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Once you ve speci ed the circle s center and radius, you test the output pixel location. If it falls inside the circle, you color it white. Otherwise, you color it black. The result is a white circle on a black background. This kernel ignores in fact, replaces the contents of the input image. As I demonstrated with the previous two examples, when you write pixel shaders, you have to think about their effects implicitly, which might take some getting used to. Rather than setting an image s scale or procedurally drawing a circle, you have to tell a program what to do with each pixel. The simple rules, when applied to every pixel, result in a consistent output. Some lters ask you to think in the same way. The convolution lter asks, How is the color of each pixel in uenced by its neighbors And the displacement map lter asks, How far away from the output pixel location should I sample the source image Of course, you can replicate these lters easily with the tools Pixel Bender provides to you. Now you ll go back to the inputs to a kernel. You ve seen shaders with zero and one image inputs. When would you use more image inputs How about building a shader to be used as a blend mode Blend modes determine how an image looks after being layered on top of another image. For example, the BlendMode.MULTIPLY mode multiplies the colors in the top image with the cumulative colors below it. A kernel that s going to be used as a blend mode should take two images and output a single image.

The control chart will only detect assignable causes. Management, operator, and engineering action will usually be necessary to eliminate the assignable cause. An action plan for responding to control chart signals is vital.

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In identifying and eliminating assignable causes, it is important to nd the underlying root cause of the problem and to attack it A cosmetic solution will not result in any real, long-term process improvement Developing an effective system for corrective action is an essential component of an effective SPC implementation We may also use the control chart as an estimating device That is, from a control chart that exhibits statistical control, we may estimate certain process parameters, such as the mean, standard deviation, and fraction nonconforming or fallout These estimates may then be used to determine the capability of the process to produce acceptable products Such process capability studies have considerable impact on many management decision problems that occur over the product cycle, including make-or-buy decisions, plant and process improvements that reduce process variability, and contractual agreements with customers or suppliers regarding product quality.

Once you have the ability to match patterns in text, replacing those patterns with desired text is a simple enough leap.

Control charts may be classi ed into two general types Many quality characteristics can be measured and expressed as numbers on some continuous scale of measurement In such cases, it is convenient to describe the quality characteristic with a measure of central tendency and a measure of variability Control charts for central tendency and variability are collectively called variables control charts The X chart is the most widely used chart for monitoring central tendency, whereas charts based on either the sample range or the sample standard deviation are used to control process variability Many quality characteristics are not measured on a continuous scale or even a quantitative scale In these cases, we may judge each unit of product as either conforming or nonconforming on the basis of whether or not it possesses certain attributes, or we may count the number of nonconformities (defects) appearing on a unit of product.

You might recognize this function, as well, from 6. You can use this method with a string pattern and a string replacement object to perform a basic substitution. Why did I write this book anyhow See Example 12-5. EXAMPLE 12-5

Control charts for such quality characteristics are called attributes control charts Control charts have had a long history of use in industry There are at least ve reasons for their popularity: Control charts are a proven technique for improving productivity A successful control chart program will reduce scrap and rework, which are the primary productivity killers in any operation If you reduce scrap and rework, productivity increases, cost decreases, and production capacity (measured in the number of good parts per hour) increases 2 Control charts are effective in defect prevention The control chart helps keep the process in control, which is consistent with the do it right the rst time philosophy It is never cheaper to sort out the good units from the bad later on than it is to build them correctly initially.

var quote:String = "I did it for the money and the ladies."; quote = quote.replace("money", "learning experience"); quote = quote.replace("ladies", "sleep deprivation"); trace(quote); //I did it for the learning experience and the sleep deprivation.

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