In this paper, a Hierarchical Cellular System (HCS) supporting two classes of users with high and low velocity is considered. Based on the analytical model of the bidirectional calloverflow scheme, a new dynamic guard...In this paper, a Hierarchical Cellular System (HCS) supporting two classes of users with high and low velocity is considered. Based on the analytical model of the bidirectional calloverflow scheme, a new dynamic guard channel assignment method is proposed In this method, the number of guard channels in both macrocells and microcells varies in realtime according to the different call traffic load in the HCS, and as a result, the lowest dissatisfaction grade of users is achieved.展开更多
Introducing a System-on-Chip （SoC） microcontroller （C8051F350） into a ceramic pressure sensor has resulted in the design of a intelligent sensor. An improved algorithm for digital phassensitive detection is used ...Introducing a System-on-Chip （SoC） microcontroller （C8051F350） into a ceramic pressure sensor has resulted in the design of a intelligent sensor. An improved algorithm for digital phassensitive detection is used to perform lock-in amplification of the sensor signal. The compensation for the sensor error is realized by the detection of the sensor＇s supply voltage and working temperature. The system also has the function of short/open circuit fault detection and can ommamicate with other digital equipment through an RS-485 communication interface. In the design, full utilization of the SoC microcontroller＇ s internal resource results in the simple hardware structure. Experimental results show that the error of the sensor is less than 0.5% at range ratio 1 ： 10. Employing the microcontroller and using lock-in amplification algorithm are an effective method for achieving an intelligent sensor of slowly-varying physical quantities, thereby improving the measuring accuracy and performance.展开更多
In DVB-IPDC system, due to the constraints of handheld devices and the broadcast nature of wireless network, packet loss is inevitable. ECDR-NC proposed is a retransmission encoding packet selection algorithm based on...In DVB-IPDC system, due to the constraints of handheld devices and the broadcast nature of wireless network, packet loss is inevitable. ECDR-NC proposed is a retransmission encoding packet selection algorithm based on the dynamic information updating, which can find the current most effective complete decoding packet. ECDR-NC can not only avoid the redundant encoding packets due to the overlapping among encoding packets, but also reduce the computational complexity compared with the traditional encoding schemes. Furthermore, the retransmission upper bound of ECDR-NC is fully controlled. In time-sensitive applications, to maximize the aggregate number of recovery packets while minimizing the total number of discarded packets due to the time limit according to the priority preference, the adaptive priority scheme EPNC is formulized, and the weighted relation graph is constructed to find the maximum-weighted encoding packets sequence according to the decoding gains. In the same network environment, the performances comparisons between PNC and EPNC show that EPNC is more efficient and more rational, and the average discarded packets ratios ofEPNC can be reduced about 18%~27%. The main contributions of this paper are an effective retransmission encoding packet selection algorithm ECDR-NC proposed, and a new adaptive priority recovery scheme EPNC introduced into DVB-IPDC system.展开更多
Proposes a reinforcement learning scheme based on a special Hierarchical Fuzzy Neural-Networks (HFNN)for solving complicated learning tasks in a continuous multi-variables environment. The output of the previous layer...Proposes a reinforcement learning scheme based on a special Hierarchical Fuzzy Neural-Networks (HFNN)for solving complicated learning tasks in a continuous multi-variables environment. The output of the previous layer in the HFNN is no longer used as if-part of the next layer, but used only in then-part. Thus it can deal with the difficulty when the output of the previous layer is meaningless or its meaning is uncertain. The proposed HFNN has a minimal number of fuzzy rules and can successfully solve the problem of rules combination explosion and decrease the quantity of computation and memory requirement. In the learning process, two HFNN with the same structure perform fuzzy action composition and evaluation function approximation simultaneously where the parameters of neural-networks are tuned and updated on line by using gradient descent algorithm. The reinforcement learning method is proved to be correct and feasible by simulation of a double inverted pendulum system.展开更多
基金Supported in part by the National Natural Science Foundation of China(No.69825102)
文摘In this paper, a Hierarchical Cellular System (HCS) supporting two classes of users with high and low velocity is considered. Based on the analytical model of the bidirectional calloverflow scheme, a new dynamic guard channel assignment method is proposed In this method, the number of guard channels in both macrocells and microcells varies in realtime according to the different call traffic load in the HCS, and as a result, the lowest dissatisfaction grade of users is achieved.
基金supported by Research Project of "SUSTSpring Bud"(No.2008BWZ042)from Shandong University of Science and Technology
文摘Introducing a System-on-Chip （SoC） microcontroller （C8051F350） into a ceramic pressure sensor has resulted in the design of a intelligent sensor. An improved algorithm for digital phassensitive detection is used to perform lock-in amplification of the sensor signal. The compensation for the sensor error is realized by the detection of the sensor＇s supply voltage and working temperature. The system also has the function of short/open circuit fault detection and can ommamicate with other digital equipment through an RS-485 communication interface. In the design, full utilization of the SoC microcontroller＇ s internal resource results in the simple hardware structure. Experimental results show that the error of the sensor is less than 0.5% at range ratio 1 ： 10. Employing the microcontroller and using lock-in amplification algorithm are an effective method for achieving an intelligent sensor of slowly-varying physical quantities, thereby improving the measuring accuracy and performance.
基金supported by the National High Technology Research and Development Program of China(863 Program )(Grant No: 2015AA01A705)the National Basic Research Program of China (Grant No:2012CB316100)+1 种基金Key Grant Project of Chinese Ministry of Education (Grant No:311031 100)Young Innovative Research Team of Sichuan Province (Grant No:2011JTD0007)
文摘In DVB-IPDC system, due to the constraints of handheld devices and the broadcast nature of wireless network, packet loss is inevitable. ECDR-NC proposed is a retransmission encoding packet selection algorithm based on the dynamic information updating, which can find the current most effective complete decoding packet. ECDR-NC can not only avoid the redundant encoding packets due to the overlapping among encoding packets, but also reduce the computational complexity compared with the traditional encoding schemes. Furthermore, the retransmission upper bound of ECDR-NC is fully controlled. In time-sensitive applications, to maximize the aggregate number of recovery packets while minimizing the total number of discarded packets due to the time limit according to the priority preference, the adaptive priority scheme EPNC is formulized, and the weighted relation graph is constructed to find the maximum-weighted encoding packets sequence according to the decoding gains. In the same network environment, the performances comparisons between PNC and EPNC show that EPNC is more efficient and more rational, and the average discarded packets ratios ofEPNC can be reduced about 18%~27%. The main contributions of this paper are an effective retransmission encoding packet selection algorithm ECDR-NC proposed, and a new adaptive priority recovery scheme EPNC introduced into DVB-IPDC system.
文摘Proposes a reinforcement learning scheme based on a special Hierarchical Fuzzy Neural-Networks (HFNN)for solving complicated learning tasks in a continuous multi-variables environment. The output of the previous layer in the HFNN is no longer used as if-part of the next layer, but used only in then-part. Thus it can deal with the difficulty when the output of the previous layer is meaningless or its meaning is uncertain. The proposed HFNN has a minimal number of fuzzy rules and can successfully solve the problem of rules combination explosion and decrease the quantity of computation and memory requirement. In the learning process, two HFNN with the same structure perform fuzzy action composition and evaluation function approximation simultaneously where the parameters of neural-networks are tuned and updated on line by using gradient descent algorithm. The reinforcement learning method is proved to be correct and feasible by simulation of a double inverted pendulum system.
