In the first part of the present paper we have explained why we manage to formulate another wave prediction model when so many of them, including the so-called third generation model, have already been in use. The win...In the first part of the present paper we have explained why we manage to formulate another wave prediction model when so many of them, including the so-called third generation model, have already been in use. The wind-wave part of the proposed model has also been given. Now we proceed to discuss the swell part,the implementation of the model as a prediction method,mumerical experiments done with ideal wind fields and hindcasts made in the Bohai Sea,in the neighboring seas adjacent to China and in the Northwest Pacific.展开更多
The authors make an endeavor to explain why a new hybrid wave model is here proposed when several such models have already been in operation and the so- called third generation wave modej is proving attractive. This p...The authors make an endeavor to explain why a new hybrid wave model is here proposed when several such models have already been in operation and the so- called third generation wave modej is proving attractive. This part of the paper is devoted to the wind wave model. Both deep and shallow water models have been developed, the former being actually a special case of the latter when water depth is great. The deep water model is exceptionally simple in form. Significant wave height is the only prognostic variable. In comparison with the usual methods to compute the energy input and dissipations empirically or by 'tuning', the proposed model has the merit that the effects of all source terms are combined into one term which is computed through empirical growth relations for significant waves, these relations being, relatively speaking, easier and more reliable to obtain than those for the source terms in the spectral energy balance equation. The discrete part of the model and the implementation of the model as a whole will be discussed in the second part of the present paper.展开更多
This paper describes a modified version of SSIB through implementing a new snow model (SAST) in Simplified Simple Biosphere Model SSIB for climate study and presents the evaluation results by testing the scheme based ...This paper describes a modified version of SSIB through implementing a new snow model (SAST) in Simplified Simple Biosphere Model SSIB for climate study and presents the evaluation results by testing the scheme based on the field data from Russia and France. The relevant equations in the scheme are given, which describe complicated interactive processes among air-vegetation-snow-soil continuum through mass and heat exchange. An efficient numerical scheme is developed to solve the nonlinear equations successfully. By using the field data from Russia and France, the function of the new scheme is evaluated. The numerical results from the scheme show good agreement with field data. It indicates that the scheme developed here is workable and can be extended for climate study. Key words Snow cover model (SAST) - SSIB - Implementing - Evaluation This work was supported by the foundation from China: 1)NSF Grant 49835010, 2) National key program G1998040900—Part 1, 3) NSF 40075019, 4) NSF 49823002.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
Background: Preventing anterior cruciate ligament(ACL) injuries is important to avoid long-term adverse health consequences. Identifying barriers to implementation of these prevention programs is crucial to reducing t...Background: Preventing anterior cruciate ligament(ACL) injuries is important to avoid long-term adverse health consequences. Identifying barriers to implementation of these prevention programs is crucial to reducing the incidence of these injuries. Our purpose was to identify barriers of implementation for ACL injury prevention programs and suggest mechanisms for reducing the barriers through application of a SocioEcological Model(SEM).Methods: Studies investigating ACL prevention program effectiveness were searched in Medline via PubMed and the Cochrane Library, and a subsequent review of the references of the identified articles, yielded 15 articles total. Inclusion criteria encompassed prospective controlled trials, published in English, with ACL injuries as the primary outcome. Studies were independently appraised by 2 reviewers for methodological quality using the PEDro scale. Barriers to implementation were identified when reported in at least 2 separate studies. A SEM was used to suggest ways to reduce the identified barriers.Results: Five barriers were identified: motivation, time requirements, skill requirements for program facilitators, compliance, and cost. The SEM suggested ways to minimize the barriers at all levels of the model from the individual through policy levels.Conclusion: Identification of barriers to program implementation and suggesting how to reduce them through the SEM is a critical first step toward enabling ACL prevention programs to be more effective and ultimately reducing the incidence of these injuries.展开更多
文摘In the first part of the present paper we have explained why we manage to formulate another wave prediction model when so many of them, including the so-called third generation model, have already been in use. The wind-wave part of the proposed model has also been given. Now we proceed to discuss the swell part,the implementation of the model as a prediction method,mumerical experiments done with ideal wind fields and hindcasts made in the Bohai Sea,in the neighboring seas adjacent to China and in the Northwest Pacific.
文摘The authors make an endeavor to explain why a new hybrid wave model is here proposed when several such models have already been in operation and the so- called third generation wave modej is proving attractive. This part of the paper is devoted to the wind wave model. Both deep and shallow water models have been developed, the former being actually a special case of the latter when water depth is great. The deep water model is exceptionally simple in form. Significant wave height is the only prognostic variable. In comparison with the usual methods to compute the energy input and dissipations empirically or by 'tuning', the proposed model has the merit that the effects of all source terms are combined into one term which is computed through empirical growth relations for significant waves, these relations being, relatively speaking, easier and more reliable to obtain than those for the source terms in the spectral energy balance equation. The discrete part of the model and the implementation of the model as a whole will be discussed in the second part of the present paper.
基金the foundation from China: 1) NSF Grant 49835010, 2) National keyprogram G1998040900-Part 1,3) NSF 40075019, 4) NSF 49823002.
文摘This paper describes a modified version of SSIB through implementing a new snow model (SAST) in Simplified Simple Biosphere Model SSIB for climate study and presents the evaluation results by testing the scheme based on the field data from Russia and France. The relevant equations in the scheme are given, which describe complicated interactive processes among air-vegetation-snow-soil continuum through mass and heat exchange. An efficient numerical scheme is developed to solve the nonlinear equations successfully. By using the field data from Russia and France, the function of the new scheme is evaluated. The numerical results from the scheme show good agreement with field data. It indicates that the scheme developed here is workable and can be extended for climate study. Key words Snow cover model (SAST) - SSIB - Implementing - Evaluation This work was supported by the foundation from China: 1)NSF Grant 49835010, 2) National key program G1998040900—Part 1, 3) NSF 40075019, 4) NSF 49823002.
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.
文摘Background: Preventing anterior cruciate ligament(ACL) injuries is important to avoid long-term adverse health consequences. Identifying barriers to implementation of these prevention programs is crucial to reducing the incidence of these injuries. Our purpose was to identify barriers of implementation for ACL injury prevention programs and suggest mechanisms for reducing the barriers through application of a SocioEcological Model(SEM).Methods: Studies investigating ACL prevention program effectiveness were searched in Medline via PubMed and the Cochrane Library, and a subsequent review of the references of the identified articles, yielded 15 articles total. Inclusion criteria encompassed prospective controlled trials, published in English, with ACL injuries as the primary outcome. Studies were independently appraised by 2 reviewers for methodological quality using the PEDro scale. Barriers to implementation were identified when reported in at least 2 separate studies. A SEM was used to suggest ways to reduce the identified barriers.Results: Five barriers were identified: motivation, time requirements, skill requirements for program facilitators, compliance, and cost. The SEM suggested ways to minimize the barriers at all levels of the model from the individual through policy levels.Conclusion: Identification of barriers to program implementation and suggesting how to reduce them through the SEM is a critical first step toward enabling ACL prevention programs to be more effective and ultimately reducing the incidence of these injuries.
