Digitized Processing Track Generation of a Computer Integrated Multi-Functional 5-Axis Laser Processing System

Based on the computer integrated and flexible laser processing system, an intelligent measuring sub-system was developed. A novel model has been built up to compensate the deviations of the main frame-structure, and a new 3-D laser tracker system is applied to adjust the accuracy of the system. To analyze the characteristic of all kind surfaces of automobile outer penal moulds and dies, classification of types of the surface、brim and ridge(or vale) area to be measured and processed has been established, resulting in one of the main processing functions of the laser processing system. According to different type of surfaces, a 2-D adaptive measuring method based on B?zier curve was developed; furthermore a 3-D adaptive measuring method based on Spline curve was also developed. According to the laser materials processing characteristics and data characteristics, necessary methods have been developed to generate processing tracks, they are explained in details. Measuring experiments and laser processing experiments were carried out to testify the above mentioned methods, which have been applied in the computer integrated and flexible laser processing system developed by the Institute of Mechanics, CAS.

The Intelligent Measuring Sub-System in the Computer Integrated and Flexible Laser Processing System

Based on the computer integrated and flexible laser processing system, develop the intelligent measuring sub-system. A novel model has been built to compensate the deviations of the main frame, a new-developed 3-D laser tracker system is applied to adjust the accuracy of the system. Analyzing the characteristic of all kinds of automobile dies, which is the main processing object of the laser processing system, classify the types of the surface and border needed to be measured and be processed. According to different types of surface and border, develop 2-D adaptive measuring method based on B?zier curve and 3-D adaptive measuring method based on spline curve. During the data processing, a new 3-D probe compensation method has been described in details. Some measuring experiments and laser processing experiments are carried out to testify the methods. All the methods have been applied in the computer integrated and flexible laser processing system invented by the Institute of Mechanics, CAS.

Randomness-efficient curve sampling

Curve samplers are sampling algorithms that proceed by viewing the domain as a vector space over a finite field, and randomly picking a low-degree curve in it as the sample. Curve samplers exhibit a nice property besides the sampling property: the restriction of low-degree polynomials over the domain to the sampled curve is still low-degree. This property is often used in combination with the sampling property and has found many applications, including PCP constructions, local decoding of codes, and algebraic PRG constructions.

The randomness complexity of curve samplers is a crucial parameter for its applications. It is known that (non-explicit) curve samplers using O(log N + log(1/δ)) random bits exist, where N is the domain size and δ is the confidence error. The question of explicitly constructing randomness-efficient curve samplers was first raised in [TU06] where they obtained curve samplers with near-optimal randomness complexity.

In this thesis, we present an explicit construction of low-degree curve samplers with optimal randomness complexity (up to a constant factor) that sample curves of degree (m log_q(1/δ))^O(1) in F_q^m. Our construction is a delicate combination of several components, including extractor machinery, limited independence, iterated sampling, and list-recoverable codes.

Estimation of the growth curve parameters in Macrobrachium rosenbergii

Growth is one of the most important characteristics of cultured species. The objective of this study was to determine the fitness of linear, log linear, polynomial, exponential and Logistic functions to the growth curves of Macrobrachium rosenbergii obtained by using weekly records of live weight, total length, head length, claw length, and last segment length from 20 to 192 days of age. The models were evaluated according to the coefficient of determination (R2), and error sum off square (ESS) and helps in formulating breeders in selective breeding programs. Twenty full-sib families consisting 400 PLs each were stocked in 20 different hapas and reared till 8 weeks after which a total of 1200 animals were transferred to earthen ponds and reared up to 192 days. The R2 values of the models ranged from 56 – 96 in case of overall body weight with logistic model being the highest. The R2 value for total length ranged from 62 to 90 with logistic model being the highest. In case of head length, the R2 value ranged between 55 and 95 with logistic model being the highest. The R2 value for claw length ranged from 44 to 94 with logistic model being the highest. For last segment length, R2 value ranged from 55 – 80 with polynomial model being the highest. However, the log linear model registered low ESS value followed by linear model for overall body weight while exponential model showed low ESS value followed by log linear model in case of head length. For total length the low ESS value was given by log linear model followed by logistic model and for claw length exponential model showed low ESS value followed by log linear model. In case of last segment length, linear model showed lowest ESS value followed by log linear model. Since, the model that shows highest R2 value with low ESS value is generally considered as the best fit model. Among the five models tested, logistic model, log linear model and linear models were found to be the best models for overall body weight, total length and head length respectively. For claw length and last segment length, log linear model was found to be the best model. These models can be used to predict growth rates in M. rosenbergii. However, further studies need to be conducted with more growth traits taken into consideration

Proposal and validation of methodologies for the categorisation of continuous variables in the development of prediction models

158 p.

The von Bertalanffy growth curve: when a good fit is not good enough

The von Bertalanffy growth function is used for length based analysis of growth and mortality patterns for management of fisheries. However, certain fish have growth patterns that the VBGF may not be able to describe adequately.e.g. the Acanthurus lineatus in Samoa. In such cases a two phase VBGF may be a useful approach.