污染源化学分析仪实时采样运行控制系统

针对废水污染源化学分析仪检测数据的实时性问题,提出了一种提高检测数据实时性的污染源化学分析仪实时采样运行控制系统,有效地解决了由于化学分析仪设计结构存在的不足而导致的检测数据滞后于当前工况的实际问题,真实的反映被监控点的实时排污状况,为监管机构提供更为贴近实际的实时分析检测数据。     

Study on error calibration of fiber optic gyroscope under intense ambient temperature variation

A novel adaptive forward linear prediction (FLP) denoising algorithm and a temperature drift modeling and compensation concept based on ambient temperature change rate for fiber-optic gyroscope (FOG) are presented to calibrate the errors caused by intense ambient temperature variation. The intense ambient temperature variation will bring large temperature errors, which will degrade the performance of FOG. To analyze the temperature variation, characteristics of FOG temperature experiments are developed at first. Then the adaptive FLP denoising algorithm is employed to eliminate the noise aiming at reducing noise interference. After that, a simple modeling concept of building the compensation model between temperature drift and ambient temperature change rate is first to be given (we have not found a report of better results in any literature). The semiphysical simulation results show that the proposed method significantly reduces the noise and drift caused by intense ambient temperature variation.     

基于概念漂移检测的制冷系统故障诊断模型自适应

制冷系统实际运行中,故障诊断模型可能出现诊断性能波动或下降等情况,需具备再学习能力以适应现场数据。本文设计了一种基于正确率阈值的概念漂移检测机制及支持向量机增量学习的故障诊断自适应模型,并将其应用于制冷剂过量故障的再学习。该算法通过两次优化选择、过滤数据信息,保留原有诊断知识,仅学习未知样本信息,可极大地节约模型学习时间,快速适应新环境。结果表明,新的故障种类出现时,诊断模型检测到概念漂移,进而通过增量学习进行自我更新,实现对新故障的学习与诊断。1 400个过量故障样本中诊断模型只需要学习600个,且保证最终模型对后续数据流具有较佳诊断性能,正确率高达99%。在现场制冷系统故障诊断应用中,诊断模型的再学习和自适应体现出良好的应用前景。     

An Effective Steganalysis Algorithm for Histogram-Shifting Based Reversible Data Hiding

To measure the security for hot searched reversible data hiding (RDH) technique, especially for the common-used histogram-shifting based RDH (denoted as HS-RDH), several steganalysis schemes are designed to detect whether some secret data has been hidden in a normal-looking image. However, conventional steganalysis schemes focused on the previous RDH algorithms, i.e., some early spatial/pixel domain-based histogram-shifting (HS) schemes, which might cause great changes in statistical characteristics and thus be easy to be detected. For recent improved methods, such as some adaptive prediction error (PE) based embedding schemes, those conventional schemes might be invalid, since those adaptive embedding mechanism would effectively reduce the embedding trace and thus increase the difficulty of steganalysis. Therefore, a novel steganalysis method is proposed in this paper to detect recent adaptive RDH schemes and provide a more effective detection tool for RDH. The contributions of this paper could be summarized as follows. (1) By analyzing the characteristics for those adaptive HS-RDH, an effective “flat ground” based detection method is designed to fast identify whether the given image is used to hide secret data; (2) According to the empirical statistical model, double check mechanism is provided to improve the detection accuracy; (3) In addition, to further improve detection ability, some detailed information for secret data, i.e., its content and embedding location are further estimated. Compared with conventional steganalysis methods, experimental results indicate that our proposed algorithm could achieve a better detection accuracy and meanwhile acquire more detailed information on secret data.     

An Adaptive Sampling Strategy for Online High-Dimensional Process Monitoring

Temporally and spatially dense data-rich environments provide unprecedented opportunities and challenges for effective process control. In this article, we propose a systematic and scalable adaptive sampling strategy for online high-dimensional process monitoring in the context of limited resources with only partial information available at each acquisition time. The proposed adaptive sampling strategy includes a broad range of applications: (1) when only a limited number of sensors is available; (2) when only a limited number of sensors can be in “ON” state in a fully deployed sensor network; and (3) when only partial data streams can be analyzed at the fusion center due to limited transmission and processing capabilities even though the full data streams have been acquired remotely. A monitoring scheme of using the sum of top-r local CUSUM statistics is developed and named as “TRAS” (top-r based adaptive sampling), which is scalable and robust in detecting a wide range of possible mean shifts in all directions, when each data stream follows a univariate normal distribution. Two properties of this proposed method are also investigated. Case studies are performed on a hot-forming process and a real solar flare process to illustrate and evaluate the performance of the proposed method.     

