Checking Finite Traces Using Alternating Automata

Alternating automata have been commonly used as a basis for static verification of reactive systems. In this paper we show how alternating automata can be used in runtime verification. We present three algorithms to check at runtime whether a reactive program satisfies a temporal specification, expressed by a linear-time temporal logic formula. The three methods start from the same alternating automaton but traverse the automaton in different ways: depth-first, breadth-first, and backwards, respectively. We then show how an extension of these algorithms, that collects statistical data while verifying the execution trace, can be used for a more detailed analysis of the runtime behavior. All three methods have been implemented and experimental results are presented.     

Nonstationary time series analysis by temporal clustering

The object of this paper is to present a model and a set of algorithms for estimating the parameters of a nonstationary time series generated by a continuous change in regime. We apply fuzzy clustering methods to the task of estimating the continuous drift in the time series distribution and interpret the resulting temporal membership matrix as weights in a time varying, mixture probability distribution function (PDF). We analyze the stopping conditions of the algorithm to infer a novel cluster validity criterion for fuzzy clustering algorithms of temporal patterns. The algorithm performance is demonstrated with three different types of signals.     

An overview of air quality analysis by big data techniques: Monitoring,forecasting, and traceability

With the rapid development of economy and the frequent occurrence of air pollution incidents, the problem of air pollution has become a hot issue of concern to the whole people. The air quality big data is generally characterized by multi-source heterogeneity, dynamic mutability, and spatial–temporal correlation, which usually uses big data technology for air quality analysis after data fusion. In recent years, various models and algorithms using big data techniques have been proposed. To summarize these methodologies of air quality study, in this paper, we first classify air quality monitoring by big data techniques into three categories, consisting of the spatial model, temporal model and spatial–temporal model. Second, we summarize the typical methods by big data techniques that are needed in air quality forecasting into three folds, which are statistical forecasting model, deep neural network model, and hybrid model, presenting representative scenarios in some folds. Third, we analyze and compare some representative air pollution traceability methods in detail, classifying them into two categories: traditional model combined with big data techniques and data-driven model. Finally, we provide an outlook on the future of air quality analysis with some promising and challenging ideas.     

地表蒸散发遥感产品比较与分析

蒸散发遥感估算与其他定量遥感反演参数相比存在算法复杂,环节多,输入参数多,不确定性来源多等不利因素,因而造成蒸散发遥感产品成熟度较低,产品较少,不能满足高时空分辨率、高精度、实时获取等应用需求的问题.从蒸散发遥感产品角度出发,选择目前国内外较为常用和知名的蒸散发遥感产品,系统分析了各产品的实际蒸散发估算模型方法、阻抗的...     

Blind source extraction using generalized autocorrelations.

This letter addresses blind (semiblind) source extraction (BSE) problem when a desired source signal has temporal structures, such as linear or nonlinear autocorrelations. Using the temporal characteristics of sources, we develop objective functions based on the generalized autocorrelations of primary sources. Maximizing the objective functions, we propose simple fixed-point source extraction algorithms. We give the stability analysis and prove convergence properties of the algorithms as the generalized autocorrelation function is linear or nonlinear. Especially, as the generalized autocorrelation function is linear, the algorithm has interesting character of "one-iteration" convergence under some conditions. Computer simulations and real-data application experiments show that the algorithms are appealing BSE methods for temporal signals of interest by capturing the linear or nonlinear autocorrelations of the desired sources.     

亚像素级图像配准算法研究

在很多应用领域,要求图像配准的精度达到亚像素级。对现有的亚像素级图像配准算法进行了分类,介绍了几种最主要的亚像素级精度的配准方法,包括基于插值的方法、扩展的相位相关法、解最优化问题法,并对这些算法的思路、性能特点和最新进展进行了综述。然后从几个评价标准对各种算法进行了比较,分析了各自影响性能的因素和性能提升的空间。最后对未来的发展趋势进行了展望。     

State of the Art on 3D Reconstruction with RGB‐D Cameras

The advent of affordable consumer grade RGB‐D cameras has brought about a profound advancement of visual scene reconstruction methods. Both computer graphics and computer vision researchers spend significant effort to develop entirely new algorithms to capture comprehensive shape models of static and dynamic scenes with RGB‐D cameras. This led to significant advances of the state of the art along several dimensions. Some methods achieve very high reconstruction detail, despite limited sensor resolution. Others even achieve real‐time performance, yet possibly at lower quality. New concepts were developed to capture scenes at larger spatial and temporal extent. Other recent algorithms flank shape reconstruction with concurrent material and lighting estimation, even in general scenes and unconstrained conditions. In this state‐of‐the‐art report, we analyze these recent developments in RGB‐D scene reconstruction in detail and review essential related work. We explain, compare, and critically analyze the common underlying algorithmic concepts that enabled these recent advancements. Furthermore, we show how algorithms are designed to best exploit the benefits of RGB‐D data while suppressing their often non‐trivial data distortions. In addition, this report identifies and discusses important open research questions and suggests relevant directions for future work.     

