系统性金融风险的监测和度量——基于中国金融体系的分析

作为动态变化的非线性复杂系统,金融体系风险始终处在不断产生和演变的状态中。基于这种认识,本文针对中国金融体系,结合国情提出了系统性金融风险的监测和度量方法。从分析结果来看,采用建立的监测体系可以及时进行风险预警,而采用主成分分析法则能实现风险准确度量,为政策制定提供参考依据。

我国系统性金融风险指数的度量与监测

国际金融危机以来，系统性金融风险的研究已经成为各国政府和学者关注的重要问题，系统性金融风险不仅会对一国的宏观经济稳定产生重要影响，更可能带来国家福利和社会财富的损失。党的十九大报告中明确提出，要健全金融监管体系，守住不发生系统性金融风险的底线．有鉴于此，本文从宏观层面选取了包含宏观经济、货币流动性、外部市场、资产泡沫四个层面的指标体系并运用主成分分析方法构建了分层面和我国宏观总体的系统性风险指数，以此来度量我国金融系统的总体风险水平，并给出了风险形成的原因。研究结果表明，2010年以来我国系统性金融风险一直呈现上升的趋势，目前已经达到较高水平。本文的研究结论对于监测和防范我国系统性金融风险，促进金融体系持续健康发展具有政策启示。

Air Pollution Concentration Approach to Potential Area Selection of the Air Quality Monitoring Station in Nakhon Ratchasima Municipality, Thailand

The purpose of the study is to generate traffic air information system） to determine a proper zone of AQMS （air analyzed were carbon monoxide （CO）, and nitrogen oxides （NOx） pollution map using mathematical model and GIS （geographic quality monitoring station） in municipality area. The pollutants which can be harmful to people living in the area. The three steps of mapping process were performed under the GIS environment using the existing vehicle emission rates and pollutant dispersion model. First, traffic volume, road network, and the emission rates of road segments varying with types of vehicle were collected from existing data. Second, the pollutant concentrations were calculated by use of CALINE4, a tool with Gaussian dispersion model. The model parameters include emission rate, wind directions and speeds, ambient temperature and observed pollutant concentration, and atmospheric stability during all seasons from the January 1, 2010 to May 31,2011 with regardless the rainy season. This resulted in concentrations at many receptor points along links of the road network. Third, distributions of pollution concentrations were generated by means of the spatial interpolation of those from receptors. The results of pollution raster-based maps are used for determining frequency of violence and combined pollution map. The resulting frequency of violence and intensity concentration will be further integrated to determine a potential area of AQMS. Finally, achieving pollution potential area of AQMS can be located as helpful basic data for efficient traffic and transportation planning.

Relationship between impact of dose distribution and energy of radioactive ray for metal contraceptive ring

Objective： We studied the relationship between impact of dose distribution and energy of radioactive ray in teletherapy with contraceptive ring. Methods： The SI Virtual Water Phantom was used to monitor medium of dose. PTW ionization chamber was used to detect the dose. AP-PA （10 cmx 10 cm） and SAD technique were adopted. Monitoring dose of different locations with different energy of radioactive ray in AP and PA respectively with and without contraceptive ring, repeated 5 times. Results： Dose of with contraceptive ring was lower than that of without contraceptive ring in AP 6 MV- X-ray （P 〈 0.05）, while in PA was no obvious difference （P 〉 0.05）. Dose of except for the center was higher than that of without contraceptive ring in AP and PA 15 MV-X-ray （P 〈 0.05）. For locations, doses of the upper, the left and right were higher than that of the center in AP 6 MV-X-ray （P 〈 0.05）, while in PA was lower （P 〉 0.05）. In AP and PA 15 MV-X-ray, doses of the upper, the left and right was higher than that of the center （P 〈 0.05）. Conclusion： Metallic contraceptive ring would effect the dose distribution of radioactive ray, but influence degree related to locations of contraceptive ring and energy of radioactive ray.

Abundance quantification by independent component analysis of hyperspectral imagery for oil spill coverage calculation

The estimation of oil spill coverage is an important part of monitoring of oil spills at sea.The spatial resolution of images collected by airborne hyper-spectral remote sensing limits both the detection of oil spills and the accuracy of estimates of their size.We consider at-sea oil spills with zonal distribution in this paper and improve the traditional independent component analysis algorithm.For each independent component we added two constraint conditions:non-negativity and constant sum.We use priority weighting by higher-order statistics,and then the spectral angle match method to overcome the order nondeterminacy.By these steps,endmembers can be extracted and abundance quantified simultaneously.To examine the coverage of a real oil spill and correct our estimate,a simulation experiment and a real experiment were designed using the algorithm described above.The result indicated that,for the simulation data,the abundance estimation error is 2.52% and minimum root mean square error of the reconstructed image is 0.030 6.We estimated the oil spill rate and area based on eight hyper-spectral remote sensing images collected by an airborne survey of Shandong Changdao in 2011.The total oil spill area was 0.224 km^2,and the oil spill rate was 22.89%.The method we demonstrate in this paper can be used for the automatic monitoring of oil spill coverage rates.It also allows the accurate estimation of the oil spill area.

