• Journal of Korean Society of Disaster and Security
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• v.14 no.1
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• pp.41-50
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• 2021

As a method of predicting the displacement of river levee in advance, Differential Interferometry (D-InSAR) kind of InSAR techniques was used to identify weak points in the area of the levee collapes near Gumgok Bridge (Somjin River) in Namwon City, which occurred in the summer of 2020. As a result of analyzing the displacement using five images each in the spring and summer of 2020, the Variation Index (V) of area where the collapse occurred was larger than that of the other areas, so the prognostic sysmptoms was detected. If the levee monitoring system is realized by analyzing the correlations with displacement results and hydrometeorological factors, it will overcome the existing limitations of system and advance ultra-precise, automated river levee maintenance technology and improve national disaster management.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.6
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• pp.605-610
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• 2020

Software Defined Networking (SDN) is setting the standard for the management of networks due to its scalability, flexibility and functionality to program the network. The Distributed Denial of Service (DDoS) attack is most widely used to attack the SDN controller to bring down the network. Different methodologies have been utilized to detect DDoS attack previously. In this paper, first the dataset is obtained by Kaggle with 84 features, and then according to the rank, the 20 highest rank features are selected using Permutation Importance Algorithm. Then, the datasets are trained and tested with Convolution Neural Network (CNN) classifier model by utilizing deep learning techniques. Our proposed solution has achieved the best results, which will allow the critical systems which need more security to adopt and take full advantage of the SDN paradigm without compromising their security.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1595-1603
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• 2020

Online defamation incidents such as Internet news comments on portal sites, SNS, and community sites are increasing in recent years. Bias and hate expressions threaten online service users in various forms, such as invasion of privacy and personal attacks, and defamation issues. In the past few years, academia and industry have been approaching in various ways to solve this problem The purpose of this study is to build a dataset and experiment with deep learning classification modeling for detecting various bias expressions as well as hate expressions. The dataset was annotated 7 labels that 10 personnel cross-checked. In this study, each of the 7 classes in a dataset of about 137,111 Korean internet news comments is binary classified and analyzed through deep learning techniques. The Proposed technique used in this study is multi-channel CNN model with attention. As a result of the experiment, the weighted average f1 score was 70.32% of performance.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.13 no.1
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• pp.131-138
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• 2003

With the Internet winning a huge popularity, there rise urgent problems which are related to Network Security Managements such as Protecting Network and Communication from un-authorized user. Accordingly, Using Security equipments have been common lately such as Intrusion Detection Systems, Firewalls and VPNs. Those systems. however, operate in individual system which are independent to me another. Their usage are so limited according to their vendors that they can not provide a corporate Security Solution. In this paper, we present a Hierarchical Security Management Model which can be applicable to a Network Security Policies consistently. We also propose a Policy Negotiation Mechanism and a Prototype which help us to manage Security Policies and Negotiations easier. The results of this research also can be one of the useful guides to developing a Security Policy Server or Security Techniques which can be useful in different environments. This study also shows that it is also possible to improve a Security Characteristics as a whole network and also to support Policy Associations among hosts using our mechanisms.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.15 no.5
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• pp.93-103
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• 2005

In this paper, we propose a RST resistant robust logo embedding technique for multimedia copyright protection Geometric manipulations are challenging attacks in that they do not introduce the quality degradation very much but make the detection process very complex and difficult. Watermark embedding in the normalized image directly suffers from smoothing effect due to the interpolation during the image normalization. This can be avoided by estimating the transform parameters using an image normalization technique, instead of embedding in the normalized image. Conventional RST resistant schemes that use full frame transform suffer from the absence of effective perceptual masking methods. Thus, we adopt $8\times8$ block DCT and calculate masking using a spatio-frequency localization of the $8\times8$ block DCT coefficients. Simulation results show that the proposed algorithm is robust against various signal processing techniques, compression and geometrical manipulations.

