• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.24-27
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• 2002

The environmental problems by using coolant demanded the new cooling methods. As one of them, the studies on the finding with compressed cold air and oil mist have been done. The cooling method using compressed cold air was effective through going down the temperature of compressed air supplied below $-25^{\circ}$ and increasing the amount of compressed cold air, but had not enough cooling effect due to the low performance of lubrication. Therefore, the cooling methods using oil mist newly were suggested. This method can satisfy both cooling effect and lubrication with only small amount of coolant, also have the benefit in the point of decreasing the environmental pollution. This paper focused on analyzing the grinding characteristics of the cooling method using oil mist. The grinding test according to compressed cold air, oil mist spray pressure and oil mist supply direction were done.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.2946-2962
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• 2017

An improved energy-efficient compressed network coding method is proposed for the data communication in the wireless sensor networks (WSNs). In the method, the compressed sensing and network coding are jointly used to improve the energy efficiency, and the two-hop neighbor information is employed to choose the next hop to further reduce the number of the transmissions. Moreover, a new packet format is designed to facilitate the intermediate node selection. To theoretically verify the efficiency of the proposed method, the expressions for the number of the transmissions and receptions are derived. Simulation results show that, the proposed method has higher energy efficiency compared with the available schemes, and it only requires a few packets to reconstruct measurements with reasonable quality.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.8
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• pp.1120-1124
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• 2013

Our objective of this study was to reduce radio frequency coil (RF) control time at 3 T MRI systems. A compressed method is proposed with a convex optimization and pseudo-inverse method in multi-channel RF coils. After applying the proposed methods, fields are homogenized with less field data. Even with 80% compression, the fields are well homogenized and localized, indicating that mapping requires only 20% of the original data. Detailed values are compared between each compressed result in and outside the region of interest at 3 T.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2018.11a
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• pp.59-61
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• 2018

Highly compressed images typically not only have low resolution, but are also affected by compression artifacts. Performing image super-resolution (SR) directly on highly compressed image would simultaneously magnify the blocking artifacts. In this paper, a SR method based on deep learning is proposed. The method is an end-to-end trainable deep convolutional neural network which performs SR on compressed images so as to reduce compression artifacts and improve image resolution. The proposed network is divided into compression artifacts removal (CAR) part and SR reconstruction part, and the network is trained by three-step training method to optimize training procedure. Experiments on JPEG compressed images with quality factors of 10, 20, and 30 demonstrate the effectiveness of the proposed method on commonly used test images and image sets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.58-78
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• 2016

Compressed sensing (CS) possesses the potential benefits for spectrum sensing of wideband signal in cognitive radio. The sparsity of signal in frequency domain denotes the number of occupied channels for spectrum sensing. This paper presents a scheme of adaptively adjusting the number of compressed measurements to reduce the unnecessary computational complexity when priori information about the sparsity of signal cannot be acquired. Firstly, a method of sparsity estimation is introduced because the sparsity of signal is not available in some cognitive radio environments, and the relationship between the amount of used data and estimation accuracy is discussed. Then the SNR of the compressed signal is derived in the closed form. Based on the SNR of the compressed signal and estimated sparsity, an adaptive algorithm of adjusting the number of compressed measurements is proposed. Finally, some simulations are performed, and the results illustrate that the simulations agree with theoretical analysis, which prove the effectiveness of the proposed adaptive adjusting of compressed measurements.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.793-797
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• 2000

The environmental problems by using coolant demanded the new cooling methods. As one of them the studies on the dry grinding with compressed cold air have been done. The cooling method using compressed cold air was effdve thmugh going down the temperature of compressed air supplied below $-25^{\circ}C$ and inneasing the amount of mmpresd cold air, but had not enough cooling effect due to the low performance of lubrication. Therefore, the cooling methods using MQL(Minimum Quantity Lubrication) or mist newly were suggested. These two methods can satisfy both cooling effect and lubrication with only small amount of coolant, also has the benefit in the point of decreasing the envimnmental pollution. This paper focused on analyzing the grindmg characteristics of the cooling method using mid. The generated heat and grinding force of the cooling method using mist were compared with them of coolant and compressed cold air. And them grinding test according to the temperature of compressed cold air, mist spray amount and mist supply direction were done.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.254-257
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• 2002

As the large Coolant amount used of a machine holds mass serious trouble recently, an environment pollution is increased, and a machine is conquering large specific gravity in an empty cost plane. It is the stage that must reexamine the parts washing that processing is later with this current way or a problem of a liquid waste treatment back. The environmental problems by using coolant demanded the new cooling methods. As one of them, the studies on the grinding with compressed cold air and oil mist have been done. The cooling method using compressed cold air was effective through going down the temperature of compressed air supplied below -$25^{\circ}C$ and increasing the amount of compressed cold air, but had not enough cooling effect due to the low performance of lubrication. Therefore, the cooling methods using oil mist newly were suggested. This method can satisfy both cooling effect and lubrication with only small amount of coolant, also have the benefit in the point of decreasing the environmental pollution. This paper focused on analyzing the grinding characteristics of the cooling method using oil mist. The grinding test according to compressed cold air and oil mist supply direction were done.

• Journal of the Semiconductor & Display Technology
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• v.19 no.4
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• pp.71-76
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• 2020

In this paper, we propose a super-resolution method that reconstructs compressed low-resolution images into high-resolution images. We propose a CNN model with a small number of parameters, and even if quantization is applied to the proposed model, super-resolution can be implemented without deteriorating the image quality. To further improve the quality of the compressed low-resolution image, a new degradation model was proposed instead of the existing bicubic degradation model. The proposed degradation model is used only in the training process and can be applied by changing only the parameter values to the original CNN model. In the super-resolution image applying the proposed degradation model, visual artifacts caused by image compression were effectively removed. As a result, our proposed method generates higher PSNR values at compressed images and shows better visual quality, compared to conventional CNN-based SR methods.

• KIEE International Transaction on Systems and Control
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• v.2D no.2
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• pp.97-107
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• 2002

In this paper, we propose the concept of orthonormalized compressed measurement for the stability analysis of discrete linear time-varying Kalman filters. Unlike previous studies that deal with the homogeneous portion of Kalman filters, the proposed Lyapunov method directly deals with the stochastically-driven system. The orthonorrmalized compressed measurement provides information on the a priori state estimate of the Kalman filter at the k-th step that is propagated from the a posteriori state estimate at the previous block of time. Since the complex multiple-step propagations of a candidate Lyapunov function with process and measurement noises can be simplified to a one-step Lyapunov propagation by the orthonormalized compressed measurement, a stochastic radius of attraction can be derived that would be impractically difficult to obtain by the conventional multiple-step Lyapunov method.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.410-421
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• 2019

Distributed compressed sensing (DCS) states that we can recover the sparse signals from very few linear measurements. Various studies about DCS have been carried out recently. In many practical applications, there is no prior information except for standard sparsity on signals. The typical example is the sparse signals have block-sparse structures whose non-zero coefficients occurring in clusters, while the cluster pattern is usually unavailable as the prior information. To discuss this issue, a new algorithm, called backtracking-based adaptive orthogonal matching pursuit for block distributed compressed sensing (DCSBBAOMP), is proposed. In contrast to existing block methods which consider the single-channel signal reconstruction, the DCSBBAOMP resorts to the multi-channel signals reconstruction. Moreover, this algorithm is an iterative approach, which consists of forward selection and backward removal stages in each iteration. An advantage of this method is that perfect reconstruction performance can be achieved without prior information on the block-sparsity structure. Numerical experiments are provided to illustrate the desirable performance of the proposed method.