A lightweight cell switching and traffic offloading scheme for energy optimization in ultra-dense heterogeneous networks

One of the major capacity boosters for 5G networks is the deployment of ultra-dense heterogeneous networks (UDHNs). However, this deployment results in a tremendous increase in the energy consumption of the network due to the large number of base stations (BSs) involved. In addition to enhanced capacity, 5G networks must also be energy efficient for it to be economically viable and environmentally friendly. Dynamic cell switching is a very common way of reducing the total energy consumption of the network, but most of the proposed methods are computationally demanding, which makes them unsuitable for application in ultra-dense network deployment with massive number of BSs. To tackle this problem, we propose a lightweight cell switching scheme also known as Threshold-based Hybrid cEll swItching Scheme (THESIS) for energy optimization in UDHNs. The developed approach combines the benefits of clustering and exhaustive search (ES) algorithm to produce a solution whose optimality is close to that of the ES (which is guaranteed to be optimal), but is computationally more efficient than ES and as such can be applied for cell switching in real networks even when their dimension is large. The performance evaluation shows that THESIS significantly reduces the energy consumption of the UDHN and can reduce the complexity of finding a near-optimal solution from exponential to polynomial complexity.     

On Hr(curl,Ω) estimates for a Maxwell type system in convex domains

In bounded convex domains, the regularity of a vector field u with its $\mathrm{div}\mathbf{u}$, $\mathrm{curl}\mathbf{u}$ in $L^r$ space and the tangential component or the normal component of u over the boundary in $L^r$ space, is established for $1<r<\infty$. As an application, we derive an $H^r(\mathrm{curl},\mathrm{\Omega })$ estimate for solutions to a Maxwell type system with an inhomogeneous boundary condition in convex domains. In contrast to the well-posed region of r in the space $H^r(\mathrm{curl},\mathrm{\Omega })$ for the Maxwell type system in Lipschitz domains given by Kar and Sini (2016) [16], we extend the well-posed region to be optimal.     

Parametric error bounds for convex approximations of two-stage mixed-integer recourse models with a random second-stage cost vector

We consider two-stage recourse models with integer restrictions in the second stage. These models are typically non-convex and hence, hard to solve. There exist convex approximations of these models with accompanying error bounds. However, it is unclear how these error bounds depend on the distributions of the second-stage cost vector q. In this paper, we derive parametric error bounds whose dependence on the distribution of q is explicit: they scale linearly in the expected value of the ${?}_1$-norm of q.     

PSO-LSSVM对LIBS定量分析精度的提高

针对土壤定量分析受基体效应影响大，LIBS定量分析精度不佳等问题，采用粒子群算法对LSSVM进行优化，提高模型的精确度。选取Pb Ⅰ 405.78 nm和Cr Ⅰ 425.44 nm作为分析谱线进行分析。采集十二个不同浓度样品的特征光谱，每个浓度样品在不同点采集20组数据，将其中17组数据设为训练集，3组数据设为预测集，用LSSVM和PSO-LSSVM两种方法建立定标模型。对比两种模型的拟合相关系数（R2）、训练集均方根误差（RMSEC）和预测集均方根误差(RMSEP)。由于自吸收效应的影响，随着浓度的增加，预测值逐渐低于实际值，LSSVM定标模型的拟合程度较低，无法达到实验要求，模型性能有待提高。利用粒子群算法对LSSVM的模型参数惩罚系数和核函数参数进行优化，得到最佳的参数组合，Pb元素为(8 096.8, 138.865 7)，Cr元素为(4 908.6, 393.563 5)，用最佳的参数组合构建LSSVM的定标模型。相比于LSSVM，PSO-LSSVM定标模型的精确度更高，Pb和Cr元素的R2提高到了0.982 8和0.985 0，拟合效果明显提升。Pb和Cr元素的训练集均方根误差由0.026 0 Wt%和0.027 2 Wt%下降到0.022 4 Wt%和0.019 1 Wt%，预测集均方根误差由0.101 8 Wt%和0.078 8 Wt% 下降到0.045 8 Wt%和0.042 0 Wt%，模型的稳定性进一步提高。说明PSO-LSSVM算法能够更好地降低土壤基体效应和自吸收效应带来的影响，提高分析结果的精确度与稳定性。     

