Considering the difficulty of integrating the depth points of nautical charts of the East China Sea into a global high-precision Grid Digital Elevation Model(Grid-DEM),we proposed a“Fusion based on Image Recognition(...Considering the difficulty of integrating the depth points of nautical charts of the East China Sea into a global high-precision Grid Digital Elevation Model(Grid-DEM),we proposed a“Fusion based on Image Recognition(FIR)”method for multi-sourced depth data fusion,and used it to merge the electronic nautical chart dataset(referred to as Chart2014 in this paper)with the global digital elevation dataset(referred to as Globalbath2002 in this paper).Compared to the traditional fusion of two datasets by direct combination and interpolation,the new Grid-DEM formed by FIR can better represent the data characteristics of Chart2014,reduce the calculation difficulty,and be more intuitive,and,the choice of different interpolation methods in FIR and the influence of the“exclusion radius R”parameter were discussed.FIR avoids complex calculations of spatial distances among points from different sources,and instead uses spatial exclusion map to perform one-step screening based on the exclusion radius R,which greatly improved the fusion status of a reliable dataset.The fusion results of different experiments were analyzed statistically with root mean square error and mean relative error,showing that the interpolation methods based on Delaunay triangulation are more suitable for the fusion of nautical chart depth of China,and factors such as the point density distribution of multiple source data,accuracy,interpolation method,and various terrain conditions should be fully considered when selecting the exclusion radius R.展开更多
The greatest difficulties in recognizing geochemical hydrocarbon anomalies are: (1) how to objectively and accurately separate anomalies from background; (2) how to distinguish hydrocarbon pool related apical anomal...The greatest difficulties in recognizing geochemical hydrocarbon anomalies are: (1) how to objectively and accurately separate anomalies from background; (2) how to distinguish hydrocarbon pool related apical anomalies from lateral anomalies controlled by faults; and (3) how to eliminate interferences. These uncertainties are serious obstacles for the wide acceptance and use of geochemical techniques in hydrocarbon exploration. In this paper, the features of hydrocarbon anomalies were analyzed based on the micro migration mechanisms. In most cases, there are two anomalous populations or point groups, which are produced by two distinct mechanisms: (1) a population that directly reflects oil and gas fields, and (2) one that is related to structures such as faults. Statistical studies show that background anomalous populations and the boundaries between them can be described by the population means, prior probabilities, which are the proportions of population sizes, and covariance matrices, when background and anomalous populations have normal distributions. When this normality condition is met, a series of formulas can be derived. The method is designed on the basis of these allows: (1) univariate anomaly recognition, (2) elimination of interferences, (3) multivariate anomaly recognition, and (4) multivariate anomaly combination which depicts a more representative picture of morphology of the anomalous target than individual anomalies. The univariate and multivariate anomaly recognition can not only separate anomalies from background objectively, but also simultaneously distinguish the two types of anomalies objectively. This method was applied to the hydrocarbon data in Yangshuiwu region, Hebei Province. The interferences from regional variation of background were eliminated, and the interpretation uncertainty was reduced greatly as the anomalous populations were separated. The method was also used in Daxing region within the confines of Beijing City, and Aershan and Jiergalangtu regions in Inner Mongolia.展开更多
Based on the widely used DRASTIC method, a fuzzy pattern recognition and optimization method was proposed and applied to the fissured-karstic aquifer of Zhangji area for assessing groundwater vulnerability to pollutio...Based on the widely used DRASTIC method, a fuzzy pattern recognition and optimization method was proposed and applied to the fissured-karstic aquifer of Zhangji area for assessing groundwater vulnerability to pollution. The result is compared with DRASTIC method. It is shown that by taking the fuzziness into consideration, the fuzzy pattern recognition and optimization method reflects more efficiently the fuzzy nature of the groundwater vulnerability to pollution and is more applicable in reality.展开更多
A new image recognition method based on fuzzy rough sets theory is proposed, and its implementation discussed. The performance of this method as applied to ferrography image recognition is evaluated. It is shown that...A new image recognition method based on fuzzy rough sets theory is proposed, and its implementation discussed. The performance of this method as applied to ferrography image recognition is evaluated. It is shown that the new method gives better results than fuzzy or rough sets method when used alone.展开更多
In the process of human behavior recognition, the traditional dense optical flow method has too many pixels and too much overhead, which limits the running speed. This paper proposed a method combing YOLOv3 (You Only ...In the process of human behavior recognition, the traditional dense optical flow method has too many pixels and too much overhead, which limits the running speed. This paper proposed a method combing YOLOv3 (You Only Look Once v3) and local optical flow method. Based on the dense optical flow method, the optical flow modulus of the area where the human target is detected is calculated to reduce the amount of computation and save the cost in terms of time. And then, a threshold value is set to complete the human behavior identification. Through design algorithm, experimental verification and other steps, the walking, running and falling state of human body in real life indoor sports video was identified. Experimental results show that this algorithm is more advantageous for jogging behavior recognition.展开更多
