In the application of moving horizon estimation (MHE) algorithm, the window length will affect the estimation accuracy and the computing efficiency. For this kind of problem, a method of parameter optimization is prop...
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In the application of moving horizon estimation (MHE) algorithm, the window length will affect the estimation accuracy and the computing efficiency. For this kind of problem, a method of parameter optimization is proposed to obtain suitable window length. Firstly, in order to facilitate online solution, the optimization problem involved in the algorithm is transformed into a quadratic programming (QP) problem in matrix form. Secondly, for the time index and the estimated residual index that measure different properties, the normalization idea is adopted to incorporate them into the same dimension to design the fitness function, and a genetic optimization algorithm based on simulated annealing mechanism is given to search for the optimal window length. Finally, the proposed parameter optimization method is verified by two cases. The results show that the parameter optimization method has the advantages of excellent local search ability and sufficient convergence, and the window length obtained by this method can better take into account the two performance indexes of the MHE algorithm and improve the estimation performance.
Since the behavior of photovoltaic (PV) modules under different operational conditions is highly nonlinear, predicting the performance of PV systems in industrial applications is becoming a major challenge issue. More...
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Since the behavior of photovoltaic (PV) modules under different operational conditions is highly nonlinear, predicting the performance of PV systems in industrial applications is becoming a major challenge issue. Moreover, the most important information required to configure an optimal PV system is unavailable in all manufacturer's datasheets. In this context, a novel method is recommended to optimize PV cells/module parameters with the ability to correctly characterize the I-V and P-V curves of different PV models. In the present article, a chaotic map is incorporated in the so-called quasi-oppositional Rao-1 algorithm to improve its efficiency, and the resulting algorithm is named quasi-oppositional logistic chaotic Rao-1 (QOLCR) algorithm. Numerical results indicate that the QOLCR algorithm has presented very good performance in terms of accuracy and robustness. The idea is to minimize the root mean square error (RMSE) between the estimated and the actual data. Simulation results in the single diode model give an RMSE of value 7.73006208 x 10(-4), and in the double diode model, an RMSE of value 7.445111655 x 10(-4) has been reached as the minimum value among the other compared optimization methods. Hence, the QOLCR approach also converges faster than the basic Rao-1 algorithm and its other variants. Moreover, the modified QO Rao-1 algorithm shows its perfectness and could be involved as tools for optimal designing of PV systems.
During fast-paced rolling of the same type strip of hot tandem rolling, the thermal expansion of the work rolls has a significant influence on the shape of the on-load roll gap, which needs to be considered in the fre...
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During fast-paced rolling of the same type strip of hot tandem rolling, the thermal expansion of the work rolls has a significant influence on the shape of the on-load roll gap, which needs to be considered in the free shifting of the work rolls. In this paper, the thermal crown evaluation index and the thermal expansion simulation model of the work roll are established, and the influence of different roll shifting parameters on the thermal crown of the roll in the service cycle of the work roll is analyzed. A special roll shifting strategy of the downstream stand is designed, and intelligent optimization is carried out with the goal of thermal roll shape and uniform wear of work rolls. The optimized roll shifting strategy can significantly improve the strip shape quality.
The accurate prediction of daily reference crop evapotranspiration (ETO) enables effective management decision-making for agricultural water resources;this is crucial for developing water-efficient agriculture. To imp...
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The accurate prediction of daily reference crop evapotranspiration (ETO) enables effective management decision-making for agricultural water resources;this is crucial for developing water-efficient agriculture. To improve the accuracy of ETO forecasts in data-deficient areas, this study uses a decision tree algorithm (classification and regression tree [CART]) to obtain the effects of various factors on ETO at typical stations in arid and semiarid regions of China. A combination of factors with considerable influence on the model was selected as the input for constructing a kernel-extreme-learning-machine (KELM) daily reference evapotranspiration prediction model, and three bionic optimization algorithms (i.e., sparrow search optimization algorithm, Harris Hawks optimization algorithm, and lion swarm optimization algorithm) were integrated to optimize KELM prediction model parameters and improve the accuracy of daily reference evapotranspiration prediction. The results indicate that temperature (maximum or minimum temperature) is the primary factor influencing ETO, and the range of importance is 0.399-0.554. RH and Ra are also key factors influencing ETO;the hybrid model optimized using the bionic optimization algorithm provides advantages over the independent KELM model, and the SSA-KELM model has the highest accuracy among hybrid models, with a root-mean-square error of 0.408-1.964, R-2 of 0.545-0.982, mean absolute error of 0.273-1.086, and Nash-Sutcliffe efficiency coefficient of 0.658-0.967. The top five factors extracted using the CART algorithm are recommended as inputs for constructing the SSA-KELM model for ETO estimation in arid and semiarid regions of China, and this model can also serve as a reference for ETO forecasting in similar regions.
