• International Journal of High-Rise Buildings
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• v.8 no.1
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• pp.71-82
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• 2019

The purpose of this study is to evaluate the indoor air quality (IAQ) and energy benefits of a dedicated outdoor air system (DOAS) and compare them with a conventional variable air volume (VAV) system. The DOAS is a decoupled system that supplies only outdoor air, while reducing its consumption using an enthalpy wheel. The VAV system supplies air that is mixed outdoor and transferred indoor. The VAV has the issue of unbalanced ventilation in each room in multiple zones because it supplies mixing air. The DOAS does not have this problem because it supplies only outdoor air. That is, the DOAS is a 100% outdoor air system and the VAV is an air conditioning system. The transient simulations of carbon dioxide concentration and energy consumption were performed using a MATLAB program based on the thermal loads from the model predicted by the TRNSYS 18 program. The results indicated that when the air volume is large, such as in summer, the distribution of air is not appropriate in the VAV system. The DOAS however, supplies the outdoor air stably. Moreover, in terms of annual primary energy consumption, the DOAS consumed approximately 40% less energy than the VAV system.

• Korean Journal of Remote Sensing
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• v.22 no.2
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• pp.141-151
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• 2006

UV-visible absorption measurement techniques using several horizone viewing directions in addition to the traditional zenith-sky pointing have been recently developed in ground-based remote sensing of atmospheric constituents. The spatial distribution of various trace gases close to the instrument can be derived by combing several viewing directions. Multi-axis differential optical absorption spectroscopy (MAX-DOAS) technique, one of the remote sensing techniques for air quality measurements, uses the scattered sunlight as a light source and measures it at various elevation angles (corresponding to the viewing directions) by sequential scanning with a stepper motor. A MAX-DOAS system developed by GIST/ADEMRC has been applied to measuring trace gases in urban air and plumes of the volcano and fossil fuel power plant in January, May, and October 2004, respectively. MAX-DOAS spectra were analyzed to identify and quantify $SO_2,\;NO_2,\;BrO,\;and\;O_4$ (based on Slant Column Densities, SCD) in the urban air, volcanic plume, and fossil fuel power plant utilizing theirs specific structured absorption features in the UV-visible region. Vertical scan through the multiple elevation angles was performed at different directions perpendicular to the plume dispersion to retrieve cross-sectional distribution of $SO_2\;or\;NO_2$ in the plumes of the volcano and fossil fuel power plant. Based on the estimated cross sections of the plumes the mixing ratios were estimated to 580 $SO_2$ ppbv in the volcanic Plume, and 337 $NO_2\;and\;227\;SO_2$ ppbv in the plume of the fossil fuel power plant, respectively.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.2
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• pp.123-129
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• 2017

This paper proposes a new nonnegative matrix factorization (NMF) based direction-of-arrival (DOA) estimation method for multiple sound sources using a dual microphone array. First of all, sound signals coming from the dual microphone array are segmented into consecutive analysis frames, and a steered-response power phase transform (SRP-PHAT) beamformer is applied to each frame so that stereo signals of each frame are represented in a time-direction domain. The time-direction outputs of SRP-PHAT are stored for a pre-defined number of frames, which is referred to as a time-direction block. Next, In order to estimate DOAs robust to noise, each time-direction block is normalized along the time by using a block subtraction technique. After that, an unsupervised NMF method is applied to the normalized time-direction block in order to cluster the directions of each sound source in a multiple sound source environments. In particular, the activation and basis matrices are used to estimate the number of sound sources and their DOAs, respectively. The DOA estimation performance of the proposed method is evaluated by measuring a mean absolute error (MAE) and the standard deviation of errors between the oracle and estimated DOAs under a three source condition, where the sources are located in [$-35{\circ}$, 5m], [$12{\circ}$, 4m], and [$38{\circ}$, 4.m] from the dual microphone array. It is shown from the experiment that the proposed method could relatively reduce MAE by 56.83%, compared to a conventional SRP-PHAT based DOA estimation method.

• The Journal of the Acoustical Society of Korea
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• v.30 no.3
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• pp.129-135
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• 2011

An algorithm is presented for estimating the 3-D location (i.e., azimuth angle, elevation angle, and range) of multiple sources with a uniform circular array (UCA) consisting of an even number of sensors. Recently the rank reduction (RARE) algorithm for partly-calibrated sensor arrays was developed. This algorithm is applicable to sensor arrays consisting of several identically oriented and calibrated linear subarrays. Assuming that a UCA consists of M sensors, it can be divided into M/2 identical linear subarrays composed of two facing sensors. Based on the structure of the subarrays, the steering vectors are decomposed into two parts: range-independent 2-D direction-of-arrival (DOA) parameters, and range-relevant 3-D location parameters. Using this property we can estimate range-independent 2-D DOAs by using the RARE algorithm. Once the 2-D DOAs are available, range estimation can be obtained for each source by defining the 1-D MUSIC spectrum. Despite its low computational complexity, the proposed algorithm can provide an estimation performance almost comparable to that of the 3-D MUSIC benchmark estimator.

