比较型算术应用题一致性效应的实验研究

As former research shown, the error rate of consistent compare word problem (such as: Mary has 5 apples, Tom has 2 apples more than Mary, how many apples does Tom has?) is much lower than the rate of inconsistent compare word problem (such as: Mary has 5 apples, she has 2 apples more than Tom, how many apples does Tom has?). This difference of error rate is named as Consistency Effect. There are different explanations about why consistency effect happens, one of them is R.E.Mayer's opinion about two kinds of problem solving strategies. As Mayer's opinion, unsuccessful problem solvers make mistakes on inconsistent problem because they use Direct Translation Strategy, problem solvers who use Problem Model Strategy will not make such kind of mistakes. In this study, three experiments with 3~(rd) graders investigate reasons for the consistency effect. The results of experiment 1 do not support the explanation of Mayer's theory of two kinds of strategy. Experiment 2 shows that there is no relation between inconsistent problem error and Impulsivity cognitive style. Experiment 3 reveals that the working memory capacity of successful inconsistent problem solvers is significant larger than the capacity of unsuccessful solvers. It is supposed that working memory could be an important factor contributing for the consistency effect.

A high-speed image sensing technique with adjustable frame rate based on an ordinary CCD

In the sinusoidal phase modulating interferometer technique, the high-speed CCD is necessary to detect the interference signals. The reason of ordinary CCD's low frame rate was analyzed, and a novel high-speed image sensing technique with adjustable frame rate based on ail ordinary CCD was proposed. And the principle of the image sensor was analyzed. When the maximum frequency and channel bandwidth were constant, a custom high-speed sensor was designed by using the ordinary CCD under the control of the special driving circuit. The frame rate of the ordinary CCD has been enhanced by controlling the number of pixels of every frame; therefore, the ordinary of CCD can be used as the high frame rate image sensor with small amount of pixels. The multi-output high-speed image sensor has the deficiencies of low accuracy, and high cost, while the high-speed image senor with small number of pixels by using this technique can overcome theses faults. The light intensity varying with time was measured by using the image sensor. The frame rate was LIP to 1600 frame per second (f/s), and the size of every frame and the frame rate were adjustable. The correlation coefficient between the measurement result and the standard values were higher than 0.98026, and the relative error was lower than 0.53%. The experimental results show that this sensor is fit to the measurements of sinusoidal phase modulating interferometer technique. (c) 2007 Elsevier GmbH. All rights reserved.

An Automatic Classification Method of Metabolic Cycle Based on Biomimetic Pattern Recognition

Correct classification of different metabolic cycle stages to identification cell cycle is significant in both human development and clinical diagnostics. However, it has no perfect method has been reached in classification of metabolic cycle yet. This paper exploringly puts forward an automatic classification method of metabolic cycle based on Biomimetic pattern recognition (BPR). As to the three phases of yeast metabolic cycle, the correct classification rate reaches 90%, 100% and 100% respectively.

Ground target detection, classification and sensor fusion in distributed fiber seismic sensor network - art. no. 683015

This paper describes the ground target detection, classification and sensor fusion problems in distributed fiber seismic sensor network. Compared with conventional piezoelectric seismic sensor used in UGS, fiber optic sensor has advantages of high sensitivity and resistance to electromagnetic disturbance. We have developed a fiber seismic sensor network for target detection and classification. However, ground target recognition based on seismic sensor is a very challenging problem because of the non-stationary characteristic of seismic signal and complicated real life application environment. To solve these difficulties, we study robust feature extraction and classification algorithms adapted to fiber sensor network. An united multi-feature (UMF) method is used. An adaptive threshold detection algorithm is proposed to minimize the false alarm rate. Three kinds of targets comprise personnel, wheeled vehicle and tracked vehicle are concerned in the system. The classification simulation result shows that the SVM classifier outperforms the GMM and BPNN. The sensor fusion method based on D-S evidence theory is discussed to fully utilize information of fiber sensor array and improve overall performance of the system. A field experiment is organized to test the performance of fiber sensor network and gather real signal of targets for classification testing.

Biomimetic (topological) pattern recognition - A new model of pattern recognition theory and its application

A new theoretical model of Pattern Recognition principles was proposed, which is based on "matter cognition" instead of "matter classification" in traditional statistical Pattern Recognition. This new model is closer to the function of human being, rather than traditional statistical Pattern Recognition using "optimal separating" as its main principle. So the new model of Pattern Recognition is called the Biomimetic Pattern Recognition (BPR)(1). Its mathematical basis is placed on topological analysis of the sample set in the high dimensional feature space. Therefore, it is also called the Topological Pattern Recognition (TPR). The fundamental idea of this model is based on the fact of the continuity in the feature space of any one of the certain kinds of samples. We experimented with the Biomimetic Pattern Recognition (BPR) by using artificial neural networks, which act through covering the high dimensional geometrical distribution of the sample set in the feature space. Onmidirectionally cognitive tests were done on various kinds of animal and vehicle models of rather similar shapes. For the total 8800 tests, the correct recognition rate is 99.87%. The rejection rate is 0.13% and on the condition of zero error rates, the correct rate of BPR was much better than that of RBF-SVM.

