Fresnel Equations Derived Using a Non-Local Hidden-Variable Particle Theory

Problem: The Fresnel equations describe the proportions of reflected and transmitted light from a surface, and are conventionally derived from wave theory continuum mechanics. Particle-based derivations of the Fresnel equations appear not to exist. Approach: The objective of this work was to derive the basic optical laws from first principles from a particle basis. The particle model used was the Cordus theory, a type of non-local hidden-variable (NLHV) theory that predicts specific substructures to the photon and other particles. Findings: The theory explains the origin of the orthogonal electrostatic and magnetic fields, and re-derives the refraction and reflection laws including Snell’s law and critical angle, and the Fresnel equations for s and p-polarisation. These formulations are identical to those produced by electromagnetic wave theory. Contribution: The work provides a comprehensive derivation and physical explanation of the basic optical laws, which appears not to have previously been shown from a particle basis. Implications: The primary implications are for suggesting routes for the theoretical advancement of fundamental physics. The Cordus NLHV particle theory explains optical phenomena, yet it also explains other physical phenomena including some otherwise only accessible through quantum mechanics (such as the electron spin g-factor) and general relativity (including the Lorentz and relativistic Doppler). It also provides solutions for phenomena of unknown causation, such as asymmetrical baryogenesis, unification of the interactions, and reasons for nuclide stability/instability. Consequently, the implication is that NLHV theories have the potential to represent a deeper physics that may underpin and unify quantum mechanics, general relativity, and wave theory.

Quantization of Action for Elementary Particles and the Principle of Least Action

The uncertainty principle is a fundamental principle of quantum mechanics, but its exact mathematical expression cannot obtain correct results when used to solve theoretical problems such as the energy levels of hydrogen atoms, one-dimensional deep potential wells, one-dimensional harmonic oscillators, and double-slit experiments. Even after approximate treatment, the results obtained are not completely consistent with those obtained by solving Schrödinger’s equation. This indicates that further research on the uncertainty principle is necessary. Therefore, using the de Broglie matter wave hypothesis, we quantize the action of an elementary particle in natural coordinates and obtain the quantization condition and a new deterministic relation. Using this quantization condition, we obtain the energy level formulas of an elementary particle in different conditions in a classical way that is completely consistent with the results obtained by solving Schrödinger’s equation. A new physical interpretation is given for the particle eigenfunction independence of probability for an elementary particle: an elementary particle is in a particle state at the space-time point where the action is quantized, and in a wave state in the rest of the space-time region. The space-time points of particle nature and the wave regions of particle motion constitute the continuous trajectory of particle motion. When an elementary particle is in a particle state, it is localized, whereas in the wave state region, it is nonlocalized.

A Chaotic Local Search-Based Particle Swarm Optimizer for Large-Scale Complex Wind Farm Layout Optimization

Wind energy has been widely applied in power generation to alleviate climate problems.The wind turbine layout of a wind farm is a primary factor of impacting power conversion efficiency due to the wake effect that reduces the power outputs of wind turbines located in downstream.Wind farm layout optimization(WFLO)aims to reduce the wake effect for maximizing the power outputs of the wind farm.Nevertheless,the wake effect among wind turbines increases significantly as the number of wind turbines increases in the wind farm,which severely affect power conversion efficiency.Conventional heuristic algorithms suffer from issues of low solution quality and local optimum for large-scale WFLO under complex wind scenarios.Thus,a chaotic local search-based genetic learning particle swarm optimizer(CGPSO)is proposed to optimize large-scale WFLO problems.CGPSO is tested on four larger-scale wind farms under four complex wind scenarios and compares with eight state-of-the-art algorithms.The experiment results indicate that CGPSO significantly outperforms its competitors in terms of performance,stability,and robustness.To be specific,a success and failure memories-based selection is proposed to choose a chaotic map for chaotic search local.It improves the solution quality.The parameter and search pattern of chaotic local search are also analyzed for WFLO problems.

The statistical observation localized equivalent-weights particle filter in a simple nonlinear model

This paper presents an improved approach based on the equivalent-weights particle filter(EWPF)that uses the proposal density to effectively improve the traditional particle filter.The proposed approach uses historical data to calculate statistical observations instead of the future observations used in the EWPF’s proposal density and draws on the localization scheme used in the localized PF(LPF)to construct the localized EWPF.The new approach is called the statistical observation localized EWPF(LEWPF-Sobs);it uses statistical observations that are better adapted to the requirements of real-time assimilation and the localization function is used to calculate weights to reduce the effect of missing observations on the weights.This approach not only retains the advantages of the EWPF,but also improves the assimilation quality when using sparse observations.Numerical experiments performed with the Lorenz 96 model show that the statistical observation EWPF is better than the EWPF and EAKF when the model uses standard distribution observations.Comparisons of the statistical observation localized EWPF and LPF reveal the advantages of the new method,with fewer particles giving better results.In particular,the new improved filter performs better than the traditional algorithms when the observation network contains densely spaced measurements associated with model state nonlinearities.

