A Generalized Two-Component Camassa-Holm System with Complex Nonlinear Terms and Waltzing Peakons

In this paper, we deal with the Cauchy problem for a generalized two-component Camassa-Holm system with waltzing peakons and complex higher-order nonlinear terms. By the classical Friedrichs regularization method and the transport equation theory, the local well-posedness of solutions for the generalized coupled Camassa-Holm system in nonhomogeneous Besov spaces and the critical Besov space B5/22,1×B5/22,1 was obtained. Besides the propagation behaviors of compactly supported solutions, the global existence and precise blow-up mechanism for the strong solutions of this system are determined. In addition to wave breaking, the another one of the most essential property of this equation is the existence of waltzing peakons and multi-peaked solitray was also obtained

Incremental Learning Method for Data with Delayed Labels

Most research on machine learning tasks relies on the availability of true labels immediately after making a prediction. However, in many cases, the ground truth labels become available with a non-negligible delay. In general, delayed labels create two problems. First, labelled data is insufficient because the label for each data chunk will be obtained multiple times. Second, there remains a problem of concept drift due to the long period of data. In this work, we propose a novel incremental ensemble learning when delayed labels occur. First, we build a sliding time window to preserve the historical data. Then we train an adaptive classifier by labelled data in the sliding time window. It is worth noting that we improve the TrAdaBoost to expand the data of the latest moment when building an adaptive classifier. It can correctly distinguish the wrong types of source domain sample classification. Finally, we integrate the various classifiers to make predictions. We apply our algorithms to synthetic and real credit scoring datasets. The experiment results indicate our algorithms have superiority in delayed labelling setting

Hardware-in-the-loop simulation technology of wide-band radar targets based on scattering center model

AbstractHardware-in-the-loop (HWIL) simulation technology can verify and evaluate the radar by simulating the radio frequency environment in an anechoic chamber. The HWIL simulation technology of wide-band radar targets can accurately generate wide-band radar target echo which stands for the radar target scattering characteristics and pulse modulation of radar transmitting signal. This paper analyzes the wide-band radar target scattering properties first. Since the responses of target are composed of many separate scattering centers, the target scattering characteristic is restructured by scattering centers model. Based on the scattering centers model of wide-band radar target, the wide-band radar target echo modeling and the simulation method are discussed. The wide-band radar target echo is reconstructed in real-time by convoluting the transmitting signal to the target scattering parameters. Using the digital radio frequency memory (DRFM) system, the HWIL simulation of wide-band radar target echo with high accuracy can be actualized. A typical wide-band radar target simulation is taken to demonstrate the preferable simulation effect of the reconstruction method of wide-band radar target echo. Finally, the radar target time-domain echo and high-resolution range profile (HRRP) are given. The results show that the HWIL simulation gives a high-resolution range distribution of wide-band radar target scattering centers

A Method for Learning a Petri Net Model Based on Region Theory

The deployment of robots in real life applications is growing. For better control and analysis of robots, modeling and learning are the hot topics in the field. This paper proposes a method for learning a Petri net model from the limited attempts of robots. The method can supplement the information getting from robot system and then derive an accurate Petri net based on region theory accordingly. We take the building block world as an example to illustrate the presented method and prove the rationality of the method by two theorems. Moreover, the method described in this paper has been implemented by a program and tested on a set of examples. The results of experiments show that our algorithm is feasible and effective

Vibration analysis of a cylinder with slight diameter and thickness variations

The cross section of circular cylinder in their dynamic model is always considered as a perfect circle, which means radii at every point on the circle are the same. In real engineering structure, there are slight fluctuations in shape of the circular cylinder which is different from those in ideal model. Meanwhile, effects of structural fluctuations on its dynamic characteristic are rarely analyzed before. To study problem mentioned above, the geometric shape of a typical, apparently symmetrical cylinder is examined experimentally to demonstrate that a small variation in diameter and thickness indeed exists in practice firstly. Because fluctuations in diameter and thickness of the cylinder are related to each other, we need to separate effects of a slight variation in its diameter and thickness on structural dynamic characteristics to search the key factor of influence. Then, two simplified modes, which are modeled by finite element method, are used to study the effects of diameter or thickness variation alone on the natural frequencies and modal shapes of the free cylinder. It is revealed that the diameter variation described by the simplified model captures the key influencing elements which affect the modal characteristics of the free cylinder. Finally, a free cylinder with both variations is analyzed numerically and the results are verified experimentally. This work illustrates that significant discrepancies inevitably exist between the measured results of an actual free cylinder and an assumed symmetrical model even if there is only a very slight variation in its geometric shape

THE EFFECT OF TRUST ON INFORMATION DIFFUSION IN ONLINE SOCIAL NETWORKS

online social networks have a explosive growth in recent years and they provide a perfect platform for information diffusion. Many models have been given to explore the information diffusion procedure and its dynamics. But the trust relationship and memory effect are ignored. Based on the complex network theory, The information diffusion model is proposed and the network users, considered as agents, are classified into susceptible, infected and recovered individuals. The users’ behaviour rule and diffusion process are designed. The proposed agent-based model is tested by simulation experiments in four different complex networks: regular network, small world network, random network and scale-free network. Moreover, the effect of four immunization strategies are explored. The research results show that the influence of users’ trust relationship on different networks is varied, and the vertex weight priority immunization strategy is the best one in all four networks

ANN RBF Based Approach of Risk Assessment for Aviation ATM Network

ATM (Asynchronous Transfer Mode) network is the core communication network of civil aviation aeronautical telecommunication network. So it is an urgent time to do scientific risk assessment for ATM network as soon as possible. According to threats and vulnerabilities existing in ATM network, which could bring bad influence to assets and missions of ATM network, even threaten the whole security situation of ATM network. This paper proposes risk assessment model based on RBF neural network. According to the established evaluation model, indexes that influencing the security situation of missions are used as input of the model and train the model. The well-trained neural network model is used to assess ATM network, while the results are compared to that of the traditional methods of scoring by experts for rounds of times. The experimental results demonstrate that the risk assessment model has strong capacities of self-learning and convergence, accords well with the complex ATM network for risk assessment

Fast blind deblurring of QR code images based on adaptive scale control.

With the development of 5G technology, the short delay requirements of commercialization and large amounts of data change our lifestyle day-to-day. In this background, this paper proposes a fast blind deblurring algorithm for QR code images, which mainly achieves the effect of adaptive scale control by introducing an evaluation mechanism. Its main purpose is to solve the out-of-focus caused by lens shake, inaccurate focus, and optical noise by speeding up the latent image estimation in the process of multi-scale division iterative deblurring. The algorithm optimizes productivity under the guidance of collaborative computing, based on the characteristics of the QR codes, such as the features of gradient and strength. In the evaluation step, the Tenengrad method is used to evaluate the image quality, and the evaluation value is compared with the empirical value obtained from the experimental data. Combining with the error correction capability, the recognizable QR codes will be output. In addition, we introduced a scale control parameter to study the relationship between the recognition rate and restoration time. Theoretical analysis and experimental results show that the proposed algorithm has high recovery efficiency and well recovery effect, can be effectively applied in industrial applications