基于 MPI ＋OpenMP 的红外弱小目标检测并行计算

为有效监控红外弱小目标运动的全过程，必须采用多个波段同时探测，但是多波段探测必然带来计算时间的大幅增长，无法满足实际应用中对目标检测实时性的要求。针对这一问题，本文提出一种基于MPI＋OpenMP的层次化并行方法，充分利用消息传递模型和共享存储模型的优势，并基于多处理器节点集群进行测试。实验结果表明，该并行程序在保证相同的检测概率的情况下加速比达到8．61，极大地提高了目标检测的效率。     

Sequential detection of three-dimensional signals under dependent noise

We study detection methods for multivariable signals under dependent noise. The main focus is on three-dimensional signals; that is, on signals in the space–time domain. Examples for such signals are multifaceted. They include geographic and climatic data as well as image data that are observed over a fixed time horizon. We assume that the signal is observed as a finite block of noisy samples whereby we are interested in detecting changes from a given reference signal. Our detector statistic is based on a sequential partial sum process, related to classical signal decomposition and reconstruction approaches applied to the sampled signal. We show that this detector process converges weakly under the no change null hypothesis that the signal coincides with the reference signal, provided that the spatial–temporal partial sum process associated with the random field of the noise terms disturbing the sampled signal converges to a Brownian motion. More generally, we also establish the limiting distribution under a wide class of local alternatives that allows for smooth as well as discontinuous changes. Our results also cover extensions to the case in which the reference signal is unknown. We conclude with an extensive simulation study of the detection algorithm.     

概论混凝土砖的检测方法

混凝土砖逐渐取代了粘土砖，在建筑、道路以及工业建筑中被广泛应用。本文以混凝土砖的检测方法为研究对象，详细探讨了不同种类的混凝土砖的检测方法，并且给出了检测的结果。     

Towards building a social emotion detection system for online news

Social emotion detection of online users has become an important task for mining public opinions. Social emotion detection aims at predicting the readers’ emotions evoked by news articles, tweets, etc. In this article, we focus on building a social emotion detection system for online news. The system is built based on the modules of document selection, Part-of-speech (POS) tagging, and social emotion lexicon generation. Empirical studies are extensively conducted on a large scale real-world collection of news articles. Experiments show that the document selection algorithm has a positive effect on the social emotion detection. The system performs better with the words and POS combination compared to a feature set consisting only of words. POS is also useful to detect emotion ambiguity of words and the context dependence of their sentiment orientations. Furthermore, the proposed method of generating the lexicon outperforms the baselines in terms of social emotion prediction.     

Non‐minimum phase switched systems: HOSM‐based fault detection and fault identification via Volterra integral equation

In this paper, the problem of continuous and discrete state estimation for a class of linear switched systems with additive faults is studied. The class of systems under study can contain non‐minimum phase zeroes in some of their ‘operating modes’. The conditions for exact reconstruction of the discrete state are given using structural properties of the switched system. The state space is decomposed into the strongly observable part, the non‐strongly observable part, and the unobservable part, to analyze the effect of the unknown inputs. State observers based on high‐order sliding mode to exactly estimate the strongly observable part and Luenberger‐like observers to estimate the remaining parts are proposed. For the case when the exact estimation of the state cannot be achieved, the ultimate bounds on the estimation errors are provided. The proposed strategy includes a high‐order sliding‐mode‐based fault detection and a fault identification scheme via the solution of a Volterra integral equation. The feasibility of the proposed method is illustrated by simulations. Copyright © 2013 John Wiley & Sons, Ltd.     

Data‐driven bounded‐error fault detection

In this paper, a new data‐driven fault‐detection method is proposed. This method is based on a new nonparametric system identification approach, which constitutes the principal contribution to this work. The fault‐detection method is a parametric model‐free approach that can be applied to nonlinear systems that work at various operating points. Not only can the fault‐detection process be applied to the steady state of each operating point, but it can also be applied to the transient state resulting from a change in the operating point. In order to detect faults, the proposed method uses an interval predictor based on bounded‐error techniques. The utilization of techniques based on bounded error enables system uncertainties to be included in an explicit way. This in turn leads to the possibility of obtaining interval predictions of the behaviour of the system, which include information on the reliability of the prediction itself. In order to show the effectiveness of the fault‐detection method, two examples are presented: in the form of a simulated process (counter‐flow shell‐and‐tube heat‐exchanger system) and an example of a real application (two‐tanks system). A comparison with two fault‐detection methods has also been included. Copyright © 2013 John Wiley & Sons, Ltd.     

辅助变量纯方位目标跟踪算法

To overcome the biased estimation of the pseudo-linear algorithm in bearings-only target tracking, a modified instrumental variable algorithm is proposed. In the new algorithm, the current bearings-only estimation angle is acquired by polyfitting several previous bearings angles. Then the estimated angle is used as the instrumental variable to get the motion parameters by applying the least-square method. The algorithm can achieve the theoretical unbiased estimation. Simulation results illustrate that the new modified instrumental variable algorithm has a better convergence rate and estimation accuracy than the existing research and that it is more suitable for engineering practice.     

Traffic violation detection based on the implicit curves family

We propose an effective algorithm for the detection of the traffic violation vehicle by employing a set of implicit function curves which can be used to describe the different regions on the roads. When the vehicles are detected to run across different road regions, we can determine in which region that vehicle lies easily and immediately by using the set of implicit functions. According to the vehicle trajectory and the relationship among different regions described by the implicit functions, we can quickly and accurately determine whether the vehicle violates the traffic rules. In order to improve the detection efficiency and accuracy greatly, we use the piecewise linear functions to approximate the set of implicit functions in actual detection. Experimental results show that our algorithm can detect the traffic violation and display the specific violated traffic rules in real time.     

Robust data reconciliation of combustion variables in multi-fuel fired industrial boilers

This paper introduces an application of simultaneous nonlinear data reconciliation and gross error detection for power plants utilizing a complex but computationally light first principle combustion model. Element and energy balances and robust techniques introduce nonlinearity and the consequent optimization problem is solved using nonlinear optimization. Data reconciliation improves estimation of process variables and enables improved sensor quality control and identification of process anomalies. The approach was applied to an industrial 200 MW_th fluidized bed boiler combusting wood, peat, bark, and slurry. The results indicate that the approach is valid and is able to perform in various process conditions. As the combustion model is generic, the method is applicable in any boiler environment.