Dynamic control in automated manufacturing: a knowledge integrated approach†

Flexibly automated facilities permit a wider variety of products as well as objectives for making those products—thus requiring manufacturing control strategies to face an environment of ever present change. To operate in this environment, a system composed of hard automation, flexible automation and humans, which can be responsive to product and process requirements, machine breakdowns and delays, engineering changes and improvement opportunities, is needed. Such a system does not fall into the realm of any current manufacturing solution techniques. Something more than exact optimization, heuristic algorithms or stochastic estimates must be utilized. The research discussed herein describes a dynamic solution strategy to operate in this changing environment with adaptive self-improving characteristics. The proposed methodology for optimizing the control of an automated manufacturing facility is an integrated approach utilizing real-time feedback from the operating facility, direct feedback from a simulation of the facility and guidance from a historical knowledge base. This system is being implemented in a knowledge based environment called CAYENE. CAYENE is a hybrid artificial intelligence system, written in Lisp, based on the idea of using object oriented programming as a unifying principle for functional, frame and rule-based programming.     

Adaptive Exponentially Weighted Moving Average Schemes Using a Kalrnan Filter

Two adaptive exponentially weighted moving average control schemes are proposed. The weighting coefficient is updated using a Kalman filter algorithm. The two test statistics incorporate an integral error term. Simulated average run lengths indicate the proposed schemes are sensitive to small process shifts, but do tend to ring false alarms when there is no process change. For medium and large process changes and trends their performance is comparable to that, of Lucas's combined Shewhart-CUSUM control scheme. Some application of the proposed schemes to correlated data indicate robust performance. Conclusions are drawn that the Kalman filter used to model a process together with a detection mechanism applied to the residuals closely resembles the work done in control theory.     

Online Burst Events Detection Oriented Real-Time Microblog Message Stream

The rapid spread of microblog messages and sensitivity of unexpected events make microblog become the public opinion center of burst events. Online burst events detection oriented real-time microblog message stream has become an important research problem in the field of microblog public opinion. Because of the large amount of real-time microblog message stream and irregular language of microblog message, it is important to process real-time microblog message stream and detect burst events accurately. In this paper, an online burst events detection framework is proposed. In this framework, abnormal messages are detected based on sliding time window and two-level hash table. Combined with event features, an online incremental clustering algorithm is used to cluster abnormal messages and detect burst events. Experimental results in the real-time microblog message stream environment show that our framework can be used in online burst events detection and has higher accuracy compared with other approaches.     

基于拉格朗日抛物线插值的毛细管粘度仪液位检测方法

为提高毛细管粘度仪液位检测的实时性,减小凹形液面和采样间隔对检测结果精度的影响,提出了一种基于拉格朗日抛物线插值的毛细管粘度仪液位检测方法。采用光纤传感器作为液位检测元件,对采样得到的液位检测电压信号进行一阶差分处理;搜索一阶差分最小采样时刻,选取该采样时刻与其前后相邻2个采样时刻的电压一阶差分值作为插值点,进行拉格朗日抛物线插值;通过插值多项式对时间求导,确定计时点,实现液位流经2个计时点的自动检测。该方法与现有基于最小二乘线性拟合方法相比,计算量小,实时性好,受采样间隔影响小。实验表明该方法的运动粘度测定重复性小于0.1%。     

随机图像选取与自适应背景更新的运动物体检测

为克服运动目标检测中易出现的光照变化、遮挡、虚假目标等现象,提出了一种随机图像选取与自适应背景更新的运动物体检测方法.该方法从视频序列中随机选取一帧图像作为初始背景,根据变化标记矩阵对背景进行自适应迭代更新,以提取可靠的背景图像,实现运动物体的检测.实验结果表明,采用该算法提取的背景不存在混合现象,且在光照变化较大以及运动物体之间存在遮挡的情况下,能够构造出可靠的背景,检测出的目标物体清晰可见.     

Towards a quantitative understanding of the within-host dynamics of influenza A infections

Although the influenza A virus has been extensively studied, a quantitative understanding of the infection dynamics is still lacking. To make progress in this direction, we designed several mathematical models and compared them with data from influenza A infections of mice. We find that the immune response (IR) plays an important part in the infection dynamics. Both an innate and an adaptive IR are required to provide adequate explanation of the data. In contrast, regrowth of epithelial cells did not seem to be an important mechanism on the time scale of the infection. We also find that different model variants for both innate and adaptive responses fit the data well, indicating the need for additional data to allow further model discrimination.     

