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Large-scale spatiotemporal data mining has created valuable insights into managing key areas of society and the economy. It has encouraged data owners to release/publish trajectory datasets. However, the ill-informed publication of such valuable datasets may lead to serious privacy implications for individuals. Moreover, as a major goal of data protection, balancing privacy and utility remains a challenging problem due to the diversity of spatiotemporal data. However, the user dimension was not considered for traditional frameworks, which limits the application at the global level as opposed to the user level. Many researchers overcome this issue by assuming that a user in the dataset generates only one trajectory. Actually, a user always generates multiple and repetitive trajectories during observation. Only considering one trajectory for one user may cause insufficient privacy protection at the trajectory level alone, as a user’s privacy can be manifested in many trajectories collectively. In addition, it demonstrates strong user correlation when using multiple and repetitive trajectories. If not considered, additional information will be lost, and the utility will be decreased. In this article, we propose a novel privacy-preserved trajectory data publishing method, i.e., IDF-OPT, which can reduce global least-information loss and guarantee strong individual privacy. Comprehensive experiments based on an actual trajectory publishing benchmark demonstrate that the proposed method maintains high practicability in trajectory data mining.

Pipes are important components for well drilling, production, and transportation in petroleum industry. However, due to factors such as vibration, corrosion, fatigue damage, etc., pipes are prone to cracks. This paper presents a novel ACFM probe with flexible sensor array for the inspection of inner and outer cracks in pipes with different diameters. The simulation model of the ACFM probe with flexible sensor array is established by the finite element method. The influences of the crack length and the lift-off on the distance between the Bz peaks and troughs are analyzed respectively. The relationship between the offset distance of the crack from the pickup coil center and the detection sensitivity is studied. The probe made by flexible PCB and the testing system are developed. The experiments are carried out. The results of experiments and simulations show that the probe with flexible sensor array can detect inner and outer surface cracks for pipes with different diameters, and both circumferential and axial cracks can be identified. The distance between the Bz peaks and troughs can measure the crack length. When the crack is offset from the center of the pickup coil, the adjacent pickup coil can compensate for the detection sensitivity.

This paper deals with the coherent radar detection with non-uniform pulse repetition intervals (PRI), which has strong electronic counter-countermeasures capabilities to enemy jammer thanks to its low interception and difficult to sort. A novel coherent processing framework for the non-uniform PRI returns is proposed to detect moving targets by exploiting the PRI relationship of the transmitting time sequences. It can achieve the range and velocity measurement without ambiguity. In addition, the optimized weighted window in the Doppler domain is designed to suppress the sidelobe levels which is introduced by the non-uniform PRI sequences. Finally, several simulation results are provided and discussed.

Driving range is one of the most critical issues for electric vehicles (EVs): running out of battery charge while driving results in serious inconvenience even comparable to a vehicle breakdown, as an effect of long fuel recharging times and lack of charging facilities. This may discourage EVs for current and potential customers. As an effect, the dimensioning of the energy subsystem of an EV is a crucial issue: the choice of the energy storage components and the policies for their management should be validated at design time through simulations, so to estimate the vehicle driving range under reference driving profiles. Thus, it is necessary to build a simulation framework that considers an EV power consumption model that accounts for the characteristics of the vehicle and the driving route, plus accurate models for all power components, including batteries and renewable power sources. The goal of this paper is to achieve such an early EV simulation, through the definition of a SystemC-AMS framework, which models simultaneously the physical and mechanical evolution, together with energy flows and environmental characteristics. The proposed solution extends the state-of-the-art framework for the simulation of electrical energy systems with support for mechanical descriptions and the AC domain, by finding a good balance between accuracy and simulation speed and by formalizing the new information and energy flows. The experimental results demonstrate that the performance of the proposed approach in terms of accuracy and simulation speed w.r.t. the current state-of-the-art and its effectiveness at supporting EV design with an enhanced exploration of the alternatives.

In this paper, we introduce an automatic method for image quality enhancement. Our method relies on a multi-level connected generator and a pair-wise relativistic discriminator. Moreover, we also incorporate auxiliary loss functions to exhibit a high-quality image enhancement. Different from other image style transfer approaches, our method has two appealing properties: 1) different scale information not only pass on the same scale but also flow from encoder to decoder freely and 2) more contextual information is provided to help the discriminator to perform pair-wise discriminative learning. The learned generative model can then automatically enhance the low-quality image into a digital single lens reflex (DSLR) quality. The extensive experiments demonstrate that the proposed framework surpasses the state-of-the-art methods with a clear margin.