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.     

Periodic autoregressive conditional duration

We propose an autoregressive conditional duration (ACD) model with periodic time-varying parameters and multiplicative error form. We name this model periodic autoregressive conditional duration (PACD). First, we study the stability properties and the moment structures of it. Second, we estimate the model parameters, using (profile and two-stage) Gamma quasi-maximum likelihood estimates (QMLEs), the asymptotic properties of which are examined under general regularity conditions. Our estimation method encompasses the exponential QMLE, as a particular case. The proposed methodology is illustrated with simulated data and two empirical applications on forecasting Bitcoin trading volume and realized volatility. We found that the PACD produces better in-sample and out-of-sample forecasts than the standard ACD.     

各向异性随机场下的边坡模糊随机可靠度分析

土性参数具有很大的空间变异性,且在水平方向和垂直方向上差异显著。基于随机变量模型的传统边坡模糊随机可靠度分析方法并未对此进行考虑。提出一种能合理考虑土性参数空间变异性的边坡模糊随机可靠度分析方法。首先,视黏聚力和内摩擦角的均值为正态模糊数,对其取不同的λ截集水平并在各截集水平上进行参数组合。其次,利用各向异性随机场模拟土性参数的空间变异性,将有限元法和Monte–Carlo模拟相结合,计算各参数组合对应的可靠度指标。再通过数学方法得到边坡在各截集水平上的可靠度指标。最后,运用加权平均法计算边坡的模糊随机可靠度指标。算例分析表明:与水平方向的空间变异性相比,垂直方向的空间变异性对边坡模糊随机可靠度的影响更为显著;不考虑土性参数的空间变异性在一般情况下会低估边坡的模糊随机可靠度指标,但在抗剪强度参数变异性较大时,反而可能会高估边坡的模糊随机可靠度指标;此外,黏聚力与内摩擦角之间的相关性对边坡失效概率的影响趋势基本不受土性参数空间变异性的干扰。     

Modeling the dynamic properties of fibre-reinforced concrete with different coating technologies of multifilament yarns

The dynamic behaviour of fibre-reinforced, cementitious composite materials is gaining increasing interest. With respect to service life dynamic loading just under the elastic limit of the material at hand is relevant to practical applications, for the resulting (stress-)waves may be focused within regions of the heterogeneous composite material. This local overstraining of the material may lead to deterioration of the structural element. In this contribution, the effect of the set-up of the reinforcing fibres on the wave scattering behaviour is investigated. Special attention is paid to layered centric configurations of these fibres, as it occurs e. g. within textile-reinforced concrete (TRC). A mechanical model is developed and solved analytically providing an efficient and robust method to describe the dynamic behaviour of given fibre configurations. This method is needed for materials which have to be described mechanically before the manufacturing process – as it is the case for TRC. The proposed model also allows for planning experiments and thus is of additional value. It is shown that the inner structure of the fibres does influence the amplitude response spectra and consequently the proposed method also may be used for non-destructively detecting the inner structure of the multifilament yarns and other related objects.     

Sequence of the most informative joints (SMIJ): A new representation for human skeletal action recognition

Much of the existing work on action recognition combines simple features with complex classifiers or models to represent an action. Parameters of such models usually do not have any physical meaning nor do they provide any qualitative insight relating the action to the actual motion of the body or its parts. In this paper, we propose a new representation of human actions called sequence of the most informative joints (SMIJ), which is extremely easy to interpret. At each time instant, we automatically select a few skeletal joints that are deemed to be the most informative for performing the current action based on highly interpretable measures such as the mean or variance of joint angle trajectories. We then represent the action as a sequence of these most informative joints. Experiments on multiple databases show that the SMIJ representation is discriminative for human action recognition and performs better than several state-of-the-art algorithms.     

Error analysis of thermal equation with moving ends

A mathematical model of the linear thermodynamic equations with moving ends, based on the Stefan Problem is considered. In this work, we are interested in obtaining existence, uniqueness and regularity using the Galerkin method and mainly to establish an error estimates of solutions in Sobolev spaces for the semi-discrete problem, with discretization of space variable, continuous time and for the completely discrete problem with discretization of space variable and time.     

Order conditions for ARKN methods solving oscillatory systems

For the perturbed oscillators in one-dimensional case, J.M. Franco designed the so-called Adapted Runge-Kutta-Nyström (ARKN) methods and derived the sufficient order conditions as well as the necessary and sufficient order conditions for ARKN methods based on the B-series theory [J.M. Franco, Runge-Kutta-Nyström methods adapted to the numerical integration of perturbed oscillators, Comput. Phys. Comm. 147 (2002) 770-787]. These methods integrate exactly the unperturbed oscillators and are highly efficient when the perturbing function is small. Unfortunately, some critical mistakes have been made in the derivation of order conditions in that paper. On the basis of the results from that paper, Franco extended directly the ARKN methods and the corresponding order conditions to multidimensional case where the perturbed function f does not depend on the first derivative y^′ [J.M. Franco, New methods for oscillatory systems based on ARKN methods, Appl. Numer. Math. 56 (2006) 1040-1053]. In this paper, we present the order conditions for the ARKN methods for the general multidimensional perturbed oscillators where the perturbed function f may depend on only y or on both y and y^′.     

7A05铝合金自由锻造工艺及锻件力学性能研究

为得到高性能7A05铝合金锻件,对铸造毛坯制备、锻件制备和锻件热处理进行试验研究。采用半连续铸造工艺制备铸造毛坯,铸造温度为705～710℃,冷却水压为0.05 MPa,铸造速度为50 mm/min;采用一次镦粗加一次拔长锻造工艺,终锻温度为330℃,εZ=87.7%;锻件采用固溶加时效热处理,固溶为(480±5)℃×2 h+水淬,时效为80℃×8 h+120℃×24 h。观察锻件的金相组织并测试其力学性能,锻件微观组织为变形纤维组织,横向力学性能Rm=400 MPa,Rp0.2=360 MPa,A=11%,锻件力学性能与板材力学性能相当。