- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Diode-pumped neodymium:yttrium aluminum garnet laser effects on the visceral pleura in an <i>ex vivo</i> porcine lung model?
摘要： Management of tree plantations needs information on the number, time, intensity and type of thinnings, and the length of the rotation. Economically optimal plantation management depends on discount rate and site fertility. This study proposed a new approach to developing management instructions for tree plantations, simultaneously for all discount rates and site classes. The method was applied to larch (Larix olgensis A. Henry) plantations in northeast China. Instead of optimizing the management of individual stands, instructions for thinning basal area, thinning intensity and rotation diameter were optimized. The instructions consisted of three equations, the first indicating the basal area at which the stand should be thinned, the second showing the optimal thinning intensity, and the third model indicating the mean diameter of trees at the moment of clear-felling. Particle swarm optimization (PSO) was employed to simultaneously optimize the coefficients of these three equation models. PSO maximized the average net present value of the management schedules of 29 larch sample plots. The obtained instructions corresponded to earlier results on the effects of different factors on optimal stand management. Management based on the instructions developed in this study resulted in almost equally profitable management as in the case where the management of every plot was optimized separately.
关键词： thinning,particle swarm optimization,tree plantations,management instructions,rotation diameter
更新于2025-09-11 14:15:04
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A Review of the Energy Performance and Life-Cycle Assessment of Building-Integrated Photovoltaic (BIPV) Systems
摘要： This paper is based on the safety method of particle swarm, and integrates the classical particle swarm algorithm and simulated annealing particle swarm algorithm. This article combines the classic particle swarm algorithm and simulated annealing particle the advantages of can improve the UAV mission in a complex spatial environment safety, reduce the unmanned aerial vehicle unable to effectively avoid the ground fixed obstacles in the process of flying and air other aircraft due to the risk of collision, made it possible to unmanned aerial vehicles and man-machine Shared airspace, able to perform various tasks for unmanned aerial vehicle safely and successfully provide effective protection.
关键词： particle swarm optimization,UAVs,simulated annealing,conflict resolution
更新于2025-09-11 14:15:04
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[IEEE 2018 International Conference on Smart Electric Drives and Power System (ICSEDPS) - Maharashtra State, India (2018.6.12-2018.6.13)] 2018 International Conference on Smart Electric Drives and Power System (ICSEDPS) - Grid connected PV system using FB-PSO
摘要： This Paper presents a MPPT (maximum power point tracking) design for a Grid connected PV (Photo-voltaic) system using FB-PSO (Forward Backward particle swarm optimization) Technique. The FB-PSO is a new improved method which results in maximum efficiency, fast tracking of maximum power point, less steady state oscillations compare to PO (Perturb and Observe) and CPSO (Conventional PSO) methods. The proposed scheme is examine under PSC (partial shading conditions) and its results are compare with other two methods. The FB-PSO algorithm is implemented in MATLAB/SIMULINK and it is observed that proposed method is best compare to PO and CPSO MPPTs.
关键词： Forward Backward particle swarm optimization (FB-PSO),Maximum point tracking (MPPT),Conventional particle swarm optimization (CPSO),Perturb and Observe (PO),Photo-voltaic (PV),partial shading conditions (PSC)
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - High Quality Isar Imaging for Target of Arbitrary Trajectory Based on Back Projection and Particle Swarm Optimization
摘要： When the target has large size or the target moves irregularly, traditional inverse synthetic aperture radar (ISAR) imaging method will lead to a poor image quality due to space variate and migration of echo envelop. In this paper, a novel method based on Back Projection (BP) and Particle Swarm Optimization (PSO) is proposed, which can achieve high quality images under the circumstances of irregular motion, large target and low signal to noise ratio (SNR). First, Target motion is modeled as a turntable, then the translational motion and rotational motion are modeled as two polynomials. Entropy of coherent superposition value of part of the imaging scene pixels based on BP algorithm is utilized as the evaluation function to estimate the polynomial coefficients based on an optimization algorithm such as PSO. Once the polynomial coefficients are estimated, a high quality image of the whole scene can be obtained by BP algorithm. The simulation results verify the effectiveness of the proposed method.
关键词： Back Projection (BP),particle swarm optimization (PSO),Inverse synthetic aperture radar (ISAR)
更新于2025-09-10 09:29:36
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Simulation of Isar Motion Compensation for Moving Targets Based on Particle Swarm Optimization
摘要： In inverse synthetic aperture radar (ISAR) imaging, the imaging results can be affected by the unexpected target motions. This results in a blurry and unrecognizable image. The motion parameters estimation is a compensation method to improve the ISAR image refocusing and quality. In this study, the backscattered echo signals are simulated by the linear geometry system of ISAR moving targets. The entropy of ISAR image is used as a criterion to evaluate the image quality. Furthermore, this entropy measure can be treated as a cost function of the particle swarm optimization (PSO) method and minimized by PSO to improve the quality of ISAR images. The experimental results showed that our proposed PSO motion estimation approach to entropy minimization for ISAR imaging can not only efficiently improve the estimation capability of motion parameters, but also significantly achieve a better performance of ISAR image refocusing.
