In order to accurately and quickly identify the safety status pattern of coalmines,a new safety status pattern recognition method based on the extension neural network (ENN) was proposed,and the design of structure of...In order to accurately and quickly identify the safety status pattern of coalmines,a new safety status pattern recognition method based on the extension neural network (ENN) was proposed,and the design of structure of network,the rationale of recognition algorithm and the performance of proposed method were discussed in detail.The safety status pattern recognition problem of coalmines can be regard as a classification problem whose features are defined in a range,so using the ENN is most appropriate for this problem.The ENN-based recognition method can use a novel extension distance to measure the similarity between the object to be recognized and the class centers.To demonstrate the effectiveness of the proposed method,a real-world application on the geological safety status pattern recognition of coalmines was tested.Comparative experiments with existing method and other traditional ANN-based methods were conducted.The experimental results show that the proposed ENN-based recognition method can identify the safety status pattern of coalmines accurately with shorter learning time and simpler structure.The experimental results also confirm that the proposed method has a better performance in recognition accuracy,generalization ability and fault-tolerant ability,which are very useful in recognizing the safety status pattern in the process of coal production.展开更多
Flatness pattern recognition is the key of the flatness control. The accuracy of the present flatness pattern recognition is limited and the shape defects cannot be reflected intuitively. In order to improve it, a nov...Flatness pattern recognition is the key of the flatness control. The accuracy of the present flatness pattern recognition is limited and the shape defects cannot be reflected intuitively. In order to improve it, a novel method via T-S cloud inference network optimized by genetic algorithm(GA) is proposed. T-S cloud inference network is constructed with T-S fuzzy neural network and the cloud model. So, the rapid of fuzzy logic and the uncertainty of cloud model for processing data are both taken into account. What's more, GA possesses good parallel design structure and global optimization characteristics. Compared with the simulation recognition results of traditional BP Algorithm, GA is more accurate and effective. Moreover, virtual reality technology is introduced into the field of shape control by Lab VIEW, MATLAB mixed programming. And virtual flatness pattern recognition interface is designed.Therefore, the data of engineering analysis and the actual model are combined with each other, and the shape defects could be seen more lively and intuitively.展开更多
In order to make the effective ECCM to the deceptive jamming, especially the angle deceptive jamming, this paper establishes a signal-processing model for anti-deceptive jamming firstly, in which two feature-extractin...In order to make the effective ECCM to the deceptive jamming, especially the angle deceptive jamming, this paper establishes a signal-processing model for anti-deceptive jamming firstly, in which two feature-extracting algorithms, i.e. the statistical algorithm and the neural network （NN） algorithm are presented, then uses the RBF NN as the classitier in the processing model. Finally the two algorithms are validated and compared through some simulations.展开更多
Threat-judgment is a complicated fuzzy inference problem. Up to now no relevant unified theory and measur-ing standard have been developed. It is very difficult to establish a threat-judgment model with high reliabili...Threat-judgment is a complicated fuzzy inference problem. Up to now no relevant unified theory and measur-ing standard have been developed. It is very difficult to establish a threat-judgment model with high reliability in the airdefense system for the naval warships. Air target threat level judgment is an important component in naval warship com-bat command decision-making systems. According to the threat level judgment of air targets during the air defense of sin-gle naval warship, a fuzzy pattern recognition model for judging the threat from air targets is established. Then an algo-rithm for identifying the parameters in the model is presented. The model has an adaptive feature and can dynamicallyupdate its parameters according to the state change of the attacking targets and the environment. The method presentedhere can be used for the air defense system threat judgment in the naval warships.展开更多
To recognize circular objects rapidly in satellite remote sensing imagery, an approach using their geometry properties is presented. The original image is segmented to be a binary one by one dimension maximum entropy ...To recognize circular objects rapidly in satellite remote sensing imagery, an approach using their geometry properties is presented. The original image is segmented to be a binary one by one dimension maximum entropy threshold algorithm and the binary image is labeled with an algorithm based on recursion technique. Then, shape parameters of all labeled regions are calculated and those regions with shape parameters satisfying certain conditions are recognized as circular objects. The algorithm is described in detail, and comparison experiments with the randomized Hough transformation （RHT） are also provided. The experimental results on synthetic images and real images show that the proposed method has the merits of fast recognition rate, high recognition efficiency and the ability of anti-noise and anti-jamming. In addition, the method performs well when some circular objects are little deformed and partly misshapen.展开更多