Risk-based water quality decision-making under small data using Bayesian network

A knowledge-based network for Section Yidong Bridge,Dongyang River,one tributary of Qiantang River,Zhejiang Province,China,is established in order to model water quality in areas under small data.Then,based on normal transformation of variables with routine monitoring data and normal assumption of variables without routine monitoring data,a conditional linear Gaussian Bayesian network is constructed.A "two-constraint selection" procedure is proposed to estimate potential parameter values under small data.Among all potential parameter values,the ones that are most probable are selected as the "representatives".Finally,the risks of pollutant concentration exceeding national water quality standards are calculated and pollution reduction decisions for decision-making reference are proposed.The final results show that conditional linear Gaussian Bayesian network and "two-constraint selection" procedure are very useful in evaluating risks when there is limited data and can help managers to make sound decisions under small data.

Monitoring and Analysis of SPM of an Industrial Town of Punjab （India）

Suspended particulate matter （SPM） levels in ambient air were monitored at Mandi Gobindgarh, an industrial town of Punjab, India located on the National Highway-1 during November 2001 to March 2002 covering spring and winter seasons to check the variation of SPM and its constituents in the town. The maximum levels of SPM varied between 594 μg/m^3 to 620 μg/m^3 at selected monitoring sites while the minimum levels varied between 209μg/m^3 to 220 μg/m^3. These values were observed always above the National Ambient Air Quality Standards （NAAQS） set by the State regulatory body. Major sources of SPM were identified as the industrial activity and traffic plying on the national highway. Collected SPM samples were further analyzed for the ignitable matter as loss on ignition （LOI） and organic tarry matter （OTM） content. Ignitable component of the SPM constituted about 45% and tarry matter in the ambient air was about 12%. Effects of meteorological parameters like temperature, wind direction and wind speed on SPM levels are discussed.

Monitoring Media Velocity Variations with Coda Wave Interferometry

Multiply scattered waves are sensitive to media changes owing to the effect of repeated sampling,superposition and amplification. Based on this characteristic,small-medium changes could be detected by using coda wave interferometry. In recent years,coda wave interferometry has been widely used in estimating velocity variation with high precision in areas such as seismology and non-destructive testing. This paper systematically presents the principle and research status of coda wave interferometry,and especial focus is placed on the research of media velocity variations by using repeating earthquakes,artificial sources,and ambient noise. Applications of coda wave interferometry can contribute to the more subtle understanding of dynamic evolution process in the medium.

Evaluation of Data Replacement Strategies for CASTNET Dry Deposition Modeling

The U.S. EPA （Environmental Protection Agency） established the CASTNET （Clean Air Status and Trends Network） and its predecessor, the NDDN （national dry deposition network）, as national air quality and meteorological monitoring networks. Both CASTNET and NDDN were designed to measure concentrations of sulfur and nitrogen gases and particles. Both networks also estimate dry deposition using an inferential model. The design was based on the concept that atmospheric dry deposition flux could be estimated as the product of a measured air pollutant concentration and a modeled deposition velocity （Vd）. The MLM （multi-layer model）, the computer model used to simulate dry deposition, requires information on meteorological conditions and vegetative cover as model input. The MLM calculates hourly Fa for each pollutant, but any missing meteorological data for an hour renders Vd missing for that hour. Because of percent completeness requirements for aggregating data for long-term estimates, annual deposition rates for some sites are not always available primarily because of missing or invalid meteorological input data. In this work, three methods for replacing missing on-site measurements are investigated. These include （1） using historical values of deposition velocity or （2） historical meteorological measurements from the site being modeled or （3） current meteorological data from nearby sites to substitute for missing inputs and thereby improve data completeness for the network＇s dry deposition estimates. Results for a CASTNET site used to test the methods show promise for using historical measurements of weekly average meteorological parameters.

Transformer Partial Discharge Monitoring Based on Optical Fiber Sensing

The power transformer is the most important equipment of the high voltage power grid, however, some traditional methods of online partial discharge monitoring have some limitations. Based on many advantages of the optical fiber sensing technology, we have done some research on fiber optics Fabry-Perot （FP） sensing which can be useful for the transformer on online partial discharge monitoring. This research aimed at improving the reliability of power system safety monitoring. We have done some work as follows： designing a set for fiber optics FP sensor preparation, according to the fabrication procedure strictly making out the sensors, building a reasonable signal demodulation system for fiber optics FP sensing, doing a preliminary analysis about online partial discharge signal monitoring, including the research on different discharge intensities with the same measuring distance and different measuring distances with the same discharge intensity, and then making a detailed analysis of the experimental results.

Inner wall running state monitoring for the main pipe of nuclear power

The heat conduction equation is solved in this paper under specific boundary conditions.The coefficients of the obtained distribution equation are simplified with the piecewise integral method.Then the associated model for the cylindrical thermal equipment is established.The relationship of the surface temperatures,the material properties and the inner wall state of the cylindrical thermal equipment is described in the associated model.This model is applied to the inner wall running state monitoring of the main pipe.A multi-channel distributed optical fiber temperature measurement system is designed to acquire the external surface temperatures of the main pipe.Then the associated model can be used to analyze the surface temperature data of the main pipe.The location and the physical dimension of the inner wall defect can be got.Therefore,the inner wall defect monitoring of the main pipe can be realized.The feasibility of this method is verified by experiment.This method also provides a theoretical basis for the real-time monitoring of the main pipe’s internal state.