全局优化问题的一个新的无参数填充函数

求全局最优化问题的填充函数算法被提出以来，参数的选取和调整一直是制约算法有效性的因素。如何在实际的计算过程中选取合适的参数，直接影响和决定了运算速度和效率。因此，构造不含参数的填充函数就显得极为重要。提出一个新的无参数的填充函数，对其理论性质进行了分析，并给出相应的填充函数算法，数值计算验证了算法的有效性。     

Performance guarantees of forward and reverse greedy algorithms for minimizing nonsupermodular nonsubmodular functions on a matroid

This letter studies the problem of minimizing increasing set functions, equivalently, maximizing decreasing set functions, over the base matroid. This setting has received great interest, since it generalizes several applied problems including actuator and sensor placement problems in control, task allocation problems, video summarization, and many others. We study two greedy heuristics, namely, the forward and reverse greedy. We provide two novel performance guarantees for the approximate solutions obtained by them depending on both submodularity ratio and curvature.     

Solution combustion synthesis,energy and environment: Best parameters for better materials

Solution combustion synthesis (SCS) is a worldwide used methodology for the preparation of inorganic ceramic and composite materials with controlled properties for a wide number of applications, from catalysis to photocatalysis and electrocatalysis, from heavy metal removal to sensoristics and electronics. The high versatility and efficiency of this technique have led to the introduction of many variants, which allowed important optimization to the prepared materials. Moreover, its ecofriendly nature encouraged further studies about the use of sustainable precursors for the preparation of nanomaterials for energy and environment, according to the concept of circular economy. On the other hand, the large variety of expressions to define SCS and the often-contradictory definitions of the SCS parameters witnessed a scarce consciousness of the potentiality of this methodology. In this review article, the most important findings about SCS and the selection criteria for its main parameters are critically reviewed, in order to give useful guidelines to those scientists who want to use this methodology for preparing materials with improved or new functional properties. This review aims as well (i) to bring more clarity in the SCS terminology (ii) to increase the awareness of the SCS as a convenient tool for the synthesis of materials and (iii) to propose a new perspective in the SCS, with special attention to the use of ecofriendly procedures. Part of the review is also dedicated to precautions and limitations of this powerful methodology.     

无穷多目标优化的Geoffrion真有效性及其在鲁棒优化中的应用

在多目标优化中,Pareto有效性体现了目标之间的妥协与补偿,而Geoffrion真有效性能保证补偿是有界的.本文对有无穷多个目标的优化问题引入了真有效性,完整保留了Geoffrion的结构.基于一族锥,揭示了Geoffrion真有效性的特点及其与Pareto有效性的区别.并将Geoffrion真有效性的思想用于鲁棒优化,得到了著名的Hurwicz决策准则.     

Gear shifting multi-objective optimization to improve vehicle performance,fuel consumption,and engine emissions

In the present work, the Adaptive-Weight Genetic Algorithm was employed in order to determine the gear shifting strategies that allow an automobile to work in the best compromise among fuel consumption, engine emissions, and vehicle performance. For the assessment of each of the three objective functions, a simulation model based on engine data and on the well-established equations of the longitudinal dynamics was developed. The driving cycle chosen for the calculations was the FTP-75, which takes into account both cold and hot starts, meaning that the transient operation during the warm-up of the catalyst is also considered.     

薄板结构加筋布局优化设计方法研究

提出一种两步优化策略，以加筋板结构的固有频率最大化为目标函数，以结构所受外载荷作用的最大静变形为约束条件，开展薄板结构加筋构件的布局优化设计研究。为了降低加筋布局优化的难度，提高优化设计的效率，将加筋等效为一系列弹性铰（点）支撑，以便快速获得加筋横向移动的灵敏度信息。在基本不改变结构重量的情形下，通过合理布局加筋位置，能显著改善结构的刚度分布，提高结构的整体承载能力。随后，小幅调整加筋的截面尺寸，以满足对结构最大变形的设计要求。最后，用两个算例验证了所提优化方法的可行性和有效性。