In order to improve the recognition rate and accuracy rate of projectiles in six sky-screens intersection test system,this work proposes a new recognition method of projectiles by combining particle swarm optimization...In order to improve the recognition rate and accuracy rate of projectiles in six sky-screens intersection test system,this work proposes a new recognition method of projectiles by combining particle swarm optimization support vector and spatial-temporal constrain of six sky-screens detection sensor.Based on the measurement principle of the six sky-screens intersection test system and the characteristics of the output signal of the sky-screen,we analyze the existing problems regarding the recognition of projectiles.In order to optimize the projectile recognition effect,we use the support vector machine and basic particle swarm algorithm to form a new recognition algorithm.We set up the particle swarm algorithm optimization support vector projectile information recognition model that conforms to the six sky-screens intersection test system.We also construct a spatial-temporal constrain matching model based on the spatial geometric relationship of six sky-screen intersection,and form a new projectile signal recognition algorithm with six sky-screens spatial-temporal information constraints under the signal classification mechanism of particle swarm optimization algorithm support vector machine.Based on experiments,we obtain the optimal penalty and kernel function radius parameters in the PSO-SVM algorithm;we adjust the parameters of the support vector machine model,train the test signal data of every sky-screen,and gain the projectile signal classification results.Afterwards,according to the signal classification results,we calculate the coordinate parameters of the real projectile by using the spatial-temporal constrain of six sky-screens detection sensor,which verifies the feasibility of the proposed algorithm.展开更多
This paper focuses on the recognition rate comparison for competing recognition algorithms, which is a common problem of many pattern recognition research areas. The paper firstly reviews some traditional recognition ...This paper focuses on the recognition rate comparison for competing recognition algorithms, which is a common problem of many pattern recognition research areas. The paper firstly reviews some traditional recognition rate comparison procedures and discusses their limitations. A new method, the posterior probability calculation（PPC） procedure is then proposed based on Bayesian technique. The paper analyzes the basic principle, process steps and computational complexity of the PPC procedure. In the Bayesian view, the posterior probability represents the credible degree（equal to confidence level） of the comparison results. The posterior probability of correctly selecting or sorting the competing recognition algorithms is derived, and the minimum sample size requirement is also pre-estimated and given out by the form of tables. To further illustrate how to use our method, the PPC procedure is used to prove the rationality of the experiential choice in one application and then to calculate the confidence level with the fixed-size datasets in another application. These applications reveal the superiority of the PPC procedure, and the discussions about the stopping rule further explain the underlying statistical causes. Finally we conclude that the PPC procedure achieves all the expected functions and be superior to the traditional methods.展开更多
In many machine learning problems, a large amount of data is available but only a few of them can be labeled easily. This provides a research branch to effectively combine unlabeled and labeled data to infer the label...In many machine learning problems, a large amount of data is available but only a few of them can be labeled easily. This provides a research branch to effectively combine unlabeled and labeled data to infer the labels of unlabeled ones, that is, to develop transductive learning. In this article, based on Pattern classification via single sphere （SSPC）, which seeks a hypersphere to separate data with the maximum separation ratio, a progressive transductive pattern classification method via single sphere （PTSSPC） is proposed to construct the classifier using both the labeled and unlabeled data. PTSSPC utilize the additional information of the unlabeled samples and obtain better classification performance than SSPC when insufficient labeled data information is available. Experiment results show the algorithm can yields better performance.展开更多
The main methods of the second phase quantitative analysis in current material science researches are manual recognition and extracting by using software such as Image Tool and Nano Measurer. The weaknesses such as hi...The main methods of the second phase quantitative analysis in current material science researches are manual recognition and extracting by using software such as Image Tool and Nano Measurer. The weaknesses such as high labor intensity and low accuracy statistic results exist in these methods. In order to overcome the shortcomings of the current methods, the Ω phase in A1-Cu-Mg-Ag alloy is taken as the research object and an algorithm based on the digital image processing and pattern recognition is proposed and implemented to do the A1 alloy TEM （transmission electron microscope） digital images process and recognize and extract the information of the second phase in the result image automatically. The top-hat transformation of the mathematical morphology, as well as several imaging processing technologies has been used in the proposed algorithm. Thereinto, top-hat transformation is used for elimination of asymmetric illumination and doing Multi-layer filtering to segment Ω phase in the TEM image. The testing results are satisfied, which indicate that the Ω phase with unclear boundary or small size can be recognized by using this method. The omission of these two kinds of Ω phase can be avoided or significantly reduced. More Ω phases would be recognized （growing rate minimum to 2% and maximum to 400% in samples）, accuracy of recognition and statistics results would be greatly improved by using this method. And the manual error can be eliminated. The procedure recognizing and making quantitative analysis of information in this method is automatically completed by the software. It can process one image, including recognition and quantitative analysis in 30 min, but the manual method such as using Image Tool or Nano Measurer need 2 h or more. The labor intensity is effectively reduced and the working efficiency is greatly improved.展开更多
