In the reliability analysis of slope, the performance functions derived from the most available stability analysis procedures of slopes are usually implicit and cannot be solved by first-order second-moment approach. ...In the reliability analysis of slope, the performance functions derived from the most available stability analysis procedures of slopes are usually implicit and cannot be solved by first-order second-moment approach. A new reliability analysis approach was presented based on three-dimensional Morgenstem-Price method to investigate three-dimensional effect of landslide in stability analyses. To obtain the reliability index, Support Vector Machine （SVM） was applied to approximate the performance function. The time-consuming of this approach is only 0.028% of that using Monte-Carlo method at the same computation accuracy. Also, the influence of time effect of shearing strength parameters of slope soils on the long-term reliability of three-dimensional slopes was investigated by this new approach. It is found that the reliability index of the slope would decrease by 52.54% and the failure probability would increase from 0.000 705% to 1.966%. In the end, the impact of variation coefficients of c andfon reliability index of slopes was taken into discussion and the changing trend was observed.展开更多
For physical ozone absorption without reaction,two parametric estimation methods,i.e.the common linear least square fitting and non-linear Simplex search methods,were applied,respectively,to determine the ozone mass t...For physical ozone absorption without reaction,two parametric estimation methods,i.e.the common linear least square fitting and non-linear Simplex search methods,were applied,respectively,to determine the ozone mass transfer coefficient during absorption and both methods give almost the same mass transfer coefficient.While for chemical absorption with ozone decomposition reaction,the common linear least square fitting method is not applicable for the evaluation of ozone mass transfer coefficient due to the difficulty of model linearization for describing ozone concentration dissolved in water.The nonlinear Simplex method obtains the mass transfer coefficient by minimizing the sum of the differences between the simulated and experimental ozone concentration during the whole absorption process,without the limitation of linear relationship between the dissolved ozone concentration and absorption time during the initial stage of absorption.Comparison of the ozone concentration profiles between the simulation and experimental data demonstrates that Simplex method may determine ozone mass transfer coefficient during absorption in an accurate and high efficiency way with wide applicability.展开更多
By using the extended F-expansion method, the exact solutions,including periodic wave solutions expressed by Jacobi elliptic functions, for (2+1)-dimensional nonlinear Schrdinger equation are derived. In the limit c...By using the extended F-expansion method, the exact solutions,including periodic wave solutions expressed by Jacobi elliptic functions, for (2+1)-dimensional nonlinear Schrdinger equation are derived. In the limit cases, the solitary wave solutions and the other type of traveling wave solutions for the system are obtained.展开更多
Outlier detection is an important task in data mining. In fact, it is difficult to find the clustering centers in some sophisticated multidimensional datasets and to measure the deviation degree of each potential outl...Outlier detection is an important task in data mining. In fact, it is difficult to find the clustering centers in some sophisticated multidimensional datasets and to measure the deviation degree of each potential outlier. In this work, an effective outlier detection method based on multi-dimensional clustering and local density(ODBMCLD) is proposed. ODBMCLD firstly identifies the center objects by the local density peak of data objects, and clusters the whole dataset based on the center objects. Then, outlier objects belonging to different clusters will be marked as candidates of abnormal data. Finally, the top N points among these abnormal candidates are chosen as final anomaly objects with high outlier factors. The feasibility and effectiveness of the method are verified by experiments.展开更多
Dimensional analysis and numerical simulations were carried out to research prediction method of breakthrough time of horizontal wells in bottom water reservoir. Four dimensionless independent variables and dimensionl...Dimensional analysis and numerical simulations were carried out to research prediction method of breakthrough time of horizontal wells in bottom water reservoir. Four dimensionless independent variables and dimensionless time were derived from 10 influencing factors of the problem by using dimensional analysis. Simulations of horizontal well in reservoir with bottom water were run to find the prediction correlation. A general and concise functional relationship for predicting breakthrough time was established based on simulation results and theoretical analysis. The breakthrough time of one conceptual model predicted by the correlation is very close to the result by Eclipse with less than 2% error. The practical breakthrough time of one well in Helder oilfield is 10 d, and the predicted results by the method is 11.2 d, which is more accurate than the analytical result. Case study indicates that the method could predict breakthrough time of horizontal well under different reservoir conditions accurately. For its university and ease of use, the method is suitable for quick prediction of breakthrough time.展开更多
