• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2357-2380
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• 2019

In this paper, we investigate the impact of hardware impairments (HWIs) on the performance of a downlink massive MIMO system. We consider a single-cell system with maximum ratio transmission (MRT) as precoding scheme, and with all the HWIs characteristics such as phase noise, distortion noise, and amplified thermal noise. Based on the system model, we derive closed-form expressions for a typical user data rate under two scenarios: when a common local oscillator (CLO) is used at the base station and when separated oscillators (SLOs) are used. We also derive closed-form expressions for the downlink transmit power required for some desired per-user data rate under each scenario. Compared to the conventional system with ideal transceiver hardware, our results show that impairments of hardware make a finite upper limit on the user's downlink channel capacity; and as the number of base station antennas grows large, it is only the hardware impairments at the users that mainly limit the capacity. Our results also show that SLOs configuration provides higher data rate than CLO at the price of higher power consumption. An approach to minimize the effect of the hardware impairments on the system performance is also proposed in the paper. In our approach, we show that by reducing the cell size, the effect of accumulated phase noise during channel estimation time is minimized and hence the user capacity is increased, and the downlink transmit power is decreased.

• Journal of Korean Society of Transportation
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• v.29 no.6
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• pp.97-106
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• 2011

Mode choice model is an essential element for estimating- the demand of new means of transportation in the planning stage as well as in the establishment phase. In general, current demand analysis model developed for the mode choice analysis applies common parameters of utility function in each region which causes inaccuracy in forecasting mode choice behavior. Several critical problems from using common parameters are: a common parameter set can not reflect different distribution of coefficient for travel time and travel cost by different population. Consequently, the resulting model fails to accurately explain policy variables such as travel time and travel cost. In particular, the nonlinear logit model applied to aggregation data is vulnerable to the aggregation error. The purpose of this paper is to consider the regional characteristics by adopting the parameters fitted to each area, so as to reduce prediction errors and enhance accuracy of the resulting mode choice model. In order to estimate parameter of each area, this study used Household Travel Survey Data of Metropolitan Transportation Authority. For the verification of the model, the value of time by marginal rate of substitution is evaluated and statistical test for resulting coefficients is also carried out. In order to crosscheck the applicability and reliability of the model, changes in mode choice are analyzed when Seoul subway line 9 is newly opened and the results are compared with those from the existing model developed without considering the regional characteristics.

• The KIPS Transactions:PartD
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• v.10D no.1
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• pp.23-32
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• 2003

Since the data is collected from disparate sources in many actual data mining environments, it is common to have data values in different abstraction levels. This paper shows that such multiple abstraction levels of data can cause undesirable effects in decision tree classification. After explaining that equalizing abstraction levels by force cannot provide satisfactory solutions of this problem, it presents a method to utilize the data as it is. The proposed method accommodates the generalization/specialization relationship between data values in both of the construction and the class assignment phase of decision tree classification. The experimental results show that the proposed method reduces classification error rates significantly when multiple abstraction levels of data are involved.

• Journal of Biomedical Engineering Research
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• v.26 no.4
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• pp.215-221
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• 2005

We describe the development of a prototype multi-channel electrical impedance tomography (EIT) system. The EIT system can be equipped with either a single-ended current source or a balanced current source. Each current source can inject current between any chosen pair of electrodes. In order to reduce the data acquisition time, we implemented multiple digital voltmeters simultaneously acquiring and demodulating voltage signals. Each voltmeter measures a differential voltage between a fixed pair of adjacent electrodes. All voltmeters are configured in a radially symmetric architecture to optimize the routing of wires and minimize cross-talks. To maximize the signal-to-noise ratio, we implemented techniques such as digital waveform generation, Howland current pump circuit with a generalized impedance converter, digital phase-sensitive demodulation, tri-axial cables with both grounded and driven shields, and others. The performance of the EIT system was evaluated in terms of common-mode rejection ratio, signal-to-noise ratio, and reciprocity error. Future design of a more innovative EIT system including battery operation, miniaturization, and wireless techniques is suggested.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.382-388
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• 2022

Satellite communication for high altitude platform stations (HAPS) and low earth orbit (LEO) systems suffer from dust and sand (DU&SA) storms in the desert regions such as Saudi Arabia. These attenuations have a distorting effect on signal fidelity at high frequency of operations. This results signal to noise ratio (SNR) to dramatically decreasing and leads to wireless transmission error. The main focus in this paper is to propose common relations between HAPS and LEO for the atmospheric impairments affecting the satellite communication networks operating above Ku-band crossing the propagation path. A double phase three dimensional relationship for HAPS and LEO systems is then presented. The comparison model present the analysis of atmospheric attenuation with specific focus on sand and dust based on particular size, visibility, adding gaseous effects for different frequency, and propagation angle to provide system operations with a predicted vision of satellite parameters' values. Skillful decision and control system (SD&CS) is proposed to control applied parameters that lead to improve satellite network performance and to get the ultimate receiving wireless signal under bad weather condition.

