An efficient method for estimating time-dependent global reliability sensitivity

Structural and Multidisciplinary Optimization - Time-dependent global reliability sensitivity can quantify the effect of input variables in their whole distribution ranges on the time-dependent...     

An efficient deconvolution algorithm for estimating oxygen consumption during muscle activities

The reconstruction of an unknown input function from noisy measurements in a biological system is an ill-posed inverse problem. Any computational algorithm for its solution must use some kind of regularization technique to neutralize the disastrous effects of amplified noise components on the computed solution. In this paper, following a hierarchical Bayesian statistical inversion approach, we seek estimates for the input function and regularization parameter (hyperparameter) that maximize the posterior probability density function. We solve the maximization problem simultaneously for all unknowns, hyperparameter included, by a suitably chosen quasi-Newton method. The optimization approach is compared to the sampling-based Bayesian approach. We demonstrate the efficiency and robustness of the deconvolution algorithm by applying it to reconstructing the time courses of mitochondrial oxygen consumption during muscle state transitions (e.g., from resting state to contraction and recovery), from the simulated noisy output of oxygen concentration dynamics on the muscle surface. The model of oxygen transport and metabolism in skeletal muscle assumes an in vitro cylindrical structure of the muscle in which the oxygen from the surrounding oxygenated solution diffuses into the muscle and is then consumed by the muscle mitochondria. The algorithm can be applied to other deconvolution problems by suitably replacing the forward model of the system.     

An efficient method for estimating null values in relational databases

Generally, a database system containing null value attributes will not operate properly. This study proposes an efficient and systematic approach for estimating null values in a relational database which utilizes clustering algorithms to cluster data, and a regression coefficient to determine the degree of influence between different attributes. Two databases are used to verify the proposed method: (1) Human resource database; and (2) Waugh's database. Furthermore, the mean of absolute error rate (MAER) and average error are used as evaluation criteria to compare the proposed method with other methods. It demonstrates that the proposed method is superior to existing methods for estimating null values in relational database systems. Jia-Wen Wang was born on September 5, 1978, in Taipei, Taiwan, Republic of China. She received the M.S. degree in information management from the National Yunlin University of Science and Technology, Yunlin, Taiwan, in 2003. Since 2003, she has been a PhD degree student in Information Management Department at the National Yunlin University of Science and Technology. Her current research interests include fuzzy systems, database systems, and artificial intelligence. Ching-Hsue Cheng received the B.S. degree in mathematics from Chinese Military Academy, Taiwan, in 1982, the M.S. degree in applied mathematics from the Chung Yuan Christian University, Taiwan, in 1988, and the Ph.D. degree in system engineering and management from National Defence University, Taiwan, in 1994. Currently, he is a professor of the Department of Information Management, National YunLin University of Technology & Science. His research interests are in decision science, soft computing, software reliability, performance evaluation, and fuzzy time series. He has published more than 120 refereed papers in these areas. He has been a principal investigator and project leader in a number of projects with government, and other research-sponsoring agencies.     

An efficient camera calibration method for vision-based head tracking

The aim of this study is to develop and evaluate an efficient camera calibration method for vision-based head tracking. Tracking head movements is important in the design of an eye-controlled human/computer interface. A vision-based head tracking system is proposed to allow the user's head movements in the design of the eye-controlled human/computer interface. We propose an efficient camera calibration method to track the three-dimensional position and orientation of the user's head accurately. We also evaluate the performance of the proposed method and the influence of the configuration of calibration points on the performance. The experimental error analysis results showed that the proposed method can provide more accurate and stable pose (i.e. position and orientation) of the camera than the direct linear transformation method which has been used in camera calibration. The results for this study can be applied to the tracking of head movements related to the eye-controlled human/computer interface and the virtual reality technology.     

An efficient visual tracking method for multiple moving targets

An efficient algorithm of the edge detection according to integrating the edge gradient with the average filter is proposed, which can significantly reduce sensitivity of the background subtraction method to noise and illumination. Taking into account the features of the target such as color, size, etc., a new modified Nearest Neighbor （NN） algorithm for data association using the target features is designed. A designed Interacting Multiple Model （IMM） filter is utilized to track the maneuvering target motion, i.e. the feature point （called the centroid of the target） motion of the target. The algorithms are validated via an example with natural video sequences. The results show the algorithms are performances and validity for visual tracking. In complex environment, the algorithm can still work well.     

