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Poor information means incomplete and insufficient information, such as small sample and unknown distribution. For point estimation under the condition of poor information, the statistical methods relied on large sample sizes and known distributions may become ineffective. For this end, a fusion method is proposed. The fusion method develops five methods, three concepts, and one rule. The five methods include the rolling mean method, the membership function method, the maximum membership grade method, the moving bootstrap method, and the arithmetic mean method. The three concepts comprise the solution set on the estimated true value, the fusion series, and the final estimated true value. The rule is the range rule. The results of the Monte Carlo simulation and of the experimental investigation on information of the quality evaluation for the tapered roller bearing indicate that the fusion method allows the number of the data to be little and the distribution to be unknown, having the reliable estimated result

In this paper we introduce a hierarchical region decomposition algorithm for triangle meshes segmentation based on quadric surface fitting. Initially, the whole mesh represents a single cluster. At every iteration, the region with largest error is divided and Lloyd iteration clustering is executed following, which make sure the segmentation has the minimum error in each hierarchical level. An error control strategy is used to get finally result automatically. Besides, users also can interpose this process by specifying the segmentation level. Each segmentation region after this process has a best fitting proxy of quadric surface. We propose an improved L 2,1 distance into a hybrid error, which is used in least square fit of quadric function. By comparing with the state-of-theart methods, our method is testified to be insensitive to noise. Model optimization and hole filling based on our algorithm are demonstrated in the last.

Point estimation is an important issue in data processing. Classical statistics is taken into account to assess mainly the true value of a population under the condition of large sample sizes and known probability distributions. If sample sizes are small and probability distributions are unknown, many statistical methods may become ineffective. For this end, a fusion method based on the information poor theory is proposed. Synthetically considering the characteristics of the various methods and effectively depicting the true value from different aspects, the fusion method is able to obtain the final estimated true value used as the most appropriate representative of the true value of a population. The results of the Monte Carlo simulation and the experimental investigation indicate that the fusion method allows the number of the data to be little and the probability distribution to be unknown