• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4795-4815
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• 2020

Drone detection can be considered as a specific sort of small object detection, which has always been a challenge because of its small size and few features. For improving the detection rate of drones, we design a Deeper SSD network, which uses large-scale input image and deeper convolutional network to obtain more features that benefit small object classification. At the same time, in order to improve object classification performance, we implemented the up-sampling modules to increase the number of features for the low-level feature map. In addition, in order to improve object location performance, we adopted the down-sampling modules so that the context information can be used by the high-level feature map directly. Our proposed Deeper SSD and its variants are successfully applied to the self-designed drone datasets. Our experiments demonstrate the effectiveness of the Deeper SSD and its variants, which are useful to small drone's detection and recognition. These proposed methods can also detect small and large objects simultaneously.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.4 no.4
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• pp.264-271
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• 2011

ILS(Instrument Landing System) is the international standard system for approach and landing guidance. ILS was adopted by ICAO(International Civil Aviation Organization) in 1947 and is currently being used in commercial systems. To design the digital transmitter and receiver modules that can be mounted in the integrated ILS, we propose the digital design methods of digital double AM modulator and demodulator using FPGA chip, DDS(Direct Digital Synthesizer) for generation of sampling clock, demodulator of DDC(Digital Down Converter) structure, and spectrum analyzer using DSP chip. We demonstrate the efficiency of the proposed design method through experiments using developed transmitter and receiver modules. This system can be used as a high-performance commercial system.

• Smart Structures and Systems
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• v.32 no.3
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• pp.135-151
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• 2023

Using 3D asphalt pavement surface data, a deep and multiscale network named CrackNet-M is proposed in this paper for pixel-level crack detection for improvements in both accuracy and robustness. The CrackNet-M consists of four function-specific architectural modules: a central branch net (CBN), a crack map enhancement (CME) module, three pooling feature pyramids (PFP), and an output layer. The CBN maintains crack boundaries using no pooling reductions throughout all convolutional layers. The CME applies a pooling layer to enhance potential thin cracks for better continuity, consuming no data loss and attenuation when working jointly with CBN. The PFP modules implement direct down-sampling and pyramidal up-sampling with multiscale contexts specifically for the detection of thick cracks and exclusion of non-crack patterns. Finally, the output layer is optimized with a skip layer supervision technique proposed to further improve the network performance. Compared with traditional supervisions, the skip layer supervision brings about not only significant performance gains with respect to both accuracy and robustness but a faster convergence rate. CrackNet-M was trained on a total of 2,500 pixel-wise annotated 3D pavement images and finely scaled with another 200 images with full considerations on accuracy and efficiency. CrackNet-M can potentially achieve crack detection in real-time with a processing speed of 40 ms/image. The experimental results on 500 testing images demonstrate that CrackNet-M can effectively detect both thick and thin cracks from various pavement surfaces with a high level of Precision (94.28%), Recall (93.89%), and F-measure (94.04%). In addition, the proposed CrackNet-M compares favorably to other well-developed networks with respect to the detection of thin cracks as well as the removal of shoulder drop-offs.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.5
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• pp.117-124
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• 2004

Various multimedia systems have been developed and their application areas widely proliferate. Thus, the interoperability is getting important among various networks and devices. The video transcoding is a technology to solve this interoperability problem among various coding standards. Transcoding can be defined as the conversion of one compressed coded data to another. In this paper, MPEG-2 to MPEG-4 transcoder in the spatial domain is compared with that in the DCT domain. The transcoder is very useful when a video sequence that is originally encoded for digital TV, DVD or satellite broadcasting is served in mobile environment. In order to compare two transcoders, all modules except motion compensation and down sampling are implemented identically. In addition, both transcoders do not search for motion vector. Instead, the decoded information is reused to the encoder. The experimental results show that the transcoder in the spatial domain is usually better than that in the DCT domain with respect to PSNR (Peak Signal-to-Noise Ratio), bitrate and execution time.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.35 no.6
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• pp.568-575
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• 2022

This paper is a study on frequency analysis and electronic noise reduction of energy storage system (ESS). We acquired 4 necessary data for about 2 minutes and 4 seconds using a sampling frequency of 10,000 Hz in ESS. Fast Fourier transform (FFT) was used for electronic noise analysis from the acquired data. As a result, it was confirmed that DC component, fundamental wave, second and higher harmonic component exist. For the attenuation of harmonics, low-pass filter (LPF) was applied. We confirmed that an attenuation of approximately 59.3% appears from the second harmonic. The presence of many harmonic components in the data of the ESS was expected to occur due to the insufficiency of optimization among the modules inside the ESS. Therefore, we propose that a national certification system for ESS should be introduced to settle down the issue properly.