Pattern‐Similarity‐Based Model for Time Series Prediction

This research proposes a pattern/shape‐similarity‐based clustering approach for time series prediction. This article uses single hidden Markov model (HMM) for clustering and combines it with soft computing techniques (fuzzy inference system/artificial neural network) for the prediction of time series. Instead of using distance function as an index of similarity, here shape/pattern of the sequence is used as the similarity index for clustering, which overcomes few of the shortcomings associated with distance‐based clustering approaches. Underlying hidden properties of time series are captured with the help of HMM. The prediction method used here exploits the pattern identification prowess of the HMM for cluster selection and the generalization and nonlinear modeling capabilities of soft computing methods to predict the output of the system. To see the validity of the proposed method in the real‐life scenario, it is tested on four different time series. The first is a benchmark Mackey–Glass time series, which is tested for delay parameters τ = 17 and τ = 30. The remaining time series are monthly sunspot data time series, Laser data time series and the last is Lorenz attractor time series. Simulation results show that the proposed method provide a better prediction performance in comparison with the existing methods.     

基于序列标注的全词消歧方法

全词消歧(All-Words Word Sense Disambiguation)可以看作一个序列标注问题,该文提出了两种基于序列标注的全词消歧方法,它们分别基于隐马尔可夫模型(Hidden Markov Model, HMM)和最大熵马尔可夫模型(Maximum Entropy Markov Model, MEMM)。首先,我们用HMM对全词消歧进行建模。然后,针对HMM只能利用词形观察值的缺点,我们将上述HMM模型推广为MEMM模型,将大量上下文特征集成到模型中。对于全词消歧这类超大状态问题,在HMM和MEMM模型中均存在数据稀疏和时间复杂度过高的问题,我们通过柱状搜索Viterbi算法和平滑策略来解决。最后,我们在Senseval-2和Senseval-3的数据集上进行了评测,该文提出的MEMM方法的F1值为0.654,超过了该评测上所有的基于序列标注的方法。     

基于分层存储理论模型的近似字符串匹配 并行算法研究

CUDA (Compute Unified Device Architecture)是一种重要的并行处理架构,但其具有相对复杂的线程管理机制和多重存储模块,从而使得基于CUDA的算法时间复杂度很难量化.针对这一问题,提出了一种分层存储理论模型—HMM (Hierarchical Memory Machine)模型,该模型所具有的分层存储结构可以有效地描述图形处理单元设备不同存储模块的物理特性,因此非常适用于对CUDA算法时间复杂度的量化评估.作为HMM模型的应用实例,文章提出了一种基于HMM模型的并行近似字符串匹配算法,并给出了相应算法时间复杂度的计算过程.与串行算法相比,该算法可以获得60倍以上的加速比.     

Incorporating Intra‐Query Term Dependencies in an Aspect Query Language Model

Query language modeling based on relevance feedback has been widely applied to improve the effectiveness of information retrieval. However, intra‐query term dependencies (i.e., the dependencies between different query terms and term combinations) have not yet been sufficiently addressed in the existing approaches. This article aims to investigate this issue within a comprehensive framework, namely the Aspect Query Language Model (AM). We propose to extend the AM with a hidden Markov model (HMM) structure to incorporate the intra‐query term dependencies and learn the structure of a novel aspect HMM (AHMM) for query language modeling. In the proposed AHMM, the combinations of query terms are viewed as latent variables representing query aspects. They further form an ergodic HMM, where the dependencies between latent variables (nodes) are modeled as the transitional probabilities. The segmented chunks from the feedback documents are considered as observables of the HMM. Then the AHMM structure is optimized by the HMM, which can estimate the prior of the latent variables and the probability distribution of the observed chunks. Our extensive experiments on three large‐scale text retrieval conference (TREC) collections have shown that our method not only significantly outperforms a number of strong baselines in terms of both effectiveness and robustness but also achieves better results than the AM and another state‐of‐the‐art approach, namely the latent concept expansion model. © 2014 Wiley Periodicals, Inc.     

