This study evaluates the effects of a fall experience caused by tripping during the repetitive stepping movements over an obstacle [obstacle-single leg forward step (OSFS) test]. The study included 147 participants wh...This study evaluates the effects of a fall experience caused by tripping during the repetitive stepping movements over an obstacle [obstacle-single leg forward step (OSFS) test]. The study included 147 participants who were divided into 2 groups: 25 fallers caused by tripping and 122 nonfallers. The subjects were asked to step forward over a 10-cm-high obstacle with 1 leg and then return to their original position, as quickly as possible, and this test was repeated for 5 times. The OSFS test was evaluated in 2 phases: the OSFS-F phase, wherein the participants stepped forward on one leg, and the OSFS-R phase, wherein they returned to their original position. Significant differences were observed in both phases of the OSFS test between the two groups, and the fallers by tripping were significantly inferior to the nonfallers. The area under the curve [AUC;area under the receiver operating characteristic (ROC) curve] was more than 0.63 for all the parameters, which was statistically significant. In conclusion, the fallers by tripping were inferior to the nonfallers in the obstacle step movement.展开更多
This study examines the effects of fall experience caused by tripping on the movement of stepping over an obstacle. The participants were divided into 3 groups (26 fallers caused by tripping, 24 fallers caused by othe...This study examines the effects of fall experience caused by tripping on the movement of stepping over an obstacle. The participants were divided into 3 groups (26 fallers caused by tripping, 24 fallers caused by other causes, and 145 non-fallers). Participants stepped forward over a 10 cm high obstacle with one leg, and then returned to their original position five times as quickly as possible. The OSFS (obstacle single leg forward step) test was measured in the following 2 phases: the OSFS-F phase, in which participants stepped out on one leg, and the OSFS-R phase, in which they returned it. Significant differences among the three groups were found in all parameters, and the fallers by tripping were significantly inferior to the non-fallers. There were no significant differences between the fallers by other reasons and the non-fallers in all parameters. The fallers by tripping were slower in the obstacle step movement than the non-fallers.展开更多
7月29日,旧金山度假租赁搜索引擎Tripping.com获得1600万美元B轮投资。本轮融资由纽约早期投资机构Steadfast Venture Capital领投,前Expedia CEO Erik Blachford、7 Seas Venture Partners、Enspire Capital、Azure Capital、
Generator tripping scheme(GTS)is the most commonly used scheme to prevent power systems from losing safety and stability.Usually,GTS is composed of offline predetermination and real-time scenario match.However,it is e...Generator tripping scheme(GTS)is the most commonly used scheme to prevent power systems from losing safety and stability.Usually,GTS is composed of offline predetermination and real-time scenario match.However,it is extremely time-consuming and labor-intensive for manual predetermination for a large-scale modern power system.To improve efficiency of predetermination,this paper proposes a framework of knowledge fusion-based deep reinforcement learning(KF-DRL)for intelligent predetermination of GTS.First,the Markov Decision Process(MDP)for GTS problem is formulated based on transient instability events.Then,linear action space is developed to reduce dimensionality of action space for multiple controllable generators.Especially,KF-DRL leverages domain knowledge about GTS to mask invalid actions during the decision-making process.This can enhance the efficiency and learning process.Moreover,the graph convolutional network(GCN)is introduced to the policy network for enhanced learning ability.Numerical simulation results obtained on New England power system demonstrate superiority of the proposed KF-DRL framework for GTS over the purely data-driven DRL method.展开更多
文摘This study evaluates the effects of a fall experience caused by tripping during the repetitive stepping movements over an obstacle [obstacle-single leg forward step (OSFS) test]. The study included 147 participants who were divided into 2 groups: 25 fallers caused by tripping and 122 nonfallers. The subjects were asked to step forward over a 10-cm-high obstacle with 1 leg and then return to their original position, as quickly as possible, and this test was repeated for 5 times. The OSFS test was evaluated in 2 phases: the OSFS-F phase, wherein the participants stepped forward on one leg, and the OSFS-R phase, wherein they returned to their original position. Significant differences were observed in both phases of the OSFS test between the two groups, and the fallers by tripping were significantly inferior to the nonfallers. The area under the curve [AUC;area under the receiver operating characteristic (ROC) curve] was more than 0.63 for all the parameters, which was statistically significant. In conclusion, the fallers by tripping were inferior to the nonfallers in the obstacle step movement.
文摘This study examines the effects of fall experience caused by tripping on the movement of stepping over an obstacle. The participants were divided into 3 groups (26 fallers caused by tripping, 24 fallers caused by other causes, and 145 non-fallers). Participants stepped forward over a 10 cm high obstacle with one leg, and then returned to their original position five times as quickly as possible. The OSFS (obstacle single leg forward step) test was measured in the following 2 phases: the OSFS-F phase, in which participants stepped out on one leg, and the OSFS-R phase, in which they returned it. Significant differences among the three groups were found in all parameters, and the fallers by tripping were significantly inferior to the non-fallers. There were no significant differences between the fallers by other reasons and the non-fallers in all parameters. The fallers by tripping were slower in the obstacle step movement than the non-fallers.
文摘7月29日,旧金山度假租赁搜索引擎Tripping.com获得1600万美元B轮投资。本轮融资由纽约早期投资机构Steadfast Venture Capital领投,前Expedia CEO Erik Blachford、7 Seas Venture Partners、Enspire Capital、Azure Capital、
基金supported by National Natural Science Foundation of China(No.U22B20111,No.U1866602)。
文摘Generator tripping scheme(GTS)is the most commonly used scheme to prevent power systems from losing safety and stability.Usually,GTS is composed of offline predetermination and real-time scenario match.However,it is extremely time-consuming and labor-intensive for manual predetermination for a large-scale modern power system.To improve efficiency of predetermination,this paper proposes a framework of knowledge fusion-based deep reinforcement learning(KF-DRL)for intelligent predetermination of GTS.First,the Markov Decision Process(MDP)for GTS problem is formulated based on transient instability events.Then,linear action space is developed to reduce dimensionality of action space for multiple controllable generators.Especially,KF-DRL leverages domain knowledge about GTS to mask invalid actions during the decision-making process.This can enhance the efficiency and learning process.Moreover,the graph convolutional network(GCN)is introduced to the policy network for enhanced learning ability.Numerical simulation results obtained on New England power system demonstrate superiority of the proposed KF-DRL framework for GTS over the purely data-driven DRL method.
