New method of modeling and interpretation wave fields monitoring data in hierarchic medium

2015 ◽  
Author(s):  
O.A. Hachay ◽  
O.Y. Khachay ◽  
A.Y. Khachay
Keyword(s):  
New Method ◽  
Monitoring Data ◽  
Wave Fields

scholarly journals A new method of spatial filtering for Schlieren visualization of ultrasound wave fields

2010 ◽  
Vol 3 (1) ◽  
pp. 935-942 ◽  
Author(s):  
Carsten Unverzagt ◽  
Sergei Olfert ◽  
Bernd Henning
Keyword(s):  
Spatial Filtering ◽  
New Method ◽  
Ultrasound Wave ◽  
Wave Fields ◽  
Schlieren Visualization

Coded T-mark continuums: a graphical method for reporting rangeland monitoring data

2010 ◽  
Vol 32 (2) ◽  
pp. 145 ◽  
Author(s):  
Gary N. Bastin ◽  
John A. Ludwig ◽  
Kate Richardson
Keyword(s):  
Graphical Method ◽  
Reference Points ◽  
New Method ◽  
Monitoring Data ◽  
Multiple Indicators ◽  
Rangeland Monitoring ◽  
Rangeland Condition ◽  
Term Management ◽  
Management Effects

In this paper we describe a new method of graphically presenting rangeland monitoring data as coded time-mark continuums. This method aims to provide people with an interest in rangelands (stakeholders) with succinct information, which they need to assess rangeland condition and change. This new method graphs data for indicators of rangeland condition as time or T-marks along gradients or continuums. The ends of these continuums are reference points, which are values for indicators defining highly functional to very dysfunctional rangeland systems. The T-marks for an indicator along its continuum are also coded as to how changes relate to combinations of recent seasonal conditions and longer-term management effects. Codes are based on a two-way matrix combining ‘seasonal quality’ (e.g. rainfall in a specified period relative to the long-term record) and expected responses from land management (i.e. increase, decrease or no change relative to that predicted from seasonal quality). Monitoring data available in the Australian Collaborative Rangeland Information System were used to illustrate the use of coded T-mark continuums. We show succinctly how one indicator changed in two different rangeland regions and how multiple indicators changed within one region.

Separation of Point and Diffuse Loads of Pollutants Using Monitoring Data of Rivers

1993 ◽  
Vol 28 (3-5) ◽  
pp. 165-175 ◽  
Author(s):  
H. Behrendt
Keyword(s):  
Heavy Metals ◽  
Reasonable Agreement ◽  
River System ◽  
Point Sources ◽  
New Method ◽  
Monitoring Data ◽  
Diffuse Sources ◽  
Dissolved Nitrogen ◽  
Lead And Zinc ◽  
Cadmium Lead

The results of a new method for estimating point and diffuse loads of rivers from analysis of monitoring data are presented (immission approach). The estimated point source loads of dissolved nitrogen, total phosphorus, and the heavy metals, cadmium, lead and zinc were compared with the loads of existing inventories. The diffuse loads of these pollutants were compared with estimations calculated on the basis of area related loads of the main diffuse sources (emission approach). Reasonable agreement was obtained in these comparisons, thus demonstrating the utility of the new method as a tool for analyzing point sources and diffuse loads of pollutants to a river system from analysis of monitoring data.

A New Method for Forecasting of Non-Point Source Pollutant Load

2014 ◽  
Vol 955-959 ◽  
pp. 1167-1171
Author(s):  
Guang Qing Zeng ◽  
Wei Zhao ◽  
Biao Han ◽  
Xiu Qin Bu ◽  
Guo Shao Su
Keyword(s):  
Point Source ◽  
Influencing Factors ◽  
Small Sample ◽  
New Method ◽  
Monitoring Data ◽  
Support Vector ◽  
Pollutant Load ◽  
Training Samples ◽  
Highly Nonlinear ◽  
Gp Model

Gaussian process (GP) is a newly developed machine learning technology based on statistical theoretical fundamentals, which has successful application in the field of solving for highly nonlinear problems. Conventional methods for forecasting of non-point source pollutant load often meet great difficulty since relationship between pollutant load and its influencing factors is highly complicated nonlinear. A new method based on GP is proposed for forecasting of non-point source pollutant load. The monitoring data of a certain river since 1976 to 1990 are preformed to obtain the training samples and test samples. Nonlinear mapping relationship between non-point source pollutant load and its influencing factors can be constructed by GP learning with the training samples. The monitoring data of a certain river since 1991 to 1993 are preformed to testify the effects of the method above. The results of case studies show that the method is feasible, effective and simple to implement for forecasting of non-point source pollutant load. It has merits of self-adaptive parameters determination and better capacity for solving nonlinear small sample problems comparing with the artificial neural networks method and Support Vector Machine method. The good performance of GP model makes it very attractive for a wide range of application in environmental engineering.

Selective Sampling and Orientation of Non-Homogeneous Tissues

1968 ◽  
Vol 26 ◽  
pp. 98-99
Author(s):  
C. C. Clawson ◽  
L. W. Anderson ◽  
R. A. Good
Keyword(s):  
Electron Microscopy ◽  
Electron Microscope ◽  
Light Microscopy ◽  
Random Sampling ◽  
New Method ◽  
Microscope Examination ◽  
Small Areas ◽  
Selective Sampling ◽  
Large Numbers ◽  
Specific Orientation

Investigations which require electron microscope examination of a few specific areas of non-homogeneous tissues make random sampling of small blocks an inefficient and unrewarding procedure. Therefore, several investigators have devised methods which allow obtaining sample blocks for electron microscopy from region of tissue previously identified by light microscopy of present here techniques which make possible: 1) sampling tissue for electron microscopy from selected areas previously identified by light microscopy of relatively large pieces of tissue; 2) dehydration and embedding large numbers of individually identified blocks while keeping each one separate; 3) a new method of maintaining specific orientation of blocks during embedding; 4) special light microscopic staining or fluorescent procedures and electron microscopy on immediately adjacent small areas of tissue.

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