To address the task scheduling problem of industrial software using Service-Oriented Architecture （SOA）， a task scheduling algorithm for SOA-based industrial software was proposed， considering the multiple attributes of tasks， the randomness， time-varying and coupling relationships of attributes， and the requirements of real-time scheduling and parallel processing of tasks. Firstly， the task scheduling problem was modeled， and a utility function was designed to evaluate the importance of the task. Then， Importance Ranking-based Scheduling Algorithm （IRSA） was proposed to schedule tasks in descending order of importance. Finally， a resource reservation mechanism and a preemptive scheduling mechanism were designed in IRSA to improve the efficiency of task scheduling. Experimental results show that compared with the four online scheduling algorithms such as First Come First Serve（FCFS）， Earliest Deadline First（EDF）， Least Laxity First（LLF）， and Fixed Priority Scheduling（FPS）， when the number of arrival tasks per second reaches 7.99， IRSA reduces the average response time of tasks by 55.83% to 61.27%， respectively， and has significant advantages on all performance metrics. Therefore， IRSA can achieve efficient task scheduling for SOA-based industrial software.

Aiming at the problem of high computational cost of the BlackBox of the engineering optimizations, a River-based Dynamic Service-oriented Optimization Computing Platform (R-DSOCP) was proposed to calculate the BlackBox in a distributed and parallel way. Firstly, the running pattern of BlackBox in the optimization algorithms was analyzed. Conforming to the dynamic service-oriented architecture and surrounding the functions of service release and lookup of River, the kernel services required for building R-DSOCP were designed. Secondly, an ACO-based BlackBox Schedule Problem (BSP) algorithm was devised. Depending on it, the scheduling service could not only choose the best computing services for BlackBox quickly but also balance the load of R-DSOCP. At Last, the experimental results show that the BlackBox can be parallel performed on the platform effectively after separating the BlackBox’s computation from the execution of the optimization algorithm. Comparing with a single computing machine, the average computing efficiency is advanced nearly n times. n is the parallel factor. Thus, with the help of High Performance Computing (HPC) technology, R-DSOCP can offer a feasible scheme for accelerating the optimization algorithm and reducing the computational expenses in the field of engineering optimization.

Concerning the problem that the linear eigentransformation method cannot capture the statistical properties of the nonlinear facial image, a Data-driven Local Eigentransformation (DLE) method for face hallucination was proposed. Firstly, some samples most similar to the input image patch were searched. Secondly, a patch-based eigentransformation method was used for modeling the relationship between the Low-Resolution (LR) and High-Resolution (HR) training samples. Finally, a post-processing approach refined the hallucinated results. The experimental results show the proposed method has better visual performance as well as 1.81dB promotion over method of locality-constrained representation in objective evaluation criterion for face image especially with noise. This method can effectively hallucinate surveillant facial images.

Under noise conditions, the black stripes of the spectrum for the uniform linear motion blurred image become obscure or even disappear. It is difficult to estimate the motion blur parameters by means of the characteristics of the black stripes. Thus, a new algorithm was proposed for motion blur parameters synchronous identification under noise conditions, which was based on the spectrum of the motion blurred image. Firstly, the contour of the white bound area was extracted using region growing algorithm, and then the minimum bounding rectangle of the contour was calculated, the motion direction and the motion length were identified simultaneously by the length, width and tilt of the minimum bounding rectangle. The experimental results indicate that, for different signal-to-noise ratios, different motion directions and motion lengths, the algorithm can be more accurate to estimate the motion blur parameters, and has good robustness. parameters, and has good robustness.