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.     

合成氢化松香乙酯的新工艺研究

以DRC为催化剂,在无溶剂条件下,通过氢化松香与乙醇的直接酯化反应合成氢化松香乙酯。探索了反应温度、催化剂用量、反应时间、物料配比等因素对反应酯化率的影响,确定最佳反应条件为:反应温度180℃,催化剂用量为原料氢化松香质量的6%,反应时间6h,醇与酸的摩尔比为3∶1。在最佳条件下酯化率达92.2%。还探讨了用阴离子交换树脂柱层析分离纯化氢化松香乙酯粗产物的方法。此外,利用红外光谱对氢化松香乙酯精制产物进行了表征;用GC-MS分别对氢化松香乙酯粗产物及其精制产物进行了定性和定量分析,比较了柱层析前后化学组成的变化。     

A binary approximating method for graspable region determination of biped climbing robots

For a biped pole-climbing robot (BiPCR) with grippers, it is an essential demand to determine the target grasp configuration for climbing and transiting between poles, with the graspable region as a priori knowledge. The graspable region on the target pole is critically important for climbing path planning and motion control. To efficiently compute the graspable region for a BiPCR, we propose a novel binary approximating method in this paper. This method may also be applied to generate the three-dimensional (3-D) workspace of a manipulator with constant orientation. The grasping problem and the concept of graspable region for a BiPCR are first introduced. The binary approximating method and the corresponding algorithms are then presented to generate the graspable region. Additional constraints on a biped climbing robot with five degrees of freedom (DoFs) are presented as a supplement to the algorithm. A series of comprehensive simulations are conducted with the five-DoF and six-DoF climbing robots to verify the effectiveness of the proposed method. Finally, the dexterity of biped climbing robots with different DoFs is discussed.     

A heuristic biomarker selection approach based on professional tennis player ranking strategy

Extracting significant features from high-dimension and small sample size biological data is a challenging problem. Recently, Micha? Draminski proposed the Monte Carlo feature selection (MC) algorithm, which was able to search over large feature spaces and achieved better classification accuracies. However in MC the information of feature rank variations is not utilized and the ranks of features are not dynamically updated. Here, we propose a novel feature selection algorithm which integrates the ideas of the professional tennis players ranking, such as seed players and dynamic ranking, into Monte Carlo simulation. Seed players make the feature selection game more competitive and selective. The strategy of dynamic ranking ensures that it is always the current best players to take part in each competition. The proposed algorithm is tested on 8 biological datasets. Results demonstrate that the proposed method is computationally efficient, stable and has favorable performance in classification.     

Effective naive Bayes nearest neighbor based image classification on GPU

Non-parametric classifier, Naive Bayes nearest neighbor, is designed with no training phase, and its performance outperforms many well-trained learning-based image classifiers. Unfortunately, despite its high accuracy, it suffers from great computational pressure from distance computations in space of local feature. This paper explores accelerating strategies from perspectives of both algorithm design and software development. Our approach integrates space decomposition capability of Product quantization (PQ) and parallel accelerating capability of underlying computational platform, Graphics processing unit (GPU). PQ is exploited to compress the indexed local features and prune the search space. GPU is used to ease most of computational pressure by processing the tasks in parallel. To achieve good parallel efficiency, a new sequential classification process is first designed and decomposed into independent components with high parallelism. Effective parallelization techniques are then presented to make use of computational resources. Parallel heap array is built to accelerate the process of feature quantization. Distance table lookup is built to speed up the process of feature search. Comparative experiments on UIUC-Sport dataset demonstrate that our integrated solution outperforms other implementations significantly on Core-quad Intel Core i7 950 CPU and GPU of NVIDIA Geforce GTX460. Scalability experiment on 80 million tiny images database shows that our approach still performs well when large-scale image database is explored.     

Adaptive Neural Control of a Hypersonic Vehicle in Discrete Time

The article investigates the discrete-time controller for the longitudinal dynamics of the hypersonic flight vehicle with throttle setting constraint. Based on functional decomposition, the dynamics can be decomposed into the altitude subsystem and the velocity subsystem. Furthermore, the discrete model could be derived using the Euler expansion. For the velocity subsystem, the controller is proposed by estimating the system uncertainty and unknown control gain separately with neural networks. The auxiliary error signal is designed to compensate the effect of throttle setting constraint. For the altitude subsystem, the desired control input is approximated by neural network while the error feedback is synthesized for the design. The singularity problem is avoided. Stability analysis proves that the errors of all the signals in the system are uniformly ultimately bounded. Simulation results show the effectiveness of the proposed controller.     

Point-based online value iteration algorithm in large POMDP

Partially observable Markov decision process (POMDP) is an ideal framework for sequential decision-making under uncertainty in stochastic domains. However, it is notoriously computationally intractable to solving POMDP in real-time system. In order to address this problem, this paper proposes a point-based online value iteration (PBOVI) algorithm which involves performing value backup at specific reachable belief points, rather than over the entire belief simplex, to speed up computation processes, exploits branch-and-bound pruning approach to prune the AND/OR tree of belief states online, and proposes a novel idea to reuse the belief states that have been searched to avoid repeated computation. The experiment and simulation results show that the proposed algorithm can simultaneously satisfy the requirement of low errors and high timeliness in real-time system.     

Robustness of globally exponential stability of delayed neural networks in the presence of random disturbances

This paper analyzes the robustness of globally exponential stability of time-varying delayed neural networks (NNs) subjected to random disturbances. Given a globally exponentially stable neural network, and in the presence of noise, we quantify how much noise intensity that the delayed neural network can remain to be globally exponentially stable. We characterize the upper bounds of the noise intensity for the delayed NNs to sustain globally exponential stability. The upper bounds of parameter uncertainty intensity are characterized by using transcendental equation. A numerical example is provided to illustrate the theoretical result.     

基于CaO引导的甲烷蒸汽重整化学链燃烧制氢系统

为在重整气中得到高纯H_2和降低尾气CO_2分离成本,建立了基于CaO引导的甲烷蒸汽重整化学链燃烧制氢系统,该系统在重整反应器中加入CaO吸收剂,用以吸收重整器内的CO_2,提高重整气中H_2浓度,形成的CaCO_3固体在煅烧器中受热分解重新生成CaO。利用Aspen Plus进行了过程模拟及热力学分析,并研究主要参数对系统性能的影响,得到优化的操作条件为:CaO循环量/CH_4比为0.5,CH_4(燃料)/CH_4比为0.35,NiO循环量/CH_4比为1.4。CaO循环量/CH_4比从0变化到0.5时,重整气中H_2浓度从0.60增长到0.99;CH_4(燃料)/CH_4比在0.25~0.45区间变化时,重整气中H_2浓度从0.86提高到0.99,产气量增加;NiO循环量/CH_4比在1~1.6区间变化时,重整气中H_2浓度从0.88增长到0.99,系统有效能效率变化较小。