Classification of plume and spatter images was studied to evaluate the welding stability. A high-speed camera was used to capture the instantaneous images of plume and spatters during high power disk laser welding. Ch...Classification of plume and spatter images was studied to evaluate the welding stability. A high-speed camera was used to capture the instantaneous images of plume and spatters during high power disk laser welding. Characteristic parameters such as the area and number of spatters, the average grayscale of a spatter image, the entropy of a spatter grayscale image, the coordinate ratio of the plume centroid and the welding point, the polar coordinates of the plume centroid were defined and extracted. Karhunen-Loeve transform method was used to change the seven characteristics into three primary characteristics to reduce the dimensions. Also, K-nearest neighbor method was used to classify the plume and spatter images into two categories such as good and poor welding quality. The results show that plume and spatter have a close relationship with the welding stability, and two categories could be recognized effectively using K-nearest neighbor method based on Karhunen-Loeve transform.展开更多
An approach for color image segmentation is proposed based on the contributions of color features to segmentation rather than the choice of a particular color space. The determination of effective color features depen...An approach for color image segmentation is proposed based on the contributions of color features to segmentation rather than the choice of a particular color space. The determination of effective color features depends on the analysis of various color features from each tested color image via the designed feature encoding. It is different from the pervious methods where self organized feature map (SOFM) is used for constructing the feature encoding so that the feature encoding can self organize the effective features for different color images. Fuzzy clustering is applied for the final segmentation when the well suited color features and the initial parameter are available. The proposed method has been applied in segmenting different types of color images and the experimental results show that it outperforms the classical clustering method. The study shows that the feature encoding approach offers great promise in automating and optimizing the segmentation of color images.展开更多
Finding channel sandbodies is an important task in oil and gas exploration due to the importance of fluvial reservoirs. It is difficult to describe fluvial reservoirs in detail owing to their frequent changes and seri...Finding channel sandbodies is an important task in oil and gas exploration due to the importance of fluvial reservoirs. It is difficult to describe fluvial reservoirs in detail owing to their frequent changes and serious intersections, as well as limitations of S/N ratio and seismic data resolution. Based on the Laohekou 3D data in Shengli Oilfield, we analyze the general characteristics of fluvial reservoirs in this area, from which we find that they are characterized by strong amplitudes on seismic profiles, high continuity on time slices, and low frequency in the frequency domain. In addition, a cluster of strong string-bead- like reflections was found after color processing and detailed interpretation. To understand this observation, we conduct forward modeling to explain the mechanism. This provides a new way to identify ancient channels in similar areas. By using the multi-attribute fusion and RGB display techniques, channel incision is more obvious and the characteristics of the channel structures are manifested much better. Finally, we introduce and apply multi-wavelet detection technology to identify weaker fluvial reservoir signals.展开更多
A new method for combining features via importance-inhibition analysis （IIA） is described to obtain more effective feature combination in learning question classification. Features are combined based on the inhibiti...A new method for combining features via importance-inhibition analysis （IIA） is described to obtain more effective feature combination in learning question classification. Features are combined based on the inhibition among features as well as the importance of individual features. Experimental results on the Chinese questions set show that, the IIA method shows a gradual increase in average and maximum accuracies at all feature combinations, and achieves great improvement over the importance analysis（IA） method on the whole. Moreover, the IIA method achieves the same highest accuracy as the one by the exhaustive method, and further improves the performance of question classification.展开更多
In order to investigate the influence of secondary classification mode on waste generation features, this study classified domestic waste generated by 310 rural and urban households at urban areas and Shuigaozhuang Vi...In order to investigate the influence of secondary classification mode on waste generation features, this study classified domestic waste generated by 310 rural and urban households at urban areas and Shuigaozhuang Village of Xiqing District into 3 groups： compostable materials, recyclable materials and toxics on the basis of the constructed secondary classification mode of domestic waste. The study focused on waste generation strength and classification features, compared the waste generation features between rural and urban residents, and analyzed the re- lation between waste generation strength and economic and cultural factors. The re- sults indicated that the average generation speed of urban domestic waste was 423.08 g/（d.capita）, and that of rural domestic waste was 629.89 g/（d.capita）, there was significant difference between rural and urban compost generation strength （P= 0.00002）, while the generation strength of recyclable materials and toxics between rural and urban areas had no significant difference （P=0.471 and P=0.099, respec- tively）. Secondary classification mode is an effective source classification mode for domestic wastes and has positive effects on waste reduction and treatment.展开更多
