Millimeter wave(mmWave)massive multiple-input multiple-output(MIMO)plays an important role in the fifth-generation(5G)mobile communications and beyond wireless communication systems owing to its potential of high capa...Millimeter wave(mmWave)massive multiple-input multiple-output(MIMO)plays an important role in the fifth-generation(5G)mobile communications and beyond wireless communication systems owing to its potential of high capacity.However,channel estimation has become very challenging due to the use of massive MIMO antenna array.Fortunately,the mmWave channel has strong sparsity in the spatial angle domain,and the compressed sensing technology can be used to convert the original channel matrix into the sparse matrix of discrete angle grid.Thus the high-dimensional channel matrix estimation is transformed into a sparse recovery problem with greatly reduced computational complexity.However,the path angle in the actual scene appears randomly and is unlikely to be completely located on the quantization angle grid,thus leading to the problem of power leakage.Moreover,multiple paths with the random distribution of angles will bring about serious interpath interference and further deteriorate the performance of channel estimation.To address these off-grid issues,we propose a parallel interference cancellation assisted multi-grid matching pursuit(PIC-MGMP)algorithm in this paper.The proposed algorithm consists of three stages,including coarse estimation,refined estimation,and inter-path cyclic iterative inter-ference cancellation.More specifically,the angular resolution can be improved by locally refining the grid to reduce power leakage,while the inter-path interference is eliminated by parallel interference cancellation(PIC),and the two together improve the estimation accuracy.Simulation results show that compared with the traditional orthogonal matching pursuit(OMP)algorithm,the normalized mean square error(NMSE)of the proposed algorithm decreases by over 14dB in the case of 2 paths.展开更多
This paper presents a method for optimal sizing of an off-grid hybrid microgrid (MG) system in order to achieve a certain load demand. The hybrid MG is made of a solar photovoltaic (PV) system, wind turbine (TW) and e...This paper presents a method for optimal sizing of an off-grid hybrid microgrid (MG) system in order to achieve a certain load demand. The hybrid MG is made of a solar photovoltaic (PV) system, wind turbine (TW) and energy storage system (ESS). The reliability of the MG system is modeled based on the loss of power supply probability (SPSP). For optimization, an enhanced Genetic Algorithm (GA) is used to minimize the total cost of the system over a 20-year period, while satisfying some reliability and operation constraints. A case study addressing optimal sizing of an off-grid hybrid microgrid in Nigeria is discussed. The result is compared with results obtained from the Brute Force and standard GA methods.展开更多
Due to the uncertainty of renewable energy power generation and the non-linearity of load demand,it becomes complicated to determine the capacity of each device in hybrid renewable energy power generation systems.This...Due to the uncertainty of renewable energy power generation and the non-linearity of load demand,it becomes complicated to determine the capacity of each device in hybrid renewable energy power generation systems.This work aims to optimize the capacity of two types of the off-grid hybrid wind-hydrogen energy system.We considered the maximum profit of the system and the minimum loss of power supply probability as optimization goals.Firstly,we established steady-state models of the wind turbine,alkaline electrolyzer,lead-acid battery,and proton exchange membrane fuel cell in matrix laboratory software to optimize the capacity.Secondly,we analyzed the operating mode of the system and determined two system structures(system contains batteries whether or not).Finally,according to the wind speed and load in the sample area,we compared the economics of the two systems and selected the optimal configuration for the area.In the same calculation example data,the non-dominated sorting genetic algorithm-II(NSGA-II)is used to optimize the capacity of each device in the two systems.The results showed that the profit of the without battery-equipped system is 32.38%higher than another system.But the power supply reliability is the opposite.To avoid the contingency of the calculation results,we used the traditional genetic algorithm(GA)and ant colony optimization(ACO)to calculate the same example.The results showed that NSGA-II is significantly better than GA and ACO in terms of iteration steps and calculation results.The required architecture for the System-I composes of 3 numbers of 10 kW wind turbines,61 sets of 12 V·240 Ah leadacid batteries,8 kW electrolytic cell,and 6 kW PEMFC.The net profit and LPSP are ¥44,315 and 0.01254 respectively.The required architecture for the System-II composes of 2 numbers of 10 kW wind turbines,24 kW electrolytic cells,and 18 kW PEMFC.Net profit and LPSP are ¥58,663 and 0.03244,respectively.This paper provided two schemes for the optimal configuration of the hybrid wind-hydrogen energy system in islanding mode,which provided a theoretical basis for practical engineering applications.展开更多
