Uncertainty estimation and Monte Carlo simulation method

In this paper, Monte Carlo Simulation (MCS) method was used as an uncertainty assessment tool for air flow, net thrust measurement. Uuncertainty sources of the net thrust measurement were analyzed, and the probability distribution characteristics of each source were discussed. Detailed MCS methodology was described including the effect of the number of simulation. Compared to the conventional sensitivity coefficient method, the MCS method has advantage in the uncertainty assessment. The MCS is comparatively simple, convenient and accurate, especially for complex or nonlinear measurement modeling equations. The uncertainty assessment result by MCS was compared with that of the conventional sensitivity coefficient method, and each method gave different result. The uncertainties in the net thrust measurement by the MCS and the conventional sensitivity coefficient method were 0.906% and 1.209%, respectively. It was concluded that the first order Taylor expansion in the conventional sensitivity coefficient method and the nonlinearity of model equation caused the difference. It was noted that the uncertainty assessment method should be selected carefully according to the mathematical characteristics of the model equation of the measurement.

In this paper, we propose a channel estimation method for radio frequency multiple-input multiple-output orthogonal frequency division multiplexing (RF-MIMO-OFDM) systems. In RF-MIMO-OFDM systems, a receiver combines the received signals at the RF front-end. This implies that a receiver can estimate only one of MIMO channels during one OFDM symbol duration. Therefore, the conventional channel estimation method requires a number of pilot OFDM symbols in proportion to the numbers of transmit antennas and receive antennas. If the channels are varying during the pilot transmission, the accuracy of the conventional channel estimation is deteriorated. In this paper, we propose a channel estimation method which can estimate channels with a shorter time period considering the antenna switching time. Our proposed method uses time-domain maximum likelihood estimation and pilot symbols which have a repetitive waveform within one OFDM symbol duration. The proposed method estimates channel impulse responses (CIRs) of multiple channels during one OFDM symbol duration. From simulation results, we show that the proposed estimation method achieves a better estimation performance than the conventional estimation method. Furthermore, the error performance is also improved by the use of the proposed method.

Single-carrier frequency-domain equalization has been adopted as the uplink scheme for broadband high speed wireless communications, in contrast to OFDM for downlink communications. An efficient channel estimation technique is inevitable for any communication system in order to retrieve the transmitted data as such. The estimation can be done either by conventional method using training symbol or by blind estimation process. By combining the equalization along with the estimation process, the bit error rate can be reduced. In this paper, the conventional estimation and equalization method with the help of training data is considered. The results show that the estimation along with adaptive equalization yields better result than with one with estimation only.

A Compact Humanoid I-VY can control torque of a joint actuator because of sensing its electric current. In addition, I-VY can measure joint angle and its velocity with potentiometers inserted into the actuators. From these information, I-VY can estimate ZMP(Zero Moment Point). On the other hand, conventional ZMP estimation method uses the force/torque information, which needs various sensors. This study compared the proposed estimation methods of ZMP applied to I-VY with the conventional method, and proposes simple posture control system by using the proposed method.

Debris flow velocities are commonly back-calculated from superelevation events which require subjective estimates of radii of curvature of bends in the debris flow channel or predicted using flow equations that require the selection of appropriate rheological models and material property inputs. This research investigated difficulties associated with the use of these conventional velocity estimation methods. Radii of curvature estimates were found to vary with the extent of the channel investigated and with the scale of the media used, and back-calculated velocities varied among different investigated locations along a channel. Distinct populations of Bingham properties were found to exist between those measured by laboratory tests and those back-calculated from field data; thus, laboratory-obtained values would not be representative of field-scale debris flow behavior. To avoid these difficulties with conventional methods, a new preliminary velocity estimation method is presented that statistically relates flow velocity to the channel slope and the flow depth. This method presents ranges of reasonable velocity predictions based on 30 previously measured velocities.

