The time-varying demands for a certain period are often assumed to be less than the basic economic order quantity （EOQ） so that total replenishment quantity rather than economic order quantity is normally considered...The time-varying demands for a certain period are often assumed to be less than the basic economic order quantity （EOQ） so that total replenishment quantity rather than economic order quantity is normally considered by most of the heuristics. This acticle focuses on a combined heuristics method for determining order quantity under generalized time-varying demands. The independent policy （IP）, abnormal independent policy （AIP） and dependent policies are studied and compared. Using the concepts of normal/abnormal periods and the properties of dependent policies, a dependent policy-based heuristics （DPH） is proposed for solving the order quantity problems with a kind of time-varying demands pattern under which the first period is normal. By merging the Silver-Meal （S-M） heuristics and the dependent policy-based heuristics （DPH）, a combined heuristics （DPH/S-M） is developed for solving order quantity problems with generalized time-varying demands. The experimentation shows that （1） for the problem with one normal period, no matter which position the normal period stands, the DPH/S-M could not guarantee better than the S-M heuristics, however it is superior to the S-M heuristics in the case that the demands in the abnormal periods are in descending order, and （2） The DPH/S-M is superior to the S-M heuristics for problems with more than one normal period, and the more the number of normal periods, the greater the improvements.展开更多
From the view of information flow, a super-network equilibrium optimization model is proposed to compute the solution of the operation architecture which is made up of a perceptive level, a command level and a firepow...From the view of information flow, a super-network equilibrium optimization model is proposed to compute the solution of the operation architecture which is made up of a perceptive level, a command level and a firepower level. Firstly, the optimized conditions of the perceptive level, command level and firepower level are analyzed respectively based on the demand of information relation,and then the information supply-and-demand equilibrium model of the operation architecture super-network is established. Secondly,a variational inequality transformation(VIT) model for equilibrium optimization of the operation architecture is given. Thirdly, the contraction projection algorithm for solving the operation architecture super-network equilibrium optimization model with fuzzy demands is designed. Finally, numerical examples are given to prove the validity and rationality of the proposed method, and the influence of fuzzy demands on the super-network equilibrium solution of operation architecture is discussed.展开更多
A laboratory scale up-flow biological activated carbon(BAC) reactor was constructed for the advanced treatment of synthetic flotation wastewater. Biodegradation of a common collector(i.e., ethyl xanthate) for non-ferr...A laboratory scale up-flow biological activated carbon(BAC) reactor was constructed for the advanced treatment of synthetic flotation wastewater. Biodegradation of a common collector(i.e., ethyl xanthate) for non-ferrous metallic ore flotation was evaluated. The results show that the two stages of domestication can improve microbial degradation ability. The BAC reactor obtains a chemical oxygen demand(COD) reduction rate of 82.5% for ethyl xanthate and its effluent COD concentration lowers to below 20 mg/L. The kinetics equation of the BAC reactor proves that the activated carbon layers at the height of 0 mm to 70 mm play a key role in the removal of flotation reagents. Ultraviolet spectral analysis indicates that most of the ethyl xanthate are degraded by microorganisms after advanced treatment by the BAC reactor.展开更多
弹上电缆,作为导弹设备进行信号、能量传输的重要载体,质量是否符合要求,直接决定了设备的可靠性。与传统电缆不同,弹上电缆,具备多芯、多头、大规模等特点。传统的测试设备往往存在线缆支持规模较小、测试针对性太强、扩展性差、自动...弹上电缆,作为导弹设备进行信号、能量传输的重要载体,质量是否符合要求,直接决定了设备的可靠性。与传统电缆不同,弹上电缆,具备多芯、多头、大规模等特点。传统的测试设备往往存在线缆支持规模较小、测试针对性太强、扩展性差、自动化程度低等缺点,本文提出了基于Test On Demand平台的弹上电缆测技术,阐述了测试原理和测试流程设计,使用该技术完成了对最多达300芯弹上电缆的导通、绝缘、耐压测试,可扩展多种型号电缆,具备通用性、扩展性强、测试速度快、自动化程度高、智能化等特点。展开更多
In order to optimize the spares configuration project at different stages during the life cycle, the factor of time is considered to relax the assumption of the spares steady demand in multi-echelon technique for reco...In order to optimize the spares configuration project at different stages during the life cycle, the factor of time is considered to relax the assumption of the spares steady demand in multi-echelon technique for recoverable item control (METRIC) theory. According to the method of systems analysis, the dynamic palm theorem is introduced to establish the prediction model of the spares demand rate, and its main influence factors are analyzed, based on which, the spares support effectiveness evaluation index system is studied, and the system optimization-oriented spares dynamic configuration method for multi-echelon multi-indenture system is proposed. Through the analysis of the optimization algorithm, the layered marginal algorithm is designed to improve the model calculation efficiency. In a given example, the multi-stage spares configuration project during its life cycle is gotten, the research result conforms to the actual status, and it can provide a new way for the spares dynamic optimization.展开更多