One of the most important features of the modern ma ch ining system in an "unmanned" factory is to change tools that have been subjec ted to wear and damage. An integrated tool condition monitoring system co...One of the most important features of the modern ma ch ining system in an "unmanned" factory is to change tools that have been subjec ted to wear and damage. An integrated tool condition monitoring system composed of multi-sensors, signal processing devices and intelligent decision making pla ns is a necessary requirement for automatic manufacturing processes. An intellig ent tool wear monitoring system will be introduced in this paper. The system is equipped with power consumption, vibration, AE and cutting force sensors, signal transformation and collection apparatus and a microcomputer. Tool condition monitoring is a pattern recognition process in which the characte ristics of the tool to be monitored are compared with those of the standard mode ls. The tool wear classification process is composed of the following parts: fea ture extraction; determination of the fuzzy membership functions of the features ; calculation of the fuzzy similarity; learning and tool wear classification. Fe atures extracted from the time domain and frequency domain for the future patter n recognition are as follows. Power consumption signal: mean value; AE-RMS sign al: mean value, skew and kutorsis; Cutting force, AE and vibration signal: mean value, standard deviation and the mean power in 10 frequency ranges. These signa l features can reflect the tool wear states comprehensively. The fuzzy approachi ng degree and the fuzzy distance between corresponding features of different obj ects are combined to describe the closeness of two fuzzy sets more accurately. A unique fuzzy driven neural network based pattern recognition algorithm has bee n developed from this research. The combination of Artificial Neural Networks (A NNs) and fuzzy logic system integrates the strong learning and classification ab ility of the former and the superb flexibility of the latter to express the dist ribution characteristics of signal features with vague boundaries. This methodol ogy indirectly solves the automatic weight assignment problem of the conventiona l fuzzy pattern recognition system and let it have greater representative power, higher training speed and be more robust. The introduction of the two-dimensio nal weighted approaching degree can make the pattern recognition process more re liable. The fuzzy driven neural network can effectively fuse multi-sensor i nformation and successfully recognize the tool wear states. Armed with the advan ced pattern recognition methodology, the established intelligent tool condition monitoring system has the advantages of being suitable for different machini ng conditions, robust to noise and tolerant to faults. Cooperated with the contr ol system of the machine tool, the optimized machining processed can be achieved .展开更多
Short-term traffic flow prediction is one of the essential issues in intelligent transportation systems(ITS). A new two-stage traffic flow prediction method named AKNN-AVL method is presented, which combines an advanc...Short-term traffic flow prediction is one of the essential issues in intelligent transportation systems(ITS). A new two-stage traffic flow prediction method named AKNN-AVL method is presented, which combines an advanced k-nearest neighbor(AKNN)method and balanced binary tree(AVL) data structure to improve the prediction accuracy. The AKNN method uses pattern recognition two times in the searching process, which considers the previous sequences of traffic flow to forecast the future traffic state. Clustering method and balanced binary tree technique are introduced to build case database to reduce the searching time. To illustrate the effects of these developments, the accuracies performance of AKNN-AVL method, k-nearest neighbor(KNN) method and the auto-regressive and moving average(ARMA) method are compared. These methods are calibrated and evaluated by the real-time data from a freeway traffic detector near North 3rd Ring Road in Beijing under both normal and incident traffic conditions.The comparisons show that the AKNN-AVL method with the optimal neighbor and pattern size outperforms both KNN method and ARMA method under both normal and incident traffic conditions. In addition, the combinations of clustering method and balanced binary tree technique to the prediction method can increase the searching speed and respond rapidly to case database fluctuations.展开更多
To avoid unstable learning, a stable adaptive learning algorithm was proposed for discrete-time recurrent neural networks. Unlike the dynamic gradient methods, such as the backpropagation through time and the real tim...To avoid unstable learning, a stable adaptive learning algorithm was proposed for discrete-time recurrent neural networks. Unlike the dynamic gradient methods, such as the backpropagation through time and the real time recurrent learning, the weights of the recurrent neural networks were updated online in terms of Lyapunov stability theory in the proposed learning algorithm, so the learning stability was guaranteed. With the inversion of the activation function of the recurrent neural networks, the proposed learning algorithm can be easily implemented for solving varying nonlinear adaptive learning problems and fast convergence of the adaptive learning process can be achieved. Simulation experiments in pattern recognition show that only 5 iterations are needed for the storage of a 15×15 binary image pattern and only 9 iterations are needed for the perfect realization of an analog vector by an equilibrium state with the proposed learning algorithm.展开更多