GBS业务流量的隐马尔可夫模型

简单介绍了GBS(全球广播业务)系统, 对其承载的业务进行分析, 提出了基于隐马尔可夫模型的业务流量建模方法. 在Qualnet中搭建了GBS仿真场景, 生成并测量了业务流量. 并在Matlab中实现了对GBS系统中四种业务流量的建模. 结果表明, 该模型只选用数据包大小和数据包间隔等受加密技术影响不大的流量特征进行建模, 更能真实地反映GBS系统的业务流量特征. 采用离散型的随机变量, 降低了建模的计算复杂度, 减少了建模时间, 提高了实时性.     

ADAPTIVE RANKS CLONE AND k‐NEAREST NEIGHBOR LIST–BASED IMMUNE MULTI‐OBJECTIVE OPTIMIZATION

Artificial immune systems (AIS) are computational systems inspired by the principles and processes of the vertebrate immune system. The AIS‐based algorithms typically exploit the immune system's characteristics of learning and adaptability to solve some complicated problems. Although, several AIS‐based algorithms have proposed to solve multi‐objective optimization problems (MOPs), little focus have been placed on the issues that adaptively use the online discovered solutions. Here, we proposed an adaptive selection scheme and an adaptive ranks clone scheme by the online discovered solutions in different ranks. Accordingly, the dynamic information of the online antibody population is efficiently exploited, which is beneficial to the search process. Furthermore, it has been widely approved that one‐off deletion could not obtain excellent diversity in the final population; therefore, a k‐nearest neighbor list (where k is the number of objectives) is established and maintained to eliminate the solutions in the archive population. The k‐nearest neighbors of each antibody are founded and stored in a list memory. Once an antibody with minimal product of k‐nearest neighbors is deleted, the neighborhood relations of the remaining antibodies in the list memory are updated. Finally, the proposed algorithm is tested on 10 well‐known and frequently used multi‐objective problems and two many‐objective problems with 4, 6, and 8 objectives. Compared with five other state‐of‐the‐art multi‐objective algorithms, namely NSGA‐II, SPEA2, IBEA, HYPE, and NNIA, our method achieves comparable results in terms of convergence, diversity metrics, and computational time.     

Increasing mapping based hidden Markov model for dynamic process monitoring and diagnosis

Hidden Markov models (HMMs) perform parameter estimation based on the forward–backward (FB) procedure and the Baum–Welch (BW) algorithm. The two algorithms together may increase the computational complexity and the difficulty to understand the algorithm structure of HMMs clearly. In this study, an increasing mapping based hidden Markov model (IMHMM) is proposed. Between the observation sequence and possible state sequence an increasing mapping is established. The re-estimation formulas for the model parameters are derived straightforwardly based on these mappings instead of FB variables. The IMHMM has simpler algorithm structure and lower storage requirement than the HMM. Based on IMHMM, an expandable process monitoring and fault diagnosis framework for large-scale dynamical process is developed. To characterize the dynamic process, a novel index considering serial correlation is used to evaluate process state. The presented methodology is carried out in Tennessee Eastman process (TEP). The results show improvement over HMM in terms of memory complexity and training time of the model. Also, the power of IMHMM can be observed compared with principal component analysis (PCA) based methods.     

