Volume 15, Number 4, 2018
Due to the prevalence of social network services, more and more attentions are paid to explore how information diffuses and users affect each other in these networks, which has a wide range of applications, such as viral marketing, reposting prediction and social recommendation. Therefore, in this paper, we review the recent advances on information diffusion analysis in social networks and its applications. Specifically, we first shed light on several popular models to describe the information diffusion process in social networks, which enables three practical applications, i.e., influence evaluation, influence maximization and information source detection. Then, we discuss how to evaluate the authority and influence based on network structures. After that, current solutions to influence maximization and information source detection are discussed in detail, respectively. Finally, some possible research directions of information diffusion analysis are listed for further study.
This paper considers a multiple unmanned aerial vehicles (UAV) formation problem and proposes a new method inspired by bird flocking and foraging behavior. A bidirectional communication network, a navigator based on bird foraging behavior, a controller based on bird interaction and a movement switch are developed for multi-UAV formation. Lyapunov′s second method and mechanical energy method are adopted for stability analysis. Parameters of the controller are optimized by Levy-flight based pigeon inspired optimization (Levy-PIO). Patrol missions along a square and an S shaped trajectory are designed to test this formation method. Simulations prove that the bird flocking and foraging strategy can accomplish the mission and obtain satisfying performance.
In this paper, a fast and accurate work-piece detection and measurement algorithm is proposed based on top-down feature extraction and bottom-up saliency estimation. Firstly, a top-down feature extraction method based on the prior knowledge of workpieces is presented, in which the contour of a work-piece is chosen as the major feature and the corresponding template of the edges is created. Secondly, a bottom-up salient region estimation algorithm is proposed, where the image boundaries are labelled as background queries, and the salient region can be detected by computing contrast against image boundary. Finally, the calibration method for vision system with telecentric lens is discussed, and the dimensions of the work-pieces are measured. In addition, strategies such as image pyramids and a stopping criterion are adopted to speed-up the algorithm. An automatic system embedded with the proposed detection and measurement algorithm combining top-down and bottom-up saliency (DM-TBS) is designed to pick out defective work-pieces without any manual auxiliary. Experiments and results demonstrate the effectiveness of the proposed method.
This paper presents a genetic programming based reconfiguration planner for metamorphic modular robots. Initially used for evolving computer programs that can solve simple problems, genetic programming (GP) has been recently used to handle various kinds of problems in the area of complex systems. This paper details how genetic programming can be used as an automatic programming tool for handling reconfiguration-planning problem. To do so, the GP evolves sequences of basic operations which are required for transforming the robot s geometric structure from its initial configuration into the target one while the total number of modules and their connectedness are preserved. The proposed planner is intended for both Crystalline and TeleCube modules which are achieved by cubical compressible units. The target pattern of the modular robot is expressed in quantitative terms of morphogens diffused on the environment. Our work presents a solution for self reconfiguration problem with restricted and unrestricted free space available to the robot during reconfiguration. The planner outputs a near optimal explicit sequence of low-level actions that allows modules to move relative to each other in order to form the desired shape.
A robust approach to elaborately reconstruct the indoor scene with a consumer depth camera is proposed in this paper. In order to ensure the accuracy and completeness of 3D scene model reconstructed from a freely moving camera, this paper proposes new 3D reconstruction methods, as follows: 1) Depth images are processed with a depth adaptive bilateral filter to effectively improve the image quality; 2) A local-to-global registration with the content-based segmentation is performed, which is more reliable and robust to reduce the visual odometry drifts and registration errors; 3) An adaptive weighted volumetric method is used to fuse the registered data into a global model with sufficient geometrical details. Experimental results demonstrate that our approach increases the robustness and accuracy of the geometric models which were reconstructed from a consumer-grade depth camera.
For news video images, caption recognizing is a useful and important step for content understanding. Caption locating is usually the first step of caption recognizing and this paper proposes a simple but effective caption locating algorithm called maximum feature score region (MFSR) based method, which mainly consists of two stages: In the first stage, up/down boundaries are attained by turning to edge map projection. Then, maximum feature score region is defined and left/right boundaries are achieved by utilizing MFSR. Experiments show that the proposed MFSR based method has superior and robust performance on news video images of different types.
