双轴自动电子倾斜测试仪

双轴自动电子倾斜测试仪是一个高精度、大量程的角度倾斜测量仪器。本文介绍了该仪器的原理和性能,给出了技术指标和实测结果,最后阐明了它的应用领域。     

Attentional negativity bias moderated by positive mood arousal

Automatically allocation of more attention to negative stimuli is called emotional negativity bias. An event-related potentials (ERPs) experiment investigated whether or not this bias was altered by positive mood arousal. The results suggested that the attention bias towards negative stimuli was attenuated when positive information was accessible.     

一种新型的多址接入中继协作方案

现有多址中继协作方案的编码增益有限且用户之间不能相互辅助,为此,文中提出了一种结合网络与信道编码的多址中继协作方案:在中继端利用网络编码将不同用户的编码冗余同时转发,目的端通过对某个用户信息的正确译码恢复出其他用户的编码冗余.通过对译码错误事件概率的分析,推导了该方案的中断率与误码率近似值表达式.仿真结果表明,随着信噪比的提升,文中方案可获得比编码协作更好的中断率和误码率性能,同时信噪比较高的用户还可辅助提升信噪比较低的用户的性能.     

近岸海浪实时仿真

自然景物的模拟是计算机图形学的一个热点问题,看上去简单的现实世界实现起来却极其复杂,特别是以海浪为代表的流体视觉仿真。海浪进入近岸区后,会出现变形和折射现象。将海洋学已有的波浪理论和计算方法应用到近岸海浪仿真中,采用椭圆余弦波构造波形,用波向线和波峰线组成的变形网格来解决波浪的折射现象,实现了近岸海浪实时仿真。实践证明该方法可以快速有效地模拟进入浅水区的海浪。     

Cluster Synchronization in a Complex Dynamical Network with Two Nonidentical Clusters

This paper further investigates cluster synchronization in a complex dynamical network with two-cluster. Each cluster contains a number of identical dynamical systems, however, the subsystems composing the two clusters can be different, i.e., the individual dynamical system in one cluster can differ from that in the other cluster. Complete synchronization within each cluster is possible only if each node from one cluster receives the same input from nodes in other cluster. In this case, the stability condition of one-cluster synchronization is known to contain two terms： the first accounts for the contribution of the inner-cluster coupling structure while the second is simply an extra linear term, which can be deduced by the ＂same-input＂ condition. Applying the connection graph stability method, the authors obtain an upper bound of input strength for one cluster if the first account is known, by which the synchronizability of cluster can be scaled. For different clusters, there are different upper bound of input strength by virtue of different dynamics and the corresponding cluster structure. Moreover, two illustrative examples are presented and the numerical simulations coincide with the theoretical analysis.     

Optimal maintenance and replacement of extraction machinery

This paper considers a problem of optimal preventive maintenance and replacement schedule of equipment devoted to extracting resources from known deposits. Typical examples are oil drills, mine shovels, etc. At most one replacement of the existing machinery by a new one is allowed. The problem is formulated as an optimal control problem subject to the state constraint that the remaining deposit at any given time is nonnegative. We show that the optimal preventive maintenance, production rates, and the replacement and salvage times of the existing machinery and the new one, if required, can be obtained by solving sequentially a series of free-end-point optimal control problems. Moreover, an algorithm based on this result is developed and used to solve two illustrative examples.     

An improved branch-and-bound algorithm to minimize the weighted flowtime on identical parallel machines with family setup times

This article investigates identical parallel machines scheduling with family setup times. The objective function being the weighted sum of completion times, the problem is known to be strongly NP-hard. We propose a constructive heuristic algorithm and three complementary lower bounds. Two of these bounds proceed by elimination of setup times or by distributing each of them to jobs of the corresponding family, while the third one is based on a lagrangian relaxation. The bounds and the heuristic are incorporated into a branch-and-bound algorithm. Experimental results obtained outperform those of the methods presented in previous works, in term of size of solved problems.     

Interval Time Series Analysis with an Application to the Sterling-Dollar Exchange Rate

Traditional econometrics has long employed ＂points＂ to measure time series data. In real life situations, however, it suffers the loss of volatility information, since many variables are bounded by intervals in a given period. To address this issue, this paper provides a new methodology for interval time series analysis. The concept of ＂interval stochastic process＂ is formally defined as a counterpart of ＂stochastic process＂ in point-based econometrics. The authors introduce the concepts of interval stationarity, interval statistics （including interval mean, interval variance, etc.） and propose an interval linear model to investigate the dynamic relationships between interval processes. A new interval-based optimization approach for estimation is proposed, and corresponding evaluation criteria are derived. To demonstrate that the new interval method provides valid results, an empirical example on the sterling-dollar exchange rate is presented.     

PDE-Based Medial Axis Extraction and Shape Manipulation of Arbitrary Meshes

Shape skeletonization （i.e., medial axis extraction） is powerful in many visual computing applications, such as pattern recognition, object segmentation, registration, and animation. In this paper, the authors expand the use of diffusion equations combined with distance field information to approximate medial axes of arbitrary 3D differential properties. It offers an alternative solids represented by polygonal meshes based on their but natural way for medial axis extraction for commonly used 3D polygonal models. By solving the PDE along time axis, this system can not only quickly extract diffusion-based medial axes of input meshes, but also allow users to visualize the extraction process at each time step. In addition, the proposed model provides users a set of manipulation toolkits to sculpt extracted medial axes, then use diffusion-based techniques to recover corresponding deformed shapes according to the original input datasets. This skeleton-based shape manipulation offers a fast and easy way for animation and deformation of complicated mesh objects.     

Designing a Hybrid Intelligent Mining System for Credit Risk Evaluation

In this study, a novel hybrid intelligent mining system integrating rough sets theory and support vector machines is developed to extract efficiently association rules from original information table for credit risk evaluation and analysis. In the proposed hybrid intelligent system, support vector machines are used as a tool to extract typical features and filter its noise, which are different from the previous studies where rough sets were only used as a preprocessor for support vector machines. Such an approach could reduce the information table and generate the final knowledge from the reduced information table by rough sets. Therefore, the proposed hybrid intelligent system overcomes the difficulty of extracting rules from a trained support vector machine classifier and possesses the robustness which is lacking for rough-set-based approaches. In addition, the effectiveness of the proposed hybrid intelligent system is illustrated with two real-world credit datasets.