Observation of Ballistic Thermal Transport in a Nonintegrable Classical Many-Body System

We report, for the first time, the observation of ballistic thermal transport in a nonintegrable classical many-body system. This claim is substantiated by appropriately incorporating long-range interactions into the system, which exhibits all characteristic hallmarks of ballistic heat transport, including the presence of equilibrium dynamical correlations exhibiting ballistic scaling, a size-independent energy current and a flat bulk temperature profile. These findings hold true for large system sizes (long times), indicating that ballistic heat transport is valid in the thermodynamic limit. The underlying mechanism is attributed to the presence of traveling discrete breathers in the relevant nonintegrabel systems surpassing conventional solitons in a nonlinear integrable Toda system.Comment: 5 Pages; 6 Figures; Including supplementary material upon requeste

An Authenticated Routing Protocol for Wireless Ad Hoc Network Based on Small World Model

Compared with traditional cellular networks, wireless ad hoc networks do not have trusted entities such as routers, since every node in the network is expected to participate in the routing function. Therefore, routing protocols need to be specifically designed for wireless ad hoc networks. In this work, we propose an authenticated routing protocol based on small world model (ARSW). With the idea originating from the small world theory, the operation of the protocol we proposed is simple and flexible. Our simulation results show the proposed ARSW not only increases packet delivery ratio, but also reduces packet delivery delay. In particularly, Using authentication theory, the proposed ARSW improves communication security

Subdiffusive Energy Transport and Antipersistent Correlations Due to the Scattering of Phonons and Discrete Breathers

While there are many physical processes showing subdiffusion and some useful particle models for understanding the underlying mechanisms have been established, a systematic study of subdiffusive energy transport is still lacking. Here we present convincing evidence that the energy subdiffusion and its antipersistent correlations take place in a Hamiltonian lattice system with both harmonic nearest-neighbor and anharmonic long-range interactions. We further understand the underlying mechanisms from the scattering of phonons and discrete breathers. Our result sheds new light on understanding the extremely slow energy transport.Comment: 5 pages, 5 figure

An Authenticated Routing Protocol for Wireless Ad Hoc Network Based on Small World Model

Compared with traditional cellular networks, wireless ad hoc networks do not have trusted entities such as routers, since every node in the network is expected to participate in the routing function. Therefore, routing protocols need to be specifically designed for wireless ad hoc networks. In this work, we propose an authenticated routing protocol based on small world model (ARSW). With the idea originating from the small world theory, the operation of the protocol we proposed is simple and flexible. Our simulation results show the proposed ARSW not only increases packet delivery ratio, but also reduces packet delivery delay. In particularly, Using authentication theory, the proposed ARSW improves communication security

Neural Synchronization Using Genetic Algorithm for Secure Key Establishment

Two neural networks that are trained on their mutual output synchronize to an identical time dependant weight vector. This novel phenomenon can be used for creation of a secure cryptographic secret-key using a public channel. Neural cryptography is a way to create shared secret key. Key generation in Tree Parity Machine neural network is done by mutual learning. Neural networks here receive common inputs and exchange their outputs. Adjusting discrete weights according to a suitable learning rule then leads to full synchronization in a finite number of steps and these identical weights are the secret key needed for encryption. A faster synchronization of the neural network has been achieved by generating the optimal weights for the sender and receiver from a genetic process. Here the best fit weight vector is found using a genetic algorithm. In this paper the performance of the genetic algorithm has been analysed by varying the number of hidden and input neurons

Parameter Selection Method for Support Vector Regression Based on Adaptive Fusion of the Mixed Kernel Function

Support vector regression algorithm is widely used in fault diagnosis of rolling bearing. A new model parameter selection method for support vector regression based on adaptive fusion of the mixed kernel function is proposed in this paper. We choose the mixed kernel function as the kernel function of support vector regression. The mixed kernel function of the fusion coefficients, kernel function parameters, and regression parameters are combined together as the parameters of the state vector. Thus, the model selection problem is transformed into a nonlinear system state estimation problem. We use a 5th-degree cubature Kalman filter to estimate the parameters. In this way, we realize the adaptive selection of mixed kernel function weighted coefficients and the kernel parameters, the regression parameters. Compared with a single kernel function, unscented Kalman filter (UKF) support vector regression algorithms, and genetic algorithms, the decision regression function obtained by the proposed method has better generalization ability and higher prediction accuracy

