Atmospheric chemistry models usually perform badly in forecasting wintertime air pollution because of their uncertainties. Generally, such uncertainties can be decreased effectively by techniques such as data assimila...Atmospheric chemistry models usually perform badly in forecasting wintertime air pollution because of their uncertainties. Generally, such uncertainties can be decreased effectively by techniques such as data assimilation（DA） and model output statistics（MOS）. However, the relative importance and combined effects of the two techniques have not been clarified. Here,a one-month air quality forecast with the Weather Research and Forecasting-Chemistry（WRF-Chem） model was carried out in a virtually operational setup focusing on Hebei Province, China. Meanwhile, three-dimensional variational（3 DVar） DA and MOS based on one-dimensional Kalman filtering were implemented separately and simultaneously to investigate their performance in improving the model forecast. Comparison with observations shows that the chemistry forecast with MOS outperforms that with 3 DVar DA, which could be seen in all the species tested over the whole 72 forecast hours. Combined use of both techniques does not guarantee a better forecast than MOS only, with the improvements and degradations being small and appearing rather randomly. Results indicate that the implementation of MOS is more suitable than 3 DVar DA in improving the operational forecasting ability of WRF-Chem.展开更多
The background error covariance plays an important role in modern data assimilation and analysis systems by determining the spatial spreading of information in the data. A novel method based on model output is propose...The background error covariance plays an important role in modern data assimilation and analysis systems by determining the spatial spreading of information in the data. A novel method based on model output is proposed to estimate background error covariance for use in Optimum Interpolation. At every model level, anisotropic correlation scales are obtained that give a more detailed description of the spatial correlation structure. Furthermore, the impact of the background field itself is included in the background error covariance. The methodology of the estimation is presented and the structure of the covariance is examined. The results of 20-year assimilation experiments are compared with observations from TOGA-TAO (The Tropical Ocean-Global Atmosphere-Tropical Atmosphere Ocean) array and other analysis data.展开更多
In view of the extensive growth of China's steel production in recent years, this paper analyzed the industrial development background and economic geography theory, and discussed the possible spatial interaction ...In view of the extensive growth of China's steel production in recent years, this paper analyzed the industrial development background and economic geography theory, and discussed the possible spatial interaction mechanism. Based on panel data of China's inter-provincial steel output from 2001 to 2015, using spatial econometric model, this paper also explored whether China's provincial steel production shows material orientation, market orientation and traffic orientation, and isolated spatial interactions of interprovincial steel output. The results showed that the inter-provincial steel production in China did show both material orientation, market orientation and traffic orientation and that there was a significant negative spatial interaction, indicating that there might be strong competition and a crowing-out effect between neighboring provinces, and that the smaller the spatial scope, the more significant the spatial interactions of steel production.展开更多
This paper investigates the problem of global output feedback stabilization for a class of feedforward nonlinear systems via linear sampled-data control. To solve the problem, we first construct a linear sampled-data ...This paper investigates the problem of global output feedback stabilization for a class of feedforward nonlinear systems via linear sampled-data control. To solve the problem, we first construct a linear sampled-data observer and controller. Then, a scaling gain is introduced into the proposed observer and controller. Finally, we use the sampled-data output feedback domination approach to find the explicit formula for choosing the scaling gain and the sampling period which renders the closed-loop system globally asymptotically stable. A simulation example is given to demonstrate the effectiveness of the proposed design procedure.展开更多