Artificial neural network classification based on high-performance liquid chromatography of urinary and serum nucleosides for the clinical diagnosis of cancer

Nucleosides in human urine and serum have frequently been studied as a possible biomedical marker for cancer, acquired immune deficiency syndrome (AIDS) and the whole-body turnover of RNAs. Fifteen normal and modified nucleosides were determined in 69 urine and 42 serum samples using high-performance liquid chromatography (HPLC). Artificial neural networks have been used as a powerful pattern recognition tool to distinguish cancer patients from healthy persons. The recognition rate for the training set reached 100%. In the validating set, 95.8 and 92.9% of people were correctly classified into cancer patients and healthy persons when urine and serum were used as the sample for measuring the nucleosides. The results show that the artificial neural network technique is better than principal component analysis for the classification of healthy persons and cancer patients based on nucleoside data. (C) 2002 Elsevier Science B.V. All rights reserved.

Modification to the Hardisty equation, regarding the relationship between sediment transport rate and particle size

Bagnold-type bed-load equations are widely used for the determination of sediment transport rate in marine environments. The accuracy of these equations depends upon the definition of the coefficient k(1) in the equations, which is a function of particle size. Hardisty (1983) has attempted to establish the relationship between k(1) and particle size, but there is an error in his analytical result. Our reanalysis of the original flume data results in new formulae for the coefficient. Furthermore, we found that the k(1) values should be derived using u(1) and u(1cr) data; the use of the vertical mean velocity in flumes to replace u(1) will lead to considerably higher k(1) values and overestimation of sediment transport rates.

开发早期油藏动态地质模型与油藏工程优化研究——以埕岛油田馆上段油藏为例

In order to developing reservoir of Upper of Ng at high-speed and high-efficient in Chengdao oilfield which is located in the bally shallow sea, the paper builds up a series of theory and means predicting and descripting reservoir in earlier period of oilfield development. There are some conclusions as follows. 1. It is the first time to form a series of technique of fine geological modeling of the channel-sandy reservoir by means of mainly seismic methods. These technique include the logging restriction seismic inversion, the whole three dimension seismic interpretation, seismic properties analysis and so on which are used to the 3-dimension distributing prediction of sandy body, structure and properties of the channel reservoir by a lot of the seismic information and a small quantity of the drilling and the logging information in the earlier stage of the oil-field development. It is the first time that these methods applied to production and the high-speed development of the shallow sea oilfield. The prediction sandy body was modified by the data of new drilling, the new reservoir prediction thinking of traced inversion is built. The applied effect of the technique was very well, according to approximately 200 wells belonging to 30 well groups in Chengdao oilfield, the drilling succeeded rate of the predicting sandy body reached 100%, the error total thickness only was 8%. 2. The author advanced the thinking and methods of the forecasting residual-oil prediction at the earlier stage of production. Based on well data and seismic data, correlation of sediment units was correlated by cycle-correlation and classification control methods, and the normalization and finely interpretation of the well logging and sedimentation micro-facies were acquired. On the region of poor well, using the logging restriction inversion technique and regarding finished drilling production well as the new restriction condition, the sand body distributing and its property were predicted again and derived 3-dimension pool geologic model including structure, reservoir, fluid, reservoir engineering parameter and producing dynamic etc. According to the reservoir geologic model, the reservoir engineering design was optimized, the tracking simulation of the reservoir numerical simulation was done by means of the dynamic data (pressure, yield and water content) of development well, the production rule and oil-water distributing rule was traced, the distributing of the remaining oil was predicted and controlled. The dynamic reservoir modeling method in metaphase of development was taken out. Based on the new drilling data, the static reservoir geologic model was momentarily modified, the research of the flow units was brought up including identifying flow units, evaluating flow units capability and establishing the fine flow units model; according to the dynamic data of production and well testing data, the dynamic tracing reservoir description was realized through the constant modification of the reservoir geologic model restricted these dynamic data by the theory of well testing and the reservoir numerical simulation. It was built the dynamic tracing reservoir model, which was used to track survey of the remaining oil on earlier period. The reservoir engineering tracking analysis technique on shallow sea oilfield was founded. After renewing the structure history since tertiary in Chengdao area by the balance section technique and estimating the activity character of the Chengbei fault by the sealing fault analysis technique, the meandering stream sediment pattern of the Upper of Ng was founded in which the meandering border was the uppermost reservoir unit. Based on the specialty of the lower rock component maturity and the structure maturity, the author founded 3 kinds of pore structure pattern in the Guanshang member of Chengdao oil-field in which the storing space mainly was primary (genetic) inter-granular pore, little was secondary solution pore and the inter-crystal pore tiny pore, and the type of throat mainly distributed as the slice shape and the contract neck shape. The positive rhythmic was briefly type included the simple positive rhythm, the complex positive rhythm and the compound rhythm. Interbed mainly is mudstone widely, the physical properties and the calcite interbed distribute localized. 5. The author synthetically analyzed the influence action of the micro-heterogeneity, the macro-heterogeneity and the structure heterogeneity to the oilfield water flood development. The efficiency of water flood is well in tiny structure of convex type or even type at top and bottom in which the water breakthrough of oil well is soon at the high part of structure when inject at the low part of structure, and the efficiency of water flood is poor in tiny structure of concave type at top and bottom. The remaining oil was controlled by sedimentary facies; the water flooding efficiency is well in the border or channel bar and is bad in the floodplain or the levee. The separation and inter layer have a little influence to the non-obvious positive rhythm reservoir, in which the remaining oil commonly locate within the 1-3 meter of the lower part of the separation and inter layer with lower water flooding efficiency.