Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm

The traditional production scheduling problem considers performance indicators such as processing time, cost, and quality as optimization objectives in manufacturing systems; however, it does not take energy consumption or environmental impacts completely into account. Therefore, this paper proposes an energy-efficient model for flexible flow shop scheduling (FFS). First, a mathematical model for a FFS problem, which is based on an energy-efficient mechanism, is described to solve multi-objective optimization. Since FFS is well known as a NP-hard problem, an improved, genetic-simulated annealing algorithm is adopted to make a significant trade-off between the makespan and the total energy consumption to implement a feasible scheduling. Finally, a case study of a production scheduling problem for a metalworking workshop in a plant is simulated. The experimental results show that the relationship between the makespan and the energy consumption may be apparently conflicting. In addition, an energy-saving decision is performed in a feasible scheduling. Using the decision method, there could be significant potential for minimizing energy consumption.     

Performance evaluation of cloud service considering fault recovery

Cloud computing is a recent trend in IT, which has attracted lots of attention. In cloud computing, service reliability and service performance are two important issues. To improve cloud service reliability, fault tolerance techniques such as fault recovery may be used, which in turn has impact on cloud service performance. Such impact deserves detailed research. Although there exist some researches on cloud/grid service reliability and performance, very few of them addressed the issues of fault recovery and its impact on service performance. In this paper, we conduct detailed research on performance evaluation of cloud service considering fault recovery. We consider recovery on both processing nodes and communication links. The commonly adopted assumption of Poisson arrivals of users’ service requests is relaxed, and the interarrival times of service requests can take arbitrary probability distribution. The precedence constraints of subtasks are also considered. The probability distribution of service response time is derived, and a numerical example is presented. The proposed cloud performance evaluation models and methods could yield results which are realistic, and thus are of practical value for related decision-makings in cloud computing.     

Rotation Averaging

This paper is conceived as a tutorial on rotation averaging, summarizing the research that has been carried out in this area; it discusses methods for single-view and multiple-view rotation averaging, as well as providing proofs of convergence and convexity in many cases. However, at the same time it contains many new results, which were developed to fill gaps in knowledge, answering fundamental questions such as radius of convergence of the algorithms, and existence of local minima. These matters, or even proofs of correctness have in many cases not been considered in the Computer Vision literature. We consider three main problems: single rotation averaging, in which a single rotation is computed starting from several measurements; multiple-rotation averaging, in which absolute orientations are computed from several relative orientation measurements; and conjugate rotation averaging, which relates a pair of coordinate frames. This last is related to the hand-eye coordination problem and to multiple-camera calibration.     

Robust and smart spectral clustering from normalized cut

How to determine the scale parameter and the cluster number are two important open issues of spectral clustering remained to be studied. In this paper, it is aimed to overcome these two problems. Firstly, we analyze the principle of spectral clustering from normalized cut. Secondly, on one hand, a weighted local scale was proposed to improve both the classification performance and robustness. On the other hand, we proposed an automatic cluster number estimation method from standpoint of Eigenvectors of its affinity matrix. Finally, a framework of robust and smart spectral clustering method was concluded; it is robust enough to deal with arbitrary distributed datasets and smart enough to estimate cluster number automatically. The proposed method was tested both on artificial datasets and UCI datasets, and experiments prove its availability.     

南方城市污水处理厂恶臭污染源调查与分析

对南方某市主要城市污水处理厂恶臭源的排放浓度进行监测,以H2S和NH3为主要监测指标。监测结果表明:在污水处理厂厂界内,臭气污染源主要集中在进水区、格栅区、曝气沉砂池以及污泥处理区域,厂界臭气浓度均不超标。同时,对该市典型污水处理厂的主要恶臭污染源H2S和NH3进行了13个月的连续监测;结果表明,构筑物内恶臭气体成分H2S的排放浓度为0.02~69.84ppm,NH3为0~0.37ppm,主要臭气污染物为H2S;污水处理厂进水泵房、格栅以及曝气沉砂池的臭气浓度呈现夏高冬低的特征,具有季节变化的特征。     

融合边缘测度的自然场景彩色图像区域分割

针对传统分割方法基于单个视觉线索的不足,提出一种结合两种局部边缘测度的自然场景彩色图像区域分割方法.首先,采用logistic回归模型对200幅彩色图像进行训练,建立边缘测度与对象边界的回归模型;然后,采用该模型预测图像中每个像素的边界置信度;最后,将置信度的函数以自适应权重的形式整合到均值漂移分割算法中,实现图像区域分割.近20幅图像的定量和目视对比实验结果表明,本文方法能有效地控制过分割和欠分割的产生,且具有更好的区域边界定位效果.     

离子液体作用下的噻吩加氢脱除机理研究

采用Materials Studio 6.1中的Dmol3程序,程序中的数字积分是medium水平,位移收敛标准、能量和受力分别是5×10^-3 A,2×10^-5 au/和4×10^-3 au/,利用DFT理论中的梯度校正GGAPW91方法和NDN机组计算。计算离子液体催化燃料油加氢脱硫过程的机理。通过对噻吩在离子液体酸性阴离子作用下的中间体和过渡态能量的研究,发现离子液体的阴离子不仅起到萃取作用,且在过渡态形成的过程中有促进加氢脱硫的作用,在一定的程度上起到催化剂的作用,并且活泼氢有利于噻吩加氢的进行。计算将为离子液体加氢脱硫机理提供重要的参考价值。     

基于泄流孔的管道内检测器运动状态分析

检测器在管道内依靠输送介质的压差产生动力,从而驱动其行走完成壁面腐蚀缺陷的扫描检测,检测器在管内的运动状态会直接影响到检测数据有效性。检测器速度一般采用泄流孔的方式控制,泄流孔的设计和检测器运动机理建模研究检测器运动状态的关键。本文采用流体力学的方法建立了检测器在管道中运动时的流场分析模型,分析了当原油以相对内检测器1.5 m/s流速通过不同结构泄流孔时的流场变化情况,结果表明单孔渐扩型泄流孔周围流场状态更加稳定,更有利于内检测器运动状态的稳定。建立了当内检测器采用单孔渐扩型泄流孔时的运动机理模型,对内检测器所受驱动力和摩擦阻力进行深入分析,得到了泄流孔开度和内检测器运动速度之间的特性关系曲线,为实现内检测器运动速度控制打下了理论基础。     

电容式油量测量系统误差研究

介绍了利用电容传感器测量飞机油箱内油量的原理,从理论上定量地分析了该电容传感器测量油量时产生的三种主要误差,这对提高油量测量系统的测量精度、减小测量误差提供了理论依据。