结合边缘特征先验引导的深度卷积显著性检测

针对当前基于深度学习的显著性检测算法缺少利用先验特征和边缘信息，且在复杂场景中难以检测出鲁棒性强的显著性区域的问题，提出了一种结合边缘特征，利用先验信息引导的全卷积神经网络显著性检测算法。该算法利用三种被经常用到的先验知识结合边缘信息形成先验图，通过注意力机制将提取的先验特征与深度特征有效融合，最终通过提出的循环卷积反馈优化策略迭代地学习改进显著性区域，从而产生更可靠的最终显著图预测。经过实验定性定量分析，对比证明了算法的可靠性。     

一种新的应用于5G中的自适应波束赋形优化算法

5G standard uses millimeter wave band to realize communication. In order to cover more reasonable in 5G communication system, beamforming technology must be adopted. To speed up the convergence, a novel algorithm based on the SWO criterion is proposed which can be applied in millimeter wave communication. The simulation results show that compared with other beamform- ing algorithms, the proposed algorithm can achieve higher convergence speed and is more realizable.     

Stochastic polynomial optimization

ABSTRACT

This paper studies stochastic optimization problems with polynomials. We propose an optimization model with sample averages and perturbations. The Lasserre-type Moment-SOS relaxations are used to solve the sample average optimization. Properties of the optimization and its relaxations are studied. Numerical experiments are presented.     

An improved iterative stochastic multi-objective acceptability analysis method for robust alternative selection in new product development

Alternative selection in new product development (NPD) is a multi-criteria decision-making (MCDM) problem. It usually starts with incomplete, imprecise or even partially missing information. Currently, most existing methods in dealing with this problem cannot work well if required information is incomplete or missing. It is acknowledged that stochastic multi-objective acceptability analysis (SMAA) can be applied to address MCDM problem with incomplete preference information and uncertain criteria measurements. In SMAA, alternatives are evaluated based on SMAA measurements (acceptability index, central weight vector and confidence factor). The discriminability of SMAA for the optimum alternative heavily depends on differences of SMAA measurements among different alternatives. Usually, a large number of alternatives and high level of uncertainty are involved in alternative selection in NPD. In this situation, the differences among SMAA measurements are not obvious, and therefore SMAA cannot deal with such problem very well. To this end, this paper proposes an improved SMAA method called Iterative-SMAA (I-SMAA) for alternative selection in NPD. In the I-SMAA, an iterative multi-step decision-making process is suggested to improve differences of SMAA measurements among different alternatives, and thus assist decision makers (DMs) to positively discern from the most preferred alternative. To enhance the decision-making efficiency, sensitive criteria are acquired in each iteration by ranking sensitivity analysis. DMs are guided to provide partial preference information and give more accurate criteria measurements for sensitive criteria rather than all criteria. Eventually, to verify the proposed method, a numerical example of the existing literature is solved with the method, and the results are compared. And then, a practical example of a preparation equipment for coal samples is further employed to verify the practicability of the proposed I-SMAA.     

精细注水配套工艺技术优化研究

本文通过对注水配套工艺的优化研究,有效提高奈曼油田分注率和配注合格率,提高精细注水开发效果。     

Multi-objective optimal allocation strategy for the energy internet in Huangpu District,Guangzhou, China

To improve the overall efficiency of the energy system, the basic structure for the energy internet of coordination and optimization of “generation-grid-load-storage” of Huangpu District, Guangzhou, China is designed, while the arrangement for the output of centralized and distributed energy module and energy storage are proposed. Taking economic benefit maximization, environmental benefit maximization and energy efficiency maximization as sub-objectives, the mathematical model of multi-objective optimal allocation and operation strategy of the energy internet is established considering supply-demand balance constraints, equipment characteristic constraints, operation mode constraints, and energy conditions constraints. The calculation results show that without considering the outsourced electricity, the balanced strategy, the economic development strategy, the environmental protection strategy, and the energy efficiency strategy are obtained by calculation, which are all superior to the traditional energy supply strategy. Moreover, considering the outsourced electricity, the proportion of outsourced electricity to total electricity is 19.8%, which is the system optimization of the energy internet under certain power demand. Compared with other strategies without outsourced electricity, the outsourced electricity strategy can have a certain emission reduction effect, but at the same time reduce the economic benefit. Furthermore, the huge difference in demand for thermal and cooling load between industrial and commercial areas results in the installed capacity of gas distributed energy stations in industrial areas being nearly twice as large as that in commercial areas. The distributed photovoltaic power generation is allocated according to the proportion of the installed roof areas of photovoltaic power generation system in residential, industrial, and commercial areas.     

Relevance- and interface-driven clustering for visual information retrieval

Search results of spatio-temporal data are often displayed on a map, but when the number of matching search results is large, it can be time-consuming to individually examine all results, even when using methods such as filtered search to narrow the content focus. This suggests the need to aggregate results via a clustering method. However, standard unsupervised clustering algorithms like $K$-means (i) ignore relevance scores that can help with the extraction of highly relevant clusters, and (ii) do not necessarily optimize search results for purposes of visual presentation. In this article, we address both deficiencies by framing the clustering problem for search-driven user interfaces in a novel optimization framework that (i) aims to maximize the relevance of aggregated content according to cluster-based extensions of standard information retrieval metrics and (ii) defines clusters via constraints that naturally reflect interface-driven desiderata of spatial, temporal, and keyword coherence that do not require complex ad-hoc distance metric specifications as in $K$-means. After comparatively benchmarking algorithmic variants of our proposed approach – RadiCAL – in offline experiments, we undertake a user study with 24 subjects to evaluate whether RadiCAL improves human performance on visual search tasks in comparison to $K$-means clustering and a filtered search baseline. Our results show that (a) our binary partitioning search (BPS) variant of RadiCAL is fast, near-optimal, and extracts higher-relevance clusters than $K$-means, and (b) clusters optimized via RadiCAL result in faster search task completion with higher accuracy while requiring a minimum workload leading to high effectiveness, efficiency, and user satisfaction among alternatives.     

红外菲涅尔透镜的注射超声辅助成型优化

为了提升聚合物红外菲涅尔透镜的光学性能,以其表面微沟槽的成型质量为目标,提出了一种高效的注射超声辅助成型方法,并对工艺参数进行了综合质量优化。首先分析了超声振动对聚合物的加热和加压效应,设计了一套一模两腔的对比试验模具;接着以红外菲涅尔透镜的调制传递函数MTF和齿形平均高度h为优化质量目标,设计了四步骤的多目标优化流程,通过试验设计、基于BP神经网络的质量目标与注射工艺参数关系建模、基于NSGA-Ⅱ的多目标优化和试验验证进行工艺参数的综合优化。实验结果表明:该多目标优化流程具有很高的精度,MTF和h的平均预测误差MPE分别为4.16%和3.32%;注射超声辅助成型的菲涅尔透镜微沟槽具有更高的复制质量,其齿沟槽平均高度h增加了15.6%,且h值的波动量随着h值的增大而增大,MTF值受齿高h均匀性的影响大于齿高h对其的影响。