In this paper, we propose a modified time-domain channel estimation algorithm for OFDM systems working under impulsive noise conditions. The proposed method employs two time-domain windows to reduce additive white Gaussian noise and impulsive noise components included in the estimated channel impulse response (CIR). Meanwhile, the impulse noise equalizer further eliminates the impulse noise. As a result, the modified algorithm can overcome the mean-square error (MSE) floor caused by the conventional method in impulsive noise environment. Simulation results demonstrate that the proposed algorithm can significantly improve the MSE performance compared with the conventional estimation method, and provides about 3dB improvement in SNR for the same BER values in DVB system.

M2M (machine-to-machine) is required and spreads rapidly in recent years. Wireless multi-hop communication is one of the communication styles to construct M2M. However, the performance estimation of such wireless multi-hop communication is not so easy because the bit error ratio and symbol rate adaptation heavily depend on various environmental conditions. Stochastic network calculus (SNC) is a theory for the performance estimation of network. However, current SNC lacks ways to take wireless random errors, branching and joining of traffic flows into calculation, these are necessary to estimate the performance of sink-tree wireless-sensor network. This paper proposed a performance estimation method for sink-tree wireless multi-hop network based on SNC. The proposed method has three attractive points. Firstly, this introduces parameters of hop number and node number considering wireless random error due to packet collision. Secondly, this treats actual sink-tree network model by considering joining of traffic. Thirdly, this separates the service curve and bounding function calculation to avoid double counting. To evaluate the accuracy of the result of the proposed method, conventional estimation methods and simulation evaluation were conducted on a typical wireless multi-hop sensor data collection network. The results validate that the proposed method achieves much higher-accuracy than conventional methods.

In this paper, we present a novel pilot-assisted channel estimation algorithm for orthogonal frequency-division multiplexing (OFDM) wireless communication systems. The proposed method uses fewer pilots, which helps to increase the bandwidth (BW) efficiency while maintaining the adequate bit-error-rate (BER). A systematical approach for optimization of the pilot pattern applied in the low-dispersive channels is proposed and the performances using both the proposed and conventional estimation methods are quantitatively compared the performance in terms of BER for a case study of DVB-T/T2 system in 2K mode. MATLAB simulations are performed to evaluate the methods in the cases of the specific channel models (urban and rural models). From the simulation results, the novel estimation method gives out mostly the same BER while requiring only about 25% pilot number per symbol, compared with the conventional methods.

In this paper a subspace processing method is introduced that can be used for direction of arrival estimation of coherent signals in an asynchronous DS-CDMA system. Conventional methods of direction of arrival estimation are not directly applicable to the case of multiple coherent signals that impinge on an antenna array from different directions. Some preprocessing is essential prior to estimation in this case. The proposed method exploits the spreading code and the path delays of the desired user to eliminate the contribution of undesired paths in the signal subspace. To this end, the signal subspace is mapped to a new subspace which contains the spatial-temporal signature of the desired signal. Once the desired subspace is created, conventional methods such as MUSIC and ESPRIT can be employed to estimate the desired directions of arrival. It is proved that the obtained direction of arrival estimator, based on the proposed method, is consistent. Also, the estimation performance is evaluated by comparing the proposed method with conventional estimation methods.

Monte Carlo (MC) simulation methods are widely used to solve complex engineering and scientific problems. Unlike other deterministic methods, MC methods use statistical sampling to produce approximate solutions. As the processed sample size N growths, the uncertainty of the solution is reduced. It is well known that the mean-square approximation error decreases as 1/N. However, for large problems like high-dimensional integrations and computationally intensive simulations, MC methods may take months or even years to obtain a solution with acceptable tolerance. The Super-Efficient (SE) Monte Carlo simulation method, originated by Umeno, produces a solution whose approximation error decreases as fast as 1/N2. However, it only applies to a small class of problems possessing certain properties. We describe an approximate SE Monte Carlo simulation method that is applicable to a wider class of problems than the original SE method, and yields a convergence rate as fast as 1/Na for 1<a<2.

