ETC中具有车型识别功能的车辆检测器设计

介绍了专门用于ETC(不停车收费系统)中一种车辆检测器的软硬件设计方法。根据车辆检测器应用环境的特点给出了基准频率校正算法,可以对基准频率进行实时校正。并采用模糊模式识别算法进行车型识别。

基于模糊模式识别的车型分类研究

根据目前中国路桥车辆收费标准,提出了一种基于模糊模式识别的车型分类系统。车辆经过环形线圈传感器时,形成感应曲线,提取感应曲线的特征并进行特征分离,利用模糊模式识别方法对车型进行匹配分类。研究结果已在路桥收费系统以及交通流量统计中得到应用。

断陷湖盆缓坡带成藏模式研究

Halfgraben-like depressions have multiple layers of subtle traps, multiple coverings of oil-bearing series and multiple types of reservoirs. But these reservoirs have features of strong concealment and are difficult to explore. For this reason, many scholars contribute efforts to study the pool-forming mechanism for this kind of basins, and establish the basis for reservoir exploration and development. However, further study is needed. This paper takes HuiMin depression as an example to study the pool-forming model for the gentle slope belts of fault-depression lake basins. Applying multi-discipline theory, methods and technologies including sedimentary geology, structural geology, log geology, seismic geology, rock mechanics and fluid mechanics, and furthermore applying the dynamo-static data of oil reservoir and computer means in maximum limitation, this paper, qualitatively and quantitatively studies the depositional system, structural framework, structural evolution, structural lithofacies and tectonic stress field, as well as fluid potential field, sealing and opening properties of controlling-oil faults and reservoir prediction, finally presents a pool-forming model, and develops a series of methods and technologies suited to the reservoir prediction of the gentle slope belt. The results obtained in this paper richen the pool-forming theory of a complex oil-gas accumulative area in the gentle slope belt of a continental fault-depression basin. The research work begins with the study of geometric shape of fracture system, then the structural form, activity stages and time-space juxtaposition of faults with different level and different quality are investigated. On the basis of study of the burial history, subsidence history and structural evolution history, this paper synthesizes the studied results of deposition system, analyses the structural lithofacies of the gentle slope belt in the HuiMing Depression and its controlling roles to oil reservoir in the different structural lithofacies belts in time-space, and presents their evolution patterns. The study of structural stress field and fluid potential field indicates that the stress field has a great change from the Dong Ying stages to nowadays. One marked point among them is that the Dong Ying double peak- shaped nose structures usually were the favorable directional area for oil and gas migration, while the QuDi horst became favorable directional area since the GuanTao stage. Based on the active regular of fractures and the information of crude oil saturation pressure, this paper firstly demonstrates that the pool-forming stages of the LingNan field were prior to the stages of the QuDi field, whici provides new eyereach and thinking for hydrocarbon exploration in the gentle slope belt. The BeiQiao-RenFeng buried hill belt is a high value area with the maximum stress values from beginning to end, thus it is a favorable directional area for oil and gas migration. The opening and sealing properties of fractures are studied. The results obtained demonstrate their difference in the hydrocarbon pool formation. The seal abilities relate not only with the quality, direction and scale of normal stress, with the interface between the rocks of two sides of a fault and with the shale smear factor (SSF), but they relate also with the juxtaposition of fault motion stage and hydrocarbon migration. In the HuiMin gentle slope belt, the fault seal has difference both in different stages, and in different location and depth in the same stage. The seal extent also displays much difference. Therefore, the fault seal has time-space difference. On the basis of study of fault seal history, together with the obtained achievement of structural stress field and fluid potential field, it is discovered that for the pool-forming process of oil and gas in the studied area the fault seal of nowadays is better than that of the Ed and Ng stages, it plays an important role to determine the oil column height and hydrocarbon preservation. However, the fault seal of the Ed and Ng stages has an important influence for the distribution state of oil and gas. Because the influential parameters are complicated and undefined, we adopt SSF in the research work. It well reflects synthetic effect of each parameter which influences fault seal. On the basis of the above studies, three systems of hydrocarbon migration and accumulation, as well as a pool-forming model are established for the gentle slope belt of the HuiMin depression, which can be applied for the prediction of regular patterns of oil-gas migration. Under guidance of the pool-forming geological model for the HuiMin slope belt, and taking seismic facies technology, log constraint evolution technology, pattern recognition of multiple parameter reservoir and discrimination technology of oil-bearing ability, this paper develops a set of methods and technologies suited to oil reservoir prediction of the gentle slope belt. Good economic benefit has been obtained.

