航天发射场测控设备的无人机模拟训练方法

针对航天发射场测控设备操作人员使用常规训练手段与实际操作环境差距较大的问题，提出了一种较为实用的无人机模拟训练方法。利用实际任务中获取的真实测量数据生成训练任务，由无人机搭载相关任务载荷执行，并结合发射场已有的测控设备来跟踪无人机完成模拟训练。通过对无人机的飞行航路模拟和任务载荷测量信号的动态模拟，可为参训人员提供接近实战的训练环境。仿真结果表明，该方法可实现与真实测量数据极为接近的模拟训练数据，因而参训人员通过模拟训练中获得的训练效果与实战基本一致，在提升了训练的真实性和针对性的同时，可有效提高操作人员的综合操作能力。     

A hybrid multi-criteria decision making approach for strategic retail location investment: Application to Turkish food retailing

Retail location selection decision is a critical and complex process which requires the evaluation and aggregation of multiple criteria and also the usage of appropriate data related to them. This study handles the problem at a strategic level and proposes a Monte Carlo simulation based multi-criteria strategic location decision model for food retailing. This model integrates two multi-criteria decision making (MCDM) methods which are Hesitant Analytic Hierarchy Process (H-AHP) and Grey Relational Analysis (GRA) methods. Firstly, H-AHP method is used to obtain the weights of criteria to be used in GRA based on the experts' judgements. Secondly, simulation based GRA is used for ranking the alternative locations. Finally, the effectiveness and the applicability of the proposed model is illustrated with an application of strategic location investment decision of food retail stores in Turkey.     

Analyzing airport security checkpoint performance using cognitive agent models

Modern airports operate under high demands and pressures, and strive to satisfy many diverse, interrelated, sometimes conflicting performance goals. Airport performance areas, such as security, safety, and efficiency are usually studied separately from each other. However, operational decisions made by airport managers often impact several areas simultaneously. Current knowledge on how different performance areas are related to each other is limited. This paper contributes to filling this gap by identifying and quantifying relations and trade-offs between the detection performance of illegal items and the average queuing time at airport security checkpoints. These relations and trade-offs were analyzed by simulations with a cognitive agent model of airport security checkpoint operations. By simulation analysis a security checkpoint performance curve with three different regions was identified. Furthermore, the importance of focus on accuracy for a security operator is shown. The results of the simulation studies were related to empirical research at an existing regional airport.     

海面舰船SAR成像全动态模拟研究综述

开展海面舰船合成孔径雷达(Synthetic Aperture Radar，SAR)成像模拟具有重要的军事应用价值。为解决模拟的逼真性问题，从模拟方法、场景构建、动态考虑等方面介绍了海面舰船SAR成像模拟的国内外研究概况，总结了全动态场景构建与实时性解决方法这两大实现全动态模拟的关键技术，指出目前国内外对于海面舰船SAR成像全动态模拟的研究基本处于空白，关键在于全动态场景构建与实时性实现，相关工作基础较好。最后对下一步研究工作进行了展望。     

Learning strategic cooperative behavior in industrial symbiosis: A game‐theoretic approach integrated with agent‐based simulation

This paper investigates the negotiation phase of industrial symbiosis relationships, where companies exchanging wastes for inputs need to develop strategies on how to share the additional costs to operate the industrial symbiosis business. The business behavior is approached as a “coopetition” problem where companies need to cooperate to reduce waste discharge costs and traditional input purchase costs and dive into competition to pay a minimum share of additional costs (i.e., waste treatment, waste transportation, and transaction costs) of operating industrial symbiosis. A noncooperative game‐theoretical model for sharing the additional costs is proposed that highlights the two strategies that companies can adopt aimed at sharing costs: a fair strategy and an opportunistic strategy. Then, an agent‐based model is used to simulate the game iterated over time and investigate how the players can adapt their strategies according to their past experience. Simulation results show that players learn that playing the fair strategy is beneficial in the long period, despite in the short period they can gain more benefit by playing the opportunistic strategy. Findings of the paper are critically important to reduce the business and managerial barriers against the formation of industrial symbiosis networks and to stimulate innovative thinking of company managers to foster the development of the circular economy. The paper proposes theoretical, managerial, and policy implications, which are discussed in detail in a comparative manner between linear and circular economy.     

