Heavy-equipment airdrop is a highly risky procedure that has a complicated system due to the secluded and complex nature of factors＇ coupling. As a result, it is difficult to study the modeling and safety simulation ...Heavy-equipment airdrop is a highly risky procedure that has a complicated system due to the secluded and complex nature of factors＇ coupling. As a result, it is difficult to study the modeling and safety simulation of this system. The dynamic model of the heavy-equipment airdrop is based on the Lagrange analytical mechanics, which has all the degrees of freedom and can accurately pinpoint the real-time coordinates and attitude of the carrier with its cargo. Unfavorable conditions accounted in the factors＇ models, including aircraft malfunctions and adverse environments, are established from a man-machine-environment perspective. Subsequently, a virtual simulation system for the safety research of the multi-factor coupling heavy-equipment airdrop is developed through MATLAB/Simulink, C language and Flightgear software. To verify the veracity of the theory, the verification model is built based on dynamic software ADAMS. Finally, the emulation is put to the test with the input of realistic accident variables to ascertain its feasibility and validity of this method.展开更多
Deepwater oil and gas projects embody high risks from geology and engineering aspects, which exert substantial influence on project valuation. But the uncer- tainties may be converted to additional value to the projec...Deepwater oil and gas projects embody high risks from geology and engineering aspects, which exert substantial influence on project valuation. But the uncer- tainties may be converted to additional value to the projects in the case of flexible management. Given the flexibility of project management, this paper extends the classical real options model to a multi-factor model which contains oil price, geology, and engineering uncertainties. It then gives an application example of the new model to evaluate deepwater oil and gas projects with a numerical analytical method. Compared with other methods and models, this multi-factor real options model contains more project information. It reflects the potential value deriving not only from oil price variation but also from geology and engi- neering uncertainties, which provides more accurate and reliable valuation information for decision makers.展开更多
In this paper, a new branch-and-bound algorithm based on the Lagrangian dual relaxation and continuous relaxation is proposed for discrete multi-factor portfolio selection model with roundlot restriction in financial ...In this paper, a new branch-and-bound algorithm based on the Lagrangian dual relaxation and continuous relaxation is proposed for discrete multi-factor portfolio selection model with roundlot restriction in financial optimization. This discrete portfolio model is of integer quadratic programming problems. The separable structure of the model is investigated by using Lagrangian relaxation and dual search. Computational results show that the algorithm is capable of solving real-world portfolio problems with data from US stock market and randomly generated test problems with up to 120 securities.展开更多
Fuzzy sets theory cannot describe the neutrality degreeof data, which has largely limited the objectivity of fuzzy time seriesin uncertain data forecasting. With this regard, a multi-factor highorderintuitionistic fuz...Fuzzy sets theory cannot describe the neutrality degreeof data, which has largely limited the objectivity of fuzzy time seriesin uncertain data forecasting. With this regard, a multi-factor highorderintuitionistic fuzzy time series forecasting model is built. Inthe new model, a fuzzy clustering algorithm is used to get unequalintervals, and a more objective technique for ascertaining membershipand non-membership functions of the intuitionistic fuzzy setis proposed. On these bases, forecast rules based on multidimensionalintuitionistic fuzzy modus ponens inference are established.Finally, contrast experiments on the daily mean temperature ofBeijing are carried out, which show that the novel model has aclear advantage of improving the forecast accuracy.展开更多
Weighting values for different habitat variables used in multi-factor habitat suitability index (HSI) modeling reflect the relative influences of different variables on distribution of fish species. Using the winter-s...Weighting values for different habitat variables used in multi-factor habitat suitability index (HSI) modeling reflect the relative influences of different variables on distribution of fish species. Using the winter-spring cohort of neon flying squid (Ommastrephes bartramii) in the Northwestern Pacific Ocean as an example, we evaluated the impact of different weighting schemes on the HSI models based on sea surface temperature, gradient of sea surface temperature and sea surface height. We compared differences in predicted fishing effort and HSI values resulting from different weighting. The weighting for different habitat variables could greatly influence HSI modeling and should be carefully done based on their relative importance in influencing the resource spatial distribution. Weighting in a multi-factor HSI model should be further studied and optimization methods should be developed to improve forecasting squid spatial distributions.展开更多
In order to solve the problem of strength instability of cemented tailings backfill(CTB)under low temperature environment(≤20℃),the strength optimization and prediction of CTB under the influence of multiple factors...In order to solve the problem of strength instability of cemented tailings backfill(CTB)under low temperature environment(≤20℃),the strength optimization and prediction of CTB under the influence of multiple factors were carried out.The response surface method(RSM)was used to design the experiment to analyze the development law of backfill strength under the coupling effect of curing temperature,sand-cement ratio and slurry mass fraction,and to optimize the mix proportion;the artificial neural network algorithm(ANN)and particle swarm optimization algorithm(PSO)were used to build the prediction model of backfill strength.According to the experimental results of RSM,the optimal mix proportion under different curing temperatures was obtained.When the curing temperature is 10-15℃,the best mix proportion of sand-cement ratio is 9,and the slurry mass fraction is 71%;when the curing temperature is 15-20℃,the best mix proportion of sand-cement ratio is 8,and the slurry mass fraction is 69%.The ANN-PSO intelligent model can accurately predict the strength of CTB,its mean relative estimation error value and correlation coefficient value are only 1.95%and 0.992,and the strength of CTB under different mix proportion can be predicted quickly and accurately by using this model.展开更多
