In this paper, the prey, biology and ecology of Agistemus exsertus was summarized. And the effect on A. exsertus of pesticide was introduced particularly. The mass-rearing research of A. exsertus was included too. The...In this paper, the prey, biology and ecology of Agistemus exsertus was summarized. And the effect on A. exsertus of pesticide was introduced particularly. The mass-rearing research of A. exsertus was included too. The data from 1970 to 2004 was collected in the world.展开更多
Reliable on line cutting tool conditioning monitoring is an essential feature of automatic machine tool and flexible manufacturing system (FMS) and computer integrated manufacturing system (CIMS). Recently artificia...Reliable on line cutting tool conditioning monitoring is an essential feature of automatic machine tool and flexible manufacturing system (FMS) and computer integrated manufacturing system (CIMS). Recently artificial neural networks (ANNs) are used for this purpose in conjunction with suitable sensory systems. The present work in Norwegian University of Science and Technology (NTNU) uses back propagation neural networks (BP) and fuzzy neural networks (FNN) to process the cutting tool state data measured with force and acoustic emission (AE) sensors, and implements a valuable on line tool condition monitoring system using the ANNs. Different ANN structures are designed and investigated to estimate the tool wear state based on the fusion of acoustic emission and force signals. Finally, four case studies are introduced for the sensing and ANN processing of the tool wear states and the failures of the tool with practical experiment examples. The results indicate that a tool wear identification system can be achieved using the sensors integration with ANNs, and that ANNs provide a very effective method of implementing sensor integration for on line monitoring of tool wear states and abnormalities.展开更多
Based on a comprehensive study of various algorithms, the automatic recognition of traditional ocular optical measuring instruments is realized. Taking a universal tools microscope(UTM) lens view image as an example, ...Based on a comprehensive study of various algorithms, the automatic recognition of traditional ocular optical measuring instruments is realized. Taking a universal tools microscope(UTM) lens view image as an example, a 2-layer automatic recognition model for data reading is established after adopting a series of pre-processing algorithms. This model is an optimal combination of the correlation-based template matching method and a concurrent back propagation(BP) neural network. Multiple complementary feature extraction is used in generating the eigenvectors of the concurrent network. In order to improve fault-tolerance capacity, rotation invariant features based on Zernike moments are extracted from digit characters and a 4-dimensional group of the outline features is also obtained. Moreover, the operating time and reading accuracy can be adjusted dy-namically by setting the threshold value. The experimental result indicates that the newly developed algorithm has optimal recognition precision and working speed. The average reading ratio can achieve 97.23%. The recognition method can automatically obtain the results of optical measuring instruments rapidly and stably without modifying their original structure, which meets the application requirements.展开更多
This paper presents a method for dynamically predicting gas emission quantity based on the wavelet neural network （WNN） toolbox. Such a method is able to predict the gas emission quantity in adjacent subsequent time...This paper presents a method for dynamically predicting gas emission quantity based on the wavelet neural network （WNN） toolbox. Such a method is able to predict the gas emission quantity in adjacent subsequent time intervals through training the WNN with even time-interval samples. The method builds successive new model with the width of sliding window remaining invariable so as to obtain a dynamic prediction method for gas emission quantity. Furthermore, the method performs prediction by a self-developed WNN toolbox. Experiments indicate that such a model can overcome the deficiencies of the traditional static prediction model and can fully make use of the feature extraction capability of wavelet base function to reflect the geological feature of gas emission quantity dynamically. The method is characterized by simplicity, flexibility, small data scale, fast convergence rate and high prediction precision. In addition, the method is also characterized by certainty and repeatability of the predicted results. The effectiveness of this method is confirmed by simulation results. Therefore, this method will exert practical significance on promoting the application of WNN.展开更多
Through analysis on drillability of frozen soil, it is concluded that the main factors affecting the drillability of frozen soil are temperature, wave velocity, impact inductility and chiseling specific work. Based on...Through analysis on drillability of frozen soil, it is concluded that the main factors affecting the drillability of frozen soil are temperature, wave velocity, impact inductility and chiseling specific work. Based on the foundation it is discussed that applying the neural networks method to classify the drillability of frozen soil is simple and feasible, and the inputted vectors quantity of networks don’t be restricted, which make the classification on drillability of frozen soil rather well match the objective practice.展开更多
