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Quantum field theories can be applied to compute various statistical properties of random tensors. In particular, signed distributions of tensor eigenvalues/vectors are the easiest, which can be computed as partition functions of four-fermi theories. Though signed distributions are different from genuine ones because of extra signs of weights, they are expected to coincide in vicinities of ends of distributions. In this paper, we perform a case study of the signed eigenvalue/vector distribution of the real symmetric order-three random tensor. The correct critical point and the correct end in the large N limit are obtained from the four-fermi theory, for which a method using the Schwinger-Dyson equation is very efficient. Since locations of ends are particularly important in applications, such as the largest eigenvalues and the best rank-one tensor approximations, signed distributions are the easiest and highly useful through the Schwinger-Dyson method. [ABSTRACT FROM AUTHOR]

In this paper, we present a new simple method for solving two integral equations of Love's type that have many applications, especially in electrostatic systems. The approach of the solution is based on an innovative technique using matrix algebra for the barycentric Lagrange interpolation. The unknown function is expressed through the product of four matrices. The kernel is interpolated twice, so we get it in the product of five matrices. Additionally, we derive an equivalent linear algebraic system to the solution by substituting the matrix-vector barycentric interpolated unknown function together with the double interpolated kernel into both sides of the integral equation. Thus, there was no need to employ the collocation method. The obtained results converge strongly with the approximate analytical solutions, in addition to being uniformly approximated, continuous, and even, which proves the validity of the solution by the presented method. [ABSTRACT FROM AUTHOR]

Regime-switching models have been heavily studied recently, as they have some clear advantages of over other non-constant volatility model to resolve the so-called smirk effect displayed when constant volatility models are used to price financial derivatives such as options. However, due to the increased model complexity, the associated computational effort usually increases as well, particularly when they are used to price American-style options. In this paper, a novel computational approach based on integral equations is presented. A distinctive feature of our approach, in comparison with other numerical approaches, is that the coupled partial differential equations (PDEs) in a PDE system have been decoupled in the Fourier space, resulting in a completely decoupled integral equation for each economical states, and thus has greatly reduced computational effort. Some examples with preliminary results for a two-state regime-switching model are used to demonstrate our approach. [ABSTRACT FROM AUTHOR]

New propulsion systems in aircrafts must meet strict regulations and emission limitations. The Flightpath 2050 goals set by the Advisory Council for Aviation Research and Innovation in Europe (ACARE) include reductions of 75%, 90%, and 65% in CO2, NOx, and noise, respectively. These goals are not fully satisfied by marginal improvements in gas turbine technology or aircraft design. A novel control design procedure for the next generation of turbofan engines is proposed in this paper to improve Full Authority Digital Engine Control (FADEC) systems and reduce the emission levels to meet the Flightpath 2050 regulations. Hence, an Adaptive Network–based Fuzzy Inference System (ANFIS), nonlinear autoregressive network with exogenous inputs (NARX) techniques, and the block-structure Hammerstein–Wiener approach are used to develop a model for a turbofan engine. The Min–Max control structure is chosen as the most widely used practical control algorithm for gas turbine aero engines. The objective function is considered to minimize the emission level for the engine in a pre-defined maneuver while keeping the engine performance in different aspects. The Genetic Algorithm (GA) is applied to find the optimized control structure. The results confirm the effectiveness of the proposed approach in emission reduction for the next generation of turbofan engines. [ABSTRACT FROM AUTHOR]

Queueing systems are modeled by equations which depend on a large number of input parameters. In practice, significant uncertainty is associated with estimates of these parameters, and this uncertainty must be considered in the analysis of the model. The objective of this paper is to propose a sensitivity analysis approach for a queueing model, presenting parameters that follow a Gaussian distribution. The approach consists in decomposing the output of the model (stationary distribution of the model) into a polynomial chaos. The sensitivity indices, allowing to quantify the contribution of each parameter to the variance of the output, are obtained directly from the coefficients of decomposition. The proposed approach is then applied to M/G/1/N queueing model. The most influential parameters are highlighted. Finally several numerical and data examples are sketched out to illustrate the accuracy of the proposed method and compare them with Monte Carlo simulation. The results of this work will be useful to practitioners in various fields of theoretical and applied sciences. [ABSTRACT FROM AUTHOR]