A new grey forecasting model based on BP neural network and Markov chain was proposed. In order to combine the grey forecasting model with neural network, an important theorem that the grey differential equation is eq...A new grey forecasting model based on BP neural network and Markov chain was proposed. In order to combine the grey forecasting model with neural network, an important theorem that the grey differential equation is equivalent to the time response model, was proved by analyzing the features of grey forecasting model（GM（1,1））. Based on this, the differential equation parameters were included in the network when the BP neural network was constructed, and the neural network was trained by extracting samples from grey system＇s known data. When BP network was converged, the whitened grey differential equation parameters were extracted and then the grey neural network forecasting model （GNNM（1,1）） was built. In order to reduce stochastic phenomenon in GNNM（1,1）, the state transition probability between two states was defined and the Markov transition matrix was established by building the residual sequences between grey forecasting and actual value. Thus, the new grey forecasting model（MNNGM（1,1）） was proposed by combining Markov chain with GNNM（1,1）. Based on the above discussion, three different approaches were put forward for forecasting China electricity demands. By comparing GM（1, 1） and GNNM（1,1） with the proposed model, the results indicate that the absolute mean error of MNNGM（1,1） is about 0.4 times of GNNM（1,1） and 0.2 times of GM（I, 1）, and the mean square error of MNNGM（1,1） is about 0.25 times of GNNM（1,1） and 0.1 times of GM（1,1）.展开更多
Aiming at the problem that the consumption data of new ammunition is less and the demand is difficult to predict,combined with the law of ammunition consumption under different damage grades,a Bayesian inference metho...Aiming at the problem that the consumption data of new ammunition is less and the demand is difficult to predict,combined with the law of ammunition consumption under different damage grades,a Bayesian inference method for ammunition demand based on Gompertz distribution is proposed.The Bayesian inference model based on Gompertz distribution is constructed,and the system contribution degree is introduced to determine the weight of the multi-source information.In the case where the prior distribution is known and the distribution of the field data is unknown,the consistency test is performed on the prior information,and the consistency test problem is transformed into the goodness of the fit test problem.Then the Bayesian inference is solved by the Markov chain-Monte Carlo(MCMC)method,and the ammunition demand under different damage grades is gained.The example verifies the accuracy of this method and solves the problem of ammunition demand prediction in the case of insufficient samples.展开更多
An accurate long-term energy demand forecasting is essential for energy planning and policy making. However, due to the immature energy data collecting and statistical methods, the available data are usually limited i...An accurate long-term energy demand forecasting is essential for energy planning and policy making. However, due to the immature energy data collecting and statistical methods, the available data are usually limited in many regions. In this paper, on the basis of comprehensive literature review, we proposed a hybrid model based on the long-range alternative energy planning (LEAP) model to improve the accuracy of energy demand forecasting in these regions. By taking Hunan province, China as a typical case, the proposed hybrid model was applied to estimating the possible future energy demand and energy-saving potentials in different sectors. The structure of LEAP model was estimated by Sankey energy flow, and Leslie matrix and autoregressive integrated moving average (ARIMA) models were used to predict the population, industrial structure and transportation turnover, respectively. Monte-Carlo method was employed to evaluate the uncertainty of forecasted results. The results showed that the hybrid model combined with scenario analysis provided a relatively accurate forecast for the long-term energy demand in regions with limited statistical data, and the average standard error of probabilistic distribution in 2030 energy demand was as low as 0.15. The prediction results could provide supportive references to identify energy-saving potentials and energy development pathways.展开更多
