• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.3
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• pp.180-191
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• 2016

Optimizing energy usage for maximum efficiency is an essential goal for manufacturing plants in every industrial manufacturing sector. The generation and distribution of purifying compressed air is a large expense incurred in practically all manufacturing processes. Not only is the generation and treatment expensive equipment of compressed air, but frequent maintenance and effective operation is also required. As a plant's compressed air system is often an integral part of the production process, it needs to be reliable, efficient, and easy to be maintain. In this paper, we study to find operating method to save energy from the adsorption dryer in the process of purifying compressed air, which is required for a clean room production site in "A" company. The compressed air passes through a pressure vessel with two "towers" filled with a material such as activated alumina, silica gel, molecular sieve or other desiccant material. This desiccant material attracts the water from the compressed air via adsorption. As the water clings to the desiccant, the desiccant particle becomes saturated. Therefore, Adsorption dryer is an extremely significant facility which removes the moisture in the air $70^{\circ}C$ below the dew point temperature while using a lot of energy. Also, the energy consumption of the adsorption dryer can be varied by various operating conditions (time, pressure, temperature, etc). Therefore, based on existing operating experiments, we have searched operating condition to maximize energy saving by changing operating conditions of the facility. However, due to a short experiment period (from September to October), further research will be focused on considering seasonality.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.3
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• pp.10-17
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• 2009

This paper proposes a compressed domain signature extraction method which can be used for CBCD (Content Based Copy Detection). Since existing signature extraction methods for the CBCD are executed in spatial domain, they need additional computations to decode the compressed video before the signature extraction. To avoid this overhead, we generate a thumbnail image directly from the compressed video without full decoding. Then we can extract the video signature from the thumbnail image. Experimental results of extracting brightness ordering information as the signature for CBCD show that our proposed method is 2.8 times faster than the spatial domain method while maintaining 80.98% accuracy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37A no.12
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• pp.1122-1132
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• 2012

Compressed sensing has been applied to many fields such as images, speech signals, radars, etc. It has been mainly applied to stationary signals, and reconstruction error could grow as compression ratios are increased by decreasing measurements. To resolve the problem, speech signals are divided into frames and processed in parallel. The frames are made sparse by dictionary learning, and adaptive compressed sensing is applied which designs the compressed sensing reconstruction matrix adaptively by using the difference between the sparse coefficient vector and its reconstruction. Through the proposed method, we could see that fast and accurate reconstruction of non-stationary signals is possible with compressed sensing.

• Proceedings of the IEEK Conference
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• 2002.06a
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• pp.311-314
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• 2002

This paper deals with an improved method of utilizing end-to-end bandwidth in the All-IP environment. The proposed method includes compression of UDP/RTP headers, and multiplexing of the RTP stream packets over the end-to-and media transfer. Although the conventional method of using TCRTP(Tunneling Multiplexed Compressed RTP) is an efficient mettled of maximizing tile network throughput, it is inadequate for the All-IP based end-to-end communication. The method is a link-layer independent solution that can be easily implemented in the NGN(Next Generation Network).

• Smart Structures and Systems
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• v.25 no.2
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• pp.123-133
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• 2020

Compressive sensing (CS) is a newly developed data acquisition and processing technique that takes advantage of the sparse structure in signals. Normally signals in their primitive space or format are reconstructed from their compressed measurements for further treatments, such as modal analysis for vibration data. This approach causes problems such as leakage, loss of fidelity, etc., and the computation of reconstruction itself is costly as well. Therefore, it is appealing to directly work on the compressed data without prior reconstruction of the original data. In this paper, a direct approach for modal analysis of damped systems is proposed by decomposing the compressed measurements with an appropriate dictionary. The damped free vibration function is adopted to form atoms in the dictionary for the following sparse decomposition. Compared with the normally used Fourier bases, the damped free vibration function spans a space with both the frequency and damping as the control variables. In order to efficiently search the enormous two-dimension dictionary with frequency and damping as variables, a two-step strategy is implemented combined with the Orthogonal Matching Pursuit (OMP) to determine the optimal atom in the dictionary, which greatly reduces the computation of the sparse decomposition. The performance of the proposed method is demonstrated by a numerical and an experimental example, and advantages of the method are revealed by comparison with another such kind method using POD technique.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1997.11a
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• pp.151-154
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• 1997

