Abstract: Visible light communication (VLC) is a new approach of optical wireless communication proposed to support the congested radio frequency (RF) spectrum. VLC systems are combined with orthogonal frequency division multiplexing (OFDM) to achieve high rate transmission and high spectral efficiency. In this paper, we investigate the Pilot-Assisted Channel Estimation for DC biased Optical OFDM (PACE-DCO-OFDM) systems to reduce the effects of the distortion on the transmitted signal. Least-square (LS) and linear minimum mean-squared error (LMMSE) estimators are implemented in MATLAB/Simulink to enhance the bit-error-rate (BER) of PACE-DCO-OFDM. Results show that DCO-OFDM system based on PACE scheme has achieved better BER performance compared to conventional system without pilot assisted channel estimation. Simulation results show that the proposed PACE-DCO-OFDM based on LMMSE algorithm can more accurately estimate the channel and achieves better BER performance when compared to the LS based PACE-DCO-OFDM and the traditional system without PACE. For the same signal to noise ratio (SNR) of 25 dB, the achieved BER is about 5×10-4 for LMMSE-PACE and 4.2×10-3 with LS-PACE while it is about 2×10-1 for system without PACE scheme.
Abstract: It is well known that the real estate price depends on a lot of factors. Each house current value is dependent on the location, room number, transportation, living convenience, year and surrounding environments. Although, there are different experienced models for housing agent to estimate the price, it is a case by case study without overall dynamic variation investigation. However, many economic parameters may more or less influence the real estate price variation. Here, the influences of most macroeconomic parameters on real estate price are investigated individually based on least-square scheme and grey correlation strategy. Then those parameters are classified into leading indices, simultaneous indices and laggard indices. In addition, the leading time period is evaluated based on least square method. The important leading and simultaneous indices can be used to establish an artificial intelligent neural network model for real estate price variation prediction. The real estate price variation of Taipei, Taiwan during 2005 ~ 2017 are chosen for this research data analysis and validation. The results show that the proposed method has reasonable prediction function for real estate business reference.
Abstract: Fault diagnosis of Linear Parameter-Varying (LPV)
system using an adaptive Kalman filter is proposed. The LPV model
is comprised of scheduling parameters, and the emulator parameters.
The scheduling parameters are chosen such that they are capable of
tracking variations in the system model as a result of changes in the
operating regimes. The emulator parameters, on the other hand,
simulate variations in the subsystems during the identification phase
and have negligible effect during the operational phase. The nominal
model and the influence vectors, which are the gradient of the feature
vector respect to the emulator parameters, are identified off-line from
a number of emulator parameter perturbed experiments. A Kalman
filter is designed using the identified nominal model. As the system
varies, the Kalman filter model is adapted using the scheduling
variables. The residual is employed for fault diagnosis. The
proposed scheme is successfully evaluated on simulated system as
well as on a physical process control system.
Abstract: This paper aims to analysis the behavior of DC corona
discharge in wire-to-plate electrostatic precipitators (ESP). Currentvoltage
curves are particularly analyzed. Experimental results show
that discharge current is strongly affected by the applied voltage. The proposed method of current identification is to use the method
of least squares. Least squares problems that of into two categories:
linear or ordinary least squares and non-linear least squares,
depending on whether or not the residuals are linear in all unknowns.
The linear least-squares problem occurs in statistical regression
analysis; it has a closed-form solution. A closed-form solution (or
closed form expression) is any formula that can be evaluated in a
finite number of standard operations. The non-linear problem has no
closed-form solution and is usually solved by iterative.
Abstract: In this paper, a linear mixed model which has two
random effects is broken up into two models. This thesis gets
the parameter estimation of the original model and an estimation’s
statistical qualities based on these two models. Then many important
properties are given by comparing this estimation with other general
estimations. At the same time, this paper proves the analysis of
variance estimate (ANOVAE) about σ2 of the original model is equal
to the least-squares estimation (LSE) about σ2 of these two models.
Finally, it also proves that this estimation is better than ANOVAE
under Stein function and special condition in some degree.
Abstract: Carbon dioxide is one of the major greenhouse gas
(GHG) contributors. It is an obligation of the industry to reduce the
amount of carbon dioxide emission to the acceptable limits.
Tremendous research and studies are reported in the past and still the
quest to find the suitable and economical solution of this problem
needed to be explored in order to develop the most plausible absorber
for carbon dioxide removal. Amino acids can be potential alternate
solvents for carbon dioxide capture from gaseous streams. This is due
to its ability to resist oxidative degradation, low volatility and its
ionic structure. In addition, the introduction of promoter-like
piperazine to amino acid helps to further enhance the solubility. In
this work, the effect of piperazine on thermo physical properties and
solubility of β-Alanine aqueous solutions were studied for various
concentrations. The measured physicochemical properties data was
correlated as a function of temperature using least-squares method
and the correlation parameters are reported together with it respective
standard deviations. The effect of activator piperazine on the CO2
loading performance of selected amino acid under high-pressure
conditions (1bar to 10bar) at temperature range of (30 to 60)oC was
also studied. Solubility of CO2 decreases with increasing temperature
and increases with increasing pressure. Quadratic representation of
solubility using Response Surface Methodology (RSM) shows that
the most important parameter to optimize solubility is system
pressure. The addition of promoter increases the solubility effect of
the solvent.
