To improve the tracking accuracy and antidisturbance performance of permanent magnet linear synchronous motor (PMLSM), in this article, a fast recursive terminal sliding-mode control (FRTSMC) with mover velocity and disturbance observer (MVDO) is proposed. The FRTSMC is designed by a recursive structure of fast terminal sliding mode and integral sliding mode. The FRTSMC is initialized with an appropriate value to reduce the sliding-mode approach time and guarantee that the tracking error converges to zero in a finite time. To further improve the antidisturbance property and suppress the chattering phenomenon, an MVDO is integrated into the FRTSMC to simultaneously estimate the mover velocity and the lumped disturbance. Lyapunov theory is employed to analyze the closed-loop stability of the composited controller. The experimental results verify the effectiveness of the proposed method in reducing convergence error and enhancing robustness compared with the conventional fast nonsingular TSMC, adaptive recursive TSMC, and nonlinear disturbance observer based FRTSMC for PMLSM.

To improve the bandwidth and thrust performance of permanent magnet synchronous linear motor (PMSLM), from the perspective of motor structure design, this article proposes a high-accuracy and fast-calculation inductance model of centralized winding based on analytic kernel-embedded elastic-net regression (AKER) to establish a comprehensive analytical model of PMSLM performance. A stochastic fractal search (SFS) algorithm is introduced to optimize the comprehensive performances of PMSLM. First, Neumann's formula of integral is used to establish the equivalent inductance analytical model (EIAM) for the three-phase winding of PMSLM, and the analytic kernel is constructed based on the EIAM. In combination with the finite element simulation samples of inductance, the analytical kernel is embedded into the elastic-net regression to establish the inductance model of high efficiency, namely AKER, and compared with the finite element mode (FEM), EIAM and other advanced methods, the superiority of AKER is verified from the accuracy and time cost. Then, based on the proposed AKER inductance model, the current and voltage equations of PMSLM are decoupled in the two-phase rotating coordinate system to establish the comprehensive analysis model of the PMSLM performance. Based on the comprehensive model, the optimization function of PMSLM is constructed, and the SFS algorithm is introduced to solve the function iteratively. Finally, the effectiveness of the proposed modeling and optimization method is verified by FEM-based control simulation and prototype experiment, respectively.

Privilege separation (privsep) is an effective technique for improving software’s security, but privsep involves decomposing software into components and assigning them different privileges. This is often laborious and error-prone. This paper contributes the following for applying privsep to C software: (1) a portable, lightweight, and distributed runtime library that abstracts externally-enforced compartment isolation; (2) an abstract compartmentalization model of software for reasoning about privsep; and (3) a privsep-aware Clang-based tool for code analysis and semi-automatic software transformation to use the runtime library. The evaluation spans 19 compartmentalizations of third-party software and examines: Security: 4 CVEs in widely-used software were rendered unexploitable; Approximate Effort Saving: on average, the synthesis-to-annotation code ratio was greater than 11.9 (i.e., 10 × lines of code were generated for each annotation); and Overhead: execution-time overhead was less than 2%, and memory overhead was linear in the number of compartments.

Recent proposals for the disaggregation of compute, memory, storage, and accelerators in data centers promise substantial operational benefits. Unfortunately, for resources like memory, this comes at the cost of performance overhead due to the potential insertion of network latency into every load and store operation. This effect is particularly felt by data-intensive systems due to the size of their working sets, the frequency at which they need to access memory, and the relatively low computation per access. This performance impairment offsets the elasticity benefit of disaggregated memory. This paper presents TELEPORT, a compute pushdown framework for data-intensive systems that run on disaggregated architectures; compared to prior work on compute pushdown, TELEPORT is unique in its efficiency and flexibility. We have developed optimization prin- ciples for several popular systems including a columnar in-memory DBMS, a graph processing system, and a MapReduce system. The evaluation results show that using TELEPORT to push down simple operators improves the performance of these systems on state-of-the-art disaggregated OSes by an order of magnitude, thus fully exploiting the elasticity of disaggregated data centers.

Traditional design optimization methods for permanent magnet linear synchronous motor (PMLSM) always pursuit the optimal result without considering the influence of uncertainties (manufacturing errors, use wear, etc.), which will result in big fluctuations for the stability of motor thrust performance. This article proposes a new filled function algorithm (NFFA) to improve the stability of PMLSM. It searches for the structure parameters that not only lay in the smooth area of thrust ripple distribution but also satisfy the thrust ripple constraints under the effect of uncertainties. First, an analytical model of thrust ripple is established for the next-step optimization. Second, a sigma level stability criterion is proposed, which will be used to judge the robustness of optimal thrust ripple under uncertainties, and a new global robust algorithm NFFA is studied to realize the stability of thrust ripple by managing the sigma level stability criterion as the convergence criterion of traditional filled function optimization algorithm (TFFA). Third, the NFFA is used to optimize the thrust ripple of PMLSM based on the analytical model. The optimization results are proven to be stable and superior compared with other methods, such as TFFA, Taguchi robust optimization method, and design for six sigma method. Finally, the experiments confirm the feasibility and validity of the proposed method.