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A diagrammatic representation of the process is presented in Figure 2. The target finding algorithm was developed to loosely mimic how human radar operators appeared to detect targets, and raw data is saved for each blip so that physically realistic target detection models can be applied to them.
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Sensor and actuator faults are detected and diagnosed in sufficient time that the fault detection model can be applied on-line as a reliable supervisor of the operation of an unattended deep-trough hydroponic system.
An early fraud detection model can be obtained by constructing decision trees or by instance-based learning.
In contrast to visual methods for alteration detection, mathematical models can be used to automatically predict lists of alterations based on certain mathematical assumptions about the data.
Beside the detection aspect, the HMM models can be analyzed to gain insight into LTR structure.
Models can be another issue.
Exhaustively sieved models can be displayed interactively.
QTL detection with such a model can be more powerful and more precise than a single trait detection [ 28].
We set out to examine to what extent the coiled-coil region influences homology detection, and subsequently test whether the CC model can be used to improve homology detection of coiled-coil proteins.
Collision detection between one spatula model and one brain model can be computed in parallel.
In addition, the model can be used for fault detection and diagnosis during operation.
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