• Title/Summary/Keyword: least squared method

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Stokes Flow Through a Microchannel with Projections of Constant Spacing (일정 간격의 돌출부를 갖는 마이크로채널 내의 스톡스 유동 해석)

  • Son, JeongSu;Jeong, Jae-Tack
    • Transactions of the Korean Society of Mechanical Engineers B
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    • v.39 no.4
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    • pp.335-341
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    • 2015
  • In this study, we analyzed a two-dimensional Stokes flow through a microchannel containing projections with constant spacing attached to each wall. The projections on the top and bottom walls were semi-circular in shape, with in-phase locations. By considering the periodicity and symmetry of the flow, the eigenfunction expansion and least squared error method were applied to determine the stream function and pressure distribution. For some typical radius and spacing values, the streamline patterns and pressure distributions in the flow field are shown, and the shear stress distributions on the boundary walls are plotted. In addition, the average pressure gradients in the microchannel are also calculated and shown with the radius and spacing of the projections. In particular, the results for the case of extremely small gaps between the projections on the top and bottom walls are in good agreement with the lubrication results.

Classification of Daily Precipitation Patterns in South Korea using Mutivariate Statistical Methods

  • Mika, Janos;Kim, Baek-Jo;Park, Jong-Kil
    • Journal of Environmental Science International
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    • v.15 no.12
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    • pp.1125-1139
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    • 2006
  • The cluster analysis of diurnal precipitation patterns is performed by using daily precipitation of 59 stations in South Korea from 1973 to 1996 in four seasons of each year. Four seasons are shifted forward by 15 days compared to the general ones. Number of clusters are 15 in winter, 16 in spring and autumn, and 26 in summer, respectively. One of the classes is the totally dry day in each season, indicating that precipitation is never observed at any station. This is treated separately in this study. Distribution of the days among the clusters is rather uneven with rather low area-mean precipitation occurring most frequently. These 4 (seasons)$\times$2 (wet and dry days) classes represent more than the half (59 %) of all days of the year. On the other hand, even the smallest seasonal clusters show at least $5\sim9$ members in the 24 years (1973-1996) period of classification. The cluster analysis is directly performed for the major $5\sim8$ non-correlated coefficients of the diurnal precipitation patterns obtained by factor analysis In order to consider the spatial correlation. More specifically, hierarchical clustering based on Euclidean distance and Ward's method of agglomeration is applied. The relative variance explained by the clustering is as high as average (63%) with better capability in spring (66%) and winter (69 %), but lower than average in autumn (60%) and summer (59%). Through applying weighted relative variances, i.e. dividing the squared deviations by the cluster averages, we obtain even better values, i.e 78 % in average, compared to the same index without clustering. This means that the highest variance remains in the clusters with more precipitation. Besides all statistics necessary for the validation of the final classification, 4 cluster centers are mapped for each season to illustrate the range of typical extremities, paired according to their area mean precipitation or negative pattern correlation. Possible alternatives of the performed classification and reasons for their rejection are also discussed with inclusion of a wide spectrum of recommended applications.

Predicting ozone warning days based on an optimal time series model (최적 시계열 모형에 기초한 오존주의보 날짜 예측)

  • Park, Cheol-Yong;Kim, Hyun-Il
    • Journal of the Korean Data and Information Science Society
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    • v.20 no.2
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    • pp.293-299
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    • 2009
  • In this article, we consider linear models such as regression, ARIMA (autoregressive integrated moving average), and regression+ARIMA (regression with ARIMA errors) for predicting hourly ozone concentration level in two areas of Daegu. Based on RASE(root average squared error), it is shown that the ARIMA is the best model in one area and that the regression+ARIMA model is the best in the other area. We further analyze the residuals from the optimal models, so that we might predict the ozone warning days where at least one of the hourly ozone concentration levels is over 120 ppb. Based on the training data in the years from 2000 to 2003, it is found that 35 ppb is a good cutoff value of residulas for predicting the ozone warning days. In on area of Daegu, our method predicts correctly one of two ozone warning days of 2004 as well as all of the remaining 364 non-warning days. In the other area, our methods predicts correctly all of one ozone warning days and 365 non-warning days of 2004.

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Design of Optimal FIR Filters for Data Transmission (데이터 전송을 위한 최적 FIR 필터 설계)

  • 이상욱;이용환
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.18 no.8
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    • pp.1226-1237
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    • 1993
  • For data transmission over strictly band-limited non-ideal channels, different types of filters with arbitrary responses are needed. In this paper. we proposed two efficient techniques for the design of such FIR filters whose response is specified in either the time or the frequency domain. In particular when a fractionally-spaced structure is used for the transceiver, these filters can be efficiently designed by making use of characteristics of oversampling. By using a minimum mean-squared error criterion, we design a fractionally-spaced FIR filter whose frequency response can be controlled without affecting the output error. With proper specification of the shape of the additive noise signals, for example, the design results in a receiver filter that can perform compromise equalization as well as phase splitting filtering for QAM demodulation. The second method ad-dresses the design of an FIR filter whose desired response can be arbitrarily specified in the frequency domain. For optimum design, we use an iterative optimization technique based on a weighted least mean square algorithm. A new adaptation algorithm for updating the weighting function is proposed for fast and stable convergence. It is shown that these two independent methods can be efficiently combined together for more complex applications.