A support vector machine (SVM) forecasting model based on rough set (RS) data preprocess was proposed by combining the rough set attribute reduction and the support vector machine regression algorithm, because there a...A support vector machine (SVM) forecasting model based on rough set (RS) data preprocess was proposed by combining the rough set attribute reduction and the support vector machine regression algorithm, because there are strong complementarities between two models. Firstly, the rough set was used to reduce the condition attributes, then to eliminate the attributes that were redundant for the forecast, Secondly, it adopted the minimum condition attributes obtained by reduction and the corresponding original data to re-form a new training sample, which only kept the important attributes affecting the forecast accuracy. Finally, it studied and trained the SVM with the training samples after reduction, inputted the test samples re-formed by the minimum condition attributes and the corresponding original data, and then got the mapping relationship model between condition attributes and forecast variables after testing it. This model was used to forecast the power supply and demand. The results show that the average absolute error rate of power consumption of the whole society and yearly maximum load are 14.21% and 13.23%, respectively, which indicates that the RS-SVM forecast model has a higher degree of accuracy.展开更多
In the research on the smart community pension service,it is found that the problems encountered in the promotion of the smart pension can not be solved in the short term because of the lack of information and the acc...In the research on the smart community pension service,it is found that the problems encountered in the promotion of the smart pension can not be solved in the short term because of the lack of information and the acceptance of the intelligent products by the elderly. This paper puts forward the research ideas based on the cognitive characteristics and demand prediction of the specific population. Focusing on the research on the pension needs of people born in the 1960 s or 1970 s,it provides support for the solution of smart pension services in the peak period of aging population in the future.展开更多
A non-linear regression model is proposed to forecast the aggregated passenger volume of Beijing-Shanghai high-speed railway(HSR) line in China. Train services and temporal features of passenger volume are studied to ...A non-linear regression model is proposed to forecast the aggregated passenger volume of Beijing-Shanghai high-speed railway(HSR) line in China. Train services and temporal features of passenger volume are studied to have a prior knowledge about this high-speed railway line. Then, based on a theoretical curve that depicts the relationship among passenger demand, transportation capacity and passenger volume, a non-linear regression model is established with consideration of the effect of capacity constraint. Through experiments, it is found that the proposed model can perform better in both forecasting accuracy and stability compared with linear regression models and back-propagation neural networks. In addition to the forecasting ability, with a definite formation, the proposed model can be further used to forecast the effects of train planning policies.展开更多
The unmanned reconnaissance aerial vehicle （URAV） plays an important role in battlefield monitoring and information acquiring because of its advantage of zero casualties, and has thus attracted considerable attentio...The unmanned reconnaissance aerial vehicle （URAV） plays an important role in battlefield monitoring and information acquiring because of its advantage of zero casualties, and has thus attracted considerable attention of the world. The URAV was developed rapidly in our country, however, no scientific assessment methods have yet been provided owing to different fight requirements of armed forces. Considering the demand of the missile artillery on the martial information, the model of information requirement of combat force, the reconnaissance ability of URAV, the survivability of URAV, and the task reliability of URAV were constructed, respectively. By synthesizing the mathematic models above, the model of developing demand was constructed on the URAV equipment. It simulated and calculated some URAV equipment developing scales, and explored a way of settling the problem of URAV equipment developing demand.展开更多
The systematism of weapon combat is the typical characteristic of a modern battlefield. The process of combat is complex and the demand description of weapon system of systems （SOS） is difficult. Granular analyzing ...The systematism of weapon combat is the typical characteristic of a modern battlefield. The process of combat is complex and the demand description of weapon system of systems （SOS） is difficult. Granular analyzing is an important method for solving the complex problem in the world. Granular thinking is introduced into the demand description of weapon SoS. Granular computing and granular combination based on a relation of compatibility is proposed. Based on the level of degree and degree of detail, the granular resolution of weapon SoS is defined and an example is illustrated at the end.展开更多
基金the National Natural Science Foundation of China (70625001 70431003+2 种基金 70601004)theKey Project of Scientific and Research of MOE (104064)the Program of New Century Excellent Talents ( NCET-04-0280) ofMOE.