To solve the traveling salesman problem with the characteristics of clustering,a novel hybrid algorithm,the ant colony algorithm combined with the C-means algorithm,is presented.In order to improve the speed of conver...To solve the traveling salesman problem with the characteristics of clustering,a novel hybrid algorithm,the ant colony algorithm combined with the C-means algorithm,is presented.In order to improve the speed of convergence,the traveling salesman problem(TSP)data is specially clustered by the C-means algorithm,then,the result is processed by the ant colony algorithm to solve the problem.The proposed algorithm treats the C-means algorithm as a new search operator and adopts a kind of local searching strategy—2-opt,so as to improve the searching performance.Given the cluster number,the algorithm can obtain the preferable solving result.Compared with the three other algorithms—the ant colony algorithm,the genetic algorithm and the simulated annealing algorithm,the proposed algorithm can make the results converge to the global optimum faster and it has higher accuracy.The algorithm can also be extended to solve other correlative clustering combination optimization problems.Experimental results indicate the validity of the proposed algorithm.展开更多
To minimize the low classification accuracy and low utilization of spatial information in traditional hyperspectral image classification methods, we propose a new hyperspectral image classification method, which is ba...To minimize the low classification accuracy and low utilization of spatial information in traditional hyperspectral image classification methods, we propose a new hyperspectral image classification method, which is based on the Gabor spatial texture features and nonparametric weighted spectral features, and the sparse representation classification method（Gabor–NWSF and SRC）, abbreviated GNWSF–SRC. The proposed（GNWSF–SRC） method first combines the Gabor spatial features and nonparametric weighted spectral features to describe the hyperspectral image, and then applies the sparse representation method. Finally, the classification is obtained by analyzing the reconstruction error. We use the proposed method to process two typical hyperspectral data sets with different percentages of training samples. Theoretical analysis and simulation demonstrate that the proposed method improves the classification accuracy and Kappa coefficient compared with traditional classification methods and achieves better classification performance.展开更多
Glyptosternoid fishes are a group of sisorid catfishes living in torrents of rivers mainly originating from the Qinghai-Tibet Plateau. Based on our survey in the Drung River Basin, seven collecting sites were investig...Glyptosternoid fishes are a group of sisorid catfishes living in torrents of rivers mainly originating from the Qinghai-Tibet Plateau. Based on our survey in the Drung River Basin, seven collecting sites were investigated and 271 glyptosternoid fishes caught belong to three species （Pareuchiloglanis kamengensis, Exostoma labiatum and Oreoglanis mocropterus）. Features of the distribution of the three catfishes were assessed. More individuals of E. labiatum were caught in the lower reaches of the Drung River with fast water velocity and it might be more adapted to a torrent habitat. The relationships between standard length （L） and weight （W） for P. kamengensis, E. labiatum and O. macropterus were also studied, and the parameter b of the L-W relationship （W = aL^b） ranged between 2. 8201 and 3. 0131. From the present study, all the three catfish species grow allometrically and the growth type of E. labiatum is the closest to a symmetrical one.展开更多
Partition-based clustering with weighted feature is developed in the framework of shadowed sets. The objects in the core and boundary regions, generated by shadowed sets-based clustering, have different impact on the ...Partition-based clustering with weighted feature is developed in the framework of shadowed sets. The objects in the core and boundary regions, generated by shadowed sets-based clustering, have different impact on the prototype of each cluster. By integrating feature weights, a formula for weight calculation is introduced to the clustering algorithm. The selection of weight exponent is crucial for good result and the weights are updated iteratively with each partition of clusters. The convergence of the weighted algorithms is given, and the feasible cluster validity indices of data mining application are utilized. Experimental results on both synthetic and real-life numerical data with different feature weights demonstrate that the weighted algorithm is better than the other unweighted algorithms.展开更多
基金Project (51175095) supported by the National Natural Science Foundation of ChinaProjects (10251009001000001,9151009001000020) supported by the Natural Science Foundation of Guangdong Province,ChinaProject (20104420110001) supported by the Specialized Research Fund for the Doctoral Program of Higher Education of China
文摘Classification of plume and spatter images was studied to evaluate the welding stability. A high-speed camera was used to capture the instantaneous images of plume and spatters during high power disk laser welding. Characteristic parameters such as the area and number of spatters, the average grayscale of a spatter image, the entropy of a spatter grayscale image, the coordinate ratio of the plume centroid and the welding point, the polar coordinates of the plume centroid were defined and extracted. Karhunen-Loeve transform method was used to change the seven characteristics into three primary characteristics to reduce the dimensions. Also, K-nearest neighbor method was used to classify the plume and spatter images into two categories such as good and poor welding quality. The results show that plume and spatter have a close relationship with the welding stability, and two categories could be recognized effectively using K-nearest neighbor method based on Karhunen-Loeve transform.