Cross-efficiency evaluation is recognized as an effective way of efficiency assessment for a set of decision making units (DMUs) in the framework of data envelopment analysis (DEA). It has been generally suggested tha...Cross-efficiency evaluation is recognized as an effective way of efficiency assessment for a set of decision making units (DMUs) in the framework of data envelopment analysis (DEA). It has been generally suggested that secondary goals be introduced for cross-efficiency evaluation owing to the non-uniqueness of optimal solutions in self-evaluation. This paper develops a variety of secondary goals in the spirit of promoting balance in the output efficiencies of the DMU under evaluation. The proposed models attempt to make each output contribute as equally as possible to the self-evaluated efficiency. In this way, the weight flexibility can for one thing be reduced by the introduced secondary goals with selections from alternate optimal solutions, in addition to counting on the dilution of flexibility in the subsequent peer-evaluation. The proposed approach might be applicable to evaluation problems in which multiple outputs are considered important and balance is encouraged to put all dimensions into sufficient use. The effectiveness of the proposed approach and its comparisons with some relevant secondary goals are illustrated empirically using numerical examples.展开更多
培育世界一流学科是建设世界一流大学的重要基础,科学构建一流学科建设评价体系,重视其底层数据来源及其计算方法,将第三方评价体系作为重要的评价参考体系,对促进一流学科建设具有重要意义。本研究利用Web of Science数据库的底层数据...培育世界一流学科是建设世界一流大学的重要基础,科学构建一流学科建设评价体系,重视其底层数据来源及其计算方法,将第三方评价体系作为重要的评价参考体系,对促进一流学科建设具有重要意义。本研究利用Web of Science数据库的底层数据和InCites科研分析工具,通过对中国排名靠前的中国农业大学、华中农业大学、南京农业大学、西北农林科技大学和华南农业大学这5所农业高校进行分析,从学术整体产出、科研质量、科学合作、热点主题等方面探讨了其发展态势。分析结果表明,农业高校建设应重视高水平科研产出,促进优势学科发展,加强学科与实践结合,并结合国家战略和社会需求明确发展方向,进行学科布局。展开更多
【目的】随着大规模新能源入网,新型电力系统在对灵活性需求提出一定要求的同时,对预测技术也有了更高的精确性要求,而传统预测方法在处理动态复杂的场景时存在一定局限性,因此,亟需研究适用于新型电力系统的预测技术。大语言模型(large...【目的】随着大规模新能源入网,新型电力系统在对灵活性需求提出一定要求的同时,对预测技术也有了更高的精确性要求,而传统预测方法在处理动态复杂的场景时存在一定局限性,因此,亟需研究适用于新型电力系统的预测技术。大语言模型(large language model,LLM)是一种基于生成式人工智能(artificial intelligence,AI)的技术,具有多模态数据融合、少样本学习、多任务处理的能力,能够为电力系统中的预测任务提供更加智能化和精准化的解决路径。为此,针对LLM在电力系统预测领域的应用现状及优势展开分析。【方法】首先,对LLM的基本架构、训练方法及其应用现状进行阐释;然后,说明了其应用在预测领域的原理及实现过程,并重点探讨了在电力负荷预测、新能源出力预测和电价预测方面的优势和潜力;最后,从数据质量管理、隐私保护及计算资源3个方面分析了目前LLM在预测应用中存在的问题,并给出了可行的解决思路。【结论】通过对比各种预测任务研究发现,与传统预测方法相比,LLM在少样本学习和多模态数据处理方面的强大能力使其更适用于复杂多变的预测场景,对LLM合理有效的应用能够为电力市场预测提供新的解决方案。展开更多
基金supported by the State Key Research and Development Program (Grant Nos. 2017YFC0209803, 2016YFC0208504, 2016YFC0203303 and 2017YFC0210106)the National Natural Science Foundation of China (Grant Nos. 91544230, 41575145, 41621005 and 41275128)
文摘Atmospheric chemistry models usually perform badly in forecasting wintertime air pollution because of their uncertainties. Generally, such uncertainties can be decreased effectively by techniques such as data assimilation（DA） and model output statistics（MOS）. However, the relative importance and combined effects of the two techniques have not been clarified. Here,a one-month air quality forecast with the Weather Research and Forecasting-Chemistry（WRF-Chem） model was carried out in a virtually operational setup focusing on Hebei Province, China. Meanwhile, three-dimensional variational（3 DVar） DA and MOS based on one-dimensional Kalman filtering were implemented separately and simultaneously to investigate their performance in improving the model forecast. Comparison with observations shows that the chemistry forecast with MOS outperforms that with 3 DVar DA, which could be seen in all the species tested over the whole 72 forecast hours. Combined use of both techniques does not guarantee a better forecast than MOS only, with the improvements and degradations being small and appearing rather randomly. Results indicate that the implementation of MOS is more suitable than 3 DVar DA in improving the operational forecasting ability of WRF-Chem.
基金supported by the National Key Program for Developing Basic Sciences(G1999032801)the National Natural Science Foundation of China(Grant No.40005007,40233033,and 40221503)
文摘The background error covariance plays an important role in modern data assimilation and analysis systems by determining the spatial spreading of information in the data. A novel method based on model output is proposed to estimate background error covariance for use in Optimum Interpolation. At every model level, anisotropic correlation scales are obtained that give a more detailed description of the spatial correlation structure. Furthermore, the impact of the background field itself is included in the background error covariance. The methodology of the estimation is presented and the structure of the covariance is examined. The results of 20-year assimilation experiments are compared with observations from TOGA-TAO (The Tropical Ocean-Global Atmosphere-Tropical Atmosphere Ocean) array and other analysis data.