In the traditional pattern classification method,it usually assumes that the object to be classified must lie in one of given(known)classes of the training data set.However,the training data set may not contain the cl...In the traditional pattern classification method,it usually assumes that the object to be classified must lie in one of given(known)classes of the training data set.However,the training data set may not contain the class of some objects in practice,and this is considered as an Open-Set Recognition(OSR)problem.In this paper,we propose a new progressive open-set recognition method with adaptive probability threshold.Both the labeled training data and the test data(objects to be classified)are put into a common data set,and the k-Nearest Neighbors(k-NNs)of each object are sought in this common set.Then,we can determine the probability of object lying in the given classes.If the majority of k-NNs of the object are from labeled training data,this object quite likely belongs to one of the given classes,and the density of the object and its neighbors is taken into account here.However,when most of k-NNs are from the unlabeled test data set,the class of object is considered very uncertain because the class of test data is unknown,and this object cannot be classified in this step.Once the objects belonging to known classes with high probability are all found,we re-calculate the probability of the other uncertain objects belonging to known classes based on the labeled training data and the objects marked with the estimated probability.Such iteration will stop when the probabilities of all the objects belonging to known classes are not changed.Then,a modified Otsu’s method is employed to adaptively seek the probability threshold for the final classification.If the probability of object belonging to known classes is smaller than this threshold,it will be assigned to the ignorant(unknown)class that is not included in training data set.The other objects will be committed to a specific class.The effectiveness of the proposed method has been validated using some experiments.展开更多
Advanced DriverAssistance Systems(ADAS)technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the road.Traffic Sign Recogn...Advanced DriverAssistance Systems(ADAS)technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the road.Traffic Sign Recognition System(TSRS)is one of themost important components ofADAS.Among the challengeswith TSRS is being able to recognize road signs with the highest accuracy and the shortest processing time.Accordingly,this paper introduces a new real time methodology recognizing Speed Limit Signs based on a trio of developed modules.Firstly,the Speed Limit Detection(SLD)module uses the Haar Cascade technique to generate a new SL detector in order to localize SL signs within captured frames.Secondly,the Speed Limit Classification(SLC)module,featuring machine learning classifiers alongside a newly developed model called DeepSL,harnesses the power of a CNN architecture to extract intricate features from speed limit sign images,ensuring efficient and precise recognition.In addition,a new Speed Limit Classifiers Fusion(SLCF)module has been developed by combining trained ML classifiers and the DeepSL model by using the Dempster-Shafer theory of belief functions and ensemble learning’s voting technique.Through rigorous software and hardware validation processes,the proposedmethodology has achieved highly significant F1 scores of 99.98%and 99.96%for DS theory and the votingmethod,respectively.Furthermore,a prototype encompassing all components demonstrates outstanding reliability and efficacy,with processing times of 150 ms for the Raspberry Pi board and 81.5 ms for the Nano Jetson board,marking a significant advancement in TSRS technology.展开更多
Mahalanobis-Taguchi system(MTS)is a kind of data mining and pattern recognition method which can identify the attribute characteristics of multidimensional data by constructing Mahalanobis distance(MD)measurement scal...Mahalanobis-Taguchi system(MTS)is a kind of data mining and pattern recognition method which can identify the attribute characteristics of multidimensional data by constructing Mahalanobis distance(MD)measurement scale.In this paper,considering the influence of irregular distribution of the sample data and abnormal variation of the normal data on accuracy of MTS,a feature recognition and selection model of the equipment state based on the improved MTS is proposed,and two aspects of the model namely construction of the original Mahalanobis space(MS)and determination of the threshold are studied.Firstly,the original training sample space is statistically controlled by the X-bar-S control chart,and extreme data of the single characteristic attribute is filtered to reduce the impact of extreme condition on the accuracy of the model,so as to construct a more robust MS.Furthermore,the box plot method is used to determine the threshold of the model.And the stability of the model and the tolerance to the extreme condition are improved by leaving sufficient range of the variation for the extreme condition which is identified as in the normal range.Finally,the improved model is compared with the traditional one based on the unimproved MTS by using the data from the literature.The result shows that compared with the traditional model,the accuracy and sensitivity of the improved model for state identification can be greatly enhanced.展开更多
