In this paper,we offer a new sparse recovery strategy based on the generalized error function.The introduced penalty function involves both the shape and the scale parameters,making it extremely flexible.For both cons...In this paper,we offer a new sparse recovery strategy based on the generalized error function.The introduced penalty function involves both the shape and the scale parameters,making it extremely flexible.For both constrained and unconstrained models,the theoretical analysis results in terms of the null space property,the spherical section property and the restricted invertibility factor are established.The practical algorithms via both the iteratively reweighted■_(1)and the difference of convex functions algorithms are presented.Numerical experiments are carried out to demonstrate the benefits of the suggested approach in a variety of circumstances.Its practical application in magnetic resonance imaging(MRI)reconstruction is also investigated.展开更多
Multispectral pedestrian detection technology leverages infrared images to provide reliable information for visible light images, demonstrating significant advantages in low-light conditions and background occlusion s...Multispectral pedestrian detection technology leverages infrared images to provide reliable information for visible light images, demonstrating significant advantages in low-light conditions and background occlusion scenarios. However, while continuously improving cross-modal feature extraction and fusion, ensuring the model’s detection speed is also a challenging issue. We have devised a deep learning network model for cross-modal pedestrian detection based on Resnet50, aiming to focus on more reliable features and enhance the model’s detection efficiency. This model employs a spatial attention mechanism to reweight the input visible light and infrared image data, enhancing the model’s focus on different spatial positions and sharing the weighted feature data across different modalities, thereby reducing the interference of multi-modal features. Subsequently, lightweight modules with depthwise separable convolution are incorporated to reduce the model’s parameter count and computational load through channel-wise and point-wise convolutions. The network model algorithm proposed in this paper was experimentally validated on the publicly available KAIST dataset and compared with other existing methods. The experimental results demonstrate that our approach achieves favorable performance in various complex environments, affirming the effectiveness of the multispectral pedestrian detection technology proposed in this paper.展开更多
Motivated by the study of regularization for sparse problems,we propose a new regularization method for sparse vector recovery.We derive sufficient conditions on the well-posedness of the new regularization,and design...Motivated by the study of regularization for sparse problems,we propose a new regularization method for sparse vector recovery.We derive sufficient conditions on the well-posedness of the new regularization,and design an iterative algorithm,namely the iteratively reweighted algorithm(IR-algorithm),for efficiently computing the sparse solutions to the proposed regularization model.The convergence of the IR-algorithm and the setting of the regularization parameters are analyzed at length.Finally,we present numerical examples to illustrate the features of the new regularization and algorithm.展开更多
A direction-of-arrival （DOA） estimation algorithm is presented based on covariance differencing and sparse signal recovery, in which the desired signal is embedded in noise with unknown covariance. The key point of ...A direction-of-arrival （DOA） estimation algorithm is presented based on covariance differencing and sparse signal recovery, in which the desired signal is embedded in noise with unknown covariance. The key point of the algorithm is to eliminate the noise component by forming the difference of original and transformed covariance matrix, as well as cast the DOA estimation considered as a sparse signal recovery problem. Concerning accuracy and complexity of estimation, the authors take a vectorization operation on difference matrix, and further enforce sparsity by reweighted l1-norm penalty. We utilize data-validation to select the regularization parameter properly. Meanwhile, a kind of symmetric grid division and refinement strategy is introduced to make the proposed algorithm effective and also to mitigate the effects of limiting estimates to a grid of spatial locations. Compared with the covariance-differencing-based multiple signal classification （MUSIC） method, the proposed is of salient features, including increased resolution, improved robustness to colored noise, distinguishing the false peaks easily, but with no requiring of prior knowledge of the number of sources.展开更多
