Remaining useful life prediction for degrading systems with random shocks considering measurement uncertainty

Degradation data have been widely used for the remaining useful life (RUL) prediction of systems. Most existing works apply a preset model to capture the degradation process and focus on the degradation process without shocks or constant shock effects. More generally, the actual degradation path is unobservable due to the existence of measurement uncertainty, which interferes with the determination of the degradation model. Besides, the effect of random shocks is usually fluctuating. Given these problems, a general degradation model with the random shock fluctuant effects considering the measurement uncertainty is first developed to describe the degradation process, and a two-step approach combining the arithmetic average filter and the Bayesian information criterion is adopted to identify the degradation path. Subsequently, the transfer processes of the actual degradation state and the abrupt change caused by shocks are depicted using a two-dimensional state-space model, and an expectation-maximization algorithm combined with the particle filtering is developed for parameter estimation. Furthermore, the explicit solution of RUL distribution is obtained when only considering harmful shocks, while a simulation method of RUL distribution is provided when both harmful and beneficial shocks exist. Finally, the effectiveness of the proposed method is verified by a numerical example and two practical case studies.     

Model-Based Design Space Exploration for FPGA-based Image Processing Applications Employing Parameterizable Approximations

The increasing demand for low power consumption and high computational performance is outpacing available technological improvements in embedded systems. Approximate computing is a novel design paradigm trying to bridge this gap by leveraging the inherent error resilience of certain applications and trading in quality to achieve reductions in resource usage. Numerous approximation methods have emerged in this research field. While these methods are commonly demonstrated in isolation, their combination can increase the achieved benefits in complex systems. However, the propagation of errors throughout the system necessitates a global optimization of parameters, leading to an exponentially growing design space. Additionally, the parameterization of approximated components must consider potential cross-dependencies between them. This work proposes a systematic approach to integrate and optimally configure parameterizable approximate components in FPGA-based applications, focusing on low-level but high-bandwidth image processing pipelines. The design space is explored by a multi-objective genetic algorithm which takes parameter dependencies between different components into account. During the exploration, appropriate models are used to estimate the quality-resource trade-off for probed solutions without the need for time-consuming synthesis. We demonstrate and evaluate the effectiveness of our approach on two image processing applications that employ multiple approximations. The experimental results show that the proposed methods are able to produce a wide range of Pareto-optimal solutions, offering various choices regarding the desired quality-resource trade-off.     

Learn from the past – sequentially one-to-one video deblurring network

With the growing availability of hand-held cameras in recent years, more and more images and videos are taken at any time and any place. However, they usually suffer from undesirable blur due to camera shake or object motion in the scene. In recent years, a few modern video deblurring methods are proposed and achieve impressive performance. However, they are still not suitable for practical applications as high computational cost or using future information as input. To address the issues, we propose a sequentially one-to-one video deblurring network (SOON) which can deblur effectively without any future information. It transfers both spatial and temporal information to the next frame by utilizing the recurrent architecture. In addition, we design a novel Spatio-Temporal Attention module to nudge the network to focus on the meaningful and essential features in the past. Extensive experiments demonstrate that the proposed method outperforms the state-of-the-art deblurring methods, both quantitatively and qualitatively, on various challenging real-world deblurring datasets. Moreover, as our method deblurs in an online manner and is potentially real-time, it is more suitable for practical applications.     

The effect of co-substitution on the microwave dielectric properties of Ba6-3xNd8+2xTi18O54(x=2/3) ceramics

In this work, the microwave dielectric properties of Ba₄(Nd_1-yBi_y)_28/3Ti_18-x(Al_1/2Ta_1/2)_xO₅₄(0≤x≤2, 0.05≤y≤0.2) ceramics co-substituted by A/B-site were studied. Firstly, (Al_1/2Ta_1/2)⁴⁺ was used for substitution at B-site. At 0≤x≤1.5, the above mentioned ceramic was found to exist in single-phase tungsten bronze structure, but at x = 2.0, the secondary phase appeared. Although the dielectric constant decreased by doping the (Al_1/2Ta_1/2)⁴⁺, but the quality factor was observed to improve by 40% and the temperature coefficient of resonant frequency decreased by 75%. Based on the above results, Bi³⁺ was introduced to Ba₄Nd_28/3Ti₁₇(Al_1/2Ta_1/2)O₅₄. The introduction of Bi³⁺ reduced the sintering temperature, greatly improved the dielectric constant, and ultimately decreased the temperature coefficient of resonant frequency, but it led to deterioration of quality factor. At last, with appropriate site-substitution content control (x = 1.0,y = 0.15), excellent comprehensive properties (ε_r = 89.0, Q × f = 5844 GHz @ 5.89 GHz，TCF = +8.7 ppm/°C) were obtained for the samples sintered at 1325 °C for 4 h.     