Automatic speech recognition (ASR) is vital for very low-resource languages for mitigating the extinction trouble. Chaha is one of the low-resource languages, which suffers from the problem of resource insufficiency a...Automatic speech recognition (ASR) is vital for very low-resource languages for mitigating the extinction trouble. Chaha is one of the low-resource languages, which suffers from the problem of resource insufficiency and some of its phonological, morphological, and orthographic features challenge the development and initiatives in the area of ASR. By considering these challenges, this study is the first endeavor, which analyzed the characteristics of the language, prepared speech corpus, and developed different ASR systems. A small 3-hour read speech corpus was prepared and transcribed. Different basic and rounded phone unit-based speech recognizers were explored using multilingual deep neural network (DNN) modeling methods. The experimental results demonstrated that all the basic phone and rounded phone unit-based multilingual models outperformed the corresponding unilingual models with the relative performance improvements of 5.47% to 19.87% and 5.74% to 16.77%, respectively. The rounded phone unit-based multilingual models outperformed the equivalent basic phone unit-based models with relative performance improvements of 0.95% to 4.98%. Overall, we discovered that multilingual DNN modeling methods are profoundly effective to develop Chaha speech recognizers. Both the basic and rounded phone acoustic units are convenient to build Chaha ASR system. However, the rounded phone unit-based models are superior in performance and faster in recognition speed over the corresponding basic phone unit-based models. Hence, the rounded phone units are the most suitable acoustic units to develop Chaha ASR systems.展开更多
In this paper,transient electromagnetic method was used to carry out the feasibility study on the detection and recognition of chamber blasting misfire.Firstly,an electromagnetic background field was established in th...In this paper,transient electromagnetic method was used to carry out the feasibility study on the detection and recognition of chamber blasting misfire.Firstly,an electromagnetic background field was established in the test;secondly,a benign conductor was preset in the chamber,and then the background field was eliminated after the electromagnetic field was measured;thirdly,the transient electromagnetic field was measured again after blasting;at last,the chamber blasting misfire was detected and recognized by comparing the change of eddy current field of the preset benign conductor before and after blasting.The test results showed that:When the buried depth of aluminum box target was no more than 30 m,transient electromagnetic method can clearly identify the position of the aluminum box;when the buried depth of aluminum box was more than30 m,the buried depth and position of the aluminum box was not sure due to the unknown level of secondary eddy current field generated by aluminum box.展开更多
Applying numerical simulation technology to investigate fluid-solid interaction involving complex curved bound-aries is vital in aircraft design,ocean,and construction engineering.However,current methods such as Latti...Applying numerical simulation technology to investigate fluid-solid interaction involving complex curved bound-aries is vital in aircraft design,ocean,and construction engineering.However,current methods such as Lattice Boltzmann(LBM)and the immersion boundary method based on solid ratio(IMB)have limitations in identifying custom curved boundaries.Meanwhile,IBM based on velocity correction(IBM-VC)suffers from inaccuracies and numerical instability.Therefore,this study introduces a high-accuracy curve boundary recognition method(IMB-CB),which identifies boundary nodes by moving the search box,and corrects the weighting function in LBM by calculating the solid ratio of the boundary nodes,achieving accurate recognition of custom curve boundaries.In addition,curve boundary image and dot methods are utilized to verify IMB-CB.The findings revealed that IMB-CB can accurately identify the boundary,showing an error of less than 1.8%with 500 lattices.Also,the flow in the custom curve boundary and aerodynamic characteristics of the NACA0012 airfoil are calculated and compared to IBM-VC.Results showed that IMB-CB yields lower lift and drag coefficient errors than IBM-VC,with a 1.45%drag coefficient error.In addition,the characteristic curve of IMB-CB is very stable,whereas that of IBM-VC is not.For the moving boundary problem,LBM-IMB-CB with discrete element method(DEM)is capable of accurately simulating the physical phenomena of multi-moving particle flow in complex curved pipelines.This research proposes a new curve boundary recognition method,which can significantly promote the stability and accuracy of fluid-solid interaction simulations and thus has huge applications in engineering.展开更多
The growing need for effective biometric identification is widely acknowledged.Human face recognition is an important area in the field of biometrics.It has been an active area of research for several decades,but stil...The growing need for effective biometric identification is widely acknowledged.Human face recognition is an important area in the field of biometrics.It has been an active area of research for several decades,but still remains a challenging problem because of the complexity of the human face.The Principal Component Analysis(PCA),or the eigenface method,is a de-facto standard in human face recognition.In this paper,the principle of PCA is introduced and the compressing and rebuilding of the image is accomplished with matlab program.展开更多
In this paper, we present a theoretical codebook design method for VQ-based fast face recognition algorithm to im-prove recognition accuracy. Based on the systematic analysis and classification of code patterns, first...In this paper, we present a theoretical codebook design method for VQ-based fast face recognition algorithm to im-prove recognition accuracy. Based on the systematic analysis and classification of code patterns, firstly we theoretically create a systematically organized codebook. Combined with another codebook created by Kohonen’s Self-Organizing Maps (SOM) method, an optimized codebook consisted of 2×2 codevectors for facial images is generated. Experimental results show face recognition using such a codebook is more efficient than the codebook consisted of 4×4 codevector used in conventional algorithm. The highest average recognition rate of 98.6% is obtained for 40 persons’ 400 images of publicly available face database of AT&T Laboratories Cambridge containing variations in lighting, posing, and expressions. A table look-up (TLU) method is also proposed for the speed up of the recognition processing. By applying this method in the quantization step, the total recognition processing time achieves only 28 msec, enabling real-time face recognition.展开更多