A Scalable Method of Maintaining Order Statistics for Big Data Stream

Recently, there are some online quantile algorithms that work on how to analyze the order statistics about the high-volume and high-velocity data stream, but the drawback of these algorithms is not scalable because they take the GK algorithm as the subroutine, which is not known to be mergeable. Another drawback is that they can’t maintain the correctness, which means the error will increase during the process of the window sliding. In this paper, we use a novel data structure to store the sketch that maintains the order statistics over sliding windows. Therefore three algorithms have been proposed based on the data structure. And the fixed-size window algorithm can keep the sketch of the last W elements. It is also scalable because of the mergeable property. The time-based window algorithm can always keep the sketch of the data in the last T time units. Finally, we provide the window aggregation algorithm which can help extend our algorithm into the distributed system. This provides a speed performance boost and makes it more suitable for modern applications such as system/network monitoring and anomaly detection. The experimental results show that our algorithm can not only achieve acceptable performance but also can actually maintain the correctness and be mergeable.     

Extraction of activity patterns on large video recordings

Extracting the hidden and useful knowledge embedded within video sequences and thereby discovering relations between the various elements to help an efficient decision-making process is a challenging task. The task of knowledge discovery and information analysis is possible because of recent advancements in object detection and tracking. The authors present how video information is processed with the ultimate aim to achieve knowledge discovery of people activity and also extract the relationship between the people and contextual objects in the scene. First, the object of interest and its semantic characteristics are derived in real-time. The semantic information related to the objects is represented in a suitable format for knowledge discovery. Next, two clustering processes are applied to derive the knowledge from the video data. Agglomerative hierarchical clustering is used to find the main trajectory patterns of people and relational analysis clustering is employed to extract the relationship between people, contextual objects and events. Finally, the authors evaluate the proposed activity extraction model using real video sequences from underground metro networks (CARETAKER) and a building hall (CAVIAR).     

Bootstrap control charts for quantiles based on log-symmetric distributions with applications to the monitoring of reliability data

In this work, a methodology to monitor a shift in the quantile of a distribution that is a member of the log-symmetric family is proposed. Because the sampling distribution of a quantile estimator is often not available, the parametric bootstrap method is used to determine this sampling distribution and to establish the control limits when the process measurements follow a log-symmetric distribution. The mentioned family is helpful for describing the behavior of data following a distribution with positive support and that is skewed to the right. Monte Carlo simulations are carried out to investigate the performance of the proposed bootstrap control charts for quantiles. An application regarding failure data due to stress on carbon fibers is presented for illustration when monitoring reliability data. This illustration shows that non-conventional models, other than the Birnbaum-Saunders, log-normal and Weibull distributions, have potential to be used in practice. Two model selection procedures are considered to assess adequacy to the data. To facilitate the public use of the proposed methodology, we have created an R package named chartslogsym whose main functions are detailed in this paper.     

The Effect of Autocorrelation on the Hotelling T2 Control Chart

One of the basic assumptions for traditional univariate and multivariate control charts is that the data are independent in time. For the latter, in many cases, the data are serially dependent (autocorrelated) and cross‐correlated because of, for example, frequent sampling and process dynamics. It is well known that the autocorrelation affects the false alarm rate and the shift‐detection ability of the traditional univariate control charts. However, how the false alarm rate and the shift‐detection ability of the Hotelling T² control chart are affected by various autocorrelation and cross‐correlation structures for different magnitudes of shifts in the process mean is not fully explored in the literature. In this article, the performance of the Hotelling T² control chart for different shift sizes and various autocorrelation and cross‐correlation structures are compared based on the average run length using simulated data. Three different approaches in constructing the Hotelling T² chart are studied for two different estimates of the covariance matrix: (i) ignoring the autocorrelation and using the raw data with theoretical upper control limits; (ii) ignoring the autocorrelation and using the raw data with adjusted control limits calculated through Monte Carlo simulations; and (iii) constructing the control chart for the residuals from a multivariate time series model fitted to the raw data. To limit the complexity, we use a first‐order vector autoregressive process and focus mainly on bivariate data. © 2014 The Authors. Quality and Reliability Engineering International Published by John Wiley & Sons Ltd.     

