In order to realize the auto-generation of clothing paper pattern making and reduce the reliance on the experience of clothing pattern makers,by simulating the experience of the clothing pattern maker through back pro...In order to realize the auto-generation of clothing paper pattern making and reduce the reliance on the experience of clothing pattern makers,by simulating the experience of the clothing pattern maker through back propagation(BP)neural network,400 children’s body measurements are collected and drawn into the clothing paper pattern,and the children’s body measurements and the pattern sizes generated through the children’s clothing structure design rules are imported into MATLAB neural network toolbox and a neural network model is established to automatically become the predicted pattern size.Then the parametric mathematical model of children’s clothing paper pattern is established and the children’s body measurements is imported into Auto-CAD parametric function to generate children’s clothing paper pattern automatically.The experimental interface and the virtual try-on interface are demonstrated and their effects are evaluated.The results show that the production rate of clothing paper patterns is improved by the auto-generation method,which is of positive significance to the intelligent production of clothing enterprises.展开更多
BACKGROUND Pit pattern classification using magnifying chromoendoscopy is the established method for diagnosing colorectal lesions. The Japan Narrow-band-imaging(NBI) Expert Team(JNET) classification is a novel NBI ma...BACKGROUND Pit pattern classification using magnifying chromoendoscopy is the established method for diagnosing colorectal lesions. The Japan Narrow-band-imaging(NBI) Expert Team(JNET) classification is a novel NBI magnifying endoscopic classification that focuses on the vessel, and surface patterns.AIM To determine the diagnostic efficacy of each category of the JNET and Pit pattern classifications for colorectal lesions.METHODS A systematic literature search was performed using PubMed, Embase, the Cochrane Library, and Web of Science databases. The pooled sensitivity, specificity, diagnostic odds ratio, and area under the summary receiver operating characteristic curve of each category of the JNET and Pit pattern classifications were calculated.RESULTS A total of 19227 colorectal lesions in 31 studies were included. The diagnostic performance of the JNET classification was equivalent to the Pit pattern classification in each corresponding category. The pooled sensitivity, specificity,and area under the curve(AUC) for each category of the JNET classification were as follows: 0.73(95%CI: 0.55-0.85), 0.99(95%CI: 0.97-1.00), and 0.97(95%CI: 0.95-0.98), respectively, for Type 1;0.88(95%CI: 0.78-0.94), 0.72(95%CI: 0.64-0.79), and 0.84(95%CI: 0.81-0.87), respectively, for Type 2 A;0.56(95%CI: 0.47-0.64), 0.91(95%CI: 0.79-0.96), and 0.72(95%CI: 0.68-0.76), respectively, for Type 2 B;0.51(95%CI: 0.42-0.61), 1.00(95%CI: 1.00-1.00), and 0.90(95%CI: 0.87-0.93), respectively, for Type 3.CONCLUSION This meta-analysis suggests that the diagnostic efficacy of the JNET classification may be equivalent to that of the Pit pattern classification. However, due to its simpler and clearer clinical application, the JNET classification should be promoted for the classification of colorectal lesions, and to guide the treatment strategy.展开更多
For the sake of exploring how the pattern of Chinese pine （Pinus massoniana Lamb） community changed after the invasion of the pine wood nematode （Bursaphelenchus xylophilus （Steiner ＆ Buhrer） Niclde） in Zhousha...For the sake of exploring how the pattern of Chinese pine （Pinus massoniana Lamb） community changed after the invasion of the pine wood nematode （Bursaphelenchus xylophilus （Steiner ＆ Buhrer） Niclde） in Zhoushan, Zhejiang Province, we established a test area in the local Chinese pine community. Landsat5 TM images from 1991 and 2006 were integrated with auxiliary data from field investigation and spectral data as additional sources of information. A method of expert knowledge classifier was applied to establish the expert knowledge dataset of the main vegetation cover types from which we obtained a forest type distribution map. The spatial patterns and stability of the forest, before and after the invasion of the pine wood nematode, were analyzed in terms of community patterns. The results indicated that the predominant coniferous forest type changed to a mixed forest. As a result, the forest structure became complex and the interaction between coniferous forest patches became weakened over the period from 1991 to 2006. Therefore, the resistance of the forest eco-system to plant diseases and insect pests and the stability of forest eco-system enhanced.展开更多
An alloy steel pattern recognition expert system,ASPRES,has been established for the purpose of computer aided optimal design of alloy steel in compositions and process pa- rameters,Pattern recognition techniques are ...An alloy steel pattern recognition expert system,ASPRES,has been established for the purpose of computer aided optimal design of alloy steel in compositions and process pa- rameters,Pattern recognition techniques are used to abstract inner relationship between mechanical properties and process variables.The ASPRES uses 2-dimensional graph as visual knowledge to represent domain expertise of specific object.Forward and back- ward chaining can be utilized by researcher in predicting sample performances or giving helpful suggestions about the chemical compositions and process parameters according to desired properties.展开更多
Blasting is one of the most important operations in the mining projects that has effective role in the whole operation physically and economically. Unsuitable blasting pattern may lead to unwanted events such as poor ...Blasting is one of the most important operations in the mining projects that has effective role in the whole operation physically and economically. Unsuitable blasting pattern may lead to unwanted events such as poor fragmentation, back break and fly rock. Multi attribute decision making(MADM) can be useful method for selecting the most appropriate blasting pattern among previously performed patterns. In this work, initially, from various already performed patterns, efficient and inefficient patterns are determined using data envelopment analysis(DEA). In the second step, after weighting impressive attributes using experts' opinion, elimination Et choice translating reality(ELECTRE) was used for ranking the efficient patterns and recognizing the most appropriate pattern in the Sungun Copper Mine, Iran. According to the obtained results, blasting pattern with the hole diameter of 15.24 cm, burden of 3 m, spacing of 4 m and stemming of 3.2 m has selected as the best pattern and has selected for future operation.展开更多
文摘In order to realize the auto-generation of clothing paper pattern making and reduce the reliance on the experience of clothing pattern makers,by simulating the experience of the clothing pattern maker through back propagation(BP)neural network,400 children’s body measurements are collected and drawn into the clothing paper pattern,and the children’s body measurements and the pattern sizes generated through the children’s clothing structure design rules are imported into MATLAB neural network toolbox and a neural network model is established to automatically become the predicted pattern size.Then the parametric mathematical model of children’s clothing paper pattern is established and the children’s body measurements is imported into Auto-CAD parametric function to generate children’s clothing paper pattern automatically.The experimental interface and the virtual try-on interface are demonstrated and their effects are evaluated.The results show that the production rate of clothing paper patterns is improved by the auto-generation method,which is of positive significance to the intelligent production of clothing enterprises.
