Optimal threshold value

Estimation of the optimal threshold value is provided by minimizing a risk function, which depends on a priori probabilities of positive and negative... 

Table 2 shows intensity of lesions on histogram thresholding. The threshold values are compared between Gamma-law transformation and contfast stretching. Minimum 0.48 and 0.28 are the optimal threshold value for the Ganuna-law transformation and contrast stretching respectively. 

However, in order to reduce the EMG features to the smallest dimension feature vector, we can remove features that have the same pattern that we can observe that from the scatter plots. In more details, in case of time domain features that are calculated based on their amplitudes, we found that features in the secondary group have the same pattern. Thus only the best one in this group is recommended that is the WL feature with 128-ms window size function. In addition, for the time domain features that obtained the frequency information, WAMP has the better in cluster separability than ZC and SSC. The optimal threshold value of WAMP is about 25 mV and the optimal threshold value of ZC and SSC is 5 and 2 mV, respectively. Furthermore, the modified version of the MAV is worse than its traditional version. The whole features in frequency domain show poor class separability. The MDF obtains the larger RES index than the MNF. Nevertheless, the MAVS is the worst classifier performance compared to the other features. Its... 

In a situation as shown in Fig. 6.12a, with the optimal AT in at the threshold, then there is no pinch. On the other hand, in a situation as shown in Fig. 6.126, with the optimum above the threshold value, there is a demand for both utilities, and there is a pinch. 

Onc-Factor-at-a-Timc Optimization One approach to optimizing the quantitative method for vanadium described earlier is to select initial concentrations for ITiOz and 1T2S04 and measure the absorbance. We then increase or decrease the concentration of one reagent in steps, while the second reagent s concentration remains constant, until the absorbance decreases in value. The concentration of the second reagent is then adjusted until a decrease in absorbance is again observed. This process can be stopped after one cycle or repeated until the absorbance reaches a maximum value or exceeds an acceptable threshold value. 

R.J, Reynolds (RJR) maintained a nicotine optimization program, the goals of which were to define the optimum nicotine level in cigarette smoke required to maximize smoker satisfaction as well as a minimum or threshold value of nicotine required for satisfaction (Piehl 1978 R.J, Reynolds 1989),... 

Every (bio)catalyst can be characterized by the three basic dimensions of merit -activity, selectivity and stability - as characterized by turnover frequency (tof) (= l/kcat), enantiomeric ratio (E value) or purity (e.e.), and melting point (Tm) or deactivation rate constant (kd). The dimensions of merit important for determining, evaluating, or optimizing a process are (i) product yield, (ii) (bio)catalyst productivity, (iii) (bio)catalyst stability, and (iv) reactor productivity. The pertinent quantities are turnover number (TON) (= [S]/[E]) for (ii), total turnover number (TTN) (= mole product/mole catalyst) for (iii) and space-time yield [kg (L d) 11 for iv). Threshold values for good biocatalyst performance are kcat > 1 s 1, E > 100 or e.e. > 99%, TTN > 104-105, and s.t.y. > 0.1 kg (L d). ... 

This criterion may be used during a sequential optimization process (see chapter 5), leading to an acceptable result and to completion of the optimization process once the threshold value has been reached. Alternatively, it may be used to establish ranges of conditions in the parameter space for which the result will be acceptable. This latter approach has been followed by Glajch et al. [415], by Haddad et al. [424] and by Weyland et al. [425] and was referred to as resolution mapping by the former. Within the permitted area(s) secondary criteria are then required to select the optimum conditions. For example, the conditions at which the k value of the last peak (k is minimal while the minimum value for Rsexceeds 1 may be chosen as the optimum. Such a composite criterion can be described as... 

The convergence criterion in the alternating least-squares optimization is based on the comparison of the fit obtained in two consecutive iterations. When the relative difference in fit is below a threshold value, the optimization is finished. Sometimes a maximum number of iterative cycles is used as the stop criterion. This method is very flexible and can be adapted to very diverse real examples, as shown in Section 11.7. 

The final geometry was obtained when the calculated forces acting on the atoms and stress on the supercell became smaller than the threshold values. To evaluate the relative expansion of the lattice induced by the interstitial hydrogen, geometry optimizations of the pure metals were also employed. 

Pattern density dependency, arising for system-on-a-chip design, is characterized with CMP process optimization, slurry selectivity, over-polish extent and dummy pattern structure. For low density devices, dummy structure is desirable under conditions that the supportive dense structure could exceed a threshold value for reduction of CMP erosioa Physical and electrical evaluations help to identify the process window for different density structure. The characterized SIT process feature will allow designers to print circuit patterns more efficiently and cram more fiinctions onto the silicon. 

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