Incomplete analytic hierarchy process with minimum weighted ordinal violations

ABSTRACT

Incomplete pairwise comparison matrices offer a natural way of expressing preferences in decision-making processes. Although ordinal information is crucial, there is a bias in the literature: cardinal models dominate. Ordinal models usually yield nonunique solutions; therefore, an approach blending ordinal and cardinal information is needed. In this work, we consider two cascading problems: first, we compute ordinal preferences, maximizing an index that combines ordinal and cardinal information; then, we obtain a cardinal ranking by enforcing ordinal constraints. Notably, we provide a sufficient condition (that is likely to be satisfied in practical cases) for the first problem to admit a unique solution and we develop a provably polynomial-time algorithm to compute it. The effectiveness of the proposed method is analyzed and compared with respect to other approaches and criteria at the state of the art.     

Modeling and design of 3-D MPPT for ultra low power RF energy harvesters

Radio-Frequency (RF) energy harvesting must cope with the limited availability and high variability of the energy source. In this paper, the modeling of an RF harvester for ultra low power environments is presented. A mathematical model based on theoretical analysis is developed. The model demonstrates that the maximum transferred power point is located in a three-dimensional space defined by the input capacitance, the output voltage, and the load resistance of the rectifier circuit. Moreover, the mathematical model returns results in substantial agreement with the SPICE simulation results, while guaranteeing a remarkable reduction of the required computation time. Furthermore, the paper reports the implementation of a mixed signal system for the 3-D MPPT, to be embedded in an RF harvester, in a 65 nm CMOS technology. The circuit exhibits a simulated power consumption lower than 100 nW, making this solution suitable for ultra low power harvesting.     

基于RGV轨导柔性线环境离散型零件生产过程管理与实践

为满足航空中小型结构件快速交付的需要,以提升航空中小型结构件制造高精度、自动化和高效率为目标,通过对航空中小型结构件加工现状进行梳理分析,搭建柔性生产线自动排程与调度平台,实现柔性生产线工艺设计与准备过程管理,简化工艺准备流程,推进快换工装的应用并进行自动化程序管理;在柔性生产线加工运行过程中采用可视化管理方法,推动自动化环境下加工过程资源保障集中化管理。最终使该管理方法在某项目中进行成功实践,取得了良好的经济效益。     

基于BIM技术和有限元的检测与分析在PC结构中的应用研究

本文建立了PC结构施工全过程的BIM模型及有限元分析模型,对施工过程进行数值模拟与动态、静态监测,对装配式结构进行了结构性能评价,为结构可视化施工监测提供了技术支持。     

小型特种精细化工过程自动化和信息化研究发展趋势探讨

针对采取小批量间歇性批次生产方式、工艺介质腐蚀性强、危险性大的小型特种精细化工生产工艺研究试验过程自动化、信息化程度不高，导致工艺研究试验中获取数据较少、过程机理研究不够透彻、人工操作多、安全风险高、研究试验消耗大及效率不高等问题，综述了期待通过智能控制、在线分析、模拟仿真和虚拟制造、工况监测及预测性维护以及信息管理和生产调度等关键技术的研究和应用，提高小型特种精细化工生产工艺过程的自动化和信息化程度，实现小型特种精细化工生产工艺过程的数字化、虚拟化和智能化，降低生产安全风险和试验消耗，提高小型特种精细化工生产工艺研究试验的成功率和效率，达到小型特种精细化工生产工艺过程的数字化设计和精准生产的目标。     

装填设备载荷影响因素分析及动力学仿真

大型货物的装填过程中影响载荷的因素较多,其中对中精度对装填载荷影响较为明显。通过动力学仿真软件ADAMS对装填过程中载荷进行模拟,并对各影响因素进行定量分析,得到对中偏差与装填载荷的关系。将仿真结果与试验数据进行对比,验证该仿真方法的正确性,可作为后续研究装填载荷的一种方法,为结构设计提供指导。     

Effect of neodymium substitution on crystalline orientation,microstructure and electric properties of sol-gel derived PZT thin films

Pb(Nd_xZr_0.52Ti_0.48)O₃ (PNZT) (x = 0%, 1%, 2%, 3%, 4%, 5%) thin films were prepared by sol-gel process to investigate the effects of neodymium substitution on crystalline orientation, microstructure and electric properties of lead zirconate titanate (PZT) films. X-ray diffraction (XRD) and scanning electron microscope (SEM) analysis showed that PNZT films with Nd doping concentration below 3% exhibited dense perovskite structure with (100) preferred orientation. The average grain size of PNZT films decreased as the Nd substitution increased. The maximum dielectric constant, remnant polarization and minimum coercive field were obtained in 2% Nd-doped PZT films. Fatigue resistance was also improved significantly with 2% Nd dopant.     

A comparative study on the effect of loading speed and surface scratches on the flexural strength of aluminosilicate glass: Annealed vs. chemically strengthened

Quasi-static and dynamic three-point-bending experiments were conducted on both annealed and chemically strengthened aluminosilicate glass scratched by different normal loads. Scratched areas were observed by optical microscope and atomic force microscope. Chemically strengthened glass shows better resistance to surface scratch. These dynamic experiments were carried out using a modified Split Hopkinson Pressure Bar (SHPB) device and a pulse-shaping technique was used to keep constant loading speed to the specimens. In tests, high-speed photography was also used to observe the failure process of the specimens. The test results showed that the flexural strength of aluminosilicate glass (AG) strongly depends on the applied loading speed. Compared with its annealed counterpart, the chemically strengthened glass (CSG) showed higher flexural strength to both static and dynamic loadings. Moreover, the three-point bending experiments were conducted on scratched AG and CSG specimens and decrease of 20–40% in flexural strength was observed. The fractography analysis showed that in dynamic loading conditions the fracture surface was not smooth and has more secondary cracks as compared to static loading.     

Applied data science in patient-centric healthcare: Adaptive analytic systems for empowering physicians and patients

We define the emerging research field of applied data science as the knowledge discovery process in which analytic systems are designed and evaluated to improve the daily practices of domain experts. We investigate adaptive analytic systems as a novel research perspective of the three intertwining aspects within the knowledge discovery process in healthcare: domain and data understanding for physician- and patient-centric healthcare, data preprocessing and modelling using natural language processing and (big) data analytic techniques, and model evaluation and knowledge deployment through information infrastructures. We align these knowledge discovery aspects with the design science research steps of problem investigation, treatment design, and treatment validation, respectively. We note that the adaptive component in healthcare system prototypes may translate to data-driven personalisation aspects including personalised medicine. We explore how applied data science for patient-centric healthcare can thus empower physicians and patients to more effectively and efficiently improve healthcare. We propose meta-algorithmic modelling as a solution-oriented design science research framework in alignment with the knowledge discovery process to address the three key dilemmas in the emerging “post-algorithmic era” of data science: depth versus breadth, selection versus configuration, and accuracy versus transparency.