基于GA-BP的汽车风振噪声声品质预测模型

目前对于汽车风振噪声的优化研究主要以声压级（Sound pressure level，SPL）作为单一评价指标，既不能全面反映噪声的物理属性，也无法考虑人耳对噪声的主观认知过程。为准确评价风振噪声，引入声品质，运用大涡模拟（Large eddy simulation，LES）对风振噪声进行数值仿真，根据实车道路试验判断仿真的准确性；对仿真结果进行声品质客观评价与主观评价，综合声品质客观评价参数与声品质主观评价试验结果建立BP神经网络预测模型；利用遗传算法（Genetic algorithm，GA），进一步对BP神经网络的结构参数进行优化，建立GA-BP声品质预测模型。研究结果表明，GA-BP声品质预测模型在训练速度和预测精度上都优于BP神经网络预测模型。预测模型基于声品质主客观评价结果，其预测值可以代替传统的声压级评价指标，为风振噪声提供更为准确合理的评价。     

基于Prophet人工智能算法的网络潮汐效应预测研究

本文使用Prophet人工智能算法研究与预测移动通信网络“潮汐效应”现象，探索网络“潮汐效应”在优化网络资源配置实现网络降本增效的作用。Prophet人工智能算法是一种简单、有效，且易于实现的人工智能算法。通过facebook的人工智能开源框架fbprophet，研究4G网络PRB利用率等网络资源指标的“潮汐效应”，并预测这些网络资源指标在未来的变化趋势，用来指导当前4G网络减容、扩容和4/5G节电节能等，实现优化网络资源配置达到降本增效的目的。     

伊拉克A油田Asmari组混积型碳酸盐岩储层隔夹层研究

伊拉克A油田Asmari组为大型混积型碳酸盐岩油藏,发育灰岩、白云岩、砂岩、泥岩和硬石膏等多种岩性,储层复杂、非均质性强,存在多种类型的隔夹层。基于岩心、薄片、测井和分析测试等资料,对研究区隔夹层类型及特征展开综合分析,识别出了泥岩、硬膏岩和泥-微晶碳酸盐岩3类隔夹层。泥岩和硬石膏致密无孔隙、基本不具有渗流能力,对流体流动可以起到阻挡作用;泥-微晶碳酸盐岩属于细粒碳酸盐岩沉积,孔隙度低,但一般裂缝较发育,具有一定渗流能力,不能对流体起到有效的隔挡作用。硬石膏主要在油田中部及北部A段发育,其对油田北部储层纵向连通起到一定隔挡作用,导致纵向压力衰竭不一致。泥岩主要在C段顶部、B4和B1层发育,平面上主要在油田南部较发育。C段顶部发育一套厚度较大、稳定分布的泥岩隔层,将C段和上部B段及A段分隔为两个独立的油水系统,并有效阻挡了C段水层锥进到B段;B4和B1层泥岩较发育,但由于油田南部区域断层和裂缝较发育,导致泥岩隔夹层未能起到有效隔挡作用。搞清研究区隔夹层类型、分布及是否起隔挡作用,对A油田各区域各小层有效动用开发、见水规律研究具有很好的指导意义。     

虚实结合的可穿戴产品适合性检验研究

目的 为了更科学地研究和检验可穿戴产品的适合性,提出一种适合性检验方法,能够精确保留现实环境中的产品佩戴关系,并能将现实与虚拟的适合性检验研究相结合,得到合理的适合性检验结果.方法 以虚拟现实眼镜的适合性检验为例,通过高精度的三维测量技术将现实环境中的人、产品以及人—产品佩戴关系转化为三维虚拟信息,并以人—产品佩戴三维模型为参考基准对齐人和产品的虚拟模型,得到保留现实佩戴关系的人—产品佩戴模型组,再应用偏差分析法得出人—机佩戴区域的可视化适合性结果和统计数据,结合主观评价方法进一步分析产品的适合性.结论 虚实结合的产品适合性检验方法可在虚拟环境中高精度地保留现实环境中的人—产品佩戴关系,并能得到可视化的适合性检验结果,为检验和指导产品的适合性提供依据.