In this paper, we propose a new watermarking algorithm for copyright protection of video data. The proposed algorithm inserts a watermark directly on the MPEG-2 bitstream. Since more and more video data are stored and transmitted in a compressed form, it is desirable to insert a watermark on the compressed bit stream to avoid the expensive full-decoding and re-encoding process. Embedding a watermark in the compressed domain, we can also avoid the effect of the compression error which may erase the watermark.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.411-425
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• 2021

In this paper, a vehicle recognition algorithm based on deep convolutional neural network and compression dictionary is proposed. Firstly, the network structure of fine vehicle recognition based on convolutional neural network is introduced. Then, a vehicle recognition system based on multi-scale pyramid convolutional neural network is constructed. The contribution of different networks to the recognition results is adjusted by the adaptive fusion method that adjusts the network according to the recognition accuracy of a single network. The proportion of output in the network output of the entire multiscale network. Then, the compressed dictionary learning and the data dimension reduction are carried out using the effective block structure method combined with very sparse random projection matrix, which solves the computational complexity caused by high-dimensional features and shortens the dictionary learning time. Finally, the sparse representation classification method is used to realize vehicle type recognition. The experimental results show that the detection effect of the proposed algorithm is stable in sunny, cloudy and rainy weather, and it has strong adaptability to typical application scenarios such as occlusion and blurring, with an average recognition rate of more than 95%.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.157-160
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• 2002

Scene change detection is the fundamental process of automatic video indexing and retrieving. In this paper we propose a method which utilizes both compressed and uncompressed domain methods to detect scene change in a video. Candidate locations for scene change are estimated from DC images and motion vector information in compressed domain. And candidate frames are verified using edge histogram distance and color histogram distance, in uncompressed domain. The experimental results show that scene change can be detected fast and correctly by proposed method.

• Smart Structures and Systems
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• v.25 no.3
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• pp.369-384
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• 2020

A wavefield sparse reconstruction technique based on compressed sensing is developed in this work to dramatically reduce the number of measurements. Firstly, a severely underdetermined representation of guided wavefield at a snapshot is established in the spatial domain. Secondly, an optimal compressed sensing model of guided wavefield sparse reconstruction is established based on l₁-norm penalty, where a suite of discrete cosine functions is selected as the dictionary to promote the sparsity. The regular, random and jittered undersampling schemes are compared and selected as the undersampling matrix of compressed sensing. Thirdly, a gradient projection method is employed to solve the compressed sensing model of wavefield sparse reconstruction from highly incomplete measurements. Finally, experiments with different excitation frequencies are conducted on an aluminum plate to verify the effectiveness of the proposed sparse reconstruction method, where a scanning laser Doppler vibrometer as the true benchmark is used to measure the original wavefield in a given inspection region. Experiments demonstrate that the missing wavefield data can be accurately reconstructed from less than 12% of the original measurements; The reconstruction accuracy of the jittered undersampling scheme is slightly higher than that of the random undersampling scheme in high probability, but the regular undersampling scheme fails to reconstruct the wavefield image; A quantified mapping relationship between the sparsity ratio and the recovery error over a special interval is established with respect to statistical modeling and analysis.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.8C
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• pp.1175-1183
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• 2004

This paper propose a method to trace a moving object based on the information directly obtained from MPEG-2 compressed video stream without decoding process. In the proposed method, the motion flow is constructed from the motion vectors involved in compressed video and then we calculate the amount of pan, tilt, zoom associated with camera operations using generalized Hough transform. The local object motion can be extracted from the motion flow after the compensation with the parameters related to the global camera motion. The moving object is designated initially by a user via bounding box. After then automatic tracking is performed based on the mean-shift algorithm of the motion flows of the object. The proposed method can improve the computation speed because the information is directly obtained from the MPEG-2 compressed video, but the object boundary is limited by blocks rather than pixels.