Panzhihua city （26°O5＇-27°21＇N, 101°OS＇- 102°15＇E）, located in a mountainous area, is one of the large cities in Sichuan province, China. A landslide occurred in the filling body of the easte...Panzhihua city （26°O5＇-27°21＇N, 101°OS＇- 102°15＇E）, located in a mountainous area, is one of the large cities in Sichuan province, China. A landslide occurred in the filling body of the eastern part of the Panzhihua airport on October 3, 2009 （hereafter called the lo.3 landslide）. We conducted field survey on the landslide and adopted emergency monitoring and warning models based on the Internet of Things （loT） to estimate the losses from the disaster and to prevent a secondary disaster from occurring. The results showed that four major features of the airport site had contributed to the landslide, i.e, high altitude, huge amount of filling rocks, deep backfilling and great difficulty of backfilling. The deformation process of the landslide had six stages and the unstable geological structure of high fillings and an earthquake were the main causes of the landslide. We adopted relative displacement sensing technology and Global System for Mobile Communications （GSM） technology to achieve remote, real-time and unattended monitoring of ground cracks in the landslide. The monitoring system, including five extensometers with measuring ranges of 200, 450 and 7oo mm, was continuously working for 17 months and released 7 warning signals with an average warning time of about 26 hours. At 10 am on 6 December 2009, the system issued a warning and on-site workers were evacuated and equipment protected immediately. At 2：20 medium-scale collapse monitoring site, which proved the reliability pm on 7 December, a occurred at the No. 5 justified the alarm and and efficiency of the monitoring system.展开更多
Expediency of this work is conditioned by the inconsistency between the market requirement of the specialists and the planning process of high educational system. For solving this problem it is important to make consu...Expediency of this work is conditioned by the inconsistency between the market requirement of the specialists and the planning process of high educational system. For solving this problem it is important to make consulting or expect system for flexible planning of teaching modules of every specialty. We make an attempt to consider this problem in two aspects： the prediction of market demand for planning taking into consideration of studies duration and scheduling of educational process. The prediction task consists in data acquisition of market requirement for each profession in discrete time interval to predict dynamic evolution of every specialty. The solution of the prediction task will be using to determination of prognostic quantity of students for each specialty. As regards the second aspect, it consists in finding a schedule of the teaching modules, i.e. the distribution of subjects in the semesters, keeping the total limits of credits, to update and adapt syllabus. In this paper, we present a genetic algorithm as a solution method for the modular scheduling problem. Genetic algorithms （GAs） allow a more general approach to the scheduling problem, which is rated using a fitness function. GA can be successfully applied to find optimized sequential schedules.展开更多
基金supported by the National Science Fund for Distinguished Young Scholars of China (Grant No. 40125015)a Research Project of the State Key Laboratory of Geohazard Prevention and Geoenvironment Protection (Grant No. SKLGP2010Z002)+1 种基金the Science and Technique Plans for Sichuan Province, China (Grant No. 2011SZ0182 and NO. 2013SZ0168)the Fundamental Science on Nuclear Waste and Environmental Security Laboratory (Grant No. 12zxnp04)
文摘Panzhihua city （26°O5＇-27°21＇N, 101°OS＇- 102°15＇E）, located in a mountainous area, is one of the large cities in Sichuan province, China. A landslide occurred in the filling body of the eastern part of the Panzhihua airport on October 3, 2009 （hereafter called the lo.3 landslide）. We conducted field survey on the landslide and adopted emergency monitoring and warning models based on the Internet of Things （loT） to estimate the losses from the disaster and to prevent a secondary disaster from occurring. The results showed that four major features of the airport site had contributed to the landslide, i.e, high altitude, huge amount of filling rocks, deep backfilling and great difficulty of backfilling. The deformation process of the landslide had six stages and the unstable geological structure of high fillings and an earthquake were the main causes of the landslide. We adopted relative displacement sensing technology and Global System for Mobile Communications （GSM） technology to achieve remote, real-time and unattended monitoring of ground cracks in the landslide. The monitoring system, including five extensometers with measuring ranges of 200, 450 and 7oo mm, was continuously working for 17 months and released 7 warning signals with an average warning time of about 26 hours. At 10 am on 6 December 2009, the system issued a warning and on-site workers were evacuated and equipment protected immediately. At 2：20 medium-scale collapse monitoring site, which proved the reliability pm on 7 December, a occurred at the No. 5 justified the alarm and and efficiency of the monitoring system.
文摘Expediency of this work is conditioned by the inconsistency between the market requirement of the specialists and the planning process of high educational system. For solving this problem it is important to make consulting or expect system for flexible planning of teaching modules of every specialty. We make an attempt to consider this problem in two aspects： the prediction of market demand for planning taking into consideration of studies duration and scheduling of educational process. The prediction task consists in data acquisition of market requirement for each profession in discrete time interval to predict dynamic evolution of every specialty. The solution of the prediction task will be using to determination of prognostic quantity of students for each specialty. As regards the second aspect, it consists in finding a schedule of the teaching modules, i.e. the distribution of subjects in the semesters, keeping the total limits of credits, to update and adapt syllabus. In this paper, we present a genetic algorithm as a solution method for the modular scheduling problem. Genetic algorithms （GAs） allow a more general approach to the scheduling problem, which is rated using a fitness function. GA can be successfully applied to find optimized sequential schedules.