传统压缩感知(CS,Compressive Sensing)成像方法一般假定目标精确位于事先划定的成像网格上,实际中由于散射点空间位置是连续分布的,因此偏离网格(Off-grid)问题必然存在.这会引起真实回波测量值与默认系统观测矩阵之间失配,导致传统CS...传统压缩感知(CS,Compressive Sensing)成像方法一般假定目标精确位于事先划定的成像网格上,实际中由于散射点空间位置是连续分布的,因此偏离网格(Off-grid)问题必然存在.这会引起真实回波测量值与默认系统观测矩阵之间失配,导致传统CS成像方法性能恶化.本文基于频率分集多输入多输出(FD-MIMO,Frequency Diverse Multiple-Input Multiple-Output)雷达,针对Off-grid目标提出了一种基于贝叶斯压缩感知的稀疏自聚焦(SAF-BCS,Sparse Autofocus Imaging Method Based on Bayesian Compressive Sensing)成像算法.该算法依据最大后验(MAP,Maximum A Posteriori)准则,利用变分贝叶斯学习技术求解含有Off-grid目标的稀疏像.与传统稀疏重构方法相比,所提方法充分利用了目标先验信息,可自适应调整参数,能够更好地反演稀疏目标,同时具有校正Off-grid目标的网格位置偏差以及估计噪声功率等优势.仿真结果表明SAF-BCS算法对网格划分不敏感,具有稳健的成像性能.展开更多
The study presents a comprehensive coupled thermo-bio-chemo-hydraulic(T-BCH)modeling framework for stabilizing soils using microbially induced calcite precipitation(MICP).The numerical model considers relevant multiph...The study presents a comprehensive coupled thermo-bio-chemo-hydraulic(T-BCH)modeling framework for stabilizing soils using microbially induced calcite precipitation(MICP).The numerical model considers relevant multiphysics involved in MICP,such as bacterial ureolytic activities,biochemical reactions,multiphase and multicomponent transport,and alteration of the porosity and permeability.The model incorporates multiphysical coupling effects through well-established constitutive relations that connect parameters and variables from different physical fields.It was implemented in the open-source finite element code OpenGeoSys(OGS),and a semi-staggered solution strategy was designed to solve the couplings,allowing for flexible model settings.Therefore,the developed model can be easily adapted to simulate MICP applications in different scenarios.The numerical model was employed to analyze the effect of various factors,including temperature,injection strategies,and application scales.Besides,a TBCH modeling study was conducted on the laboratory-scale domain to analyze the effects of temperature on urease activity and precipitated calcium carbonate.To understand the scale dependency of MICP treatment,a large-scale heterogeneous domain was subjected to variable biochemical injection strategies.The simulations conducted at the field-scale guided the selection of an injection strategy to achieve the desired type and amount of precipitation.Additionally,the study emphasized the potential of numerical models as reliable tools for optimizing future developments in field-scale MICP treatment.The present study demonstrates the potential of this numerical framework for designing and optimizing the MICP applications in laboratory-,prototype-,and field-scale scenarios.展开更多
基金supported in part by the Beijing Natural Science Foundation under Grant No.L202003the National Natural Science Foundation of China under Grant U22B2001 and 62271065the Project of China Railway Corporation under Grant N2022G048.
文摘Millimeter wave(mmWave)massive multiple-input multiple-output(MIMO)plays an important role in the fifth-generation(5G)mobile communications and beyond wireless communication systems owing to its potential of high capacity.However,channel estimation has become very challenging due to the use of massive MIMO antenna array.Fortunately,the mmWave channel has strong sparsity in the spatial angle domain,and the compressed sensing technology can be used to convert the original channel matrix into the sparse matrix of discrete angle grid.Thus the high-dimensional channel matrix estimation is transformed into a sparse recovery problem with greatly reduced computational complexity.However,the path angle in the actual scene appears randomly and is unlikely to be completely located on the quantization angle grid,thus leading to the problem of power leakage.Moreover,multiple paths with the random distribution of angles will bring about serious interpath interference and further deteriorate the performance of channel estimation.To address these off-grid issues,we propose a parallel interference cancellation assisted multi-grid matching pursuit(PIC-MGMP)algorithm in this paper.The proposed algorithm consists of three stages,including coarse estimation,refined estimation,and inter-path cyclic iterative inter-ference cancellation.More specifically,the angular resolution can be improved by locally refining the grid to reduce power leakage,while the inter-path interference is eliminated by parallel interference cancellation(PIC),and the two together improve the estimation accuracy.Simulation results show that compared with the traditional orthogonal matching pursuit(OMP)algorithm,the normalized mean square error(NMSE)of the proposed algorithm decreases by over 14dB in the case of 2 paths.
文摘This paper presents a method for optimal sizing of an off-grid hybrid microgrid (MG) system in order to achieve a certain load demand. The hybrid MG is made of a solar photovoltaic (PV) system, wind turbine (TW) and energy storage system (ESS). The reliability of the MG system is modeled based on the loss of power supply probability (SPSP). For optimization, an enhanced Genetic Algorithm (GA) is used to minimize the total cost of the system over a 20-year period, while satisfying some reliability and operation constraints. A case study addressing optimal sizing of an off-grid hybrid microgrid in Nigeria is discussed. The result is compared with results obtained from the Brute Force and standard GA methods.