Control‐oriented modeling and deployment of tensegrity–membrane systems

This study addresses control‐oriented modeling and control design of tensegrity–membrane systems. Lagrange's method is used to develop a control‐oriented model for a generic system. The equations of motion are expressed as a set of differential‐algebraic equations (DAEs). For control design, the DAEs are converted into second‐order ordinary differential equations (ODEs) based on coordinate partitioning and coordinate mapping. Because the number of inputs is less than the number of state variables, the system belongs to the class of underactuated nonlinear systems. A nonlinear adaptive controller based on the collocated partial feedback linearization (PFL) technique is designed for system deployment. The stability of the closed‐loop system for the actuated coordinates is studied using the Lyapunov stability theory. Because of system complexity, numerical tests are used to conduct stability analysis for the dynamics of the underactuated coordinates, which represents the system's zero dynamics. For the tensegrity–membrane systems studied in this work, analytical proof of zero dynamics stability remains an open theoretical problem. An H_∞ controller is implemented for rapid stabilization of the system at the final deployed configuration. Simulations are conducted to test the performance of the two controllers. The simulation results are presented and discussed in detail. Copyright © 2016 John Wiley & Sons, Ltd.     

An enhanced multiphase Chan–Vese model for the remote sensing image segmentation

The level set method has been widely used in image segmentation; however, the complexity of the computation has restricted its application field. Also, it is a big challenge to segment remote sensing image mainly because of the complex terrain. In this paper, an enhanced multiphase phase level set method based on the Chan–Vese (C‐V) model is proposed for segmenting remote sensing images. Compared with the C‐V model, two main contributions of the proposed model mainly include the following: First, we introduce a new strategy of initialization in which the contours of the first k biggest connected regions are extracted as the initial curves (k is the number of level set functions); Second, to increase the accuracy, a morphological gradient component is added to the original intensity image. To investigate the effectiveness and efficiency of the proposed model, we have applied it to analyze different kinds of images, including synthetic, real, and remote sensing images. The experimental results have shown that our method is able to achieve better segmentation with less computational consumption compared with the traditional multiphase C‐V model and local and global intensity fitting model. Copyright © 2013 John Wiley & Sons, Ltd.     

Work stealing with private integer–vector–matrix data structure for multi‐core branch‐and‐bound algorithms

In this paper, the focus is put on multi‐core branch‐and‐bound algorithms for solving large‐scale permutation‐based optimization problems. We investigate five work stealing (WS) strategies with a new data structure called integer–vector–matrix (IVM). In these strategies, each thread has a private IVM allowing the local management of a set of subproblems enumerated using a factorial system. The WS strategies differ in the way the victim thread is selected and the granularity of stolen work units (intervals of factoradics). To assess the efficiency of the private IVM‐based WS approach, the five WS strategies have been extensively experimented on the flowshop scheduling permutation problem and compared with their conventional linked‐list‐based counterparts. The obtained results demonstrate that the IVM‐based WS outperforms the linked‐list‐based one in terms of CPU time, memory usage and number of performed WS operations. Copyright © 2016 John Wiley & Sons, Ltd.     

Harmony search-based hidden Markov model optimization for online classification of single trial eegs during motor imagery tasks

This paper presents an improved method based on single trial EEG data for the online classification of motor imagery tasks for brain-computer interface (BCI) applications. The ultimate goal of this research is the development of a novel classification method that can be used to control an interactive robot agent platform via a BCI system. The proposed classification process is an adaptive learning method based on an optimization process of the hidden Markov model (HMM), which is, in turn, based on meta-heuristic algorithms. We utilize an optimized strategy for the HMM in the training phase of time-series EEG data during motor imagery-related mental tasks. However, this process raises important issues of model interpretation and complexity control. With these issues in mind, we explore the possibility of using a harmony search algorithm that is flexible and thus allows the elimination of tedious parameter assignment efforts to optimize the HMM parameter configuration. In this paper, we illustrate a sequential data analysis simulation, and we evaluate the optimized HMM. The performance results of the proposed BCI experiment show that the optimized HMM classifier is more capable of classifying EEG datasets than ordinary HMM during motor imagery tasks.     

