Through discussion of the time-distance curve characteristics of the direct waveand from the front,side and rear of the reflection waves of the seismic reflection methodfor advanced exploration in mines,and analysis o...Through discussion of the time-distance curve characteristics of the direct waveand from the front,side and rear of the reflection waves of the seismic reflection methodfor advanced exploration in mines,and analysis of several major interference waves inmines,the differences in time-distance curve,frequency,apparent velocity between theeffective wave and interference wave in the seismic reflection method for advanced explorationare obtained.According to the differences,the effective wave is extracted andthe interference wave is filtered and the system's precision and accuracy is improved.展开更多
Intravascular ultrasound can provide clear real-time cross-sectional images,including lumen and plaque.In practice,to identify the plaques tissues in different pathological changes is very important.However,the graysc...Intravascular ultrasound can provide clear real-time cross-sectional images,including lumen and plaque.In practice,to identify the plaques tissues in different pathological changes is very important.However,the grayscale differences of them are not so apparent.In this paper a new textural characteristic space vector was formed by the combination of Co-occurrence Matrix and fraction methods.The vector was projected to the new characteristic space after multiplied by a projective matrix which can best classify those plaques according to the Fisher linear discriminant.Then the classification was completed in the new vector space.Experimental results found that the veracity of this classification could reach up to 88%,which would be an accessorial tool for doctors to identify each plaque.展开更多
In order to achieve failure prediction without manual intervention for distributed systems, a novel failure feature analysis and extraction approach to automate failure prediction is proposed. Compared with the tradit...In order to achieve failure prediction without manual intervention for distributed systems, a novel failure feature analysis and extraction approach to automate failure prediction is proposed. Compared with the traditional methods which focus on building heuristic rules or models, the autonomic prediction approach analyzes the nonlinear correlation of failure features by recognizing failure patterns. Failure data are sorted according to the nonlinear correlation and failure signature is proposed for autonomic prediction. In addition, the Manifold Learning algorithm named supervised locally linear embedding is applied to achieve feature extraction. Based on the runtime monitoring of failure metrics, the experimental results indicate that the proposed method has better performance in terms of both correlation recognition precision and feature extraction quality and thus it can be used to design efficient autonomic failure prediction for distributed systems.展开更多
In this paper we revise the moment theory for pattern recognition designed, to extract patterns from the noisy character datas, and develop unconstrained handwritten. Amazigh character recognition method based upon or...In this paper we revise the moment theory for pattern recognition designed, to extract patterns from the noisy character datas, and develop unconstrained handwritten. Amazigh character recognition method based upon orthogonal moments and neural networks classifier. We argue that, given the natural flexibility of neural network models and the extent of parallel processing that they allow, our algorithm is a step forward in character recognition. More importantly, following the approach proposed, we apply our system to two different databases, to examine the ability to recognize patterns under noise. We discover overwhelming support for different style of writing. Moreover, this basic conclusion appears to remain valid across different levels of smoothing and insensitive to the nuances of character patterns. Experiments tested the effect of set size on recognition accuracy which can reach 97.46%. The novelty of the proposed method is independence of size, slant, orientation, and translation. The performance of the proposed method is experimentally evaluated and the promising results and findings are presented. Our method is compared to K-NN （k-nearest neighbors） classifier algorithm; results show performances of our method.展开更多
基金Supported by the National Natural Science Foundation of China(50375026)the National Basic Research Program of China(2005cb221500)+1 种基金the National Key Technology R&D Program(2006BAK03B01)the National Natural Science Foundation Key Program(50534080)
文摘Through discussion of the time-distance curve characteristics of the direct waveand from the front,side and rear of the reflection waves of the seismic reflection methodfor advanced exploration in mines,and analysis of several major interference waves inmines,the differences in time-distance curve,frequency,apparent velocity between theeffective wave and interference wave in the seismic reflection method for advanced explorationare obtained.According to the differences,the effective wave is extracted andthe interference wave is filtered and the system's precision and accuracy is improved.
文摘Intravascular ultrasound can provide clear real-time cross-sectional images,including lumen and plaque.In practice,to identify the plaques tissues in different pathological changes is very important.However,the grayscale differences of them are not so apparent.In this paper a new textural characteristic space vector was formed by the combination of Co-occurrence Matrix and fraction methods.The vector was projected to the new characteristic space after multiplied by a projective matrix which can best classify those plaques according to the Fisher linear discriminant.Then the classification was completed in the new vector space.Experimental results found that the veracity of this classification could reach up to 88%,which would be an accessorial tool for doctors to identify each plaque.
基金Supported by the National High Technology Research and Development Programme of China （ No. 2007AA01Z401 ） and the National Natural Science Foundation of China （No. 90718003, 60973027）.
文摘In order to achieve failure prediction without manual intervention for distributed systems, a novel failure feature analysis and extraction approach to automate failure prediction is proposed. Compared with the traditional methods which focus on building heuristic rules or models, the autonomic prediction approach analyzes the nonlinear correlation of failure features by recognizing failure patterns. Failure data are sorted according to the nonlinear correlation and failure signature is proposed for autonomic prediction. In addition, the Manifold Learning algorithm named supervised locally linear embedding is applied to achieve feature extraction. Based on the runtime monitoring of failure metrics, the experimental results indicate that the proposed method has better performance in terms of both correlation recognition precision and feature extraction quality and thus it can be used to design efficient autonomic failure prediction for distributed systems.
文摘In this paper we revise the moment theory for pattern recognition designed, to extract patterns from the noisy character datas, and develop unconstrained handwritten. Amazigh character recognition method based upon orthogonal moments and neural networks classifier. We argue that, given the natural flexibility of neural network models and the extent of parallel processing that they allow, our algorithm is a step forward in character recognition. More importantly, following the approach proposed, we apply our system to two different databases, to examine the ability to recognize patterns under noise. We discover overwhelming support for different style of writing. Moreover, this basic conclusion appears to remain valid across different levels of smoothing and insensitive to the nuances of character patterns. Experiments tested the effect of set size on recognition accuracy which can reach 97.46%. The novelty of the proposed method is independence of size, slant, orientation, and translation. The performance of the proposed method is experimentally evaluated and the promising results and findings are presented. Our method is compared to K-NN （k-nearest neighbors） classifier algorithm; results show performances of our method.
