It is well known that inertial integrated navigation systems can provide accurate navigation information. In these systems, inertial sensor random error often becomes the limiting factor to get a better performance. S...
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It is well known that inertial integrated navigation systems can provide accurate navigation information. In these systems, inertial sensor random error often becomes the limiting factor to get a better performance. So it is imperative to have accurate characterization of the random error. Allan variance analysis technique has a good performance in analyzing inertial sensor random error, and it is always used to characterize various types of the random error terms. This paper proposes a new method named optimization iterative algorithm based on nonnegative constraint applied to Allan variance analysis technique to estimate parameters of the random error terms. The parameter estimates by this method are nonnegative and optimal, and the estimation process does not have matrix nearly singular issues. Testing with simulation data and the experimental data of a fiber optical gyro, the parameters estimated by the presented method are compared against other excellent methods with good agreement;moreover, the objective function has the minimum value. (C) 2014 COSPAR. Published by Elsevier Ltd. All rights reserved.
The paper proposes a new least energy cost optimization algorithm for the deficiency of the low energy adaptive clustering hierarchy(LEACH) protocol. In the perspective of the energy, the algorithm uses the least ener...
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The paper proposes a new least energy cost optimization algorithm for the deficiency of the low energy adaptive clustering hierarchy(LEACH) protocol. In the perspective of the energy, the algorithm uses the least energy cost to decide whether the network clustering is on the optimal state, meanwhile the status of the cluster energy cost is imported to conduct clustering gradually towards minimizing energy consumption. Based on the theoretic analysis of the energy consumption, it is proved to have a longer network lifecycle which performs better than the LEACH.
The solution algorithm of the aeroengine nonlinear model is a classic problem, which is relatively mature in theory. But this question is still studied today, because the mathematical character of the aeroengine nonli...
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ISBN:
(纸本)9781510806450
The solution algorithm of the aeroengine nonlinear model is a classic problem, which is relatively mature in theory. But this question is still studied today, because the mathematical character of the aeroengine nonlinear model is so complex, that it is difficult to be convergent using the traditional iteration algorithm. So an idea is proposed to solve this problem, which is conversing the model solution to an optimization problem, and using optimization algorithms to solve the difficulties. In order to ensure the calculation speed, the iteration optimization method is chosen, and a new improved hybrid iteration optimization methods is design. In order to verify the improved algorithm, the simulation experiment was carried out taking an given turbofan engine as an example. The simulation results shows that, the solution precision is improved in some degree, when the new algorithm is used.
Money demand is one of the most important economic variables which are a critical component in appointing and choosing appropriate monetary policy, because it determines the transmission of policy-driven change in mon...
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Money demand is one of the most important economic variables which are a critical component in appointing and choosing appropriate monetary policy, because it determines the transmission of policy-driven change in monetary aggregates to the real sector. In this paper, the data of economic indicators in Iran are presented for estimating the money demand using biogeography-based optimization (BBO) algorithm, particle swarm optimization (PSO) algorithm, and a new hybrid metaheuristic method based on biogeography-based optimization and particle swarm optimization algorithm (BBPSO). The data are used in two forms (i.e. linear and exponential) to estimate money demand values based on true liquidity, Consumer price index, GDP, lending interest rate, Inflation, and official exchange rate. The available data are partly used for finding optimal or near-optimal values of weighting parameters (1974-2013) and partly for testing the models (2014-2018). The performance of methods is evaluated using mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). According to the simulation results, the proposed method (i.e. BBPSO) outperformed the other models. The findings proved that the recommended method was an appropriate tool for effective money demand prediction in Iran. These data were the result of a comprehensive look at the most influential factors for money market demand. With this method, the demand side of this market was clearly defined. Along with other markets, the consequences of economic policy could be analyzed and predicted. The article provides a method for observing the effect of economic scenarios on the money market and the analysis obtained by this proposed method allows experts, public sector economics, and monetary economist to see a clearer explanation of the country's liquidity plan. The method presented in this article can be beneficial for the policy makers and monetary authorities during their decision-making process. (C) 20
This paper deals with the global energy consumption to forecast future projections based on primary energy, global oil, coal and natural gas consumption using a hybrid Cuckoo optimization algorithm and information of ...
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This paper deals with the global energy consumption to forecast future projections based on primary energy, global oil, coal and natural gas consumption using a hybrid Cuckoo optimization algorithm and information of British Petroleum Company plc and BP Amoco plc. The Artificial Neural Network (ANN) has some significant disadvantages, such as training slowly, easiness to fall into local optimal point, and sensitivity of the initial weights and bias. To overcome the shortcomings, an improved ANN structure, that is optimized by the Cuckoo optimization algorithm (COA), is proposed in this paper (COANN). The performance of the COANN is evaluated with Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (CC) between the output of the model and the actual dataset. Finally, CO2 emission in the world by 2050 is forecasted using COANN. The findings showed that COANN is a helpful and reliable tool for monitoring global warming. This proposed method will assist experts, policy planners and researchers who study greenhouse gases. The method can be used as a potential tool for policymakers and governments to make policy on global warming monitoring and control. The proposed method can play a key role in the global climate changes policies and can have a significant impact on the efficiency or inefficiency of government's intervention policies. (C) 2021 The Author(s). Published by Elsevier B.V.
