It is important to study the contributions of climate change and human activities to cropland changes in the fields of both climate change and land use change. Relationships between cropland changes and driving forces...It is important to study the contributions of climate change and human activities to cropland changes in the fields of both climate change and land use change. Relationships between cropland changes and driving forces were qualitatively studied in most of the previous researches. However, the quantitative assessments of the contributions of climate change and human activities to cropland changes are needed to be explored for a better understanding of the dynamics of land use changes. We systematically reviewed the methods of identifying the contributions of climate change and human activities to cropland changes at quantitative aspects, including model analysis, mathematical statistical method, framework analysis, index assessment and difference comparison. Progress of the previous researches on quantitative evaluation of the contributions was introduced. Then we discussed four defects in the assessment of the contributions of climate change and human activities. For example, the methods were lack of comprehensiveness, and the data need to be more accurate and abundant. In addition, the scale was single and the explanations were biased. Moreover, we concluded a clue about quantitative approach to assess the contributions from synthetically aspect to specific driving forces. Finally, the solutions of the future researches on data, scale and explanation were proposed.展开更多
In this paper,the improved canonical quantization method of the self dual field is given in order to overcome linear combination problem about the second class constraint and the first class constraint number maximiza...In this paper,the improved canonical quantization method of the self dual field is given in order to overcome linear combination problem about the second class constraint and the first class constraint number maximization problem in the Dirac method.In the improved canonical quantization method,there are no artificial linear combination and the first class constraint number maximization problems,at the same time,the stability of the system is considered.Therefore,the improved canonical quantization method is more natural and easier accepted by people than the usual Dirac method.We use the improved canonical quantization method to realize the canonical quantization of the self dual field,which has relation with string theory successfully and the results are equal to the results by using the Dirac method.展开更多
Support vector machine （SVM） is a widely used tool in the field of image processing and pattern recognition. However, the parameters selection of SVMs is a dilemma in disease identification and clinical diagnosis. T...Support vector machine （SVM） is a widely used tool in the field of image processing and pattern recognition. However, the parameters selection of SVMs is a dilemma in disease identification and clinical diagnosis. This paper proposed an improved parameter optimization method based on traditional particle swarm optimization （PSO） algorithm by changing the fitness function in the traditional evolution process of SVMs. Then, this PSO method was combined with simulated annealing global searching algorithm to avoid local convergence that traditional PSO algorithms usually run into. And this method has achieved better results which reflected in the receiver-operating characteristic curves in medical images classification and has gained considerable identification accuracy in clinical disease detection.展开更多
基金National Natural Science Foundation of China,No.41401113,No.41371002,No.41471091The Science and Technology Strategic Pilot of the Chinese Academy of Sciences,No.XDA05090310The Key Project of Physical Geography of Hebei Province
文摘It is important to study the contributions of climate change and human activities to cropland changes in the fields of both climate change and land use change. Relationships between cropland changes and driving forces were qualitatively studied in most of the previous researches. However, the quantitative assessments of the contributions of climate change and human activities to cropland changes are needed to be explored for a better understanding of the dynamics of land use changes. We systematically reviewed the methods of identifying the contributions of climate change and human activities to cropland changes at quantitative aspects, including model analysis, mathematical statistical method, framework analysis, index assessment and difference comparison. Progress of the previous researches on quantitative evaluation of the contributions was introduced. Then we discussed four defects in the assessment of the contributions of climate change and human activities. For example, the methods were lack of comprehensiveness, and the data need to be more accurate and abundant. In addition, the scale was single and the explanations were biased. Moreover, we concluded a clue about quantitative approach to assess the contributions from synthetically aspect to specific driving forces. Finally, the solutions of the future researches on data, scale and explanation were proposed.
基金Supported by National Natural Science Foundation of China under Grant Nos. 11275017 and 11173028
文摘In this paper,the improved canonical quantization method of the self dual field is given in order to overcome linear combination problem about the second class constraint and the first class constraint number maximization problem in the Dirac method.In the improved canonical quantization method,there are no artificial linear combination and the first class constraint number maximization problems,at the same time,the stability of the system is considered.Therefore,the improved canonical quantization method is more natural and easier accepted by people than the usual Dirac method.We use the improved canonical quantization method to realize the canonical quantization of the self dual field,which has relation with string theory successfully and the results are equal to the results by using the Dirac method.
文摘Support vector machine （SVM） is a widely used tool in the field of image processing and pattern recognition. However, the parameters selection of SVMs is a dilemma in disease identification and clinical diagnosis. This paper proposed an improved parameter optimization method based on traditional particle swarm optimization （PSO） algorithm by changing the fitness function in the traditional evolution process of SVMs. Then, this PSO method was combined with simulated annealing global searching algorithm to avoid local convergence that traditional PSO algorithms usually run into. And this method has achieved better results which reflected in the receiver-operating characteristic curves in medical images classification and has gained considerable identification accuracy in clinical disease detection.
