• Journal of Korean Society for Atmospheric Environment
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• v.27 no.6
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• pp.650-662
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• 2011

The statistical tools such as empirical orthogonal function (EOF), and singular value decomposition (SVD) have been applied to analyze the characteristic of air pollutant over southeast Asia as well as to evaluate Zimeke's tropospheric column ozone (ZTO) determined by tropospheric residual method. In this study, we found that the EOF and SVD analyses are useful methods to extract the most significant temporal and spatial pattern from enormous amounts of satellite data. The EOF analyses with OMI $NO_2$ and OMI HCHO over southeast Asia revealed that the spatial pattern showed high correlation with fire count (r=0.8) and the EOF analysis of CO (r=0.7). This suggests that biomass burning influences a major seasonal variability on $NO_2$ and HCHO over this region. The EOF analysis of ZTO has indicated that the location of maximum ZTO was considerably shifted westward from the location of maximum of fire count and maximum month of ZTO occurred a month later than maximum month (March) of $NO_2$, HCHO and CO. For further analyses, we have performed the SVD analyses between ZTO and ozone precursor to examine their correlation and to check temporal and spatial consistency between two variables. The spatial pattern of ZTO showed latitudinal gradient that could result from latitudinal gradient of stratospheric ozone and temporal maximum of ZTO in March appears to be associated with stratospheric ozone variability that shows maximum in March. These results suggest that there are some sources of error in the tropospheric residual method associated with cloud height error, low efficiency of tropospheric ozone, and low accuracy in lower stratospheric ozone.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.9A
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• pp.1022-1030
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• 2004

The next generation cellular radio systems require high data rate transmission and large system capacity In order to meet these requirements, multiple antennas can be used at the base and mobile stations, forming MIMO(multiple-input, multiple-output) channels This paper considers a downlink MIMO system assuming a large number of base station antennas, a small number of mobile station antennas, and rich-scattering, quasi-stationary, and flat-fading channel environments When the channel state information is given at the base station in a single user system, a MIMO technique with SVD(singular value decomposition) and water-filling can achieve the maximal downlink channel capacity. In multi-user environments, however, SDMA(space division multiple acces) technique can be used to further increase the total channel capacity supported by the base station This paper proposes a MIMO SDMA technique which can transmit parallel data streams to each of multiple users. The proposed method. can achieve higher total channel capacity than SVD-based MIMO techniques or conventional SDMA using smart antennas.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2002.11a
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• pp.407-411
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• 2002

Ternary sequences are digital codes consisting of discrete values -1, 0, and 1 only. They are advantageous in that the correlation can be carried out using additions only Also, they feature an ideal circular autocorrelation function, but in channel characterization tasks, the usual requirement is that the linear autocorrelation function be ideal, i.e., a Kronecker delta function. In this article, we consider two approaches to improving their linear autocorrelation or crosscorrelation properties: one is an inverse filtering method with theresholding and the other is a singular value decomposition (SVD) method. Both methods are simulated under noisy circumstances. The inverse filtering method resulted in an improvement in peak sidelobe level of about 1㏈ at an SNR of 30㏈, and the SVD method showed similar performances, albeit more sensitive to noise depending on the singular value selection strategy.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.591-598
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• 2021

It's possible to get the word-vector by the statistical SVD or deep-learning CBOW and LSTM methods and theses ones learn the contexts of forward/backward words or the sequence of following words. It's used to analyze the poems by Ki Hyung-do with similar words recommended by the word-vector showing the core images of the poetry. It seems at first sight that the words don't go well with the images but they express the similar style described by the reference words once you look close the contexts of the specific poems. The word-vector can analogize the words having the same relations with the ones between the representative words for the core images of the poems. Therefore you can analyze the poems in depth and in variety with the similarity and analogy operations by the word-vector estimated with the statistical SVD or deep-learning CBOW and LSTM methods.

• Atmosphere
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• v.20 no.4
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• pp.495-503
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• 2010

Satellite data have an intrinsic problem due to a number of various physical parameters, which can have a similar effect on measured radiance. Most evaluations of satellite performance have relied on comparisons with limited spatial and temporal resolution of ground-based measurements such as soundings and in-situ measurements. In order to overcome this problem, a new way of satellite data evaluation is suggested with statistical tools such as empirical orthogonal function(EOF), and singular value decomposition(SVD). The EOF analyses with OMI and OMI HCHO over northeast Asia show that the spatial pattern show high correlation with population density. This suggests that human activity is a major source of as well as HCHO over this region. However, this analysis is contradictory to the previous finding with GOME HCHO that biogenic activity is the main driving mechanism(Fu et al., 2007). To verify the source of HCHO over this region, we performed the EOF analyses with vegetation and HCHO distribution. The results showed no coherence in the spatial and temporal pattern between two factors. Rather, the additional SVD analysis between $NO_2$ and HCHO shows consistency in spatial and temporal coherence. This outcome suggests that the anthropogenic emission is the main source of HCHO over the region. We speculate that the previous study appears to be due to low temporal and spatial resolution of GOME measurements or uncertainty in model input data.