For some reasons, engineers build their product 3D mo del according to a set of related engineering drawings. The problem is how we ca n know the 3D model is correct. The manual checking is very boring and time cons u...For some reasons, engineers build their product 3D mo del according to a set of related engineering drawings. The problem is how we ca n know the 3D model is correct. The manual checking is very boring and time cons uming, and still could not avoid mistakes. Thus, we could not confirm the model, maybe try checking again. It will effect the production preparing cycle greatly , and should be solved in a intelligent way. The difficulties are quite obvious, unlike word checking in a word processing package, the checking described above is not a comparison between same items. One is 2D drawing, the another is 3D mo del, they are not in the same dimension. So, we should make a change for compari son in the same dimension. If we can rebuild a 3D model through related 2D drawi ngs automatically, that’s great. We can not only compare two 3D models to check and correct, but also omit the manual process itself completely. Unfortunately, we can not build such a 3D model automatically right now. So only one way left: compare two 2D drawings, one is the original, the another is processed from tha t manual built one.The method is to select a drawing as a background, rotate th e 3D model and make projections, compare projection with the background automati cally to find a case which they meet each other in certain amount of error ( tolerance), otherwise alarm. This process can be repeated many times if needed t o fulfil the checking task. Also, this is a man-machine system, computer does h ard working, man keeps final decision. The project involved in CAD, VRML, patter n recognition, image capture and comparison, artificial intelligence.展开更多
To find out all dependency relationships in which metaphors probably exist between syntax constituents in a given sentence,a dependency tree matching algorithm oriented to Chinese metaphor processing is proposed based...To find out all dependency relationships in which metaphors probably exist between syntax constituents in a given sentence,a dependency tree matching algorithm oriented to Chinese metaphor processing is proposed based on a research of unordered tree inclusion matching.In this algorithm,the pattern library is composed of formalization dependency syntax trees that are derived from large-scale metaphor sentences.These kinds of metaphor sentences are saved in the pattern library in advance.The main process of this algorithm is up-down searching and bottom-up backtracking revising.The algorithm discovers potential metaphoric structures in Chinese sentences from metaphoric dependency pattern library.Finally,the feasibility and efficiency of the new matching algorithm are further testified by the results of a series of experiments on dependency pattern library.Hence,accurate dependency relationships can be achieved through this algorithm.展开更多
Partial label learning aims to learn a multi-class classifier,where each training example corresponds to a set of candidate labels among which only one is correct.Most studies in the label space have only focused on t...Partial label learning aims to learn a multi-class classifier,where each training example corresponds to a set of candidate labels among which only one is correct.Most studies in the label space have only focused on the difference between candidate labels and non-candidate labels.So far,however,there has been little discussion about the label correlation in the partial label learning.This paper begins with a research on the label correlation,followed by the establishment of a unified framework that integrates the label correlation,the adaptive graph,and the semantic difference maximization criterion.This work generates fresh insight into the acquisition of the learning information from the label space.Specifically,the label correlation is calculated from the candidate label set and is utilized to obtain the similarity of each pair of instances in the label space.After that,the labeling confidence for each instance is updated by the smoothness assumption that two instances should be similar outputs in the label space if they are close in the feature space.At last,an effective optimization program is utilized to solve the unified framework.Extensive experiments on artificial and real-world data sets indicate the superiority of our proposed method to state-of-art partial label learning methods.展开更多
This paper summarizes the research results dealing with washer and nut taxonomy and knowledge base design, making the use of fuzzy methodology. In particular, the theory of fuzzy membership functions, similarity matri...This paper summarizes the research results dealing with washer and nut taxonomy and knowledge base design, making the use of fuzzy methodology. In particular, the theory of fuzzy membership functions, similarity matrices, and the operation of fuzzy inference play important roles.A realistic set of 25 washers and nuts are employed to conduct extensive experiments and simulations.The investigation includes a complete demonstration of engineering design. The results obtained from this feasibility study are very encouraging indeed because they represent the lower bound with respect to performance, namely correctrecognition rate, of what fuzzy methodology can do. This lower bound shows high recognition rate even with noisy input patterns, robustness in terms of noise tolerance, and simplicity in hardware implementation. Possible future works are suggested in the conclusion.展开更多
In this paper, we first make a brief review on the fundamental properties of artificial neural networks (ANN) and the basic models, and explore emphatically some potential application of artificial neural networks in ...In this paper, we first make a brief review on the fundamental properties of artificial neural networks (ANN) and the basic models, and explore emphatically some potential application of artificial neural networks in the area of product quality diagnosis, prediction and control, state supervision and classification, factor recognition, and expert system based diagnosis, then set up the ANN models and expert system for quality forecasting, monitoring and diagnosing. We point out that combining ANN with other techniques will have the broad development and application of perspectives. Finally, the paper gives out some practical applications for the models and the system.展开更多
In this report several practical issues about moment invariants with application to image classification are concerned. A modified formulation for the approximation of the moments of digital images is suggested. Four ...In this report several practical issues about moment invariants with application to image classification are concerned. A modified formulation for the approximation of the moments of digital images is suggested. Four computational procedures and their corresponding noise performances are studied in detail.展开更多