Dropout and other feature noising schemes have shown promise in controlling over-fitting by artificially corrupting the training data. Though extensive studies have been performed for generalized linear models, little...Dropout and other feature noising schemes have shown promise in controlling over-fitting by artificially corrupting the training data. Though extensive studies have been performed for generalized linear models, little has been done for support vector machines （SVMs）, one of the most successful approaches for supervised learning. This paper presents dropout training for both linear SVMs and the nonlinear extension with latent representation learning. For linear SVMs, to deal with the intractable expectation of the non-smooth hinge loss under corrupting distributions, we develop an iteratively re-weighted least square （IRLS） algorithm by exploring data augmentation techniques. Our algorithm iteratively minimizes the expectation of a re- weighted least square problem, where the re-weights are analytically updated. For nonlinear latent SVMs, we con- sider learning one layer of latent representations in SVMs and extend the data augmentation technique in conjunction with first-order Taylor-expansion to deal with the intractable expected hinge loss and the nonlinearity of latent representa- tions. Finally, we apply the similar data augmentation ideas to develop a new IRLS algorithm for the expected logistic loss under corrupting distributions, and we further develop a non-linear extension of logistic regression by incorporating one layer of latent representations. Our algorithms offer insights on the connection and difference between the hinge loss and logistic loss in dropout training. Empirical results on several real datasets demonstrate the effectiveness of dropout training on significantly boosting the classification accuracy of both linear and nonlinear SVMs.展开更多
Rice straw is a major kind of biomass that can be utilized as lignocellulosic materials and renewable energy.Rapid prediction of the lignocellulose(cellulose,hemicellulose,and lignin)and organic elements(carbon,hydrog...Rice straw is a major kind of biomass that can be utilized as lignocellulosic materials and renewable energy.Rapid prediction of the lignocellulose(cellulose,hemicellulose,and lignin)and organic elements(carbon,hydrogen,nitrogen,and sulfur)of rice straw would help to decipher its growth mechanisms and thereby improve its sustainable usages.In this study,364 rice straw samples featuring different rice subspecies(japonica and indica),growing seasons(early-,middle-,and late-season),and growing environments(irrigated and rainfed)were collected,the differences among which were examined by multivariate analysis of variance.Statistic results showed that the cellulose content exhibited significant differences among different growing seasons at a significant level(p<0.01),and the contents of cellulose and nitrogen had significant differences between different growing environments(p<0.01).Near infrared reflectance spectroscopy(NIRS)models for predicting the lignocellulosic and organic elements were developed based on two algorithms including partial least squares(PLS)and competitive adaptive reweighted sampling-partial least squares(CARS-PLS).Modeling results showed that most CARS-PLS models are of higher accuracy than the PLS models,possibly because the CARS-PLS models selected optimal combinations of wavenumbers,which might have enhanced the signal of chemical bonds and thereby improved the predictive efficiency.As a major contributor to the applications of rice straw,the nitrogen content was predicted precisely by the CARS-PLS model.Generally,the CARS-PLS models efficiently quantified the lignocellulose and organic elements of a wide variety of rice straw.The acceptable accuracy of the models allowed their practical applications.展开更多
A variety of alternating direction methods have been proposed for solving a class of optimization problems. The applications in computed tomography (CT) perform well in image reconstruction. The reweighted schemes wer...A variety of alternating direction methods have been proposed for solving a class of optimization problems. The applications in computed tomography (CT) perform well in image reconstruction. The reweighted schemes were applied in l1-norm and total variation minimization for signal and image recovery to improve the convergence of algorithms. In this paper, we present a reweighted total variation algorithm using the alternating direction method (ADM) for image reconstruction in CT. The numerical experiments for ADM demonstrate that adding reweighted strategy reduces the computation time effectively and improves the quality of reconstructed images as well.展开更多