• Journal of IKEEE
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• v.25 no.2
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• pp.362-370
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• 2021

In this paper, we propose the Autonomous Stair-climbing system based on data from ToF sensors and IMU in developing stair-climbing robots to passive wheelchair users. Autonomous stair-climbing system are controlled by separating the timing of landing gear operation by location and utilizing state machines. To prove the theory, we construct and experiment with standard model stairs. Through an experiment to get the Attack angle, the average error of operating landing gear was 2.19% and the average error of the Attack angle was 2.78%, and the step division and status transition of the autonomous stair-climbing system were verified. As a result, the performance of the proposed techniques will reduce constraints of transportation handicapped.

• Journal of Convergence for Information Technology
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• v.11 no.2
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• pp.25-30
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• 2021

In this paper, we studied the coding technique for flicker mitigation and BER performance improvement in visible light communication system. In order to increase the transmission speed of visible light communication, a multi-transmission multi-LED transmission system using a plurality of LEDs is being actively studied. However, when data is transmitted through N LEDs in a multi-LED visible light communication system using N LEDs, there is a continuous zero section in which 0 is transmitted simultaneously according to the data sequence, and since the transmission section of 1 is different, flickering Or, a phenomenon in which the dimming level changes occurs. The visible light communication system is a communication system that simultaneously performs communication and lighting functions. Therefore, transmission efficiency of communication and brightness of lighting must be considered at the same time. To solve this problem, we proposed a flicker reduction mapping that can alleviate flicker and dimming level problems, improve error detection and BER performance through coding mapping of each LED data sequence.

• Journal of Internet of Things and Convergence
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• v.6 no.4
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• pp.65-72
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• 2020

In this paper, we developed a web-based DApp system based on a private blockchain by applying machine learning techniques to automatically identify Android malicious apps that are continuously increasing rapidly. The optimal machine learning model that provides 96.2587% accuracy for Android malicious app identification was selected to the authorized experimental data, and automatic identification results for Android malicious apps were recorded/managed in the Hyperledger Fabric blockchain system. In addition, a web-based DApp system was developed so that users who have been granted the proper authority can use the blockchain system. Therefore, it is possible to further improve the security in the Android mobile app usage environment through the development of the machine learning-based Android malicious app identification block chain DApp system presented. In the future, it is expected to be able to develop enhanced security services that combine machine learning and blockchain for general-purpose data.

• Journal of the Korea Society for Simulation
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• v.29 no.4
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• pp.1-7
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• 2020

Early detection and classification of crop diseases play significant role to help farmers to reduce disease spread and to increase agricultural productivity. Recently, many researchers have used deep learning techniques like convolutional neural network (CNN) classifier for crop disease inspection with dataset of crop leaf images (e.g., PlantVillage dataset). These researches present over 90% of classification accuracy for crop diseases, but they have ability to detect only the pre-trained diseases. This paper proposes an efficient disease inspection CNN model for new crops not used in the pre-trained model. First, we present a benchmark crop disease classifier (CDC) for the crops in PlantVillage dataset using VGG16. Then we build a modified crop disease classifier (mCDC) to inspect diseases for untrained crops. The performance evaluation results show that the proposed model outperforms the benchmark classifier.

• Food Science of Animal Resources
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• v.42 no.1
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• pp.1-17
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• 2022

Lipids are one of the major macronutrients essential for adequate growth and maintenance of human health. Their structure is not only complex but also diverse, which makes systematic and holistic analyses challenging; consequently, little is known regarding the relationship between phenotype and mechanism of action. In recent years, rapid advancements have been made in the fields of lipidomics and bioinformatics. In comparison with traditional approaches, mass spectrometry-based lipidomics can rapidly identify as well as quantify >1,000 lipid species at the same time, facilitating comprehensive, robust analyses of lipids in tissues, cells, and body fluids. Accordingly, lipidomics is now being widely applied in various fields, particularly food and nutrition science. In this review, we discuss lipid classification, extraction techniques, and detection and analysis using lipidomics. We also cover how lipidomics is being used to assess food obtained from livestock and poultry. The information included herein should serve as a reference to determine how to characterize lipids in animal food samples, enhancing our understanding of the application of lipidomics in the field in animal husbandry.