The definition of rough similarity degree is given based on the axiomatic similarity degree, and the properties of rough similarity degree are listed. Using the properties of rough similarity degree, the method of clu...The definition of rough similarity degree is given based on the axiomatic similarity degree, and the properties of rough similarity degree are listed. Using the properties of rough similarity degree, the method of clustering in rough systems can be obtained. After clustering, a new sample can be recognized by the principle of maximal rough similarity degree.展开更多
Based on rough similarity degree of rough sets and close degree of fuzzy sets, the definitions of rough similarity degree and rough close degree of rough fuzzy sets are given, which can be used to measure the similar ...Based on rough similarity degree of rough sets and close degree of fuzzy sets, the definitions of rough similarity degree and rough close degree of rough fuzzy sets are given, which can be used to measure the similar degree between two rough fuzzy sets. The properties and theorems are listed. Using the two new measures, the method of clustering in the rough fuzzy system can be obtained. After clustering, the new fuzzy sample can be recognized by the principle of maximal similarity degree.展开更多
基金Project(107021) supported by the Key Foundation of Chinese Ministry of Education Project(2009643013) supported by China Scholarship Fund
文摘In order to accurately and quickly identify the safety status pattern of coalmines,a new safety status pattern recognition method based on the extension neural network (ENN) was proposed,and the design of structure of network,the rationale of recognition algorithm and the performance of proposed method were discussed in detail.The safety status pattern recognition problem of coalmines can be regard as a classification problem whose features are defined in a range,so using the ENN is most appropriate for this problem.The ENN-based recognition method can use a novel extension distance to measure the similarity between the object to be recognized and the class centers.To demonstrate the effectiveness of the proposed method,a real-world application on the geological safety status pattern recognition of coalmines was tested.Comparative experiments with existing method and other traditional ANN-based methods were conducted.The experimental results show that the proposed ENN-based recognition method can identify the safety status pattern of coalmines accurately with shorter learning time and simpler structure.The experimental results also confirm that the proposed method has a better performance in recognition accuracy,generalization ability and fault-tolerant ability,which are very useful in recognizing the safety status pattern in the process of coal production.
基金Project(LJRC013)supported by the University Innovation Team of Hebei Province Leading Talent Cultivation,China
文摘Flatness pattern recognition is the key of the flatness control. The accuracy of the present flatness pattern recognition is limited and the shape defects cannot be reflected intuitively. In order to improve it, a novel method via T-S cloud inference network optimized by genetic algorithm(GA) is proposed. T-S cloud inference network is constructed with T-S fuzzy neural network and the cloud model. So, the rapid of fuzzy logic and the uncertainty of cloud model for processing data are both taken into account. What's more, GA possesses good parallel design structure and global optimization characteristics. Compared with the simulation recognition results of traditional BP Algorithm, GA is more accurate and effective. Moreover, virtual reality technology is introduced into the field of shape control by Lab VIEW, MATLAB mixed programming. And virtual flatness pattern recognition interface is designed.Therefore, the data of engineering analysis and the actual model are combined with each other, and the shape defects could be seen more lively and intuitively.
文摘In order to make the effective ECCM to the deceptive jamming, especially the angle deceptive jamming, this paper establishes a signal-processing model for anti-deceptive jamming firstly, in which two feature-extracting algorithms, i.e. the statistical algorithm and the neural network （NN） algorithm are presented, then uses the RBF NN as the classitier in the processing model. Finally the two algorithms are validated and compared through some simulations.
基金This project was supported by the National Defense Foundation of China(40108070103)
文摘Threat-judgment is a complicated fuzzy inference problem. Up to now no relevant unified theory and measur-ing standard have been developed. It is very difficult to establish a threat-judgment model with high reliability in the airdefense system for the naval warships. Air target threat level judgment is an important component in naval warship com-bat command decision-making systems. According to the threat level judgment of air targets during the air defense of sin-gle naval warship, a fuzzy pattern recognition model for judging the threat from air targets is established. Then an algo-rithm for identifying the parameters in the model is presented. The model has an adaptive feature and can dynamicallyupdate its parameters according to the state change of the attacking targets and the environment. The method presentedhere can be used for the air defense system threat judgment in the naval warships.
文摘To recognize circular objects rapidly in satellite remote sensing imagery, an approach using their geometry properties is presented. The original image is segmented to be a binary one by one dimension maximum entropy threshold algorithm and the binary image is labeled with an algorithm based on recursion technique. Then, shape parameters of all labeled regions are calculated and those regions with shape parameters satisfying certain conditions are recognized as circular objects. The algorithm is described in detail, and comparison experiments with the randomized Hough transformation （RHT） are also provided. The experimental results on synthetic images and real images show that the proposed method has the merits of fast recognition rate, high recognition efficiency and the ability of anti-noise and anti-jamming. In addition, the method performs well when some circular objects are little deformed and partly misshapen.
