• Progress in Medical Physics
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• v.26 no.2
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• pp.106-111
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• 2015

In Tomotherapy the couch sags during the treatment due to the weight of the patient. In this study, we developed a simple method to obtain the amount of the sag and the pitch angle of the couch using the image processing technique of MVCT images in Tomotherapy. Using the method we evaluated the sag and pitch of couch for 22 head and neck patients and one craniospinal irradiation (CSI) patient. The sag and the average pitch angle of couch were 0.40~1.54 mm and $0.7^{\circ}$ for head and neck patients, respectively. For head and neck patients, the sag increased as the longitudinal length of the irradiation volume increased and the pitch angle showed no relationship with the longitudinal length. For the CSI patient the sag was 4.97 mm. Using the method the amount of the couch sag could be measured easily and the measured data could be useful in determination of margins considering the table sag error.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37A no.11
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• pp.927-936
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• 2012

In this paper, we study joint feedback design for interference channel (IC). We develop a simple iterative algorithm for the joint feedback design to maximize the expected rate when the transmitters use partial channel-state information (CSI) obtained by the feedback link. Also, from the simulation result, we show that the performance gain is obtained compared to the conventional scheme.

• Journal of Communications and Networks
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• v.15 no.4
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• pp.338-351
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• 2013

Multiple-input multiple-output (MIMO) communication may provide high spectral efficiency through the deployment of a very large number of antenna elements at the base stations. The gains from massive MIMO communication come from the use of multi-user MIMO on the uplink and downlink, but with a large excess of antennas at the base station compared to the number of served users. Initial work on massive MIMO did not fully address several practical issues associated with its deployment. This paper considers the impact of channel aging on the performance of massive MIMO systems. The effects of channel variation are characterized as a function of different system parameters assuming a simple model for the channel time variations at the transmitter. Channel prediction is proposed to overcome channel aging effects. The analytical results on aging show how capacity is lost due to time variation in the channel. Numerical results in a multicell network show that massive MIMO works even with some channel variation and that channel prediction could partially overcome channel aging effects.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1682-1701
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• 2015

In this paper, we propose an intergraded unified imperfect CSI model and investigate the joined effects of feedback delay and channel estimation errors (CEE) for two-hop relaying systems with transmit beamforming and relay selection. We derived closed-form expressions for important performance measures including the exact analysis and lower bounds of outage probability as well as error performance. The ergodic capacity is also included with closed-form results. Furthermore, diversity and coding gains based on the asymptotic analysis at high SNRs are also presented, which are simple and concise and provide new analytical insights into the corresponding power allocation scheme. The analysis indicates that delay effect results in the coding gain loss and the diversity order loss, while CEE will merely cause the coding gain loss. Numerical results verify the theoretical analysis and illustrate the system is more sensitive to transmit beamforming delay compared with relay selection delay and also verify the superiority of optimum power allocation. We further investigate the outage loss due to the CEE and feedback delays, which indicates that the effect of the CEE is more influential at low-to-medium SNR, and then it will hand over the dominate role to the feedback delay.

• The Bulletin of The Korean Astronomical Society
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• v.38 no.1
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• pp.61.1-61.1
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• 2013

In this study we apply Support Vector Machine (SVM) to the prediction of geo-effective halo coronal mass ejections (CMEs). The SVM, which is one of machine learning algorithms, is used for the purpose of classification and regression analysis. We use halo and partial halo CMEs from January 1996 to April 2010 in the SOHO/LASCO CME Catalog for training and prediction. And we also use their associated X-ray flare classes to identify front-side halo CMEs (stronger than B1 class), and the Dst index to determine geo-effective halo CMEs (stronger than -50 nT). The combinations of the speed and the angular width of CMEs, and their associated X-ray classes are used for input features of the SVM. We make an attempt to find the best model by using cross-validation which is processed by changing kernel functions of the SVM and their parameters. As a result we obtain statistical parameters for the best model by using the speed of CME and its associated X-ray flare class as input features of the SVM: Accuracy=0.66, PODy=0.76, PODn=0.49, FAR=0.72, Bias=1.06, CSI=0.59, TSS=0.25. The performance of the statistical parameters by applying the SVM is much better than those from the simple classifications based on constant classifiers.