For inefficient trajectory planning of six-degree-of-freedom industrial manipulators, a tra-jectory planning algorithm based on an improved multiverse algorithm (IMVO) for time, energy, and impact optimization are pro...
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For inefficient trajectory planning of six-degree-of-freedom industrial manipulators, a tra-jectory planning algorithm based on an improved multiverse algorithm (IMVO) for time, energy, and impact optimization are proposed. The multi-universe algorithm has better robustness and conver-gence accuracy in solving single-objective constrained optimization problems than other algorithms. In contrast, it has the disadvantage of slow convergence and quickly falls into local optimum. This paper proposes a method to improve the wormhole probability curve, adaptive parameter adjustment, and population mutation fusion to improve the convergence speed and global search capability. In this paper, we modify MVO for multi-objective optimization to derive the Pareto solution set. We then construct the objective function by a weighted approach and optimize it using IMVO. The results show that the algorithm improves the timeliness of the six-degree-of-freedom manipulator trajectory operation within a specific constraint and improves the optimal time, energy consumption, and impact problems in the manipulator trajectory planning.
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
The standard localization approach is characterized by a fixed position distribution of the anchor nodes, which cannot be dynamically modified based on the deployment environment. This paper proposes a novel approach ...
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The standard localization approach is characterized by a fixed position distribution of the anchor nodes, which cannot be dynamically modified based on the deployment environment. This paper proposes a novel approach combining Radial Bias (RB) with the Seeker optimization algorithm (SOA) to address the challenges of energy-constrained target localization and tracking. The RB technique enhances localization accuracy by refining the position estimates of the target, while the SOA optimizes sensor deployment and data transmission paths to minimize energy consumption. By integrating these two methodologies, ensures a balance between precision in tracking and energy efficiency. Extensive simulations shown this technique surpasses existing methods in terms of both accuracy in determining the location and the duration of network operation. This makes it attractive option for applications of energy-constrained WSNs. The investigation examines the outcome of the particle count in the RBSO algorithm, specifically for values of 5, 10, 15, 20, and 25. The simulation results show that the recommended strategy decreases particles, speeds up positioning and tracking, and maintains target localization and tracking accuracy. It is seen that the proposed RadB_SOA achieves 12.4 % of transmission error, 14.6 % of ranging error, 96.3 % of localization coverage, 98.65 % of PDR, and 21.56 % of energy consumption. center dot The Radial Bias-Seeker optimization algorithm (RadB_SOA) suggested enhances the precision in target localization and optimizes energy usage in wireless sensor networks. center dot Simulation outcomes reveal improved tracking accuracy, minimized transmission and ranging errors, as well as increased localization coverage over current techniques. center dot The research presents an extensive evaluation of particle count fluctuations in RBSO, demonstrating enhanced positioning speed and precision with network efficiency.
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.
As an important component of the vessel's Dynamic Positioning(DP) System, thrust allocation determines the control input of each thruster device from the control law. Thrust allocation problems can be formulated a...
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ISBN:
(纸本)9781467374439
As an important component of the vessel's Dynamic Positioning(DP) System, thrust allocation determines the control input of each thruster device from the control law. Thrust allocation problems can be formulated as nonlinear optimization problems. A chaos Particle Swarm optimization(PSO) algorithm combined with multi-agent scheme is proposed for the thrust allocation in this paper. The algorithm which uses multi-agent topological structure has three functions that keeps the diversity of the particle swarm population, improving particle swarm global search ability, and enhancing information diversity. Relying on chaotic local search to get rid of local optima, it can also improve the convergence precision. The numerical simulations are conducted to demonstrate the effectiveness of the proposed methods, and the results are compared with PSO algorithm.
In order to overcome the energy hole problem and long data gathering latency problem in some wireless sensor networks (WSNs), lifetime optimization algorithm with multiple mobile sink nodes for wireless sensor network...
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ISBN:
(纸本)9783662469811;9783662469804
In order to overcome the energy hole problem and long data gathering latency problem in some wireless sensor networks (WSNs), lifetime optimization algorithm with multiple mobile sink nodes for wireless sensor networks (LOA_MMSN) is proposed. LOA_MMSN analyzes the constraints, establishes network optimization model, and decomposes the model into movement path selection model and lifetime optimization model with known movement paths. Finally, the two models are solved. Simulation results show that LOA_MMSN can extend the network lifetime, balance node energy consumption and reduce data gathering latency. Under certain conditions, it outper forms Ratio_w, TPGF and lifetime optimization algorithm with single mobile sink node for WSNs.
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