This paper focuses on land resource consumption due to urban sprawl. Special attention is given to shrinking regions, characterized by economic decline, demographic change, and high unemployment rates. In these region...This paper focuses on land resource consumption due to urban sprawl. Special attention is given to shrinking regions, characterized by economic decline, demographic change, and high unemployment rates. In these regions, vast terrain is abandoned and falls derelict. A geographic information system （GIS） based multi-criteria decision tool is introduced to determine the reuse potential of derelict terrain, to investigate the possible reuse options （housing, business and trade, industry, services, tourism and leisure, and re-greening）, and to visualize the best reuse options for groups of sites on a regional scale. Achievement functions for attribute data are presented to assess the best reuse options based on a multi-attribute technique. The assessment tool developed is applied to a model region in Germany. The application of the assessment tool enables communities to become aware of their resources of derelict land and their reuse potential.展开更多
Due to the strict requirements of extremely high accuracy and fast computational speed, real-time transient stability assessment(TSA) has always been a tough problem in power system analysis.Fortunately, the developme...Due to the strict requirements of extremely high accuracy and fast computational speed, real-time transient stability assessment(TSA) has always been a tough problem in power system analysis.Fortunately, the development of artificial intelligence and big data technologies provide the new prospective methods to this issue, and there have been some successful trials on using intelligent method, such as support vector machine(SVM) method.However, the traditional SVM method cannot avoid false classification, and the interpretability of the results needs to be strengthened and clear.This paper proposes a new strategy to solve the shortcomings of traditional SVM,which can improve the interpretability of results, and avoid the problem of false alarms and missed alarms.In this strategy, two improved SVMs, which are called aggressive support vector machine(ASVM) and conservative support vector machine(CSVM), are proposed to improve the accuracy of the classification.And two improved SVMs can ensure the stability or instability of the power system in most cases.For the small amount of cases with undetermined stability, a new concept of grey region(GR) is built to measure the uncertainty of the results, and GR can assessment the instable probability of the power system.Cases studies on IEEE 39-bus system and realistic provincial power grid illustrate the effectiveness and practicability of the proposed strategy.展开更多
With the requirements for high performance results in the today’s mobile, global, highly competitive, and technology-based business world, business professionals have to get supported by convenient mobile decision su...With the requirements for high performance results in the today’s mobile, global, highly competitive, and technology-based business world, business professionals have to get supported by convenient mobile decision support systems (DSS). To give an improved support to mobile business professionals, it is necessary to go further than just allowing a simple remote access to a Business Intelligence platform. In this paper, the need for actual context-aware mobile Geospatial Business Intelligence (GeoBI) systems that can help capture, filter, organize and structure the user mobile context is exposed and justified. Furthermore, since capturing, structuring, and modeling mobile contextual information is still a research issue, a wide inventory of existing research work on context and mobile context is provided. Then, step by step, we methodologically identify relevant contextual information to capture for mobility purposes as well as for BI needs, organize them into context-dimensions, and build a hierarchical mobile GeoBI context model which (1) is geo-spatial-extended, (2) fits with human perception of mobility, (3) takes into account the local context interactions and information-sharing with remote contexts, and (4) matches with the usual hierarchical aggregated structure of BI data.展开更多
基金supported by the German Federal Ministry of Education and Research (BMBF Berlin)the Federal Office of Building and Regional Planning (BBR Bonn)the State of Thuringia and the State Development Corporation (LEG) Thuringia
文摘This paper focuses on land resource consumption due to urban sprawl. Special attention is given to shrinking regions, characterized by economic decline, demographic change, and high unemployment rates. In these regions, vast terrain is abandoned and falls derelict. A geographic information system （GIS） based multi-criteria decision tool is introduced to determine the reuse potential of derelict terrain, to investigate the possible reuse options （housing, business and trade, industry, services, tourism and leisure, and re-greening）, and to visualize the best reuse options for groups of sites on a regional scale. Achievement functions for attribute data are presented to assess the best reuse options based on a multi-attribute technique. The assessment tool developed is applied to a model region in Germany. The application of the assessment tool enables communities to become aware of their resources of derelict land and their reuse potential.
基金supported by Science and Technology Project of State Grid Corporation of ChinaNational Natural Science Foundation of China (No.51777104)China State Key Laboratory of Power System (No.SKLD16Z08)
文摘Due to the strict requirements of extremely high accuracy and fast computational speed, real-time transient stability assessment(TSA) has always been a tough problem in power system analysis.Fortunately, the development of artificial intelligence and big data technologies provide the new prospective methods to this issue, and there have been some successful trials on using intelligent method, such as support vector machine(SVM) method.However, the traditional SVM method cannot avoid false classification, and the interpretability of the results needs to be strengthened and clear.This paper proposes a new strategy to solve the shortcomings of traditional SVM,which can improve the interpretability of results, and avoid the problem of false alarms and missed alarms.In this strategy, two improved SVMs, which are called aggressive support vector machine(ASVM) and conservative support vector machine(CSVM), are proposed to improve the accuracy of the classification.And two improved SVMs can ensure the stability or instability of the power system in most cases.For the small amount of cases with undetermined stability, a new concept of grey region(GR) is built to measure the uncertainty of the results, and GR can assessment the instable probability of the power system.Cases studies on IEEE 39-bus system and realistic provincial power grid illustrate the effectiveness and practicability of the proposed strategy.
文摘With the requirements for high performance results in the today’s mobile, global, highly competitive, and technology-based business world, business professionals have to get supported by convenient mobile decision support systems (DSS). To give an improved support to mobile business professionals, it is necessary to go further than just allowing a simple remote access to a Business Intelligence platform. In this paper, the need for actual context-aware mobile Geospatial Business Intelligence (GeoBI) systems that can help capture, filter, organize and structure the user mobile context is exposed and justified. Furthermore, since capturing, structuring, and modeling mobile contextual information is still a research issue, a wide inventory of existing research work on context and mobile context is provided. Then, step by step, we methodologically identify relevant contextual information to capture for mobility purposes as well as for BI needs, organize them into context-dimensions, and build a hierarchical mobile GeoBI context model which (1) is geo-spatial-extended, (2) fits with human perception of mobility, (3) takes into account the local context interactions and information-sharing with remote contexts, and (4) matches with the usual hierarchical aggregated structure of BI data.
