• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3730-3744
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• 2020

This study proposes a deep learning-based evolutionary recommendation model for heterogeneous big data integration, for which collaborative filtering and a neural-network algorithm are employed. The proposed model is used to apply an individual's importance or sensory level to formulate a recommendation using the decision-making feedback. The evolutionary recommendation model is based on the Deep Neural Network (DNN), which is useful for analyzing and evaluating the feedback data among various neural-network algorithms, and the DNN is combined with collaborative filtering. The designed model is used to extract health information from data collected by the Korea National Health and Nutrition Examination Survey, and the collaborative filtering-based recommendation model was compared with the deep learning-based evolutionary recommendation model to evaluate its performance. The RMSE is used to evaluate the performance of the proposed model. According to the comparative analysis, the accuracy of the deep learning-based evolutionary recommendation model is superior to that of the collaborative filtering-based recommendation model.

• East Asian Economic Review
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• v.17 no.4
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• pp.385-415
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• 2013

Inter-regional free trade agreements (FTAs) - notably between Asia and Latin America - are growing in numbers and complexity. There is an absence of an agreed methodology for empirical assessments on the content of FTAs and little research. This paper proposes a framework to assess liberalization in FTAs in goods and services and new trade policy issues relating to regulatory barriers. Next, it applies this framework to studying the 22 Asia-Latin America FTAs in existence. The findings suggest that Asia-Latin American FTAs have laid the foundations for inter-regional integration by liberalizing the trade in goods and services and reducing some regulatory barriers. Deepening FTAs and adopting structural reforms will enhance Asia-Latin American integration in the future.

• International Journal of Computer Science & Network Security
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• v.23 no.10
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• pp.89-96
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• 2023

Intrusion detection has been widely studied in both industry and academia, but cybersecurity analysts always want more accuracy and global threat analysis to secure their systems in cyberspace. Big data represent the great challenge of intrusion detection systems, making it hard to monitor and analyze this large volume of data using traditional techniques. Recently, deep learning has been emerged as a new approach which enables the use of Big Data with a low training time and high accuracy rate. In this paper, we propose an approach of an IDS based on cloud computing and the integration of big data and deep learning techniques to detect different attacks as early as possible. To demonstrate the efficacy of this system, we implement the proposed system within Microsoft Azure Cloud, as it provides both processing power and storage capabilities, using a convolutional neural network (CNN-IDS) with the distributed computing environment Apache Spark, integrated with Keras Deep Learning Library. We study the performance of the model in two categories of classification (binary and multiclass) using CSE-CIC-IDS2018 dataset. Our system showed a great performance due to the integration of deep learning technique and Apache Spark engine.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.41-68
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• 2019

Multi-view learning considers data from various viewpoints as well as attempts to integrate various information from data. Multi-view learning has been studied recently and has showed superior performance to a model learned from only a single view. With the introduction of deep learning techniques to a multi-view learning approach, it has showed good results in various fields such as image, text, voice, and video. In this study, we introduce how multi-view learning methods solve various problems faced in human behavior recognition, medical areas, information retrieval and facial expression recognition. In addition, we review data integration principles of multi-view learning methods by classifying traditional multi-view learning methods into data integration, classifiers integration, and representation integration. Finally, we examine how CNN, RNN, RBM, Autoencoder, and GAN, which are commonly used among various deep learning methods, are applied to multi-view learning algorithms. We categorize CNN and RNN-based learning methods as supervised learning, and RBM, Autoencoder, and GAN-based learning methods as unsupervised learning.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2002.02a
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• pp.274-284
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• 2002

Deep drawing process for rectangular drawn section is different with that for axisymmetric circular one. Therefore deep drawing process for rectangular drawn section requires several intermediate steps to generate the final configuration without any significant defect. In this study, finite element analysis for multi-stage deep drawing process for high precision rectangular cases is carried out especially for an extreme aspect ratio. The analysis is performed using rigid-plastic finite element method with an explicit time integration scheme of the commercial program, LS-DYNA3D. The sheet blank is modeled using eight-node continuum brick elements. The results of analysis show that the irregular contact condition between blank and die affects the occurrence of failure, and the difference of aspect ratio in the drawn section leads to non-uniform metal flow, which may cause failure. A series of experiments for multi-stage deep drawing process for the rectangular cases are conducted, and the deformation configuration and the thickness distribution of the drawn rectangular cases are investigated by comparing with the results of the numerical analysis. The numerical analysis with an explicit time integration scheme shows good agreement with the experimental observation.

