Developing feasible and optimal schedules of jobs on one machine

The paper considers the properties of feasible and optimal scheduling of jobs on one machine under constraints on the terms of the beginning and completion of jobs and on partial sequences of job performance. The established properties and the lower-bound estimates of the length of the optimal schedule are used to develop methods for the exact and approximate solutions of the formulated problem by sequential optimization algorithms. The proposed algorithms are illustrated by numerical examples and can be successfully applied to solve these problems in the absence of constraints.     

Comparative evaluation of linear edge detection methods

A comparative evaluation of the most commonly used linear methods for edge detection in grayscale images are presented. Detectors based on the first and second derivatives of image brightness are considered. The method for automatic edge tracking in grayscale images is proposed. The model for assessing errors and artifacts caused by sampling during digitization of real input images is proposed. Investigation of edge detectors isotropy and errors caused by input images sampling is conducted. The advantage of the Isotropic operator for edge tracking is shown. The noise immunity of linear edge detection methods is assessed and the superiority of 3 × 3 gradient operators for noisy images is shown. Isotropic and Sobel operators are identified to be optimal on a basis of sampling errors, output noise level, and computational complexity.     

Control of downrange and crossrange motion of a descent module in a certain range of reentry angles with load factor constraints

Results concerning the terminal guidance algorithm at the reentry of the descent module into the Earth atmosphere are presented. This algorithm simultaneously eliminates predicted deviations in the downrange and crossrange directions. The restriction on the tolerable load factor n ?? 3 is taken into account. Three reentry angles are analyzed: the medium angle ?1.5°, the steep angle ?1.9°, and the flat angle ?1.2°. The motion of the descent module is considered in the disturbed Earth atmosphere represented by the TsNIIMash model. This model includes variations of the atmosphere density and the wind field. It is demonstrated that, depending on the reentry angle, the proposed algorithm guarantees the downrange maneuver zone in the range 1240?C1890 km and the crossrange maneuver zone of 210?C220 km on one side. The maximum guidance error at the altitude of 1 km does not exceed 0.8 km, and the average error is 0.1 km. The algorithm uses up to three bank angle reversals and uses the numerical prediction of the remaining part of the trajectory when choosing the guidance parameters. The concept of the automatic adjustment of the bank angle reference function depending on the entry angle and the prescribed distance to the target point in the maneuver zone is formulated.     

Hierarchical streamline bundles

Effective 3D streamline placement and visualization play an essential role in many science and engineering disciplines. The main challenge for effective streamline visualization lies in seed placement, i.e., where to drop seeds and how many seeds should be placed. Seeding too many or too few streamlines may not reveal flow features and patterns either because it easily leads to visual clutter in rendering or it conveys little information about the flow field. Not only does the number of streamlines placed matter, their spatial relationships also play a key role in understanding the flow field. Therefore, effective flow visualization requires the streamlines to be placed in the right place and in the right amount. This paper introduces hierarchical streamline bundles, a novel approach to simplifying and visualizing 3D flow fields defined on regular grids. By placing seeds and generating streamlines according to flow saliency, we produce a set of streamlines that captures important flow features near critical points without enforcing the dense seeding condition. We group spatially neighboring and geometrically similar streamlines to construct a hierarchy from which we extract streamline bundles at different levels of detail. Streamline bundles highlight multiscale flow features and patterns through clustered yet not cluttered display. This selective visualization strategy effectively reduces visual clutter while accentuating visual foci, and therefore is able to convey the desired insight into the flow data.     

Interactive image segmentation based on level sets of probabilities

In this paper, we present a robust and accurate algorithm for interactive image segmentation. The level set method is clearly advantageous for image objects with a complex topology and fragmented appearance. Our method integrates discriminative classification models and distance transforms with the level set method to avoid local minima and better snap to true object boundaries. The level set function approximates a transformed version of pixelwise posterior probabilities of being part of a target object. The evolution of its zero level set is driven by three force terms, region force, edge field force, and curvature force. These forces are based on a probabilistic classifier and an unsigned distance transform of salient edges. We further propose a technique that improves the performance of both the probabilistic classifier and the level set method over multiple passes. It makes the final object segmentation less sensitive to user interactions. Experiments and comparisons demonstrate the effectiveness of our method.     