There are many motors in operation or on standby in nuclear power plants,and the startup of group motors will have a great impact on the voltage of the emergency bus.At present,there is no special or inexpensive softw...There are many motors in operation or on standby in nuclear power plants,and the startup of group motors will have a great impact on the voltage of the emergency bus.At present,there is no special or inexpensive software to solve this problem,and the experience of engineers is not accurate enough.Therefore,this paper developed a method and system for the startup calculation of group motors in nuclear power plants and proposed an automatic generation method of circuit topology in nuclear power plants.Each component in the topology was given its unique number,and the component class could be constructed according to its type and upper and lower connections.The subordination and topology relationship of switches,buses,and motors could be quickly generated by the program according to the component class,and the simplified direct power flow algorithm was used to calculate the power flow for the startup of group motors according to the above relationship.Then,whether the bus voltage is in the safe range and whether the voltage exceeds the limit during the startup of the group motor could be judged.The practical example was used to verify the effectiveness of the method.Compared with other professional software,the method has high efficiency and low cost.展开更多
A method about fault identification is proposed to solve the relationship among fault features of large rotating machinery, which is extremely complicated and nonlinear. This paper studies the rotor test-rig and the c...A method about fault identification is proposed to solve the relationship among fault features of large rotating machinery, which is extremely complicated and nonlinear. This paper studies the rotor test-rig and the clustering of data sets and fault pattern recognitions. The present method firstly maps the data from their original space to a high dimensional Kernel space which makes the highly nonlinear data in low-dimensional space become linearly separable in Kernel space. It highlights the differences among the features of the data set. Then fuzzy C-means （FCM） is conducted in the Kernel space. Each data is assigned to the nearest class by computing the distance to the clustering center. Finally, test set is used to judge the results. The convergence rate and clustering accuracy are better than traditional FCM. The study shows that the method is effective for the accuracy of pattern recognition on rotating machinery.展开更多
Dimensionality reduction methods play an important role in face recognition. Principal component analysis(PCA) and two-dimensional principal component analysis(2DPCA) are two kinds of important methods in this field. ...Dimensionality reduction methods play an important role in face recognition. Principal component analysis(PCA) and two-dimensional principal component analysis(2DPCA) are two kinds of important methods in this field. Recent research seems like that 2DPCA method is superior to PCA method. To prove if this conclusion is always true, a comprehensive comparison study between PCA and 2DPCA methods was carried out. A novel concept, called column-image difference(CID), was proposed to analyze the difference between PCA and 2DPCA methods in theory. It is found that there exist some restrictive conditions when2 DPCA outperforms PCA. After theoretical analysis, the experiments were conducted on four famous face image databases. The experiment results confirm the validity of theoretical claim.展开更多
An attribute recognition model for safe thickness assessment between a concealed karst cave and a tunnel is established based on the attribute mathematic theory.The model can be applied to carrying out risk classifica...An attribute recognition model for safe thickness assessment between a concealed karst cave and a tunnel is established based on the attribute mathematic theory.The model can be applied to carrying out risk classification of the safe thickness between a concealed karst cave and a tunnel and to guarantee construction’s safety in tunnel engineering.Firstly,the assessment indicators and classification standard of safe thickness between a concealed karst cave and a tunnel are studied based on the perturbation method.Then some attribute measurement functions are constructed to compute the attribute measurement of each single index and synthetic attribute measurement.Finally,the identification and classification of risk assessment of safe thickness between a concealed karst cave and a tunnel are recognized by the confidence criterion.The results of two engineering application show that the evaluation results agree well with the site situations in construction.The results provide a good guidance for the tunnel construction.展开更多
A new multi-level analysis method of introducing the super-element modeling method, derived from the multi-level analysis method first proposed by O. F. Hughes, has been proposed in this paper to solve the problem of ...A new multi-level analysis method of introducing the super-element modeling method, derived from the multi-level analysis method first proposed by O. F. Hughes, has been proposed in this paper to solve the problem of high time cost in adopting a rational-based optimal design method for ship structural design. Furthermore,the method was verified by its effective application in optimization of the mid-ship section of a container ship. A full 3-D FEM model of a ship,suffering static and quasi-static loads, was used as the analyzing object for evaluating the structural performance of the mid-ship module, including static strength and buckling performance. Research results reveal that this new method could substantially reduce the computational cost of the rational-based optimization problem without decreasing its accuracy, which increases the feasibility and economic efficiency of using a rational-based optimal design method in ship structural design.展开更多