• Asia-Pacific Journal of Atmospheric Sciences
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• v.54 no.4
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• pp.527-544
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• 2018

This paper presents a nighttime sea fog detection algorithm incorporating unsupervised learning technique. The algorithm is based on data sets that combine brightness temperatures from the $3.7{\mu}m$ and $10.8{\mu}m$ channels of the meteorological imager (MI) onboard the Communication, Ocean and Meteorological Satellite (COMS), with sea surface temperature from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA). Previous algorithms generally employed threshold values including the brightness temperature difference between the near infrared and infrared. The threshold values were previously determined from climatological analysis or model simulation. Although this method using predetermined thresholds is very simple and effective in detecting low cloud, it has difficulty in distinguishing fog from stratus because they share similar characteristics of particle size and altitude. In order to improve this, the unsupervised learning approach, which allows a more effective interpretation from the insufficient information, has been utilized. The unsupervised learning method employed in this paper is the expectation-maximization (EM) algorithm that is widely used in incomplete data problems. It identifies distinguishing features of the data by organizing and optimizing the data. This allows for the application of optimal threshold values for fog detection by considering the characteristics of a specific domain. The algorithm has been evaluated using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) vertical profile products, which showed promising results within a local domain with probability of detection (POD) of 0.753 and critical success index (CSI) of 0.477, respectively.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.6
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• pp.86-93
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• 2009

We investigate lattice-reduction-aided precoding techniques for multi-user multiple-input multiple-output (MIMO) channels. When assuming full knowledge of the channel state information only at the transmitter, a vector perturbation (VP) is a promising precoding scheme that approaches sum capacity and has simple receiver. However, its encoding is nondeterministic polynomial time (NP)-hard problem. Vector perturbation using lattice reduction algorithms can remarkably reduce its encoding complexity. In this paper, we propose a vector perturbation scheme using Seysen's lattice reduction (VP-SLR) with simultaneously reducing primal basis and dual one. Simulation results show that the proposed VP-SLR has better bit error rate (BER) and larger capacity than vector perturbation with Lenstra-Lenstra-Lovasz lattice reduction (VP-LLL) in addition to less encoding complexity.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.1
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• pp.22-32
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• 2011

In this paper, we first analyze the differences of underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) systems and conventional terrestrial OFDM system, and give a simple introduction of the backgrounds. By considering the real UWA channel environments, the measured channel data is used to generate the UWA channel model and calculate the relative parameters for underwater OFDM systems. Practical least square (LS) based channel estimation with linear interpolation are adopted to obtain the channel state information (CSI) at receiver side. As multi-input multi-output (MIMO) processing techniques, Alamouti code is implemented and evaluated to perform for space time block coding (STBC) and space frequency block coding (SFBC) for UWA OFDM systems with the MIMO configuration of $2{\times}1$, at the same time, $1{\times}2$ maximum ratio combining (MRC) is performed for the purpose of comparison. The simulation results show that, with perfect channel estimation, SFBC failed to work duo to the serious frequency selectivity of UWA channel environments. When the practical channel estimation is applied, in the case of STBC, the proposed 4-column pilot pattern gives better performance about 7dB than SISO system.

• Journal of The Korean Astronomical Society
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• v.45 no.2
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• pp.31-38
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• 2012

In this study we apply Support Vector Machine (SVM) to the prediction of geo-effective halo coronal mass ejections (CMEs). The SVM, which is one of machine learning algorithms, is used for the purpose of classification and regression analysis. We use halo and partial halo CMEs from January 1996 to April 2010 in the SOHO/LASCO CME Catalog for training and prediction. And we also use their associated X-ray flare classes to identify front-side halo CMEs (stronger than B1 class), and the Dst index to determine geo-effective halo CMEs (stronger than -50 nT). The combinations of the speed and the angular width of CMEs, and their associated X-ray classes are used for input features of the SVM. We make an attempt to find the best model by using cross-validation which is processed by changing kernel functions of the SVM and their parameters. As a result we obtain statistical parameters for the best model by using the speed of CME and its associated X-ray flare class as input features of the SVM: Accuracy=0.66, PODy=0.76, PODn=0.49, FAR=0.72, Bias=1.06, CSI=0.59, TSS=0.25. The performance of the statistical parameters by applying the SVM is much better than those from the simple classifications based on constant classifiers.