Two Types of Haar Wavelet Neural Networks for Nonlinear System Identification

Since wavelet transform uses the multi-scale (or multi-resolution) techniques for time series, wavelet transform is suitable for modeling complex signals. Haar wavelet transform is the most commonly used and the simplest one. The Haar wavelet neural network (HWNN) applies the Harr wavelet transform as active functions. It is easy for HWNN to model a nonlinear system at multiple time scales and sudden transitions. In this paper, two types of HWNN, feedforward and recurrent wavelet neural networks, are used to model discrete-time nonlinear systems, which are in the forms of the NARMAX model and state-space model. We first propose an optimal method to determine the structure of HWNN. Then two stable learning algorithms are given for the shifting and broadening coefficients of the wavelet functions. The stability of the identification procedures is proven.     

A scalable multi-attribute hybrid overlay for range queries on the cloud

Cloud computing has become a promising paradigm as next generation computing model, by providing computation, software, data access, and storage services that do not need to know the location of physical resources interconnected across the globe providing such services. In such an environment, important issues as information sharing and resource/service discovery arise. In order to overcome critical limitations in centralized approaches for information sharing and resource/service discovery, this paper proposes a framework of a scalable multi-attribute hybrid overlay featured with decentralized information sharing, flexible resource/service discovery, fault tolerance and load balancing. Additionally, the proposed hybrid overlay integrates a structured P2P system with an unstructured one to support complex queries. Mechanisms such as load balancing and fault tolerance implemented in our proposed system to improve the overall system performance are also discussed. Experimental results show that the performance of the proposed approach is feasible and stable, as the proposed hybrid overlay improves system performance by reducing the number of routing hops and balancing the load by migrating requests.     

Exploring latent browsing graph for question answering recommendation

In this paper, we develop a framework of Question Answering Pages (referred to as QA pages) recommendation. Our proposed framework consists of the two modules: the off-line module to determine the importance of QA pages and the on-line module for on-line QA page recommendation. In the off-line module, we claim that the importance of QA pages could be discovered from user click streams. If the QA pages are of higher importance, many users will click and spend their time on these QA pages. Moreover, the relevant relationships among QA pages are captured by the browsing behavior on these QA pages. As such, we exploit user click streams to model the browsing behavior among QA pages as QA browsing graph structures. The importance of QA pages is derived from our proposed QA browsing graph structures. However, we observe that the QA browsing graph is sparse and that most of the QA pages do not link to other QA pages. This is referred to as a sparsity problem. To overcome this problem, we utilize the latent browsing relations among QA pages to build a QA Latent Browsing Graph. In light of QA latent browsing graph, the importance score of QA pages (referred to as Latent Browsing Rank) and the relevance score of QA pages (referred to as Latent Browsing Recommendation Rank) are proposed. These scores demonstrate the use of a QA latent browsing graph not only to determine the importance of QA pages but also to recommend QA pages. We conducted extensive empirical experiments on Yahoo! Asia Knowledge Plus to evaluate our proposed framework.     

Input‐to‐state stability of min‐max MPC scheme for nonlinear time‐varying delay systems

This paper studies the robustness problem of the min–max model predictive control (MPC) scheme for constrained nonlinear time‐varying delay systems subject to bounded disturbances. The notion of the input‐to‐state stability (ISS) of nonlinear time‐delay systems is introduced. Then by using the Lyapunov–Krasovskii method, a delay‐dependent sufficient condition is derived to guarantee input‐to‐state practical stability (ISpS) of the closed‐loop system by way of nonlinear matrix inequalities (NLMI). In order to lessen the online computational demand, the non‐convex min‐max optimization problem is then converted to a minimization problem with linear matrix inequality (LMI) constraints and a suboptimal MPC algorithm is provided. Finally, an example of a truck‐trailer is used to illustrate the effectiveness of the proposed results. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society