Traction power substation balance and losses estimation in AC railways using a power transfer device through Monte Carlo analysis

The high dynamic power requirements present in modern railway transportation systems raise research challenges for an optimal operation of railway electrification. This paper presents a Monte Carlo analysis on the application of a power transfer device installed in the neutral zone and exchanging active power between two sections. The main analyzed parameters are the active power balance in the two neighbor traction power substations and the system power losses. A simulation framework is presented to comprise the desired analysis and a universe of randomly distributed scenarios are tested to evaluate the effectiveness of the power transfer device system. The results show that the density of trains and the relative branch length of a traction power substation should be considered in the evaluation phase of the best place to install a power transfer device, towards the reduction of the operational power losses, while maintaining the two substations balanced in terms of active power.

Monte Carlo Analysis of Nodes Deployment in Wireless Sensor Network Using Range-Free Location Methods

A Monte Carlo Analysis of nodes deployment for large-scale and non-homogeneous wireless sensor networks, has been done. Through simulations of random deployments of nodes over a square area using different densities, assuming that our network is composed by Anchor nodes （special sensors with known position） and simple Sensor nodes, the latter are supposed to estimate their own position after being placed within the coverage area with the minimum Anchor nodes needed to ＇feed＇ them with the necessary information. The goal is then to assist decision-makers in selecting among different alternatives to deploy the networks, according to resources features and availability, hence this method provides an estimate value of how many Anchor nodes should be deployed in a given area to trigger the location algorithm in the greatest possible number of Sensor nodes in the network.

Path Tracking Controller Design of Automated Parking Systems via NMPC with an Instructible Solution

Parking difficulties have become a social issue that people have to solve.Automated parking system is practicable for quick par operations without a driver which can also greatly reduces the probability of parking accidents.The paper proposes a Lyapunov-based nonlinear model predictive controller embedding an instructable solution which is generated by the modified rear-wheel feedback method(RF-LNMPC)in order to improve the overall path tracking accuracy in parking conditions.Firstly,A discrete-time RF-LNMPC considering the position and attitude of the parking vehicle is proposed to increase the success rate of automated parking effectively.Secondly,the RF-LNMPC problem with a multi-objective cost function is solved by the Interior-Point Optimization,of which the iterative initial values are described as the instructable solutions calculated by combining modified rear-wheel feedback to improve the performance of local optimal solution.Thirdly,the details on the computation of the terminal constraint and terminal cost for the linear time-varying case is presented.The closed-loop stability is verified via Lyapunov techniques by considering the terminal constraint and terminal cost theoretically.Finally,the proposed RF-LNMPC is implemented on a selfdriving Lincoln MKZ platform and the experiment results have shown improved performance in parallel and vertical parking conditions.The Monte Carlo analysis also demonstrates good stability and repeatability of the proposed method which can be applied in practical use in the near future.

ANALYSIS OF CIRCUIT TOLERANCE BASED ON RANDOM SET THEORY

Monte Carlo Analysis has been an accepted method for circuit tolerance analysis, but the heavy computational complexity has always prevented its applications. Based on random set theory, this paper presents a simple and flexible tolerance analysis method to estimate circuit yield. It is the alternative to Monte Carlo analysis, but reduces the number of calculations dramatically.

Multiple Parameters Side Channel Signal Analysis Using in Hardware Trojan Detection

As the process of economic globalization deepens,a large variety of integrated circuit designers tends to move the chip's manufacturing to the developing country.But during the globalization of semiconductor design and fabrication process,integrated circuits are suffering from increasing malicious alterations from the untrusted foundries,which pose a serious threat to the military,finance,transportation and other critical systems.An noninvasive approach was presented to measure the physical "sidechannel"parameter of a chip such as current or delay,which is effectively capable of detecting the malicious hardware alternations.The intrinsic relationship of a circuit's side-channel parameters was exploited to distinguish the effect of a Trojan in the presence of large random noise and process noise,such as the Dynamic current(IDDT)versus the maximum operating frequency(Fmax)correlation.The Monte Carlo analysis in Hspice using ± 20% Gauss variations in transistor threshold voltage(Vth)was carried out to simulate the circuit state in the real world.Simulation Results show that this approach is effective to detect the ultra-small Trojans.

Risk analysis of slope instability of levees under river sand mining conditions

Levees are affected by over-exploitation of river sand and river adjustments after the formation of sand pits. The slope stability is seriously threatened, drawing wide concern among experts and scholars in the area of water conservancy. This study analyzed the uncertainties of slope stability of levees under river sand mining conditions, including uncertainty caused by interest- driven over-exploitation by sand mining contractors, and uncertainty of the distance from the slope or sand pit to the bottom of the levee under the action of cross-flow force after the sand pit forms. Based on the results of uncertainty analysis, the distribution and related parameters of these uncertainties were estimated according to the Yangtze River sand mining practice. A risk model of the slope instability of a levee under river sand mining conditions was built, and the possibility of slope instability under different slope gradients in a certain reach of the Yangtze River was calculated with the Monte Carlo method and probability combination method. The results indicated that the probability of instability risk rose from 2.38% to 4.74% as the pits came into being.

