Performance Histogram Curve: Abstractive Visualization and Analysis of NBA Games

NBA game is one of the world’s most exciting sports. Multiple datasets for a NBA game are available, e.g., play-by-play data, shot-position data, twitter and videos. Existing methods for visualizing game data commonly focus on the composition of multi-variate information of a game process. In this paper, we introduce a new parametric modeling approach, Performance Histogram Curve (PHC), that locally and adaptively encodes the game progression with game-related features derived from the play-by-play data. By transforming a PHC into the two-dimensional space with a two-phase projection technique, we create a unique 2D line representation. The 2D representation and auxiliary views abstracts the progress of a game and the performance of each team along the timeline. Our integrated system favor browsing a single play, analyzing game performance, and comparing multiple games. We conducted two case studies to demonstrate the effectiveness of our approach.     

Modeling and solving mixed-model assembly line balancing problem with setups. Part I: A mixed integer linear programming model

This paper is the first one of the two papers entitled “modeling and solving mixed-model assembly line balancing problem with setups”, which has the aim of developing the mathematical programming formulation of the problem and solving it with a hybrid meta-heuristic approach. In this current part, a mixed-integer linear mathematical programming (MILP) model for mixed-model assembly line balancing problem with setups is developed. The proposed MILP model considers some particular features of the real world problems such as parallel workstations, zoning constraints, and sequence dependent setup times between tasks, which is an actual framework in assembly line balancing problems. The main endeavor of Part-I is to formulate the sequence dependent setup times between tasks in type-I mixed-model assembly line balancing problem. The proposed model considers the setups between the tasks of the same model and the setups because of the model switches in any workstation. The capability of our MILP is tested through a set of computational experiments. Part-II tackles the problem with a multiple colony hybrid bees algorithm. A set of computational experiments is also carried out for the proposed approach in Part-II.     

基于时间冗余的显著测量误差处理方法

提出基于时间冗余的不可删显著误差处理方法。对于该类特殊变量，采用线性回归的方法，根据历史数据进行预测，进而代替实际到量值进行协调运算。大大降低显著误差对协调数据的污染，确保协调物度。通过实例数据仿真计算证实该方法的有效性。     

六自由度机械臂约束预测控制系统的设计

本文提出了一种基于约束预测控制的机械臂实时运动控制方法.该控制方法分为两层,分别设计了约束预测控制器和跟踪控制器.其中,约束预测控制器在考虑系统物理约束的条件下,在线为跟踪控制器生成参考轨迹;跟踪控制器采用最优反馈控制律,使机械臂沿参考轨迹运动.为了简化控制器的设计和在线求解,本文采用输入输出线性化的方式简化机械臂动力学模型.同时,为了克服扰动,在约束预测控制器中引入前馈策略,提出了带前馈一反馈控制结构的预测控制设计.因此,本文设计的控制器可以使机械臂在满足物理约束的条件下快速稳定地跟踪到目标位置.通过在PUMA560机理模型上进行仿真实验,验证了预测控制算法的可行性和有效性.     

一类离散混沌系统的传输受限同步问题

研究了一类离散混沌系统信息受限条件下的同步问题.将具有离散形式的混沌驱动系统和具有控制输入的响应系统通过一个有限容量信道的通讯网络进行连接.在这种情况下,设计了合适的编解码方法使得传输误差在一定的条件下达到渐近稳定.同时,在线性矩阵不等式条件下,获得了同步误差方程关于传输误差是输入状态稳定,从而实现了混沌系统在信道容量有限条件下的完全同步.最后,通过Fold混沌系统验证了本文方法的有效性.     

零售商资金约束供应链中的金融服务与营运管理协调策略

基于零售商存在资金约束和破产成本的条件假设,研究由一个供应商与一个零售商组成的二级供应链中的外部金融服务和营运管理协调策略.研究表明,不确定需求分布下零售商的融资能力和订购水平是有限的.资金不足零售商可通过外部金融服务实现最优订购,为供应链创造价值,并且金融市场的竞争程度、零售商的初始资金量和破产成本率会影响零售商的订购策略、供应商的批发价策略和供应链绩效.     

怒江流域云南区段降雨时空变化分析

利用怒江流域云南区段及其毗邻地区77个站点1980~2009年雨量资料,以及怒江干流道街坝站1957~2015年和南汀河支流姑老河站1960~2015年降雨、径流资料,研究该流域降雨区域分异特征及变化趋势。结果表明:怒江流域云南区段多年平均面雨量达1 336.7 mm,远大于全省平均降雨量;怒江干流云南区段上游贡山地区和下游支流苏帕河流域,以及支流南汀河流域下游多年平均降雨量达1 500 mm以上,而干流的中游怒江坝干热河谷不足1 000 mm,降水空间分布格局与西南季风强弱及纵向岭谷地形密切相关;怒江流域云南区段降水量呈下降趋势,与怒江上游西藏区域相反,支流南汀河流域面雨量减少速率(56 mm/10 a)大于怒江干流云南区段(29 mm/10 a);怒江流域云南区段夏季降水占全年降水量的51%,怒江干流云南区段与支流南汀河流域降水量差异主要表现在夏季;怒江流域云南区段春季降水量呈增加趋势,夏季、秋季和冬季降水量呈减少趋势;怒江干流道街坝站、支流南汀河姑老河站降雨量长周期分别为36 a和33 a左右,从降雨变化长周期来看,2002年以来的少水期将持续到2018~2020年。怒江干流云南区段降水量变化的Hurst指数值为0.69,表明其未来趋势与过去一致,仍将延续降水量减少的变化趋势。     

On control of discrete-time state-dependent jump linear systems with probabilistic constraints: A receding horizon approach

In this article, we consider a receding horizon control of discrete-time state-dependent jump linear systems, a particular kind of stochastic switching systems, subject to possibly unbounded random disturbances and probabilistic state constraints. Due to the nature of the dynamical system and the constraints, we consider a one-step receding horizon. Using inverse cumulative distribution function, we convert the probabilistic state constraints to deterministic constraints, and obtain a tractable deterministic receding horizon control problem. We consider the receding horizon control law to have a linear state-feedback and an admissible offset term. We ensure mean square boundedness of the state variable via solving linear matrix inequalities off-line, and solve the receding horizon control problem on-line with control offset terms. We illustrate the overall approach applied on a macroeconomic system.     

An analysis,design and tuning of Cascade Control Systems in the presence of constraints in actuator and process outputs

In the presence of rate constraints in actuator, design of cascade controller based on the primary controller conditional integration can result into closed-loop system oscillations. Stability analysis, performed by the describing function technique and confirmed by simulation, demonstrates that the solution based on the Anti-Reset Windup Cascade Control System (ARW CCS) structure is successful. Design and tuning of the ARW CCS secondary controller is a standard ARW single-input single-output problem. In the present paper tuning is proposed for the ARW CCS primary controller. For the serial process modeling simple rules are derived and confirmed by experimental results, obtained on a drum type boiler of a 210 MWe lignite coal fired unit. General design of the ARW CCS is based on the parallel process modeling and optimization of the primary controller. Optimization is performed in the frequency domain, under constraints on the maximum sensitivity, multiplicative uncertainty bound and sensitivity to measurement noise. Simulation and experimental results on a laboratory thermal plant demonstrate effectiveness of the proposed optimization.