Performance of local search heuristics on scheduling a class of pipelined multiprocessor tasks

This paper presents the evaluation of the solution quality of heuristic algorithms developed for scheduling multiprocessor tasks for a class of multiprocessor architectures designed to exploit temporal and spatial parallelism simultaneously. More specifically, we deal with multi-level or partitionable architectures where MIMD parallelism and multiprogramming support are the two main characteristics of the system. We investigate scheduling a number of pipelined multiprocessor tasks with arbitrary processing times and arbitrary processor requirements in this system. The scheduling problem consists of two interrelated sub-problems, which are finding a sequence of pipelined multiprocessor tasks on a processor and finding a proper mapping of tasks to the processors that are already being sequenced. For the solution of the second problem, various techniques are available. However, the problem remains of generating a feasible sequence for the pipelined operations. We employed three well-known local search heuristic algorithms that are known to be robust methods applicable to various optimization problems. These are Simulated Annealing, Tabu Search, and Genetic Algorithms. We then conduct computational experiments and evaluate the reduction achieved in completion time by each heuristic. We have also compared the results with well-known simple list-based heuristics.     

Natural actor-critic algorithms

We present four new reinforcement learning algorithms based on actor-critic, natural-gradient and function-approximation ideas, and we provide their convergence proofs. Actor-critic reinforcement learning methods are online approximations to policy iteration in which the value-function parameters are estimated using temporal difference learning and the policy parameters are updated by stochastic gradient descent. Methods based on policy gradients in this way are of special interest because of their compatibility with function-approximation methods, which are needed to handle large or infinite state spaces. The use of temporal difference learning in this way is of special interest because in many applications it dramatically reduces the variance of the gradient estimates. The use of the natural gradient is of interest because it can produce better conditioned parameterizations and has been shown to further reduce variance in some cases. Our results extend prior two-timescale convergence results for actor-critic methods by Konda and Tsitsiklis by using temporal difference learning in the actor and by incorporating natural gradients. Our results extend prior empirical studies of natural actor-critic methods by Peters, Vijayakumar and Schaal by providing the first convergence proofs and the first fully incremental algorithms.     

Join operations in temporal databases

Joins are arguably the most important relational operators. Poor implementations are tantamount to computing the Cartesian product of the input relations. In a temporal database, the problem is more acute for two reasons. First, conventional techniques are designed for the evaluation of joins with equality predicates rather than the inequality predicates prevalent in valid-time queries. Second, the presence of temporally varying data dramatically increases the size of a database. These factors indicate that specialized techniques are needed to efficiently evaluate temporal joins.We address this need for efficient join evaluation in temporal databases. Our purpose is twofold. We first survey all previously proposed temporal join operators. While many temporal join operators have been defined in previous work, this work has been done largely in isolation from competing proposals, with little, if any, comparison of the various operators. We then address evaluation algorithms, comparing the applicability of various algorithms to the temporal join operators and describing a performance study involving algorithms for one important operator, the temporal equijoin. Our focus, with respect to implementation, is on non-index-based join algorithms. Such algorithms do not rely on auxiliary access paths but may exploit sort orderings to achieve efficiency.Received: 17 October 2002, Accepted: 26 July 2003, Published online: 28 October 2003Edited by: T. Sellis     

Improving the localization accuracy of targets by using their spatial–temporal relationships in wireless sensor networks

Due to the low cost and capabilities of sensors, wireless sensor networks (WSNs) are promising for military and civilian surveillance of people and vehicles. One important aspect of surveillance is target localization. A location can be estimated by collecting and analyzing sensing data on signal strength, time of arrival, time difference of arrival, or angle of arrival. However, this data is subject to measurement noise and is sensitive to environmental conditions, so its location estimates can be inaccurate. In this paper, we add a novel process to further improve the localization accuracy after the initial location estimates are obtained from some existing algorithm. Our idea is to exploit the consistency of the spatial–temporal relationships of the targets we track. Spatial relationships are the relative target locations in a group and temporal relationships are the locations of a target at different times. We first develop algorithms that improve location estimates using spatial and temporal relationships of targets separately, and then together. We prove mathematically that our methods improve the localization accuracy. Furthermore, we relax the condition that targets should strictly keep their relative positions in the group and also show that perfect time synchronization is not required. Simulations were also conducted to test the algorithms. They used initial target location estimates from existing signal-strength and time-of-arrival algorithms and implemented our own algorithms. The results confirmed improved localization accuracy, especially in the combined algorithms. Since our algorithms use the features of targets and not the underlying WSNs, they can be built on any localization algorithm whose results are not satisfactory.     

From Crispness to Fuzziness: Three Algorithms for Soft Sequential Pattern Mining

Most real world databases consist of historical and numerical data such as sensor, scientific or even demographic data. In this context, classical algorithms extracting sequential patterns, which are well adapted to the temporal aspect of data, do not allow numerical information processing. Therefore, the data are pre-processed to be transformed into a binary representation, which leads to a loss of information. Fuzzy algorithms have been proposed to process numerical data using intervals, particularly fuzzy intervals, but none of these methods is satisfactory. Therefore this paper completely defines the concepts linked to fuzzy sequential pattern mining. Using different fuzzification levels, we propose three methods to mine fuzzy sequential patterns and detail the resulting algorithms (SpeedyFuzzy, MiniFuzzy, and TotallyFuzzy). Finally, we assess them through different experiments, thus revealing the robustness and the relevancy of this work.     