文摘In view of the extensive growth of China's steel production in recent years, this paper analyzed the industrial development background and economic geography theory, and discussed the possible spatial interaction mechanism. Based on panel data of China's inter-provincial steel output from 2001 to 2015, using spatial econometric model, this paper also explored whether China's provincial steel production shows material orientation, market orientation and traffic orientation, and isolated spatial interactions of interprovincial steel output. The results showed that the inter-provincial steel production in China did show both material orientation, market orientation and traffic orientation and that there was a significant negative spatial interaction, indicating that there might be strong competition and a crowing-out effect between neighboring provinces, and that the smaller the spatial scope, the more significant the spatial interactions of steel production.
基金This work was supported by the National Natural Science Foundation of China （Nos. 61104068, 61273119） Natural Science Foundation of Jiangsu Province （No. BK2010200）+1 种基金 China Postdoctoral Science Foundation Founded Project （No. 2012M511176） the Fundamental Research Funds for the Central Universities （No. 2242013R30006）.
文摘This paper investigates the problem of global output feedback stabilization for a class of feedforward nonlinear systems via linear sampled-data control. To solve the problem, we first construct a linear sampled-data observer and controller. Then, a scaling gain is introduced into the proposed observer and controller. Finally, we use the sampled-data output feedback domination approach to find the explicit formula for choosing the scaling gain and the sampling period which renders the closed-loop system globally asymptotically stable. A simulation example is given to demonstrate the effectiveness of the proposed design procedure.
文摘Cross-efficiency evaluation is recognized as an effective way of efficiency assessment for a set of decision making units (DMUs) in the framework of data envelopment analysis (DEA). It has been generally suggested that secondary goals be introduced for cross-efficiency evaluation owing to the non-uniqueness of optimal solutions in self-evaluation. This paper develops a variety of secondary goals in the spirit of promoting balance in the output efficiencies of the DMU under evaluation. The proposed models attempt to make each output contribute as equally as possible to the self-evaluated efficiency. In this way, the weight flexibility can for one thing be reduced by the introduced secondary goals with selections from alternate optimal solutions, in addition to counting on the dilution of flexibility in the subsequent peer-evaluation. The proposed approach might be applicable to evaluation problems in which multiple outputs are considered important and balance is encouraged to put all dimensions into sufficient use. The effectiveness of the proposed approach and its comparisons with some relevant secondary goals are illustrated empirically using numerical examples.
文摘培育世界一流学科是建设世界一流大学的重要基础,科学构建一流学科建设评价体系,重视其底层数据来源及其计算方法,将第三方评价体系作为重要的评价参考体系,对促进一流学科建设具有重要意义。本研究利用Web of Science数据库的底层数据和InCites科研分析工具,通过对中国排名靠前的中国农业大学、华中农业大学、南京农业大学、西北农林科技大学和华南农业大学这5所农业高校进行分析,从学术整体产出、科研质量、科学合作、热点主题等方面探讨了其发展态势。分析结果表明,农业高校建设应重视高水平科研产出,促进优势学科发展,加强学科与实践结合,并结合国家战略和社会需求明确发展方向,进行学科布局。
文摘【目的】随着大规模新能源入网,新型电力系统在对灵活性需求提出一定要求的同时,对预测技术也有了更高的精确性要求,而传统预测方法在处理动态复杂的场景时存在一定局限性,因此,亟需研究适用于新型电力系统的预测技术。大语言模型(large language model,LLM)是一种基于生成式人工智能(artificial intelligence,AI)的技术,具有多模态数据融合、少样本学习、多任务处理的能力,能够为电力系统中的预测任务提供更加智能化和精准化的解决路径。为此,针对LLM在电力系统预测领域的应用现状及优势展开分析。【方法】首先,对LLM的基本架构、训练方法及其应用现状进行阐释;然后,说明了其应用在预测领域的原理及实现过程,并重点探讨了在电力负荷预测、新能源出力预测和电价预测方面的优势和潜力;最后,从数据质量管理、隐私保护及计算资源3个方面分析了目前LLM在预测应用中存在的问题,并给出了可行的解决思路。【结论】通过对比各种预测任务研究发现,与传统预测方法相比,LLM在少样本学习和多模态数据处理方面的强大能力使其更适用于复杂多变的预测场景,对LLM合理有效的应用能够为电力市场预测提供新的解决方案。
