With the ever-increasing popularity of mobile computing technology, a wide range of computational resources or services (e.g., movies, food, and places of interest) are migrating to the mobile infrastructure or devices (e.g., mobile phones, PDA, and smart watches), imposing heavy burdens on the service selection decisions of users. In this situation, service recommendation has become one of the promising ways to alleviate such burdens. In general, the service usage data used to make service recommendation are produced by various mobile devices and collected by distributed edge platforms, which leads to potential leakage of user privacy during the subsequent cross-platform data collaboration and service recommendation process. Locality-Sensitive Hashing (LSH) technique has recently been introduced to realize the privacy-preserving distributed service recommendation. However, existing LSH-based recommendation approaches often consider only one quality dimension of services, without considering the multidimensional recommendation scenarios that are more complex but more common. In view of this drawback, we improve the traditional LSH and put forward a novel LSH-based service recommendation approach named SerRecmulti-qos, to protect users’ privacy over multiple quality dimensions during the distributed mobile service recommendation process.

With the increasing volume of web services in the cloud environment, Collaborative Filtering-(CF-) based service recommendation has become one of the most effective techniques to alleviate the heavy burden on the service selection decisions of a target user. However, the service recommendation bases, that is, historical service usage data, are often distributed in different cloud platforms. Two challenges are present in such a cross-cloud service recommendation scenario. First, a cloud platform is often not willing to share its data to other cloud platforms due to privacy concerns, which decreases the feasibility of cross-cloud service recommendation severely. Second, the historical service usage data recorded in each cloud platform may update over time, which reduces the recommendation scalability significantly. In view of these two challenges, a novel privacy-preserving and scalable service recommendation approach based on SimHash, named SerRec SimHash , is proposed in this paper. Finally, through a set of experiments deployed on a real distributed service quality dataset WS-DREAM, we validate the feasibility of our proposal in terms of recommendation accuracy and efficiency while guaranteeing privacy-preservation.

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The advertised quality of an Internet of things service is not always trustable due to the exaggerated quality propagation and dynamic network environment. Therefore, it is more trustable to evaluate the Internet of things service quality based on the historical execution records of service. However, an Internet of things service often has multiple historical records whose invocation time and location are different, which makes it necessary to weigh each historical record of an identical Internet of things service. Besides, for different candidate Internet of things services, their invocation frequencies are often varied, which may also affect the final service selection decision of target user. In view of the above two challenges, a novel service selection approach ''Time-Location-Frequency''-aware Service Selection Approach is put forward in this article. In Time-Location-Frequency-aware Service Selection Approach, we first weigh each historical record of an Internet of things service, based on its service invocation time and location; afterward, we weigh each candidate Internet of things service based on its invocation frequency; finally, with the derived two kinds of weights, we evaluate each candidate Internet of things service and return the quality-optimal one to the target user. At last, through a set of experiments deployed on a real service quality data set WS-DREAM, we validate the feasibility of our proposal.

The bridge stay cable, one of the most critical components in cable-stayed bridges, is vulnerable to vibrations owing to its low inherent damping capacity. Thus effective vibration control technology for bridge stay cables is extremely critical to safe operations of cable-stayed bridges. Several countermeasures have been presented and/or implemented to mitigate this vibration; however the passive method can only add a small amount of damping to the cables, excessive energy demand of active control devices severely limits its practicality, the semi-active control methods still have the drawbacks of complex state estimation module and a large amount of control algorithm calculation. This paper proposes a practical magnetorheological pseudo negative stiffness (MR-PNS) control system coupled with control strategy for bridge stay cables. The current reference point is introduced in the dynamic modeling of the MR-PNS control system to characterize the current control strategy. This paper investigates the adjustable of MR-PNS control system performance and energy consumption caused by different current strategies. Taking the vibration control of the Nanjing Second Yangtze River Bridge J20 cable as an example, the simulation results highlight the advantages of the MR-PNS control system that the failure area is small, the quasi-optimal area is wide, and it can still keep sort of vibration damping performance in the degenerate area. The model cable vibration control test proves the feasibility and efficiency of the single input MR-PNS bridge stay cables control method.

Magnetorheological (MR) damper is a semi-active control device designed by utilizing the instantaneous fluid-solid conversion characteristics of MR fluid, thus the microstructure of MR fluid fundamentally determines the mechanical properties of MR dampers. In order to study the influence of MR fluid microstructure on the macroscopic mechanical properties of MR dampers, a micro-macro mathematical model for MR dampers was proposed to describe the dynamic properties of MR dampers affected by the microstructure of MR fluid. Firstly, the micromodel of MR fluid was brought into classic quasi-static model and the double-Sigmoid model to propose a mathematical model, which considers the MR fluid microstructure by expressing the yield force parameter in the traditional double-Sigmoid model with the microstructure parameters of MR fluid. By analyzing the data of the performance test of a single-coil MR damper, the parameters of the proposed mathematical model were fitted. The proposed micro-macro model for MR dampers was verified by comparing the results calculated by this model with the performance test data. Based on the proposed micro-macro mathematical model, the nonlinear hysteretic curves with different MR fluid microstructure parameters can be numerically analyzed and compared. Finally, the influences of the volume fraction, size, and coating thickness of ferromagnetic particles on the mechanical properties of MR dampers were revealed and discussed. The research can provide guidance for the preparation and formulation optimization of high-performance MR fluid.

Aiming at the problems of weak generalization ability and long training time in most fault diagnosis models based on deep learning, such as support vector machines and random forest algorithms, one intelligent diagnosis method of rolling bearing fault based on the improved convolution neural network and light gradient boosting machine is proposed. At first, the convolution layer is used to extract the features of the original signal. Second, the generalization ability of the model is improved by replacing the full connection layer with the global average pooling layer. Then, the extracted features are classified by a light gradient boosting machine. Finally, the verification experiment is carried out, and the experimental result shows that the average training and diagnosis time of the model is only 39.73 s and 0.09 s, respectively, and the average classification accuracy of the model is 99.72% and 95.62%, respectively, on the same and variable load test sets, which indicates that the diagnostic efficiency and classification accuracy of the proposed model are better than those of other comparison models.