Internal Sorting Methods

Internal sorting methods are possible when all of the items to be accessed fit in a computer\u27s high-speed internal memory. There are quite a few (Knuth\u27s third volume of The Art of Computer Programming covers 14 in total) but I will go over the four I found to be most versatile and useful. Each algorithm that I cover has a specific benefit that merits its\u27 use in computer science. Some have faster run times (Heapsort), simpler code (Straight Insertion), run with a smaller memory space (Quicksort), or work well with large sets (Radix Sorting). Different sorting tasks lead users to unique sorting algorithms and so we have many variations of organization systems

Unbranched Catacondensed Polygonal Systems Containing Hexagons and Tetragons

An algebraic solution for the isomer numbers of unbranched a-4- catafusenes is presented. An a-4-catafusene is a catacondensed polygonal system consisting of exactly o: tetragons each and otherwise only hexagons. This analysis, which makes use of certain triangular matrices including the Pascal triangle, is a continuation of a previous work on di-4-catafusenes. By serendipity, the problem was reversed in the sence that the systems were considered as possessing \u277 hexagons each and otherwise only tetragons. Under this viewpoint the enumeration problem could be solved more directly and led to explicit formulas. Finally, the resuIts are applied to catafusenes as a special case

Unbranched Catacondensed Polygonal Systems Containing Hexagons and Tetragons

An algebraic solution for the isomer numbers of unbranched a-4- catafusenes is presented. An a-4-catafusene is a catacondensed polygonal system consisting of exactly o: tetragons each and otherwise only hexagons. This analysis, which makes use of certain triangular matrices including the Pascal triangle, is a continuation of a previous work on di-4-catafusenes. By serendipity, the problem was reversed in the sence that the systems were considered as possessing \u277 hexagons each and otherwise only tetragons. Under this viewpoint the enumeration problem could be solved more directly and led to explicit formulas. Finally, the resuIts are applied to catafusenes as a special case

The development of in-process surface roughness prediction systems in turning operation using accelerometer

Three in-process surface roughness prediction (ISRP) systems using linear multiple regression, fuzzy logic, and fuzzy nets algorisms, respectively, were developed to allow the prediction of real time surface roughness of a work piece on a turning operation. The surface roughness is predicted from feed rate, spindle speed, depth of cut, and machining vibration that is detected and collected by an accelerometer.;Two groups of data were collected for two cutters with nose radii of 0.016 and 0.031 inches, respective. A total of 162 training data sets and 54 testing data sets for each cutter were applied to train and test the system. While the multiple-regression-based system applied the linear relationships of the dependent variables and the dependent variable for the prediction, the fuzzy-logic-based and the fuzzy-nets-based systems relied on fuzzy theory for the prediction. The fuzzy rule banks employed in the fuzzy-logic-based system was generated with expert\u27s experiences as well as observation results from the experiments. Whereas, the rule banks employed in the fuzz-nets-system were rule banks self-extracted from the training data by the fuzzy-nets self-learning algorithm.;The predicted surface roughness values were compared with corresponding measured values. The average prediction accuracy with the three algorithms, linear multiple regression, fuzzy logic, and fuzzy nets algorisms, was 92.78%, 89.06%, and 95.70%, respectively. The use of the accelerometer was found valuable in increasing the prediction The Fuzzy-nets-based In-process Surface Roughness Prediction System was considered the best among the three tested systems. This conclusion relies on not only the best average prediction accuracy achieved, but also the self-learning ability of the fuzzy nets algorism