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.
Catalysis Letters - Microbial electrosynthesis (MES) is an effective approach to driving the CO2 reduction to multi-carbon organic products using renewable energy. In this work, the MES of acetate... 相似文献
A series of high oil-absorption resins with low cross-linking degree were synthesized by suspension polymerization using stearyl methacrylate(SMA), 2-Ethylhexyl methacrylate(EHMA), and styrene(St) as monomers.Response surface methodology(RSM) with central composite design(CCD) was also applied to determine the optimal parameters that are mainly known to affect their synthesis. Thus, the effects of the monomer mass ratio(EHMA:SMA), the rigid monomer(St) dosage, the porous agent(acetone) dosage, and their pairwise interaction on the resin's oil-absorption capacity were analyzed, highlighting PSES-R_2 as the resin with the optimum performance. The pure oil-absorption rates of PSES-R2 for gasoline, diesel, and kerosene were 11.19 g·g~(-1),16.25 g·g~(-1), and 14.84 g·g~(-1), respectively, while the oil removal rates from oily wastewater were 98.82%,65.11%, and 99.63%, respectively. 相似文献