Motion and Special Relativity in Complex Spaces

A natural extension of the Lorentz transformation to its complex version was constructed together with a parallel extension of the Minkowski M4 model for special relativity (SR) to complex C4 space-time. As the [signed] absolute values of complex coordinates of the underlying motion’s characterization in C4 one obtains a Newtonian-like type of motion whereas as the real parts of the complex motion’s description and of the complex Lorentz transformation, all the SR theory as modeled by M4 real space-time can be recovered. This means all the SR theory is preserved in the real subspace M4 of the space-time C4 while becoming simpler and clearer in the new complex model’s framework. Since velocities in the complex model can be determined geometrically, with no primary use of time, time turns out to be definable within the equivalent theory of the reduced complex C4 model to the C3 “para-space” model. That procedure allows us to separate time from the (para)space and consider all the SR theory as a theory of C3 alone. On the other hand, the complex time defined within the C3 theory is interpreted and modeled by the single separate C1 complex plane. The possibility for application of the C3 model to quantum mechanics is suggested. As such, the model C3 seems to have unifying abilities for application to different physical theories.

Complex Time Series Analysis Based on Conditional Random Fields

A fundamental problem with complex time series analysis involves data prediction and repair.However,existing methods are not accurate enough for complex and multidimensional time series data.In this paper,we propose a novel approach,a complex time series predic-tion model,which is based on the conditional randomfield(CRF)and recurrent neural network(RNN).This model can be used as an upper-level predictor in the stacking process or be trained using deep learning methods.Our approach is more accurate than existing methods in some suitable scenarios,as shown in the experimental results.

Species distribution of polymeric aluminium ferrum——timed complexation colorimetric analysis method of Al-Fe-Ferron

The effects of the calorimetric buffer solutions were investigated while the two colorimetric reactions of AI-ferron complex and Fe-ferron complex occurred individually, and the effects of the testing wavelength and the pH of the solutions were also investigated. A timed complexatian colorimetric analysis method of Al-Fe-ferron in view of the total concentration of {AI + Fe} was then established to determine the species distribution of polymeric Al-Fe. The testing wavelength was recommended at 362 net and the testing pH value was 5. With a comparison of the ratios of n(Al)/n(Fe), the standard adsorption curves of the polymeric Al-Fe solutions were derived from the experimental results. Furthermore, the solutions' composition were carious in both the molar n(Al)/n(Fe) ratios, i.e. 0/0, 5/5, 9/1 and 0/10, and the concentrations associated with the total ( Al + Fe which ranged from 10(-5) to 10(-4) mol/L..

Stability analysis of synchronization in discrete-time complex dynamical networks

In this paper, by using both the linear stability analysis and Lyapunov function approach, some conditions for stabilizing synchronization behavior in a discrete-time complex dynamical network were derived. These conditions were determined by the coupling strength and the eigenvalues of coupling configuration matrix. Furthermore, some explicit results were obtained when the coupling map between the nodes is equal to the dynamics function of the network, which implies that the product of the coupling strength and the eigenvalues is bounded.

Fault diagnosis of time-delay complex dynamical networks using output signals

This paper proposes a novel approach for fault diagnosis of a time-delay complex dynamical network. Unlike the other methods, assuming that the dynamics of the network can be described by a linear stochastic model, or using the state variables of nodes in the network to design an adaptive observer, it only uses the output variable of the nodes to design an observer and an adaptive law of topology matrix in the observer of a complex network, leading to simple design of the observer and easy realisation of topology monitoring for the complex networks in real engineering. The proposed scheme can monitor any changes of the topology structure of a time-delay complex network. The effectiveness of this method is successfully demonstrated by virtue of a complex networks with Lorenz model.

Parameter Estimation and Topology Identification of Uncertain General Fractional-order Complex Dynamical Networks with Time Delay

Complex networks have attracted much attention from various fields of sciences and engineering in recent years.However, many complex networks have various uncertain information, such as unknown or uncertain system parameters and topological structure, which greatly affects the system dynamics.Thus, the parameter estimation and structure identification problem has theoretical and practical importance for uncertain complex dynamical networks. This paper investigates identification of unknown system parameters and network topologies in uncertain fractional-order complex network with time delays(including coupling delay and node delay). Based on the stability theorem of fractional-order differential system and the adaptive control technique, a novel and general method is proposed to address this challenge. Finally two representative examples are given to verify the effectiveness of the proposed approach.