In this paper, the authors study the effectiveness of the Japanese monetary policy set by the Bank of Japan (BOJ) to contrast the three major crises that the country has experienced since the second half of the 90s: t...In this paper, the authors study the effectiveness of the Japanese monetary policy set by the Bank of Japan (BOJ) to contrast the three major crises that the country has experienced since the second half of the 90s: that of the lost decade, that of 2008 and that of the Covid-19 pandemic. To this end, they use a particular type of mathematical-statistical model that is widely applied today in the economic field, namely a simultaneous equation model (SEM). This simultaneous equation model is estimated through an Iteratively reweighted least squares (IRLS) using quarterly historical series in the sample period Q1 1994 - Q2 2020. All data are in real terms. The results, appropriately compared with those of other authors, suggest that the monetary policy has a (limited) impact only on the interbank market. The fiscal policy, instead, has a greater ability to influence the money supply, the private consumption and the inflation expectations.展开更多
In this paper, we proposed an iterative reweighted l1?penalty regression approach to solve the line spectral estimation problem. In each iteration process, we first use the ideal of Bayesian lasso to update the sparse...In this paper, we proposed an iterative reweighted l1?penalty regression approach to solve the line spectral estimation problem. In each iteration process, we first use the ideal of Bayesian lasso to update the sparse vectors;the derivative of the penalty function forms the regularization parameter. We choose the anti-trigonometric function as a penalty function to approximate the?l0? norm. Then we use the gradient descent method to update the dictionary parameters. The theoretical analysis and simulation results demonstrate the effectiveness of the method and show that the proposed algorithm outperforms other state-of-the-art methods for many practical cases.展开更多
In this paper, we present continuous iteratively reweighted least squares algorithm (CIRLS) for solving the linear models problem by convex relaxation, and prove the convergence of this algorithm. Under some condition...In this paper, we present continuous iteratively reweighted least squares algorithm (CIRLS) for solving the linear models problem by convex relaxation, and prove the convergence of this algorithm. Under some conditions, we give an error bound for the algorithm. In addition, the numerical result shows the efficiency of the algorithm.展开更多
The generalized l1 greedy algorithm was recently introduced and used to reconstruct medical images in computerized tomography in the compressed sensing framework via total variation minimization. Experimental results ...The generalized l1 greedy algorithm was recently introduced and used to reconstruct medical images in computerized tomography in the compressed sensing framework via total variation minimization. Experimental results showed that this algorithm is superior to the reweighted l1-minimization and l1 greedy algorithms in reconstructing these medical images. In this paper the effectiveness of the generalized l1 greedy algorithm in finding random sparse signals from underdetermined linear systems is investigated. A series of numerical experiments demonstrate that the generalized l1 greedy algorithm is superior to the reweighted l1-minimization and l1 greedy algorithms in the successful recovery of randomly generated Gaussian sparse signals from data generated by Gaussian random matrices. In particular, the generalized l1 greedy algorithm performs extraordinarily well in recovering random sparse signals with nonzero small entries. The stability of the generalized l1 greedy algorithm with respect to its parameters and the impact of noise on the recovery of Gaussian sparse signals are also studied.展开更多
Analysis of stock recruitment (SR) data is most often done by fitting various SR relationship curves to the data. Fish population dynamics data often have stochastic variations and measurement errors, which usually re...Analysis of stock recruitment (SR) data is most often done by fitting various SR relationship curves to the data. Fish population dynamics data often have stochastic variations and measurement errors, which usually result in a biased regression analysis. This paper presents a robust regression method, least median of squared orthogonal distance (LMD), which is insensitive to abnormal values in the dependent and independent variables in a regression analysis. Outliers that have significantly different variance from the rest of the data can be identified in a residual analysis. Then, the least squares (LS) method is applied to the SR data with defined outliers being down weighted. The application of LMD and LMD based Reweighted Least Squares (RLS) method to simulated and real fisheries SR data is explored.展开更多
