In the traditional flatness pattern recognition neural network, the topologic configurations need to be rebuilt with a changing width of cold strip. Furthermore, the large learning assignment, slow convergence, and lo...In the traditional flatness pattern recognition neural network, the topologic configurations need to be rebuilt with a changing width of cold strip. Furthermore, the large learning assignment, slow convergence, and local minimum in the network are observed. Moreover, going by the structure of the traditional neural network, according to experience, the model is time-consuming and complex. Thus, a new approach of flatness pattern recognition is proposed based on the CMAC （cerebellar model articulation controllers） neural network. The difference in fuzzy distances between samples and the basic patterns is introduced as the input of the CMAC network. Simultaneously, the adequate learning rate is improved in the error correction algorithm of this neural network. The new approach with advantages, such as high learning speed, good generalization, and easy implementation, is efficient and intelligent. The simulation results show that the speed and accuracy of the flatness pattern recognition model are obviously im proved.展开更多
In the current mobile IPv6 (MIPv6) systems for the System architecture evaluation (SAE) networks, such as 4th generation (4G) mobile network, the data delivery is performed basing on a centralized mobility network anc...In the current mobile IPv6 (MIPv6) systems for the System architecture evaluation (SAE) networks, such as 4th generation (4G) mobile network, the data delivery is performed basing on a centralized mobility network anchor between Evolved Node B (eNB) and Serving Gateways (S-GW), and also between S-GW and Packet Data Network Gateway (P-GW). However, the existing network has many obstacles, including suboptimal data routing, injection of unwanted data traffic into mobile core network and the requirement of capital expenditure. To handle these challenges, here we describe a flat mobile core network scheme donated by F-EPC, based SAE mobile network. In the proposed scheme, the P-GW and S-GW gateways are features as one node named Cellular Gateway (C-GW). Further, we proposed to distribute and increase the number of C-GW in mobile core network, the Mobility Management Entity (MME) functioned as centralizing mobility anchor and allocating the IP address for the User Entity (UE). In this paper, the explained results of a simulation analysis showed that the proposed scheme provides a superior performance compared with the current 4G architecture in terms of total transmission delay, handover delay and initial attach procedure.展开更多
The accuracy of present flatness predictive method is limited and it just belongs to software simulation. In order to improve it, a novel flatness predictive model via T-S cloud reasoning network implemented by digita...The accuracy of present flatness predictive method is limited and it just belongs to software simulation. In order to improve it, a novel flatness predictive model via T-S cloud reasoning network implemented by digital signal processor(DSP) is proposed. First, the combination of genetic algorithm(GA) and simulated annealing algorithm(SAA) is put forward, called GA-SA algorithm, which can make full use of the global search ability of GA and local search ability of SA. Later, based on T-S cloud reasoning neural network, flatness predictive model is designed in DSP. And it is applied to 900 HC reversible cold rolling mill. Experimental results demonstrate that the flatness predictive model via T-S cloud reasoning network can run on the hardware DSP TMS320 F2812 with high accuracy and robustness by using GA-SA algorithm to optimize the model parameter.展开更多
A tribal-owned network of aerosol monitors and meteorological stations was installed at Ts’aahudaaneekk’onh Denh (Beaver), Gwichyaa Zheh (Fort Yukon), Jalgiitsik (Chalkyitsik), and Danzhit Khànlaii (Circle) in ...A tribal-owned network of aerosol monitors and meteorological stations was installed at Ts’aahudaaneekk’onh Denh (Beaver), Gwichyaa Zheh (Fort Yukon), Jalgiitsik (Chalkyitsik), and Danzhit Khànlaii (Circle) in the Yukon Flats, Alaska. Surface inversions occurred under calm wind conditions due to radiative cooling. In May, local emissions governed air quality with worst conditions related to road and river dust. As the warm season progressed, worst air quality was due to transport of pollutants from upwind wildfires. During situations without smoke or when smoke existed at layers above the surface inversion, concentrations of particulate matter of less than 2.5 micrometer in diameter or less (PM2.5) were explainable by the local emissions;24-h means remained below 25 μg·m-3. Absorption of solar radiation in the smoke layer and upward scattering enhanced stability and fostered the persistence of the surface inversions. During smoke episodes without the presence of a surface inversion, daily mean concentrations exceeded 35 μg·m-3 often for several consecutive days, at all sites. Then concentrations temporally reached levels considered unhealthy.展开更多