An effective pseudospectral method for constraint dynamic optimisation problems with characteristic times

Dynamic optimisation problem with characteristic times, widely existing in many areas, is one of the frontiers and hotspots of dynamic optimisation researches. This paper considers a class of dynamic optimisation problems with constraints that depend on the interior points either fixed or variable, where a novel direct pseudospectral method using Legendre–Gauss (LG) collocation points for solving these problems is presented. The formula for the state at the terminal time of each subdomain is derived, which results in a linear combination of the state at the LG points in the subdomains so as to avoid the complex nonlinear integral. The sensitivities of the state at the collocation points with respect to the variable characteristic times are derived to improve the efficiency of the method. Three well-known characteristic time dynamic optimisation problems are solved and compared in detail among the reported literature methods. The research results show the effectiveness of the proposed method.     

An identification method for estimating the inertia parameters of a manipulator

This article presents an off-line identification method to estimate the minimal knowledge of the inertia parameters for determining the dynamic model of a manipulator. A new approach is proposed to find a set of the minimal knowledge of the inertia parameters. This set is recursively estimated by moving one joint at a time. The off-line identification procedure also provides a sufficient condition for a persistently exciting trajectory. A simulation example of Stanford arm illustrates the validity and simplicity of the identification procedure.     

流程雁阵的多目标跟踪优化方法

流程雁阵(Process Goose Queue,PGQ)[1]为流程系统的分解协调优化提供了一个新的方法,然而,目前PGQ方法尚存在许多不足,如简单地将个体PGQ的状态跟踪处理为单目标优化问题,这与实际流程操作不符;而多级PGQ系统优化仍采用传统的数学规划方法,对模型要求苛刻且依赖于初值的选取。为此,论文提出了一个面向流程雁阵多目标跟踪的优化方法。首先对多级PGQ系统进行了结构优化,然后将NSGA-Ⅱ用于多级PGQ系统中个体PGQ的多目标优化求解,得到Pareto解集;在此基础上,将逼近理想解捧序法(TOPSIS)和扩展傅里叶幅值灵敏度分析法(EFAST)应用于个体PGQ的多目标决策,并从Pareto解集选取最优解在多级PGQ系统中逐级传递,实现流程系统的分解协调优化。仿真实例验证了方法的可行性和有效性。     

An improved computationally efficient identification method for errors-in-variables models based on v-gap optimisation

A v-gap optimisation-based frequency-domain identification method is proposed to estimate a nominal normalised right graph symbol for errors-in-variables models. The proposed method follows the similar identification idea from a previous research work but with improved computational efficiency using interior-point (IP) algorithms. By imposing an inner function constraint instead of frequency point-wise constraints to normalise the graph symbol, the number of involved equality constraints for the v-gap optimisation is related to the nominal model order instead of the data length. As a consequence, the computational complexity of the proposed IP-based identification algorithm is much lower than that of linear matrix inequalities-based algorithms. Due to the fact that the data length is typically much larger than the finite nominal model order, the number of saved equality constraints is close to that of the employed data points. Finally, two numerical simulation examples are given to verify the proposed identification method.     

一个效益型QoS参数匹配的估算方法

在网格计算商业应用环境中,保障用户服务质量(QoS)需求和服务使用效益以及优化系统资源效能是一个重要环节.为保证服务供求双方QoS需求的均衡,提出了在QoS参数匹配时,对用户QoS期望值进行估算的方法.该方法根据效益原理,针对服务提供方的QoS能力,估算出一组均衡的QoS参数值,为服务选择提供依据,使用户能获得较高的服务使用效益.实例分析表明,该方法能可靠地估算出有效的QoS期望值.     

A particle swarm optimisation algorithm with interactive swarms for tracking multiple targets

We propose a novel particle swarm optimisation algorithm that uses a set of interactive swarms to track multiple pedestrians in a crowd. The proposed method improves the standard particle swarm optimisation algorithm with a dynamic social interaction model that enhances the interaction among swarms. In addition, we integrate constraints provided by temporal continuity and strength of person detections in the framework. This allows particle swarm optimisation to be able to track multiple moving targets in a complex scene. Experimental results demonstrate that the proposed method robustly tracks multiple targets despite the complex interactions among targets that lead to several occlusions.     