One of the greatest challenges in the design of a gun is to balance muzzle velocity and recoil,especially for guns on aircrafts and deployable vehicles.To resolve the conflict between gun power and recoil force,a conc...One of the greatest challenges in the design of a gun is to balance muzzle velocity and recoil,especially for guns on aircrafts and deployable vehicles.To resolve the conflict between gun power and recoil force,a concept of rarefaction wave gun(RAVEN)was proposed to significantly reduce the weapon recoil and the heat in barrel,while minimally reducing the muzzle velocity.The main principle of RAVEN is that the rarefaction wave will not reach the projectile base until the muzzle by delaying the venting time of an expansion nozzle at the breech.Developed on the RAVEN principle,the purpose of this paper is to provide an engineering method for predicting the performance of a low-recoil gun with front nozzle.First,a two-dimensional two-phase flow model of interior ballistic during the RAVEN firing cycle was established.Numerical simulation results were compared with the published data to validate the reliability and accuracy.Next,the effects of the vent opening times and locations were investigated to determine the influence rules on the performance of the RAVEN with front nozzle.Then according to the results above,simple nonlinear fitting formulas were provided to explain how the muzzle velocity and the recoil force change with the vent opening time and location.Finally,a better vent venting opening time corresponding to the vent location was proposed.The findings should make an important contribution to the field of engineering applications of the RAVEN.展开更多
The searching method of failure surface which consists of complex geological structures in high and steep rock slopes was studied. Based on computer simulation technology and Monte-Carlo method, three dimensional mult...The searching method of failure surface which consists of complex geological structures in high and steep rock slopes was studied. Based on computer simulation technology and Monte-Carlo method, three dimensional multi-scale geological structures such as engineering scale and statistical scale structures of the slope were simulated. The searching method of failure route which consists of joints and rock bridges was determined via simulation annealing method by considering the shear strength of joints or rock bridges in one supposed route. When shear strengths of all the supposed routes were computed, the least shear strength route was considered failure route. Then, the inclined slice of joint slices and rock bridge slices were separated according to the position of joints and rock bridges. For the rock bridge slices, by distinguishing the failure model, the force direction to the next slice was defined. Finally, the limit equilibrium equations for every slice were established, and the slope stability factor was obtained. One practical example indicates that the discussed method is more closely to the real condition.展开更多
In this paper the Hamming distance is used to contr ol individual difference in the process of creating an original population, and a peak-depot is established to preserve information of different peak-points. So me n...In this paper the Hamming distance is used to contr ol individual difference in the process of creating an original population, and a peak-depot is established to preserve information of different peak-points. So me new methods are also put forward to improve optimization performance of genet ic algorithm, such as point-cast method and neighborhood search strategy around peak-points. The methods are used to deal with genetic operation besides of cr ossover and mutation, in order to obtain a global optimum solution and avoid GA ’s premature convergence. By means of many control rules and a peak-depot, the new algorithm carries out optimum search surrounding several peak-points. Alon g with evolution of individuals of population, the fitness of peak-points of pe ak-depot increases continually, and a global optimum solution can be obtained. The new algorithm searches around several peak-points, which increases the prob ability to obtain the global optimum solution to the best. By using some example s to test the modified genetic algorithm, the results indicate what we have done makes the modified genetic algorithm effectively to solve both of linear optimi zation problems and nonlinear optimization problems with restrictive functions.展开更多
