Measuring the Targeting Accuracy of China’s Urban Dibao System

After China eradicated absolute poverty in 2020,the problems of relative poverty and urban poverty will draw more attention.Social protection system in urban areas lays the groundwork for economic transition and social stability.The targeting accuracy of urban minimum livelihood guarantee(Dibao)system is the key to the success of the system.After analyzing urban Dibao’s targeting practice and performance with household survey data,this study found that the issuance of Dibao payments took account of household income,assets and demographic characteristics to ensure minimum livelihood guarantee and meet recipients’urgent needs.This practice is of great importance during China’s economic transition.Under the multidimensional review mechanism,the exclusion error of urban Dibao is in the range of 38.45% and 66.28%,and the inclusion error is between 54.59% and 69.17%.By 2013,Dibao’s targeting efficiency improved significantly over 2007.In evaluating Dibao’s targeting efficiency,it is more appropriate to adopt multidimensional criteria instead of income alone.Multidimensional evaluation is also of great importance for evaluating Dibao’s targeting policy.

ENSO Predictions in an Intermediate Coupled Model Influenced by Removing Initial Condition Errors in Sensitive Areas： A Target Observation Perspective

Previous studies indicate that ENSO predictions are particularly sensitive to the initial conditions in some key areas（socalled ＂sensitive areas＂）. And yet, few studies have quantified improvements in prediction skill in the context of an optimal observing system. In this study, the impact on prediction skill is explored using an intermediate coupled model in which errors in initial conditions formed to make ENSO predictions are removed in certain areas. Based on ideal observing system simulation experiments, the importance of various observational networks on improvement of El Ni n？o prediction skill is examined. The results indicate that the initial states in the central and eastern equatorial Pacific are important to improve El Ni n？o prediction skill effectively. When removing the initial condition errors in the central equatorial Pacific, ENSO prediction errors can be reduced by 25%. Furthermore, combinations of various subregions are considered to demonstrate the efficiency on ENSO prediction skill. Particularly, seasonally varying observational networks are suggested to improve the prediction skill more effectively. For example, in addition to observing in the central equatorial Pacific and its north throughout the year,increasing observations in the eastern equatorial Pacific during April to October is crucially important, which can improve the prediction accuracy by 62%. These results also demonstrate the effectiveness of the conditional nonlinear optimal perturbation approach on detecting sensitive areas for target observations.

Impacts of space-time-frequency synchronization errors onwide-band target echo characteristics of bistatic/multistatic radar

Bistatic/multistatic radar has great potential advantages over its monostatic counterpart. However, the separation of a transmitter and a receiver leads to difficulties in locating the target position accurately and guaranteeing space-timefrequency synchronization of the transmitter and the receiver.The error model of space-time-frequency synchronization in a motion platform of bistatic/multistatic radar is studied. The relationship between the space synchronization error and the transmitter platform position, receiver platform position, moving state, and beam pointing error, is analyzed. The effect of space synchronization error on target echo power is studied. The target scattering characteristics are restructured by many separate scattering centers of the target in high frequency regions. Based on the scattering centers model of the radar target, this radar target echo model and the simulation method are discussed. The algorithm of bistatic/multistatic radar target echo accurately reflects the scattering characteristics of the radar target, pulse modulation speciality of radar transmitting signals, and spacetime-frequency synchronization error characteristics between the transmitter station and the receiver station. The simulation of bistatic radar is completed in computer, and the results of the simulation validate the feasibility of the method.

Energy Efficient Power Allocation Scheme for Distributed Antenna Systems with Target BER Constraint

The energy efficiency(EE) of distributed antenna system with quality of service(Qo S) requirement is investigated over composite Rayleigh fading channel,where the shadow fading,path loss and Rayleigh fading are all considered. Our aim is to maximize the EE which is defined as the ratio of the transmission rate to the total consumed power subject to the maximum transmit power of each remote antenna constraint and Qo S(target BER) requirement. According to the definition of EE and using the upper bound of average EE,the optimized objective function is provided. Based on this,utilizing Karush-KuhnTucker conditions and numerical calculation,a suboptimal energy efficient power allocation(PA) scheme is developed,and the closedform expression of PA coefficients is derived. The scheme may obtain the EE performance close to the existing optimal scheme. Moreover,it has relatively lower complexity than the existing scheme because only the statistic channel information and less iteration are required. Simulation results show the presented scheme is valid and can meet the target BER requirement,and the EE can be increased as target BER requirement decreases.

Reactive Navigation of Underwater Mobile Robot Using ANFIS Approach in a Manifold Manner

Learning and self-adaptation ability is highly required to be integrated in path planning algorithm for underwater robot during navigation through an unspecified underwater environment. High frequency oscillations during underwater motion are responsible for nonlinearities in dynamic behavior of underwater robot as well as uncertainties in hydrodynamic coefficients. Reactive behaviors of underwater robot are designed considering the position and orientation of both target and nearest obstacle from robot s current position. Human like reasoning power and approximation based learning skill of neural based adaptive fuzzy inference system（ANFIS）has been found to be effective for underwater multivariable motion control. More than one ANFIS models are used here for achieving goal and obstacle avoidance while avoiding local minima situation in both horizontal and vertical plane of three dimensional workspace.An error gradient approach based on input-output training patterns for learning purpose has been promoted to spawn trajectory of underwater robot optimizing path length as well as time taken. The simulation and experimental results endorse sturdiness and viability of the proposed method in comparison with other navigational methodologies to negotiate with hectic conditions during motion of underwater mobile robot.