Abstract: In this paper, according to the classical algorithm
LSQR for solving the least-squares problem, an iterative method is
proposed for least-squares solution of constrained matrix equation. By
using the Kronecker product, the matrix-form LSQR is presented to
obtain the like-minimum norm and minimum norm solutions in a
constrained matrix set for the symmetric arrowhead matrices. Finally,
numerical examples are also given to investigate the performance.
Abstract: Annihilations, phase shifts, scattering lengths and
elastic cross sections of low energy positrons scattering from
magnesium atoms were studied using the least-squares variational
method (LSVM). The possibility of positron binding to the
magnesium atoms is investigated. A trial wave function is suggested
to represent e+-Mg elastic scattering and scattering parameters were
derived to estimate the binding energy and annihilation rates. The
trial function is taken to depend on several adjustable parameters, and
is improved iteratively by increasing the number of terms. The
present results have the same behavior as reported semi-empirical,
theoretical and experimental results. Especially, the estimated
positive scattering length supports the possibility of positronmagnesium
bound state system that was confirmed in previous
experimental and theoretical work.
Abstract: In statistics parameter theory, usually the
parameter estimations have two kinds, one is the least-square
estimation (LSE), and the other is the best linear unbiased
estimation (BLUE). Due to the determining theorem of
minimum variance unbiased estimator (MVUE), the parameter
estimation of BLUE in linear model is most ideal. But since
the calculations are complicated or the covariance is not
given, people are hardly to get the solution. Therefore, people
prefer to use LSE rather than BLUE. And this substitution
will take some losses. To quantize the losses, many scholars
have presented many kinds of different relative efficiencies in
different views. For the linear weighted regression model, this
paper discusses the relative efficiencies of LSE of β to BLUE
of β. It also defines two new relative efficiencies and gives
their lower bounds.
Abstract: High density electrical prospecting has been widely
used in groundwater investigation, civil engineering and
environmental survey. For efficient inversion, the forward modeling
routine, sensitivity calculation, and inversion algorithm must be
efficient. This paper attempts to provide a brief summary of the past
and ongoing developments of the method. It includes reviews of the
procedures used for data acquisition, processing and inversion of
electrical resistivity data based on compilation of academic literature.
In recent times there had been a significant evolution in field survey
designs and data inversion techniques for the resistivity method. In
general 2-D inversion for resistivity data is carried out using the
linearized least-square method with the local optimization technique
.Multi-electrode and multi-channel systems have made it possible to
conduct large 2-D, 3-D and even 4-D surveys efficiently to resolve
complex geological structures that were not possible with traditional
1-D surveys. 3-D surveys play an increasingly important role in very
complex areas where 2-D models suffer from artifacts due to off-line
structures. Continued developments in computation technology, as
well as fast data inversion techniques and software, have made it
possible to use optimization techniques to obtain model parameters to
a higher accuracy. A brief discussion on the limitations of the
electrical resistivity method has also been presented.
Abstract: A new relative efficiency in linear model in reference is
instructed into the linear weighted regression, and its upper and lower
bound are proposed. In the linear weighted regression model, for the
best linear unbiased estimation of mean matrix respect to the
least-squares estimation, two new relative efficiencies are given, and
their upper and lower bounds are also studied.
Abstract: This paper presents a Gaussian process model-based
short-term electric load forecasting. The Gaussian process model is
a nonparametric model and the output of the model has Gaussian
distribution with mean and variance. The multiple Gaussian process
models as every hour ahead predictors are used to forecast future
electric load demands up to 24 hours ahead in accordance with the
direct forecasting approach. The separable least-squares approach that
combines the linear least-squares method and genetic algorithm is
applied to train these Gaussian process models. Simulation results
are shown to demonstrate the effectiveness of the proposed electric
load forecasting.
Abstract: A time domain approach is used in this paper to identify unknown dynamic forces applied on two dimensional frames using the measured dynamic structural responses for a sub-structure in the two dimensional frame. In this paper a sub-structure finite element model with short length of measurement from only three or four accelerometers is required, and an iterative least-square algorithm is used to identify the unknown dynamic force applied on the structure. Validity of the method is demonstrated with numerical examples using noise-free and noise-contaminated structural responses. Both harmonic and impulsive forces are studied. The results show that the proposed approach can identify unknown dynamic forces within very limited iterations with high accuracy and shows its robustness even noise- polluted dynamic response measurements are utilized.
