• The Journal of the Acoustical Society of Korea
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• v.38 no.1
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• pp.106-113
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• 2019

In this paper, we propose an algorithm to classify the received active pulse when the active sonar system is operated as a non-cooperative mode. The proposed algorithm uses CNN (Convolutional Neural Networks) which shows good performance in various fields. As an input of CNN, time frequency analysis data which performs STFT (Short Time Fourier Transform) of the received signal is used. The CNN used in this paper consists of two convolution and pulling layers. We designed a database based neural network and a pulse feature based neural network according to the output layer design. To verify the performance of the algorithm, the data of 3110 CW (Continuous Wave) pulses and LFM (Linear Frequency Modulated) pulses received from the actual ocean were processed to construct training data and test data. As a result of simulation, the database based neural network showed 99.9 % accuracy and the feature based neural network showed about 96 % accuracy when allowing 2 pixel error.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.1
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• pp.105-114
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• 2006

Ubiquitous Sensor Network consists of a number of sensor nodes with a limited computation power and limited communication capabilities, and a sensor node is able to communicate with each other at anytime and in any place. Due to the rapid research and development in sensor networks, it will rapidly grow into environments where hmm beings can interact in an intuitive way with sensing objects which can be PDAs, sensors, or even clothes in the future. We are aiming at realizing an Unattended Secure Security System to apply it to Ubiquitous Sensor Network. In this paper, the vulnerabilities in the Unattended security system are identified, and a new protocol called OMAC-SNEP is proposed for the Unattended Secure Security System. Because the CBC-MAC in SNEP is not secure unless the message length is fixed, the CBC-MAC in SNEP was replaced with OMAC in SNEP. We have shown that the proposed protocol is secure for my bit length of messages and is almost as efficient as the CBC-MAC with only one key. OMAC-SNEP can be used not only in Unattended Security System, but also any other Sensor Networks.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.4
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• pp.161-169
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• 2011

There is a consensus in the field of vehicular network security that public key cryptography should be used to secure communications. A certificate revocation list (CRL) should be distributed quickly to all the vehicles in the network to protect them from malicious users and malfunctioning equipment as well as to increase the overall security and safety of vehicular networks. Thus, a major challenge in vehicular networks is how to efficiently distribute CRLs. This paper proposes a CRL distribution method aided by terrestrial digital multimedia broadcasting (T-DMB). By using T-DMB data broadcasting channels as alternative communication channels, the proposed method can broaden the network coverage, achieve real-time delivery, and enhance transmission reliability. Even if roadside units are not deployed or only sparsely deployed, vehicles can obtain recent CRLs from the T-DMB infrastructure. A new transport protocol expert group (TPEG) CRL application was also designed for the purpose of broadcasting CRLs over the T-DMB infrastructure.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.4
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• pp.515-521
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• 2020

Recently, data traffic has exploded due to development of various industries, which causes problems about losing of efficiency and overloaded existing networks. To solve these problems, network slicing, which uses a virtualization technology and provides a network optimized for various services, has received a lot of attention. In this paper, we propose a resource allocation method using credit value. In the method using the clustering technology, an operation for selecting a cluster is performed whenever an allocation request for various services occurs. On the other hand, in the proposed method, the credit value is set by using the residual capacity and balancing so that the slice request can be processed without performing the operation required for cluster selection. To prove proposed method, we perform processing time and balancing simulation. As a result, the processing time and the error factor of the proposed method are reduced by about 13.72% and about 7.96% compared with the clustering method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.6
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• pp.700-705
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• 2020

Generative adversarial networks are methods of generating images by opposing two neural networks. When generating the image, randomly generated noise is rearranged to generate the image. The image generated by this method is not generated well depending on the noise, and it is difficult to generate a proper image when the number of pixels of the image is small In addition, the speed and size of data accumulation in data classification increases, and there are many difficulties in labeling them. In this paper, to solve this problem, we propose a technique to generate noise based on random noise using real data. Since the proposed system generates an image based on the existing image, it is confirmed that it is possible to generate a more natural image, and if it is used for learning, it shows a higher hit rate than the existing method using the hostile neural network respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.10
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• pp.1254-1260
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• 2019

