The quantum measurement problem and selection of classical states

The problem of selection of preferred basis during passage from quantum to classical systems is treated with the help of a simple example of a 2-state system like the sugar molecule. A simple principle leading to this selection is stated and demonstrated in case of the chosen example. The principle, stated simply is that the preferred basis is the one in which the system environment interaction hamiltonian is diagonal. Talk given at the International Symposium on Theoretical Physics, Bangalore, November 1984.     

Inverse estimation of the pulse parameters of a time-varying laser pulse to obtain desired temperature at the material surface

A proper selection of the pulse parameters is essential to achieve desired temperature at the material surface. This leads to obtain the required metallurgical changes in the surface vicinity when a time-varying laser pulse is used in a heating process such as surface modification. In this paper, the conjugate gradient method (CGM) for parameter estimation is successfully applied to estimate the unknown laser pulse parameters for those purposes during laser heating process. The determination of the pulse parameters is treated as a one-dimensional, transient, non-linear inverse heat conduction problem (IHCP). Based on a sensitivity analysis, the inverse problem is solved as an optimization problem comparing a desired temperature at the surface and a calculated one where the objective function is minimized by CGM. The method has been applied to a test case of a heating process on steel, appropriate pulse parameters and desired temperature distribution can also be returned.     

An automated parameter selection procedure for finite-element model updating and its applications

Finite-element model updating is an inverse problem to identify and correct uncertain modeling parameters, which leads to better predictions of the dynamic behavior of a target structure. Unlike other inverse problems, the restrictions on selecting parameters are very high since the updated model should maintain its physical meaning. That is, only the regions with modeling errors should be parameterized and the variations of the parameters should be kept small while the updated results give acceptable correlations with experimental data. To avoid an ill-conditioned numerical problem, the number of parameters should be kept as small as possible. Thus it is very difficult to select an adequate set of updating parameters which meet all these requirements. In this paper, the importance of updating parameter selection is illustrated through a case study, and an automated procedure to guide the parameter selection is suggested based on simple observations. The effectiveness of the suggested procedure is tested with two example problems, one is a simulated case study and the other is a real engineering structure.     

Joint optimization of component carrier selection and resource allocation in 5G carrier aggregation system

In this paper, we consider joint optimization of Component Carrier (CC) selection and resource allocation in 5G Carrier Aggregation (CA) system. Firstly, the upper-bound system throughput with determined number of CCs is derived and it is proved by using graph theory that the throughput optimization problem is NP hard. Then we propose a greedy based algorithm to solve this problem and prove that the proposed algorithm can achieve at least 1/2 of the optimal performance in the worst case. At last, we evaluate the throughput and computational complexity performance through a variety of simulations. Simulation results show that the proposed algorithm can obtain better performance comparing with existing schemes while keeping the computation complexity at an acceptable level.     

The application of simple selection rules to photoemission from clean and adsorbate covered metal single crystals

The selection rules governing the uv-photoemission process from clean and adsorbate covered surfaces, as a consequence of the symmetry properties of the system, are discussed. It is shown that the symmetries of the initial states giving rise to features in the angle-resolved photoemission spectra can be readily derived from photon polarization dependent measurements. Symmetry based selection rules are also applied to the problem of adsorbate molecule orientation for the case of adsorbate covered surfaces.     

Interband transitions in a spherical quantum layer in the presence of an electric field: Spherical rotator model

The interband transitions in a spherical GaAs quantum layer in the presence of an arbitrarily directed electric field are studied theoretically within the framework of the rigid spherical rotator model. The problem is solved under the assumption that the external field is a perturbation. Within the framework of the dipole approximation an expression for the interband absorption coefficient is obtained, and the absorption threshold frequency is determined. The corresponding selection rules are derived. A comparison with the case of quantum transitions in a spherical quantum layer in the presence of a radial electric field is performed.     