In this paper,the performance of a two-stage three-phase grid coupled solar photovoltaic generating system(SPVGS)is analyzed by using a novel reweighted Lo norm variable step size continuous mixed p-norm(RLo-VSSCMPN)o...In this paper,the performance of a two-stage three-phase grid coupled solar photovoltaic generating system(SPVGS)is analyzed by using a novel reweighted Lo norm variable step size continuous mixed p-norm(RLo-VSSCMPN)of a voltage source inverter(VSI)control scheme.The efficacy of the system is determined by considering unbalanced grid voltage,DC offset,voltage sag and swell,unbalanced load and variations in solar insolation.RLo-VSSCMPN is used for inverter control and it ex-tracts fundamental components of load current for generating the reference grid current with a faster convergence rate and lesser steady state oscillations.With the proposed control,harmonics in the grid current follows the IEEE-519 norm and the performance is also satisfactory under varying environmental/load conditions.The power generated from SPvGS is transferred optimally using a DC-DC boost converter utilizing the incremental conductance(INC)maximum power point technique.The proposed system is simulated using MATLAB/Simulink 2018a and test results are verified experimentally using dSPACE1202 in the laboratory to ensure the validity of a novel proposed robust RLo-VSSCMPN.Index Terms-INC maximum power point tracker,power quality,reweighted LoVSSCMPN algorithm,solar PV generating system,total harmonic distortion,voltage source inverter.展开更多
The prediction of the heating and cooling loads of a building is an essential aspect in studies involving the analysis of energy consumption in buildings. An accurate estimation of heating and cooling load leads to be...The prediction of the heating and cooling loads of a building is an essential aspect in studies involving the analysis of energy consumption in buildings. An accurate estimation of heating and cooling load leads to better management of energy related tasks and progressing towards an energy efficient building. With increasing global energy demands and buildings being major energy consuming entities, there is renewed interest in studying the energy performance of buildings. Alternative technologies like Artificial Intelligence (AI) techniques are being widely used in energy studies involving buildings. This paper presents a review of research in the area of forecasting the heating and cooling load of buildings using AI techniques. The results discussed in this paper demonstrate the use of AI techniques in the estimation of the thermal loads of buildings. An accurate prediction of the heating and cooling loads of buildings is necessary for forecasting the energy expenditure in buildings. It can also help in the design and construction of energy efficient buildings.展开更多
文摘牛奶中的蛋白质含量会影响牛奶的品质,利用高光谱图像的光谱特征信息研究对牛奶蛋白质含量预测的可行性。本文提出一种基于竞争性自适应重加权算法(competitive adaptive reweighted sampling, CARS)和连续投影算法(successive projections algorithm, SPA)结合多层前馈神经网络(back propagation, BP)的预测建模方法,实验以含有不同浓度蛋白质的牛奶为对象,利用可见光/近红外高光谱成像系统共采集到5种牛奶共计250组高光谱数据,通过实验对比选择采用标准化方法对获取到的吸收光谱预处理,然后采用CARS结合SPA筛选特征波长,得到18个特征波长,建立CARS-SPA-BP模型,经过试验,CARS-SPA-BP模型的训练集决定系数和测试集决定系数R;和R;分别达到0.971和0.968,训练集均方根误差(root mean square error of calibration,RMSEC)和测试集均方根误差(root mean square error of prediction,RMSEP)达到了0.033和0.034。研究发现,采用CARS结合SPA筛选的牛奶特征波长建立的多层前馈神经网络模型,其模型预测结果与全波长建模相比并没有明显降低,因此将CARS结合SPA用于波长筛选并且结合BP神经网络基本可以完成对牛奶蛋白质含量的预测。为验证CARS-SPA-BP模型的预测能力,在相同数据环境下,使用较为传统的偏最小二乘回归(partial least squares regression, PLSR)进行建模,实验结果表明,CARS-SPA-BP相较于PLSR,R;和RMSEP均有明显提升。研究表明,CARS-SPA-BP可充分利用牛奶光谱特征信息实现较高精度的牛奶蛋白质含量检测。
基金supported by the Zhejiang Provincial Natural Science Foundation of China under grant No.LQ21A010003.
文摘In this paper,we offer a new sparse recovery strategy based on the generalized error function.The introduced penalty function involves both the shape and the scale parameters,making it extremely flexible.For both constrained and unconstrained models,the theoretical analysis results in terms of the null space property,the spherical section property and the restricted invertibility factor are established.The practical algorithms via both the iteratively reweighted■_(1)and the difference of convex functions algorithms are presented.Numerical experiments are carried out to demonstrate the benefits of the suggested approach in a variety of circumstances.Its practical application in magnetic resonance imaging(MRI)reconstruction is also investigated.
