Objective:Explore the characteristics of acupoints in the treatment of stroke with complex network and point mutual information method.Methods:The complex network and point wise mutual information system-developed by ...Objective:Explore the characteristics of acupoints in the treatment of stroke with complex network and point mutual information method.Methods:The complex network and point wise mutual information system-developed by Chinese academy of Chinese medical sciences wereused to analyze the specific acupoints,compatibility,frequency etc.Results:174 acumoxibustion prescriptions were collected,including 163 acupoints.among them eighteen acupoints were used more than 30 times such as Hegu(LI4),Zusanli(ST36),Quchi(LI11)and Fengshi(GB31).The combinations of 31 acupoints were used more than 15 times,such as the combination of Quchi(LI11)and Zusanli(ST36),the combination of acupoint Quchi(LI11)and Jianyu(LI15),Hegu and Quchi(LI11).The most commonly used treatment method for stroke treatment is to dredge the Yangming meridian and Shaoyang meridian through acupuncture the multiple acupoints located on these two meridians..The commonly used acupoints are mainly distributed in the limbs,head and face.The most commonly used specific acupoint is intersection acupoint.The usage frequency of specific acupoints are higher than that of non-specific acupoints.Conclusion:Dredging the collaterals,dispelling wind-evil and restoring consciousness are the main principle for the treatment of stroke.Specific acupoints in head,face and climbs maybe the main targeted acupoints.Combination of Yang meridians with other meridians is needed to improve the effects.The Yangming meridian and Shaoyang meridian are most used meridians and Hegu(LI4),Quchi(LI11)and Zusanli(ST36)are the most used acupionts.展开更多
The flash points of organic compounds were estimated using a hybrid method that includes a simple group contribution method (GCM) implemented in an artificial neural network (ANN) with particle swarm optimization (PSO...The flash points of organic compounds were estimated using a hybrid method that includes a simple group contribution method (GCM) implemented in an artificial neural network (ANN) with particle swarm optimization (PSO). Different topologies of a multilayer neural network were studied and the optimum architecture was determined. Property data of 350 compounds were used for training the network. To discriminate different substances the molecular structures defined by the concept of the classical group contribution method were given as input variables. The capabilities of the network were tested with 155 substances not considered in the training step. The study shows that the proposed GCM+ANN+PSO method represent an excellent alternative for the estimation of flash points of organic compounds with acceptable accuracy (AARD = 1.8%; AAE = 6.2 K).展开更多
Under the environment of electric power market, economic dispatch (ED) problem should consider network constraints, unit ramp rates, besides the basic constraints. For this problem, it is important to establish the ef...Under the environment of electric power market, economic dispatch (ED) problem should consider network constraints, unit ramp rates, besides the basic constraints. For this problem, it is important to establish the effective model and algorithm. This paper examines the decoupled conditions that affect the solution optimality to this problem. It proposes an effective model and solution method. Based on the look-ahead technique, it finds the number of time intervals to guarantee the solution optimality. Next, an efficient technique for finding the optimal solution via the interior point methods is described. Test cases, which include dispatching six units over 5 time intervals on the IEEE 30 test system with line flows and ramp constraints are presented. Results indicate that the computational effort as measured by iteration counts or execution time varies only modestly with the problem size.展开更多
In order to forecast projectile impact points quickly and accurately,aprojectile impact point prediction method based on generalized regression neural network（GRNN）is presented.Firstly,the model of GRNN forecasting ...In order to forecast projectile impact points quickly and accurately,aprojectile impact point prediction method based on generalized regression neural network（GRNN）is presented.Firstly,the model of GRNN forecasting impact point is established;secondly,the particle swarm algorithm（PSD）is used to optimize the smooth factor in the prediction model and then the optimal GRNN impact point prediction model is obtained.Finally,the numerical simulation of this prediction model is carried out.Simulation results show that the maximum range error is no more than 40 m,and the lateral deviation error is less than0.2m.The average time of impact point prediction is 6.645 ms,which is 1 300.623 ms less than that of numerical integration method.Therefore,it is feasible and effective for the proposed method to forecast projectile impact points,and thus it can provide a theoretical reference for practical engineering applications.展开更多