机场行李提取中行李箱图像识别与旋转仿真

为了减少机场旅客提取行李的时间,缓解机场拥挤问题,设计了一种行李箱自动识别和旋转系统。该系统主要包括行李箱图像预处理和角度计算与旋转两大部分。图像预处理对行李箱图像进行灰度处理、运用中值滤波对行李箱图像进行降噪、对行李箱图像进行二值化处理;然后对图像中有效轮廓进行截取;最后计算行李箱旋转角度并进行旋转。通过应用Matlab仿真验证了本文所提出的图像识别、处理和角度计算等的有效性和适用性。将行李箱把手旋转到靠近旅客的一侧可达到缩短旅客提取行李箱的时间,促进机场高效运行的目标,符合机场运营的需要,能够提高机场的运营和管理水平。     

A supply chain network equilibrium model with a smoothing newton solution scheme

ABSTRACT

Considering that a supply chain comprises several independent decision makers, a supply chain network equilibrium model that consists of manufacturers, retailers and consumers is developed. After analysing the optimal conditions of various decision makers in the model, the equilibrium condition is established as an equivalent, finite-dimensional variational inequality formulation and is solved by a smoothing Newton method. The global and quadratic convergence of the method is established. The numerical results show the rapid convergence of the method. Additionally, the rapid convergence of the smoothing Newton method is beneficial when solving a complicated network model in the real world.     

政府大数据赋能城市创新：理论机制与经验证据*

中国城市创新存在着明显的发展不平衡、协同性差的问题。政府大数据作为公共信息资源的宝库，是赋能城市创新、实现协同发展的关键要素。本文基于政府传播视角，通过Netlogo仿真实验识别了影响政府大数据传播效果的关键因素，并将其纳入企业公共信息资本积累函数，以此拓展了企业横向创新内生增长模型并探究了政府大数据赋能城市创新的微观机制；此外，本文以2014年实施的“信息惠民国家试点”政策为准自然实验，采用双重差分法实证检验了政府大数据对城市创新的赋能作用。理论研究表明：激励政府提高传播意愿和优化大数据传播情景能通过改善政府传播效果促进企业公共信息资本积累提量增速，加速企业创新、赋能城市创新；实证研究发现：信息惠民国家试点政策对试点城市创新指数的平均提升作用约为29%。为此，有序推广政府大数据惠民试点工程、多措多举推动政府传播环境的优化与制度供给，是助力城市创新的可行思路。     

FFORMPP: Feature-based forecast model performance prediction

This paper introduces a novel meta-learning algorithm for time series forecast model performance prediction. We model the forecast error as a function of time series features calculated from historical time series with an efficient Bayesian multivariate surface regression approach. The minimum predicted forecast error is then used to identify an individual model or a combination of models to produce the final forecasts. It is well known that the performance of most meta-learning models depends on the representativeness of the reference dataset used for training. In such circumstances, we augment the reference dataset with a feature-based time series simulation approach, namely GRATIS, to generate a rich and representative time series collection. The proposed framework is tested using the M4 competition data and is compared against commonly used forecasting approaches. Our approach provides comparable performance to other model selection and combination approaches but at a lower computational cost and a higher degree of interpretability, which is important for supporting decisions. We also provide useful insights regarding which forecasting models are expected to work better for particular types of time series, the intrinsic mechanisms of the meta-learners, and how the forecasting performance is affected by various factors.     

Aircraft turnaround time estimation in early design phases: Simulation tools development and application to the case of box-wing architecture

This work deals with the problem of estimating the turnaround time in the early stages of aircraft design. The turnaround time has a significant impact in terms of marketability and value creation potential of an aircraft and, for this reason, it should be considered as an important driver of fuselage and cabin design decisions. Estimating the turnaround time during the early stages of aircraft design is therefore an essential task. This task becomes even more decisive when designers explore unconventional aircraft architectures or, in general, are still evaluating the fuselage design and its internal layout. In particular, it is of paramount importance to properly estimate the boarding and deboarding times, which contribute for up the 40% to the overall turnaround time. For this purpose, a tool, called SimBaD, has been developed and validated with publicly available data for existing aircraft of different classes. In order to demonstrate SimBaD capability of evaluating the influence of fuselage and cabin features on the turnaround time, its application to an unconventional box-wing aircraft architecture, known as PrandtlPlane, is presented as case study. Finally, considering standard scenarios provided by aircraft manufacturers, a comparison between the turnaround time of the PrandtlPlane and the turnaround time of a conventional competitor aircraft is presented.