基金supported by the Henan Provincial Science and Technology Research Project under Grants 232102211006,232102210044,232102211017,232102210055 and 222102210214the Science and Technology Innovation Project of Zhengzhou University of Light Industry under Grant 23XNKJTD0205+1 种基金the Undergraduate Universities Smart Teaching Special Research Project of Henan Province under Grant Jiao Gao[2021]No.489-29the Doctor Natural Science Foundation of Zhengzhou University of Light Industry under Grants 2021BSJJ025 and 2022BSJJZK13.
文摘Multispectral pedestrian detection technology leverages infrared images to provide reliable information for visible light images, demonstrating significant advantages in low-light conditions and background occlusion scenarios. However, while continuously improving cross-modal feature extraction and fusion, ensuring the model’s detection speed is also a challenging issue. We have devised a deep learning network model for cross-modal pedestrian detection based on Resnet50, aiming to focus on more reliable features and enhance the model’s detection efficiency. This model employs a spatial attention mechanism to reweight the input visible light and infrared image data, enhancing the model’s focus on different spatial positions and sharing the weighted feature data across different modalities, thereby reducing the interference of multi-modal features. Subsequently, lightweight modules with depthwise separable convolution are incorporated to reduce the model’s parameter count and computational load through channel-wise and point-wise convolutions. The network model algorithm proposed in this paper was experimentally validated on the publicly available KAIST dataset and compared with other existing methods. The experimental results demonstrate that our approach achieves favorable performance in various complex environments, affirming the effectiveness of the multispectral pedestrian detection technology proposed in this paper.
基金Project supported by the National Natural Science Foundation of China(No.61603322)the Research Foundation of Education Bureau of Hunan Province of China(No.16C1542)
文摘Motivated by the study of regularization for sparse problems,we propose a new regularization method for sparse vector recovery.We derive sufficient conditions on the well-posedness of the new regularization,and design an iterative algorithm,namely the iteratively reweighted algorithm(IR-algorithm),for efficiently computing the sparse solutions to the proposed regularization model.The convergence of the IR-algorithm and the setting of the regularization parameters are analyzed at length.Finally,we present numerical examples to illustrate the features of the new regularization and algorithm.
基金supported by the National Natural Science Foundation of China(61171137)
文摘A direction-of-arrival （DOA） estimation algorithm is presented based on covariance differencing and sparse signal recovery, in which the desired signal is embedded in noise with unknown covariance. The key point of the algorithm is to eliminate the noise component by forming the difference of original and transformed covariance matrix, as well as cast the DOA estimation considered as a sparse signal recovery problem. Concerning accuracy and complexity of estimation, the authors take a vectorization operation on difference matrix, and further enforce sparsity by reweighted l1-norm penalty. We utilize data-validation to select the regularization parameter properly. Meanwhile, a kind of symmetric grid division and refinement strategy is introduced to make the proposed algorithm effective and also to mitigate the effects of limiting estimates to a grid of spatial locations. Compared with the covariance-differencing-based multiple signal classification （MUSIC） method, the proposed is of salient features, including increased resolution, improved robustness to colored noise, distinguishing the false peaks easily, but with no requiring of prior knowledge of the number of sources.