Reversed-phase high-performance liquid chromatographic investigation of urinary normal and modified nucleosides of cancer patients

Post-transcriptional modifications in RNA give rise to free modified ribonucleosides circulating in the blood stream and excreted in urine. Due to their abnormal levels in conjunction with several tumor diseases, they have been suggested as possible tumor markers. The developed RP-HPLC method has been applied to analyze the urinary nucleosides in 34 urinary samples from 15 kinds of cancer patients. The statistical analyses showed the urinary nucleoside excretion, especially modified nucleoside levels, in cancer patients were significantly higher than those in normal healthy volunteers. Factor analysis was used to classify the patients with cancer and normal healthy humans. It was found that using 15 urinary nucleoside levels or only five modified nucleoside levels as data vectors the factor analysis plot displayed two almost separate clusters representing each group. (C) 1999 Elsevier Science B.V. All rights reserved.

A Theory of Networks for Appxoimation and Learning

Learning an input-output mapping from a set of examples, of the type that many neural networks have been constructed to perform, can be regarded as synthesizing an approximation of a multi-dimensional function, that is solving the problem of hypersurface reconstruction. From this point of view, this form of learning is closely related to classical approximation techniques, such as generalized splines and regularization theory. This paper considers the problems of an exact representation and, in more detail, of the approximation of linear and nolinear mappings in terms of simpler functions of fewer variables. Kolmogorov's theorem concerning the representation of functions of several variables in terms of functions of one variable turns out to be almost irrelevant in the context of networks for learning. We develop a theoretical framework for approximation based on regularization techniques that leads to a class of three-layer networks that we call Generalized Radial Basis Functions (GRBF), since they are mathematically related to the well-known Radial Basis Functions, mainly used for strict interpolation tasks. GRBF networks are not only equivalent to generalized splines, but are also closely related to pattern recognition methods such as Parzen windows and potential functions and to several neural network algorithms, such as Kanerva's associative memory, backpropagation and Kohonen's topology preserving map. They also have an interesting interpretation in terms of prototypes that are synthesized and optimally combined during the learning stage. The paper introduces several extensions and applications of the technique and discusses intriguing analogies with neurobiological data.

Template Matching: Matched Spatial Filters and Beyond

Template matching by means of cross-correlation is common practice in pattern recognition. However, its sensitivity to deformations of the pattern and the broad and unsharp peaks it produces are significant drawbacks. This paper reviews some results on how these shortcomings can be removed. Several techniques (Matched Spatial Filters, Synthetic Discriminant Functions, Principal Components Projections and Reconstruction Residuals) are reviewed and compared on a common task: locating eyes in a database of faces. New variants are also proposed and compared: least squares Discriminant Functions and the combined use of projections on eigenfunctions and the corresponding reconstruction residuals. Finally, approximation networks are introduced in an attempt to improve filter design by the introduction of nonlinearity.

Mean Field Theory for Sigmoid Belief Networks

We develop a mean field theory for sigmoid belief networks based on ideas from statistical mechanics. Our mean field theory provides a tractable approximation to the true probability distribution in these networks; it also yields a lower bound on the likelihood of evidence. We demonstrate the utility of this framework on a benchmark problem in statistical pattern recognition -- the classification of handwritten digits.

Fuzzy-Rough Sets Assisted Attribute Selection

R. Jensen and Q. Shen. Fuzzy-Rough Sets Assisted Attribute Selection. IEEE Transactions on Fuzzy Systems, vol. 15, no. 1, pp. 73-89, 2007.

Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches

R. Jensen and Q. Shen. Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough Based Approaches. IEEE Transactions on Knowledge and Data Engineering, 16(12): 1457-1471. 2004.

Feature Selection based on Rough Sets and Particle Swarm Optimization

X. Wang, J. Yang, X. Teng, W. Xia, and R. Jensen. Feature Selection based on Rough Sets and Particle Swarm Optimization. Pattern Recognition Letters, vol. 28, no. 4, pp. 459-471, 2007.

Rough sets, their extensions and applications

Q. Shen and R. Jensen, 'Rough sets, their extensions and applications,' International Journal of Automation and Computing (IJAC), vol. 4, no. 3, pp. 217-218, 2007.

Fuzzy-Rough Data Reduction with Ant Colony Optimization.

R. Jensen and Q. Shen, 'Fuzzy-Rough Data Reduction with Ant Colony Optimization,' Fuzzy Sets and Systems, vol. 149, no. 1, pp. 5-20, 2005.

Selecting Informative Features with Fuzzy-Rough Sets and its Application for Complex Systems Monitoring.

Q. Shen and R. Jensen, 'Selecting Informative Features with Fuzzy-Rough Sets and its Application for Complex Systems Monitoring,' Pattern Recognition, vol. 37, no. 7, pp. 1351-1363, 2004.

Tolerance-based and Fuzzy-Rough Feature Selection.

R. Jensen and Q. Shen, 'Tolerance-based and Fuzzy-Rough Feature Selection,' Proceedings of the 16th International Conference on Fuzzy Systems (FUZZ-IEEE'07), pp. 877-882, 2007.

Finding Rough Set Reducts with SAT.

R. Jensen, Q. Shen and A. Tuson, 'Finding Rough Set Reducts with SAT,' Proceedings of the 10th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, LNAI 3641, pp. 194-203, 2005.