• International Journal of Contents
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• v.14 no.1
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• pp.1-6
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• 2018

Text, as one of the most influential inventions of humanity, has played an important role in human life since ancient times. The rich and precise information embodied in text is very useful in a wide range of vision-based applications such as the text data extracted from images that can provide information for automatic annotation, indexing, language translation, and the assistance systems for impaired persons. Therefore, natural-scene text detection with active research topics regarding computer vision and document analysis is very important. Previous methods have poor performances due to numerous false-positive and true-negative regions. In this paper, a fully-convolutional-network (FCN)-based method that uses supervised architecture is used to localize textual regions. The model was trained directly using images wherein pixel values were used as inputs and binary ground truth was used as label. The method was evaluated using ICDAR-2013 dataset and proved to be comparable to other feature-based methods. It could expedite research on text detection using deep-learning based approach in the future.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.2
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• pp.27-33
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• 2020

Backward compatibility is one of the key issues for radio equipment supporting IEEE 802.11, the typical wireless local area networks (WLANs) communication protocol. For a successful packet decoding with the backward compatibility, the frame format detection is a core precondition. This paper presents a novel frame format detection method based on a deep learning procedure for WLANs affiliated with IEEE 802.11. Considering that the detection performance of conventional methods is degraded mainly due to the poor performances in the symbol synchronization and/or channel estimation in low signal-to-noise-ratio environments, we propose a novel detection method based on convolutional neural network (CNN) that replaces the entire conventional detection procedures. The proposed deep learning network provides a robust detection directly from the receive data. Through extensive computer simulations performed in the multipath fading channel environments (modeled by Project IEEE 802.11 Task Group ac), the proposed method exhibits superb improvement in the frame format detection compared to the conventional method.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.2
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• pp.45-52
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• 2020

This paper is focused on the modulation scheme detection of the IEEE 802.11 standard. In the IEEE 802.11ac standard, the information of the modulation scheme is indicated by the modulation coding scheme (MCS) included in the VHT-SIG-A of the preamble field. Transmitting end determines the MCS index suitable for the low signal to noise ratio (SNR) situation and transmits the data accordingly. Since data field decoding can take place only when the receiving end acquires the MCS index information of the frame. Therefore, accurate MCS detection must be guaranteed before data field decoding. However, since the MCS index information is the information obtained through preamble field decoding, the detection rate can be affected significantly in a low SNR situation. In this paper, we propose a relatively robust modulation classification method based on deep learning to solve the low detection rate problem with a conventional method caused by a low SNR.

• Annual Conference of KIPS
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• 2017.11a
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• pp.302-305
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• 2017

사이버 공격은 점차 다양해지고, 그 위험성은 날로 심각해지고 있다. 가장 강력한 공격 중 하나는 DDoS (Distributed Denial of Service) 공격이다. 본 논문에서는 다양한 사이버 공격을 분류하고 이에 따른 방법 기법을 서술하겠다. 특히, 최신 DDoS 공격 탐지 방법을 소개하고 딥러닝 (Deep Learning)을 활용한 최신 방어 기법 연구에 대해 살펴보도록 하겠다.

• Annual Conference of KIPS
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• 2017.04a
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• pp.949-950
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• 2017

딥러닝(Deep Learning)을 효과적으로 연구하고 개발할 수 있도록 도와주는 다양한 딥러닝 프레임워크(Deep Learning Framework)가 있다. 딥러닝 프레임워크는 현재 100 가지도 넘는 종류가 있다. 그렇기 때문에 개발의 목적에 가장 적합한 딥러닝 프레임워크를 선택하는 것은 쉽지 않다. 본고에서는 5가지 대표적인 딥러닝 프레임워크에 대해서 각각의 특징을 분석하고 비교한다. 이를 통하여 딥러닝을 개발하기 전에 개발 목적에 적합한 프레임워크를 선택할 수 있는 간단한 안목을 제시한다.

• Journal of Broadcast Engineering
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• v.23 no.5
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• pp.718-721
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• 2018

This paper presents an inter-prediction technique using deep learning, where a virtual reference frame of the current frame is synthesized by using the reconstructed frames to improve coding efficiency. Experimental results demonstrate that the proposed algorithm provides 1.9% BD-rate reduction on average as compared to HEVC reference software in the Random Access condition.

• Electronics and Telecommunications Trends
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• v.33 no.4
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• pp.33-42
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• 2018

Object classification and detection are fundamental technologies in computer vision and its applications. Recently, a deep-learning based approach has shown significant improvement in terms of object classification and detection. This report reviews the progress of deep-learning based object classification and detection in views of the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), and analyzes recent trends of object classification and detection technology and its applications.

• Journal of Positioning, Navigation, and Timing
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• v.9 no.3
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• pp.169-173
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• 2020

Location-based services are used as core services in various fields. In particular, in the field of public services such as emergency rescue, accurate location estimation technology is very important. Recently, the technology of tracking the location of self-isolation subjects for COVID-19 has become a major issue. Therefore, location estimation technology using personal smart devices is being studied in various ways, and the most widely used method is to use GPS. Other representative methods are using Wi-Fi, Pedestrian Dead Reckoning (PDR), Bluetooth Low Energy (BLE) beacons, and LTE signals. In this paper, we introduced a positioning technology using deep learning based on LTE Channel State Information-Reference Signal (CSI-RS) data, and confirmed the possibility through an outdoor location estimation experiment using a commercial LTE signal.

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.183-191
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• 2020

Today, taking photos using smartphones has become an essential element of modern people. According to these social changes, modern people need a larger storage capacity, and the number of unnecessary photos has increased. To support the storage, cloud-based photo storage services from various platforms have appeared, and many people are using the services. As the number of photos increases, it is difficult for users to find the photos they want, and it takes a lot of time to organize. In this paper, we propose a cloud-based photo management service that facilitates photo management by classifying photos and recommending unnecessary photos using deep learning. The service provides the function of tagging photos by identifying what the subject is, the function of checking for wrongly taken photos, and the function of recommending similar photos. By using the proposed service, users can easily manage photos and use storage capacity efficiently.

• Computers and Concrete
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• v.25 no.5
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• pp.433-445
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• 2020

A method is proposed in this paper to estimate the workability of self-compacting concrete (SCC) in different mixing conditions with different mixers and mixing volumes by recording the mixing process based on deep learning (DL). The SCC mixing videos were transformed into a series of image sequences to fit the DL model to predict the SF and VF values of SCC, with four groups in total and approximately thirty thousand image sequence samples. The workability of three groups SCC whose mixing conditions were learned by the DL model, was estimated. One additionally collected group of the SCC whose mixing condition was not learned, was also predicted. The results indicate that whether the SCC mixing condition is included in the training set and learned by the model, the trained model can estimate SCC with different workability effectively at the same time. Our goal to estimate SCC workability in different mixing conditions is achieved.