Detecting collaborative fraudsters who manipulate opinions in social media is becoming extremely important in order to provide reliable information, in which, however, the diversity in different groups of collaborative fraudsters presents a significant challenge to existing collaborative fraudsters detection methods. These methods often detect collaborative fraudsters as the largest group of users who have the strongest relation with each other in the social media, consequently overlooking the other groups of fraudsters that are with strong user relation yet small group size. This paper introduces a novel network embedding-based framework NEST and its instance BEST to address this issue. NEST detects multiple groups of collaborative fraudsters by two steps. In the first step, to disclose user collaboration, it represents users according to their social relations. Then, in the second step, to identify the collaborative fraudsters, it detects the user groups with anomalous large group density in its representation space. BEST instantiates NEST by using a bipartite network embedding method to represent users and adopting a fast density group detection method based on the k-dimensional tree. Our experiments show BEST (i) performs significantly better in detecting fraudsters on four real-word social media data sets, and (ii) effectively detects multiple groups of collaborative fraudsters, compared to three state-of-the-art competitors.
As a two-terminal mechanical element, the inerter has been successfully applied in various mechanical fields, such as automotive engineering and civil engineering, for passive control and semiactive control. In this paper, a hydraulic electric inerter is considered an active device to suppress the vibration of a vehicle suspension system. The components and working principle of the hydraulic electric inerter are first introduced. On the basis of a force test of the hydraulic electric inerter, nonlinear factors such as friction, the damping force, and the elastic effect are analyzed, and parameter identification methods are adopted to identify the detailed parameters. A dynamic model of the vehicle suspension system employing a nonlinear hydraulic electric inerter is established, and the predictive controller is designed to further improve the vibration isolation performance of the suspension system. Numerical simulations show that the performance of the vehicle ISD (inerter-spring-damper) suspension system is significantly improved compared to the passive suspension. Finally, bench tests are carried out, and the advantages of vehicle ISD suspension are demonstrated. The RMS (root-mean-square) value of the vehicle body acceleration and the RMS value of the suspension working space are reduced by 16.1% and 8.9%, respectively.
This article concerns a hybrid vehicle suspension system that can regenerate energy from vibrations. To further improve the performance of the hybrid vehicle suspension system, the design of the energy-regenerative circuit is investigated. First, the force tests of the linear motor used in the hybrid vehicle suspension were carried out, and the key parameters of the linear motor were obtained. Then, the selection procedures of the protective resistance, inductance, and initial terminal voltage of the super capacitor were discussed. These aforementioned parameters' values were determined by considering the impact of the hybrid suspension on the dynamic performance indexes and the energy-regenerative efficiency. Simulations showed that, in comparison to the original hybrid suspension system, the designed hybrid suspension effectively improved the energy-regenerative efficiency, and that the dynamic performance indexes of the suspension were synchronously improved. Given the result of the simulation analysis, which were validated by bench tests, it is shown that the optimized energy-regenerative circuit presents an enhanced regeneration efficiency, with an improvement of nearly 13% compared to the original suspension system.
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