Robust stabilization of nonlinear stochastic 2‐D systems: LaSalle‐type theorem approach

LaSalle theorem (also known as the LaSalle invariance principle) plays an essential role in the systems and control theory. Recently, it has been extensively studied and developed for various types of one‐dimensional (1‐D) systems including deterministic and stochastic 1‐D systems in discrete‐ and continuous‐time domains. For two‐dimensional (2‐D) systems, such studies have received considerably less attention. In this article, based on discrete martingale theory, a LaSalle‐type theorem is first developed for a class of discrete‐time nonlinear stochastic 2‐D systems described by a Roesser model. The proposed result can be regarded as an extension of stochastic Lyapunov‐like theorem, which guarantees the convergence almost surely of system state trajectories. Extensions to the problem of optimal guaranteed cost control of nonlinear stochastic 2‐D systems are also presented. The proposed schemes are then utilized to derive tractable synthesis conditions of a suboptimal state‐feedback controller for uncertain 2‐D systems with multiplicative stochastic noises. The effectiveness of the obtained results is illustrated by given numerical examples and simulations.     

A new approach based on a discrete hidden Markov model using the Rocchio algorithm for the diagnosis of heart valve diseases

Abstract: Application of the Doppler ultrasound technique in the diagnosis of heart diseases has been increasing in the last decade since it is non‐invasive, practicable and reliable. In this study, a new approach based on the discrete hidden Markov model (DHMM) is proposed for the diagnosis of heart valve disorders. For the calculation of hidden Markov model (HMM) parameters according to the maximum likelihood approach, HMM parameters belonging to each class are calculated by using training samples that only belong to their own classes. In order to calculate the parameters of DHMMs, not only training samples of the related class but also training samples of other classes are included in the calculation. Therefore HMM parameters that reflect a class's characteristics are more represented than other class parameters. For this aim, the approach was to use a hybrid method by adapting the Rocchio algorithm. The proposed system was used in the classification of the Doppler signals obtained from aortic and mitral heart valves of 215 subjects. The performance of this classification approach was compared with the classification performances in previous studies which used the same data set and the efficiency of the new approach was tested. The total classification accuracy of the proposed approach (95.12%) is higher than the total accuracy rate of standard DHMM (94.31%), continuous HMM (93.5%) and support vector machine (92.67%) classifiers employed in our previous studies and comparable with the performance levels of classifications using artificial neural networks (95.12%) and fuzzy‐C‐means/CHMM (95.12%).     

A Survey on Data‐Driven Video Completion

Image completion techniques aim to complete selected regions of an image in a natural looking manner with little or no user interaction. Video Completion, the space–time equivalent of the image completion problem, inherits and extends both the difficulties and the solutions of the original 2D problem, but also imposes new ones—mainly temporal coherency and space complexity (videos contain significantly more information than images). Data‐driven approaches to completion have been established as a favoured choice, especially when large regions have to be filled. In this survey, we present the current state of the art in data‐driven video completion techniques. For unacquainted researchers, we aim to provide a broad yet easy to follow introduction to the subject (including an extensive review of the image completion foundations) and early guidance to the challenges ahead. For a versed reader, we offer a comprehensive review of the contemporary techniques, sectioned out by their approaches to key aspects of the problem.     

Shear–rotation–warp volume rendering

Shear–warp volume rendering has the advantages of a moderate image quality and a fast rendering speed. However, in the case of dynamic changes in the opacity transfer function, the efficiency of memory access drops, as the method cannot exploit pre‐classified volumes. In this paper, we propose an efficient algorithm that exploits the spatial locality of memory references for interactive classifications. The algorithm inserts a rotation matrix when factorizing the viewing transformation, so that it may perform a scanline‐based traversal in both object space and image space. In addition, we present solutions to some problems of the proposed method, namely inaccurate front‐to‐back composition, the occurrence of holes, and increased computation. Our method is noticeably faster than traditional shear‐warp rendering methods because of an improved utilization of cache memory. Copyright © 2005 John Wiley & Sons, Ltd.     

