Mössbauer spectra processing using neural networks

A Mössbauer spectrum can be understood as a sum of Lorentz functions. The aim of this work is to develop an automated algorithm for Mössbauer spectra processing. The required output of this algorithm is the number of Lorentz functions contained in the spectrum and the values of parameters of these functions, i.e. values of their amplitudes, positions and halfwidths. For this purpose a modified brute-force method was used. There are tested all possible combinations of parameters of the Lorentz functions whether they properly fit measured spectrum or not. A set of parameters having the best fit with the spectrum is the output of this method. Testing all possible combinations takes a huge computational power so an optimization process was developed to speed up the computational process. This is achieved by decreasing the number of tested combinations. The optimization process is based on a mathematical analysis and neural network processing. An evolutional algorithms were used for proper setting up of the neural weights.     

激光并行加工DM码实验研究

DM（data matrix）码信息存储容量大、编码范围广、可靠性高,是信息交流的重要载体。为了实现DM码并行加工方法,分析了DM码间距值与灰度值对识别时间的影响规律,并对不同情况下的DM码进行多次扫描,记录识读时间,确定了DM码识读成功的临界圆点直径为11像素,临界灰度值为220。在此基础上,设计了一种基于空间光调制器的DM码并行加工方法,并在石英玻璃内部进行了激光并行加工DM码实验验证,与传统的单光束激光标刻二维码的方式相比,加工效率得到了提高。     

Visual image processing by neural networks with nonlocal coupling

The possibilities of using homogeneous artificial neural networks both for encoding and back reconstruction of visual images are investigated. A conjugate reverse neural network with an opposite time direction is used for the image reconstruction. Information-entropic characteristics of the image and its different-level histograms are used for quality monitoring of the dynamic image processing. Experimental data on the dynamic behavior of these values in computer simulation for pure images are reported. Comparison with the thermodynamic entropy, subject to a monotonic change, is made. Special cases of nonmonotonic entropy change are considered and examples of the dynamic processing of images with code redundancy are demonstrated.Radiophysical Research Institute. Translated from Izvestiya Vysshikh Uchebnykh Zavedenii, Radiofizika, Vol. 37, No. 9, pp. 1195–1213, September, 1994.     

基于JASMIN并行框架的2.5维粒子模拟程序NEPTUNE2D的研制

介绍了2.5维自主研制的并行电磁粒子模拟程序NEPTUNE2D初步研发情况。该程序基于JASMIN并行自适应结构网格支撑框架研制,并行效能高,可扩展性强,且支持动态负载平衡;采用新型PIC算法替代传统算法,避免求解泊松方程修正电场,更适用于大规模并行计算;程序支持r-z坐标系下的器件仿真,可应用于高功率微波器件、电真空器件的快速模拟设计。该程序现已完成电磁场更新、粒子推进、电磁场注入/引出、粒子发射/吸收等基本物理功能模块的研制,并通过同轴线、圆波导、同轴二极管及无箔二极管算例模拟验证了模块的正确性。最后,应用NEPTUNE2D程序设计了一个高效同轴相对论返波管,给出了粒子模拟结果和并行性能测试结果。     

基于JASMIN并行框架的2.5维粒子模拟程序NEPTUNE2D的研制北大核心CSCD

介绍了2.5维自主研制的并行电磁粒子模拟程序NEPTUNE2D初步研发情况。该程序基于JASMIN并行自适应结构网格支撑框架研制,并行效能高,可扩展性强,且支持动态负载平衡;采用新型PIC算法替代传统算法,避免求解泊松方程修正电场,更适用于大规模并行计算;程序支持r-z坐标系下的器件仿真,可应用于高功率微波器件、电真空器件的快速模拟设计。该程序现已完成电磁场更新、粒子推进、电磁场注入/引出、粒子发射/吸收等基本物理功能模块的研制,并通过同轴线、圆波导、同轴二极管及无箔二极管算例模拟验证了模块的正确性。最后,应用NEPTUNE2D程序设计了一个高效同轴相对论返波管,给出了粒子模拟结果和并行性能测试结果。     

Martingale approach to neural networks with hierarchically structured information

A modified version of a model with hierarchically structured information, originally proposed by Parga and Virasoro, is studied in the context of martingale theory. The number of generations is allowed to be arbitrary but fixed. In the calculation of the storage capacity both the patterns and all their ancestors have to be taken into account as retrieval states. Due to the martingale property, the free energy greatly simplifies and for the retrieval states is shown to reduce to the one of the Hopfield model. So the storage capacity and retrieval quality of both models are identical. Numerical stimulations confirm the theoretical predictions.     

