure, number of nodes and number of layers, then it was compared with a radial basis function surrogate model using various sampling methods and numbers. The results show that our DNN could be used as an efficient surrogate model for the deep drawing process.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.1
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• pp.18-23
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• 2006

The complex system synchronization methods improve based on synchronization theory; however, due to deeper level of complexity within complex system compared to that of chaos system, it is difficult to synchronize complex signals from complex system. In this paper, we proposed coupled-synchronization theory in the n-double scroll circuit and new embedding driven-synchronization theory, a method of accomplishing synchronization with only one parameter out of may parameters, in hyper-chaos circuit to apply synchronization in the complex system. By applying proposed synchronization method using computer simulation, we confirmed the accomplishment of superior synchronization in complex system.