The performance of cutting machines in terms of energy consumption and vibration directly affects the production costs. In this work, our aim was to evaluate the performance of cutting machines using hybrid intelligen...The performance of cutting machines in terms of energy consumption and vibration directly affects the production costs. In this work, our aim was to evaluate the performance of cutting machines using hybrid intelligent models. For this purpose, a systematic experimental work was performed. A database of the carbonate and granite rocks was established, in which the physical and mechanical properties of these rocks (i.e., UCS, elastic modulus, Mohs hardness, and Schmiazek abrasivity factor) and the operational parameters (i.e., depth of cut and feed rate) were considered as the input parameters. The predictive models were developed incorporating a combination of the multi-layered perceptron artificial neural networks and genetic algorithm (GANN-BP) and the support vector regression method and Cuckoo optimization algorithm (COA-SVR). The results obtained indicated that the performance of the developed GANN-BP and COA-SVR models was close to each other and that these models had good agreements with the measured values. These results also showed that these proposed models were suitable tools in evaluating the performance of cutting machines.展开更多
Driven by the challenge of integrating large amount of experimental data, classification technique emerges as one of the major and popular tools in computational biology and bioinformatics research. Machine learning m...Driven by the challenge of integrating large amount of experimental data, classification technique emerges as one of the major and popular tools in computational biology and bioinformatics research. Machine learning methods, especially kernel methods with Support Vector Machines （SVMs） are very popular and effective tools. In the perspective of kernel matrix, a technique namely Eigen- matrix translation has been introduced for protein data classification. The Eigen-matrix translation strategy has a lot of nice properties which deserve more exploration. This paper investigates the major role of Eigen-matrix translation in classification. The authors propose that its importance lies in the dimension reduction of predictor attributes within the data set. This is very important when the dimension of features is huge. The authors show by numerical experiments on real biological data sets that the proposed framework is crucial and effective in improving classification accuracy. This can therefore serve as a novel perspective for future research in dimension reduction problems.展开更多
基金Project(50878082) supported by the National Natural Science Foundation of ChinaProject(200631880237) supported by the Science and Technology Program of West Transportation of the Ministry of Transportation of ChinaKey Project(09JJ3104) supported by the Natural Science Foundation of Hunan Province, China
文摘In the reliability analysis of slope, the performance functions derived from the most available stability analysis procedures of slopes are usually implicit and cannot be solved by first-order second-moment approach. A new reliability analysis approach was presented based on three-dimensional Morgenstem-Price method to investigate three-dimensional effect of landslide in stability analyses. To obtain the reliability index, Support Vector Machine （SVM） was applied to approximate the performance function. The time-consuming of this approach is only 0.028% of that using Monte-Carlo method at the same computation accuracy. Also, the influence of time effect of shearing strength parameters of slope soils on the long-term reliability of three-dimensional slopes was investigated by this new approach. It is found that the reliability index of the slope would decrease by 52.54% and the failure probability would increase from 0.000 705% to 1.966%. In the end, the impact of variation coefficients of c andfon reliability index of slopes was taken into discussion and the changing trend was observed.
基金Project supported by China Postdoctoral Science Foundation （20100481488）, Key Fund Project of Advanced Research of the Weapon Equipment （9140A33040512JB3401）.
基金Project(2011467001)supported by the Ministry of Environment Protection of ChinaProject(2010DFB94130)supported by the Ministry of Science and Technology of China
文摘For physical ozone absorption without reaction,two parametric estimation methods,i.e.the common linear least square fitting and non-linear Simplex search methods,were applied,respectively,to determine the ozone mass transfer coefficient during absorption and both methods give almost the same mass transfer coefficient.While for chemical absorption with ozone decomposition reaction,the common linear least square fitting method is not applicable for the evaluation of ozone mass transfer coefficient due to the difficulty of model linearization for describing ozone concentration dissolved in water.The nonlinear Simplex method obtains the mass transfer coefficient by minimizing the sum of the differences between the simulated and experimental ozone concentration during the whole absorption process,without the limitation of linear relationship between the dissolved ozone concentration and absorption time during the initial stage of absorption.Comparison of the ozone concentration profiles between the simulation and experimental data demonstrates that Simplex method may determine ozone mass transfer coefficient during absorption in an accurate and high efficiency way with wide applicability.