Localized dissolution initiated at single and clustered intermetallic particles during immersion of Al-Cu-Mg alloy in sodium chloride solution

Aiming at understanding how intermetallic phases response when AA2024-T3 aluminium alloy is exposed to chloridecontainingaqueous medium, scanning electron microscopy was employed to provide morphological information on alloy surfacebefore and after corrosion testing. Energy dispersive X-ray spectroscopy was carried out to determine compositional change inintermetallic particles. Atomic force microscopy was used to examine topographical variation introduced by the reactions ofintermetallic phases. Transmission electron microscopy combined with ultramicrotomy was carried out on dealloyed Al2CuMgparticles and their periphery region. It is found that dealloyed Al2CuMg particles exhibited porous, polycrystalline structurecomprised of body-centred cubic copper particles with sizes of 5 to 20 nm. Aluminium matrix started to trench in the periphery ofAl2CuMg particles at the early stage of dealloying. Development of trenching in Al.Cu.Fe.Mn.（Si） particle’s periphery was notuniform and took longer time to initiate than Al2CuMg dealloying. Localized corrosion at a cluster of Al2CuMg and Al2Cu particleswas mainly associated with Al2CuMg particles.

Adaptive Multi-Updating Strategy Based Particle Swarm Optimization

Particle swarm optimization(PSO)is a stochastic computation tech-nique that has become an increasingly important branch of swarm intelligence optimization.However,like other evolutionary algorithms,PSO also suffers from premature convergence and entrapment into local optima in dealing with complex multimodal problems.Thus this paper puts forward an adaptive multi-updating strategy based particle swarm optimization(abbreviated as AMS-PSO).To start with,the chaotic sequence is employed to generate high-quality initial particles to accelerate the convergence rate of the AMS-PSO.Subsequently,according to the current iteration,different update schemes are used to regulate the particle search process at different evolution stages.To be specific,two different sets of velocity update strategies are utilized to enhance the exploration ability in the early evolution stage while the other two sets of velocity update schemes are applied to improve the exploitation capability in the later evolution stage.Followed by the unequal weightage of acceleration coefficients is used to guide the search for the global worst particle to enhance the swarm diversity.In addition,an auxiliary update strategy is exclusively leveraged to the global best particle for the purpose of ensuring the convergence of the PSO method.Finally,extensive experiments on two sets of well-known benchmark functions bear out that AMS-PSO outperforms several state-of-the-art PSOs in terms of solution accuracy and convergence rate.

Hybrid particle swarm optimization with differential evolution and chaotic local search to solve reliability-redundancy allocation problems

In order to solve reliability-redundancy allocation problems more effectively, a new hybrid algorithm named CDEPSO is proposed in this work, which combines particle swarm optimization （PSO） with differential evolution （DE） and a new chaotic local search. In the CDEPSO algorithm, DE provides its best solution to PSO if the best solution obtained by DE is better than that by PSO, while the best solution in the PSO is performed by chaotic local search. To investigate the performance of CDEPSO, four typical reliability-redundancy allocation problems were solved and the results indicate that the convergence speed and robustness of CDEPSO is better than those of PSO and CPSO （a hybrid algorithm which only combines PSO with chaotic local search）. And, compared with the other six improved meta-heuristics, CDEPSO also exhibits more robust performance. In addition, a new performance was proposed to more fairly compare CDEPSO with the same six improved recta-heuristics, and CDEPSO algorithm is the best in solving these problems.

A Learning Particle Swarm Optimization Algorithm for Odor Source Localization

This paper is concerned with the problem of odor source localization using multi-robot system. A learning particle swarm optimization algorithm, which can coordinate a multi-robot system to locate the odor source, is proposed. First, in order to develop the proposed algorithm, a source probability map for a robot is built and updated by using concentration magnitude information, wind information, and swarm information. Based on the source probability map, the new position of the robot can be generated. Second, a distributed coordination architecture, by which the proposed algorithm can run on the multi-robot system, is designed. Specifically, the proposed algorithm is used on the group level to generate a new position for the robot. A consensus algorithm is then adopted on the robot level in order to control the robot to move from the current position to the new position. Finally, the effectiveness of the proposed algorithm is illustrated for the odor source localization problem.

