• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.3
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• pp.420-427
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• 2020

The U.S-China disputes have attracted worldwide attention since it took place. However, the disputes between China and the US are no longer limited to the competition in traditional industries, and the competition in 5G industries is becoming more intense. This paper analyzes the reasons for US sanctions on Huawei and puts forward some Suggestions for its countermeasures. With the continuous trade exchanges between China and the United States and the acceleration of China's rise, the related industries in the United States will inevitably be impacted by the related industries in China. Despite U.S. sanctions, the fast speed and effective cost of 5G in China is further improving China's competitiveness. However, under the economic sanctions of the United States, how to survive and further develop China's 5G industry needs in-depth research.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.6
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• pp.802-808
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• 2020

The IoT(Internet of Things) is based on the development of sensor technology and high-speed communication infrastructure, and the number of IoT connected to the network is increasing more than the number of people, and the increase is also very fast. In the field of defense, IoT is being deployed in various fields such as operations, military, base defense, and informatization, and the need is also increasing. Unlike the existing PC/server information protection system, cyber threats are also increasing as IoT sensors, which are vulnerable to information protection, are increasing in the network, so it is necessary to study the platform to protect the defense information and communication network. we investigated the case of connecting wired and wireless IoT to the defense network, and presented an efficient interlocking design method of the IoT integrated independent network with enhanced security by minimizing the contact point with the defense network.

• Journal of Convergence for Information Technology
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• v.11 no.2
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• pp.41-46
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• 2021

The method of the edge detection algorithm based on grayscale mathematical morphology has the advantage that image noise can be removed and processed in parallel, and the operation speed is fast. However, the method of detecting the edge of an image using a single structural scale element may be affected by image information. The characteristics of grayscale morphology may be limited to the edge information result of the operation result by repeatedly performing expansion, erosion, opening, and containment operations by repeating structural elements. In this paper, we propose an edge detection algorithm that applies a structural element with strong directionality to noise and then applies weighted entropy to each pixel information in the element. The result of applying the multi-scale structural element applied to the image and the result of applying the directional weighted entropy were compared and analyzed, and the simulation result showed that the proposed algorithm is superior in edge detection.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.4
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• pp.191-197
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• 2021

To calculate the induced charge of atoms in molecular dynamics, linear equations for the induced charges need to be solved. As induced charges are determined at each time step, the process involves considerable computational costs. Hence, an efficient method for calculating the induced charge distribution is required when analyzing large systems. This paper introduces the Uzawa method for solving saddle point problems, which occur in linear systems, for the solution of the Lagrange equation with constraints. We apply the accelerated Uzawa algorithm, which reduces computational costs noticeably using the Schur complement and preconditioned conjugate gradient methods, in order to overcome the drawback of the Uzawa parameter, which affects the convergence speed, and increase the efficiency of the matrix operation. Numerical models of molecular dynamics in which two gold nanoparticles are placed under external electric fields reveal that the proposed method provides improved results in terms of both convergence and efficiency. The computational cost was reduced by approximately 1/10 compared to that for the Gaussian elimination method, and fast convergence of the conjugate gradient, as compared to the basic Uzawa method, was verified.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.406-412
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• 2022

Storing large amounts of data has always been a big problem from the beginning of computing history. Big Data has made huge advancements in improving business processes by finding the customers' needs using prediction models based on web and social media search. The main purpose of big data stream processing frameworks is to allow programmers to directly query the continuous stream without dealing with the lower-level mechanisms. In other words, programmers write the code to process streams using these runtime libraries (also called Stream Processing Engines). This is achieved by taking large volumes of data and analyzing them using Big Data frameworks. Streaming platforms are an emerging technology that deals with continuous streams of data. There are several streaming platforms of Big Data freely available on the Internet. However, selecting the most appropriate one is not easy for programmers. In this paper, we present a detailed description of two of the state-of-the-art and most popular streaming frameworks: Apache Ignite and Hazelcast. In addition, the performance of these frameworks is compared using selected attributes. Different types of databases are used in common to store the data. To process the data in real-time continuously, data streaming technologies are developed. With the development of today's large-scale distributed applications handling tons of data, these databases are not viable. Consequently, Big Data is introduced to store, process, and analyze data at a fast speed and also to deal with big users and data growth day by day.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.104-113
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• 2022