A semi-analytical approach using artificial neural network for dielectrophoresis generated by parallel electrodes

In this paper, we present a semi-analytical approach to obtain the DEP force generated by parallel electrodes. By solving the electric potential equation using the separation of variables method, a solution was found in the form of a Fourier series with unknown coefficients. The unknown coefficients were determined by training a linear artificial neural network with the appropriate data satisfied on the boundary. This results of calculated electric field and DEP force for both planar electrode system and 3D electrode system are validated by comparison with the numerical results obtained using the commercial software CFD-ACE+.     

A packet routing strategy using neural networks on scale-free networks

We propose a dynamic packet routing strategy by using neural networks on scale-free networks. In this strategy, in order to determine the nodes to which the packets should be transmitted, we use path lengths to the destinations of the packets, and adjust the connection weights of the neural networks attached to the nodes from local information and the path lengths. The performances of this strategy on scale-free networks which have the same degree distribution and different degree correlations are compared to one another. Our numerical simulations confirm that this routing strategy is more effective than the shortest path based strategy on scale-free networks with any degree correlations and that the performance of our strategy on assortative scale-free networks is better than that on disassortative and uncorrelated scale-free networks.     

Nonintersecting string model and graphical approach: Equivalence with a Potts model

Using a graphical method we establish the exact equivalence of the partition function of aq-state nonintersecting string (NIS) model on an arbitrary planar, even-valenced, lattice with that of a q²-state Potts model on a related lattice. The NIS model considered in this paper is one in which the vertex weights are expressible as sums of those of basic vertex types, and the resulting Potts model generally has multispin interactions. For the square and Kagomé lattices this leads to the equivalence of a staggered NIS model with Potts models with anisotropic pair interactions, indicating that these NIS models have a first-order transition forq > 2. For the triangular lattice the NIS model turns out to be the five-vertex model of Wu and Lin and it relates to a Potts model with two- and three-site interactions. The most general model we discuss is an oriented NIS model which contains the six-vertex model and the NIS models of Stroganov and Schultz as special cases.     

Optical microwave generation using two parallel DFB lasers integrated with Y-branch waveguide coupler

A new device of two parallel distributed feedback (DFB) lasers integrated monolithically with Y-branch waveguide coupler was fabricated by means of quantum well intermixing. Optical microwave signal was generated in the Y-branch waveguide coupler through frequency beating of the two laser modes coming from two DFB laser in parallel, which had a small difference in frequency. Continuous rapid tuning of optical microwave signal from 13 to 42GHz were realized by adjusting independently the driving currents injected into the two DFB lasers.     

Linear matrix inequality approach to exponential synchronization of a class of chaotic neural networks with time-varying delays

In this paper, a synchronization scheme for a class of chaotic neural networks with time-varying delays is presented. This class of chaotic neural networks covers several well-known neural networks, such as Hopfield neural networks, cellular neural networks, and bidirectional associative memory networks. The obtained criteria are expressed in terms of linear matrix inequalities, thus they can be efficiently verified. A comparison between our results and the previous results shows that our results are less restrictive.     

Solving nonlinear soliton equations using improved physics-informed neural networks with adaptive mechanisms

Partial differential equations(PDEs) are important tools for scientific research and are widely used in various fields. However, it is usually very difficult to obtain accurate analytical solutions of PDEs, and numerical methods to solve PDEs are often computationally intensive and very time-consuming. In recent years, Physics Informed Neural Networks(PINNs) have been successfully applied to find numerical solutions of PDEs and have shown great potential. All the while, solitary waves have been ...     