Water quality assessment of lakes is important to determine functional zones of water use.Considering the fuzziness during the partitioning process for lake water quality in an arid area,a multiplex model of fuzzy clu...Water quality assessment of lakes is important to determine functional zones of water use.Considering the fuzziness during the partitioning process for lake water quality in an arid area,a multiplex model of fuzzy clustering with pattern recognition was developed by integrating transitive closure method,ISODATA algorithm in fuzzy clustering and fuzzy pattern recognition.The model was applied to partition the Ulansuhai Lake,a typical shallow lake in arid climate zone in the west part of Inner Mongolia,China and grade the condition of water quality divisions.The results showed that the partition well matched the real conditions of the lake,and the method has been proved accurate in the application.展开更多
基金Supported by the National Key R&D Program of China (No.2023YFC3008100)the National Natural Science Foundation of China (No.U23A2033)
文摘Considering the difficulty of integrating the depth points of nautical charts of the East China Sea into a global high-precision Grid Digital Elevation Model(Grid-DEM),we proposed a“Fusion based on Image Recognition(FIR)”method for multi-sourced depth data fusion,and used it to merge the electronic nautical chart dataset(referred to as Chart2014 in this paper)with the global digital elevation dataset(referred to as Globalbath2002 in this paper).Compared to the traditional fusion of two datasets by direct combination and interpolation,the new Grid-DEM formed by FIR can better represent the data characteristics of Chart2014,reduce the calculation difficulty,and be more intuitive,and,the choice of different interpolation methods in FIR and the influence of the“exclusion radius R”parameter were discussed.FIR avoids complex calculations of spatial distances among points from different sources,and instead uses spatial exclusion map to perform one-step screening based on the exclusion radius R,which greatly improved the fusion status of a reliable dataset.The fusion results of different experiments were analyzed statistically with root mean square error and mean relative error,showing that the interpolation methods based on Delaunay triangulation are more suitable for the fusion of nautical chart depth of China,and factors such as the point density distribution of multiple source data,accuracy,interpolation method,and various terrain conditions should be fully considered when selecting the exclusion radius R.
文摘The greatest difficulties in recognizing geochemical hydrocarbon anomalies are: (1) how to objectively and accurately separate anomalies from background; (2) how to distinguish hydrocarbon pool related apical anomalies from lateral anomalies controlled by faults; and (3) how to eliminate interferences. These uncertainties are serious obstacles for the wide acceptance and use of geochemical techniques in hydrocarbon exploration. In this paper, the features of hydrocarbon anomalies were analyzed based on the micro migration mechanisms. In most cases, there are two anomalous populations or point groups, which are produced by two distinct mechanisms: (1) a population that directly reflects oil and gas fields, and (2) one that is related to structures such as faults. Statistical studies show that background anomalous populations and the boundaries between them can be described by the population means, prior probabilities, which are the proportions of population sizes, and covariance matrices, when background and anomalous populations have normal distributions. When this normality condition is met, a series of formulas can be derived. The method is designed on the basis of these allows: (1) univariate anomaly recognition, (2) elimination of interferences, (3) multivariate anomaly recognition, and (4) multivariate anomaly combination which depicts a more representative picture of morphology of the anomalous target than individual anomalies. The univariate and multivariate anomaly recognition can not only separate anomalies from background objectively, but also simultaneously distinguish the two types of anomalies objectively. This method was applied to the hydrocarbon data in Yangshuiwu region, Hebei Province. The interferences from regional variation of background were eliminated, and the interpretation uncertainty was reduced greatly as the anomalous populations were separated. The method was also used in Daxing region within the confines of Beijing City, and Aershan and Jiergalangtu regions in Inner Mongolia.
基金Project (No. ICA4-CT-2001-10039) supported by Manporivers(Management policies for priority water pollutants and their effects onfoods and human health: general methodology and application toChinese river basins)
文摘Based on the widely used DRASTIC method, a fuzzy pattern recognition and optimization method was proposed and applied to the fissured-karstic aquifer of Zhangji area for assessing groundwater vulnerability to pollution. The result is compared with DRASTIC method. It is shown that by taking the fuzziness into consideration, the fuzzy pattern recognition and optimization method reflects more efficiently the fuzzy nature of the groundwater vulnerability to pollution and is more applicable in reality.
文摘A new image recognition method based on fuzzy rough sets theory is proposed, and its implementation discussed. The performance of this method as applied to ferrography image recognition is evaluated. It is shown that the new method gives better results than fuzzy or rough sets method when used alone.
文摘In the process of human behavior recognition, the traditional dense optical flow method has too many pixels and too much overhead, which limits the running speed. This paper proposed a method combing YOLOv3 (You Only Look Once v3) and local optical flow method. Based on the dense optical flow method, the optical flow modulus of the area where the human target is detected is calculated to reduce the amount of computation and save the cost in terms of time. And then, a threshold value is set to complete the human behavior identification. Through design algorithm, experimental verification and other steps, the walking, running and falling state of human body in real life indoor sports video was identified. Experimental results show that this algorithm is more advantageous for jogging behavior recognition.
基金supported by the National Natural Science Foundation of China(Nos.U20B2067).
