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The creep-fatigue resistance of turbine rotor seriously affects the reliability performance and service lifetime of aircraft engine. Creep-fatigue reliability assessment is an effective measure to quantify the uncertain creep-fatigue damage and evaluate the creep-fatigue reliable life for turbine rotor. To improve the modeling accuracy and simulation efficiency of creep-fatigue reliability assessment, a multi-surrogate collaboration approach (MSCA) is proposed by absorbing the strengths of the proposed dynamic neural network surrogate (DNNS) into distributed collaborative strategy. The creep-fatigue reliability assessment of a typical turbine rotor is regarded as one case to estimate the presented MSCA with respect to the fluctuations of multi-physical variables and the variabilities of multi-model parameters. The assessment results reveal that the creep-fatigue reliable life of turbine rotor under reliability degree of 0.998 7 is 629 cycles, and the fatigue strength coefficient and holding creep time play a leading role on creep-fatigue reliable life since their effect probabilities of 27 % and 19 %, respectively. Comparison of various methods (direct Monte Carlo simulation, response surface, neural network surrogate, DNNS) shows that the presented MSCA holds high efficiency and accuracy in creep-fatigue reliability assessment of turbine rotor.

Metamodels in lieu of time-demanding performance functions can accelerate the reliability analysis effectively. In this paper, we propose an efficient collaborative active learning strategy-based augmented radial basis function metamodel (CAL-ARBF), for reliability analysis with implicit and nonlinear performance functions. For generating the suitable samples, a CAL function is first designed to constrain the new samples being generated in sensitivity region, near limit state surface and keep certain distances mutually. Then by adjusting the adjustment coefficient of CAL function, the CAL-ARBF is mathematically modeled and the corresponding reliability analysis theory is developed. The effectiveness of the proposed approach is validated by four numerical samples, including global nonlinear problem, local nonlinear problem, nonlinear oscillator and truss structure. Through comparison of several state-of-the-art methods, the proposed CAL-ARBF is demonstrated to possess the computational advantages in efficiency and accuracy for reliability analysis.

Due to the immense benefits of guaranteeing user privacy, popular messaging platforms have shown enthusiasm for deploying End-to-End Encryption (E2EE). However, E2EE could be misused for bypassing media moderation, opening a shortcut for the viral spreading of harmful media. Private hash-matching techniques are proposed to identify harmful content in E2EE. Unfortunately, the pioneering solution incurs prohibitively high latency due to redundant user-cloud interactions for a private inspection. In this paper, we design Entbergen for efficient inspection of E2EE media by differentially handling harmless and harmful ingredients. For this, a novel Private-2D BloOm filter with Fuzzy Query (PBO-FQ) is designed for local, agile, and private media hash matching. It is proposed as the first structure that adapts inverted index and differential privacy (DP) towards seamless integration of sketch and mask encoding. With PBO-FQ, Entbergen can instantly filter out harmless media and only pays attention to the small-scale counterparts by scrutinizing them privately based on homomorphic encryption. Security analysis shows that Entbergen can effectively fulfil the desired privacy requirements. Extensive evaluations demonstrate that Entbergen is sufficiently efficient (w.r.t. computation and communication overhead) for working on mobile devices and can easily scale to real-world inspection with a large database. [ABSTRACT FROM AUTHOR]

Commercial clouds now heavily use CPUs in DL (deep learning) because there are large numbers of CPUs which would otherwise sit idle during off-peak periods. Following the trend, CPU vendors have not only released high-performance many-core CPUs but also developed efficient math kernel libraries. However, current DL platforms cannot scale well to a large number of CPU cores, making many-core CPUs inefficient in DL computation. We analyze the memory access patterns of various layers and identify the root cause of the low scalability, i.e., the per-layer barriers that are implicitly imposed by current platforms which assign one single instance (i.e., one batch of input data) to a CPU. The barriers cause severe memory bandwidth contention and CPU starvation in the access-intensive layers (like activation and BN). This paper presents a novel approach called ParaX, which boosts the performance of DL on multi-NUMA (non-uniform memory access) many-core CPUs by effectively alleviating bandwidth contention and CPU starvation. Our key idea is to assign one instance to each CPU core instead of to the entire CPU, so as to remove the per-layer barriers on the executions of the many cores. ParaX designs an ultralight scheduling policy which sufficiently overlaps the access-intensive layers with the compute-intensive ones to avoid contention, and proposes a NUMA-aware gradient server mechanism for training which leverages shared memory to substantially reduce the overhead of per-iteration parameter synchronization. We have implemented ParaX on MXNet. Extensive evaluation on a two-NUMA Intel 8280 CPU shows that ParaX significantly improves the training/inference throughput for all tested models (for image recognition and natural language processing) by $1.73\times \sim 2.93{\times}$ 1. 73 × ∼ 2. 93 × . [ABSTRACT FROM AUTHOR]

A phased array usually suffers from severe gain drop when its beam steers in a wide range. In this article, an alleviating technique based on a gain-compensating approach is proposed. Isolated element pattern (IEP) and array factor are jointly considered. When calculating the array factor, mutual couplings between antennas in an array are also included. The IEP is optimized in such a way that the element gain is nearly inversely proportional to the peak gain of the array factor. As a result, the peak gain of the overall array beam can be kept almost the same in a wide scanning range. As an example, a cylindrical dielectric resonator antenna (DRA) array is investigated. In such a design, a dip is introduced to the top of the element pattern so that the top peak in the array factor envelope can be compensated. This idea is demonstrated by using an X-band cylindrical DRA, with its beam shaped by using a loading peripheral metal ring and two top dielectric slabs. This shaped-beam DRA element is used to construct a nine-element H-plane linear phased array. The element and array prototypes were fabricated and measured, and reasonable agreement between the measured and simulated results is observed. The measured 3-dB beamwidths of the DRA element are 172° and 149° in the E- and H-planes, respectively. It is found that the H-plane main beam of the DRA array can scan from $- \mathrm{72}^{\circ }$ to $+ \mathrm{72}^{\circ }$ with a small gain fluctuation of less than 0.9 dB. [ABSTRACT FROM AUTHOR]