In this paper, we propose deep learning-based target distance and velocity estimation technique for OFDM radar systems. In the proposed technique, the 2D periodogram is obtained via 2D fast Fourier transform (FFT) from the reflected signal after removing the modulation effect. The periodogram is the input to the conventional and proposed estimators. The peak of the 2D periodogram represents the target, and the constant false alarm rate (CFAR) algorithm is the most popular conventional technique for the target's distance and speed estimation. In contrast, the proposed method is designed using the multiple output convolutional neural network (CNN). Unlike the conventional CFAR, the proposed estimator is easier to use because it does not require any additional information such as noise power. According to the simulation results, the proposed CNN improves the mean square error (MSE) by more than 5 times compared with the conventional CFAR, and the proposed estimator becomes more accurate as the number of transmitted OFDM symbols increases.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1545-1550
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• 2021

We propose an optimal variable step size adjustment method for the simplified subband affine projection algorithm (Simplified SAP; SSAP) in a subband structure based on a polyphase decomposition technique. The proposed method provides an optimal step size derived to minimize the mean square deviation(MSD) at the time of updating the coefficients of the subband adaptive filter. Application of the proposed optimal step size in the SSAP algorithm using colored input signals ensures fast convergence speed and small steady-state error. The results of computer simulations performed using AR(2) signals and real voices as input signals prove the validity of the proposed optimal step size for the SSAP algorithm. Also, the simulation results show that the proposed algorithm has a faster convergence rate and good steady-state error compared to the existing other adaptive algorithms.

• Smart Media Journal
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• v.11 no.5
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• pp.48-55
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• 2022

Tomato crops are easy to expose to disease and spread in a short period of time, so late measures against disease are directly related to production and sales, which can cause damage. Therefore, there is a need for a service that enables early prevention by simply and accurately diagnosing tomato diseases in the field. In this paper, we construct a system that applies a deep learning-based model in which ImageNet transition is learned in advance to classify and serve nine classes of tomatoes for disease and normal cases. We use the input of MobileNet, ResNet, with a deep learning-based CNN structure that builds a lighter neural network using a composite product for the image set of leaves classifying tomato disease and normal from the Plant Village dataset. Through the learning of two proposed models, it is possible to provide fast and convenient services using MobileNet with high accuracy and learning speed.

• Journal of the Korea Society of Computer and Information
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• v.26 no.4
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• pp.47-53
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• 2021

In this paper, we propose a quadtree-based optimization technique that enables fast Super-resolution(SR) computation by efficiently classifying and dividing physics-based simulation data required to calculate SR. The proposed method reduces the time required for quadtree computation by downscaling the smoke simulation data used as input data. By binarizing the density of the smoke in this process, a quadtree is constructed while mitigating the problem of numerical loss of density in the downscaling process. The data used for training is the COCO 2017 Dataset, and the artificial neural network uses a VGG19-based network. In order to prevent data loss when passing through the convolutional layer, similar to the residual method, the output value of the previous layer is added and learned. In the case of smoke, the proposed method achieved a speed improvement of about 15 to 18 times compared to the previous approach.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.393-402
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• 2021

The first TBM introduced in Korea was the gripper TBM, which was applied to the Gudeok Waterway Tunnel in 1985. In the initial stage of the introduction of the gripper TBM, many applications were mainly focused on waterway tunnels (Tunnel Mechanized Construction Design, 2008). Currently, the construction range of gripper TBM in Korea is widely applied to not only waterway tunnels, but also subways, railway tunnels, and TBM+NATM expansion. Overseas, gripper TBM is generally applied, and even when NATM tunnel is applied, it is applied as an exploration tunnel because of the excellent advance rate of gripper TBM and used as an evacuation tunnel after completion. Due to the fast excavation speed, the application of the gripper TBM in the rock section of weathered rock or higher can minimize the environmental and civil complaints caused by creating a large number of work areas when planning long tunnels or mountain tunnels. In this study, the work process of the general gripper TBM was analyzed by analyzing the construction cycle and the gripper TBM with a diameter of 2.6~5.0 m, which was applied the most in Korea. Downtime was investigated and analyzed.