文摘In the traditional pattern classification method,it usually assumes that the object to be classified must lie in one of given(known)classes of the training data set.However,the training data set may not contain the class of some objects in practice,and this is considered as an Open-Set Recognition(OSR)problem.In this paper,we propose a new progressive open-set recognition method with adaptive probability threshold.Both the labeled training data and the test data(objects to be classified)are put into a common data set,and the k-Nearest Neighbors(k-NNs)of each object are sought in this common set.Then,we can determine the probability of object lying in the given classes.If the majority of k-NNs of the object are from labeled training data,this object quite likely belongs to one of the given classes,and the density of the object and its neighbors is taken into account here.However,when most of k-NNs are from the unlabeled test data set,the class of object is considered very uncertain because the class of test data is unknown,and this object cannot be classified in this step.Once the objects belonging to known classes with high probability are all found,we re-calculate the probability of the other uncertain objects belonging to known classes based on the labeled training data and the objects marked with the estimated probability.Such iteration will stop when the probabilities of all the objects belonging to known classes are not changed.Then,a modified Otsu’s method is employed to adaptively seek the probability threshold for the final classification.If the probability of object belonging to known classes is smaller than this threshold,it will be assigned to the ignorant(unknown)class that is not included in training data set.The other objects will be committed to a specific class.The effectiveness of the proposed method has been validated using some experiments.
文摘Advanced DriverAssistance Systems(ADAS)technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the road.Traffic Sign Recognition System(TSRS)is one of themost important components ofADAS.Among the challengeswith TSRS is being able to recognize road signs with the highest accuracy and the shortest processing time.Accordingly,this paper introduces a new real time methodology recognizing Speed Limit Signs based on a trio of developed modules.Firstly,the Speed Limit Detection(SLD)module uses the Haar Cascade technique to generate a new SL detector in order to localize SL signs within captured frames.Secondly,the Speed Limit Classification(SLC)module,featuring machine learning classifiers alongside a newly developed model called DeepSL,harnesses the power of a CNN architecture to extract intricate features from speed limit sign images,ensuring efficient and precise recognition.In addition,a new Speed Limit Classifiers Fusion(SLCF)module has been developed by combining trained ML classifiers and the DeepSL model by using the Dempster-Shafer theory of belief functions and ensemble learning’s voting technique.Through rigorous software and hardware validation processes,the proposedmethodology has achieved highly significant F1 scores of 99.98%and 99.96%for DS theory and the votingmethod,respectively.Furthermore,a prototype encompassing all components demonstrates outstanding reliability and efficacy,with processing times of 150 ms for the Raspberry Pi board and 81.5 ms for the Nano Jetson board,marking a significant advancement in TSRS technology.
基金the National Natural Science Foundation of China(No.71401016)the Shaanxi Provincial Natural Science Foundation of China(No.2019JM-495)the Fundamental Research Funds for Central Universities of Chang'an University(Nos.300102228110 and 300102228402)。
文摘Mahalanobis-Taguchi system(MTS)is a kind of data mining and pattern recognition method which can identify the attribute characteristics of multidimensional data by constructing Mahalanobis distance(MD)measurement scale.In this paper,considering the influence of irregular distribution of the sample data and abnormal variation of the normal data on accuracy of MTS,a feature recognition and selection model of the equipment state based on the improved MTS is proposed,and two aspects of the model namely construction of the original Mahalanobis space(MS)and determination of the threshold are studied.Firstly,the original training sample space is statistically controlled by the X-bar-S control chart,and extreme data of the single characteristic attribute is filtered to reduce the impact of extreme condition on the accuracy of the model,so as to construct a more robust MS.Furthermore,the box plot method is used to determine the threshold of the model.And the stability of the model and the tolerance to the extreme condition are improved by leaving sufficient range of the variation for the extreme condition which is identified as in the normal range.Finally,the improved model is compared with the traditional one based on the unimproved MTS by using the data from the literature.The result shows that compared with the traditional model,the accuracy and sensitivity of the improved model for state identification can be greatly enhanced.
文摘Automatic speech recognition (ASR) is vital for very low-resource languages for mitigating the extinction trouble. Chaha is one of the low-resource languages, which suffers from the problem of resource insufficiency and some of its phonological, morphological, and orthographic features challenge the development and initiatives in the area of ASR. By considering these challenges, this study is the first endeavor, which analyzed the characteristics of the language, prepared speech corpus, and developed different ASR systems. A small 3-hour read speech corpus was prepared and transcribed. Different basic and rounded phone unit-based speech recognizers were explored using multilingual deep neural network (DNN) modeling methods. The experimental results demonstrated that all the basic phone and rounded phone unit-based multilingual models outperformed the corresponding unilingual models with the relative performance improvements of 5.47% to 19.87% and 5.74% to 16.77%, respectively. The rounded phone unit-based multilingual models outperformed the equivalent basic phone unit-based models with relative performance improvements of 0.95% to 4.98%. Overall, we discovered that multilingual DNN modeling methods are profoundly effective to develop Chaha speech recognizers. Both the basic and rounded phone acoustic units are convenient to build Chaha ASR system. However, the rounded phone unit-based models are superior in performance and faster in recognition speed over the corresponding basic phone unit-based models. Hence, the rounded phone units are the most suitable acoustic units to develop Chaha ASR systems.