This article describes numerical simulation of gas pipeline network operation using high-accuracy computational fluid dynamics (CFD) simulators of the modes of gas mixture transmission through long, multi-line pipelin...This article describes numerical simulation of gas pipeline network operation using high-accuracy computational fluid dynamics (CFD) simulators of the modes of gas mixture transmission through long, multi-line pipeline systems (CFD-simulator). The approach used in CFD-simulators for modeling gas mixture transmission through long, branched, multi-section pipelines is based on tailoring the full system of fluid dynamics equations to conditions of unsteady, non-isothermal processes of the gas mixture flow. Identification, in a CFD-simulator, of safe parameters for gas transmission through compressor stations amounts to finding the interior points of admissible sets described by systems of nonlinear algebraic equalities and inequalities. Such systems of equalities and inequalities comprise a formal statement of technological, design, operational and other constraints to which operation of the network equipment is subject. To illustrate the practicability of the method of numerical simulation of a gas transmission network, we compare computation results and gas flow parameters measured on-site at the gas transmission enter-prise.展开更多
Clustering approaches are one of the probabilistic load flow(PLF)methods in distribution networks that can be used to obtain output random variables,with much less computation burden and time than the Monte Carlo simu...Clustering approaches are one of the probabilistic load flow(PLF)methods in distribution networks that can be used to obtain output random variables,with much less computation burden and time than the Monte Carlo simulation(MCS)method.However,a challenge of the clustering methods is that the statistical characteristics of the output random variables are obtained with low accuracy.This paper presents a hybrid approach based on clustering and Point estimate methods.In the proposed approach,first,the sample points are clustered based on the𝑙-means method and the optimal agent of each cluster is determined.Then,for each member of the population of agents,the deterministic load flow calculations are performed,and the output variables are calculated.Afterward,a Point estimate-based PLF is performed and the mean and the standard deviation of the output variables are obtained.Finally,the statistical data of each output random variable are modified using the Point estimate method.The use of the proposed method makes it possible to obtain the statistical properties of output random variables such as mean,standard deviation and probabilistic functions,with high accuracy and without significantly increasing the burden of calculations.In order to confirm the consistency and efficiency of the proposed method,the 10-,33-,69-,85-,and 118-bus standard distribution networks have been simulated using coding in Python®programming language.In simulation studies,the results of the proposed method have been compared with the results obtained from the clustering method as well as the MCS method,as a criterion.展开更多
文摘Objective:Explore the characteristics of acupoints in the treatment of stroke with complex network and point mutual information method.Methods:The complex network and point wise mutual information system-developed by Chinese academy of Chinese medical sciences wereused to analyze the specific acupoints,compatibility,frequency etc.Results:174 acumoxibustion prescriptions were collected,including 163 acupoints.among them eighteen acupoints were used more than 30 times such as Hegu(LI4),Zusanli(ST36),Quchi(LI11)and Fengshi(GB31).The combinations of 31 acupoints were used more than 15 times,such as the combination of Quchi(LI11)and Zusanli(ST36),the combination of acupoint Quchi(LI11)and Jianyu(LI15),Hegu and Quchi(LI11).The most commonly used treatment method for stroke treatment is to dredge the Yangming meridian and Shaoyang meridian through acupuncture the multiple acupoints located on these two meridians..The commonly used acupoints are mainly distributed in the limbs,head and face.The most commonly used specific acupoint is intersection acupoint.The usage frequency of specific acupoints are higher than that of non-specific acupoints.Conclusion:Dredging the collaterals,dispelling wind-evil and restoring consciousness are the main principle for the treatment of stroke.Specific acupoints in head,face and climbs maybe the main targeted acupoints.Combination of Yang meridians with other meridians is needed to improve the effects.The Yangming meridian and Shaoyang meridian are most used meridians and Hegu(LI4),Quchi(LI11)and Zusanli(ST36)are the most used acupionts.
