The Cournot game under a fuzzy decision environment

The conventional precision-based decision analysis methodology is invalid for business decision analysis when precise assessment data seldom exist. This paper considers the Cournot game with fuzzy demand and fuzzy costs that are assumed to be triangular fuzzy numbers. Our model utilizes the weighted center of gravity (WCoG) method to defuzzify the fuzzy profit function into a crisp value. We derive the equilibrium Cournot quantity of each firm by simultaneously solving the first-order condition of each firm. Our model explicitly derives the necessary condition to avoid an unreasonable outcome of negative equilibrium quantities and lack of flexibility for modification of the ranking method. In addition, we examine the standard deviation of the fuzzy profit resulting from the fuzziness of each firm’s cost and market demand functions. We conduct sensitivity analysis to investigate the effect of parameter perturbations on firms’ outcomes. The results indicate that the center of parameter plays an important role in sensitivity analysis and dominates over variations in equilibrium quantity due to a perturbation of fuzzy parameters.     

Stacking graphic elements to avoid over-plotting

An ongoing challenge for information visualization is how to deal with over-plotting forced by ties or the relatively limited visual field of display devices. A popular solution is to represent local data density with area (bubble plots, treemaps), color (heatmaps), or aggregation (histograms, kernel densities, pixel displays). All of these methods have at least one of three deficiencies:1) magnitude judgments are biased because area and color have convex downward perceptual functions, 2) area, hue, and brightness have relatively restricted ranges of perceptual intensity compared to length representations, and/or 3) it is difficult to brush or link to individual cases when viewing aggregations. In this paper, we introduce a new technique for visualizing and interacting with datasets that preserves density information by stacking overlapping cases. The overlapping data can be points or lines or other geometric elements, depending on the type of plot. We show real-dataset applications of this stacking paradigm and compare them to other techniques that deal with over-plotting in high-dimensional displays.     

基于图像处理的不规则形体面积测量系统的实现

为解决不规则形状物体面积的快速测量问题,设计实现了一套基于数字图像处理技术的面积测量系统;利用常用硬件构建了合理的系统硬件结构,并开发了适合非计算机专业人士操作的视窗工作软件,将硬件采集到的待测物某表面图像数据经过软件处理之后就可以得到并显示出待测物该表面的面积;实验证明,该系统测量物体面积的精度为士1.5%以内,可以实现对不规则形体面积的有效测量,操作简单、数据可视性好且测量稳定可靠.     

基于MCF52223USB数据采集系统的实时性研究

详细分析了基于USB传输的数据采集系统的实时特性。针对实时工业控制环境,研究了采用32位微处理器MCF52223进行AD采集,并通过USB总线通信方式向PC机传送AD数据的多路数据采集系统的实时特性,并从软件滤波、数据传输等方面提出了提高系统实时性的有效方法,最后较为具体地给出了MCU方和PC方的编程方法。     

Coverage-guided test generation for continuous and hybrid systems

In this paper, we describe a formal framework for conformance testing of continuous and hybrid systems, using the international standard ‘Formal Methods in Conformance Testing’ FMCT. We propose a novel test coverage measure for these systems, which is defined using the star discrepancy notion. This coverage measure is used to quantify the validation ‘completeness’. It is also used to guide input stimulus generation by identifying the portions of the system behaviors that are not adequately examined. We then propose a test generation method, which is based on a robotic motion planning algorithm and is guided by the coverage measure. This method was implemented in a prototype tool that can handle high dimensional systems (up to 100 dimensions).     

基于网络状态参数估计的主动队列管理PI改进算法

作为一种重要的主动队列管理手段,PI控制器算法通过积分嚣的引入有效地消除了队列长度控制的稳态误差,在提高网络吞吐的同时缩短了排队时延.但是PI控制器不能根据网络状态变化而自动调整控制参数,故当网络流量变化时PI控制器的收敛速度很慢.基于TCP-AQM系统模型,对经过中间节点的活动连接数、平均往返时间和前向链路容量等3个参数进行估计.通过计算击中概率的倒数,估计出活动流数;通过计算单位时间的数据包数,估计出网络容量;通过往返时延、活动流数、网络容量以及丢包概率在稳态时的关系式,估算出平均往返时延.在此基础上,提出了对网络状态变化自适应调整控制参数改进的快速收敛PI算法——FCPI算法.仿真结果表明,该算法有效提高了算法的收敛速度,并且鲁棒性好,易于实现,适用于未来高速网络的路由器.     

Audio-visual speech recognition integrating 3D lip information obtained from the Kinect

Audio-visual speech recognition (AVSR) has shown impressive improvements over audio-only speech recognition in the presence of acoustic noise. However, the problems of region-of-interest detection and feature extraction may influence the recognition performance due to the visual speech information obtained typically from planar video data. In this paper, we deviate from the traditional visual speech information and propose an AVSR system integrating 3D lip information. The Microsoft Kinect multi-sensory device was adopted for data collection. The different feature extraction and selection algorithms were applied to planar images and 3D lip information, so as to fuse the planar images and 3D lip feature into the visual-3D lip joint feature. For automatic speech recognition (ASR), the fusion methods were investigated and the audio-visual speech information was integrated into a state-synchronous two stream Hidden Markov Model. The experimental results demonstrated that our AVSR system integrating 3D lip information improved the recognition performance of traditional ASR and AVSR system in acoustic noise environments.     

Twitter summarization with social-temporal context

Twitter is one of the most popular social media platforms for online users to create and share information. Tweets are short, informal, and large-scale, which makes it difficult for online users to find reliable and useful information, arising the problem of Twitter summarization. On the one hand, tweets are short and highly unstructured, which makes traditional document summarization methods difficult to handle Twitter data. On the other hand, Twitter provides rich social-temporal context beyond texts, bringing about new opportunities. In this paper, we investigate how to exploit social-temporal context for Twitter summarization. In particular, we provide a methodology to model temporal context globally and locally, and propose a novel unsupervised summarization framework with social-temporal context for Twitter data. To assess the proposed framework, we manually label a real-world Twitter dataset. Experimental results from the dataset demonstrate the importance of social-temporal context in Twitter summarization.     

Analysis and improvement of the quantum image matching

We investigate the quantum image matching algorithm proposed by Jiang et al. (Quantum Inf Process 15(9):3543–3572, 2016). Although the complexity of this algorithm is much better than the classical exhaustive algorithm, there may be an error in it: After matching the area between two images, only the pixel at the upper left corner of the matched area played part in following steps. That is to say, the paper only matched one pixel, instead of an area. If more than one pixels in the big image are the same as the one at the upper left corner of the small image, the algorithm will randomly measure one of them, which causes the error. In this paper, an improved version is presented which takes full advantage of the whole matched area to locate a small image in a big image. The theoretical analysis indicates that the network complexity is higher than the previous algorithm, but it is still far lower than the classical algorithm. Hence, this algorithm is still efficient.