Antenna design and implementation for the future space Ultra-Long wavelength radio telescope     

Linjie Chen  Amin Aminaei  Leonid I. Gurvits  Marc Klein Wolt  Hamid Reza Pourshaghaghi  Yihua Yan  Heino Falcke 《Experimental Astronomy》2018,45(2):231-253

In radio astronomy, the Ultra-Long Wavelengths (ULW) regime of longer than 10 m (frequencies below 30 MHz), remains the last virtually unexplored window of the celestial electromagnetic spectrum. The strength of the science case for extending radio astronomy into the ULW window is growing. However, the opaqueness of the Earth’s ionosphere makes ULW observations by ground-based facilities practically impossible. Furthermore, the ULW spectrum is full of anthropogenic radio frequency interference (RFI). The only radical solution for both problems is in placing an ULW astronomy facility in space. We present a concept of a key element of a space-borne ULW array facility, an antenna that addresses radio astronomical specifications. A tripole–type antenna and amplifier are analysed as a solution for ULW implementation. A receiver system with a low power dissipation is discussed as well. The active antenna is optimized to operate at the noise level defined by the celestial emission in the frequency band 1 ? 30 MHz. Field experiments with a prototype tripole antenna enabled estimates of the system noise temperature. They indicated that the proposed concept meets the requirements of a space-borne ULW array facility.  相似文献   

Prediction of riverine suspended sediment discharge using fuzzy logic algorithms, and some implications for estuarine settings     

Saeed Jafari Mianaei  Ali Reza Keshavarzi 《Geo-Marine Letters》2010,30(1):35-45

The ability of fuzzy logic algorithms to model relationships between stream flow and suspended sediment discharge was investigated using daily measurements of stream flow and suspended sediment discharge for the Escanaba River mouth station, situated on the shore of Lake Michigan and operated by the US Geological Survey. Three different configurations of inputs were applied, whereby the inputs were fuzzified into fuzzy subsets of variables by means of triangular membership functions. The relationships between inputs and suspended sediment discharge (output) were represented by a set of fuzzy rule expressed in IF–THEN format. The weighted average method served for defuzzification. The commonly used sediment rating curve was also applied to the data, and its performance compared with that of the three models by means of statistical analyses. For all three models, suspended sediment discharge predicted by the fuzzy logic algorithm was in satisfactory agreement with observations. Furthermore, the fuzzy logic algorithms performed better than the sediment rating curve, particularly at higher rates of suspended sediment discharge (in this study, more than 50  × 10⁶ g/day). Considered collectively, the use of fuzzy logic algorithms is suggested as a simple and effective approach for better prediction of suspended sediment discharge, also for estuaries.  相似文献   

Numerical Prediction of Induced Pressure and Lift of the Planing Surfaces     

Hassan GHASSEMI  Ahmad Reza KOHANSAL  Mahmoud GHIASSI 《中国海洋工程》2009,23(2):221-232

This paper discusses the numerical prediction of the induced pressure and lift of the planing surfaces in a steady motion based on the potential flow solver as well as the spray drag by use of the practical method.The numerical method for computation of the induced pressure and lift is potential-based boundary element method.Special technique is identified to present upwash geometry and to determine the spray drag.Numerical results of a planing flat plate and planing craft model 4666 are presented.It is shown that the method is robust and efficient and the results agree well with the experimental measurements with various Froude humors.  相似文献   

Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques     

Jian Zhou  Yingui Qiu  Danial Jahed Armaghani  Wengang Zhang  Chuanqi Li  Shuangli Zhu  Reza Tarinejad 《地学前缘(英文版)》2021,(3):201-213

A reliable and accurate prediction of the tunnel boring machine(TBM) performance can assist in minimizing the relevant risks of high capital costs and in scheduling tunneling projects.This research aims to develop six hybrid models of extreme gradient boosting(XGB) which are optimized by gray wolf optimization(GWO), particle swarm optimization(PSO), social spider optimization(SSO), sine cosine algorithm(SCA), multi verse optimization(MVO) and moth flame optimization(MFO), for estimation of the TBM penetration rate(PR).To do this, a comprehensive database with 1286 data samples was established where seven parameters including the rock quality designation, the rock mass rating, Brazilian tensile strength(BTS), rock mass weathering, the uniaxial compressive strength(UCS), revolution per minute and trust force per cutter(TFC), were set as inputs and TBM PR was selected as model output.Together with the mentioned six hybrid models, four single models i.e., artificial neural network, random forest regression, XGB and support vector regression were also built to estimate TBM PR for comparison purposes.These models were designed conducting several parametric studies on their most important parameters and then, their performance capacities were assessed through the use of root mean square error, coefficient of determination, mean absolute percentage error, and a10-index.Results of this study confirmed that the best predictive model of PR goes to the PSO-XGB technique with system error of(0.1453, and 0.1325), R~2 of(0.951, and 0.951), mean absolute percentage error(4.0689, and 3.8115), and a10-index of(0.9348, and 0.9496) in training and testing phases, respectively.The developed hybrid PSO-XGB can be introduced as an accurate, powerful and applicable technique in the field of TBM performance prediction.By conducting sensitivity analysis, it was found that UCS, BTS and TFC have the deepest impacts on the TBM PR.  相似文献   

A GIS‐based multi‐criteria seismic vulnerability assessment using the integration of granular computing rule extraction and artificial neural networks          下载免费PDF全文