Complex Networks from Chaotic Time Series on Riemannian Manifold

Complex networks are important paradigms for analyzing the complex systems as they allow understanding the structural properties of systems composed of different interacting entities. In this work we propose a reliable method for constructing complex networks from chaotic time series. We first estimate the covariance matrices, then a geodesic-based distance between the covariance matrices is introduced. Consequently the network can be constructed on a Riemannian manifold where the nodes and edges correspond to the covariance matrix and geodesic-based distance, respectively. The proposed method provides us with an intrinsic geometry viewpoint to understand the time series.

Pinning synchronization of time-varying delay coupled complex networks with time-varying delayed dynamical nodes

This paper deals with the pinning synchronization of nonlinearly coupled complex networks with time-varying coupling delays and time-varying delays in the dynamical nodes.We control a part of the nodes of the complex networks by using adaptive feedback controllers and adjusting the time-varying coupling strengths.Based on the Lyapunov-Krasovskii stability theory for functional differential equations and a linear matrix inequality（LMI）,some sufficient conditions for the synchronization are derived.A numerical simulation example is also provided to verify the correctness and the effectiveness of the proposed scheme.

Synchronization of High-order Discrete-time Linear Complex Networks with Time-varying Delays

Synchronization of high-order discrete-time complex networks with undirected topologies is studied and the impacts of time delays are investigated. Firstly,by the state decomposition,synchronization problems are transformed into asymptotic stability ones of multiple lower dimensional time-delayed subsystems. Then,linear matrix inequality( LMI) criteria for synchronization are given,which can guarantee the scalability of complex networks since they only include three LMI constraints independent of the number of agents. Moreover,an explicit expression of the synchronization function is presented,which can describe the synchronization behavior of all agents in complex networks. Finally,a numerical example is given to demonstrate the theoretical results,where it is shown that if the gain matrices of synchronization protocols satisfy LMI criteria for synchronization,synchronization can be achieved.

Load-redistribution strategy based on time-varying load against cascading failure of complex network

Cascading failure can cause great damage to complex networks, so it is of great significance to improve the network robustness against cascading failure. Many previous existing works on load-redistribution strategies require global information, which is not suitable for large scale networks, and some strategies based on local information assume that the load of a node is always its initial load before the network is attacked, and the load of the failure node is redistributed to its neighbors according to their initial load or initial residual capacity. This paper proposes a new load-redistribution strategy based on local information considering an ever-changing load. It redistributes the loads of the failure node to its nearest neighbors according to their current residual capacity, which makes full use of the residual capacity of the network. Experiments are conducted on two typical networks and two real networks, and the experimental results show that the new load-redistribution strategy can reduce the size of cascading failure efficiently.

Robust synchronization of uncertain complex systems with time-varying delays

Synchronization analysis and design problems for uncertain time-delayed high-order complex systems with dynamic output feedback synchronization protocols are investigated. By stating projection on the synchronization subspace and the complement synchronization subspace, synchronization problems are transformed into simultaneous stabilization problems of multiple subsystems related to eigenvalues of the Laplacian matrix of the interaction topology of a complex system. In terms of linear matrix inequalities(LMIs), sufficient conditions for robust synchronization are presented, which include only five LMI constraints.By the changing variable method, sufficient conditions for robust synchronization in terms of LMIs and matrix equalities are given,which can be checked by the cone complementarily linearization approach. The effectiveness of theoretical results is shown by numerical examples.

Generic reconstruction technology based on RST for multivariate time series of complex process industries

In order to effectively analyse the multivariate time series data of complex process,a generic reconstruction technology based on reduction theory of rough sets was proposed,Firstly,the phase space of multivariate time series was originally reconstructed by a classical reconstruction technology.Then,the original decision-table of rough set theory was set up according to the embedding dimensions and time-delays of the original reconstruction phase space,and the rough set reduction was used to delete the redundant dimensions and irrelevant variables and to reconstruct the generic phase space,Finally,the input vectors for the prediction of multivariate time series were extracted according to generic reconstruction results to identify the parameters of prediction model.Verification results show that the developed reconstruction method leads to better generalization ability for the prediction model and it is feasible and worthwhile for application.

Time Scale Approach to One Parameter Plane Motion by Complex Numbers

This paper presents building one-parameter motion by complex numbers on a time scale. Firstly, we assumed that E and E′ were moving in a fixed time scale complex plane and {0, e1,e2} and {0', e'1,e'2} ?were their orthonormal frames, respectively. By using complex numbers, we investigated the delta calculus equations of the motion on T. Secondly, we gave the velocities and their union rule on the time scale. Finally, by using the delta-derivative, we got interesting results and theorems for the instantaneous rotation pole and the pole curves (trajectory). In kinematics, investigating one-parameter motion by complex numbers is important for simplifying motion calculation. In this study, our aim is to obtain an equation of motion by using complex numbers on the time scale.