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Frequency of different maxillary sinus septal patterns found on cone-beam computed tomography and predicting the associated risk of sinus membrane perforation during sinus lifting

  • Sigaroudi, Ali Khalighi;Kajan, Zahra Dalili;Rastgar, Shabnam;Asli, Hamid Neshandar
    • Imaging Science in Dentistry
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    • v.47 no.4
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    • pp.261-267
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    • 2017
  • Purpose: Analyzing different patterns of maxillary sinus septa in cone-beam computed tomography (CBCT) images and predicting maxillary sinus membrane perforations. Materials and Methods: In this cross-sectional study, CBCT images of 222 patients ranging from 20 to 81 years old were evaluated. One hundred fifty-two patients (93 females and 59 males) who had maxillary sinus septa in axial views were included in this study. Cross-sectional images were used to determine classifications of sinus septa and the risk of membrane perforation using a method modified from Al-Faraje et al. Variables of sex, age, and dental status were considered. Chi-squared and Kruskal-Wallis tests were used for data analysis(P<.05). Results: In this study, 265 maxillary sinus septal patterns were found. The mean age of the patients was $44.1{\pm}14.7$ years old. The Class I and VII-div II patterns had the greatest and least prevalence, respectively. Furthermore, there was a significant relationship between the location of septa and the frequency of membrane perforation risk (P<.05). In this study, the relationship of different patterns of septa with dental status did not differ significantly (P>0.05). Conclusion: A higher prevalence of moderate risk of membrane perforation in the molar region relative to the premolar region was observed. Furthermore, maxillary sinus septa occur most frequently in the molar region, demonstrating the importance of paying attention to this region during sinus lift surgery. This study did not show any relationship between tooth loss and the presence of septa.

Geometric Regualrization of Irregular Building Polygons: A Comparative Study

  • Sohn, Gun-Ho;Jwa, Yoon-Seok;Tao, Vincent;Cho, Woo-Sug
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.25 no.6_1
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    • pp.545-555
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    • 2007
  • 3D buildings are the most prominent feature comprising urban scene. A few of mega-cities in the globe are virtually reconstructed in photo-realistic 3D models, which becomes accessible by the public through the state-of-the-art online mapping services. A lot of research efforts have been made to develop automatic reconstruction technique of large-scale 3D building models from remotely sensed data. However, existing methods still produce irregular building polygons due to errors induced partly by uncalibrated sensor system, scene complexity and partly inappropriate sensor resolution to observed object scales. Thus, a geometric regularization technique is urgently required to rectify such irregular building polygons that are quickly captured from low sensory data. This paper aims to develop a new method for regularizing noise building outlines extracted from airborne LiDAR data, and to evaluate its performance in comparison with existing methods. These include Douglas-Peucker's polyline simplication, total least-squared adjustment, model hypothesis-verification, and rule-based rectification. Based on Minimum Description Length (MDL) principal, a new objective function, Geometric Minimum Description Length (GMDL), to regularize geometric noises is introduced to enhance the repetition of identical line directionality, regular angle transition and to minimize the number of vertices used. After generating hypothetical regularized models, a global optimum of the geometric regularity is achieved by verifying the entire solution space. A comparative evaluation of the proposed geometric regulator is conducted using both simulated and real building vectors with various levels of noise. The results show that the GMDL outperforms the selected existing algorithms at the most of noise levels.

A Study on the Postprocessing of Channel Estimates in LTE System (LTE 시스템 채널 추정치의 후처리 기법 연구)