基金supported by the Inner Mongolia Science and Technology Program under Grant 2021GG0336by the Open Fund of Key Laboratory of Wind Energy and Solar Energy Technology(Inner Mongolia University of Technology),Ministry of Education(No.2020ZD01)in China.
文摘Due to the uncertainty of renewable energy power generation and the non-linearity of load demand,it becomes complicated to determine the capacity of each device in hybrid renewable energy power generation systems.This work aims to optimize the capacity of two types of the off-grid hybrid wind-hydrogen energy system.We considered the maximum profit of the system and the minimum loss of power supply probability as optimization goals.Firstly,we established steady-state models of the wind turbine,alkaline electrolyzer,lead-acid battery,and proton exchange membrane fuel cell in matrix laboratory software to optimize the capacity.Secondly,we analyzed the operating mode of the system and determined two system structures(system contains batteries whether or not).Finally,according to the wind speed and load in the sample area,we compared the economics of the two systems and selected the optimal configuration for the area.In the same calculation example data,the non-dominated sorting genetic algorithm-II(NSGA-II)is used to optimize the capacity of each device in the two systems.The results showed that the profit of the without battery-equipped system is 32.38%higher than another system.But the power supply reliability is the opposite.To avoid the contingency of the calculation results,we used the traditional genetic algorithm(GA)and ant colony optimization(ACO)to calculate the same example.The results showed that NSGA-II is significantly better than GA and ACO in terms of iteration steps and calculation results.The required architecture for the System-I composes of 3 numbers of 10 kW wind turbines,61 sets of 12 V·240 Ah leadacid batteries,8 kW electrolytic cell,and 6 kW PEMFC.The net profit and LPSP are ¥44,315 and 0.01254 respectively.The required architecture for the System-II composes of 2 numbers of 10 kW wind turbines,24 kW electrolytic cells,and 18 kW PEMFC.Net profit and LPSP are ¥58,663 and 0.03244,respectively.This paper provided two schemes for the optimal configuration of the hybrid wind-hydrogen energy system in islanding mode,which provided a theoretical basis for practical engineering applications.
文摘传统压缩感知(CS,Compressive Sensing)成像方法一般假定目标精确位于事先划定的成像网格上,实际中由于散射点空间位置是连续分布的,因此偏离网格(Off-grid)问题必然存在.这会引起真实回波测量值与默认系统观测矩阵之间失配,导致传统CS成像方法性能恶化.本文基于频率分集多输入多输出(FD-MIMO,Frequency Diverse Multiple-Input Multiple-Output)雷达,针对Off-grid目标提出了一种基于贝叶斯压缩感知的稀疏自聚焦(SAF-BCS,Sparse Autofocus Imaging Method Based on Bayesian Compressive Sensing)成像算法.该算法依据最大后验(MAP,Maximum A Posteriori)准则,利用变分贝叶斯学习技术求解含有Off-grid目标的稀疏像.与传统稀疏重构方法相比,所提方法充分利用了目标先验信息,可自适应调整参数,能够更好地反演稀疏目标,同时具有校正Off-grid目标的网格位置偏差以及估计噪声功率等优势.仿真结果表明SAF-BCS算法对网格划分不敏感,具有稳健的成像性能.
基金support from the OpenGeoSys communitypartially funded by the Prime Minister Research Fellowship,Ministry of Education,Government of India with the project number SB21221901CEPMRF008347.
文摘The study presents a comprehensive coupled thermo-bio-chemo-hydraulic(T-BCH)modeling framework for stabilizing soils using microbially induced calcite precipitation(MICP).The numerical model considers relevant multiphysics involved in MICP,such as bacterial ureolytic activities,biochemical reactions,multiphase and multicomponent transport,and alteration of the porosity and permeability.The model incorporates multiphysical coupling effects through well-established constitutive relations that connect parameters and variables from different physical fields.It was implemented in the open-source finite element code OpenGeoSys(OGS),and a semi-staggered solution strategy was designed to solve the couplings,allowing for flexible model settings.Therefore,the developed model can be easily adapted to simulate MICP applications in different scenarios.The numerical model was employed to analyze the effect of various factors,including temperature,injection strategies,and application scales.Besides,a TBCH modeling study was conducted on the laboratory-scale domain to analyze the effects of temperature on urease activity and precipitated calcium carbonate.To understand the scale dependency of MICP treatment,a large-scale heterogeneous domain was subjected to variable biochemical injection strategies.The simulations conducted at the field-scale guided the selection of an injection strategy to achieve the desired type and amount of precipitation.Additionally,the study emphasized the potential of numerical models as reliable tools for optimizing future developments in field-scale MICP treatment.The present study demonstrates the potential of this numerical framework for designing and optimizing the MICP applications in laboratory-,prototype-,and field-scale scenarios.