• Phonetics and Speech Sciences
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• v.4 no.3
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• pp.103-110
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• 2012

In this paper, a target signal detection method using multiple signal classification (MUSIC) algorithm is proposed. The MUSIC algorithm is a subspace-based direction of arrival (DOA) estimation method. Using the inverse of the eigenvalue-weighted eigen spectra, the algorithm detects the DOAs of multiple sources. To apply the algorithm in target signal detection for GSC-based beamforming, we utilize its spectral response for the DOA of the target source in noisy conditions. The performance of the proposed target signal detection method is compared with those of the normalized cross-correlation (NCC), the fixed beamforming, and the power ratio method. Experimental results show that the proposed algorithm significantly outperforms the conventional ones in receiver operating characteristics (ROC) curves.

• Journal of Positioning, Navigation, and Timing
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• v.9 no.4
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• pp.367-378
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• 2020

This paper introduces the development of the iterative multiple signal classification (MUSIC)-based direction-of-arrival (DOA) estimation system using a rotator that can control the direction of antenna for the global navigation satellite system (GNSS) interference. The system calculates the spatial spectrum according to the noise eigenvector of all dimensions to measure the number of signals (NOS). Also, to detect the false peak, the system adjusts the array antenna's direction and checks the change's peak angles. The phase delay and gain correction values for system calibration are calculated in consideration of the chamber's structure and the characteristics of radio waves. The developed system estimated DOAs of interferences located about 1km away. The field test results show that the developed system can estimate the DOA without NOS information and detect the false peak even though the inter-element spacing is longer than the half-wavelength of the interference.

• Journal of Positioning, Navigation, and Timing
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• v.9 no.1
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• pp.7-13
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• 2020

As a countermeasure against the threat of jamming which can disrupt operation of the Global Positioning System (GPS) receivers, various kinds of technique to estimate the Direction-Of-Arrivals (DOAs) of incoming jamming signals have been widely studied, and among them, the MUltiple SIgnal Classification (MUSIC) algorithm is known to provide very high resolution. However, since the previous studies regarding the MUSIC algorithm does not consider the orientation of each antenna element of antenna arrays, there is a possibility that DOA estimation performance degrades in the case of a misaligned antenna array whose antenna elements are not oriented along the same direction. As an effort to solve this problem, there exists a previous work which presents an MUSIC-based method for DOA estimation. However, the error between the real and measured values of each antenna orientation is not taken into consideration. Therefore, in this paper, the effect of the aforementioned error on the DOA estimation performance in the case of a misaligned antenna array is analyzed by simulations.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.2 s.314
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• pp.102-107
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• 2007

Since the Matrix Pencil (MP) method can be performed to estimate multiple DOAs by using only single snapshot, this method is suitable for short data length or when the environment is dynamic. As the number of array increases, the computational load increases due to complex number computation. This paper presents an approach based on a unitary matrix pencil (MP) algorithm to reduce the computational load. Unitary transformation for the MP method has been suggested and formulated successfully. The computer simulation shows that the error rate of proposed method agree with that of MP for different SNR values.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.5
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• pp.177-187
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• 2014

This paper proposes a fast subspace tracking methods, which is called GVFF FAPI, based on FAPI (Fast Approximated Power Iteration) method and GVFF RLS (Gradient-based Variable Forgetting Factor Recursive Lease Squares). Since the conventional FAPI uses a constant forgetting factor for estimating covariance matrix of source signals, it has difficulty in applying to non-stationary environments such as continuously changing DOAs of source signals. To overcome the drawback of conventioanl FAPI method, the GVFF FAPI uses the gradient-based variable forgetting factor derived from an improved means square error (MSE) analysis of RLS. In order to achieve the decreased subspace error in non-stationary environments, the GVFF-FAPI algorithm used an improved forgetting factor updating equation that can produce a fast decreasing forgetting factor when the gradient is positive and a slowly increasing forgetting factor when the gradient is negative. Our numerical simulations show that GVFF-FAPI algorithm offers lower subspace error and RMSE (Root Mean Square Error) of tracked DOAs of source signals than conventional FAPI based MUSIC (MUltiple SIgnal Classification).

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.153-159
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• 2011

The MUSIC (MUltiple SIgnal Classification) method estimates the directions of arrival (DOAs) of the signals impinging on a sensor array based on the fact that the noise subspace is orthogonal to the signal subspace. In the conventional MUSIC, an estimate of the basis for the noise subspace is obtained by eigendecomposing the sample matrix, which is computationally expensive. In this paper, we present a simple DOA estimation method which finds an estimate of the signal subspace basis directly from the columns of the sample matrix from which the noise power components are removed. DOA estimates are obtained by searching for minimum points of a cost function which is defined using the estimated signal subspace basis. The minimum points are efficiently found through the Brent method which employs parabolic interpolation. Simulation shows that the simple estimation method virtually has the same performance as the complex conventional method based on the eigendecomposition.