Suchergebnisse

In the cloud manufacturing environment, the interaction and cooperation between enterprises become more convenient. The most optimal green supplier selection through cloud manufacturing platform can improve the production quality and sustainable development efficiency of enterprises. However, as alternative suppliers are all over the world, there is a risk that the selected supplier is not ideal. Therefore, in this paper, a hybrid supplier selection model based on TODIM method is proposed to choose the optimal green supplier under cloud manufacturing platform. Considering the characteristics of suppliers in the cloud manufacturing environment and green criteria, the comprehensive green supplier evaluation index system is constructed. The creative application of heterogeneous evaluation information including crisp numbers, interval numbers, and probabilistic linguistic values, can express the evaluation information of different subjects comprehensively. In order to consider human judgment and the importance of information provided by raw data, the criteria weights are determined by integrating fuzzy BWM (Best-worst method) weights and objective entropy weights. Then, with the full consideration of decision maker's risk attitude, the TODIM method is used to process heterogeneous evaluation information and calculate the priority of the green suppliers. The proposed method is novel, and allows multi-subjects to participate in the assessment process and considers the risk attitude of decision-makers in the green supplier selection process. An empirical study of green supplier selection in the cloud manufacturing environment is conducted. Sensitivity analysis and comparative analysis indicate that the proposed selection model for green supplier is reliable and effective.

With increasing demands in real-time or near real-time remotely sensed imagery applications in such as military deployments, quick response to terrorist attacks and disaster rescue, the on-board geometric calibration problem has attracted the attention of many scientists in recent years. This paper presents an on-board geometric calibration method for linear CCD sensor arrays using FPGA chips. The proposed method mainly consists of four modules—Input Data, Coefficient Calculation, Adjustment Computation and Comparison—in which the parallel computations for building the observation equations and least squares adjustment, are implemented using FPGA chips, for which a decomposed matrix inversion method is presented. A Xilinx Virtex-7 FPGA VC707 chip is selected and the MOMS-2P data used for inflight geometric calibration from DLR (Köln, Germany), are employed for validation and analysis. The experimental results demonstrated that: (1) When the widths of floating-point data from 44-bit to 64-bit are adopted, the FPGA resources, including the utilizations of FF, LUT, memory LUT, I/O and DSP48, are consumed at a fast increasing rate; thus, a 50-bit data width is recommended for FPGA-based geometric calibration. (2) Increasing number of ground control points (GCPs) does not significantly consume the FPGA resources, six GCPs is therefore recommended for geometric calibration. (3) The FPGA-based geometric calibration can reach approximately 24 times faster speed than the PC-based one does. (4) The accuracy from the proposed FPGA-based method is almost similar to the one from the inflight calibration if the calibration model and GCPs number are the same.

With increasing demands in real-time or near real-time remotely sensed imagery applications in such as military deployments, quick response to terrorist attacks and disaster rescue, the on-board geometric calibration problem has attracted the attention of many scientists in recent years. This paper presents an on-board geometric calibration method for linear CCD sensor arrays using FPGA chips. The proposed method mainly consists of four modules—Input Data, Coefficient Calculation, Adjustment Computation and Comparison—in which the parallel computations for building the observation equations and least squares adjustment, are implemented using FPGA chips, for which a decomposed matrix inversion method is presented. A Xilinx Virtex-7 FPGA VC707 chip is selected and the MOMS-2P data used for inflight geometric calibration from DLR (Köln, Germany), are employed for validation and analysis. The experimental results demonstrated that: (1) When the widths of floating-point data from 44-bit to 64-bit are adopted, the FPGA resources, including the utilizations of FF, LUT, memory LUT, I/O and DSP48, are consumed at a fast increasing rate; thus, a 50-bit data width is recommended for FPGA-based geometric calibration. (2) Increasing number of ground control points (GCPs) does not significantly consume the FPGA resources, six GCPs is therefore recommended for geometric calibration. (3) The FPGA-based geometric calibration can reach approximately 24 times faster speed than the PC-based one does. (4) The accuracy from the proposed FPGA-based method is almost similar to the one from the inflight calibration if the calibration model and GCPs number are the same.

The Chinese government intends to upgrade its current provincial carbon emission trading pilots to a nationwide scheme by 2015. This study investigates two of scenarios: separated provincial markets and a linked inter-provincial market. The carbon abateme

The Chinese government intends to upgrade its current provincial carbon emission trading pilots to a nationwide scheme by 2015. This study investigates two of scenarios: separated provincial markets and a linked inter-provincial market. The carbon abateme

Self-stereotyping and self-anchoring are two important cognitive pathways that facilitate individuals' social identification. We applied the integrative model of social identification to examine the effects of these cognitive pathways on social identification of 325 Chinese undergraduate
students, and further explored whether self-esteem as an individual factor plays a moderating role in this relationship. Participants were randomly assigned to one of three groups: selfstereotyping, self-anchoring, and control. We manipulated selfanchoring and self-stereotyping via mindset
priming and then measured self-esteem and social identification. Results show that the level of in-group identification was higher in the self-stereotyping and self-anchoring groups than in the control group. Furthermore, the moderation effects analyses revealed that in the condition of high
selfesteem, there was no significant difference in the level of in-group identification between the self-anchoring and self-stereotyping groups. However, in the condition of low self-esteem, the in-group identification of the self-stereotyping group was higher than that of the self-anchoring
group. This study provides the first empirical evidence that self-esteem could moderate the effects of cognitive pathways on social identification, and provides evidence for the role of social identification cognitive pathways in a non-Western sample.