A gradient-free adaptation method for nonlinear active noise control

Active Noise Control (ANC) problems are often affected by nonlinear effects, such as saturation and distortion of microphones and loudspeakers. Nonlinear models and specific adaptation algorithms must be employed to properly account for these effects. The nonlinear structure of the problem complicates the application of gradient-based Least Mean Squares (LMS) algorithms, due to the fact that exact gradient calculation requires executing nonlinear recursive filtering operations, which pose computational and stability issues. One favored solution to this problem consists in neglecting recursive terms in the gradient calculation, an approximation which is not always without consequences on the convergence performance. Besides, an efficient application of nonlinear models cannot avoid some form of model structure selection, to avoid the well-known effects of overparametrization and to reduce the computational load on-line. Unfortunately, the standard ANC setting configures an indirect identification problem, due to the presence of the secondary path in the control loop. In the nonlinear case, this destroys the linear regression structure of the problem even if the control filter is linear-in-the-parameters, thereby making it impossible to apply the many existing model selection methods for linear regression problems. A simple and computationally wise low demanding approach is here proposed for parameter estimation and model structure selection that provides an answer to the mentioned issues. The proposed method avoids altogether the use of the error gradient and relies on direct cost function evaluations. A virtualization scheme is used to assess the accuracy improvements when the model is subject to parametric or structural modifications, without directly affecting the control performance. Several simulation examples are discussed to show the effectiveness of the proposed algorithms.     

Genetic optimisation of a neural damage locator

A critical problem in structural health monitoring (SHM) based on pattern recognition methods is the correct selection of features, i.e. measured and processed data for the diagnosis. Various selection strategies have been applied in the past and one approach that has proved effective is the use of combinatorial optimisation methods. This paper presents a case study based on a scheme for damage location in an aircraft wing. The feature selection algorithm is a Genetic Algorithm and the locator (classifier) is an artificial neural network. A comparison is made with the results obtained when the features are selected on the basis of engineering judgement. The study is seen to raise some issues relating to model complexity and generalisation and these matters are discussed in some detail.     

Positron diffusion in solids and the reconstruction of inhomogeneous defect distributions from lifetime measurements

The time dependent diffusion trapping equations for positrons implanted into inhomogeneous solids are analyzed. This problem is of central importance in the study of polycrystalline materials and for the application of pulsed positron beams to defect studies in materials research. The main problem in previous investigations was the necessity to solve the time-dependent diffusion equation. It prevented analytical treatment in all but the simplest applications. For the first time this difficulty is eliminated by invoking a new concept, the observable local annihilation characteristics for local implantation of positrons into the thermalized ensemble. It will be shown that the local annihilation characteristics are governed by field equations which reduce to the well known quantities of the standard trapping model in the case of homogeneous defect distributions. Furthermore, inhomogeneous defect distributions are uniquely determined from the field equations provided the local annihilation characteristics are known. Analytical solutions are derived and applied successfully to recent experimental results for a selection of simple, but realistic problems. The formal procedure includes internal drift fields and could be extended to cover also the epithermal period of positron thermalization, if necessary.     

基于不动点理论的多系统兼容接收机频点选择问题的研究与遗传算法实现

基于不动点理论研究了多系统兼容接收机的频点选择问题,并引入遗传算法解决了这一问题.将Banach不动点理论引入频点空间,提出并证明了频点空间中频点集的遗传进化过程反映了频点空间呈现出的一种不动点物理特性;经过频点空间算子的足够多次的反复遗传进化作用,频点空间中的任一迭代序列源频点集都将最终收敛于唯一不动点目标频点集,而该不动点目标频点集中的目标频点即为该频点选择问题的最优解.完成了遗传算法应用于频点选择问题的理论研究与实现.仿真结果表明,该算法能够有效地解决多系统兼容接收机的频点选择问题. 关键词： 多系统 接收机 遗传算法 不动点     

Innovative investigation on ion flows by an ad hoc Lagrangian formulation

A specialised Lagrangian formulation is here recommended to describe the main performances of unipolar ion flows driven by partial discharges. The subject fits into a number of electrostatic applications also governed by fluid-dynamic laws, which is why it needs careful examination by a solid and comprehensive approach. A reasoned selection of referential case studies are proposed in order to better appreciate the value of this predictive framework. The interdisciplinary character of the general problem under examination makes the role of the Lagrangian approach pivotal for the physical interpretation of theoretical results. These are given by resolving the Euler-Lagrange differential equation associated to individual examples.     

