Rate adaption algorithms

Rate-adaptive pacing relies on the performance of sensors to provide input to the pacemaker regarding the appropriate heart rate for the activity. An algorithm then converts the sensor data to a specific heart rate response. Currently available sensor systems are so-called open loop because an external algorithm must be apphed to the sensor data to determine an appropriate heart rate. A closed-loop system would internally regulate the heart rate response based on the sensor data without requiring adjustment of an external algorithm. 

Special programming features such as rate adaption, magnet response algorithms to suppress atrial fibrillation, mode switch response, and premature ventricular contraction responses should be programmed off. ... 

Adaptive Control. An adaptive control strategy is one in which the controller characteristics, ie, the algorithm or the control parameters within it, are automatically adjusted for changes in the dynamic characteristics of the process itself (34). The incentives for an adaptive control strategy generally arise from two factors common in many process plants (/) the process and portions thereof are really nonlinear and (2) the process state, environment, and equipment s performance all vary over time. Because of these factors, the process gain and process time constants vary with process conditions, eg, flow rates and temperatures, and over time. Often such variations do not cause an unacceptable problem. In some instances, however, these variations do cause deterioration in control performance, and the controllers need to be retuned for the different conditions. 

FIGURE 6-6. Decision algorithm for long-term ventricular rate control with oral drug therapy for patients with paroxysmal or permanent atrial fibrillation, bpm, beats per minute CCB, calcium channel blocker (diltiazem or verapamil) HF, heart failure LV, left ventricular function LVEF, left ventricular ejection fraction. (Algorithm adapted with permission from Tisdale JE, Moser LR. Tachyarrhythmias. In Mueller BA, Bertch KE, Dunsworth TS, et al. (eds.) Pharmacotherapy Self-Assessment Program, 4th ed. Kansas City American College of Clinical Pharmacy 2001 ... 

This function is called numerous times from the Matlab ODE solver. In the example it is the ode45 which is the standard Runge-Kutta algorithm. ode45 requires as parameters the file name of the inner function, ode autocat. m, the vector of initial concentrations, cO, the rate constants, k, and the total amount of time for which the reaction should be modelled (20 time units in the example). The solver returns the vector t at which the concentrations were calculated and the concentrations themselves, the matrix C. Note that due to the adaptive step size control, the concentrations are computed at times t which are not predefined. 

Fig. 6. Chemotype enrichment rates using a variety of structure-based virtual screening algorithms and constraint settings for CDK2. DOCK search incorporating target class critical pharmacophore constraints denoted by the mark. Adapted from ref. 70.

Figure 13 Schematic of an automated system for producing nanoparticles with desired properties. The set up is an adaptation of the system shown in Figure 8. The emission spectra of the emergent nanoparticles recorded by the CCD are passed to an intelligent control algorithm that repeatedly updates the reaction temperature and the injection rates of the two reagents until particles with the desired properties are obtained.

Figure 20 Application of the dynamic simplex to the compensation of system-drift. An artificial example is considered here in which the temperature is ramped linearly with time and the simplex aims to compensate for the changes in the reaction temperature by modifying the flow rate accordingly. The plot compares the change in the peak wavelength when the flow rate is held fixed at its initial value of 12 llmin 1 and when it is adapted dynamically by the simplex algorithm. In the former case, the peak wavelength increases steadily with time due to the increasing temperature which increases the growth rate of the particles. In the latter case, the peak wavelength remains fairly close to its initial value of 508 nm.

We adapt our model for numerical simulation with the help of the Gillespie algorithm [10], which enables the system to jump to the next event via the calculation of the waiting time before any event will occur. Following the approach suggested by us [50], we stochastically model the system where several events can happen with different probabilities. Suppose that in some moment of time we have a set of N probable events with rates Ri, where the i — th event has the rate Ri and i = 1... N. Then by generating two uniformly distributed in (0, 1) random numbers RN and RN2, we estimate the time T after which the next event would occur as ... 

The limited space for this review precludes a proper review of the extensive literature on this subject. I will confine this section to results concerning setting selection stringency and mutation rate, as these issues are critical to any laboratory implementation of search. There are four relevant bodies of literature adaptive walk studies, theoretical immune system studies, formal spin glass studies, and theoretical genetic algorithm studies. Much of the adaptive walk literature has already been discussed above. 

Bak, T. The interaction of mutation rate, selection, and self-adaptation within a genetic algorithm In Parallel Problem Solving from Nature 2 Manner, R., Manderick, B., Eds. Elsevier Science Publishers B. V. ... 

To test the classification performance of the adaptive wavelet, the coefficients from each of the bands (at level 2) at initialization and at termination of the algorithm were used as inputs to the classifier. The results are summarized for both the training and test data in Table I. At initialization the coefficients in band(2,0) gave the best classification rates closely followed by band(2,l). At completion the classification performance of band(2,0) has further improved. 

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