Application of Monte Carlo filtering method in regional sensitivity analysis of AASHTOWare Pavement ME design

Since AASHTO released the Mechanistic-Empirical Pavement Design Guide （MEPDG） for public review in 2004, many highway research agencies have performed sensitivity ana- lyses using the prototype MEPDG design software. The information provided by the sensitivity analysis is essential for design engineers to better understand the MEPDG design models and to identify important input parameters for pavement design. In literature, different studies have been carried out based on either local or global sensitivity analysis methods, and sensitivity indices have been proposed for ranking the importance of the input parameters. In this paper, a regional sensitivity analysis method, Monte Carlo filtering （MCF）, is presented. The MCF method maintains many advantages of the global sensitivity analysis, while focusing on the regional sensitivity of the MEPDG model near the design criteria rather than the entire problem domain. It is shown that the information obtained from the MCF method is more helpful and accurate in guiding design engineers in pavement design practices. To demonstrate the proposed regional sensitivity method, a typical three-layer flexible pavement structure was analyzed at input level 3. A detailed procedure to generate Monte Carlo runs using the AASHTOWare Pavement ME Design software was provided. The results in the example show that the sensitivity ranking of the input parameters in this study reasonably matches with that in a previous study under a global sensitivity analysis. Based on the analysis results, the strengths, practical issues, and applications of the MCF method were further discussed,

Quantifying the impact of mineralogical heterogeneity on reactive transport modeling of CO_(2)+O_(2) in-situ leaching of uranium

CO_(2)+O_(2) in-situ leaching(ISL)of sandstonetype uranium ore represents the third generation of solution mining in China.In this study,reactive transport modeling of the interaction between hydrodynamic and geochemical reactions is performed to enable better prediction and regulation of the CO_(2)+O_(2) in-situ leaching process of uranium.Geochemical reactions between mining solutions and rock,and the kinetic uranium dissolution controlled by O_(2)(aq)and bicarbonate(HCO_(3)-)are considered in the CO_(2)+O_(2) ISL reactive transport model of a typical sandstone-hosted uranium ore deposit in northern China.The reactive leaching of uranium is most sensitive to the spatial distribution of the mineralogical properties of the uranium deposit.Stochastic geostatistical models are used to represent the uncertainty on the spatial distribution of mineral grades.A Monte Carlo analysis was also performed to simulate the uranium production variability over an entire set of geostatistical realizations.The ISL stochastic simulation performed with the selected geostatistical realizations approximates the uranium production variability well.The simulation results of the ISL reactive transport model show that the extent of the uranium plume is highly dependent on mineralogical heterogeneity.The uncertainty analysis suggests the effect of uranium grade heterogeneity was found to be important to improve the accurate capture of the uncertainty.This study provides guidance for the accurate simulation and dynamic regulation of the CO_(2)+O_(2) leaching process of uranium at the scale of large mining areas.

Inverse uncertainty characteristics of pollution source identification for river chemical spill incidents by stochastic analysis

Identifying source information after river chemical spill occurrences is critical for emergency responses.However,the inverse uncertainty characteristics of this kind of pollution source inversion problem have not yet been clearly elucidated.To fill this gap,stochastic analysis approaches,including a regional sensitivity analysis method,identifiability plot and perturbation methods,were employed to conduct an empirical investigation on generic inverse uncertainty characteristics under a well-accepted uncertainty analysis framework.Case studies based on field tracer experiments and synthetic numerical tracer experiments revealed several new rules.For example,the release load can be most easily inverted,and the source location is responsible for the largest uncertainty among the source parameters.The diffusion and convection processes are more sensitive than the dilution and pollutant attenuation processes to the optimization of objective functions in terms of structural uncertainty.The differences among the different objective functions are smaller for instantaneous release than for continuous release cases.Small monitoring errors affect the inversion results only slightly,which can be ignored in practice.Interestingly,the estimated values of the release location and time negatively deviate from the real values,and the extent is positively correlated with the relative size of the mixing zone to the objective river reach.These new findings improve decision making in emergency responses to sudden water pollution and guide the monitoring network design.

Variation tolerant and stability simulation of low power SRAM cell analysis using FGMOS

With technology scaling,stability,power dissipation,and device variability,the impact of process,voltage and temperature(PVT)variations has become dominant for static random access memory(SRAM)analysis for productivity and failure.In this paper,ten-transistors(10T)and low power eight-transistors SRAM cells are redesigned using floating-gate MOS transistors(FGMOS).Power centric parameters viz.read power,write power,hold power and delay are the performance analysis metrics.Further,the stochastic parameter variation to study the variability tolerance of the redesigned cell,PVT variations and Monte Carlo simulations have been carried out for 10T FGMOS SRAM cell.Stability has been illustrated with the conventional butterfly method giving read static noise margin(RSNM)and write static noise margin(WSNM)metrics for read stability and write ability,respectively.A comparative analysis with standard six-transistor SRAM cell is carried out.HSPICE simulative analysis has been carried out for 32 nm technology node.The redesigned FGMOS SRAM cells provide improved performance.Also,these are robust and reliability efficient with comparable stability.

Stochastic model updating using distance discrimination analysis

This manuscript presents a stochastic model updating method, taking both uncertainties in models and variability in testing into account. The updated finite element（FE） models obtained through the proposed technique can aid in the analysis and design of structural systems. The authors developed a stochastic model updating method integrating distance discrimination analysis（DDA） and advanced Monte Carlo（MC） technique to（1） enable more efficient MC by using a response surface model,（2） calibrate parameters with an iterative test-analysis correlation based upon DDA, and（3） utilize and compare different distance functions as correlation metrics. Using DDA, the influence of distance functions on model updating results is analyzed. The proposed stochastic method makes it possible to obtain a precise model updating outcome with acceptable calculation cost. The stochastic method is demonstrated on a helicopter case study updated using both Euclidian and Mahalanobis distance metrics. It is observed that the selected distance function influences the iterative calibration process and thus, the calibration outcome, indicating that an integration of different metrics might yield improved results.