In connection with the characteristics of multi-disturbance and nonlinearity of a system for flatness control in cold rolling process, a new intelligent PID control algorithm was proposed based on a cloud model, neura...In connection with the characteristics of multi-disturbance and nonlinearity of a system for flatness control in cold rolling process, a new intelligent PID control algorithm was proposed based on a cloud model, neural network and fuzzy integration. By indeterminacy artificial intelligence, the problem of fixing the membership functions of input variables and fuzzy rules was solved in an actual fuzzy system and the nonlinear mapping between variables was implemented by neural network. The algorithm has the adaptive learning ability of neural network and the indetermi- nacy of a cloud model in processing knowledge, which makes the fuzzy system have more persuasion in the process of knowledge inference, realizing the online adaptive regulation of PID parameters and avoiding the defects of the traditional PID controller. Simulation results show that the algorithm is simple, fast and robust with good control performance and application value.展开更多
Steel strips are the main of steel products and flatness is an important quality indicator of steel strips. Flatness control is the key and highly difficult technique of strip mills. The bottle-neck restricting the im...Steel strips are the main of steel products and flatness is an important quality indicator of steel strips. Flatness control is the key and highly difficult technique of strip mills. The bottle-neck restricting the improvement of flatness control techniques is that the research on flatness theories and control mathematic models is not in accordance with the requirement of technique developments. To build a simple, rapid and accurate explicit formulation control model has become an urgent need for the development of flatness control technique. This paper puts forward the conception of dynamic effective matrix based on the effective matrix method for flatness control proposed by the authors under the consideration of the influence of the change of parameters in roiling processes on the effective matrix, and the concept is validated by industrial productions. Three methods of the effective matrix generation are induced： the calculation method based on the flatness prediction model; the calculation method based on the data excavation in rolling processes and the direct calculation method based on the network model. A fuzzy neural network effective matrix model is built based on the clusters, and then the network structure is optimized and the high-speed-calculation problem of the dynamic effective matrix is solved. The flatness control scheme for cold strip mills is proposed based on the dynamic effective matrix. On stand 5 of the 1 220 mm five-stand 4-high cold strip tandem mill, the industrial experiment with the control methods of tilting roll and bending roll is done by the control scheme of the static effective matrix and the dynamic effective matrix, respectively. The experiment result proves that the control effect of the dynamic effective matrix is much better than that of the static effective matrix. This paper proposes a new idea and method for the dynamic flatness control in the rolling processes of cold strip mills and develops the theory and model of the flatness control effective matrix method.展开更多
In order to increase the precision of flatness control, considering the principle and the measured data of rolling process essence, the theory-intelligent dynamic matrix model of flatness control is established by usi...In order to increase the precision of flatness control, considering the principle and the measured data of rolling process essence, the theory-intelligent dynamic matrix model of flatness control is established by using theory and in-telligent methods synthetically. The network model for rapidly calculating the theory effective matrix is established by the BP network optimized by the particle swarm algorithm. The network model for rapidly calculating the meas- urement effective matrix is established by the RBF network optimized by the cluster algorithm. The flatness control model can track the practical situation of roiling process by on-line selVlearning. The scheme for flatness control quantity calculation is established by combining the theory control matrix and the measurement control matrix. The simulation result indicates that the establishment of theory-intelligent dynamic matrix model of flatness control with stable control process and high precision supplies a new way and method for studying flatness on-line control model.展开更多