文摘The time-varying demands for a certain period are often assumed to be less than the basic economic order quantity （EOQ） so that total replenishment quantity rather than economic order quantity is normally considered by most of the heuristics. This acticle focuses on a combined heuristics method for determining order quantity under generalized time-varying demands. The independent policy （IP）, abnormal independent policy （AIP） and dependent policies are studied and compared. Using the concepts of normal/abnormal periods and the properties of dependent policies, a dependent policy-based heuristics （DPH） is proposed for solving the order quantity problems with a kind of time-varying demands pattern under which the first period is normal. By merging the Silver-Meal （S-M） heuristics and the dependent policy-based heuristics （DPH）, a combined heuristics （DPH/S-M） is developed for solving order quantity problems with generalized time-varying demands. The experimentation shows that （1） for the problem with one normal period, no matter which position the normal period stands, the DPH/S-M could not guarantee better than the S-M heuristics, however it is superior to the S-M heuristics in the case that the demands in the abnormal periods are in descending order, and （2） The DPH/S-M is superior to the S-M heuristics for problems with more than one normal period, and the more the number of normal periods, the greater the improvements.
基金supported by the National Natural Science Foundation of China (71771216,71701209)Shaanxi Natural Science Foundation (2019 JQ-250)。
文摘From the view of information flow, a super-network equilibrium optimization model is proposed to compute the solution of the operation architecture which is made up of a perceptive level, a command level and a firepower level. Firstly, the optimized conditions of the perceptive level, command level and firepower level are analyzed respectively based on the demand of information relation,and then the information supply-and-demand equilibrium model of the operation architecture super-network is established. Secondly,a variational inequality transformation(VIT) model for equilibrium optimization of the operation architecture is given. Thirdly, the contraction projection algorithm for solving the operation architecture super-network equilibrium optimization model with fuzzy demands is designed. Finally, numerical examples are given to prove the validity and rationality of the proposed method, and the influence of fuzzy demands on the super-network equilibrium solution of operation architecture is discussed.
基金Project(201209013)supported by Special Fund for Environmental Scientific Research in the Public Interest,China
文摘A laboratory scale up-flow biological activated carbon(BAC) reactor was constructed for the advanced treatment of synthetic flotation wastewater. Biodegradation of a common collector(i.e., ethyl xanthate) for non-ferrous metallic ore flotation was evaluated. The results show that the two stages of domestication can improve microbial degradation ability. The BAC reactor obtains a chemical oxygen demand(COD) reduction rate of 82.5% for ethyl xanthate and its effluent COD concentration lowers to below 20 mg/L. The kinetics equation of the BAC reactor proves that the activated carbon layers at the height of 0 mm to 70 mm play a key role in the removal of flotation reagents. Ultraviolet spectral analysis indicates that most of the ethyl xanthate are degraded by microorganisms after advanced treatment by the BAC reactor.