关键词： motion compensation,entropy minimization,particle swarm optimization (PSO),inverse synthetic aperture radar
更新于2025-09-10 09:29:36
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A hybrid intelligent GMPPT algorithm for partial shading PV system
摘要： Maximum power extraction for PV systems under partial shading conditions (PSCs) relies on the optimal global maximum power point tracking (GMPPT) method used. This paper proposes a novel maximum power point tracking (MPPT) control method for PV system with reduced steady-state oscillation based on improved particle swarm optimization (PSO) algorithm and variable step perturb and observe (P&O) method. Firstly, the grouping idea of shuffled frog leaping algorithm (SFLA) is introduced in the basic PSO algorithm (PSO–SFLA), ensuring the differences among particles and the searching of global extremum. Furthermore, adaptive speed factor is introduced into the improved PSO to improve the convergence of the PSO–SFLA under PSCs. And then, the variable step P&O (VSP&O) method is used to track the maximum power point (MPP) accurately with the change of environment. Finally, the superiority of the proposed method over the conventional P&O method and the standard PSO method in terms of tracking speed and steady-state oscillations is highlighted by simulation results under fast variable PSCs.
关键词： Variable step P&O (VSP&O),Global maximum power point tracking (GMPPT),Under partial shading conditions (PSCs),Particle swarm optimization (PSO),Photovoltaic (PV) system,Adaptive speed factor,Shuffled frog leaping algorithm (SFLA)
更新于2025-09-10 09:29:36
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An Ensemble Framework For Day-Ahead Forecast of PV Output in Smart Grids
摘要： The uncertainty associated with solar photo-voltaic (PV) power output is a big challenge to design, manage and implement effective demand response and management strategies. Therefore, an accurate PV power output forecast is an utmost importance to allow seamless integration and a higher level of penetration. In this research, a neural network ensemble (NNE) scheme is proposed, which is based on particle swarm optimization (PSO) trained feedforward neural network (FNN). Five different FFN structures with varying network complexities are used to achieve the diverse and accurate forecast results. These results are combined using trim aggregation after removing the upper and lower forecast error extremes. Correlated variables namely wavelet transformed historical power output of PV, solar irradiance, wind speed, temperature and humidity are applied as inputs to the multivariate NNE. Clearness index is used to classify days into clear, cloudy and partial cloudy days. Test case studies are designed to predict the solar output for these days selected from all seasons. The performance of the proposed framework is analyzed by applying training data set of different resolution, length and quality from seven solar PV sites of the University of Queensland, Australia. The forecast results demonstrate that the proposed framework improves the forecast accuracy significantly in comparison with individual and benchmark models.
关键词： clear day (CD),solar irradiance,cloudy day (CLD),clearness index,PV power output forecasting,ensemble network (EN),neural network ensemble (NNE),partially cloudy day (PCD),particle swarm optimization
更新于2025-09-10 09:29:36
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Estimation of the particle size distribution of colloids from multiangle dynamic light scattering measurements with particle swarm optimization
摘要： In this paper particle Swarm Optimization (PSO) algorithms are applied to estimate the particle size distribution (PSD) of a colloidal system from the average PSD diameters, which are measured by multi-angle dynamic light scattering. The system is considered a nonlinear inverse problem, and for this reason the estimation procedure requires a Tikhonov regularization method. The inverse problem is solved through several PSO strategies. The evaluated PSOs are tested through three simulated examples corresponding to polystyrene (PS) latexes with different PSDs, and two experimental examples obtained by simply mixing 2 PS standards. In general, the evaluation results of the PSOs are excellent; and particularly, the PSO with the Trelea’s parameter set shows a better performance than other implemented PSOs.
关键词： inverse problem,particle swarm optimization algorithm,particle size distribution,Swarm Intelligence,dynamic light scattering
更新于2025-09-09 09:28:46
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[IEEE 2018 International Conference On Advances in Communication and Computing Technology (ICACCT) - Sangamner, India (2018.2.8-2018.2.9)] 2018 International Conference On Advances in Communication and Computing Technology (ICACCT) - Optimal Sizing of Battery-Ultracapacitor Hybrid Energy Storage Device in a Standalone Photovoltaic System
摘要： In standalone renewable energy systems, it is most essential to deploy energy storage devices to compensate for the intermittent and random output power generation. Since the last few years, hybrid energy storage devices (HESDs) composed of two or more energy storage technologies are being adopted in these power systems. In this paper, a standalone photovoltaic system with battery-ultracapacitor HESD has been considered for case study. A suitable problem formulation with the necessary objective function and constraints has been developed for the system. A variant of one of the popular meta-heuristic techniques, particle swarm optimization (PSO) has been used to address the optimization problem. The simulation results along with the convergence characteristics of the algorithm have been presented. It is observed that the proposed technique can produce comparable results.
关键词： Battery,standalone photovoltaic system,ultracapacitor,hybrid energy storage,particle swarm optimization (PSO)
更新于2025-09-09 09:28:46
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Near-infrared spectra quantitative analysis for flue gas of thermal power plant based on wavelength selection
摘要： This paper proposed a near-infrared (NIR) spectra quantitative analysis method for flue gas of thermal power plant based on wavelength selection. For the proposed method, the self-adaptive accelerated particle swarm optimization is presented for determining the most representative wavelengths of NIR spectral signals and is combined with partial least square for predicting the various contents of the real flue gas dataset. The proposed method chooses the current own optimal or the current global optimal as the reference state randomly and accelerated updates of the flight velocity by the reference state, then the particle state is updated based on the new velocity self-adaptively. The experimental results of a real flue gas dataset verified that the proposed method has higher predictive ability and could overcome the premature convergence.
关键词： near-infrared spectrum,wavelength selection,fuel gas,Thermal power plant,self-adaptive accelerated discrete particle swarm optimization
更新于2025-09-09 09:28:46