Hossein Sheikhian  Mahmoud Reza Delavar  Alfred Stein 《Transactions in GIS》2017,21(6):1237-1259

This study proposes multi‐criteria group decision‐making to address seismic physical vulnerability assessment. Granular computing rule extraction is combined with a feed forward artificial neural network to form a classifier capable of training a neural network on the basis of the rules provided by granular computing. It provides a transparent structure despite the traditional multi‐layer neural networks. It also allows the classifier to be applied on a set of rules for each incoming pattern. Drawbacks of original granular computing (GrC) are covered, where some input patterns remained unclassified. The study was applied to classify seismic vulnerability of the statistical units of the city of Tehran, Iran. Slope, seismic intensity, height and age of the buildings were effective parameters. Experts ranked 150 randomly selected sample statistical units with respect to their degree of seismic physical vulnerability. Inconsistency of the experts' judgments was investigated using the induced ordered weighted averaging (IOWA) operator. Fifty‐five classification rules were extracted on which a neural network was based. An overall accuracy of 88%, κ = 0.85 and R² = 0.89 was achieved. A comparison with previously implemented methodologies proved the proposed method to be the most accurate solution to the seismic physical vulnerability of Tehran.  相似文献   

Risk assessment of agricultural water requirement based on a multi-model ensemble framework,southwest of Iran     

Zamani  Reza  Akhond-Ali  Ali-Mohammad  Roozbahani  Abbas  Fattahi  Rouhollah 《Theoretical and Applied Climatology》2017,129(3-4):1109-1121

Water shortage and climate change are the most important issues of sustainable agricultural and water resources development. Given the importance of water availability in crop production, the present study focused on risk assessment of climate change impact on agricultural water requirement in southwest of Iran, under two emission scenarios (A2 and B1) for the future period (2025–2054). A multi-model ensemble framework based on mean observed temperature-precipitation (MOTP) method and a combined probabilistic approach Long Ashton Research Station-Weather Generator (LARS-WG) and change factor (CF) have been used for downscaling to manage the uncertainty of outputs of 14 general circulation models (GCMs). The results showed an increasing temperature in all months and irregular changes of precipitation (either increasing or decreasing) in the future period. In addition, the results of the calculated annual net water requirement for all crops affected by climate change indicated an increase between 4 and 10 %. Furthermore, an increasing process is also expected regarding to the required water demand volume. The most and the least expected increase in the water demand volume is about 13 and 5 % for A2 and B1 scenarios, respectively. Considering the results and the limited water resources in the study area, it is crucial to provide water resources planning in order to reduce the negative effects of climate change. Therefore, the adaptation scenarios with the climate change related to crop pattern and water consumption should be taken into account.

  相似文献   

Standard thermodynamic data for Rhodochrosite from equilibrium decomposition curve     

Ali Reza Fazeli  J. A. K. Tareen  B. Basavalingu  G. T. Bhandage 《Journal of Earth System Science》1991,100(1):37-39

Hydrothermal equilibrium decomposition curve for MnCO₃⇌MnO + CO₂ in the total CO₂ pressure range of 100–1700 bars and temperature range of 500–800°C was studied. The standard thermodynamic data obtained are: ΔH⁰ _f= − 894.382 ± 0.74 kj/mol and ΔG⁰ _f = − 822.170 ± 0.74 kj/mol. These values are more negative than the reported calorimetric data.  相似文献   

A Geometric Computational Model for Calculation of Longwall Face Effect on Gate Roadways     

Hamid Mohammadi  Mohammad Ali Ebrahimi Farsangi  Hossein Jalalifar  Ali Reza Ahmadi 《Rock Mechanics and Rock Engineering》2016,49(1):303-314

In this paper a geometric computational model (GCM) has been developed for calculating the effect of longwall face on the extension of excavation-damaged zone (EDZ) above the gate roadways (main and tail gates), considering the advance longwall mining method. In this model, the stability of gate roadways are investigated based on loading effects due to EDZ and caving zone (CZ) above the longwall face, which can extend the EDZ size. The structure of GCM depends on four important factors: (1) geomechanical properties of hanging wall, (2) dip and thickness of coal seam, (3) CZ characteristics, and (4) pillar width. The investigations demonstrated that the extension of EDZ is a function of pillar width. Considering the effect of pillar width, new mathematical relationships were presented to calculate the face influence coefficient and characteristics of extended EDZ. Furthermore, taking GCM into account, a computational algorithm for stability analysis of gate roadways was suggested. Validation was carried out through instrumentation and monitoring results of a longwall face at Parvade-2 coal mine in Tabas, Iran, demonstrating good agreement between the new model and measured results. Finally, a sensitivity analysis was carried out on the effect of pillar width, bearing capacity of support system and coal seam dip.  相似文献   

An efficient artificial intelligence model for prediction of tropical storm surge   总被引：1，自引：0，他引：1  

M. Reza Hashemi  Malcolm L. Spaulding  Alex Shaw  Hamed Farhadi  Matt Lewis 《Natural Hazards》2016,80(1):471-486

  相似文献   

Regional modeling of rainfall-induced landslides using TRIGRS model by incorporating plant cover effects: case study in Hulu Kelang,Malaysia     

Nader Saadatkhah  Shattri Mansor  Azman Kassim  Lee Min Lee  Reza Saadatkhah  Ali Sobhanmanesh 《Environmental Earth Sciences》2016,75(5):445

  相似文献