Max-margin Metric Learning for Speaker Recognition

Probabilistic linear discriminant analysis (PLDA) is a popular normalization approach for the i-vector model, and has delivered state-of-the-art performance in speaker recognition. A potential problem of the PLDA model, however, is that it essentially assumes Gaussian distributions over speaker vectors, which is not always true in practice. Additionally, the objective function is not directly related to the goal of the task, e.g., discriminating true speakers and imposters. In this paper, we propose a max-margin metric learning approach to solve the problems. It learns a linear transform with a criterion that the margin between target and imposter trials are maximized. Experiments conducted on the SRE08 core test show that compared to PLDA, the new approach can obtain comparable or even better performance, though the scoring is simply a cosine computation

An LCA study of an electricity coal supply chain

Purpose: The aim of this paper is to provide methods to find the emission source and estimate the amount of waste gas emissions in the electricity coal supply chain, establish the model of the environmental impact (burden) in the electricity coal supply chain, detect the critical factor which causes significant environmental impact, and then identify the key control direction and reduce amount of environmental pollution in the electricity coal supply chain. Design/methodology/approach: In this context, life cycle inventory and life cycle assessment of China’s electricity coal were established in three difference stages: coal mining, coal transportation, and coal burning. Then the outcomes were analyzed with the aim to reduce waste gases emissions’ environmental impact in the electricity coal supply chain from the perspective of sensitivity analysis. Findings: The results and conclusion are as follow: (1) In terms of total waste gas emissions in electricity coal supply chain, CO2 is emitted in the greatest quantity, accounting for 98-99 wt% of the total waste gas emissions. The vast majority of the CO2, greater than 93%, is emitted from the power plant when the coal is combusted. (2) Other than CO2, the main waste gas is CH4, SO2 and so on. CH4 is mainly emitted from Coal Bed Methane (CBM), so the option is to consider capturing some of the CH4 from underground mines for an alternative use. SO2 is mainly emitted from power plant when the coal is combusted. (3) The environmental burden of coal burning subsystem is greatest, followed by the coal mining subsystem, and finally the coal transportation subsystem. Improving the coal-burning efficiency of coal-fired power plant in electricity coal supply chain is the most effective way to reduce the environmental impact of waste gas emissions. (4) Of the three subsystems examined (coal mining, coal transportation, and coal burning), transportation requires the fewest resources and has the lowest waste gas emissions. However, the energy consumption for this subsystem is significant (excluding the mine mouth case), and transportation distance is found to have a substantial effect on the oil consumption and non-coal energy consumption. (5) In electricity coal supply chain, the biggest environmental impact of waste gas emissions is GWP, followed by EP, AP, POCP and ODP, and regional impact is greater than the global impact. Practical implications: The model and methodology established in this paper could be used for environmental impact assessment of waste gas emissions in electricity coal supply chain and sensitivity analysis in China, and it could supply reference and example for similar researches. The data information on life cycle inventory, impact assessment and sensitivity analysis could supply theory and data reference for waste gas emissions control in electricity coal supply chain. Originality/value: To the best of our knowledge, this is the first time to study the environmental influence of electricity coal supply chain by employing a LCA approach from life cycle of electricity coalPeer Reviewe

Experimental investigation of nonlinear interface effects in a jointed beam

Dynamic response of assembled structures is strongly dependent on the mechanical behavior of joint interfaces. However, the effect of mechanical joints on structural dynamics is not yet fully understood due to the complex multi-scale, multi-physics and nonlinear characteristics of frictional contact interfaces. A monolithic beam without joint interfaces and a jointed beam with a typical shear lap joint are fabricated. Both structures are subjected to hammer impact excitation to identify the nonlinear effect of joint interfaces. The time-domain responses of both structures are first compared directly to identify the effect of joints. Experimental modal analysis is further used to yield mode shape, frequency and modal damping. The effects of joint interfaces on structural modal properties are investigated. Finally, the wavelet transform analysis and empirical mode decomposition are used to analyze recorded time-domain signals to quantify the effect of joint interfaces on dynamic behavior in time-frequency domain. The results show that joint interfaces play a critical role in dynamic response of assembled structure. The effective stiffness and damping of the lap joint bolt are amplitude dependent. Nonlinear effect of joints on dynamical response is obvious in high-frequency