Goal-balancing process for goal formation in the fractal manufacturing system

The coordination architecture and mechanisms are proposed for the goal-balancing process (GBP), which is one of the components of goal-formation process (GFP) in the fractal manufacturing system (FrMS). Since each agent in the FrMS generates, achieves and modifies its own goal autonomously during the coordination process with other agents, it is necessary to develop a systematic methodology for the goal-formations in a manufacturing system: GFP takes charge of this methodology, which enables the formation of and the changes in goal through coordination between agents in real-time in the distributed and dynamic systems. The GFP is composed of three sub-processes: the goal-generating process (GGP), the goal-harmonizing process (GHP), and the GBP. The GGP makes and propagates goals for all fractals. The GHP then eliminates or reduces possible conflicts and interferences between goals generated in GGP. The GBP is the post-process of GHP; it refines the fractal's goal, which has been ridded of conflict during the GHP to enhance the global performance of the system, rather than maximizing the performance of each fractal. For the development of the GBP we proposed a mechanism for unit goal-balancing process (UGBP), which is the basic process of GBP that adopts the, fuzzy decision-making, and distributed problem solving approaches.     

Efficient grid deformation using deterministic sampling-based data reduction

Spring analogies and the point-by-point interpolating approaches have been widely used for the grid deformation, and both require solution of a linear system of equations. Depending on the problem, the resulting system of equations may be defined by a large-dimensional matrix. Thus, sampling for a subset of the grids is essential in order to achieve an efficient grid deformation. This article presents an efficient grid deformation algorithm developed via deterministic data sampling. From the position data of the deformed grids, proper orthogonal decomposition and discrete empirical interpolation method are employed to define the subset of the grids. Herein, symmetric rank-one update is considered to choose the additional grids (oversampling). And it facilitates the deterministic data sampling approach and realizes the improved stability within the data reduction procedure. Such deterministic data sampling approach is applied to the moving submesh approach and radial basis function (RBF) interpolations. Specifically, for an RBF interpolation, boundaries of a deformable body are directly introduced within the data reduction procedure to improve the computational efficiency. Two- and three-dimensional examples are used to evaluate the relevant computational efficiency of the proposed methods. It is found that computational time consumed by the present method is two orders of magnitude smaller than that of the existing method while maintaining the quality of the deformed grids.     

Kalman filtering for adaptive nonuniformity correction in infrared focal-plane arrays

A novel statistical approach is undertaken for the adaptive estimation of the gain and bias nonuniformity in infrared focal-plane array sensors from scene data. The gain and the bias of each detector are regarded as random state variables modeled by a discrete-time Gauss-Markov process. The proposed Gauss-Markov framework provides a mechanism for capturing the slow and random drift in the fixed-pattern noise as the operational conditions of the sensor vary in time. With a temporal stochastic model for each detector's gain and bias at hand, a Kalman filter is derived that uses scene data, comprising the detector's readout values sampled over a short period of time, to optimally update the detector's gain and bias estimates as these parameters drift. The proposed technique relies on a certain spatiotemporal diversity condition in the data, which is satisfied when all detectors see approximately the same range of temperatures within the periods between successive estimation epochs. The performance of the proposed technique is thoroughly studied, and its utility in mitigating fixed-pattern noise is demonstrated with both real infrared and simulated imagery.     

A High Sensitivity Microwave Sampling System with Adaptive Time-Drift Compensation

A new microwave sampling system is presented whose sensitivity has been increased by digital processing of the measurement data. The signal processing gets more complicated due to a time drift caused by electronic circuits in the sampling oscilloscope. An adaptive method to compensate for this time drift has been developed. The influence of noise is discussed, giving the remaining time uncertainty as a function of the signal-to-noise ratio. Finally an example illustrates how this method can be applied in an automatic sampling system.     

Process Capability Analysis for NonLinear Profiles Using Depth Functions

There are practical situations in which the quality of a process or product can be better characterized by a functional relationship between a response variable and one or more explanatory variables, which is called profile. Such profiles frequently can be represented adequately using linear or nonlinear models. While there are several studies in monitoring profiles, there are few studies to evaluate the capability of a process with profile quality characteristic; specifically, there is no method in the literature to analyze process capability characterized by nonlinear profiles. In this paper, we propose two methods to measure the capability of these processes, based on the concept of functional depth. These methods do not have distributional assumptions and extend to functional data the Process Capability Indexes proposed by Clements 1 to measure the capability of a process characterized by a random variable. Performance of the proposed methods is evaluated through simulation studies. An example illustrates the applicability of these methods. Copyright © 2013 John Wiley & Sons, Ltd.