This article attempted to construct a multi-factor quantitative stock selection model,analyze the financial indicators and transaction data of listed companies in detail via the big data statistical test method,and to...This article attempted to construct a multi-factor quantitative stock selection model,analyze the financial indicators and transaction data of listed companies in detail via the big data statistical test method,and to find out the alpha excess return relative to the market in the case of short stock index futures as a hedge in the Chinese market.展开更多
This paper focuses on how to measure the interest rate risk. The conventional measure methods of interest rate risk are reviewed and the duration concept is generalized to stochastic duration in the Markovian HJM fram...This paper focuses on how to measure the interest rate risk. The conventional measure methods of interest rate risk are reviewed and the duration concept is generalized to stochastic duration in the Markovian HJM framework. The generalized stochastic duration of the coupon bond is defined as the time to maturity of a zero coupon bond having the same instantaneous variance as the coupon bond. According to this definition., the authors first present the framework of Markovian HJM model, then deduce the measures of stochastic duration in some special cases which cover some extant interest term structure.展开更多
Covariance matrix plays an important role in risk management, asset pricing, and portfolio allocation. Covariance matrix estimation becomes challenging when the dimensionality is comparable or much larger than the sam...Covariance matrix plays an important role in risk management, asset pricing, and portfolio allocation. Covariance matrix estimation becomes challenging when the dimensionality is comparable or much larger than the sample size. A widely used approach for reducing dimensionality is based on multi-factor models. Although it has been well studied and quite successful in many applications, the quality of the estimated covariance matrix is often degraded due to a nontrivial amount of missing data in the factor matrix for both technical and cost reasons. Since the factor matrix is only approximately low rank or even has full rank, existing matrix completion algorithms are not applicable. We consider a new matrix completion paradigm using the factor models directly and apply the alternating direction method of multipliers for the recovery. Numerical experiments show that the nuclear-norm matrix completion approaches are not suitable but our proposed models and algorithms are promising.展开更多
基金co-supported by the National Natural Science Foundation of China (Nos. 61374145 and U1333131)
文摘Heavy-equipment airdrop is a highly risky procedure that has a complicated system due to the secluded and complex nature of factors＇ coupling. As a result, it is difficult to study the modeling and safety simulation of this system. The dynamic model of the heavy-equipment airdrop is based on the Lagrange analytical mechanics, which has all the degrees of freedom and can accurately pinpoint the real-time coordinates and attitude of the carrier with its cargo. Unfavorable conditions accounted in the factors＇ models, including aircraft malfunctions and adverse environments, are established from a man-machine-environment perspective. Subsequently, a virtual simulation system for the safety research of the multi-factor coupling heavy-equipment airdrop is developed through MATLAB/Simulink, C language and Flightgear software. To verify the veracity of the theory, the verification model is built based on dynamic software ADAMS. Finally, the emulation is put to the test with the input of realistic accident variables to ascertain its feasibility and validity of this method.
基金supported from the National Science and Technology Major Project under Grant No.2011ZX05030
文摘Deepwater oil and gas projects embody high risks from geology and engineering aspects, which exert substantial influence on project valuation. But the uncer- tainties may be converted to additional value to the projects in the case of flexible management. Given the flexibility of project management, this paper extends the classical real options model to a multi-factor model which contains oil price, geology, and engineering uncertainties. It then gives an application example of the new model to evaluate deepwater oil and gas projects with a numerical analytical method. Compared with other methods and models, this multi-factor real options model contains more project information. It reflects the potential value deriving not only from oil price variation but also from geology and engi- neering uncertainties, which provides more accurate and reliable valuation information for decision makers.
基金Project supported by the National Natural Science Foundation of China （Grant Nos.70518001. 70671064）
文摘In this paper, a new branch-and-bound algorithm based on the Lagrangian dual relaxation and continuous relaxation is proposed for discrete multi-factor portfolio selection model with roundlot restriction in financial optimization. This discrete portfolio model is of integer quadratic programming problems. The separable structure of the model is investigated by using Lagrangian relaxation and dual search. Computational results show that the algorithm is capable of solving real-world portfolio problems with data from US stock market and randomly generated test problems with up to 120 securities.
