GPU-OSDDA: a bit-vector GPU-based deadlock detection algorithm for single-unit resource systems

This article presents a GPU-based single-unit deadlock detection methodology and its algorithm, GPU-OSDDA. Our GPU-based design utilizes parallel hardware of GPU to perform computations and thus is able to overcome the major limitation of prior hardware-based approaches by having the capability of handling thousands of processes and resources, whilst achieving real-world run-times. By utilizing a bit-vector technique for storing algorithm matrices and designing novel, efficient algorithmic methods, we not only reduce memory usage dramatically but also achieve two orders of magnitude speedup over CPU equivalents. Additionally, GPU-OSDDA acts as an interactive service to the CPU, because all of the aforementioned computations and matrix management techniques take place on the GPU, requiring minimal interaction with the CPU. GPU-OSDDA is implemented on three GPU cards: Tesla C2050, Tesla K20c, and Titan X. Our design shows overall speedups of 6-595X over CPU equivalents.     

基于指尖触控的互动投影系统

随着人机交互技术的不断发展,互动投影技术的应用逐渐广泛.该技术通过摄像机等传感器技术感知周围场景动态并结合投影的显示功能实现交互的方式,如增强现实、结构光编码、三维重建等.基于触控的互动投影技术在未来的嵌入式人机交互系统中具有广阔的前景.文章基于单目摄像机与投影仪系统实现能够在任意平面徒手触摸的交互.系统的功能主要通过前景区域分割、指尖点的定位以及触摸检测三个步骤来实现.与目前相关领域的研究文献进行的对比实验证明,所提算法在稳定性、准确性以及实时性等方面具有一定优势.     

A new method for expiration code detection and recognition using gabor features based collaborative representation

Text in images and video contains important information for visual content understanding, indexing, and recognizing. Extraction of this information involves preprocessing, localization and extraction of the text from a given image. In this paper, we propose a novel expiration code detection and recognition algorithm by using Gabor features and collaborative representation based classification. The proposed system consists of four steps: expiration code location, character isolation, Gabor features extraction and characters recognition. For expiration code detection, the Gabor energy (GE) and the maximum energy difference (MED) are extracted. The performance of the recognition algorithm is tested over three Gabor features: GE, magnitude response (MR) and imaginary response (IR). The Gabor features are classified based on collaborative representation based classifier (GCRC). To encompass all frequencies and orientations, downsampling and principal component analysis (PCA) are applied in order to reduce the features space dimensionality. The effectiveness of the proposed localization algorithm is highlighted and compared with other existing methods. Extensive testing shows that the suggested detection scheme outperforms existing methods in terms of detection rate for large image database. Also, GCRC show very competitive results compared with Gabor feature sparse representation based classification (GSRC). Also, the proposed system outperforms the nearest neighbor (NN) classifier and the collaborative representation based classification (CRC).     

Performance comparison of reduced models for leak detection in water distribution networks

This paper presents a methodology for comparing the performance of model-reduction strategies to be used with a diagnostic methodology for leak detection in water distribution networks. The goal is to find reduction strategies that are suitable for error-domain model falsification, a model based data interpretation methodology. Twelve reduction strategies are derived from five strategy categories. Categories differ according to the manner in which nodes are selected for deletion. A node is selected for deletion according to: (1) the diameter of the pipes; (2) the number of pipes linked to a node; (3) the angle of the pipes in the case of two-pipe nodes; (4) the distribution of the water demand; and, (5) a pair-wise combination of some categories.The methodology is illustrated using part of a real network. Performance is evaluated first by judging the equivalency of the reduced network with the initial network (before the application of any reduction procedure) and secondly, by assessing the compatibility with the diagnostic methodology. The results show that for each reduction strategy the equivalency of networks is verified. Computational time can be reduced to less than 20% of the non-reduced network in the best case. Results of diagnostic performance show that the performance decreases when using reduced networks. The reduction strategy with the best diagnostic performance is that based on the angle of two-pipe nodes, with an angle threshold of 165°. In addition, the sensitivity of the performance of the reduced networks to variation in leak intensity is evaluated. Results show that the reduction strategies where the number of nodes is significantly reduced are the most sensitive.Finally this paper describes a Pareto analysis that is used to select the reduction strategy that is a good compromise between reduction of computational time and performance of the diagnosis. In this context, the extension strategy is the most attractive.     