In this paper, we describe new hybrid genetic algorithm and particle swarm optimization to solve the sum of earliness and the number of tardy job on two-machines flow shop schedule problem is NP- hard. The study discusses a hybrid genetic algorithm and particle swarm optimization (HGA-PSO) to tackle the presented mission. Extensive experiments, based on computers, suggest that the proposed mathematical models are efficient in solving flow shop problems with GA solved to n = 3000 while PSO solved to n = 500 job. [ABSTRACT FROM AUTHOR]

The benefit of this paper is to find the circular divison of the factor group when r is an odd number, where Q2r is denoted to Quaternion group of order 4r, such that for each positive integer r, there are two generators a and b for Q2r satisfies Q2r = {ah bk, 0 ≤ h ≤ 2r − 1, k = 0, 1} which has the following properties {a2r = b4 = I, barb−1 = a−r}. [ABSTRACT FROM AUTHOR]

Real-time crime forecasting is important. However, accurate prediction of when and where the next crime will happen is difficult. No known physical model provides a reasonable approximation to such a complex system. Historical crime data are sparse in both space and time and the signal of interests is weak. In this work, the authors first present a proper representation of crime data. The authors then adapt the spatial temporal residual network on the well represented data to predict the distribution of crime in Los Angeles at the scale of hours in neighborhood-sized parcels. These experiments as well as comparisons with several existing approaches to prediction demonstrate the superiority of the proposed model in terms of accuracy. Finally, the authors present a ternarization technique to address the resource consumption issue for its deployment in real world. This work is an extension of our short conference proceeding paper [Wang, B., Zhang, D., Zhang, D. H., et al., Deep learning for real time Crime forecasting, 2017, arXiv: 1707.03340]. [ABSTRACT FROM AUTHOR]

This paper deals with the observability and sensitivity analysis of the lightcurve measurement for use in the estimation of instantaneous pose (orientation) and shape geometry. Since several reflectance models exist that are typically used in obtaining a synthetic lightcurve measurement, they are compared to assess the observability or/and 'sensitivity' to determine the 'best model' for use. These measurement models are nonlinear with implicit and non-trivial dependency on the states; a numerical Jacobian as well as an unscented Kalman filter derived observation matrix are synthesized for each choice of the measurement model and compared. As the linearization is about an attitude (orientation) trajectory, distinct cases are considered to elaborate the results. For the cases evaluated here, it is shown that the attitude and shape/size can indeed be estimated from the lightcurve, but this is also dependent upon the initial conditions (subsequent attitude trajectory). Linear observability analysis of a discretized system is also performed with respect to the attitude states and shape/size parameters using the singular value decomposition of the observability matrix synthesized for a batch of measurements. The results are summarized for various initial conditions of the resident space object's attitude and angular velocity states. [ABSTRACT FROM AUTHOR]

The equations of motion of a bicycle are highly nonlinear and rolling of wheels without slipping can only be expressed by nonholonomic constraint equations. A geometrical theory of general nonholonomic constrained systems on fibered manifolds and their jet prolongations, based on so-called Chetaev-type constraint forces, was proposed and developed in the last decade by O. Krupková (Rossi) in 1990’s. Her approach is suitable for study of all kinds of mechanical systems-without restricting to Lagrangian, time-independent, or regular ones, and is applicable to arbitrary constraints (holonomic, semiholonomic, linear, nonlinear or general nonholonomic). The goal of this paper is to apply Krupková’s geometric theory of nonholonomic mechanical systems to study a concrete problem in nonlinear nonholonomic dynamics, i.e., autonomous bicycle. The dynamical model is preserved in simulations in its original nonlinear form without any simplifying. The results of numerical solutions of constrained equations of motion, derived within the theory, are in good agreement with measurements and thus they open the possibility of direct application of the theory to practical situations. [ABSTRACT FROM AUTHOR]

Aquatic organisms such as copepods exhibit remarkable responses to changes in ambient flows, especially shear gradients, when foraging, mating and escaping. To accomplish these tasks, the sensory system of the organism must decode the local sensory measurements to detect the flow properties. Evidence suggests that organisms sense differences in the hydrodynamic signal rather than absolute values of the ambient flow. In this paper, we develop a mathematical framework for shear flow detection using a bioinspired sensory system that measures only differences in velocity. We show that the sensory system is capable of reconstructing the properties of the ambient shear flow under certain conditions on the flow sensors. We discuss these conditions and provide explicit expressions for processing the sensory measurements and extracting the flow properties. These findings suggest that by combining suitable velocity sensors and physics-based methods for decoding sensory measurements, we obtain a powerful approach for understanding and developing underwater sensory systems. [ABSTRACT FROM AUTHOR]