Enhanced Particle Swarm Optimization Based Local Search for Reactive Power Compensation Problem

This paper presents an enhanced Particle Swarm Optimization (PSO) algorithm applied to the reactive power compensation (RPC) problem. It is based on the combination of Genetic Algorithm (GA) and PSO. Our approach integrates the merits of both genetic algorithms (GAs) and particle swarm optimization (PSO) and it has two characteristic features. Firstly, the algorithm is initialized by a set of a random particle which traveling through the search space, during this travel an evolution of these particles is performed by a hybrid PSO with GA to get approximate no dominated solution. Secondly, to improve the solution quality, dynamic version of pattern search technique is implemented as neighborhood search engine where it intends to explore the less-crowded area in the current archive to possibly obtain more nondominated solutions. The proposed approach is carried out on the standard IEEE 30-bus 6-generator test system. The results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto optimal nondominated solutions of the multiobjective RPC.

The theoretical research on localization using improved particle filter for mine rescue robot in unknown underground space

The localization for mine rescue robot in unknown space of coal mine emer- gency was researched,basing on the requirement of mine rescue robot localization which enters the scene of the coal mine accident,unknown circumstance,the unstructured, non-Gaussian and nonlinear work-space for robot works.The localization using particle filter was proposed to which is applied in mine rescue robot in unknown under-ground space.Meanwhile,focusing on severe particle sample degeneracy in the primary particle filter,an improved particle filter was proposed to reinforce the stability of particle filter.Be- ing compared with localization using extended Kalman filter through simulation experiment the localization using particle filter is proved to have more locating accuracy in unknown underground space and better computational real-time ability,which solves the pre-local- ization problem of robot underground.

Mobile robot simultaneous localization and map building based on improved particle filter

We present an investigation into the use of pan tilt zoom camera and sonar sensors for simuhaneous localization and mapping with artificial colored landmarks. An improved particle filter is applied to estimate a posterior of the pose of the robot, in which each particle has associated it with an entire map. The distributions of landmarks are also represented by particle sets, where separate particles are used to represent the robot and the landmarks. Hough transform is used to extract line segments from sonar observations and build map simultaneously. The key advantage of our method is that the full posterior over robot poses and landmarks can be nonlinearly approximated at every point in time by particles. Especially the landmarks are affixed on the moving robots, which can reduce the impact of the depletion problem and the impoverishment problem produced by basic particle filter. Experimental results show that this approach has advantages over the basic particle filter and the extended Kalman filter.

Plastic Flow Macrolocalization: Autowave and Quasi-Particle

A new approach is proposed to describe the autowave processes responsible for plastic deformation localiza-tion in metals and alloys. The existence of a quasi-particle, which corresponds to a localized plastic flow autowave, is postulated and its characteristics are determined. The above postulate leads to a number of cor-ollaries and quantitative assessments that are considered herein. The deformation processes occurring on the macro- and micro-scale levels are found to be directly related.

Effects of combined treatment of bronchial arterial chemoembolization and radioactive particle implantation on tumor markers and T lymphocyte subsets in locally advanced non-small cell lung cancer

Objective: To investigate the effects of bronchial arterial chemoembolization combined with radioactive particle implantation on the level of serum tumor markers and T lymphocyte subsets in patients with locally advanced non-small cell lung cancer. Methods: A total of 91 cases of locally advanced non-small cell lung cancer patients according to the random data table were divided into the control group (n=45) and observation group (n=46) according to the random data table. Patients in the control group was treated with bronchial arterial chemoembolization, on the basis of the control group, patients in the observation group were treated with radioactive particle implantation, the serum tumor markers and T lymphocyte subsets of the two groups were compared before and after treatment. Results: The levels of CEA, NSE, CA125, CD4+, CD8+, CD4+/CD8+ and NK in the two groups before the treatment were not statistically significant. Compared with the group before treatment, levels of CEA, NSE, CA125and CD8+ of the two groups after treatment were significantly decreased, and after treatment the level of CEA, NSE, CA125and CD8+ in the observation group was significantly lower than those of the control group;The levels of CD4+, CD4+/CD8+ and NK in the two groups after treatment were significantly higher than those in the group before treatment, and the observation group levels were significantly higher than those of the control group. Conclusion: Bronchial artery embolization combined with radioactive particle implantation for locally advanced non-small cell lung cancer, can effectively reduce the serum tumor markers level, improve the level of T cell subsets of patients, has important clinical value.

Hierarchical particle filter tracking algorithm based on multi-feature fusion

A hierarchical particle filter（HPF） framework based on multi-feature fusion is proposed.The proposed HPF effectively uses different feature information to avoid the tracking failure based on the single feature in a complicated environment.In this approach,the Harris algorithm is introduced to detect the corner points of the object,and the corner matching algorithm based on singular value decomposition is used to compute the firstorder weights and make particles centralize in the high likelihood area.Then the local binary pattern（LBP） operator is used to build the observation model of the target based on the color and texture features,by which the second-order weights of particles and the accurate location of the target can be obtained.Moreover,a backstepping controller is proposed to complete the whole tracking system.Simulations and experiments are carried out,and the results show that the HPF algorithm with the backstepping controller achieves stable and accurate tracking with good robustness in complex environments.