Stability and synchronization of fractional-order complex-valued neural networks with time delay: LMI approach

In this paper, we investigate the problem of stability and synchronization of fractional-order complex-valued neural networks with time delay. By using Lyapunov–Krasovskii functional approach, some linear matrix inequality (LMI) conditions are proposed to ensure that the equilibrium point of the addressed neural networks is globally Mittag–Leffler stable. Moreover, some sufficient conditions for projective synchronization of considered fractional-order complex-valued neural networks are derived in terms of LMIs. Finally, two numerical examples are given to demonstrate the effectiveness of our theoretical results.     

Particle-in-cell simulations with charge-conserving current deposition on graphic processing units

We present an implementation of a 2D fully relativistic, electromagnetic particle-in-cell code, with charge-conserving current deposition, on parallel graphics processors (GPU) with CUDA. The GPU implementation achieved a one particle-step process time of 2.52 ns for cold plasma runs and 9.15 ns for extremely relativistic plasma runs, which are respectively 81 and 27 times faster than a single threaded state-of-art CPU code. A particle-based computation thread assignment was used in the current deposition scheme and write conflicts among the threads were resolved by a thread racing technique. A parallel particle sorting scheme was also developed and used. The implementation took advantage of fast on-chip shared memory, and can in principle be extended to 3D.     

Noise-robust realization of Turing-complete cellular automata by using neural networks with pattern representation

A modularly-structured neural network model is considered. Each module, which we call a ‘cell’, consists of two parts: a Hopfield neural network model and a multilayered perceptron. An array of such cells is used to simulate the Rule 110 cellular automaton with high accuracy even when all the units of neural networks are replaced by stochastic binary ones. We also find that noise not only degrades but also facilitates computation if the outputs of multilayered perceptrons are below the threshold required to update the states of the cells, which is a stochastic resonance in computation.     

Linear matrix inequality approach for robust stability analysis for stochastic neural networks with time-varying delay

This paper studies the problem of linear matrix inequality (LMI) approach to robust stability analysis for stochastic neural networks with a time-varying delay. By developing a delay decomposition approach,the information of the delayed plant states can be taken into full consideration. Based on the new Lyapunov-Krasovskii functional,some inequality techniques and stochastic stability theory,new delay-dependent stability criteria are obtained in terms of LMIs. The proposed results prove the less conservatism,which are realized by choosing new Lyapunov matrices in the decomposed integral intervals. Finally,numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed LMI method.     

Analysis and models of bilateral investment treaties using a social networks approach

Bilateral investment treaties (BITs) are agreements between two countries for the reciprocal encouragement, promotion and protection of investments in each other’s territories by companies based in either country. Germany and Pakistan signed the first BIT in 1959 and since then, BITs are one of the most popular and widespread form of international agreement. In this work we study the proliferation of BITs using a social networks approach. We propose a network growth model that dynamically replicates the empirical topological characteristics of the BIT network.     

Characteristics and generation of elastic turbulence in a three-dimensional parallel plate channel using direct numerical simulation

Direct numerical simulations(DNSs) of purely elastic turbulence in rectilinear shear flows in a three-dimensional(3D) parallel plate channel were carried out,by which numerical databases were established.Based on the numerical databases,the present paper analyzed the structural and statistical characteristics of the elastic turbulence including flow patterns,the wall effect on the turbulent kinetic energy spectrum,and the local relationship between the flow motion and the microstructures' behavior.Moreover,to address the underlying physical mechanism of elastic turbulence,its generation was presented in terms of the global energy budget.The results showed that the flow structures in elastic turbulence were 3D with spatial scales on the order of the geometrical characteristic length,and vortex tubes were more likely to be embedded in the regions where the polymers were strongly stretched.In addition,the patterns of microstructures' elongation behave like a filament.From the results of the turbulent kinetic energy budget,it was found that the continuous energy releasing from the polymers into the main flow was the main source of the generation and maintenance of the elastic turbulent status.     

Application of artificial neural networks for unfolding neutron spectra by using a scintillation detector

The unfolding of neutron spectra from the pulse height distribution measured by a BC501A scintillation detector is accomplished by the application of artificial neural networks (ANN). A simple linear neural network without biases and hidden layers is adopted. A set of monoenergetic detector response functions in the energy range from 0.25 MeV to 16 MeV with an energy interval of 0.25 MeV are generated by the Monte Carlo code O5S in the training phase of the unfolding process. The capability of ANN was demon...