A new type of the spatio-temporal queries is the Intelligent Probabilistic Collision Detection Query ( IPCDQ for short). In this paper, we focus on efficiently processing the IPCDQ on moving objects with uncertainty. Given two sets O and Q of objects, each of which moves with uncertain speed and direction, a time instant t, and a probability threshold P, the IPCDQ returns each pair of objects ( o, q) (where $$o \in O$$ and $$q \in Q$$ ), whose probability of colliding with each other is greater than or equal to P at time t. The pairs of objects satisfying the IPCDQ are termed the collision-possible pairs (or CPPs for short). We utilize a $$R^{lsd}$$ -tree, in which the spatially proximate objects with similar uncertain speeds and directions are grouped together, to effectively manage the moving objects in O. Similarly, a $$R^{lsd}$$ -tree is used to index the moving objects in Q. Then, with the two $$R^{lsd}$$ -trees for O and Q, respectively, we develop the specialized index traversals combined with three pruning criteria, the location-pruning criterion, the angle-pruning criterion, and the speed-pruning criterion to efficiently determine the objects that may collide with each other. Besides, to provide the more useful information to the user, we propose a probability model to quantify the possibility of each object pair being the query result. Comprehensive experiments demonstrate the efficiency and the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

In the conventional output voltage feedback loop design of an active power factor corrector (PFC), the bandwidth of the loop is limited to a frequency significantly lower than the input line voltage frequency. This is due to the fact that the compensator placed in the outputvoltage feedback loop is usually designed to have a narrow bandwidth in order to filter the voltage ripple of twice the line frequency coming from the PFC output dc voltage. This feedback loop is designed with this filtering effect because a relatively high ripple voltage would cause considerable distortion in the reference of the line current feedback loop. However, a low bandwidth voltage feedback loop would bring poor dynamic response which usually causes large overshoots and dips during load transient change. Therefore, a compromise is generally made for the traditional design of the output voltage feedback loop, which leads to an issue that both the steady and the dynamic behaviours of the PFC are not optimal. In this paper, mathematic model of the steady behaviour of a PFC with voltage ripple in the output-voltage feedback loop is studied using two parameters: the amplitude of the relative voltage ripple on the control output signal and its phase lag angle of the voltage feedback loop. On the basis of the analysis, a second-order voltage harmonic injection to the output-voltage feedback loop is used to eliminate the voltage harmonic components from the voltage feedback loop. Finally, the feasibility of the proposed second-order harmonic injection method was verified by simulation and experimental results. [ABSTRACT FROM AUTHOR]

Electronic cash is an electronic form of currency, which allows the cash transactions over communication networks under privacy protections. However, it still has some aspects that have not been well studied. Known constructions suffer from at least one of the following limitations: (1) relying on a random oracle, (2) not supporting multiple bank setting and users dynamically joining or (3) prohibitively expensive. In this paper, we propose a new electronic cash system that avoids all these limitations. In other words, our system is anonymous against chosen-ciphertext attack (CCA) in the standard model, and supports multiple banks enrolling and users dynamically joining, which is achieved by the utilization of non-interactive zero-knowledge proof and dynamic group signature. Finally, in the standard model, a formal security proof is given to claim that our system has CCA anonymity, unforgeability, traceability and no double-spending. Compared with the existing systems, ours has advantages of both the efficiency and security. [ABSTRACT FROM AUTHOR]

Many electricity market participants have a requirement to calculate the probabilistic risk measures, such as earnings at risk (EaR) and value at risk (VaR), for compliance reporting purposes. This requirement is currently hindered by the lack of analytical representations for forecasts of demand (load) and price curves; this motivates numerical simulation and models that need extensive calibration. In this paper, we derive an analytical representation of a state demand forecast which is the aggregated usage of all electricity consumers in a particular region (such as New South Wales or Victoria). We have used two probabilistic benchmarks from the Australian energy market operator as input, which are expressed as forecasted probability of exceedance.Due to a number of considerations, including asymmetry of these quantiles with respect to the median, we have selected a series of truncated lognormal distributions with two parameters. The procedure of finding these parameters has been reduced to solving (for every half-hour) a single nonlinear equation. As a result, the two-year half-hourly forecast (expected curve) and demand volatility are found by explicit integration with the set of derived distributions. We have also tested an alternative method based on simplifying assumptions; using a nontruncated lognormal distribution, we found that under the test conditions this method produces an identical forward load and volatility curve. [ABSTRACT FROM PUBLISHER]