In order to predict the danger of coal and gas outburst in mine coal layer correctly, on the basis of the VLBP and LMBP algorithm in Matlab neural network toolbox, one kind of modified BP neural network was put forth ...In order to predict the danger of coal and gas outburst in mine coal layer correctly, on the basis of the VLBP and LMBP algorithm in Matlab neural network toolbox, one kind of modified BP neural network was put forth to speed up the network convergence speed in this paper. Firstly, according to the characteristics of coal and gas outburst, five key influencing factors such as excavation depth, pressure of gas, and geologic destroy degree were selected as the judging indexes of coal and gas outburst. Secondly, the prediction model for coal and gas outburst was built. Finally, it was verified by practical examples. Practical application demonstrates that, on the one hand, the modified BP prediction model based on the Matlab neural network toolbox can overcome the disadvantages of constringency and, on the other hand, it has fast convergence speed and good prediction accuracy. The analysis and computing results show that the computing speed by LMBP algorithm is faster than by VLBP algorithm but needs more memory. And the resuits show that the prediction results are identical with actual results and this model is a very efficient prediction method for mine coal and gas outburst, and has an important practical meaning for the mine production safety. So we conclude that it can be used to predict coal and gas outburst precisely in actual engineering.展开更多
In art of the canvas, the brush stroke in Chinese painting and calligraphy is one of the important behavior means of the canvas, have independent aesthetic value at the same time, contemporary, one that is with plural...In art of the canvas, the brush stroke in Chinese painting and calligraphy is one of the important behavior means of the canvas, have independent aesthetic value at the same time, contemporary, one that is with pluralism, varied painting idea and painting skill and technique of art is great and abundant, traditional in the canvas works ＂ The brush stroke in Chinese painting and calligraphy ＂ Already not merely can be spoken to the limit by the simple scribbling and wiping of pen, and Chinese comfortable brush stroke in Chinese painting and calligraphy, technique of writing incorporate visual language and spiritual intension created that can enrich the picture greatly in the canvas is created. This text attempts to canvass the canvas and create the feasibility that incorporated into comfortable brush stroke in Chinese painting and calligraphy and expansionary from two respect factors of cultural idea and skill and technique and tool material.展开更多
Fuzzy neural networks （FNN） based on Gaussian membership functions can effectively control the motion of underwater vehicles. However, their operating processes and training algorithms are complicated, placing great...Fuzzy neural networks （FNN） based on Gaussian membership functions can effectively control the motion of underwater vehicles. However, their operating processes and training algorithms are complicated, placing great demands on embedded hardware. This paper presents an advanced FNN with an S membership function matching the motion characteristics of mini underwater vehicles with wings. A leaming algorithm was then developed. Simulation results showed that the modified FNN is a simpler algorithm with faster calculations and improves responsiveness, compared with a Gaussian membership function-based FNN. It is applicable for mini underwater vehicles that don＇t need accurate positioning but must have good maneuverability.展开更多
The swelling behavior of argillaceous rocks is a complex phenomenon and has been determined using a lot of indexes in the literature. Determining the required modeling indexes that need to be performed requires expens...The swelling behavior of argillaceous rocks is a complex phenomenon and has been determined using a lot of indexes in the literature. Determining the required modeling indexes that need to be performed requires expensive tests and extensive time in different laboratories. In some of the cases, it is too diffi- cult to find a relation between the effective variables and swelling potential. This paper suggests a method for modeling the time dependent swelling pressure of argillaceous rocks. The trend of short term swelling potential during the first 3 days of the swelling pressure testing is used for modeling the long term swelling pressure of mudstone that is recorded during months. The artificial neural network (ANN) as a power tool is used for modeling this nonlinear and complex behavior. This method enables predicting the swelling potential of argillaceous rocks when the required indexes and also correlation between them is unattainable. This method facilitates the model of all studied samples under a unique formulation.展开更多
Recently, research into pathological cytology were intended to put in places of artificial intelligence systems based on the development of new diagnostic technologies and the cell image segmentation. These technologi...Recently, research into pathological cytology were intended to put in places of artificial intelligence systems based on the development of new diagnostic technologies and the cell image segmentation. These technologies are not intended to substitute the human expert but to facilitate his task. The objective of this work is to develop a method for diagnosing cancer cervical smears using cervical-vaginal segmented to build the authors＇ database and a human supervisor and as an automatic tool manage and monitor the execution of the operation of diagnostic and proposing corrective actions if necessary. The Supervisor Smart is manufactured by the technique of neural networks with a success rate of 43.3% followed by the technique of fuzzy logic with a success rate equal to 56.7% and finally to improve this rate we used neuro-fuzzy approach which has a rate which reaches 94%.展开更多
文摘In this paper, the prey, biology and ecology of Agistemus exsertus was summarized. And the effect on A. exsertus of pesticide was introduced particularly. The mass-rearing research of A. exsertus was included too. The data from 1970 to 2004 was collected in the world.