The objective is to develop an approach for the determination of the target reliability index for serviceability limit state (SLS) of single piles. This contributes to conducting the SLS reliability-based design (RBD) of piles. Based on a two-parameter, hyperbolic curve-fitting equation describing the load-settlement relation of piles, the SLS model factor is defined. Then, taking into account the uncertainties of load-settlement model, load and bearing capacity of piles, the formula for computing the SLS reliability index (βsls) is obtained using the mean value first order second moment (MVFOSM) method. Meanwhile, the limit state function for conducting the SLS reliability analysis by the Monte Carlo simulation (MCS) method is established. These two methods are finally applied to determine the SLS target reliability index. Herein, the limiting tolerable settlement (slt) is treated as a random variable. For illustration, four load test databases from South Africa are compiled again to conduct reliability analysis and present the recommended target reliability indices. The results indicate that the MVFOSM method overestimates βsls compared to that computed by the MCS method. Besides, both factor of safety (FS) and slt are key factors influencing βsls, so the combination of FS and βsls is welcome to be used for the SLS reliability analysis of piles when slt is determined. For smaller slt, pile types and soils conditions have significant influence on the SLS target reliability indices; for larger slt, slt is the major factor having influence on the SLS target reliability indices. This proves that slt is the most key parameter for the determination of the SLS target reliability index.

This paper deals with the reliability assessment of electrical power supply systems under redundancy. For doing so, three probabilistic safety analysis (PSA) methods are proposed and developed namely fault tree analysis (FTA), reliability block diagram (RBD), and Monte Carlo Simulation (MC) method. The FTA method is performed using RiskSpectrumPSA® software and the RBD and MC methods are coded using Matlab software. The electrical power supply system reliability is analyzed and enhanced considering various cases of redundancy. The FTA method is based on qualitative and quantitative reliability assessment. The quantitative assessment calculates the failure probability of the top event, and the qualitative approach determines the minimal cut sets (MCS). However, the RBD and MC methods are proposed to compute the system failure probability. The considered methods are applied on three case studies and the obtained results are compared to each other. The obtained results show that the three methods provide almost the same results. Furthermore, the reliability of the electrical power supply is clearly enhanced with the number of redundant components.

Evaluation of concepts of fin efficiency using Monte Carlo Simulation method

This article aims to investigate the means to obtain optimal hot stamping process parameters and the influence of the stochastic variability of these parameters on forming quality. A multi-objective stochastic approach, integrating response surface methodology (RSM), multi-objective genetic algorithm optimization non-dominated sorting genetic algorithm II (NSGA-II) and the Monte Carlo simulation (MCS) method is proposed in this article to achieve this goal. RSM was used to establish the relationship between the process parameters and forming quality indices. NSGA-II was utilized to obtain a Pareto frontier, which consists of a series of optimal process parameters. The MCS method was employed to study and reduce the influence of a stochastic property of these process parameters on forming quality. The results confirmed the efficiency of the proposed multi-objective stochastic approach during optimization of the hot stamping process. Robust optimal process parameters guaranteeing good forming quality were also obtained using this approach.

Blackbody (BB) cavities as standard radiation sources are used in calibration for various infrared (IR) instruments in IR detection, imaging, material emissivity measuring etc. A surface pitted with cell-like mini cavities as honeycombs whose radiant characteristics is evaluated by Monte Carlo method (MCM) is applied to develop a new surface BB. To improve the precision of MCM, make it more reliable, three requisite factors are considered. First, set up correct physical model in which the radiation of a surface comes from light emitting points across the surface consistently. Second, the uniformity of random number generators are investigated, which is the precondition for the MCM simulation. Third, to perform simulating with tremendous sampling number, after all light points and their rays are tracked, the statistical results become stable, fluctuate slightly, which increase the exactitude of MCM. To implement MCM simulation, every ray of the IR lights is tracked, the position of a light point, the direction of a ray, the state that if the ray is absorbed or reflected at a reflecting point etc. are all regarded as random variables which are sampled by random number according to their probability distribution. The effective emissivity of the surface is evaluated statistically via tracking numerous rays which involves setting up the ray equation, and coordinates transformation etc. The relationships between the accuracy of computation results and simulating model, sampling method, uniformity of pseudo-random number, and sampling number have been discussed. The results show the surface with honeycombs can provide a consistent radiation near to be a diffuse emitter even the material of the surface including spectral components, and the MCM simulating results provide the criteria for surface BB design.