Different from sculling forward of water striders with their hairy water-repellent legs, water spiders walked very quickly on water surfaces. By using a shadow method, the walking of water spiders had been studied. Th...Different from sculling forward of water striders with their hairy water-repellent legs, water spiders walked very quickly on water surfaces. By using a shadow method, the walking of water spiders had been studied. The three-dimensional trajectories and the supporting forces of water spider legs during walking forward were achieved. Results showed that the leg movement could be divided into three phases： slap, stroke, and retrieve. Employing an effective strategy to improving walking efficiency, the sculling legs supported most of its body weight while other legs were lifted to reduce the lateral water resistance, which was similar to the strategy of water striders. These findings could help guiding the design of water walking robots with high efficiency.展开更多
Load forecasting can increase the efficiency of modern energy systems with built-in measuring systerms by providing a more accurate peak power shaving performance and thus more reliable control.An analysis of an integ...Load forecasting can increase the efficiency of modern energy systems with built-in measuring systerms by providing a more accurate peak power shaving performance and thus more reliable control.An analysis of an integrated CO2 heat pump and chiller system with a hot water storage system is presented in this paper.Drastic power fluctuations,which can be reduced with load forecasting,are found in historical operation records.A model that aims to forecast the ventilation system heating demand is thus established on the basis of a long short-term memory(LSTM)network.The model can successfully forecast the one hour ahead power using records of the past 48h of the system operation data and the ambient temperature.The mean absolute percentage error(MAPE)of the forecast results of the LSTM-based model is 10.70%,which is respectively 2.2%and 7.25%better than the MAPEs of the forecast results of the support vector regression based and persistence method based models.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.51323006 and 51425502)the Tribology Science Fund of State Key Laboratory of Tribology(Grant No.SKLTKF17B18)
文摘Different from sculling forward of water striders with their hairy water-repellent legs, water spiders walked very quickly on water surfaces. By using a shadow method, the walking of water spiders had been studied. The three-dimensional trajectories and the supporting forces of water spider legs during walking forward were achieved. Results showed that the leg movement could be divided into three phases： slap, stroke, and retrieve. Employing an effective strategy to improving walking efficiency, the sculling legs supported most of its body weight while other legs were lifted to reduce the lateral water resistance, which was similar to the strategy of water striders. These findings could help guiding the design of water walking robots with high efficiency.
基金the Special Program for Innovation Methodology of the Ministry of Science and Technology of China(No.2016IM010100)。
文摘Load forecasting can increase the efficiency of modern energy systems with built-in measuring systerms by providing a more accurate peak power shaving performance and thus more reliable control.An analysis of an integrated CO2 heat pump and chiller system with a hot water storage system is presented in this paper.Drastic power fluctuations,which can be reduced with load forecasting,are found in historical operation records.A model that aims to forecast the ventilation system heating demand is thus established on the basis of a long short-term memory(LSTM)network.The model can successfully forecast the one hour ahead power using records of the past 48h of the system operation data and the ambient temperature.The mean absolute percentage error(MAPE)of the forecast results of the LSTM-based model is 10.70%,which is respectively 2.2%and 7.25%better than the MAPEs of the forecast results of the support vector regression based and persistence method based models.