文摘Reliable on line cutting tool conditioning monitoring is an essential feature of automatic machine tool and flexible manufacturing system (FMS) and computer integrated manufacturing system (CIMS). Recently artificial neural networks (ANNs) are used for this purpose in conjunction with suitable sensory systems. The present work in Norwegian University of Science and Technology (NTNU) uses back propagation neural networks (BP) and fuzzy neural networks (FNN) to process the cutting tool state data measured with force and acoustic emission (AE) sensors, and implements a valuable on line tool condition monitoring system using the ANNs. Different ANN structures are designed and investigated to estimate the tool wear state based on the fusion of acoustic emission and force signals. Finally, four case studies are introduced for the sensing and ANN processing of the tool wear states and the failures of the tool with practical experiment examples. The results indicate that a tool wear identification system can be achieved using the sensors integration with ANNs, and that ANNs provide a very effective method of implementing sensor integration for on line monitoring of tool wear states and abnormalities.
文摘Based on a comprehensive study of various algorithms, the automatic recognition of traditional ocular optical measuring instruments is realized. Taking a universal tools microscope(UTM) lens view image as an example, a 2-layer automatic recognition model for data reading is established after adopting a series of pre-processing algorithms. This model is an optimal combination of the correlation-based template matching method and a concurrent back propagation(BP) neural network. Multiple complementary feature extraction is used in generating the eigenvectors of the concurrent network. In order to improve fault-tolerance capacity, rotation invariant features based on Zernike moments are extracted from digit characters and a 4-dimensional group of the outline features is also obtained. Moreover, the operating time and reading accuracy can be adjusted dy-namically by setting the threshold value. The experimental result indicates that the newly developed algorithm has optimal recognition precision and working speed. The average reading ratio can achieve 97.23%. The recognition method can automatically obtain the results of optical measuring instruments rapidly and stably without modifying their original structure, which meets the application requirements.
文摘This paper presents a method for dynamically predicting gas emission quantity based on the wavelet neural network （WNN） toolbox. Such a method is able to predict the gas emission quantity in adjacent subsequent time intervals through training the WNN with even time-interval samples. The method builds successive new model with the width of sliding window remaining invariable so as to obtain a dynamic prediction method for gas emission quantity. Furthermore, the method performs prediction by a self-developed WNN toolbox. Experiments indicate that such a model can overcome the deficiencies of the traditional static prediction model and can fully make use of the feature extraction capability of wavelet base function to reflect the geological feature of gas emission quantity dynamically. The method is characterized by simplicity, flexibility, small data scale, fast convergence rate and high prediction precision. In addition, the method is also characterized by certainty and repeatability of the predicted results. The effectiveness of this method is confirmed by simulation results. Therefore, this method will exert practical significance on promoting the application of WNN.
文摘Through analysis on drillability of frozen soil, it is concluded that the main factors affecting the drillability of frozen soil are temperature, wave velocity, impact inductility and chiseling specific work. Based on the foundation it is discussed that applying the neural networks method to classify the drillability of frozen soil is simple and feasible, and the inputted vectors quantity of networks don’t be restricted, which make the classification on drillability of frozen soil rather well match the objective practice.