The search efficiency of differential evolution (DE) algorithm is greatly impacted by its control parameters. Although many adaptation/self-adaptation techniques can automatically find suitable control parameters for the DE, most techniques are based on population information which may be misleading in solving complex optimization problems. Therefore, a self-adaptive DE (i.e., JADE) using two-phase parameter control scheme (TPC-JADE) is proposed to enhance the performance of DE in the current study. In the TPC-JADE, an adaptation technique is utilized to generate the control parameters in the early population evolution, and a well-known empirical guideline is used to update the control parameters in the later evolution stages. The TPC-JADE is compared with four state-of-the-art DE variants on two famous test suites (i.e., IEEE CEC2005 and IEEE CEC2015). Results indicate that the overall performance of the TPC-JADE is better than that of the other compared algorithms. In addition, the proposed algorithm is utilized to obtain optimal nutrient and inducer feeding for the Lee-Ramirez bioreactor. Experimental results show that the TPC-JADE can perform well on an actual dynamic optimization problem.
Electronic line-shafting (ELS) is the most popular control strategy for printing machines with shaftless drives. A slidingmode controller for tracking control is designed in this study as the first step towards an improved ELS control scheme. This controller can eliminate the negative effects on synchronization precision resulting from the friction at low speed present in the pre-registration step of a shaftless driven printing machine. Moreover, it can eliminate the synchronization error of the printing process resulting from nonlinearities and load disturbances. Based on observer techniques, the unknown components of load torque and system parameter variations are estimated. On this basis, a novel ELS control method using equivalent load-torque observers is proposed. Experimental results demonstrate the effectiveness of the proposed control system for four-axis position control.
Polyvinyl chloride (PVC) polymerizing process is a typical complicated industrial process with the characteristics of large inertia, big time delay and nonlinearity. Firstly, for the general nonlinear and discrete time system, a design scheme of model-free adaptive (MFA) controller is given. Then, particle swarm optimization (PSO) algorithm is applied to optimizing and setting the key parameters for controller tuning. After that, the MFA controller is used to control the system of polymerizing temperature. Finally, simulation results are given to show that the MAC strategy based on PSO obtains a good controlling performance index.
This paper addresses the cooperative output regulation problem of a class of multi-agent systems (MASs). Each agent is a switched linear system. We propose an agent-dependent multiple Lyapunov function (MLF) approach to design the switching law for each switched agent. The distributed controller for each agent is constructed based on a dynamic compensator. A sufficient condition for the solvability of the output regulation problem of switched agent networks is presented by distributed controllers and agent-dependent multiple Lyapunov function approach. Finally, simulation results demonstrate the effectiveness of our theory.
The geometrical and topological information of 3D computer aided design (CAD) models should be represented as a neutral format file to exchange the data between different CAD systems. Exchange of 3D CAD model data implies that the companies must exchange complete information about their products, all the way from design, manufacturing to inspection and shipping. This information should be available to each relevant partner over the entire life cycle of the product. This led to the development of an international standard organization (ISO) neutral format file named as standard for the exchange of product model data (STEP). It has been observed from the literature, the feature recognition systems developed were identified as planar, cylindrical, conical and to some extent spherical and toroidal surfaces. The advanced surface features such as B-spline and its subtypes are not identified. Therefore, in this work, a STEP-based feature recognition system is developed to recognize B-spline surface features and its sub-types from the 3D CAD model represented in AP203 neutral file format. The developed feature recognition system is implemented in Java programming language and the product model data represented in STEP AP203 format is interpreted through Java standard data access interface (JSDAI). The developed system could recognize B-spline surface features such as B-Spline surface with knots, quasi uniform surface, uniform surface, rational surface and Bezier surface. The application of extracted B-spline surface features information is discussed with reference to the toolpath generation for STEP-NC (STEP AP238).