A FastSLAM Algorithm Based on the Improved Auxiliary Particle Filter with Stirling Interpolation

The choice of the particle's distribution model and the consistency of the result are very important for FastSLAM.The improved auxiliary variable model with FastSLAM,and Stirling Interpolation which is used to approximate the nonlinear functions are provided.This approach improves the precision of the approximation for the nonlinear functions,conquers the drawback of the FastSLAM1.0 by using a model ignoring the measurement data,enhances the estimation consistency of the robot pose,and reduces the degradation speed of the particle in FastSLAM algorithm.Simulation results demonstrate the excellence of the proposed algorithm and give the noise parameter influence on the proposed algorithm.

Density Function Theory Study on the Reaction Mechanism of Cerium with Oxygen for Ce-bearing Aerosol Particle Formation

We obtained the reaction mechanism of cerium with oxygen by means of density functional theory (DFT) under the explosive or detonation situation. The energy level order of cerium was calculated and the potential energy profile was plotted. The properties of the bonds of all special structures in the reaction path were analyzed using the method of the electron localization function (ELF). The results indicate that the final reaction pathway can be expressed as Ce+O2→ FC→TS→IM→O-Ce-O, and the formation of Ce2O3 has not been found. In addition, the ELF diagram of the final product CeO2 shows that Ce bonds with both O atoms and the bond angles increase significantly.

Quantum particle swarm optimization for micro-grid system with consideration of consumer satisfaction and benefit of generation side

Considering comprehensive benefit of micro-grid system and consumers,we establish a mathematical model with the goal of the maximum consumer satisfaction and the maximum benefit of power generation side in the view of energy management.An improved multi-objective local mutation adaptive quantum particle swarm optimization(MO-LM-AQPSO)algorithm is adopted to obtain the Pareto frontier of consumer satisfaction and the benefit of power generation side.The optimal solution of the non-dominant solution is selected with introducing the power shortage and power loss to maximize the benefit of power generation side,and its reasonableness is verified by numerical simulation.Then,translational load and time-of-use electricity price incentive mechanism are considered and reasonable peak-valley price ratio is adopted to guide users to actively participate in demand response.The simulation results show that the reasonable incentive mechanism increases the benefit of power generation side and improves the consumer satisfaction.Also the mechanism maximizes the utilization of renewable energy and effectively reduces the operation cost of the battery.

Modified Wave Particle Duality and Black Hole Physics

de Broglie relation is revisited,in consideration of a generalization of canonical commuting relation.Thepossible effects on particle's localization and black hole physics are also discussed,in a heuristic manner.

Effects of particle size on residual stresses of metal matrix composites

A finite element analysis was carned out on the development of residual stresses during the cooling process from the fabrication temperature in the SiCp reinforced Al matrix composites. In the simulation, the two-dimensional and random distribution multi-particle unit cell model and plane strain conditions were used. By incorporating the Taylor-based nonlocal plasticity theory, the effect of particle size on the nature, magnitude and distribution of residual stresses of the composites was studied. The magnitude thermal-stress-induced plastic deformation during cooling was also calculated. The results show similarities in the patterns of thermal residual stress and strain distributions for all ranges of particle size. However, they show differences in magnitude of thermal residual stress as a result of strain gradient effect. The average thermal residual stress increases with decreasing particle size, and the residual plastic strain decreases with decreasing particle size.

A Hybrid Optimizer Based On Firefly Algorithm And Particle Swarm Optimization Algorithm

As two widely used evolutionary algorithms,particle swarm optimization(PSO)and firefly algorithm(FA)have been successfully applied to diverse difficult applications.And extensive experiments verify their own merits and characteristics.To efficiently utilize different advantages of PSO and FA,three novel operators are proposed in a hybrid optimizer based on the two algorithms,named as FAPSO in this paper.Firstly,the population of FAPSO is divided into two sub-populations selecting FA and PSO as their basic algorithm to carry out the optimization process,respectively.To exchange the information of the two sub-populations and then efficiently utilize the merits of PSO and FA,the sub-populations share their own optimal solutions while they have stagnated more than a predefined threshold.Secondly,each dimension of the search space is divided into many small-sized sub-regions,based on which much historical knowledge is recorded to help the current best solution to carry out a detecting operator.The purposeful detecting operator enables the population to find a more promising sub-region,and then jumps out of a possible local optimum.Lastly,a classical local search strategy,i.e.,BFGS QuasiNewton method,is introduced to improve the exploitative capability of FAPSO.Extensive simulations upon different functions demonstrate that FAPSO is not only outperforms the two basic algorithm,i.e.,FA and PSO,but also surpasses some state-of-the-art variants of FA and PSO,as well as two hybrid algorithms.