A novel methanol-tolerant oxygen-reduction catalyst, Iridium-selenium (Ir-Se) chalcogenide, was synthesized by chemical precipitation in an organic solvent. Auger electron spectroscopy (AES) analysis confirmed that the synthesized Ir-Se chalcogenide had a chemical formula of Ir4Se. This chalcogenide showed strong catalytic activity towards the oxygen reduction reaction (ORR) and a high methanol tolerance. It was found that most of the oxygen could be directly reduced to water through a four-electron pathway with less than 10% hydrogen peroxide (H2O2) being produced during the ORR. The improvement in catalytic activity of the Ir-Se chalcogenide in comparison with that of pure Ir might be attributed to the effect of a bimetallic interaction. 相似文献
An analysis is given of the performance of the standard forgetting factor recursive least squares (RLS) algorithm when used for tracking time-varying linear regression models. Three basic results are obtained: (1) the ‘P-matrix’ in the algorithm remains bounded if and only if the (time-varying) covariance matrix of the regressors is uniformly non-singular; (2) if so, the parameter tracking error covariance matrix is of the order O(μ + γ2/μ), where μ = 1 - λ, λ is the forgetting factor and γ is a quantity reflecting the speed of the parameter variations; (3) this covariance matrix can be arbitrarily well approximated (for small enough μ) by an expression that is easy to compute. 相似文献
In the context of human-robot and robot-robot interactions, the better cooperation can be achieved by predicting the other party’s subsequent actions based on the current action of the other party. The time duration for adjustment is not sufficient provided by short term forecasting models to robots. A longer duration can by achieved by mid-term forecasting. But the mid-term forecasting models introduce the previous errors into the follow-up forecasting and amplified gradually, eventually invalidating the forecasting. A new mid-term forecasting with error suppression based on restricted Boltzmann machine(RBM) is proposed in this paper. The proposed model can suppress the error amplification by replacing the previous inputs with their features, which are retrieved by a deep belief network(DBN). Furthermore, a new mechanism is proposed to decide whether the forecasting result is accepted or not. The model is evaluated with several datasets. The reported experiments demonstrate the superior performance of the proposed model compared to the state-of-the-art approaches.
