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The system lambda is the marginal cost of production in $ per MWH. Economists have strict de nitions for marginal costs. In our case, the marginal cost refers to the cost in $ per MWH of the most expensive unit that is dispatched in a least-cost dispatch. For the example in the previous section, the system lambda during hour 11 is the variable cost of the Pegasus unit, $57.20 per MWH. The relationship between the system lambda and the least-cost dispatch is that all resources that operate at a cost less than the system lambda are dispatched and all units that operate at a cost greater than the system lambda are off line. A unit with a cost equal to the system lambda is dispatched to the level required to meet load. Suppose that a system lambda is known. With the above relationship, the leastcost dispatch can be set. In the example from the previous section, if we start with the system lambda for hour 11 at $57.20 per MWH, we know to dispatch all units with a production cost of less than $57.20 per MWH. We then know to balance the remaining load with Pegasus.

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range of available gray levels from black to white Given that there is adequate illumination, it is usually desirable to utilize the full range of the camera and make minor adjustments for brightness and contrast later (if necessary) using an image processor If the camera s signal is not adjusted properly or spans a limited range of amplitudes, it may not be possible to adjust the gray-level range subsequently using the processor Increasing the gain or contrast control amplifies the video signal (multiplies the signal by a constant) and brings the bright features close to the white or saturated value on the monitor Because the signal is amplified by multiplication, the voltage range corresponding to the black and white end points in the image is increased For example, a signal ranging from 0 to 10 multiplied by 10 becomes 0 to 100, and the voltage range is increased 10-fold.

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The Kalman lter maintains two types of variables: 1 Estimated State Vector The components of the estimated state vector include the following: (a) The variables of interest (ie, what we want or need to know, such as position and velocity) (b) ``Nuisance variables'' that are of no intrinsic interest but may be necessary to the estimation process These nuisance variables may include, for example, the selective availability errors of the GPS satellites We generally do not wish to know their values but may be obliged to calculate them to improve the receiver estimate of position (c) The Kalman lter state variables for a speci c application must include all those system dynamic variables that are measurable by the sensors used in that application For example, a Kalman lter for a system containing accelerometers and rate gyroscopes must contain acceleration and rotation rate components to which these instruments respond.

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A sketch showing the effects of gain and offset adjustments on the PMTs of a confocal microscope is shown in Figure 12-12 Offset The offset or black level is a brightness control that is used to set dark features in the image close to the black level on the monitor Offset works differently from gain In this adjustment a positive or negative voltage is added to the image signal to adjust the amplitude of the darkest features in an image to a voltage corresponding to black Offset changes the amplitude of the entire voltage signal, but since it is added to the signal, it does not change the voltage difference between high- and low-voltage amplitudes contained in the original signal.

The acceleration and angular rate components do not have to be those along the sensor input axes, however The Kalman lter state variables could be the components along locally level earth- xed coordinates, even though the sensors measure components in vehicle-body- xed coordinates In similar fashion, the Kalman lter state variables for GPS-only navigation must contain the position coordinates of the receiver antenna, but these could be geodetic latitude, longitude, and altitude with respect to a reference ellipsoid or geocentric latitude, longitude, and altitude with respect to a reference sphere, or ECEF Cartesian coordinates, or ECI coordinates, or any equivalent coordinates 2 A Covariance Matrix: a Measure of Estimation Uncertainty The equations used to propagate the covariance matrix (collectively called the Riccati equation) model and manage uncertainty, taking into account how sensor noise and dynamic uncertainty contribute to uncertainty about the estimated system state.

By maintaining an estimate of its own estimation uncertainty and the relative uncertainty in the various sensor outputs, the Kalman lter is able to combine all sensor information ``optimally,'' in the sense that the resulting estimate minimizes any quadratic loss function of estimation error, including the mean-squared value of any linear combination of state estimation errors The Kalman gain is the optimal.

For example, a signal ranging from 0 to 10 volts modified with an offset of 5 volts becomes 5 to 15 volts, but the difference remains 10 volts as it was originally Both gain and offset affect the contrast of the video image Video contrast CV is defined as CV A IS IB /(IB IV),.

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java itext pdf remove text

java itext pdf remove text

java itext pdf remove text

java itext pdf remove text

java itext pdf remove text

java itext pdf remove text

java itext pdf remove text

java itext pdf remove text

java itext pdf remove text

java itext pdf remove text

java itext pdf remove text

java itext pdf remove text

java itext pdf remove text