文摘Dropout and other feature noising schemes have shown promise in controlling over-fitting by artificially corrupting the training data. Though extensive studies have been performed for generalized linear models, little has been done for support vector machines （SVMs）, one of the most successful approaches for supervised learning. This paper presents dropout training for both linear SVMs and the nonlinear extension with latent representation learning. For linear SVMs, to deal with the intractable expectation of the non-smooth hinge loss under corrupting distributions, we develop an iteratively re-weighted least square （IRLS） algorithm by exploring data augmentation techniques. Our algorithm iteratively minimizes the expectation of a re- weighted least square problem, where the re-weights are analytically updated. For nonlinear latent SVMs, we con- sider learning one layer of latent representations in SVMs and extend the data augmentation technique in conjunction with first-order Taylor-expansion to deal with the intractable expected hinge loss and the nonlinearity of latent representa- tions. Finally, we apply the similar data augmentation ideas to develop a new IRLS algorithm for the expected logistic loss under corrupting distributions, and we further develop a non-linear extension of logistic regression by incorporating one layer of latent representations. Our algorithms offer insights on the connection and difference between the hinge loss and logistic loss in dropout training. Empirical results on several real datasets demonstrate the effectiveness of dropout training on significantly boosting the classification accuracy of both linear and nonlinear SVMs.
基金We would like to acknowledge the support given by the Innovation Team Project of the Ministry of Education(IRT_17R105)the China Agriculture Research System(CARS-36)Program for Changjiang Scholars.
文摘Rice straw is a major kind of biomass that can be utilized as lignocellulosic materials and renewable energy.Rapid prediction of the lignocellulose(cellulose,hemicellulose,and lignin)and organic elements(carbon,hydrogen,nitrogen,and sulfur)of rice straw would help to decipher its growth mechanisms and thereby improve its sustainable usages.In this study,364 rice straw samples featuring different rice subspecies(japonica and indica),growing seasons(early-,middle-,and late-season),and growing environments(irrigated and rainfed)were collected,the differences among which were examined by multivariate analysis of variance.Statistic results showed that the cellulose content exhibited significant differences among different growing seasons at a significant level(p<0.01),and the contents of cellulose and nitrogen had significant differences between different growing environments(p<0.01).Near infrared reflectance spectroscopy(NIRS)models for predicting the lignocellulosic and organic elements were developed based on two algorithms including partial least squares(PLS)and competitive adaptive reweighted sampling-partial least squares(CARS-PLS).Modeling results showed that most CARS-PLS models are of higher accuracy than the PLS models,possibly because the CARS-PLS models selected optimal combinations of wavenumbers,which might have enhanced the signal of chemical bonds and thereby improved the predictive efficiency.As a major contributor to the applications of rice straw,the nitrogen content was predicted precisely by the CARS-PLS model.Generally,the CARS-PLS models efficiently quantified the lignocellulose and organic elements of a wide variety of rice straw.The acceptable accuracy of the models allowed their practical applications.
文摘A variety of alternating direction methods have been proposed for solving a class of optimization problems. The applications in computed tomography (CT) perform well in image reconstruction. The reweighted schemes were applied in l1-norm and total variation minimization for signal and image recovery to improve the convergence of algorithms. In this paper, we present a reweighted total variation algorithm using the alternating direction method (ADM) for image reconstruction in CT. The numerical experiments for ADM demonstrate that adding reweighted strategy reduces the computation time effectively and improves the quality of reconstructed images as well.
文摘In this paper, the authors study the effectiveness of the Japanese monetary policy set by the Bank of Japan (BOJ) to contrast the three major crises that the country has experienced since the second half of the 90s: that of the lost decade, that of 2008 and that of the Covid-19 pandemic. To this end, they use a particular type of mathematical-statistical model that is widely applied today in the economic field, namely a simultaneous equation model (SEM). This simultaneous equation model is estimated through an Iteratively reweighted least squares (IRLS) using quarterly historical series in the sample period Q1 1994 - Q2 2020. All data are in real terms. The results, appropriately compared with those of other authors, suggest that the monetary policy has a (limited) impact only on the interbank market. The fiscal policy, instead, has a greater ability to influence the money supply, the private consumption and the inflation expectations.
文摘In this paper, we proposed an iterative reweighted l1?penalty regression approach to solve the line spectral estimation problem. In each iteration process, we first use the ideal of Bayesian lasso to update the sparse vectors;the derivative of the penalty function forms the regularization parameter. We choose the anti-trigonometric function as a penalty function to approximate the?l0? norm. Then we use the gradient descent method to update the dictionary parameters. The theoretical analysis and simulation results demonstrate the effectiveness of the method and show that the proposed algorithm outperforms other state-of-the-art methods for many practical cases.