Calculations of the complete morphological phase diagram for nonequilibrium growth of a spherical crystal under arbitrary surface kinetics

Complete morphological diagrams (with stable, metastable, and absolutely unstable regions) were calculated for the problem of morphology selection under the conditions of nonequilibrium growth of a spherical crystal taking into account arbitrary kinetic process rates at the boundary and a linear dependence of the growth rate on supersaturation. The consideration was performed by jointly using linear stability analysis and the principle of maximum entropy production. The principal difference between kinetically and diffusion-controlled crystal growth is the possibility of the coexistence of three or more morphological phases under the same conditions in the former case. It was shown that, at the transition point, the rate of accretion of the growing crystal mass increased in a jump. The jump value was studied as a function of the parameters of the problem.     

An Adaptive Rank Aggregation-Based Ensemble Multi-Filter Feature Selection Method in Software Defect Prediction

Feature selection is known to be an applicable solution to address the problem of high dimensionality in software defect prediction (SDP). However, choosing an appropriate filter feature selection (FFS) method that will generate and guarantee optimal features in SDP is an open research issue, known as the filter rank selection problem. As a solution, the combination of multiple filter methods can alleviate the filter rank selection problem. In this study, a novel adaptive rank aggregation-based ensemble multi-filter feature selection (AREMFFS) method is proposed to resolve high dimensionality and filter rank selection problems in SDP. Specifically, the proposed AREMFFS method is based on assessing and combining the strengths of individual FFS methods by aggregating multiple rank lists in the generation and subsequent selection of top-ranked features to be used in the SDP process. The efficacy of the proposed AREMFFS method is evaluated with decision tree (DT) and naïve Bayes (NB) models on defect datasets from different repositories with diverse defect granularities. Findings from the experimental results indicated the superiority of AREMFFS over other baseline FFS methods that were evaluated, existing rank aggregation based multi-filter FS methods, and variants of AREMFFS as developed in this study. That is, the proposed AREMFFS method not only had a superior effect on prediction performances of SDP models but also outperformed baseline FS methods and existing rank aggregation based multi-filter FS methods. Therefore, this study recommends the combination of multiple FFS methods to utilize the strength of respective FFS methods and take advantage of filter–filter relationships in selecting optimal features for SDP processes.     

Assessing impact force localization by using a particle swarm optimization algorithm

This paper focuses on the inverse problem regarding force localization in the case of impacts not concentrated at a point but which occur on elastic beams. Following the identification approach proposed to solve this problem and which is based on the reciprocity theorem, the impact location characteristics were determined by using particle swarm optimization algorithm. To eliminate numerical trouble due to the trivial solutions appearing in this formulation, the fitness function was customized by introducing a set of weighting coefficients. Four different formulations of the fitness function were considered and their performances with regards to the number of sensors used and their positions were analyzed. They enabled a selection of the best combination of weighting coefficients to be used in the context of an impact force localization process based on the particle swarm optimization technique. Three sensors were found to be required and comparison with a genetic algorithm has revealed the effectiveness of the proposed method in terms of accuracy and computational time.     

An Evolution Based on Various Energy Strategies

The simplest model of the evolution of agents with different energy strategies is considered. The model is based on the most general thermodynamic ideas and includes the procedures for selection, inheritance, and variability. The problem of finding a universal strategy (principle) as a selection of possible competing strategies is solved. It is shown that when there is non-equilibrium between the medium and agents, a direction in the evolution of agents arises, but at the same time, depending on the conditions of the evolution, different strategies can be successful. However, for this case, the simulation results reveal that in the presence of significant competition of agents, the strategy that has the maximum total energy dissipation of agents arising as a result of evolution turns out to be successful. Thus, it is not the specific strategy that is universal, but the maximization of dissipation. This result discovers an interesting connection between the basic principles of Darwin–Wallace evolution and the maximum entropy production principle.     