A Theoretical Comparison among Recursive Algorithms for Fast Computation of Zernike Moments Using the Concept of Time Complexity

Zernike polynomials have been used in different fields such as optics, astronomy, and digital image analysis for many years. To form these polynomials, Zernike moments are essential to be determined. One of the main issues in realizing the moments is using factorial terms in their equation which causes higher time complexity. As a solution, several methods have been presented to reduce the time complexity of these polynomials in recent years. The purpose of this research is to study several methods among the most popular recursive methods for fast Zernike computation and compare them together by a global theoretical evaluation system called worst-case time complexity. In this study, we have analyzed the selected algorithms and calculated the worst-case time complexity for each one. After that, the results are represented and explained and finally, a conclusion has been made by comparing these criteria among the studied algorithms. According to time complexity, we have observed that although some algorithms such as Wee method and Modified Prata method were successful in having the smaller time complexities, some other approaches did not make any significant difference compared to the classical algorithm.

Novel criteria for exponential synchronization of inner time-varying complex networks with coupling delay

This paper mainly investigates the exponential synchronization of an inner time-varying complex network with coupling delay. Firstly, the synchronization of complex networks is decoupled into the stability of the corresponding dynamical systems. Based on the Lyapunov function theory, some sufficient conditions to guarantee its stability with any given convergence rate are derived, thus the synchronization of the networks is achieved. Finally, the results are illustrated by a simple time-varying network model with a coupling delay. All involved numerical simulations verify the correctness of the theoretical analysis.

Transmission time in the reflectionless complex potential

A phase time definition directly obtained from the Schr6dinger equation is used to investigate the time delay of a particle scattered by complex reflectionless potential. The artifacts introduced by truncating in the numerical simulation are clarified. The time delay of the transmitted wave packet is found to be equal to the reflection time of the truncated potential. Both time delays are the same as the traversal time in the free space, but shorter than the time taken by a classical particle to pass the same potential.

Synchronization and Exponential Estimates of Complex Networks with Mixed Time-varying Coupling Delays

Exponential estimates and sufficient conditions for the exponential synchronization of complex dynamical networks with bounded time-varying delays are given in terms of linear matrix inequalities （LMIs）. A generalized complex networks model involving both neutral delays and retarded ones is presented. The exponential synchronization problem of the complex networks is converted equivalently into the exponential stability problem of a group of uncorrelated delay functional differential equations with mixed timevarying delays. By utilizing the free weighting matrix technique, a less conservative delay-dependent synchronization criterion is derived. An illustrative example is provided to demonstrate the effectiveness of the proposed method.

Fixed-Time and Finite-Time Synchronization for a Class of Output-Coupling Complex Networks via Continuous Control

This paper mainly investigates the finite-time and fixed-time synchronization problem for a class of general output-coupling complex networks with output feedback nodes. The fixed-time and finite-time synchronization protocols are presented based on continuous controller strategies which can efficaciously eliminate chattering phenomenon existing in some previous results. Several sufficient conditions ensuring fixed-time and finite-time synchronization are derived by employing Lyapunov stability theory, linear matrix inequality (LMI) and adaptive technique. Furthermore, aimed at the model of this article, we study the problem of adaptive coupling strength in fixed-time synchronization which is rarely involved in previous results. Finally, several numerical examples are given to illustrate the effectiveness of our results.

Research on characters of surrounding rock in complex geology conditions and supporting time

The methods combined by test, field monitoring and theoretical analysis were adopted to do the systemic research on the rock mass from micro-structure to macro-deformation, and rheological model of Jinchuan rock mass was established to discuss the reasonable supporting time. Results show that supporting after suitable stress and displacement release can benefit for the long-term stability of surrounding rock.

REDUCED-COMPLEXITY DECODING ALGORITHMS FOR UNITARY SPACE-TIME CODES

Two reduced-complexity decoding algorithms for unitary space-time codes based on tree-structured constellation are presented. In this letter original unitary space-time constellation is divided into several groups. Each one is treated as the leaf nodes set of a subtree. Choosing the unitary signals that represent each group as the roots of these subtrees generates a tree-structured constellation. The proposed tree search decoder decides to which sub tree the receive signal belongs by searching in the set of subtree roots. The final decision is made after a local search in the leaf nodes set of the se-lected sub tree. The adjacent subtree joint decoder performs joint search in the selected sub tree and its “surrounding” subtrees,which improves the Bit Error Rate (BER) performance of purely tree search method. The exhaustively search in the whole constellation is avoided in our proposed decoding al-gorithms,a lower complexity is obtained compared to that of Maximum Likelihood (ML) decoding. Simulation results have also been provided to demonstrate the feasibility of these new methods.