文摘An approach for color image segmentation is proposed based on the contributions of color features to segmentation rather than the choice of a particular color space. The determination of effective color features depends on the analysis of various color features from each tested color image via the designed feature encoding. It is different from the pervious methods where self organized feature map (SOFM) is used for constructing the feature encoding so that the feature encoding can self organize the effective features for different color images. Fuzzy clustering is applied for the final segmentation when the well suited color features and the initial parameter are available. The proposed method has been applied in segmenting different types of color images and the experimental results show that it outperforms the classical clustering method. The study shows that the feature encoding approach offers great promise in automating and optimizing the segmentation of color images.
基金sponsored by The Science and Technology Research Project,Shengli Oilfield (Grant No. YKW1002)
文摘Finding channel sandbodies is an important task in oil and gas exploration due to the importance of fluvial reservoirs. It is difficult to describe fluvial reservoirs in detail owing to their frequent changes and serious intersections, as well as limitations of S/N ratio and seismic data resolution. Based on the Laohekou 3D data in Shengli Oilfield, we analyze the general characteristics of fluvial reservoirs in this area, from which we find that they are characterized by strong amplitudes on seismic profiles, high continuity on time slices, and low frequency in the frequency domain. In addition, a cluster of strong string-bead- like reflections was found after color processing and detailed interpretation. To understand this observation, we conduct forward modeling to explain the mechanism. This provides a new way to identify ancient channels in similar areas. By using the multi-attribute fusion and RGB display techniques, channel incision is more obvious and the characteristics of the channel structures are manifested much better. Finally, we introduce and apply multi-wavelet detection technology to identify weaker fluvial reservoir signals.
基金The National Natural Science Foundation of China(No.61003112,61170181)the Open Research Fund of State Key Laboratory for Novel Softw are Technology of China(No.KFKT2010B02)the Key Project of Natural Science Research for Anhui Colleges of China(No.KJ2011A048)
文摘A new method for combining features via importance-inhibition analysis （IIA） is described to obtain more effective feature combination in learning question classification. Features are combined based on the inhibition among features as well as the importance of individual features. Experimental results on the Chinese questions set show that, the IIA method shows a gradual increase in average and maximum accuracies at all feature combinations, and achieves great improvement over the importance analysis（IA） method on the whole. Moreover, the IIA method achieves the same highest accuracy as the one by the exhaustive method, and further improves the performance of question classification.
基金Supported by Agricultural Scientific and Technological Achievement Transformation and Popularization Project of Tianjin(201003010)~~
文摘In order to investigate the influence of secondary classification mode on waste generation features, this study classified domestic waste generated by 310 rural and urban households at urban areas and Shuigaozhuang Village of Xiqing District into 3 groups： compostable materials, recyclable materials and toxics on the basis of the constructed secondary classification mode of domestic waste. The study focused on waste generation strength and classification features, compared the waste generation features between rural and urban residents, and analyzed the re- lation between waste generation strength and economic and cultural factors. The re- sults indicated that the average generation speed of urban domestic waste was 423.08 g/（d.capita）, and that of rural domestic waste was 629.89 g/（d.capita）, there was significant difference between rural and urban compost generation strength （P= 0.00002）, while the generation strength of recyclable materials and toxics between rural and urban areas had no significant difference （P=0.471 and P=0.099, respec- tively）. Secondary classification mode is an effective source classification mode for domestic wastes and has positive effects on waste reduction and treatment.