基金Supported by the National Natural Science Foundation Project（50604008） and Scientific Research Fund of Hunan Provincial Education Department（06B029）, China Postdoctoral Science Foundation Project（2005038559）
文摘In order to predict the danger of coal and gas outburst in mine coal layer correctly, on the basis of the VLBP and LMBP algorithm in Matlab neural network toolbox, one kind of modified BP neural network was put forth to speed up the network convergence speed in this paper. Firstly, according to the characteristics of coal and gas outburst, five key influencing factors such as excavation depth, pressure of gas, and geologic destroy degree were selected as the judging indexes of coal and gas outburst. Secondly, the prediction model for coal and gas outburst was built. Finally, it was verified by practical examples. Practical application demonstrates that, on the one hand, the modified BP prediction model based on the Matlab neural network toolbox can overcome the disadvantages of constringency and, on the other hand, it has fast convergence speed and good prediction accuracy. The analysis and computing results show that the computing speed by LMBP algorithm is faster than by VLBP algorithm but needs more memory. And the resuits show that the prediction results are identical with actual results and this model is a very efficient prediction method for mine coal and gas outburst, and has an important practical meaning for the mine production safety. So we conclude that it can be used to predict coal and gas outburst precisely in actual engineering.
文摘In art of the canvas, the brush stroke in Chinese painting and calligraphy is one of the important behavior means of the canvas, have independent aesthetic value at the same time, contemporary, one that is with pluralism, varied painting idea and painting skill and technique of art is great and abundant, traditional in the canvas works ＂ The brush stroke in Chinese painting and calligraphy ＂ Already not merely can be spoken to the limit by the simple scribbling and wiping of pen, and Chinese comfortable brush stroke in Chinese painting and calligraphy, technique of writing incorporate visual language and spiritual intension created that can enrich the picture greatly in the canvas is created. This text attempts to canvass the canvas and create the feasibility that incorporated into comfortable brush stroke in Chinese painting and calligraphy and expansionary from two respect factors of cultural idea and skill and technique and tool material.
基金the Fundamental Research Foundation of Harbin Engineering University Foundation under Grant No.HEUFT08001
文摘Fuzzy neural networks （FNN） based on Gaussian membership functions can effectively control the motion of underwater vehicles. However, their operating processes and training algorithms are complicated, placing great demands on embedded hardware. This paper presents an advanced FNN with an S membership function matching the motion characteristics of mini underwater vehicles with wings. A leaming algorithm was then developed. Simulation results showed that the modified FNN is a simpler algorithm with faster calculations and improves responsiveness, compared with a Gaussian membership function-based FNN. It is applicable for mini underwater vehicles that don＇t need accurate positioning but must have good maneuverability.
文摘The swelling behavior of argillaceous rocks is a complex phenomenon and has been determined using a lot of indexes in the literature. Determining the required modeling indexes that need to be performed requires expensive tests and extensive time in different laboratories. In some of the cases, it is too diffi- cult to find a relation between the effective variables and swelling potential. This paper suggests a method for modeling the time dependent swelling pressure of argillaceous rocks. The trend of short term swelling potential during the first 3 days of the swelling pressure testing is used for modeling the long term swelling pressure of mudstone that is recorded during months. The artificial neural network (ANN) as a power tool is used for modeling this nonlinear and complex behavior. This method enables predicting the swelling potential of argillaceous rocks when the required indexes and also correlation between them is unattainable. This method facilitates the model of all studied samples under a unique formulation.
文摘Recently, research into pathological cytology were intended to put in places of artificial intelligence systems based on the development of new diagnostic technologies and the cell image segmentation. These technologies are not intended to substitute the human expert but to facilitate his task. The objective of this work is to develop a method for diagnosing cancer cervical smears using cervical-vaginal segmented to build the authors＇ database and a human supervisor and as an automatic tool manage and monitor the execution of the operation of diagnostic and proposing corrective actions if necessary. The Supervisor Smart is manufactured by the technique of neural networks with a success rate of 43.3% followed by the technique of fuzzy logic with a success rate equal to 56.7% and finally to improve this rate we used neuro-fuzzy approach which has a rate which reaches 94%.
