Both the OECD and the WTO have accumulated systematic data on the magnitude of support going to farmers as a result of farm policies. The datasets are collected for different purposes, but both give a detailed picture...Both the OECD and the WTO have accumulated systematic data on the magnitude of support going to farmers as a result of farm policies. The datasets are collected for different purposes, but both give a detailed picture of the evolution of these policies. This paper extends recent work on the compatibility or otherwise of the two attempts at policy monitoring by considering the categorization of individual policy instruments in Norway, Switzerland, the US, and the EU. The results show how the OECD dataset, particularly with respect to the link between direct payments and production requirements, complements that of the WTO. Many payments classified in the WTO Green Box require production, raising the possibility that they may distort production and trade. Though the issue of correct notifications to the WTO is the province of lawyers, the implications for modeling and policy analysis are of interest to economists, and the broader question of improving the consistency of the two datasets is of importance in the quest for transparency in the interpretation of changes in farm policies.展开更多
For the deficiency that the traditional single forecast methods could not forecast electronic equipment states, a combined forecast method based on the hidden Markov model（HMM） and least square support vector machin...For the deficiency that the traditional single forecast methods could not forecast electronic equipment states, a combined forecast method based on the hidden Markov model（HMM） and least square support vector machine（LS-SVM） is presented. The multi-agent genetic algorithm（MAGA） is used to estimate parameters of HMM to overcome the problem that the Baum-Welch algorithm is easy to fall into local optimal solution. The state condition probability is introduced into the HMM modeling process to reduce the effect of uncertain factors. MAGA is used to estimate parameters of LS-SVM. Moreover, pruning algorithms are used to estimate parameters to get the sparse approximation of LS-SVM so as to increase the ranging performance. On the basis of these, the combined forecast model of electronic equipment states is established. The example results show the superiority of the combined forecast model in terms of forecast precision,calculation speed and stability.展开更多
This paper presents a novel adaptive wide-band compressed spectrum sensing scheme for cognitive radio(CR)networks.Compared to the traditional CSS-based CR scenarios,the proposed approach reconstructs neither the recei...This paper presents a novel adaptive wide-band compressed spectrum sensing scheme for cognitive radio(CR)networks.Compared to the traditional CSS-based CR scenarios,the proposed approach reconstructs neither the received signal nor its spectrum during the compressed sensing procedure.On the contrary,a precise estimation of wide spectrum support is recovered with a fewer number of compressed measurements.Then,the spectrum occupancy is determined directly from the reconstructed support vector.To carry out this process,a data-driven methodology is utilized to obtain the mini-mum number of necessary samples required for support reconstruction,and a closed-form expression is obtained that optimally estimates the number of desired samples as a function of the sparsity level and number of channels.Following this phase,an adjustable sequential framework is developed where the first step predicts the optimal number of compressed measurements and the second step recovers the sparse support and makes sensing decision.Theoretical analysis and numerical simulations demonstrate the improvement achieved with the proposed algorithm to significantly reduce both sampling costs and average sensing time without any deterioration in detection performance.Furthermore,the remainder of the sensing time can be employed by secondary users for data transmission,thus leading to the enhancement of the total throughput of the CR network.展开更多
Joint sparse recovery(JSR)in compressed sensing(CS)is to simultaneously recover multiple jointly sparse vectors from their incomplete measurements that are conducted based on a common sensing matrix.In this study,the ...Joint sparse recovery(JSR)in compressed sensing(CS)is to simultaneously recover multiple jointly sparse vectors from their incomplete measurements that are conducted based on a common sensing matrix.In this study,the focus is placed on the rank defective case where the number of measurements is limited or the signals are significantly correlated with each other.First,an iterative atom refinement process is adopted to estimate part of the atoms of the support set.Subsequently,the above atoms along with the measurements are used to estimate the remaining atoms.The estimation criteria for atoms are based on the principle of minimum subspace distance.Extensive numerical experiments were performed in noiseless and noisy scenarios,and results reveal that iterative subspace matching pursuit(ISMP)outperforms other existing algorithms for JSR.展开更多
文摘Both the OECD and the WTO have accumulated systematic data on the magnitude of support going to farmers as a result of farm policies. The datasets are collected for different purposes, but both give a detailed picture of the evolution of these policies. This paper extends recent work on the compatibility or otherwise of the two attempts at policy monitoring by considering the categorization of individual policy instruments in Norway, Switzerland, the US, and the EU. The results show how the OECD dataset, particularly with respect to the link between direct payments and production requirements, complements that of the WTO. Many payments classified in the WTO Green Box require production, raising the possibility that they may distort production and trade. Though the issue of correct notifications to the WTO is the province of lawyers, the implications for modeling and policy analysis are of interest to economists, and the broader question of improving the consistency of the two datasets is of importance in the quest for transparency in the interpretation of changes in farm policies.
文摘For the deficiency that the traditional single forecast methods could not forecast electronic equipment states, a combined forecast method based on the hidden Markov model（HMM） and least square support vector machine（LS-SVM） is presented. The multi-agent genetic algorithm（MAGA） is used to estimate parameters of HMM to overcome the problem that the Baum-Welch algorithm is easy to fall into local optimal solution. The state condition probability is introduced into the HMM modeling process to reduce the effect of uncertain factors. MAGA is used to estimate parameters of LS-SVM. Moreover, pruning algorithms are used to estimate parameters to get the sparse approximation of LS-SVM so as to increase the ranging performance. On the basis of these, the combined forecast model of electronic equipment states is established. The example results show the superiority of the combined forecast model in terms of forecast precision,calculation speed and stability.
文摘This paper presents a novel adaptive wide-band compressed spectrum sensing scheme for cognitive radio(CR)networks.Compared to the traditional CSS-based CR scenarios,the proposed approach reconstructs neither the received signal nor its spectrum during the compressed sensing procedure.On the contrary,a precise estimation of wide spectrum support is recovered with a fewer number of compressed measurements.Then,the spectrum occupancy is determined directly from the reconstructed support vector.To carry out this process,a data-driven methodology is utilized to obtain the mini-mum number of necessary samples required for support reconstruction,and a closed-form expression is obtained that optimally estimates the number of desired samples as a function of the sparsity level and number of channels.Following this phase,an adjustable sequential framework is developed where the first step predicts the optimal number of compressed measurements and the second step recovers the sparse support and makes sensing decision.Theoretical analysis and numerical simulations demonstrate the improvement achieved with the proposed algorithm to significantly reduce both sampling costs and average sensing time without any deterioration in detection performance.Furthermore,the remainder of the sensing time can be employed by secondary users for data transmission,thus leading to the enhancement of the total throughput of the CR network.
基金supported by the National Natural Science Foundation of China(61771258)the Postgraduate Research and Practice Innovation Program of Jiangsu Province(KYCX 210749)。
文摘Joint sparse recovery(JSR)in compressed sensing(CS)is to simultaneously recover multiple jointly sparse vectors from their incomplete measurements that are conducted based on a common sensing matrix.In this study,the focus is placed on the rank defective case where the number of measurements is limited or the signals are significantly correlated with each other.First,an iterative atom refinement process is adopted to estimate part of the atoms of the support set.Subsequently,the above atoms along with the measurements are used to estimate the remaining atoms.The estimation criteria for atoms are based on the principle of minimum subspace distance.Extensive numerical experiments were performed in noiseless and noisy scenarios,and results reveal that iterative subspace matching pursuit(ISMP)outperforms other existing algorithms for JSR.