基金Supported by the Natural Science Foundation of Zhejiang Province,No. LQ20H160061Medical Health Science and Technology Project of Zhejiang Provincial Health Commission,No. 2018255969。
文摘BACKGROUND Pit pattern classification using magnifying chromoendoscopy is the established method for diagnosing colorectal lesions. The Japan Narrow-band-imaging(NBI) Expert Team(JNET) classification is a novel NBI magnifying endoscopic classification that focuses on the vessel, and surface patterns.AIM To determine the diagnostic efficacy of each category of the JNET and Pit pattern classifications for colorectal lesions.METHODS A systematic literature search was performed using PubMed, Embase, the Cochrane Library, and Web of Science databases. The pooled sensitivity, specificity, diagnostic odds ratio, and area under the summary receiver operating characteristic curve of each category of the JNET and Pit pattern classifications were calculated.RESULTS A total of 19227 colorectal lesions in 31 studies were included. The diagnostic performance of the JNET classification was equivalent to the Pit pattern classification in each corresponding category. The pooled sensitivity, specificity,and area under the curve(AUC) for each category of the JNET classification were as follows: 0.73(95%CI: 0.55-0.85), 0.99(95%CI: 0.97-1.00), and 0.97(95%CI: 0.95-0.98), respectively, for Type 1;0.88(95%CI: 0.78-0.94), 0.72(95%CI: 0.64-0.79), and 0.84(95%CI: 0.81-0.87), respectively, for Type 2 A;0.56(95%CI: 0.47-0.64), 0.91(95%CI: 0.79-0.96), and 0.72(95%CI: 0.68-0.76), respectively, for Type 2 B;0.51(95%CI: 0.42-0.61), 1.00(95%CI: 1.00-1.00), and 0.90(95%CI: 0.87-0.93), respectively, for Type 3.CONCLUSION This meta-analysis suggests that the diagnostic efficacy of the JNET classification may be equivalent to that of the Pit pattern classification. However, due to its simpler and clearer clinical application, the JNET classification should be promoted for the classification of colorectal lesions, and to guide the treatment strategy.
文摘For the sake of exploring how the pattern of Chinese pine （Pinus massoniana Lamb） community changed after the invasion of the pine wood nematode （Bursaphelenchus xylophilus （Steiner ＆ Buhrer） Niclde） in Zhoushan, Zhejiang Province, we established a test area in the local Chinese pine community. Landsat5 TM images from 1991 and 2006 were integrated with auxiliary data from field investigation and spectral data as additional sources of information. A method of expert knowledge classifier was applied to establish the expert knowledge dataset of the main vegetation cover types from which we obtained a forest type distribution map. The spatial patterns and stability of the forest, before and after the invasion of the pine wood nematode, were analyzed in terms of community patterns. The results indicated that the predominant coniferous forest type changed to a mixed forest. As a result, the forest structure became complex and the interaction between coniferous forest patches became weakened over the period from 1991 to 2006. Therefore, the resistance of the forest eco-system to plant diseases and insect pests and the stability of forest eco-system enhanced.
文摘An alloy steel pattern recognition expert system,ASPRES,has been established for the purpose of computer aided optimal design of alloy steel in compositions and process pa- rameters,Pattern recognition techniques are used to abstract inner relationship between mechanical properties and process variables.The ASPRES uses 2-dimensional graph as visual knowledge to represent domain expertise of specific object.Forward and back- ward chaining can be utilized by researcher in predicting sample performances or giving helpful suggestions about the chemical compositions and process parameters according to desired properties.
文摘Blasting is one of the most important operations in the mining projects that has effective role in the whole operation physically and economically. Unsuitable blasting pattern may lead to unwanted events such as poor fragmentation, back break and fly rock. Multi attribute decision making(MADM) can be useful method for selecting the most appropriate blasting pattern among previously performed patterns. In this work, initially, from various already performed patterns, efficient and inefficient patterns are determined using data envelopment analysis(DEA). In the second step, after weighting impressive attributes using experts' opinion, elimination Et choice translating reality(ELECTRE) was used for ranking the efficient patterns and recognizing the most appropriate pattern in the Sungun Copper Mine, Iran. According to the obtained results, blasting pattern with the hole diameter of 15.24 cm, burden of 3 m, spacing of 4 m and stemming of 3.2 m has selected as the best pattern and has selected for future operation.