文摘By using the extended F-expansion method, the exact solutions,including periodic wave solutions expressed by Jacobi elliptic functions, for (2+1)-dimensional nonlinear Schrdinger equation are derived. In the limit cases, the solitary wave solutions and the other type of traveling wave solutions for the system are obtained.
基金Project(61362021)supported by the National Natural Science Foundation of ChinaProject(2016GXNSFAA380149)supported by Natural Science Foundation of Guangxi Province,China+1 种基金Projects(2016YJCXB02,2017YJCX34)supported by Innovation Project of GUET Graduate Education,ChinaProject(2011KF11)supported by the Key Laboratory of Cognitive Radio and Information Processing,Ministry of Education,China
文摘Outlier detection is an important task in data mining. In fact, it is difficult to find the clustering centers in some sophisticated multidimensional datasets and to measure the deviation degree of each potential outlier. In this work, an effective outlier detection method based on multi-dimensional clustering and local density(ODBMCLD) is proposed. ODBMCLD firstly identifies the center objects by the local density peak of data objects, and clusters the whole dataset based on the center objects. Then, outlier objects belonging to different clusters will be marked as candidates of abnormal data. Finally, the top N points among these abnormal candidates are chosen as final anomaly objects with high outlier factors. The feasibility and effectiveness of the method are verified by experiments.
基金Project(2011ZX05009-004)supported by the National Science and Technology Major Projects of China
文摘Dimensional analysis and numerical simulations were carried out to research prediction method of breakthrough time of horizontal wells in bottom water reservoir. Four dimensionless independent variables and dimensionless time were derived from 10 influencing factors of the problem by using dimensional analysis. Simulations of horizontal well in reservoir with bottom water were run to find the prediction correlation. A general and concise functional relationship for predicting breakthrough time was established based on simulation results and theoretical analysis. The breakthrough time of one conceptual model predicted by the correlation is very close to the result by Eclipse with less than 2% error. The practical breakthrough time of one well in Helder oilfield is 10 d, and the predicted results by the method is 11.2 d, which is more accurate than the analytical result. Case study indicates that the method could predict breakthrough time of horizontal well under different reservoir conditions accurately. For its university and ease of use, the method is suitable for quick prediction of breakthrough time.
基金supported by the National Natural Science Foundation of China(Grant No.11502114)the Fundamental Research Funds for the Central Universities(Grant No.30918011323)
文摘One of the greatest challenges in the design of a gun is to balance muzzle velocity and recoil,especially for guns on aircrafts and deployable vehicles.To resolve the conflict between gun power and recoil force,a concept of rarefaction wave gun(RAVEN)was proposed to significantly reduce the weapon recoil and the heat in barrel,while minimally reducing the muzzle velocity.The main principle of RAVEN is that the rarefaction wave will not reach the projectile base until the muzzle by delaying the venting time of an expansion nozzle at the breech.Developed on the RAVEN principle,the purpose of this paper is to provide an engineering method for predicting the performance of a low-recoil gun with front nozzle.First,a two-dimensional two-phase flow model of interior ballistic during the RAVEN firing cycle was established.Numerical simulation results were compared with the published data to validate the reliability and accuracy.Next,the effects of the vent opening times and locations were investigated to determine the influence rules on the performance of the RAVEN with front nozzle.Then according to the results above,simple nonlinear fitting formulas were provided to explain how the muzzle velocity and the recoil force change with the vent opening time and location.Finally,a better vent venting opening time corresponding to the vent location was proposed.The findings should make an important contribution to the field of engineering applications of the RAVEN.