In the last years, the carbon footprint reduction has gained great relevance in the energy industry. Thus, it is necessary to choose approaches that weight the results not only evaluating economic benefits but also em...
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In the last years, the carbon footprint reduction has gained great relevance in the energy industry. Thus, it is necessary to choose approaches that weight the results not only evaluating economic benefits but also emphasizing the environmental impact. In order to measure this impact, the key parameter is the CO2 emission in the atmosphere. The most powerful mean to satisfy this compromise between economic benefits and emission decrease is represented by the concept of Smart Grid. A Smart Grid implies a joint participation between information network and electric grid. In order to acquire the data from the electric grid, transmit them through the IT network, compute and translate them into commands to the plant devices, an 'intelligent brain' is necessary. In order to embed a small local network in the larger VPP a delocalized intelligent device is necessary, able to interface with the Smart Grid. An optimization algorithm performs this function of intelligent delocalized brain by setting different set-points for the energy devices on field. In this paper a purposefully developed optimization algorithm is described, with the aim of optimizing the operations of an existent trigeneration plant managing both RES and fossil energy sources. The plant analysed is a real plant located in central Italy, provided by several generators (PV, CHP, absorption chiller, electric chiller, gas boiler and a wind turbine). The results are yielded by a MATLAB/Simulink simulation tool, where all plant devices are characterized by datasheet information and on-field measurements. The benefits evaluation of the algorithm optimized management is obtained by embedding inside Simulink the optimization logic and executing it during the simulation runtime. The performance is compared with conventional thermal led management operations simulated in the same platform. The comparison is mainly based on economic costs but also considers CO2 emissions and primary energy consumption. The analysis tak
Reliability is a parameter of evaluating network performance and expected path length can index the contribution of s-t paths to network reliability. It is meaningful to observe the important part of network performan...
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ISBN:
(纸本)9781467390262
Reliability is a parameter of evaluating network performance and expected path length can index the contribution of s-t paths to network reliability. It is meaningful to observe the important part of network performance in light of the reliability and path length. In this paper, we attempt to reveal the important part of network performance based on reliability. Conversely we consider the optimization problem of two-terminal reliability with expected-path length constraint. Next, we transform the problem into searching delta-maximum graph, in which its expected path length is not greater than delta(0) and reliability is maximum. Further, we find a rule of removing redundant subgraphs and propose an algorithm to search the optimal solution. Simulation shows the effectiveness of the proposed algorithm.
Basing on the introduction of Multidisciplinary Design optimization (MDO), Multidisciplinary Design optimization method based on iSIGHT is given, which includes one general process model and one optimization algorithm...
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ISBN:
(纸本)9783037851913
Basing on the introduction of Multidisciplinary Design optimization (MDO), Multidisciplinary Design optimization method based on iSIGHT is given, which includes one general process model and one optimization algorithm. optimization of one bearing is selected as one example. According to its application, it approves that MDO methods can solve practical engineering problems more effectively because of comprehensive consideration of the internal problems in all disciplines.
The dissertation puts forward a network model based on analysis of energy constrained multicast routing optimization algorithm. Combined network model, energy model, combination of extreme elements and improved geneti...
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The dissertation puts forward a network model based on analysis of energy constrained multicast routing optimization algorithm. Combined network model, energy model, combination of extreme elements and improved genetic algorithms, it presents an energy constrained QoS multicast routing optimization algorithm. The simulation result shows that the algorithm is feasible and effective and provides an available approach to Ad Hoc networks QoS multicast routing.
In order to improve the efficiency of traffic signal control for an over-saturated intersection group, a nondominated sorting genetic algorithm Ⅱ（NSGA-Ⅱ） based traffic signal control optimization algorithm is prop...
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In order to improve the efficiency of traffic signal control for an over-saturated intersection group, a nondominated sorting genetic algorithm Ⅱ（NSGA-Ⅱ） based traffic signal control optimization algorithm is proposed. The throughput maximum and average queue ratio minimum for the critical route of the intersection group are selected as the optimization objectives of the traffic signal control for the over-saturated condition. The consequences of the efficiency between traffic signal timing plans generated by the proposed algorithm and a commonly utilized signal timing optimization software Synchro are compared in a VISSIM signal control application programming interfaces （SCAPI） simulation environment by using real filed observed traffic data. The simulation results indicate that the signal timing plan generated by the proposed algorithm is more efficient in managing oversaturated flows at intersection groups, and, thus, it has the capability of optimizing signal timing under the over-saturated conditions.
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