基金supported by Project of the National Natural Science Foundation of China(Grant No.62073256)in part by Shaanxi Provincial Science and Technology Department(Grant No.2020GY-125)。
文摘In order to improve the recognition rate and accuracy rate of projectiles in six sky-screens intersection test system,this work proposes a new recognition method of projectiles by combining particle swarm optimization support vector and spatial-temporal constrain of six sky-screens detection sensor.Based on the measurement principle of the six sky-screens intersection test system and the characteristics of the output signal of the sky-screen,we analyze the existing problems regarding the recognition of projectiles.In order to optimize the projectile recognition effect,we use the support vector machine and basic particle swarm algorithm to form a new recognition algorithm.We set up the particle swarm algorithm optimization support vector projectile information recognition model that conforms to the six sky-screens intersection test system.We also construct a spatial-temporal constrain matching model based on the spatial geometric relationship of six sky-screen intersection,and form a new projectile signal recognition algorithm with six sky-screens spatial-temporal information constraints under the signal classification mechanism of particle swarm optimization algorithm support vector machine.Based on experiments,we obtain the optimal penalty and kernel function radius parameters in the PSO-SVM algorithm;we adjust the parameters of the support vector machine model,train the test signal data of every sky-screen,and gain the projectile signal classification results.Afterwards,according to the signal classification results,we calculate the coordinate parameters of the real projectile by using the spatial-temporal constrain of six sky-screens detection sensor,which verifies the feasibility of the proposed algorithm.
基金supported by the National Natural Science Foundation of China(61101179)
文摘This paper focuses on the recognition rate comparison for competing recognition algorithms, which is a common problem of many pattern recognition research areas. The paper firstly reviews some traditional recognition rate comparison procedures and discusses their limitations. A new method, the posterior probability calculation（PPC） procedure is then proposed based on Bayesian technique. The paper analyzes the basic principle, process steps and computational complexity of the PPC procedure. In the Bayesian view, the posterior probability represents the credible degree（equal to confidence level） of the comparison results. The posterior probability of correctly selecting or sorting the competing recognition algorithms is derived, and the minimum sample size requirement is also pre-estimated and given out by the form of tables. To further illustrate how to use our method, the PPC procedure is used to prove the rationality of the experiential choice in one application and then to calculate the confidence level with the fixed-size datasets in another application. These applications reveal the superiority of the PPC procedure, and the discussions about the stopping rule further explain the underlying statistical causes. Finally we conclude that the PPC procedure achieves all the expected functions and be superior to the traditional methods.
基金supported by the National Natural Science of China(6057407560705004).
文摘In many machine learning problems, a large amount of data is available but only a few of them can be labeled easily. This provides a research branch to effectively combine unlabeled and labeled data to infer the labels of unlabeled ones, that is, to develop transductive learning. In this article, based on Pattern classification via single sphere （SSPC）, which seeks a hypersphere to separate data with the maximum separation ratio, a progressive transductive pattern classification method via single sphere （PTSSPC） is proposed to construct the classifier using both the labeled and unlabeled data. PTSSPC utilize the additional information of the unlabeled samples and obtain better classification performance than SSPC when insufficient labeled data information is available. Experiment results show the algorithm can yields better performance.
基金Project(51171209)supported by the National Natural Science Foundation of China
文摘The main methods of the second phase quantitative analysis in current material science researches are manual recognition and extracting by using software such as Image Tool and Nano Measurer. The weaknesses such as high labor intensity and low accuracy statistic results exist in these methods. In order to overcome the shortcomings of the current methods, the Ω phase in A1-Cu-Mg-Ag alloy is taken as the research object and an algorithm based on the digital image processing and pattern recognition is proposed and implemented to do the A1 alloy TEM （transmission electron microscope） digital images process and recognize and extract the information of the second phase in the result image automatically. The top-hat transformation of the mathematical morphology, as well as several imaging processing technologies has been used in the proposed algorithm. Thereinto, top-hat transformation is used for elimination of asymmetric illumination and doing Multi-layer filtering to segment Ω phase in the TEM image. The testing results are satisfied, which indicate that the Ω phase with unclear boundary or small size can be recognized by using this method. The omission of these two kinds of Ω phase can be avoided or significantly reduced. More Ω phases would be recognized （growing rate minimum to 2% and maximum to 400% in samples）, accuracy of recognition and statistics results would be greatly improved by using this method. And the manual error can be eliminated. The procedure recognizing and making quantitative analysis of information in this method is automatically completed by the software. It can process one image, including recognition and quantitative analysis in 30 min, but the manual method such as using Image Tool or Nano Measurer need 2 h or more. The labor intensity is effectively reduced and the working efficiency is greatly improved.