文摘In this paper, we present continuous iteratively reweighted least squares algorithm (CIRLS) for solving the linear models problem by convex relaxation, and prove the convergence of this algorithm. Under some conditions, we give an error bound for the algorithm. In addition, the numerical result shows the efficiency of the algorithm.
文摘The generalized l1 greedy algorithm was recently introduced and used to reconstruct medical images in computerized tomography in the compressed sensing framework via total variation minimization. Experimental results showed that this algorithm is superior to the reweighted l1-minimization and l1 greedy algorithms in reconstructing these medical images. In this paper the effectiveness of the generalized l1 greedy algorithm in finding random sparse signals from underdetermined linear systems is investigated. A series of numerical experiments demonstrate that the generalized l1 greedy algorithm is superior to the reweighted l1-minimization and l1 greedy algorithms in the successful recovery of randomly generated Gaussian sparse signals from data generated by Gaussian random matrices. In particular, the generalized l1 greedy algorithm performs extraordinarily well in recovering random sparse signals with nonzero small entries. The stability of the generalized l1 greedy algorithm with respect to its parameters and the impact of noise on the recovery of Gaussian sparse signals are also studied.
文摘Analysis of stock recruitment (SR) data is most often done by fitting various SR relationship curves to the data. Fish population dynamics data often have stochastic variations and measurement errors, which usually result in a biased regression analysis. This paper presents a robust regression method, least median of squared orthogonal distance (LMD), which is insensitive to abnormal values in the dependent and independent variables in a regression analysis. Outliers that have significantly different variance from the rest of the data can be identified in a residual analysis. Then, the least squares (LS) method is applied to the SR data with defined outliers being down weighted. The application of LMD and LMD based Reweighted Least Squares (RLS) method to simulated and real fisheries SR data is explored.
文摘In this paper,the performance of a two-stage three-phase grid coupled solar photovoltaic generating system(SPVGS)is analyzed by using a novel reweighted Lo norm variable step size continuous mixed p-norm(RLo-VSSCMPN)of a voltage source inverter(VSI)control scheme.The efficacy of the system is determined by considering unbalanced grid voltage,DC offset,voltage sag and swell,unbalanced load and variations in solar insolation.RLo-VSSCMPN is used for inverter control and it ex-tracts fundamental components of load current for generating the reference grid current with a faster convergence rate and lesser steady state oscillations.With the proposed control,harmonics in the grid current follows the IEEE-519 norm and the performance is also satisfactory under varying environmental/load conditions.The power generated from SPvGS is transferred optimally using a DC-DC boost converter utilizing the incremental conductance(INC)maximum power point technique.The proposed system is simulated using MATLAB/Simulink 2018a and test results are verified experimentally using dSPACE1202 in the laboratory to ensure the validity of a novel proposed robust RLo-VSSCMPN.Index Terms-INC maximum power point tracker,power quality,reweighted LoVSSCMPN algorithm,solar PV generating system,total harmonic distortion,voltage source inverter.
文摘The prediction of the heating and cooling loads of a building is an essential aspect in studies involving the analysis of energy consumption in buildings. An accurate estimation of heating and cooling load leads to better management of energy related tasks and progressing towards an energy efficient building. With increasing global energy demands and buildings being major energy consuming entities, there is renewed interest in studying the energy performance of buildings. Alternative technologies like Artificial Intelligence (AI) techniques are being widely used in energy studies involving buildings. This paper presents a review of research in the area of forecasting the heating and cooling load of buildings using AI techniques. The results discussed in this paper demonstrate the use of AI techniques in the estimation of the thermal loads of buildings. An accurate prediction of the heating and cooling loads of buildings is necessary for forecasting the energy expenditure in buildings. It can also help in the design and construction of energy efficient buildings.