An implicit relevance feedback method for CBIR with real-time eye tracking

Relevance feedback is an efficient approach to improve the performance of content-based image retrieval systems, and implicit relevance feedback approaches, which gather users’ feedback by biometric devices (e.g. eye tracker), have extensively investigated in recent years. This paper proposes a novel image retrieval system with implicit relevance feedback, named eye tracking based relevance feedback system (ETRFs). ETRFs is composed of three main modules: image retrieval subsystem based on bag-of-word architecture; user relevance assessment that implicitly acquires relevant images with the help of a modern eye tracker; and relevance feedback module that applies a weighted query expansion method to fuse users’ relevance feedback. ETRFs is implemented online and real-time, which makes it remarkably distinguish from other offline systems. Ten subjects participate our experiments on the dataset of Oxford buildings and UKBench. The experimental results demonstrate that ETRFs achieves notable improvement for image retrieval performance.     

An interacting Fuzzy-Fading-Memory-based Augmented Kalman Filtering method for maneuvering target tracking

In this paper, the interaction and combination of Fuzzy Fading Memory (FFM) technique and Augmented Kalman Filtering (AUKF) method are presented for the state estimation of non-linear dynamic systems in presence of maneuver. It is shown that the AUKF method in conjunction with the FFM technique (FFM-AUKF) can estimate the target states appropriately since the FFM tunes the covariance matrix of the AUKF method in presence of unknown target accelerations by using a fuzzy system. In addition, the benefits of both FFM technique and AUKF method are employed in the scheme of well-known Interacting Multiple Model (IMM) algorithm. The proposed Fuzzy IMM (FIMM) algorithm does not need the predefinition and adjustment of sub-filters with respect to the target maneuver and reduces the number of required sub-filters to cover the wide range of unknown target accelerations. The Monte Carlo simulation analysis shows the effectiveness of the above-mentioned methods in maneuvering target tracking.     

Markerless human articulated tracking using hierarchical particle swarm optimisation

In this paper, we address markerless full-body articulated human motion tracking from multi-view video sequences acquired in a studio environment. The tracking is formulated as a multi-dimensional non-linear optimisation and solved using particle swarm optimisation (PSO), a swarm-intelligence algorithm which has gained popularity in recent years due to its ability to solve difficult non-linear optimisation problems. We show that a small number of particles achieves accuracy levels comparable with several recent algorithms. PSO initialises automatically, does not need a sequence-specific motion model and recovers from temporary tracking divergence through the use of a powerful hierarchical search algorithm (HPSO). We compare experimentally HPSO with particle filter (PF), annealed particle filter (APF) and partitioned sampling annealed particle filter (PSAPF) using the computational framework provided by Balan et al. HPSO accuracy and consistency are better than PF and compare favourably with those of APF and PSAPF, outperforming it in sequences with sudden and fast motion. We also report an extensive experimental study of HPSO over ranges of values of its parameters.     

Post-stratified calibration method for estimating quantiles

The estimation of quantiles in the presence of auxiliary information is discussed. Calibration and poststratification techniques provide simple and practical procedures for incorporating auxiliary information into the estimation of distribution functions, which can offer some useful gains in efficiency. The estimator proposed combines these techniques and possesses a number of desirable properties, including yielding a genuine distribution function, providing simplicity of computation and generalizing Silva and Skinner’s estimator. This proposed procedure is compared to alternative methods. On the basis of simulation studies, the proposed post-stratified calibration estimator presents a good level of performance and comprises a valid alternative to other estimators of the distribution function.     

An optimisation strategy for industrial metal forming processes

Product improvement and cost reduction have always been important goals in the metal forming industry. The rise of finite element (FEM) simulations for processes has contributed to these goals in a major way. More recently, coupling FEM simulations to mathematical optimisation techniques has shown the potential to make a further giant contribution to product improvement and cost reduction. Much research on the optimisation of metal forming processes has been published during the last couple of years. Although the results are impressive, the optimisation techniques are generally only applicable to specific optimisation problems for specific products and specific metal forming processes. As a consequence, applying optimisation techniques to other metal forming problems requires a lot of optimisation expertise, which forms a barrier for more general industrial application of these techniques. In this paper, we overcome this barrier by proposing a generally applicable optimisation strategy that makes use of FEM simulations of metal forming processes. It consists of a structured methodology for modelling optimisation problems related to metal forming. Subsequently, screening is applied to reduce the size of the optimisation problem by selecting only the most important design variables. Finally, the reduced optimisation problem is solved by an efficient optimisation algorithm. The strategy is generally applicable in a sense that it is not constrained to a certain type of metal forming problem, product or process. Also, any FEM code may be included in the strategy. Furthermore, the structured approach for modelling and solving optimisation problems should enable non-optimisation specialists to apply optimisation techniques to improve their products and processes. The optimisation strategy has been successfully applied to a hydroforming process, which demonstrates the potential of the optimisation of metal forming processes in general and more specific the proposed optimisation strategy.