• KIEE International Transactions on Power Engineering
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• v.4A no.1
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• pp.33-41
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• 2004

Knowledge of partial discharge (PD) is important to accurately diagnose and predict the condition of insulation. The PD phenomenon is highly complex and seems to be random in its occurrence. This paper indicates the possible use of chaos theory for the recognition and distinction concerning PD signals. Chaos refers to a state where the predictive abilities of a systems future are lost and the system is rendered aperiodic. The analysis of PD using deterministic chaos comprises of the study of the basic system dynamics of the PD phenomenon. This involves the construction of the PD attractor in state space. The simulation results show that the variance of an orthogonal axis in an attractor of chaos theory increases according to the magnitude and the number of PDs. However, it is difficult to clearly identify the characteristics of the PDs. Thus, we calculated the magnitude on an orthogonal axis in an attractor using singular value decomposition (SVD) and principal component analysis (PCA) to extract the numerical characteristics. In this paper, we proposed the condition monitoring method for gas insulated switchgear (GIS) using SVD for efficient calculation of the variance. Thousands of simulations have proven the accuracy and effectiveness of the proposed algorithm.

• The KIPS Transactions:PartB
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• v.10B no.7
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• pp.725-734
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• 2003

In this paper, we propose the text summarization method using PCA (Principal Component Analysis) and SVD (Singular Value Decomposition). The proposed method presents a summary by extracting significant sentences based on the distances between thematic words and sentences. To extract thematic words, we use both word frequency and co-occurence information that result from performing PCA. To extract significant sentences, we exploit Euclidean distances between thematic word vectors and sentence vectors that result from carrying out SVD. Experimental results using newspaper articles show that the proposed method is superior to the method using either word frequency or only PCA.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.4
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• pp.752-757
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• 2006

In the case of the requirement of high speed transmission, OFDM is a powerful technique employed in communications systems suffering from frequency selective fading. In this paper, we apply an optimal adaptive bitloading algorithm technique. The BER performance of the fixed-rate SISO and adaptive SISO is simulated. Specially, we can decompose the MIMO channel into the SISO channel by making use of the singular value decomposition(SVD) assuming channel knowledge in a multipath environment. As a results of simulation, we confirmed that the BER enhancement of MIMO-OFDM system with the bitloadins algorithm was superior to the SISO-OFDM system.

• Journal of Korean Society for Atmospheric Environment
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• v.21 no.3
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• pp.315-327
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• 2005

The total of 35 target VOCs (volatile organic compounds), which were included in the TO-14, was selected to develop a VOCs' source profile matrix of the Yeosu Petrochemical Complex and to test its collinearity by singular value decomposition(SVD) technique. The VOCs collected in canisters were sampled from 12 different sources such as 8 direct emission sources (refinery, painting, wastewater treatment plant, incinerator, petrochemical processing, oil storage, fertilizer plant, and iron mill) and 4 general area sources (gasoline vapor emission, graphic art activity, vehicle emission, and asphalt paving activity) in this study area, and then those samples were analyzed by GC/MS. Initially the resulting raw data for each profile were scaled and normalized through several data treatment steps, and then specific VOCs showing major weight fractions were intensively reviewed and compared by introducing many other related studies. Next, all of the source profiles were tested in terms of degree of collinearity by SVD technique. The study finally could provide a proper VOCs' source profile in the study area, which can give opportunities to apply various receptor models properly including chemical mass balance (CMB).

• International Journal of Aeronautical and Space Sciences
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• v.17 no.2
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• pp.184-194
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• 2016

In existing identification methods for on-orbit spacecraft, such as eigensystem realization algorithm (ERA) and subspace method identification (SMI), singular value decomposition (SVD) is used frequently to estimate the modal parameters. However, these identification methods are often used to process the linear time-invariant system, and there is a lower computation efficiency using the SVD when the system order of spacecraft is high. In this study, to improve the computational efficiency in identifying time-varying modal parameters of large spacecraft, a faster recursive algorithm called fast approximated power iteration (FAPI) is employed. This approach avoids the SVD and can be provided as an alternative spacecraft identification method, and the latest modal parameters obtained can be applied for updating the controller parameters timely (e.g. the self-adaptive control problem). In numerical simulations, two large flexible spacecraft models, the Engineering Test Satellite-VIII (ETS-VIII) and Soil Moisture Active/Passive (SMAP) satellite, are established. The identification results show that this recursive algorithm can obtain the time-varying modal parameters, and the computation time is reduced significantly.