• Knowledge Management Research
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• v.21 no.1
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• pp.27-40
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• 2020

Response surface methodology (RSM) empirically studies the relationship between a response variable and input variables in the product or process development phase. The ultimate goal of RSM is to find an optimal condition of the input variables that optimizes (maximizes or minimizes) the response variable. RSM can be seen as a knowledge management tool in terms of creating and utilizing data, information, and knowledge about a product production and service operations. In the field of product or process development, most real-world problems often involve a simultaneous consideration of multiple response variables. This is called a multiple response surface (MRS) problem. Various approaches have been proposed for MRS optimization, which can be classified into loss function approach, priority-based approach, desirability function approach, process capability approach, and probability-based approach. In particular, the loss function approach is divided into univariate and multivariate approaches at large. This paper focuses on the univariate approach. The univariate approach first obtains the mean square error (MSE) for individual response variables. Then, it aggregates the MSE's into a single objective function. It is common to employ the weighted sum or the Tchebycheff metric for aggregation. Finally, it finds an optimal condition of the input variables that minimizes the objective function. When aggregating, the relative weights on the MSE's should be taken into account. However, there are few studies on how to determine the weights systematically. In this study, we propose an interactive procedure to determine the weights through considering a decision maker's preference. The proposed method is illustrated by the 'colloidal gas aphrons' problem, which is a typical MRS problem. We also discuss the extension of the proposed method to the weighted MSE (WMSE).

• Journal of Advanced Navigation Technology
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• v.26 no.5
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• pp.319-324
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• 2022

If radio interference occurs or there is no navigation device, radio navigation of high-speed moving object becomes impossible. Nevertheless, if there are multiple ground stations and precise range measurement between the high-speed moving object and the ground station can be secured, it is possible to estimate the position of moving object. This paper proposes a position estimation method using high-precision TDOA measurement generated using TLM signal. In the proposed method, a common error of moving object is removed using the TDOA measurements. The measurements is generated based on TLM signal including SOQPSK PN symbol capable of precise timing synchronization. Therefore, since precise timing synchronization of the system has been performed, the timing error between ground stations has a very small value. This improved the position estimation performance by increasing the accuracy of the measured values. The proposed method is verified through software-based simulation, and the performance of estimated position satisfies the target performance.

• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.357-363
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• 2023

In underwater signal processing, separating individual signals from mixed signals has long been a challenge due to low signal quality. The common method using Short-time Fourier transform for spectrogram analysis has faced criticism for its complex parameter optimization and loss of phase data. We propose a Triple-path Recurrent Neural Network, based on the Dual-path Recurrent Neural Network's success in long time series signal processing, to handle three-dimensional tensors from multi-channel sensor input signals. By dividing input signals into short chunks and creating a 3D tensor, the method accounts for relationships within and between chunks and channels, enabling local and global feature learning. The proposed technique demonstrates improved Root Mean Square Error and Scale Invariant Signal to Noise Ratio compared to the existing method.

• Journal of Korea Water Resources Association
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• v.45 no.8
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• pp.839-851
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• 2012

When we evaluate the water supply capacity of a river basin, it is a common practice to gradually increase the water demand and check if the water demands are met. This practice is not only used in the simulation approach, but also in the optimization approach. However, this trial and error approach is a tedious task. Hence, we propose a two-phase method. In the first phase, by assuming that the decision maker has complete information on inflow data, we use a goal programming model that can generate the maximum water supply capacity at one time. In the second phase, we simulate the real-time operation for the critical period by utilizing the water supply capacity given by the goal programming model under the condition that there is no foresight of inflow. We applied the two-phase method to the Geum-River basin, where multi-purpose weirs were newly constructed. By comparing the results of the goal programming model with those of the real-time simulation model we could comprehend and estimate the effect of perfect inflow data on the water supply capacity.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.104-111
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• 2018

The sensorless control of high efficiency air compressors using a permanent magnet type synchronous motor as an oil-free air compressor is quite common. However, due to the nature of the air compressor, it is difficult to install a position sensor. In order to control the permanent magnet type synchronous motor at variable speed, the inclusion of a position sensor to grasp the position of the rotor is essential. Therefore, in order to achieve sensorless control, it is essential to use a permanent magnet type synchronous motor in the compressor. The position estimation method based on the back electromotive force, which is widely used as the sensorless control method, has a limitation in that position errors occur due either to the phase delay caused by the use of a stationary coordinate system or to the estimated back electromotive force in the transient state caused by the use of a synchronous coordinate system. Therefore, in this paper, we propose a method of estimating the position and velocity using a rotation angle tracking observer and reducing the speed ripple through a disturbance observer. An experimental apparatus was constructed using Freescale's MPU and the feasibility of the proposed algorithm was examined. It was confirmed that even if a position error occurs at a certain point in time, the position correction value converges to the actual vector position when the position error value is found.