Confirmation of basic technological parameters of tension levellers is the most important factor of leveling strip. Up to now, most factories have used experts’ experience to decide these parameters, without any esta...Confirmation of basic technological parameters of tension levellers is the most important factor of leveling strip. Up to now, most factories have used experts’ experience to decide these parameters, without any established rule to follow. For better quality of strip, a valid method is needed to decide technological parameters precisely and reasonably. In this paper, a method is used based on neural network and genetic algorithm. Neural network has a good ability to extract rules from work process of tension levellers. Then using neural network, which has learned from a lot of working samples, to be the evaluation of fitness, genetic algorithm could easily find the best or better technological parameters. At the end of this paper, examinations are given to show the effect of this method.展开更多
For the problems occurring in a least square method model, a fuzzy model, and a neural network model for flatness pattern recognition, a fuzzy neural network model for flatness pattern recognition with only three-inpu...For the problems occurring in a least square method model, a fuzzy model, and a neural network model for flatness pattern recognition, a fuzzy neural network model for flatness pattern recognition with only three-input and three output signals was proposed with Legendre orthodoxy polynomial as basic pattern, based on fuzzy logic expert experiential knowledge and genetic-BP hybrid optimization algorithm. The model not only had definite physical meanings in its inner nodes, but also had strong self-adaptability, anti interference ability, high recognition precision, and high velocity, thereby meeting the demand of high-precision flatness control for cold strip mill and providing a convenient, practical, and novel method for flatness pattern recognition.展开更多
Under the requirement of everything over IP, network service shows the following characteristics:(1) network service increases its richness;(2) broadband streaming media becomes the mainstream. To achieve unified mult...Under the requirement of everything over IP, network service shows the following characteristics:(1) network service increases its richness;(2) broadband streaming media becomes the mainstream. To achieve unified multi-service bearing in the IP network, the largescale access convergence network architecture is proposed. This flat access convergence structure with ultra-small hops, which shortens the service transmission path, reduces the complexity of the edge of the network, and achieves IP strong waist model with the integration of computation, storage and transmission. The key technologies are also introduced in this paper, including endto-end performance guarantee for real time interactive services, fog storing mechanism, and built-in safety transmission with integration of aggregation and control.展开更多
基金National Natural Science Foundation of China (50675186)
文摘In the traditional flatness pattern recognition neural network, the topologic configurations need to be rebuilt with a changing width of cold strip. Furthermore, the large learning assignment, slow convergence, and local minimum in the network are observed. Moreover, going by the structure of the traditional neural network, according to experience, the model is time-consuming and complex. Thus, a new approach of flatness pattern recognition is proposed based on the CMAC （cerebellar model articulation controllers） neural network. The difference in fuzzy distances between samples and the basic patterns is introduced as the input of the CMAC network. Simultaneously, the adequate learning rate is improved in the error correction algorithm of this neural network. The new approach with advantages, such as high learning speed, good generalization, and easy implementation, is efficient and intelligent. The simulation results show that the speed and accuracy of the flatness pattern recognition model are obviously im proved.
文摘In the current mobile IPv6 (MIPv6) systems for the System architecture evaluation (SAE) networks, such as 4th generation (4G) mobile network, the data delivery is performed basing on a centralized mobility network anchor between Evolved Node B (eNB) and Serving Gateways (S-GW), and also between S-GW and Packet Data Network Gateway (P-GW). However, the existing network has many obstacles, including suboptimal data routing, injection of unwanted data traffic into mobile core network and the requirement of capital expenditure. To handle these challenges, here we describe a flat mobile core network scheme donated by F-EPC, based SAE mobile network. In the proposed scheme, the P-GW and S-GW gateways are features as one node named Cellular Gateway (C-GW). Further, we proposed to distribute and increase the number of C-GW in mobile core network, the Mobility Management Entity (MME) functioned as centralizing mobility anchor and allocating the IP address for the User Entity (UE). In this paper, the explained results of a simulation analysis showed that the proposed scheme provides a superior performance compared with the current 4G architecture in terms of total transmission delay, handover delay and initial attach procedure.
