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number of defects, and assist in determining which process variables should be carefully controlled during manufacturing to prevent high defect levels and erratic process performance. An Optimization Experiment In a characterization experiment, we are interested in determining which factors affect the response. A logical next step is to determine the region in the important factors that leads to an optimum response. For example, if the response is cost, we will look for a region of minimum cost. As an illustration, suppose that the yield of a chemical process is in uenced by the operating temperature and the reaction time. We are currently operating the process at 155 F and 1.7 hours of reaction time, and the current process yield is around 75%. Figure S14-2 shows a view of the time temperature space from above. In this graph we have connected points of constant yield with lines. These lines are yield contours, and we have shown the contours at 60, 70, 80, 90, and 95% yield. To locate the optimum, we might begin with a factorial experiment such as we described below, with the two factors, time and temperature,

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180 Temperature ( F)

170 82% 160 75% 150

that is, Ei is 0 except for bit i, which is 1. Then we have Ei = 231-i. Denote the bits of Q 2 as Q2=

Figure S14-2 Contour plot of yield as a function of reaction time and reaction temperature, illustrating an optimization experiment.

56% 140

(qo,qi,q2,...,qx).

1.5 Time (hr)

14-3

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run at two levels each at 10 F and 0.5 hours above and below the current operating conditions. This two-factor factorial design is shown in Fig. S14-2. The average responses observed at the four points in the experiment (145 F, 1.2 hours; 145 F, 2.2 hours; 165 F, 1.2 hours; and 165 F, 2.2 hours) indicate that we should move in the general direction of increased temperature and lower reaction time to increase yield. A few additional runs could be performed in this direction to locate the region of maximum yield. A Product Design Example We can also use experimental design in the development of new products. For example, suppose that a group of engineers are designing a door hinge for an automobile. The product characteristic is the check effort, or the holding ability, of the latch that prevents the door from swinging closed when the vehicle is parked on a hill. The check mechanism consists of a leaf spring and a roller. When the door is opened, the roller travels through an arc causing the leaf spring to be compressed. To close the door, the spring must be forced aside, and this creates the check effort. The engineering team thinks that check effort is a function of the following factors: 1. 2. 3. 4. 5. Roller travel distance Spring height from pivot to base Horizontal distance from pivot to spring Free height of the reinforcement spring Free height of the main spring

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From (5.54), we see that, Q2 is known, and all other terms on the right-handside of (5.55) arc known, so we have determined W2. Then letting X z = W2, we obtain Qz which satisfies the required output conditions at step 2. After repeating a similar process for each of steps 0 through 15, we will ) have determined a message Mo = ( X o ,X I ,. . . , X I S for which all of the output conditions in these steps hold. We could then simply test the remaining conditions and if all hold, we have found a collision. If any condition beyond step 15 does not hold, then we could select a new random A& and repeat the entire process. Since each condition is expected to hold at random with a probability of about 1 / 2 , this yields an attack with a work factor on the order of 2': where c is the number of conditions in steps 16 through 63. Using only single-step modificat,ions provides a feasible shortcut attack. However, it is possible to fiirt,her reduce the work factor by using the multist>epmodification technique described in the next section.

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Good morning, I have the 128 barcode font in my Excel. I thought that the formula to convert the string went something like ="'*&A2&*'".

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