Characteristic value filtering

Some of the linear combinations will be well defined and others poorly defined. The latter may be eliminated in a filtering procedure, referred to in the literature under the names characteristic value filtering, eigenvalue filtering, and principal component analysis. If the parameter set is not homogeneous, but includes different types, relative scaling is important. Watkin (1994) recommends that the unit be scaled such that similar shifts in all parameters lead to similar changes in the error function S. 

This causes the control-to-output characteristic to add gain and phase above the location of this zero. This can be a problem with respect to the stability of the supply. Unfortunately, many capacitor manufacturers do not present the value of the ESR for their capacitors. Typically, the zero caused by the output filter capacitor falls in the following range ... 

Health Hazards Information - Recommended Personal Protective Equipment Filter respirator Symptoms Following Exposure No apparent toxicity General Treatment for Exposure None required Toxicity by Inhalation (ThresholdLimit Value) Data not available Short-Term Inhalation Limits Data not available Toxicity by Ingestion Data not available Late Toxicity Causes central Vapor (Gas) Irritant Characteristics Data not available Liquid or Solid Irritant Characteristics Data not available Odor Threshold Data not available. 

The spectral characteristics of the source, photocells, and the three filters are such that approximate I.C.I. tristimulus values may be calculated (5) and from these a specification in terms of luminous reflectance, dominant wave length, and purity can be obtained. Hardy has cautioned (3), however, that the usefulness of such an instrument as a tristimulus colorimeter depends upon the standardization and constancy of the spectral characteristics of the light source, cell, and filters. 

Because of the assumptions underlying its derivation, the Kozeny-Carman equation is not valid at void fractions greater than 0.7 to 0.8 (Billings and Wilder, op. cit.). In addition, in situ measurement of the void fraction of a dust layer on a filter fabric is extremely difficult and has seldom even been attempted. The structure of the layer is dependent on the character of the fabric surface as well as on tfie characteristics of the dust, whereas the application of Eq. (17-12) implicitly assumes that K2 is dependent only on the properties of the dust. A smooth fabric surface permits the dust to become closely packed, leading to a relatively high value of K2. If the surface is napped or has numerous extended fibrils, the dust cake formed will be more porous and have a lower value of K2 [Billings and Wilder, op. cit. Snyder and Pring, Ind. Eng. Chem., 47, 960 (1955) and K. T. Semrau, unpublished data, SRI International, Menlo Park, Calif., 1952-1953]. 

With analytical methods such as x-ray fluorescence (XRF), proton-induced x-ray emission (PIXE), and instrumental neutron activation analysis (INAA), many metals can be simultaneously analyzed without destroying the sample matrix. Of these, XRF and PEXE have good sensitivity and are frequently used to analyze nickel in environmental samples containing low levels of nickel such as rain, snow, and air (Hansson et al. 1988 Landsberger et al. 1983 Schroeder et al. 1987 Wiersema et al. 1984). The Texas Air Control Board, which uses XRF in its network of air monitors, reported a mean minimum detectable value of 6 ng nickel/m (Wiersema et al. 1984). A detection limit of 30 ng/L was obtained using PIXE with a nonselective preconcentration step (Hansson et al. 1988). In these techniques, the sample (e.g., air particulates collected on a filter) is irradiated with a source of x-ray photons or protons. The excited atoms emit their own characteristic energy spectrum, which is detected with an x-ray detector and multichannel analyzer. INAA and neutron activation analysis (NAA) with prior nickel separation and concentration have poor sensitivity and are rarely used (Schroeder et al. 1987 Stoeppler 1984). 

Fuel viscosity directly influences the pumping and atomization characteristics of the fuel. High-viscosity fuel may be difficult to efficiently pump through lines and filters unless it is heated. Also, high-viscosity fuels may not atomize and finely disperse when injected into the combustion chamber of an engine or into the firebox of a boiler. Incomplete atomization results in poor fuel efficiency and high hydrocarbon emission values. 

The cloud point test is one of the most commonly used methods to evaluate the low-temperature characteristics of distillate fuel. The cloud point temperature identifies the point when wax begins to form into crystals large enough to become visible in the fuel. At this temperature, wax can settle from fuel, deposit onto fuel filters, and interfere with the flow of fuel through small tubes and pipes. During cold weather months, distillate fuels with lower cloud point values are refined and blended to minimize the low-temperature problems associated with wax. 

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