On asymptotics of the solution of the moving oscillator problem

Asymptotic behavior of the solution of the moving oscillator problem is examined for large and small values of the spring stiffness for the general case of non-zero beam initial conditions. In the limiting case of infinite spring stiffness, it is shown that the moving oscillator problem for a simply supported beam is not equivalent, in a strict sense, to the moving mass problem. The two problems are shown to be equivalent in terms of the beam displacements but are not equivalent in terms of stresses (the higher order derivatives of the two solutions differ). In the general case, the force acting on the beam from the oscillator is shown to contain a high-frequency component , which does not vanish and can even grow when the spring coefficient tends to infinity. The magnitude of this force and its dependence on the oscillator parameters can be estimated by considering the asymptotics of the solution for the initial stage of the oscillator motion. It is shown that, for the case of a simply supported beam, the magnitude of the high-frequency force depends linearly on the oscillator eigenfrequency and velocity. The deficiency of the moving mass model is principally that it fails to predict stresses in the supporting structure. For small values of the spring stiffness, the moving oscillator problem is shown to be equivalent to the moving force problem. The discussion is amply illustrated by results of numerical experiments.     

Random Evolutionary Dynamics Driven by Fitness and House-of-Cards Mutations: Sampling Formulae

We first revisit the multi-allelic mutation-fitness balance problem, especially when mutations obey a house of cards condition, where the discrete-time deterministic evolutionary dynamics of the allelic frequencies derives from a Shahshahani potential. We then consider multi-allelic Wright–Fisher stochastic models whose deviation to neutrality is from the Shahshahani mutation/selection potential. We next focus on the weak selection, weak mutation cases and, making use of a Gamma calculus, we compute the normalizing partition functions of the invariant probability densities appearing in their Wright–Fisher diffusive approximations. Using these results, generalized Ewens sampling formulae (ESF) from the equilibrium distributions are derived. We start treating the ESF in the mixed mutation/selection potential case and then we restrict ourselves to the ESF in the simpler house-of-cards mutations only situation. We also address some issues concerning sampling problems from infinitely-many alleles weak limits.     

Basis of polarization-dressed states of an atom in an elliptically polarized resonant field

A basis of polarization-dressed states is proposed for atomic energy levels degenerate in the total angular momentum projections in the case of interaction with elliptically polarized light. It is shown that instead of selection rules for the magnetic quantum number, the interaction in this basis can be presented as the sum of direct transitions between corresponding pairs of polarization-dressed states of the upper and lower levels. The explicit form of the basis is derived for ten possible combinations of dipole transitions between energy levels with angular momenta J = 0, 1/2, 1, 3/2, and 2. The problem of Rabi oscillations in such a system is considered as an application.     

Immersing method for the multichannel scattering problem

We propose a method for solution of the multichannel scattering problem. In particular, the scattering problem is considered when a particle makes a finite motion in the transverse direction of the scattering. In this case the scattering becomes multichannel, which is connected with the presence of discrete energy levels of the transverse movement of the particle. For the case of two-channel scattering, the problem is formulated up to the end. A method for determination of scattering amplitudes for the potential V=V(x,y) is proposed.     

An approximation for the boundary optimal control problem of a heat equation defined in a variable domain

In this paper, we consider a numerical approximation for the boundary optimal control problem with the control constraint governed by a heat equation defined in a variable domain. For this variable domain problem, the boundary of the domain is moving and the shape of theboundary is defined by a known time-dependent function. By making use of the Galerkin finite element method, we first project the original optimal control problem into a semi-discrete optimal control problem governed by a system of ordinary differential equations. Then, based on the aforementioned semi-discrete problem, we apply the control parameterization method to obtain an optimal parameter selection problem governed by a lumped parameter system, which can be solved as a nonlinear optimization problem by a Sequential Quadratic Programming （SQP） algorithm. The numerical simulation is given to illustrate the effectiveness of our numerical approximation for the variable domain problem with the finite element method and the control parameterization method.