基金The National Key Technology R&D Program of China during the 11th Five-Year Plan Period(No.2006BAH02A06)
文摘To solve the traveling salesman problem with the characteristics of clustering,a novel hybrid algorithm,the ant colony algorithm combined with the C-means algorithm,is presented.In order to improve the speed of convergence,the traveling salesman problem(TSP)data is specially clustered by the C-means algorithm,then,the result is processed by the ant colony algorithm to solve the problem.The proposed algorithm treats the C-means algorithm as a new search operator and adopts a kind of local searching strategy—2-opt,so as to improve the searching performance.Given the cluster number,the algorithm can obtain the preferable solving result.Compared with the three other algorithms—the ant colony algorithm,the genetic algorithm and the simulated annealing algorithm,the proposed algorithm can make the results converge to the global optimum faster and it has higher accuracy.The algorithm can also be extended to solve other correlative clustering combination optimization problems.Experimental results indicate the validity of the proposed algorithm.
基金supported by the National Natural Science Foundation of China(No.61275010)the Ph.D.Programs Foundation of Ministry of Education of China(No.20132304110007)+1 种基金the Heilongjiang Natural Science Foundation(No.F201409)the Fundamental Research Funds for the Central Universities(No.HEUCFD1410)
文摘To minimize the low classification accuracy and low utilization of spatial information in traditional hyperspectral image classification methods, we propose a new hyperspectral image classification method, which is based on the Gabor spatial texture features and nonparametric weighted spectral features, and the sparse representation classification method（Gabor–NWSF and SRC）, abbreviated GNWSF–SRC. The proposed（GNWSF–SRC） method first combines the Gabor spatial features and nonparametric weighted spectral features to describe the hyperspectral image, and then applies the sparse representation method. Finally, the classification is obtained by analyzing the reconstruction error. We use the proposed method to process two typical hyperspectral data sets with different percentages of training samples. Theoretical analysis and simulation demonstrate that the proposed method improves the classification accuracy and Kappa coefficient compared with traditional classification methods and achieves better classification performance.
文摘Glyptosternoid fishes are a group of sisorid catfishes living in torrents of rivers mainly originating from the Qinghai-Tibet Plateau. Based on our survey in the Drung River Basin, seven collecting sites were investigated and 271 glyptosternoid fishes caught belong to three species （Pareuchiloglanis kamengensis, Exostoma labiatum and Oreoglanis mocropterus）. Features of the distribution of the three catfishes were assessed. More individuals of E. labiatum were caught in the lower reaches of the Drung River with fast water velocity and it might be more adapted to a torrent habitat. The relationships between standard length （L） and weight （W） for P. kamengensis, E. labiatum and O. macropterus were also studied, and the parameter b of the L-W relationship （W = aL^b） ranged between 2. 8201 and 3. 0131. From the present study, all the three catfish species grow allometrically and the growth type of E. labiatum is the closest to a symmetrical one.
基金Supported by the National Natural Science Foundation of China(61139002)~~
文摘Partition-based clustering with weighted feature is developed in the framework of shadowed sets. The objects in the core and boundary regions, generated by shadowed sets-based clustering, have different impact on the prototype of each cluster. By integrating feature weights, a formula for weight calculation is introduced to the clustering algorithm. The selection of weight exponent is crucial for good result and the weights are updated iteratively with each partition of clusters. The convergence of the weighted algorithms is given, and the feasible cluster validity indices of data mining application are utilized. Experimental results on both synthetic and real-life numerical data with different feature weights demonstrate that the weighted algorithm is better than the other unweighted algorithms.