基金supported by the National Natural Science Foundation of China(61309022)
文摘Fuzzy sets theory cannot describe the neutrality degreeof data, which has largely limited the objectivity of fuzzy time seriesin uncertain data forecasting. With this regard, a multi-factor highorderintuitionistic fuzzy time series forecasting model is built. Inthe new model, a fuzzy clustering algorithm is used to get unequalintervals, and a more objective technique for ascertaining membershipand non-membership functions of the intuitionistic fuzzy setis proposed. On these bases, forecast rules based on multidimensionalintuitionistic fuzzy modus ponens inference are established.Finally, contrast experiments on the daily mean temperature ofBeijing are carried out, which show that the novel model has aclear advantage of improving the forecast accuracy.
基金supported by the National 863 project (2007AA092201 2007AA092202)+4 种基金National Development and Reform Commission Project (2060403)"Shu Guang" Project (08GG14) from Shanghai Municipal Education CommissionShanghai Leading Academic Discipline Project (Project S30702)supported by the National Distantwater Fisheries Engineering Research Center, and Scientific Observing and Experimental Station of Oceanic Fishery Resources, Ministry of Agriculture, ChinaYong Chen’s involvement in the project was supported by the Shanghai Dongfang Scholar Program
文摘Weighting values for different habitat variables used in multi-factor habitat suitability index (HSI) modeling reflect the relative influences of different variables on distribution of fish species. Using the winter-spring cohort of neon flying squid (Ommastrephes bartramii) in the Northwestern Pacific Ocean as an example, we evaluated the impact of different weighting schemes on the HSI models based on sea surface temperature, gradient of sea surface temperature and sea surface height. We compared differences in predicted fishing effort and HSI values resulting from different weighting. The weighting for different habitat variables could greatly influence HSI modeling and should be carefully done based on their relative importance in influencing the resource spatial distribution. Weighting in a multi-factor HSI model should be further studied and optimization methods should be developed to improve forecasting squid spatial distributions.
基金the National Key Technology Research and Development Program of China(Nos.2018YFC1900603 and 2018YFC0604604)。
文摘In order to solve the problem of strength instability of cemented tailings backfill(CTB)under low temperature environment(≤20℃),the strength optimization and prediction of CTB under the influence of multiple factors were carried out.The response surface method(RSM)was used to design the experiment to analyze the development law of backfill strength under the coupling effect of curing temperature,sand-cement ratio and slurry mass fraction,and to optimize the mix proportion;the artificial neural network algorithm(ANN)and particle swarm optimization algorithm(PSO)were used to build the prediction model of backfill strength.According to the experimental results of RSM,the optimal mix proportion under different curing temperatures was obtained.When the curing temperature is 10-15℃,the best mix proportion of sand-cement ratio is 9,and the slurry mass fraction is 71%;when the curing temperature is 15-20℃,the best mix proportion of sand-cement ratio is 8,and the slurry mass fraction is 69%.The ANN-PSO intelligent model can accurately predict the strength of CTB,its mean relative estimation error value and correlation coefficient value are only 1.95%and 0.992,and the strength of CTB under different mix proportion can be predicted quickly and accurately by using this model.
基金Supported by National Natural Science Foundation of China(11961005)Guangdong Province General University Characteristic Innovation Project(2018KTSCX253).
文摘This article attempted to construct a multi-factor quantitative stock selection model,analyze the financial indicators and transaction data of listed companies in detail via the big data statistical test method,and to find out the alpha excess return relative to the market in the case of short stock index futures as a hedge in the Chinese market.
文摘This paper focuses on how to measure the interest rate risk. The conventional measure methods of interest rate risk are reviewed and the duration concept is generalized to stochastic duration in the Markovian HJM framework. The generalized stochastic duration of the coupon bond is defined as the time to maturity of a zero coupon bond having the same instantaneous variance as the coupon bond. According to this definition., the authors first present the framework of Markovian HJM model, then deduce the measures of stochastic duration in some special cases which cover some extant interest term structure.
基金supported by National Natural Science Foundation of China(Grant Nos.10971122,11101274 and 11322109)Scientific and Technological Projects of Shandong Province(Grant No.2009GG10001012)Excellent Young Scientist Foundation of Shandong Province(Grant No.BS2012SF025)
文摘Covariance matrix plays an important role in risk management, asset pricing, and portfolio allocation. Covariance matrix estimation becomes challenging when the dimensionality is comparable or much larger than the sample size. A widely used approach for reducing dimensionality is based on multi-factor models. Although it has been well studied and quite successful in many applications, the quality of the estimated covariance matrix is often degraded due to a nontrivial amount of missing data in the factor matrix for both technical and cost reasons. Since the factor matrix is only approximately low rank or even has full rank, existing matrix completion algorithms are not applicable. We consider a new matrix completion paradigm using the factor models directly and apply the alternating direction method of multipliers for the recovery. Numerical experiments show that the nuclear-norm matrix completion approaches are not suitable but our proposed models and algorithms are promising.