文摘One of the most important features of the modern ma ch ining system in an "unmanned" factory is to change tools that have been subjec ted to wear and damage. An integrated tool condition monitoring system composed of multi-sensors, signal processing devices and intelligent decision making pla ns is a necessary requirement for automatic manufacturing processes. An intellig ent tool wear monitoring system will be introduced in this paper. The system is equipped with power consumption, vibration, AE and cutting force sensors, signal transformation and collection apparatus and a microcomputer. Tool condition monitoring is a pattern recognition process in which the characte ristics of the tool to be monitored are compared with those of the standard mode ls. The tool wear classification process is composed of the following parts: fea ture extraction; determination of the fuzzy membership functions of the features ; calculation of the fuzzy similarity; learning and tool wear classification. Fe atures extracted from the time domain and frequency domain for the future patter n recognition are as follows. Power consumption signal: mean value; AE-RMS sign al: mean value, skew and kutorsis; Cutting force, AE and vibration signal: mean value, standard deviation and the mean power in 10 frequency ranges. These signa l features can reflect the tool wear states comprehensively. The fuzzy approachi ng degree and the fuzzy distance between corresponding features of different obj ects are combined to describe the closeness of two fuzzy sets more accurately. A unique fuzzy driven neural network based pattern recognition algorithm has bee n developed from this research. The combination of Artificial Neural Networks (A NNs) and fuzzy logic system integrates the strong learning and classification ab ility of the former and the superb flexibility of the latter to express the dist ribution characteristics of signal features with vague boundaries. This methodol ogy indirectly solves the automatic weight assignment problem of the conventiona l fuzzy pattern recognition system and let it have greater representative power, higher training speed and be more robust. The introduction of the two-dimensio nal weighted approaching degree can make the pattern recognition process more re liable. The fuzzy driven neural network can effectively fuse multi-sensor i nformation and successfully recognize the tool wear states. Armed with the advan ced pattern recognition methodology, the established intelligent tool condition monitoring system has the advantages of being suitable for different machini ng conditions, robust to noise and tolerant to faults. Cooperated with the contr ol system of the machine tool, the optimized machining processed can be achieved .
基金Project(2012CB725403)supported by the National Basic Research Program of ChinaProjects(71210001,51338008)supported by the National Natural Science Foundation of ChinaProject supported by World Capital Cities Smooth Traffic Collaborative Innovation Center and Singapore National Research Foundation Under Its Campus for Research Excellence and Technology Enterprise(CREATE)Programme
文摘Short-term traffic flow prediction is one of the essential issues in intelligent transportation systems(ITS). A new two-stage traffic flow prediction method named AKNN-AVL method is presented, which combines an advanced k-nearest neighbor(AKNN)method and balanced binary tree(AVL) data structure to improve the prediction accuracy. The AKNN method uses pattern recognition two times in the searching process, which considers the previous sequences of traffic flow to forecast the future traffic state. Clustering method and balanced binary tree technique are introduced to build case database to reduce the searching time. To illustrate the effects of these developments, the accuracies performance of AKNN-AVL method, k-nearest neighbor(KNN) method and the auto-regressive and moving average(ARMA) method are compared. These methods are calibrated and evaluated by the real-time data from a freeway traffic detector near North 3rd Ring Road in Beijing under both normal and incident traffic conditions.The comparisons show that the AKNN-AVL method with the optimal neighbor and pattern size outperforms both KNN method and ARMA method under both normal and incident traffic conditions. In addition, the combinations of clustering method and balanced binary tree technique to the prediction method can increase the searching speed and respond rapidly to case database fluctuations.
基金Project(50276005) supported by the National Natural Science Foundation of China Projects (2006CB705400, 2003CB716206) supported by National Basic Research Program of China
文摘To avoid unstable learning, a stable adaptive learning algorithm was proposed for discrete-time recurrent neural networks. Unlike the dynamic gradient methods, such as the backpropagation through time and the real time recurrent learning, the weights of the recurrent neural networks were updated online in terms of Lyapunov stability theory in the proposed learning algorithm, so the learning stability was guaranteed. With the inversion of the activation function of the recurrent neural networks, the proposed learning algorithm can be easily implemented for solving varying nonlinear adaptive learning problems and fast convergence of the adaptive learning process can be achieved. Simulation experiments in pattern recognition show that only 5 iterations are needed for the storage of a 15×15 binary image pattern and only 9 iterations are needed for the perfect realization of an analog vector by an equilibrium state with the proposed learning algorithm.