非线性过程控制系统传感器故障检测新方法

针对电厂热力系统故障检测和定位准确性低的问题,提出了基于鲁棒输入训练网络的传感器故障检测模型;采用带参数限制项的目标函数对网络进行训练,并在测试目标函数中引入影响因子,增加了模型训练精度,抑制了网络计算过程故障数据对正常值的影响,减小了残差污染,提高了模型准确性;以某300 MW电厂热力系统20组测点为对象进行算例分析,通过反复的实验,结果表明,该模型能够更加准确地对非线性系统故障点进行检测和分离,并更加精确重构各变量真实值,验证了该模型用于非线性过程传感器故障检测的有效性和可靠性.     

一种新的基于弧段提取的椭圆检测方法

为了克服椭圆检测过程中对椭圆完整性和边缘梯度信息依赖性较强的缺点,提高椭圆目标的检测速度,提出了一种新的基于弧段提取的椭圆拟合方法;首先将梯度方向符号相同的相邻边缘点连接成弧段,然后根据弧段的凸性和象限分类定义新的弧选择策略,利用位置约束和弧对的椭圆中心估计提取候选椭圆,最后采用改进的拟合算法拟合椭圆;实验结果表明,基于弧段提取的椭圆拟合方法相对于LMEDS算法和RHT3具有更好的准确性、鲁棒性和稳定性,实时性也有一定的提高。     

一种新的珍珠层厚度测量方法

珍珠层厚度是珍珠价值最主要的衡量指标之一,目前珍珠层厚度的无损测量方法主要采用光学相干层析成像技术和X射线技术;光学相干层析成像技术在实际测量时精度较低;在研究X射线测量原理的基础上,提出了一种新的珠层厚度测量方法;首先介绍了灰度图像的边缘识别算法和珍珠核、珍珠外圆度拟合算法,其次提出了一种引入对照机制的珍珠层测量算法,最后还对算法的误差进行了分析,算法的误差可以控制在0.02％以内;实际的测量也验证了该测量方法具有很高的精度.     

基于贝叶斯估计的水下机器人罗盘故障检测

以电子磁通门罗盘为例对自主水下机器人(Autonomous Underwater Vehicles,AUVs)传感器故障检测方法展开研究.针对电子罗盘极易受到周围磁场的影响而产生时对时错的数据这一问题,首先联立AUV的运动学和动力学方程得到AUV的全状态非线性模型;然后分析了电子罗盘的误差来源,建立了误差的模型;在此基础上,提出了基于贝叶斯估计的故障检测方法并给出了该方法离散形式的递推迭代公式;最后参照实际使用的电子罗盘的参数和AUV的机动能力,设计了仿真实验和外场试验,通过实验验证了该方法的正确性和有效性.     

改进相位一致的岩心图像裂缝提取方法

针对传统相位一致算法提取裂缝连续性差的缺点,提出改进的相位一致算法.首先对岩心图像进行预提取,增大目标和背景区域的对比度,并标记疑似目标像素点;然后,利用双圆投影断点检测算子判定并重建岩心图像裂缝中的断点;最后利用相位一致算法延伸裂缝走向的特性,再次对岩心图像中的裂缝目标进行提取,并进行图像去噪处理.实验结果表明,改进后的相位一致算法,能够保留提取效果不受光照亮度影响、对目标区域敏感度高的传统相位一致算法的优点,并且有效改善所提取裂缝的连续性.