土壤氡测量在马家滩—新上海庙地区砂岩型铀矿勘查中的应用

在鄂尔多斯盆地马家滩—新上海庙地区进行了土壤氡浓度测量的网度试验研究,其结果显示在区域土壤氡测量中,测线线距采用2～3km,测量点距为200～400m,可对盆地进行快速评价,同时提出了测区北部苏家井—东湾—鸳鸯湖一带、中南部石槽村—张家庙子—马家滩一带、中西部碎石井(满闸坑—猫头梁)一带可作为重点成矿区进一步开展研究工作,为该盆地成矿区预测和砂岩型铀矿找矿提供依据。

Wavelet Analytical Forecasting Method of Water Consumption

A new method of short-term forecasting for water consumption in municipal supply water networks based on wavelet transformation is introduced. By wavelet decomposing commonly used in the signal field, water consumption per hour is decomposed into many series. Trend item, cycle item and random item are separated from the original time series in this way.Then by analyzing, building a model, forecasting every series and composing the results, the forecasting value of the original consumption is received. Simulation results show that this forecasting method is faster and more accurate, of which the error is less than 2%, indicating that the wavelet analytical method is practicable.

Soft Sensor for Ammonia Concentration at the Ammonia Converter Outlet Based on an Improved Group Search Optimization and BP Neural Network

The ammonia synthesis reactor is the core unit in the whole ammonia synthesis production. The ammonia concentration at the ammonia converter outlet is a significant process variable, which reflects directly the production efficiency. However, it is hard to be measured reliably online in real applications. In this paper, a soft sensor based on BP neural network (BPNN) is applied to estimate the ammonia concentration. A modified group search optimization with nearest neighborhood (GSO-NH) is proposed to optimize the weights and thresholds of BPNN. GSO-NH is integrated with BPNN to build a soft sensor model. Finally, the soft sensor model based on BPNN and GSO-NH (GSO-NH-NN) is used to infer the outlet ammonia concentration in a real-world application. Three other modeling methods are applied for comparison with GSO-NH-NN. The results show that the soft sensor based on GSO-NH-NN has a good prediction performance with high accuracy. Moreover, the GSO-NH-NN also provides good generalization ability to other modeling problems in ammonia synthesis production.

Evaluation of Latest TMPA and CMORPH Precipitation Products with Independent Rain Gauge Observation Networks over High-latitude and Low-latitude Basins in China

The Tropical Rainfall Measuring Mission （TRMM） Multi-satellite Precipitation Analysis （TMPA） and National Oceanic and Atmospheric Administration （NOAA） Climate Prediction Center （CPC） morphing technique （CMORPH） are two important multi-satellite precipitation products in TRMM-era and perform important functions in GPM-era. Both TMPA and CMORPH systems simultaneously upgraded their retrieval algorithms and released their latest version of precipitation data in 2013. In this study, the latest TMPA and CMORPH products （i.e., Version-7 real-time TMPA （T-rt） and gauge-adjusted TMPA （T-adj）, and Version- 1.0 real-time CMORPH （C-rt） and Version-l.0 gauge-adjusted CMORPH （C-adj）） are evaluated and intercompared by using independent rain gauge observations for a 12-year （2000--2011） period over two typical basins in China with different geographical and climate conditions. Results indicate that all TMPA and CMORPH products tend to overestimate precipitation for the high-latitude semiarid Laoha River Basin and underestimate it for the low-latitude humid Mishui Basin. Overall, the satellite precipitation products exhibit superior performance over Mishui Basin than that over Laoha River Basin. The C-adj presents the best performance over the high-latitude Laoha River Basin, whereas T-adj showed the best performance over the low-latitude Mishui Basin. The two gauge-adjusted products demonstrate potential in water resource management. However, the accuracy of two real-time satellite precipitation products demonstrates large variability in the two validation basins. The C-rt reaches a similar accuracy level with the gauge-adjusted satellite precipitation products in the high-latitude Laoha River Basin, and T-rt performs well in the low-latitude Mishui Basin. The study also reveals that all satellite precipitation products obviously overestimate light rain amounts and events over Laoha River Basin, whereas they underestimate the amount and events over Mishui Basin. The findings of the precision characteristics associated with the latest TMPA and CMORPH precipitation products at different basins will offer satellite pre- cipitation users an enhanced understanding of the applicability of the latest TMPA and CMORPH for water resource management, hydrologic process simulation, and hydrometeorological disaster prediction in other similar regions in China. The findings will also be useful for IMERG algorithm development and update in GPM-era.