文摘弹上电缆,作为导弹设备进行信号、能量传输的重要载体,质量是否符合要求,直接决定了设备的可靠性。与传统电缆不同,弹上电缆,具备多芯、多头、大规模等特点。传统的测试设备往往存在线缆支持规模较小、测试针对性太强、扩展性差、自动化程度低等缺点,本文提出了基于Test On Demand平台的弹上电缆测技术,阐述了测试原理和测试流程设计,使用该技术完成了对最多达300芯弹上电缆的导通、绝缘、耐压测试,可扩展多种型号电缆,具备通用性、扩展性强、测试速度快、自动化程度高、智能化等特点。
基金supported by the National Defense Pre-research Project in 13th Five-Year(41404050502)the National Defense Science and Technology Fund of the Central Military Commission(2101140)
文摘In order to optimize the spares configuration project at different stages during the life cycle, the factor of time is considered to relax the assumption of the spares steady demand in multi-echelon technique for recoverable item control (METRIC) theory. According to the method of systems analysis, the dynamic palm theorem is introduced to establish the prediction model of the spares demand rate, and its main influence factors are analyzed, based on which, the spares support effectiveness evaluation index system is studied, and the system optimization-oriented spares dynamic configuration method for multi-echelon multi-indenture system is proposed. Through the analysis of the optimization algorithm, the layered marginal algorithm is designed to improve the model calculation efficiency. In a given example, the multi-stage spares configuration project during its life cycle is gotten, the research result conforms to the actual status, and it can provide a new way for the spares dynamic optimization.
基金Project(70572090) supported by the National Natural Science Foundation of China
文摘A new grey forecasting model based on BP neural network and Markov chain was proposed. In order to combine the grey forecasting model with neural network, an important theorem that the grey differential equation is equivalent to the time response model, was proved by analyzing the features of grey forecasting model（GM（1,1））. Based on this, the differential equation parameters were included in the network when the BP neural network was constructed, and the neural network was trained by extracting samples from grey system＇s known data. When BP network was converged, the whitened grey differential equation parameters were extracted and then the grey neural network forecasting model （GNNM（1,1）） was built. In order to reduce stochastic phenomenon in GNNM（1,1）, the state transition probability between two states was defined and the Markov transition matrix was established by building the residual sequences between grey forecasting and actual value. Thus, the new grey forecasting model（MNNGM（1,1）） was proposed by combining Markov chain with GNNM（1,1）. Based on the above discussion, three different approaches were put forward for forecasting China electricity demands. By comparing GM（1, 1） and GNNM（1,1） with the proposed model, the results indicate that the absolute mean error of MNNGM（1,1） is about 0.4 times of GNNM（1,1） and 0.2 times of GM（I, 1）, and the mean square error of MNNGM（1,1） is about 0.25 times of GNNM（1,1） and 0.1 times of GM（1,1）.
基金the Army Scientific Research(KYSZJWJK1744,012016012600B11403).
文摘Aiming at the problem that the consumption data of new ammunition is less and the demand is difficult to predict,combined with the law of ammunition consumption under different damage grades,a Bayesian inference method for ammunition demand based on Gompertz distribution is proposed.The Bayesian inference model based on Gompertz distribution is constructed,and the system contribution degree is introduced to determine the weight of the multi-source information.In the case where the prior distribution is known and the distribution of the field data is unknown,the consistency test is performed on the prior information,and the consistency test problem is transformed into the goodness of the fit test problem.Then the Bayesian inference is solved by the Markov chain-Monte Carlo(MCMC)method,and the ammunition demand under different damage grades is gained.The example verifies the accuracy of this method and solves the problem of ammunition demand prediction in the case of insufficient samples.
基金Project(51606225) supported by the National Natural Science Foundation of ChinaProject(2016JJ2144) supported by Hunan Provincial Natural Science Foundation of ChinaProject(502221703) supported by Graduate Independent Explorative Innovation Foundation of Central South University,China
文摘An accurate long-term energy demand forecasting is essential for energy planning and policy making. However, due to the immature energy data collecting and statistical methods, the available data are usually limited in many regions. In this paper, on the basis of comprehensive literature review, we proposed a hybrid model based on the long-range alternative energy planning (LEAP) model to improve the accuracy of energy demand forecasting in these regions. By taking Hunan province, China as a typical case, the proposed hybrid model was applied to estimating the possible future energy demand and energy-saving potentials in different sectors. The structure of LEAP model was estimated by Sankey energy flow, and Leslie matrix and autoregressive integrated moving average (ARIMA) models were used to predict the population, industrial structure and transportation turnover, respectively. Monte-Carlo method was employed to evaluate the uncertainty of forecasted results. The results showed that the hybrid model combined with scenario analysis provided a relatively accurate forecast for the long-term energy demand in regions with limited statistical data, and the average standard error of probabilistic distribution in 2030 energy demand was as low as 0.15. The prediction results could provide supportive references to identify energy-saving potentials and energy development pathways.