• Structural Engineering and Mechanics
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• v.75 no.3
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• pp.357-367
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• 2020

The objective of this article is investigation of dynamic response of thick multilayer functionally graded (FG) beam under generalized dynamic forces. The plane stress problem is exploited to describe the constitutive equation of thick FG beam to get realistic and accurate response. Applied dynamic forces are assumed to be sinusoidal harmonic, sinusoidal pulse or triangle in time domain and point load. Equations of motion of deep FG beam are derived based on the Hamilton principle from kinematic relations and constitutive equations of plane stress problem. The numerical finite element procedure is adopted to discretize the space domain of structure and transform partial differential equations of motion to ordinary differential equations in time domain. Numerical time integration method is used to solve the system of equations in time domain and find the time responses. Numerical parametric studies are performed to illustrate effects of force type, graduation parameter, geometrical and stacking sequence of layers on the time response of deep multilayer FG beams.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.204-214
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• 2017

Artificial intelligence, especially deep learning technology, is penetrating the majority of research areas, including the field of bioinformatics. However, deep learning has some limitations, such as the complexity of parameter tuning, architecture design, and so forth. In this study, we analyze these issues and challenges in regards to its applications in bioinformatics, particularly genomic analysis and medical image analytics, and give the corresponding approaches and solutions. Although these solutions are mostly rule of thumb, they can effectively handle the issues connected to training learning machines. As such, we explore the tendency of deep learning technology by examining several directions, such as automation, scalability, individuality, mobility, integration, and intelligence warehousing.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.2
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• pp.152-157
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• 2013

In this paper, we represent the implementation and performance analysis of vector tracking loop based GPS receiver for jamming environment. The vector tracking loop navigation performance is compared by simulation with conventional tracking loop. The simulation results shows that vector tracking loop is more robust than conventional tracking loop in jamming environment. The vector tracking loop can gain 2dB in jamming performance capability over a conventional GPS receiver. Also, Anti-jamming performance of INS Doppler aiding and deep integration method are compared.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2003.05a
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• pp.258-261
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• 2003

In this study, finite element analysis for multi-stage deep drawing process of rectangular configuration with extreme aspect ratio is carried out especially for the blank design. The analysis of rectangular deep drawing process with extreme aspect ratio is likewise very difficult with respect to the design process parameters including the intermediate die profile. In order to solve the difficulties, numerical approach using finite element method is performed in the present analysis and design. A series of experiments for multi-stage rectangular deep drawing process are conducted and the deformed configuration is investigated by comparing with the results of the finite element analysis. Additionally, to minimize amount of removal material after trimming process, finite element simulation is applied for the blank modification. The analysis incorporates brick elements for a rigid-plastic finite element method with an explicit time integration scheme using LS-DYNA3D.

• Korean Journal of Radiology
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• v.21 no.5
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• pp.511-525
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• 2020

Chest X-ray radiography and computed tomography, the two mainstay modalities in thoracic radiology, are under active investigation with deep learning technology, which has shown promising performance in various tasks, including detection, classification, segmentation, and image synthesis, outperforming conventional methods and suggesting its potential for clinical implementation. However, the implementation of deep learning in daily clinical practice is in its infancy and facing several challenges, such as its limited ability to explain the output results, uncertain benefits regarding patient outcomes, and incomplete integration in daily workflow. In this review article, we will introduce the potential clinical applications of deep learning technology in thoracic radiology and discuss several challenges for its implementation in daily clinical practice.