  • Yoo, Kyung-Yul
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.60 no.1
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    • pp.205-213
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    • 2011
  • The Long Term Evolution (LTE) system is designed to provide a high quality data service for fast moving mobile users. It is based on the Orthogonal Frequency Division Multiplexing (OFDM) and relies its channel estimation on the training samples which are systematically built within the transmitting data. Either a preamble or a lattice type is used for the distribution of training samples and the latter suits better for the multipath fading channel environment whose channel frequency response (CFR) fluctuates rapidly with time. In the lattice-type structure, the estimation of the CFR makes use of the least squares estimate (LSE) for each pilot samples, followed by an interpolation both in time-and in frequency-domain to fill up the channel estimates for subcarriers corresponding to data samples. All interpolation schemes should rely on the pilot estimates only, and thus, their performances are bounded by the quality of pilot estimates. However, the additive noise give rise to high fluctuation on the pilot estimates, especially in a communication environment with low signal-to-noise ratio. These high fluctuations could be monitored in the alternating high values of the first forward differences (FFD) between pilot estimates. In this paper, we analyzed statistically those FFD values and propose a postprocessing algorithm to suppress high fluctuations in the noisy pilot estimates. The proposed method is based on a localized adaptive moving-average filtering. The performance of the proposed technique is verified on a multipath environment suggested on a 3GPP LTE specification. It is shown that the mean-squared error (MSE) between the actual CFR and pilot estimates could be reduced up to 68% from the noisy pilot estimates.

2.4kbps Speech Coding Algorithm Using the Sinusoidal Model (정현파 모델을 이용한 2.4kbps 음성부호화 알고리즘)

  • 백성기;배건성
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.27 no.3A
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    • pp.196-204
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    • 2002
  • The Sinusoidal Transform Coding(STC) is a vocoding scheme based on a sinusoidal model of a speech signal. The low bit-rate speech coding based on sinusoidal model is a method that models and synthesizes speech with fundamental frequency and its harmonic elements, spectral envelope and phase in the frequency region. In this paper, we propose the 2.4kbps low-rate speech coding algorithm using the sinusoidal model of a speech signal. In the proposed coder, the pitch frequency is estimated by choosing the frequency that makes least mean squared error between synthetic speech with all spectrum peaks and speech synthesized with chosen frequency and its harmonics. The spectral envelope is estimated using SEEVOC(Spectral Envelope Estimation VOCoder) algorithm and the discrete all-pole model. The phase information is obtained using the time of pitch pulse occurrence, i.e., the onset time, as well as the phase of the vocal tract system. Experimental results show that the synthetic speech preserves both the formant and phase information of the original speech very well. The performance of the coder has been evaluated in terms of the MOS test based on informal listening tests, and it achieved over the MOS score of 3.1.

Vision-based Real-time Lane Detection and Tracking for Mobile Robots in a Constrained Track Environment

  • Kim, Young-Ju
    • Journal of the Korea Society of Computer and Information
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    • v.24 no.11
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    • pp.29-39
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    • 2019
  • As mobile robot applications increase in real life, the need of low cost autonomous driving are gradually increasing. We propose a novel vision-based real-time lane detection and tracking system that supports autonomous driving of mobile robots in constrained tracks which are designed considering indoor driving conditions of mobile robots. Considering the processing of lanes with various shapes and the pre-adjustment of operation parameters, the system structure with multi-operation modes are designed. In parameter tuning mode, thresholds of the color filter is dynamically adjusted based on the geometric property of the lane thickness. And in the unstable input mode of curved tracks and the stable input mode of straight tracks, lane feature pixels are adaptively extracted based on the geometric and temporal characteristics of the lanes and the lane model is fitted using the least-squared method. The track centerline is calculated using lane models and the motion model is simplified and tracked by a linear Kalman filter. In the driving experiments, it was confirmed that even in low-performance robot configurations, real-time processing produces the accurate autonomous driving in the constrained track.

Soft computing based mathematical models for improved prediction of rock brittleness index

  • Abiodun I. Lawal;Minju Kim;Sangki Kwon
    • Geomechanics and Engineering
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    • v.33 no.3
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    • pp.279-289
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    • 2023
  • Brittleness index (BI) is an important property of rocks because it is a good index to predict rockburst. Due to its importance, several empirical and soft computing (SC) models have been proposed in the literature based on the punch penetration test (PPT) results. These models are very important as there is no clear-cut experimental means for measuring BI asides the PPT which is very costly and time consuming to perform. This study used a novel Multivariate Adaptive regression spline (MARS), M5P, and white-box ANN to predict the BI of rocks using the available data in the literature for an improved BI prediction. The rock density, uniaxial compressive strength (σc) and tensile strength (σt) were used as the input parameters into the models while the BI was the targeted output. The models were implemented in the MATLAB software. The results of the proposed models were compared with those from existing multilinear regression, linear and nonlinear particle swarm optimization (PSO) and genetic algorithm (GA) based models using similar datasets. The coefficient of determination (R2), adjusted R2 (Adj R2), root-mean squared error (RMSE) and mean absolute percentage error (MAPE) were the indices used for the comparison. The outcomes of the comparison revealed that the proposed ANN and MARS models performed better than the other models with R2 and Adj R2 values above 0.9 and least error values while the M5P gave similar performance to those of the existing models. Weight partitioning method was also used to examine the percentage contribution of model predictors to the predicted BI and tensile strength was found to have the highest influence on the predicted BI.