文摘The flash points of organic compounds were estimated using a hybrid method that includes a simple group contribution method (GCM) implemented in an artificial neural network (ANN) with particle swarm optimization (PSO). Different topologies of a multilayer neural network were studied and the optimum architecture was determined. Property data of 350 compounds were used for training the network. To discriminate different substances the molecular structures defined by the concept of the classical group contribution method were given as input variables. The capabilities of the network were tested with 155 substances not considered in the training step. The study shows that the proposed GCM+ANN+PSO method represent an excellent alternative for the estimation of flash points of organic compounds with acceptable accuracy (AARD = 1.8%; AAE = 6.2 K).
文摘Under the environment of electric power market, economic dispatch (ED) problem should consider network constraints, unit ramp rates, besides the basic constraints. For this problem, it is important to establish the effective model and algorithm. This paper examines the decoupled conditions that affect the solution optimality to this problem. It proposes an effective model and solution method. Based on the look-ahead technique, it finds the number of time intervals to guarantee the solution optimality. Next, an efficient technique for finding the optimal solution via the interior point methods is described. Test cases, which include dispatching six units over 5 time intervals on the IEEE 30 test system with line flows and ramp constraints are presented. Results indicate that the computational effort as measured by iteration counts or execution time varies only modestly with the problem size.
基金Project Funded by Chongqing Changjiang Electrical Appliances Industries Group Co.,Ltd
文摘In order to forecast projectile impact points quickly and accurately,aprojectile impact point prediction method based on generalized regression neural network（GRNN）is presented.Firstly,the model of GRNN forecasting impact point is established;secondly,the particle swarm algorithm（PSD）is used to optimize the smooth factor in the prediction model and then the optimal GRNN impact point prediction model is obtained.Finally,the numerical simulation of this prediction model is carried out.Simulation results show that the maximum range error is no more than 40 m,and the lateral deviation error is less than0.2m.The average time of impact point prediction is 6.645 ms,which is 1 300.623 ms less than that of numerical integration method.Therefore,it is feasible and effective for the proposed method to forecast projectile impact points,and thus it can provide a theoretical reference for practical engineering applications.
文摘This article describes numerical simulation of gas pipeline network operation using high-accuracy computational fluid dynamics (CFD) simulators of the modes of gas mixture transmission through long, multi-line pipeline systems (CFD-simulator). The approach used in CFD-simulators for modeling gas mixture transmission through long, branched, multi-section pipelines is based on tailoring the full system of fluid dynamics equations to conditions of unsteady, non-isothermal processes of the gas mixture flow. Identification, in a CFD-simulator, of safe parameters for gas transmission through compressor stations amounts to finding the interior points of admissible sets described by systems of nonlinear algebraic equalities and inequalities. Such systems of equalities and inequalities comprise a formal statement of technological, design, operational and other constraints to which operation of the network equipment is subject. To illustrate the practicability of the method of numerical simulation of a gas transmission network, we compare computation results and gas flow parameters measured on-site at the gas transmission enter-prise.
文摘Clustering approaches are one of the probabilistic load flow(PLF)methods in distribution networks that can be used to obtain output random variables,with much less computation burden and time than the Monte Carlo simulation(MCS)method.However,a challenge of the clustering methods is that the statistical characteristics of the output random variables are obtained with low accuracy.This paper presents a hybrid approach based on clustering and Point estimate methods.In the proposed approach,first,the sample points are clustered based on the𝑙-means method and the optimal agent of each cluster is determined.Then,for each member of the population of agents,the deterministic load flow calculations are performed,and the output variables are calculated.Afterward,a Point estimate-based PLF is performed and the mean and the standard deviation of the output variables are obtained.Finally,the statistical data of each output random variable are modified using the Point estimate method.The use of the proposed method makes it possible to obtain the statistical properties of output random variables such as mean,standard deviation and probabilistic functions,with high accuracy and without significantly increasing the burden of calculations.In order to confirm the consistency and efficiency of the proposed method,the 10-,33-,69-,85-,and 118-bus standard distribution networks have been simulated using coding in Python®programming language.In simulation studies,the results of the proposed method have been compared with the results obtained from the clustering method as well as the MCS method,as a criterion.