文摘For some reasons, engineers build their product 3D mo del according to a set of related engineering drawings. The problem is how we ca n know the 3D model is correct. The manual checking is very boring and time cons uming, and still could not avoid mistakes. Thus, we could not confirm the model, maybe try checking again. It will effect the production preparing cycle greatly , and should be solved in a intelligent way. The difficulties are quite obvious, unlike word checking in a word processing package, the checking described above is not a comparison between same items. One is 2D drawing, the another is 3D mo del, they are not in the same dimension. So, we should make a change for compari son in the same dimension. If we can rebuild a 3D model through related 2D drawi ngs automatically, that’s great. We can not only compare two 3D models to check and correct, but also omit the manual process itself completely. Unfortunately, we can not build such a 3D model automatically right now. So only one way left: compare two 2D drawings, one is the original, the another is processed from tha t manual built one.The method is to select a drawing as a background, rotate th e 3D model and make projections, compare projection with the background automati cally to find a case which they meet each other in certain amount of error ( tolerance), otherwise alarm. This process can be repeated many times if needed t o fulfil the checking task. Also, this is a man-machine system, computer does h ard working, man keeps final decision. The project involved in CAD, VRML, patter n recognition, image capture and comparison, artificial intelligence.
基金Project(50474033)supported by the National Natural Science Foundation of China
文摘To find out all dependency relationships in which metaphors probably exist between syntax constituents in a given sentence,a dependency tree matching algorithm oriented to Chinese metaphor processing is proposed based on a research of unordered tree inclusion matching.In this algorithm,the pattern library is composed of formalization dependency syntax trees that are derived from large-scale metaphor sentences.These kinds of metaphor sentences are saved in the pattern library in advance.The main process of this algorithm is up-down searching and bottom-up backtracking revising.The algorithm discovers potential metaphoric structures in Chinese sentences from metaphoric dependency pattern library.Finally,the feasibility and efficiency of the new matching algorithm are further testified by the results of a series of experiments on dependency pattern library.Hence,accurate dependency relationships can be achieved through this algorithm.
基金supported by the National Natural Science Foundation of China(62176197,61806155)the National Natural Science Foundation of Shaanxi Province(2020GY-062).
文摘Partial label learning aims to learn a multi-class classifier,where each training example corresponds to a set of candidate labels among which only one is correct.Most studies in the label space have only focused on the difference between candidate labels and non-candidate labels.So far,however,there has been little discussion about the label correlation in the partial label learning.This paper begins with a research on the label correlation,followed by the establishment of a unified framework that integrates the label correlation,the adaptive graph,and the semantic difference maximization criterion.This work generates fresh insight into the acquisition of the learning information from the label space.Specifically,the label correlation is calculated from the candidate label set and is utilized to obtain the similarity of each pair of instances in the label space.After that,the labeling confidence for each instance is updated by the smoothness assumption that two instances should be similar outputs in the label space if they are close in the feature space.At last,an effective optimization program is utilized to solve the unified framework.Extensive experiments on artificial and real-world data sets indicate the superiority of our proposed method to state-of-art partial label learning methods.
文摘This paper summarizes the research results dealing with washer and nut taxonomy and knowledge base design, making the use of fuzzy methodology. In particular, the theory of fuzzy membership functions, similarity matrices, and the operation of fuzzy inference play important roles.A realistic set of 25 washers and nuts are employed to conduct extensive experiments and simulations.The investigation includes a complete demonstration of engineering design. The results obtained from this feasibility study are very encouraging indeed because they represent the lower bound with respect to performance, namely correctrecognition rate, of what fuzzy methodology can do. This lower bound shows high recognition rate even with noisy input patterns, robustness in terms of noise tolerance, and simplicity in hardware implementation. Possible future works are suggested in the conclusion.
文摘In this paper, we first make a brief review on the fundamental properties of artificial neural networks (ANN) and the basic models, and explore emphatically some potential application of artificial neural networks in the area of product quality diagnosis, prediction and control, state supervision and classification, factor recognition, and expert system based diagnosis, then set up the ANN models and expert system for quality forecasting, monitoring and diagnosing. We point out that combining ANN with other techniques will have the broad development and application of perspectives. Finally, the paper gives out some practical applications for the models and the system.
文摘In this report several practical issues about moment invariants with application to image classification are concerned. A modified formulation for the approximation of the moments of digital images is suggested. Four computational procedures and their corresponding noise performances are studied in detail.
基金the Fujian Provincial Natural Science Foundation of China (Z051049, 2006J0391).
文摘The definition of rough similarity degree is given based on the axiomatic similarity degree, and the properties of rough similarity degree are listed. Using the properties of rough similarity degree, the method of clustering in rough systems can be obtained. After clustering, a new sample can be recognized by the principle of maximal rough similarity degree.
基金the Fujian Provincial Natural Science Foundation of China (Z0510492006J0391)
文摘Based on rough similarity degree of rough sets and close degree of fuzzy sets, the definitions of rough similarity degree and rough close degree of rough fuzzy sets are given, which can be used to measure the similar degree between two rough fuzzy sets. The properties and theorems are listed. Using the two new measures, the method of clustering in the rough fuzzy system can be obtained. After clustering, the new fuzzy sample can be recognized by the principle of maximal similarity degree.
