A sensor scheduling problem was considered for a class of hybrid systems named as the stochastic linear hybrid system （SLHS）. An algorithm was proposed to select one （or a group of） sensor at each time from a set ...A sensor scheduling problem was considered for a class of hybrid systems named as the stochastic linear hybrid system （SLHS）. An algorithm was proposed to select one （or a group of） sensor at each time from a set of sensors. Then, a hybrid estimation algorithm was designed to compute the estimates of the continuous and discrete states of the SLHS based on the observations from the selected sensors. As the sensor scheduling algorithm is designed such that the Bayesian decision risk is minimized, the true discrete state can be better identified. Moreover, the continuous state estimation performance of the proposed algorithm is better than that of hybrid estimation algorithms using only predetermined sensors. Finallyo the algorithms are validated through an illustrative target tracking example.展开更多
To solve the problem of energy transmission in the Internet of Things(IoTs),an energy transmission schedule over a Rayleigh fading channel in the energy harvesting system(EHS)with a dedicated energy source(ES)is consi...To solve the problem of energy transmission in the Internet of Things(IoTs),an energy transmission schedule over a Rayleigh fading channel in the energy harvesting system(EHS)with a dedicated energy source(ES)is considered.According to the channel state information(CSI)and the battery state,the charging duration of the battery is determined to jointly minimize the energy consumption of ES,the battery's deficit charges and overcharges during energy transmission.Then,the joint optimization problem is formulated using the weighted sum method.Using the ideas from the Q-learning algorithm,a Q-learning-based energy scheduling algorithm is proposed to solve this problem.Then,the Q-learning-based energy scheduling algorithm is compared with a constant strategy and an on-demand dynamic strategy in energy consumption,the battery's deficit charges and the battery's overcharges.The simulation results show that the proposed Q-learning-based energy scheduling algorithm can effectively improve the system stability in terms of the battery's deficit charges and overcharges.展开更多
Complex terrain and working equipment in coal mine underground need a way to ensure coal mine safety. In this paper, the way to monitor the real-time status of underground equipment was put forward, and it was proved ...Complex terrain and working equipment in coal mine underground need a way to ensure coal mine safety. In this paper, the way to monitor the real-time status of underground equipment was put forward, and it was proved to be effective as commanding and dispatching system. Monitoring system for underground equipment based on panoramic images was effectively combined with real-time sensor data and static panoramic images of underground surrounding, which not only realizes real-time status monitoring for underground equipment, but also gets a direct scene for underground surrounding. B/S mode was applied in the monitoring system and this is convenient for users to monitor the equipment. Meantime, it can reduce the waste of the data resource.展开更多
基金Foundation item： Project（2012AA051603） supported by the National High Technology Research and Development Program 863 Plan of China
文摘A sensor scheduling problem was considered for a class of hybrid systems named as the stochastic linear hybrid system （SLHS）. An algorithm was proposed to select one （or a group of） sensor at each time from a set of sensors. Then, a hybrid estimation algorithm was designed to compute the estimates of the continuous and discrete states of the SLHS based on the observations from the selected sensors. As the sensor scheduling algorithm is designed such that the Bayesian decision risk is minimized, the true discrete state can be better identified. Moreover, the continuous state estimation performance of the proposed algorithm is better than that of hybrid estimation algorithms using only predetermined sensors. Finallyo the algorithms are validated through an illustrative target tracking example.
基金The National Natural Science Foundation of China(No.51608115).
文摘To solve the problem of energy transmission in the Internet of Things(IoTs),an energy transmission schedule over a Rayleigh fading channel in the energy harvesting system(EHS)with a dedicated energy source(ES)is considered.According to the channel state information(CSI)and the battery state,the charging duration of the battery is determined to jointly minimize the energy consumption of ES,the battery's deficit charges and overcharges during energy transmission.Then,the joint optimization problem is formulated using the weighted sum method.Using the ideas from the Q-learning algorithm,a Q-learning-based energy scheduling algorithm is proposed to solve this problem.Then,the Q-learning-based energy scheduling algorithm is compared with a constant strategy and an on-demand dynamic strategy in energy consumption,the battery's deficit charges and the battery's overcharges.The simulation results show that the proposed Q-learning-based energy scheduling algorithm can effectively improve the system stability in terms of the battery's deficit charges and overcharges.
基金Supported by the National Natural Science Foundation of China （51075029）
文摘Complex terrain and working equipment in coal mine underground need a way to ensure coal mine safety. In this paper, the way to monitor the real-time status of underground equipment was put forward, and it was proved to be effective as commanding and dispatching system. Monitoring system for underground equipment based on panoramic images was effectively combined with real-time sensor data and static panoramic images of underground surrounding, which not only realizes real-time status monitoring for underground equipment, but also gets a direct scene for underground surrounding. B/S mode was applied in the monitoring system and this is convenient for users to monitor the equipment. Meantime, it can reduce the waste of the data resource.
