11514442
A test pattern ordering algorithm for diagnosis with truncated fail data
In this paper, we propose a test pattern ordering algorithm for fault diagnosis. Test pattern ordering is effective in situations where the fail log is truncated and contains a limited number of fail data. In such cases, higher diagnostic resolution can be achieved with the test set appropriately ordered. Test pattern ordering is independent of the diagnosis algorithm used. The higher resolution achieved by test pattern ordering is obtained at no additional cost once the test patterns have been appropriately ordered. Experimental results on two industrial designs are presented to demonstrate the effectiveness of the proposed method
21571419
N-distinguishing Tests for Enhanced Defect Diagnosis
Diagnostic ATPG has traditionally been used to generate test patterns that distinguish pairs of modeled faults. In this work, we investigate the use of n-distinguishing test sets, which distinguish pairs of single stuck-at faults n times, to enhance the probability of distinguishing unmodeled defects. The basis for the use of n-distinguishing test sets to enhance defect diagnosis is similar to that for using n-detection test sets to improve the detection of unmodeled defects. We use a heuristic to target a subset of fault pairs for n-distinguishing in order to improve the efficacy of the patterns generated for aiding diagnosis. Experimental results on the larger ISCAS benchmark circuits are presented to demonstrate the improvements in defect diagnostic resolution due to the use of n-distinguishing test sets. We use randomly selected resistive bridges to represent unmodeled defects. The experimental results also show that the coverage of unmodeled defects by n-distinguishing test sets is similar to that by n-detection test sets even though the number of n-distinguishing tests is typically smaller. This suggests the possibility of using n-distinguishing test sets in place of n-detection test sets in manufacturing test.
7335001
Pivotbrowser: a tag-space image searching prototype
We propose a novel iterative searching and refining prototype for tagged images. This prototype, named PivotBrowser, captures semantically similar tag sets in a structure called pivot. By constructing a pivot for a textual query, PivotBrowser first selects candidate images possibly relevant to the query. The tags contained in these candidate images are then selected in terms of their tag relevances to the pivot. The shortlisted tags are clustered and one of the tag clusters is used to select the results from the candidate images. Ranking of the images in each partition is based on their relevance to the tag cluster. With the guidance of the tag clusters presented, a user is able to perform searching and iterative query refinement.
17959493
A Fast Document Classification Algorithm Based on Improved KNN
A novel KNN classification algorithm combining model and evidence theory is proposed in this paper. The new method not only overcomes the main shortage of lazy learning in traditional KNN, but also takes the distances between samples to be recognized and samples in k-neighbors into account. At the same time the method resolves the unrecognizable cases of unknown samples. Applying the classification algorithm into the document recognition, experimental results show its satisfied recognition rate and fast categorization speed
39222179
A New ART Neural Networks for Remote Sensing Image Classification
A new ART2A-C algorithm based on fuzzy operators to cluster the remote sensing images and aerials is proposed in this paper. By combining two ART ANNs with higher performance, the traditional ART2A-C is developed with the fuzzy operators introduced in matching rule. Then the proposed method is applied to the classification and the new network is implemented as well as two other existed ARTs respectively. Experimental results show that the new method outperforms the traditional ones.
30047573
Self-adaptive Statistics Management for Efficient Query Processing
Consistently good performance required by mission-critical information systems has made it a pressing demand for self-tuning technologies in DBMSs. Automated Statistics management is an important step towards a self-tuning DBMS and plays a key role in improving the quality of execution plans generated by the optimizer, and hence leads to shorter query processing times. In this paper, we present SASM, a framework for Self-Adaptive Statistics Management where, using query feedback information, an appropriate set of histograms is recommended and refined, and through histogram refining and reconstruction, fixed amount of memory is dynamically distributed to histograms which are most useful to the current workload. Extensive experiments showed the effectiveness of our techniques.
195918627
The Design and Realization of E-Journals' Categorizing and Navigating System.
This article gives a detailed description of how to design and realize a network full-text e-journalstategorizing and navigating retrieval system considering the characteristics of electronic resources and introducing specialty concept.