Disturbance observer–based adaptive boundary layer sliding mode controller for a type of nonlinear multiple‐input multiple‐output system

In this paper, a disturbance observer–based adaptive boundary layer sliding mode controller (ABLSMC) is proposed to compensate external disturbance and system uncertainty for a class of output coupled multiple‐input multiple‐output (MIMO) nonlinear systems. To show the effectiveness of the proposed ABLMSC, a traditional adaptive sliding mode controller (ASMC) is also designed. The stability of the closed‐loop system is examined by using the Lyapunov stability approach. The proposed control approach is implemented for a class of nonlinear output coupled MIMO systems. For real‐time validation, a coupled tank system is considered for study. Finally, simulation and real‐time results show that the proposed ABLMSC gives better performance such as reduced chattering and energy efficiency than that of the ASMC and some reported works in the literature.     

Synchronization Criterion and Control Scheme for Lur'e Type Complex Dynamical Networks with Switching Topology and Coupling Time‐Varying Delay

In this paper, the synchronization problem is addressed in the context of Lur'e type complex switched network (CSN) with coupling time‐varying delay in which every node is a Lur'e system. Based on the Lyapunov–Krasovskii theory and linear matrix inequality (LMI) technique, a delay‐dependent synchronization criterion and a decentralized state feedback dynamic controller for synchronization of CSNs have been proposed. By choosing a common Lyapunov–Krasovskii functional and using the combined reciprocal convex technique, some previously ignored terms can be reconsidered and less conservative conditions can be obtained. In addition, by using an eigenvalue‐decoupling method and convex optimization theory, high‐dimension LMIs are decoupled into a set of low‐dimension ones and the computation complexity of the criterion can be significantly reduced. The effectiveness and applicability of the suggested control solution is verified and assessed through the analysis for two numerical examples.     

Combining penalty‐based and Gauss–Seidel methods for solving stochastic mixed‐integer problems

In this paper, we propose a novel decomposition approach (named PBGS) for stochastic mixed‐integer programming (SMIP) problems, which is inspired by the combination of penalty‐based Lagrangian and block Gauss–Seidel methods. The PBGS method is developed such that the inherent decomposable structure that SMIP problems present can be exploited in a computationally efficient manner. The performance of the proposed method is compared with the progressive hedging (PH) method, which also can be viewed as a Lagrangian‐based method for obtaining solutions for SMIP problems. Numerical experiments performed using instances from the literature illustrate the efficiency of the proposed method in terms of computational performance and solution quality.     

A TWO‐STAGE WIN–WIN MULTIATTRIBUTE NEGOTIATION MODEL: OPTIMIZATION AND THEN CONCESSION

Many automated negotiation models have been developed to solve the conflict in many distributed computational systems. However, the problem of finding win–win outcome in multiattribute negotiation has not been tackled well. To address this issue, based on an evolutionary method of multiobjective optimization, this paper presents a negotiation model that can find win–win solutions of multiple attributes, but needs not to reveal negotiating agents’ private utility functions to their opponents or a third‐party mediator. Moreover, we also equip our agents with a general type of utility functions of interdependent multiattributes, which captures human intuitions well. In addition, we also develop a novel time‐dependent concession strategy model, which can help both sides find a final agreement among a set of win–win ones. Finally, lots of experiments confirm that our negotiation model outperforms the existing models developed recently. And the experiments also show our model is stable and efficient in finding fair win–win outcomes, which is seldom solved in the existing models.     

An Efficient Algorithm for Modelling Duration in Hidden Markov Models, with a Dramatic Application

For many years, the hidden Markov model (HMM) has been one of the most popular tools for analysing sequential data. One frequently used special case is the left-right model, in which the order of the hidden states is known. If knowledge of the duration of a state is available it is not possible to represent it explicitly with an HMM. Methods for modelling duration with HMM’s do exist (Rabiner in Proc. IEEE 77(2):257–286, [1989]), but they come at the price of increased computational complexity. Here we present an efficient and robust algorithm for modelling duration in HMM’s, and this algorithm is successfully used to control autonomous computer actors in a theatrical play.