HSCP:a hardware and software combined platform to improve precision in active network measurement

In this paper,an active network measurement platform is proposed which is a combination of hardware and software.Its innovation lies in the high performance of hardware combined with features that the software is easy to program,which retains software flexibility at the same time.By improving the precision of packet timestamp programmable hardware equipment,it provides packet sending control more accurately and supports the microsecond packet interval.We have implemented a model on the NetMagic platform,and done some experiments to analyze the accuracy difference of the user,the kernel and hardware timestamp.

GPS Rapid Static and Kinematic Positioning Based on GPS Active Network

This paper presents a data processing strategy for GPS kinematic positioning by using a GPS active network to model the GPS errors in double difference observable.Firstly,the double difference residuals are estimated between the reference stations in the active network.Then the errors at a user station are predicted as the network corrections to user measurements,based on the location of the user.Finally conventional kinematic positioning algorithms can be applied to determine the position of the user station.As an example,continuous 24_hour GPS data in March 2001 has been processed by this method.It clearly demonstrates that,after applying these corrections to a user within the network,both the success rate for ambiguity resolution and the positioning accuracy have been significantly improved.

Fine Measurements and Analysis of Temperature Gradients in the Wells of the Jinsha River Groundwater Observational Network

Fine measurements have been conducted to temperatures and their gradients of six wells of the Jinsha River Groundwater Observational Network.The results show that the influence depths of sun radiation heat are 50m to 125m,average temperature gradients in the wells range from 0.11 to 2.81℃/hm and most are 1～2℃/hm,and the temperature gradients on varied depth sections of one well are highly changeable.Lithology of strata and their integrity,particularly high-angle crashed fault zones,have imposed major effects on the influence depths of sun radiation heat and temperature gradients of the wells.The micro dynamic characteristics of water temperature,such as coseismic effects,tidal effects and anomalies of the wells prior to earthquakes,probably depend,to a large degree,on the temperature gradients of the depths at which the water temperature sensors are settled.

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.

Road Density Analysis Based on Skeleton Partitioning for Road Generalization

This paper proposes an algorithm for road density analysis based on skeleton partitioning. Road density provides metric and statistical information about overall road distribution at the macro level. Existing measurements of road density based on grid method, fractal geometry and mesh density are reviewed, and a new method for computing road density based on skeleton partitioning is proposed. Experiments illustrate that road density based on skeleton partitioning may reveal the overall road distribution. The proposed measurement is further tested against road maps at 1:10k scale and their generalized version at 1:50k scale. By comparing the deletion percentage within different density interval, a road density threshold can be found, which indicate the need for further operations during generalization. Proposed road density may be used to examine the quality of road generalization, to explore the variation of road network through temporal and spatial changes, and it also has future usage in urban planning, transportation and estates evaluation practice.

Development of a Concentration Measurement Technique for Steady State Solid-liquid Mixing using a Neural Network

A measurement technique that can measure the concentration of the solid particles in liquid flow was developed.The measurement system consists of a color camera and three LCD displays.The solid particles were put at the bottom of a cylindrical mixing tank in which JetA1 oil was filled.Transient mixing of the solid particles was performed by rotating a propeller type agitator with three different rotation speed(500,600,700 r/min).Mixing state was visualized by the LCD displays and a color camcorder.The color intensity of the glass particles changes with their concentration.The color information was decoded into three principle colors R,G,and B so that,the calibration curve of color-to-concentration was performed using these information.A neural network was used for this calibration.The transient concentration field of the solid particles was quantitatively visualized.