Semidefinite programming approach for TDOA/GROA based source localization

Time-differences-of-arrival （TDOA） and gain-ratios-of- arrival （GROA） measurements are used to determine the passive source location. Based on the measurement models, the con- strained weighted least squares （CWLS） estimator is presented. Due to the nonconvex nature of the CWLS problem, it is difficult to obtain its globally optimal solution. However, according to the semidefinite relaxation, the CWLS problem can be relaxed as a convex semidefinite programming problem （SDP）, which can be solved by using modern convex optimization algorithms. Moreover, this relaxation can be proved to be tight, i.e., the SDP solves the relaxed CWLS problem, and this hence guarantees the good per- formance of the proposed method. Furthermore, this method is extended to solve the localization problem with sensor position errors. Simulation results corroborate the theoretical results and the good performance of the proposed method.

Method for uncertain multi-attribute decisionmaking with preference information in the form of interval numbers complementary judgment matrix

The uncertain multi-attribute decision-making problems because of the information about attribute weights being known partly, and the decision maker＇s preference information on alternatives taking the form of interval numbers complementary to the judgment matrix, are investigated. First, the decision-making information, based on the subjective uncertain complementary preference matrix on alternatives is made uniform by using a translation function, and then an objective programming model is established. The attribute weights are obtained by solving the model, thus the overall values of the alternatives are gained by using the additive weighting method. Second, the alternatives are ranked, by using the continuous ordered weighted averaging （C-OWA） operator. A new approach to the uncertain multi-attribute decision-making problems, with uncertain preference information on alternatives is proposed. It is characterized by simple operations and can be easily implemented on a computer. Finally, a practical example is illustrated to show the feasibility and availability of the developed method.

Very Short-Term Probabilistic Prediction Method for Wind Speed Based on ALASSO-Nonlinear Quantile Regression and Integrated Criterion

To enhance the performance of the prediction intervals (PIs), a novel very short-term probabilistic prediction method for wind speed via nonlinear quantile regression (NQR) based on adaptive least absolute shrinkage and selection operator (ALASSO) and integrated criterion (IC) is proposed. The ALASSO method is studied for shrinkage of output weights and selection of variables. Furthermore, for the better performance of PIs, composite weighted linear programming (CWLP) is proposed to modify the conventional linear programming cost function of quantile regression (QR), by combining it with Bayesian information criterion (BIC) as an IC to optimize the coefficients of PIs. Then, the multiple fold cross model (MFCM) is utilized to improve the PIs performance. Multistep probabilistic prediction of 15-minute wind speed is performed based on the real wind farm data from the northeast of China. The effectiveness of the proposed approach is validated through the performances' comparisons with conventional methods.

Determining an optimum pattern of mixed planting from organic and non-organic crops with regard to economic and environmental indicators:A case study of cucumber in Kerman,Iran

Today,planning procedures which are used in different fields of decision making such as determination of optimum planting patterns are considered as important management issues.The importance of optimum planting patterns is highlighted when we learn that agricultural measures may have environmental side effects.Most economical analyses only focus on increasing economic gains of the farmers without regard to its environmental consequences.Therefore,one can argue that efficient managers should consider multiple purposes that cover both economic and environmental goals at the same time.This study attempted to identify an optimum mixed model of organic and non-organic production systems using a combination of AHP(Analytical Hierarchy Process)approach andWeighted Goal Programming to consider environmental and economic indicators simultaneously.This procedure was employed in the current design to determine and compare an optimum pattern of mixed planting of organic and non-organic products.The study sample was cucumber,investigated in four farming systems:organic open field farming system,nonorganic greenhouse farming system,non-organic tunnel farming system and open field non-organic farming system in Jiroft,Kerman,with regard to paper indices.Following the proposed MCDM(Multiple Criteria Decision Making)model,cucumber planting in open field non-organic farming system was replaced by open field organic farming system.Economic and environmental indicators rose by 11.97%and 21.40%respectively in MCDM proposed plan in comparison with the existing farm plan,which indicates the feasibility of MCDM proposed plan in terms of economic and environmental indicators.