基金Project(70901025) supported by the National Natural Science Foundation of China
文摘A support vector machine (SVM) forecasting model based on rough set (RS) data preprocess was proposed by combining the rough set attribute reduction and the support vector machine regression algorithm, because there are strong complementarities between two models. Firstly, the rough set was used to reduce the condition attributes, then to eliminate the attributes that were redundant for the forecast, Secondly, it adopted the minimum condition attributes obtained by reduction and the corresponding original data to re-form a new training sample, which only kept the important attributes affecting the forecast accuracy. Finally, it studied and trained the SVM with the training samples after reduction, inputted the test samples re-formed by the minimum condition attributes and the corresponding original data, and then got the mapping relationship model between condition attributes and forecast variables after testing it. This model was used to forecast the power supply and demand. The results show that the average absolute error rate of power consumption of the whole society and yearly maximum load are 14.21% and 13.23%, respectively, which indicates that the RS-SVM forecast model has a higher degree of accuracy.
基金This paper is the research result of the social science planning project of Chongqing(subject number:2018QNYS70)and the educational planning project of Chongqing(subject number:2017-GX-273).
文摘In the research on the smart community pension service,it is found that the problems encountered in the promotion of the smart pension can not be solved in the short term because of the lack of information and the acceptance of the intelligent products by the elderly. This paper puts forward the research ideas based on the cognitive characteristics and demand prediction of the specific population. Focusing on the research on the pension needs of people born in the 1960 s or 1970 s,it provides support for the solution of smart pension services in the peak period of aging population in the future.
基金Project(2014YJS080) supported by the Fundamental Research Funds for the Central Universities of China
文摘A non-linear regression model is proposed to forecast the aggregated passenger volume of Beijing-Shanghai high-speed railway(HSR) line in China. Train services and temporal features of passenger volume are studied to have a prior knowledge about this high-speed railway line. Then, based on a theoretical curve that depicts the relationship among passenger demand, transportation capacity and passenger volume, a non-linear regression model is established with consideration of the effect of capacity constraint. Through experiments, it is found that the proposed model can perform better in both forecasting accuracy and stability compared with linear regression models and back-propagation neural networks. In addition to the forecasting ability, with a definite formation, the proposed model can be further used to forecast the effects of train planning policies.
文摘The unmanned reconnaissance aerial vehicle （URAV） plays an important role in battlefield monitoring and information acquiring because of its advantage of zero casualties, and has thus attracted considerable attention of the world. The URAV was developed rapidly in our country, however, no scientific assessment methods have yet been provided owing to different fight requirements of armed forces. Considering the demand of the missile artillery on the martial information, the model of information requirement of combat force, the reconnaissance ability of URAV, the survivability of URAV, and the task reliability of URAV were constructed, respectively. By synthesizing the mathematic models above, the model of developing demand was constructed on the URAV equipment. It simulated and calculated some URAV equipment developing scales, and explored a way of settling the problem of URAV equipment developing demand.
文摘The systematism of weapon combat is the typical characteristic of a modern battlefield. The process of combat is complex and the demand description of weapon system of systems （SOS） is difficult. Granular analyzing is an important method for solving the complex problem in the world. Granular thinking is introduced into the demand description of weapon SoS. Granular computing and granular combination based on a relation of compatibility is proposed. Based on the level of degree and degree of detail, the granular resolution of weapon SoS is defined and an example is illustrated at the end.