基金Project(E2015203354)supported by Natural Science Foundation of Steel United Research Fund of Hebei Province,ChinaProject(ZD2016100)supported by the Science and the Technology Research Key Project of High School of Hebei Province,China+1 种基金Project(LJRC013)supported by the University Innovation Team of Hebei Province Leading Talent Cultivation,ChinaProject(16LGY015)supported by the Basic Research Special Breeding of Yanshan University,China
文摘The accuracy of present flatness predictive method is limited and it just belongs to software simulation. In order to improve it, a novel flatness predictive model via T-S cloud reasoning network implemented by digital signal processor(DSP) is proposed. First, the combination of genetic algorithm(GA) and simulated annealing algorithm(SAA) is put forward, called GA-SA algorithm, which can make full use of the global search ability of GA and local search ability of SA. Later, based on T-S cloud reasoning neural network, flatness predictive model is designed in DSP. And it is applied to 900 HC reversible cold rolling mill. Experimental results demonstrate that the flatness predictive model via T-S cloud reasoning network can run on the hardware DSP TMS320 F2812 with high accuracy and robustness by using GA-SA algorithm to optimize the model parameter.
基金Tribal Resilience Program for financial support of this study.
文摘A tribal-owned network of aerosol monitors and meteorological stations was installed at Ts’aahudaaneekk’onh Denh (Beaver), Gwichyaa Zheh (Fort Yukon), Jalgiitsik (Chalkyitsik), and Danzhit Khànlaii (Circle) in the Yukon Flats, Alaska. Surface inversions occurred under calm wind conditions due to radiative cooling. In May, local emissions governed air quality with worst conditions related to road and river dust. As the warm season progressed, worst air quality was due to transport of pollutants from upwind wildfires. During situations without smoke or when smoke existed at layers above the surface inversion, concentrations of particulate matter of less than 2.5 micrometer in diameter or less (PM2.5) were explainable by the local emissions;24-h means remained below 25 μg·m-3. Absorption of solar radiation in the smoke layer and upward scattering enhanced stability and fostered the persistence of the surface inversions. During smoke episodes without the presence of a surface inversion, daily mean concentrations exceeded 35 μg·m-3 often for several consecutive days, at all sites. Then concentrations temporally reached levels considered unhealthy.
基金Sponsored by National High-tech Research and Development Project of China(2009AA04Z143)Natural Science Foundation of Hebei Province of China(E2006001038)Science and Technology Project of Hebei Province of China(10212101D)
文摘In connection with the characteristics of multi-disturbance and nonlinearity of a system for flatness control in cold rolling process, a new intelligent PID control algorithm was proposed based on a cloud model, neural network and fuzzy integration. By indeterminacy artificial intelligence, the problem of fixing the membership functions of input variables and fuzzy rules was solved in an actual fuzzy system and the nonlinear mapping between variables was implemented by neural network. The algorithm has the adaptive learning ability of neural network and the indetermi- nacy of a cloud model in processing knowledge, which makes the fuzzy system have more persuasion in the process of knowledge inference, realizing the online adaptive regulation of PID parameters and avoiding the defects of the traditional PID controller. Simulation results show that the algorithm is simple, fast and robust with good control performance and application value.