文摘In this paper,transient electromagnetic method was used to carry out the feasibility study on the detection and recognition of chamber blasting misfire.Firstly,an electromagnetic background field was established in the test;secondly,a benign conductor was preset in the chamber,and then the background field was eliminated after the electromagnetic field was measured;thirdly,the transient electromagnetic field was measured again after blasting;at last,the chamber blasting misfire was detected and recognized by comparing the change of eddy current field of the preset benign conductor before and after blasting.The test results showed that:When the buried depth of aluminum box target was no more than 30 m,transient electromagnetic method can clearly identify the position of the aluminum box;when the buried depth of aluminum box was more than30 m,the buried depth and position of the aluminum box was not sure due to the unknown level of secondary eddy current field generated by aluminum box.
基金WJD,JYZ,CLC,ZX,and ZGY were supported by the National Natural Science Foundation of China(Grant Number 51705143)the Education Department of Hunan Province(Grant Number 22B0464)the Postgraduate Scientific Research Innovation Project of Hunan Province(Grant Number QL20230249).
文摘Applying numerical simulation technology to investigate fluid-solid interaction involving complex curved bound-aries is vital in aircraft design,ocean,and construction engineering.However,current methods such as Lattice Boltzmann(LBM)and the immersion boundary method based on solid ratio(IMB)have limitations in identifying custom curved boundaries.Meanwhile,IBM based on velocity correction(IBM-VC)suffers from inaccuracies and numerical instability.Therefore,this study introduces a high-accuracy curve boundary recognition method(IMB-CB),which identifies boundary nodes by moving the search box,and corrects the weighting function in LBM by calculating the solid ratio of the boundary nodes,achieving accurate recognition of custom curve boundaries.In addition,curve boundary image and dot methods are utilized to verify IMB-CB.The findings revealed that IMB-CB can accurately identify the boundary,showing an error of less than 1.8%with 500 lattices.Also,the flow in the custom curve boundary and aerodynamic characteristics of the NACA0012 airfoil are calculated and compared to IBM-VC.Results showed that IMB-CB yields lower lift and drag coefficient errors than IBM-VC,with a 1.45%drag coefficient error.In addition,the characteristic curve of IMB-CB is very stable,whereas that of IBM-VC is not.For the moving boundary problem,LBM-IMB-CB with discrete element method(DEM)is capable of accurately simulating the physical phenomena of multi-moving particle flow in complex curved pipelines.This research proposes a new curve boundary recognition method,which can significantly promote the stability and accuracy of fluid-solid interaction simulations and thus has huge applications in engineering.
文摘The growing need for effective biometric identification is widely acknowledged.Human face recognition is an important area in the field of biometrics.It has been an active area of research for several decades,but still remains a challenging problem because of the complexity of the human face.The Principal Component Analysis(PCA),or the eigenface method,is a de-facto standard in human face recognition.In this paper,the principle of PCA is introduced and the compressing and rebuilding of the image is accomplished with matlab program.
文摘In this paper, we present a theoretical codebook design method for VQ-based fast face recognition algorithm to im-prove recognition accuracy. Based on the systematic analysis and classification of code patterns, firstly we theoretically create a systematically organized codebook. Combined with another codebook created by Kohonen’s Self-Organizing Maps (SOM) method, an optimized codebook consisted of 2×2 codevectors for facial images is generated. Experimental results show face recognition using such a codebook is more efficient than the codebook consisted of 4×4 codevector used in conventional algorithm. The highest average recognition rate of 98.6% is obtained for 40 persons’ 400 images of publicly available face database of AT&T Laboratories Cambridge containing variations in lighting, posing, and expressions. A table look-up (TLU) method is also proposed for the speed up of the recognition processing. By applying this method in the quantization step, the total recognition processing time achieves only 28 msec, enabling real-time face recognition.
基金Key Project of National Natural Science Foundation of China(52237008)Beijing Municipal Education Commission Research Program Funding Project(KM202111232022)。
文摘There are many motors in operation or on standby in nuclear power plants,and the startup of group motors will have a great impact on the voltage of the emergency bus.At present,there is no special or inexpensive software to solve this problem,and the experience of engineers is not accurate enough.Therefore,this paper developed a method and system for the startup calculation of group motors in nuclear power plants and proposed an automatic generation method of circuit topology in nuclear power plants.Each component in the topology was given its unique number,and the component class could be constructed according to its type and upper and lower connections.The subordination and topology relationship of switches,buses,and motors could be quickly generated by the program according to the component class,and the simplified direct power flow algorithm was used to calculate the power flow for the startup of group motors according to the above relationship.Then,whether the bus voltage is in the safe range and whether the voltage exceeds the limit during the startup of the group motor could be judged.The practical example was used to verify the effectiveness of the method.Compared with other professional software,the method has high efficiency and low cost.