Automatic events extraction in pre-stack seismic data based on edge detection in slant-stacked peak amplitude profiles

Events picking is one of the fundamental tasks in interpreting seismic data. To extract the correct travel-time of reflected waves, picking events in a wide range of source-receiver offsets is needed. Compared to post-stack seismic data, pre-stack seismic data has an accurate horizon and abundant travel-time, amplitude, and frequency while the waveform of post-stack data is damaged by normal move-out (NMO) applications. In this paper, we focus on automatic event extraction from pre-stack reflection seismic data. With the deep development of oil-gas exploration, the difficulty of petroleum exploration is being increased. Auto recognition and picking of seismic horizon is presented as the basis for oil-gas detection. There is a correspondence between the real geology horizon and events of seismic profiles. As a result, firstly, recognizing and tracing continuous events from real seismic records are needed to acquire significant horizon locations. Picking events is in this context the recognition and tracing of waves reflected from the same interfaces according to kinematics and dynamic characteristics of seismic waves. Current extraction algorithms are well able to trace these events of the seismic profile and are undergoing great development and utilization. In this paper, a method is proposed to pick travel-time and local continuous events based on edges obtained by slant-stacked peak amplitude section (SSPA). How to calculate the SSPA section is discussed in detail. The new method can improve the efficiency and accuracy without windowing and manual picking of seed points. The event curves obtained from both the synthetic layered model and field record have validated the high accuracy and efficiency of the proposed methodology

Effective detection of seismic events by non-classical receptive field visual cognitive modelling.

The detection and up-picking of the seismic events are critical for seismic data analysis and interpretation. Events picking can be used for sequence stratigraphic analysis, reservoir feature extraction, the determining of the subsequent reflection interface, the improving of the SNR and the storage prediction. The research of the events picking is very significant for the seismic exploration. In order to overcome the existing events picking methods have the same sensitivity to noise, we propose a non-classical receptive field visual cognitive method for the events picking UP. Vision is an important functional organ for human beings to observe and recognize the world. About 80% of the information obtained by human beings from the outside world comes from the visual system, which fully shows that visual information is huge_ and also shows that human beings have a high utilization rate of visual information. How to transfer some typical information processing mechanism and target recognition function of human vision to machine is one of the most important and urgent tasks in the field of computer vision and artificial intelligence. The introduction of computer vision technology into geophysical prospecting is still in its infancy in the field of seismic exploration, our research fill the blank of this field, where the use of visual features to improve the seismic data processing and rapid realization of oil and gas exploration, will become the vane of the future direction of research and development. As a basic research work in the crossing field, this paper has made a breakthrough in the research methods and ideas, and the research content can be summarized as the following four aspects: 1. The proposed method implements the function of environmental suppression and spatial enhancement of the bio-visual primary visual cortex, which is applies to the ore-stack seismic data, as ore-stack seismic data contains abundant information such as amplitude and frequency to reflect tiny structures of the formation. 2. The seismic data is preprocessed to obtain the wavelet fusion of the envelope peak instantaneous frequency (EPIF) and the slant stack peak amplitude (SSPA), which can maximum the limit to provide optimal quality data. 3. An adaptive Gabor filter direction selection method is proposed to provide a reliable angle range and improve the recognition rate of filter decomposition. In addition. by adopting an anisotropic environmental suppression method, our method can detect edge variability more accurately than the isotropic method. 4. With the enhanced contour aggregation, cocircular constraint is adopted and combined with the characteristics of low curvature and continuous changing curvature, which is unique to the seismic events, to establish a consistent spatial structure perception model. The events picked by our method is more continuous and accurate than the existing methods, and doesn't require human interaction, which is beneficial for subsequent seismic interpretation and reservoir prediction