Recently, 5G cellular systems have been attracted great attention as a key enabler for Industry 4.0. In this paper, we propose a novel random access based on pilot-assisted opportunistic transmission to support internet-of-things (IoT) scenario in cellular networks. A key feature of our proposed scheme is to enable each of IoT devices to attempt opportunistic transmission of its data packet in Step 3 with randomly selected uplink pilot signal. Both the opportunistic transmission and the pilot randomization in Step 3 are effective to significantly mitigate the occurrence of packet collisions. We mathematically analyze our proposed scheme in terms of packet collision probability and uplink resource efficiency. Through simulations, we verify the validity of our analysis and evaluate the performance of our proposed scheme. Numerical results show that our proposed scheme outperforms other competitive schemes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.567-575
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• 2020

As online social networks are used as a critical medium for modern people's information sharing and relationship, their users are increasing rapidly every year. This not only increases usage but also surpasses the existing media in terms of information credibility. Therefore, emerging marketing strategies are deliberately attacking social networks. As a result, public opinion, which should be formed naturally, is artificially formed by online attacks, and many people trust it. Therefore, many studies have been conducted to detect agents attacking online social networks. In this paper, we analyze the trends of researches attempting to detect such online social network attackers, focusing on researches using social network graph characteristics. While the existing content-based techniques may represent classification errors due to privacy infringement and changes in attack strategies, the graph-based method proposes a more robust detection method using attacker patterns.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.285-289
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• 2020

The configuration of hierarchical wireless networks is provided to support diverse network environments. In the base station, two system state can be basically considered for the operation management so that the state transition may be occurred between active and sleep modes. Hence, to reduce energy consumption the system operation management of the low power should be considered to the base station system. In this paper we consider the analytical model of Discontinuous Reception (DRX) to investigate the system management. We provide the analysis scheme of base station system by the DRX model, and the low power factor would be investigated for the energy consumption. We also use the finite-state Markov system model that in a system state period the wireless resource request and the operation of service call arrival interval is considered to numerically analyze the performance of energy saving operations of base station.

• International journal of advanced smart convergence
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• v.10 no.1
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• pp.1-11
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• 2021

Document page segmentation is an important step in building a quality optical character recognition module. The study examined already existing work on the topic of page segmentation and focused on the development of a segmentation model that has greater functional significance for application in an organization, as well as broad capabilities for managing the quality of the model. The main problems of document segmentation were highlighted, which include a complex background of intersecting objects. As classes for detection, not only classic text, table and figure were selected, but also additional types, such as signature, logo and table without borders (or with partially missing borders). This made it possible to pose a non-trivial task of detecting non-standard document elements. The authors compared existing neural network architectures for object detection based on published research data. The most suitable architecture was RetinaNet. To ensure the possibility of quality control of the model, a method based on neural network modeling using the RetinaNet architecture is proposed. During the study, several models were built, the quality of which was assessed on the test sample using the Mean average Precision metric. The best result among the constructed algorithms was shown by a model that includes four neural networks: the focus of the first neural network on detecting tables and tables without borders, the second - seals and signatures, the third - pictures and logos, and the fourth - text. As a result of the analysis, it was revealed that the approach based on four neural networks showed the best results in accordance with the objectives of the study on the test sample in the context of most classes of detection. The method proposed in the article can be used to recognize other objects. A promising direction in which the analysis can be continued is the segmentation of tables; the areas of the table that differ in function will act as classes: heading, cell with a name, cell with data, empty cell.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.49-57
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• 2022

Recently, various uses of artificial intelligence have been made possible through the deep artificial neural network structure of machine learning, demonstrating human-like capabilities. Unfortunately, the deep structure of the artificial neural network has not yet been accurately interpreted. This part is acting as anxiety and rejection of artificial intelligence. Among these problems, we solve the capability part of artificial neural networks. Calculate the size of the artificial neural network structure and calculate the size of data that the artificial neural network can process. The calculation method uses the group method used in mathematics to calculate the size of data and artificial neural networks using an order that can know the structure and size of the group. Through this, it is possible to know the capabilities of artificial neural networks, and to relieve anxiety about artificial intelligence. The size of the data and the deep artificial neural network are calculated and verified through numerical experiments.