基金supported by the National Natural Science Foundation of China(51675253)
文摘A method about fault identification is proposed to solve the relationship among fault features of large rotating machinery, which is extremely complicated and nonlinear. This paper studies the rotor test-rig and the clustering of data sets and fault pattern recognitions. The present method firstly maps the data from their original space to a high dimensional Kernel space which makes the highly nonlinear data in low-dimensional space become linearly separable in Kernel space. It highlights the differences among the features of the data set. Then fuzzy C-means （FCM） is conducted in the Kernel space. Each data is assigned to the nearest class by computing the distance to the clustering center. Finally, test set is used to judge the results. The convergence rate and clustering accuracy are better than traditional FCM. The study shows that the method is effective for the accuracy of pattern recognition on rotating machinery.
基金supported by the support plan for the development of Marxist theoretical discipline in Shanghai in 2017(Marxist theory teaching research on“Young and Middle-aged Talents”project)In 2017,The special topic on“Chinese Citizens’Political Identity Since the 18th CPC National Congress”in the“Research on Xi Jinping’s Important Thoughts In The New Era”held by Shanghai International Studies UniversityThe research has been funded by the basic scientific research fee of the central colleges and universities
基金Projects(50275150,61173052)supported by the National Natural Science Foundation of China
文摘Dimensionality reduction methods play an important role in face recognition. Principal component analysis(PCA) and two-dimensional principal component analysis(2DPCA) are two kinds of important methods in this field. Recent research seems like that 2DPCA method is superior to PCA method. To prove if this conclusion is always true, a comprehensive comparison study between PCA and 2DPCA methods was carried out. A novel concept, called column-image difference(CID), was proposed to analyze the difference between PCA and 2DPCA methods in theory. It is found that there exist some restrictive conditions when2 DPCA outperforms PCA. After theoretical analysis, the experiments were conducted on four famous face image databases. The experiment results confirm the validity of theoretical claim.
基金Projects(51509147,51879153) supported by the National Natural Science Foundation of ChinaProjects(2017JC002,2017JC001) supported by the Fundamental Research Funds of Shandong University,China
文摘An attribute recognition model for safe thickness assessment between a concealed karst cave and a tunnel is established based on the attribute mathematic theory.The model can be applied to carrying out risk classification of the safe thickness between a concealed karst cave and a tunnel and to guarantee construction’s safety in tunnel engineering.Firstly,the assessment indicators and classification standard of safe thickness between a concealed karst cave and a tunnel are studied based on the perturbation method.Then some attribute measurement functions are constructed to compute the attribute measurement of each single index and synthetic attribute measurement.Finally,the identification and classification of risk assessment of safe thickness between a concealed karst cave and a tunnel are recognized by the confidence criterion.The results of two engineering application show that the evaluation results agree well with the site situations in construction.The results provide a good guidance for the tunnel construction.
基金Supported by the Project of Ministry of Education and Finance(No.200512)the Project of the State Key Laboratory of ocean engineering(GKZD010053-10)
文摘A new multi-level analysis method of introducing the super-element modeling method, derived from the multi-level analysis method first proposed by O. F. Hughes, has been proposed in this paper to solve the problem of high time cost in adopting a rational-based optimal design method for ship structural design. Furthermore,the method was verified by its effective application in optimization of the mid-ship section of a container ship. A full 3-D FEM model of a ship,suffering static and quasi-static loads, was used as the analyzing object for evaluating the structural performance of the mid-ship module, including static strength and buckling performance. Research results reveal that this new method could substantially reduce the computational cost of the rational-based optimization problem without decreasing its accuracy, which increases the feasibility and economic efficiency of using a rational-based optimal design method in ship structural design.
基金Supported by the National Natural Science Foundation of China (No.50269001, 50569002, 50669004)Natural Science Foundation of Inner Mongolia (No.200208020512, 200711020604)The Key Scientific and Technologic Project of the 10th Five-Year Plan of Inner Mongolia (No.20010103)
文摘Water quality assessment of lakes is important to determine functional zones of water use.Considering the fuzziness during the partitioning process for lake water quality in an arid area,a multiplex model of fuzzy clustering with pattern recognition was developed by integrating transitive closure method,ISODATA algorithm in fuzzy clustering and fuzzy pattern recognition.The model was applied to partition the Ulansuhai Lake,a typical shallow lake in arid climate zone in the west part of Inner Mongolia,China and grade the condition of water quality divisions.The results showed that the partition well matched the real conditions of the lake,and the method has been proved accurate in the application.
