A new method for distributing power usage across a sensor network

We present a method for more uniformly distributing the energy burden across a wireless ground-based sensor network communicating with an overhead unmanned aerial vehicle (UAV). A subset of sensor nodes, termed a transmit cluster, receives and aggregates data gathered by the entire network, and forms a distributed antenna array, concentrating the radiated transmission into a narrow beam aimed towards the UAV. Because these duties are power-intensive, the role of transmit cluster must be shifted to different nodes as time progresses. We present an algorithm to reassign the transmit cluster, specifying the time that should elapse between reassignments and the number of hops that should be placed between successive transmit clusters in order to achieve three competing goals: first, we wish to better and more broadly spread the energy load across the sensor network while, second, minimizing the energy expended in moving the transmit cluster, all the while, third, reducing to the extent practicable the time to bring the UAV and the sensor network’s beam into alignment. Additionally, we present a method for reconfiguring the communication burden between the ground-based sensor network and the UAV. We describe and analyze two alternative strategies to bring the UAV and the sensor network’s beam into alignment, while minimizing the energy expended by the sensor network. The performance of the two strategies is compared in terms of probability of beam-UAV alignment as a function of time, and the expected time to alignment. We examine the performance tradeoff between the choice of strategy and parameters of the sensor network that affect power conservation.     

The Food Choice Questionnaire: Factorial invariant over western urban populations?

To assess the degree of measurement invariance of the Food Choice Questionnaire (FCQ) across western urban populations, it was filled out by demographically comparable samples in Canada (163 English speaking students, original version), Belgium (Flanders, N = 176, Dutch translation), and Italy (N = 163, Italian translation). Reliability of the FCQ-scales was moderate to good, but sometimes differed from the normative values. Item analysis pinpointed items with skewed distributions and low item-total correlations. Subsequent confirmatory and exploratory factor analyses revealed a suboptimal fit for the FCQ-model in all samples, with small to considerable divergences from the original configuration. The findings do not support the generalizability of the FCQ’s factor structure, but suggest that its items and underlying constructs may have different connotations across western urban populations. Explanations for the lack of convergence in factor structure and implications for research are discussed.     

Flexible and robust CAD models for design automation

This paper explores novel methodologies for enabling Multidisciplinary Design Optimization (MDO) of complex engineering products. To realize MDO, Knowledge Based Engineering (KBE) is adopted with the aim of achieving design reuse and automation. The aim of the ongoing research at Linköping University is to shift from manual modeling of disposable geometries to Computer Aided Design (CAD) automation by introducing generic high level geometry templates. Instead of repeatedly modeling similar instances of objects, engineers should be able to create more general models that can represent entire classes of objects. The proposed methodology enables utilization of commercial design tools, hence taking industrial feasibility into consideration. The concept of High Level CAD templates (HLCt) will be proposed and discussed as the building blocks of flexible and robust CAD models, which in turn enables high fidelity geometry in the MDO loop. Furthermore, quantification of the terms flexibility and robustness are presented, providing a means to measure the quality of the geometry models. Finally, application examples are presented in which the outlined framework is evaluated. The applications have been chosen from three ongoing research projects aimed at automating the design of transport aircraft, industrial robots, and micro air vehicles.     

Smart pricing scheme: A multi-layered scoring rule application

Defining appropriate pricing strategy for smart environment is important and complex task at the same time. It holds the primal fraction in Demand Response (DR) program. In our work, we devise an incentive based smart dynamic pricing scheme for consumers facilitating a multi-layered scoring rule. The proposed strategy characterizes both incentive based DR and price based DR programs facilities. This mechanism is applied between consumer agents (CA) to electricity provider agent (EP) and EP to Generation Company (GENCO). Based on the Continuous Ranked Probability Score (CRPS), a hierarchical scoring system is formed among these entities, CA–EP–GENCO. As CA receives the dynamic day-ahead pricing signal from EP, it will schedule the household appliances to lower price period and report the prediction in a form of a probability distribution function to EP. EP, in similar way reports the aggregated demand prediction to GENCO. Finally, GENCO computes the base discount after running a cost-optimization problem. GENCO will reward EP with a fraction of discount based on their prediction accuracy. EP will do the same to CA based on how truthful they were reporting their intentions on device scheduling. The method is tested on real data provided by Ontario Power Company and we show that this scheme is capable to reduce energy consumption and consumers’ payment.     

盈余质量与公司治理关系的实证研究

对公司治理系统中的主要特征变量与盈余质量关系的统计分析结果表明:独立董事的比例和盈余质量之间存在倒U型曲线关系;董事会会议次数和盈余质量正相关;而董事会规模和盈余质量的关系不显著;债务约束不构成中国上市公司盈余管理的动机;而提高净资产收益率的动机非常明显.政策结论在于:单纯的独立董事制度改革对提高盈余质量的作用有限,公司治理改革必须配合市场改革措施才能更有效地发挥提高盈余质量的作用.     

AHP综合排序算法分析及其改进

层次分析法(AHP)是分析多目标评价、多指标决策问题时的一种常用的算法工具,但是传统AHP在进行综合排序时,却存在一定的算法缺陷,AHP不能保持各方案之间的独立性,导致当某些方案的指标值或者备选方案数量发生变化时,即使在同一评价标准下,AHP法也不能始终保证方案的排序的一致.针对这一问题,分析了AHP产生排序不一致的具体原因,指出其算法会导致最底层相对重要值会随着方案的数量的增加逐渐减少.提出用各方案与理想方案之间的相对效用指标代替传统AHP的综合排序指标,以使其能够始终保持各方案排序的一致性.给出的算例验证新方法的有效性.     

Dual–primal proximal point algorithms for extended convex programming

This paper presents a class of dual–primal proximal point algorithms (PPAs) for extended convex programming with linear constraints. By choosing appropriate proximal regularization matrices, the application of the general PPA to the equivalent variational inequality of the extended convex programming with linear constraints can result in easy proximal subproblems. In theory, the sequence generated by the general PPA may fail to converge since the proximal regularization matrix is asymmetric sometimes. So we construct descent directions derived from the solution obtained by the general PPA. Different step lengths and descent directions are chosen with the negligible additional computational load. The global convergence of the new algorithms is proved easily based on the fact that the sequences generated are Fejér monotone. Furthermore, we provide a simple proof for the O(1/t) convergence rate of these algorithms.