基金Project(50539100) supported by the National Natural Science Foundation of ChinaProject(BK2006171) supported by the Jiangsu Natural Science Foundation
文摘The searching method of failure surface which consists of complex geological structures in high and steep rock slopes was studied. Based on computer simulation technology and Monte-Carlo method, three dimensional multi-scale geological structures such as engineering scale and statistical scale structures of the slope were simulated. The searching method of failure route which consists of joints and rock bridges was determined via simulation annealing method by considering the shear strength of joints or rock bridges in one supposed route. When shear strengths of all the supposed routes were computed, the least shear strength route was considered failure route. Then, the inclined slice of joint slices and rock bridge slices were separated according to the position of joints and rock bridges. For the rock bridge slices, by distinguishing the failure model, the force direction to the next slice was defined. Finally, the limit equilibrium equations for every slice were established, and the slope stability factor was obtained. One practical example indicates that the discussed method is more closely to the real condition.
文摘In this paper the Hamming distance is used to contr ol individual difference in the process of creating an original population, and a peak-depot is established to preserve information of different peak-points. So me new methods are also put forward to improve optimization performance of genet ic algorithm, such as point-cast method and neighborhood search strategy around peak-points. The methods are used to deal with genetic operation besides of cr ossover and mutation, in order to obtain a global optimum solution and avoid GA ’s premature convergence. By means of many control rules and a peak-depot, the new algorithm carries out optimum search surrounding several peak-points. Alon g with evolution of individuals of population, the fitness of peak-points of pe ak-depot increases continually, and a global optimum solution can be obtained. The new algorithm searches around several peak-points, which increases the prob ability to obtain the global optimum solution to the best. By using some example s to test the modified genetic algorithm, the results indicate what we have done makes the modified genetic algorithm effectively to solve both of linear optimi zation problems and nonlinear optimization problems with restrictive functions.
基金Project(11039)supported by Shahrood University of Technology,Iran
文摘The performance of cutting machines in terms of energy consumption and vibration directly affects the production costs. In this work, our aim was to evaluate the performance of cutting machines using hybrid intelligent models. For this purpose, a systematic experimental work was performed. A database of the carbonate and granite rocks was established, in which the physical and mechanical properties of these rocks (i.e., UCS, elastic modulus, Mohs hardness, and Schmiazek abrasivity factor) and the operational parameters (i.e., depth of cut and feed rate) were considered as the input parameters. The predictive models were developed incorporating a combination of the multi-layered perceptron artificial neural networks and genetic algorithm (GANN-BP) and the support vector regression method and Cuckoo optimization algorithm (COA-SVR). The results obtained indicated that the performance of the developed GANN-BP and COA-SVR models was close to each other and that these models had good agreements with the measured values. These results also showed that these proposed models were suitable tools in evaluating the performance of cutting machines.
基金supported by Research Grants Council of Hong Kong under Grant No.17301214HKU CERG Grants,Fundamental Research Funds for the Central Universities+2 种基金the Research Funds of Renmin University of ChinaHung Hing Ying Physical Research Grantthe Natural Science Foundation of China under Grant No.11271144
文摘Driven by the challenge of integrating large amount of experimental data, classification technique emerges as one of the major and popular tools in computational biology and bioinformatics research. Machine learning methods, especially kernel methods with Support Vector Machines （SVMs） are very popular and effective tools. In the perspective of kernel matrix, a technique namely Eigen- matrix translation has been introduced for protein data classification. The Eigen-matrix translation strategy has a lot of nice properties which deserve more exploration. This paper investigates the major role of Eigen-matrix translation in classification. The authors propose that its importance lies in the dimension reduction of predictor attributes within the data set. This is very important when the dimension of features is huge. The authors show by numerical experiments on real biological data sets that the proposed framework is crucial and effective in improving classification accuracy. This can therefore serve as a novel perspective for future research in dimension reduction problems.