Exploiting Temporal Relations in Mining Hepatitis Data

Various data mining methods have been developed last few years for hepatitis study using a large temporal and relational database given to the research community. In this work we introduce a novel temporal abstraction method to this study by detecting and exploiting temporal patterns and relations between events in viral hepatitis such as “event A slightly happened before event B and B simultaneously ended with event C”. We developed algorithms to first detect significant temporal patterns in temporal sequences and then to identify temporal relations between these temporal patterns. Many findings by data mining methods applied to transactions/graphs of temporal relations shown to be significant by physician evaluation and matching with published in Medline.     

视频数据的错误隐藏算法研究

由于实时视频数据流在存储或传输中的错误、丢包等原因,解码器接收到的数据流可能不完整,无法正常解码。错误隐藏是解决这个问题的方法之一,文章回顾了当前的主要错误隐藏算法。根据视频编码的特点,对传输信道的错误隐藏算法进行了分类,包括时间、空间、时-空结合以及频域的相关算法,并对它们进行了比较和评述;同时对存储系统的错误隐藏算法也进行了分类比较和评述。最后对未来研究方向进行了展望,给出了若干值得研究的问题。     

三支决策的时空性

从三支决策发展历史和已有研究出发,在总结和分析三支决策近年来理论、方法、算法及应用的基础上,基于时间和空间两个维度,分别提出了时间三支决策模型和空间三支决策模型。时间三支决策注重在动态决策环境下对序贯决策进行诠释;空间三支决策主要基于“多层次”和“多视角”的粒计算思想对最优粒层和粒度进行选择。此外,对三支决策的时空性作了深入探讨和分析,厘清了三支决策发展过程和研究脉络。最后,对三支决策的研究现状进行总结,并给出未来发展方向。     

建筑电气系统故障诊断综述

从建筑电气系统研究现状、应用前景以及故障诊断方法等方面进行阐述;介绍信号分析、解析模型和人工智能算法三大类中用于解决建筑电气系统故障诊断的典型算法;综述三类方法在故障诊断中的模型选择、学习算法和实际应用等方面的研究进展;探讨信号分析、解析模型和人工智能算法在建筑电气系统故障诊断中的理论分析,以及待解决的问题和未来的发展前景等。     

New architectures: Performance highlights and new algorithms

Parallel computers are having a profound impact on computational science. Recently highly parallel machines have taken the lead as the fastest supercomputers, a trend that is likely to accelerate in the future. We describe some of these new computers, and issues involved in using them. We present elliptic PDE solutions currently running at 3.8 gigaflops, and an atmospheric dynamics model running at 1.7 gigaflops, on a 65 536-processor computer.

One intrinsic disadvantage of a parallel machine is the need to perform inter-processor communication. It is important to ensure that such communication time is maintained at a small fraction of computation time. We analyze standard multigrid algorithms in two and three dimensions from this point of view, indicating that performance efficiencies in excess of 95% are attainable under suitable conditions on moderately parallel machines. We also demonstrate that such performance is not attainable for multigrid on massively parallel computers, as indicated by an example of poor multigrid efficiency on 65 536 processors. The fundamental difficulty is the inability to keep 65 536 processors busy when operating on very coarse grids.

Most algorithms used for implementing applications on parallel machines have been derived directly from algorithms designed for serial machines. The previously mentioned multigrid example indicates that such ‘parallelized’ algorithms may not always be optimal. Parallel machines open the possibility of finding totally new approaches to solving standard tasks—intrinsically parallel algorithms. In particular, we present a class of superconvergent multiple scale methods that were motivated directly by massevely parallel machines. These methods differ from standard multigrid methods in an intrinsic way, and allow all processors to be used at all times, even when processing on the coarsest grid levels. Their serial versions are not sensible algorithms. The idea that parallel hardware—the Connection Machine in this case—can lead to discovery of new mathematical algorithms was surprising for us.     

Identifying predictive multi-dimensional time series motifs: an application to severe weather prediction

We introduce an efficient approach to mining multi-dimensional temporal streams of real-world data for ordered temporal motifs that can be used for prediction. Since many of the dimensions of the data are known or suspected to be irrelevant, our approach first identifies the salient dimensions of the data, then the key temporal motifs within each dimension, and finally the temporal ordering of the motifs necessary for prediction. For the prediction element, the data are assumed to be labeled. We tested the approach on two real-world data sets. To verify the generality of the approach, we validated the application on several subjects from the CMU Motion Capture database. Our main application uses several hundred numerically simulated supercell thunderstorms where the goal is to identify the most important features and feature interrelationships which herald the development of strong rotation in the lowest altitudes of a storm. We identified sets of precursors, in the form of meteorological quantities reaching extreme values in a particular temporal sequence, unique to storms producing strong low-altitude rotation. The eventual goal is to use this knowledge for future severe weather detection and prediction algorithms.