基金supported by National Natural Science Foundation of China(Grant No. 50675186)Hebei Provincial Major Natural Science Foundation of China (Grant No. E2006001038)
文摘Steel strips are the main of steel products and flatness is an important quality indicator of steel strips. Flatness control is the key and highly difficult technique of strip mills. The bottle-neck restricting the improvement of flatness control techniques is that the research on flatness theories and control mathematic models is not in accordance with the requirement of technique developments. To build a simple, rapid and accurate explicit formulation control model has become an urgent need for the development of flatness control technique. This paper puts forward the conception of dynamic effective matrix based on the effective matrix method for flatness control proposed by the authors under the consideration of the influence of the change of parameters in roiling processes on the effective matrix, and the concept is validated by industrial productions. Three methods of the effective matrix generation are induced： the calculation method based on the flatness prediction model; the calculation method based on the data excavation in rolling processes and the direct calculation method based on the network model. A fuzzy neural network effective matrix model is built based on the clusters, and then the network structure is optimized and the high-speed-calculation problem of the dynamic effective matrix is solved. The flatness control scheme for cold strip mills is proposed based on the dynamic effective matrix. On stand 5 of the 1 220 mm five-stand 4-high cold strip tandem mill, the industrial experiment with the control methods of tilting roll and bending roll is done by the control scheme of the static effective matrix and the dynamic effective matrix, respectively. The experiment result proves that the control effect of the dynamic effective matrix is much better than that of the static effective matrix. This paper proposes a new idea and method for the dynamic flatness control in the rolling processes of cold strip mills and develops the theory and model of the flatness control effective matrix method.
基金Item Sponsored by National High-Tech Research and Development Project of China(2009AA04Z143)Natural Science Foundation of Hebei Province of China(E2006001038)Hebei Provincial Science and Technology Project of China(10212101D)
文摘In order to increase the precision of flatness control, considering the principle and the measured data of rolling process essence, the theory-intelligent dynamic matrix model of flatness control is established by using theory and in-telligent methods synthetically. The network model for rapidly calculating the theory effective matrix is established by the BP network optimized by the particle swarm algorithm. The network model for rapidly calculating the meas- urement effective matrix is established by the RBF network optimized by the cluster algorithm. The flatness control model can track the practical situation of roiling process by on-line selVlearning. The scheme for flatness control quantity calculation is established by combining the theory control matrix and the measurement control matrix. The simulation result indicates that the establishment of theory-intelligent dynamic matrix model of flatness control with stable control process and high precision supplies a new way and method for studying flatness on-line control model.
文摘Confirmation of basic technological parameters of tension levellers is the most important factor of leveling strip. Up to now, most factories have used experts’ experience to decide these parameters, without any established rule to follow. For better quality of strip, a valid method is needed to decide technological parameters precisely and reasonably. In this paper, a method is used based on neural network and genetic algorithm. Neural network has a good ability to extract rules from work process of tension levellers. Then using neural network, which has learned from a lot of working samples, to be the evaluation of fitness, genetic algorithm could easily find the best or better technological parameters. At the end of this paper, examinations are given to show the effect of this method.
基金Item Sponsored by National Natural Science Foundation of China and Shanghai Baosteel Group Co(50675186)Provincial Natural Science Foundation of Hebei Province of China(E2006001038)
文摘For the problems occurring in a least square method model, a fuzzy model, and a neural network model for flatness pattern recognition, a fuzzy neural network model for flatness pattern recognition with only three-input and three output signals was proposed with Legendre orthodoxy polynomial as basic pattern, based on fuzzy logic expert experiential knowledge and genetic-BP hybrid optimization algorithm. The model not only had definite physical meanings in its inner nodes, but also had strong self-adaptability, anti interference ability, high recognition precision, and high velocity, thereby meeting the demand of high-precision flatness control for cold strip mill and providing a convenient, practical, and novel method for flatness pattern recognition.
基金supported by The National Key Technology R&D Program (Grant No. 2011BAH19B00)The National Basic Research Program of China (973) (Grant No. 2012CB315900)The National High Technology Research and Development Program of China (863) (Grant No. 2015AA016102)
文摘Under the requirement of everything over IP, network service shows the following characteristics:(1) network service increases its richness;(2) broadband streaming media becomes the mainstream. To achieve unified multi-service bearing in the IP network, the largescale access convergence network architecture is proposed. This flat access convergence structure with ultra-small hops, which shortens the service transmission path, reduces the complexity of the edge of the network, and achieves IP strong waist model with the integration of computation, storage and transmission. The key technologies are also introduced in this paper, including endto-end performance guarantee for real time interactive services, fog storing mechanism, and built-in safety transmission with integration of aggregation and control.