Abstract: Clusters of Microcalcifications (MCCs) are most frequent symptoms of Ductal Carcinoma in Situ (DCIS) recognized by mammography. Least-Square Support Vector Machine (LS-SVM) is a variant of the standard SVM. In the paper, LS-SVM is proposed as a classifier for classifying MCCs as benign or malignant based on relevant extracted features from enhanced mammogram. To establish the credibility of LS-SVM classifier for classifying MCCs, a comparative evaluation of the relative performance of LS-SVM classifier for different kernel functions is made. For comparative evaluation, confusion matrix and ROC analysis are used. Experiments are performed on data extracted from mammogram images of DDSM database. A total of 380 suspicious areas are collected, which contain 235 malignant and 145 benign samples, from mammogram images of DDSM database. A set of 50 features is calculated for each suspicious area. After this, an optimal subset of 23 most suitable features is selected from 50 features by Particle Swarm Optimization (PSO). The results of proposed study are quite promising.
Abstract: In this paper we report the quantitative structure activity relationship of novel bis-triazole derivatives for predicting the activity profile. The full model encompassed a dataset of 46 Bis- triazoles. Tripos Sybyl X 2.0 program was used to conduct CoMSIA QSAR modeling. The Partial Least-Squares (PLS) analysis method was used to conduct statistical analysis and to derive a QSAR model based on the field values of CoMSIA descriptor. The compounds were divided into test and training set. The compounds were evaluated by various CoMSIA parameters to predict the best QSAR model. An optimum numbers of components were first determined separately by cross-validation regression for CoMSIA model, which were then applied in the final analysis. A series of parameters were used for the study and the best fit model was obtained using donor, partition coefficient and steric parameters. The CoMSIA models demonstrated good statistical results with regression coefficient (r2) and the cross-validated coefficient (q2) of 0.575 and 0.830 respectively. The standard error for the predicted model was 0.16322. In the CoMSIA model, the steric descriptors make a marginally larger contribution than the electrostatic descriptors. The finding that the steric descriptor is the largest contributor for the CoMSIA QSAR models is consistent with the observation that more than half of the binding site area is occupied by steric regions.
Abstract: Microscopic emission and fuel consumption models
have been widely recognized as an effective method to quantify real
traffic emission and energy consumption when they are applied with
microscopic traffic simulation models. This paper presents a
framework for developing the Microscopic Emission (HC, CO, NOx,
and CO2) and Fuel consumption (MEF) models for light-duty
vehicles. The variable of composite acceleration is introduced into
the MEF model with the purpose of capturing the effects of historical
accelerations interacting with current speed on emission and fuel
consumption. The MEF model is calibrated by multivariate
least-squares method for two types of light-duty vehicle using
on-board data collected in Beijing, China by a Portable Emission
Measurement System (PEMS). The instantaneous validation results
shows the MEF model performs better with lower Mean Absolute
Percentage Error (MAPE) compared to other two models. Moreover,
the aggregate validation results tells the MEF model produces
reasonable estimations compared to actual measurements with
prediction errors within 12%, 10%, 19%, and 9% for HC, CO, NOx
emissions and fuel consumption, respectively.
Abstract: This paper deals with the optimal design of two-channel recursive parallelogram quadrature mirror filter (PQMF) banks. The analysis and synthesis filters of the PQMF bank are composed of two-dimensional (2-D) recursive digital all-pass filters (DAFs) with nonsymmetric half-plane (NSHP) support region. The design problem can be facilitated by using the 2-D doubly complementary half-band (DC-HB) property possessed by the analysis and synthesis filters. For finding the coefficients of the 2-D recursive NSHP DAFs, we appropriately formulate the design problem to result in an optimization problem that can be solved by using a weighted least-squares (WLS) algorithm in the minimax (L∞) optimal sense. The designed 2-D recursive PQMF bank achieves perfect magnitude response and possesses satisfactory phase response without requiring extra phase equalizer. Simulation results are also provided for illustration and comparison.
Abstract: The concept of order reduction by least-squares moment matching and generalised least-squares methods has been extended about a general point ?a?, to obtain the reduced order models for linear, time-invariant dynamic systems. Some heuristic criteria have been employed for selecting the linear shift point ?a?, based upon the means (arithmetic, harmonic and geometric) of real parts of the poles of high order system. It is shown that the resultant model depends critically on the choice of linear shift point ?a?. The validity of the criteria is illustrated by solving a numerical example and the results are compared with the other existing techniques.
Abstract: In this article, an adaptive least-squares mixed finite element method is studied for pseudo-parabolic integro-differential equations. The solutions of least-squares mixed weak formulation and mixed finite element are proved. A posteriori error estimator is constructed based on the least-squares functional and the posteriori errors are obtained.
Abstract: This paper deals with the project selection problem. Project selection problem is one of the problems arose firstly in the field of operations research following some production concepts from primary product mix problem. Afterward, introduction of managerial considerations into the project selection problem have emerged qualitative factors and criteria to be regarded as well as quantitative ones. To overcome both kinds of criteria, an analytic network process is developed in this paper enhanced with fuzzy sets theory to tackle the vagueness of experts- comments to evaluate the alternatives. Additionally, a modified version of Least-Square method through a non-linear programming model is augmented to the developed group decision making structure in order to elicit the final weights from comparison matrices. Finally, a case study is considered by which developed structure in this paper is validated. Moreover, a sensitivity analysis is performed to validate the response of the model with respect to the condition alteration.