A Closed-Loop Automatic Data-Mining Approach for Preventive Network Monitoring

As telecommunication networks grow in size and complexity, monitoring systems need to scale up accordingly. Alarm data generated in a large network are often highly correlated. These correlations can be explored to simplify the process of network fault management, by reducing the number of alarms presented to the network-monitoring operator. This makes it easier to react to network failures. But in some scenarios, it is highly desired to prevent the occurrence of these failures by predicting the occurrence of alarms before hand. This work investigates the usage of data mining methods to generate knowledge from historical alarm data, and using such knowledge to train a machine learning system, in order to predict the occurrence of the most relevant alarms in the network. The learning system was designed to be retrained periodically in order to keep an updated knowledge base.     

Rotation,scaling and translation invariant texture recognition by Bessel-Fourier moments

The ideal of Bessel-Fourier moments (BFMs) for image analysis and only rotation invariant image cognition has been proposed recently. In this paper, we extend the previous work and propose a new method for rotation, scaling and translation (RST) invariant texture recognition using Bessel-Fourier moments. Compared with the others moments based methods, the radial polynomials of Bessel-Fourier moments have more zeros and these zeros are more evenly distributed. It makes Bessel-Fourier moments more suitable for invariant texture recognition as a generalization of orthogonal complex moments. In the experiment part, we got three testing sets of 16, 24 and 54 texture images by way of translating, rotating and scaling them separately. The correct classification percentages (CCPs) are compared with that of orthogonal Fourier-Mellin moments and Zernike moments based methods in both noise-free and noisy condition. Experimental results validate the conclusion of theoretical derivation: BFM performs better in recognition capability and noise robustness in terms of RST texture recognition under both noise-free and noisy condition when compared with orthogonal Fourier-Mellin moments and Zernike moments based methods.     

Topology Optimization in Aircraft and Aerospace Structures Design

Topology optimization has become an effective tool for least-weight and performance design, especially in aeronautics and aerospace engineering. The purpose of this paper is to survey recent advances of topology optimization techniques applied in aircraft and aerospace structures design. This paper firstly reviews several existing applications: (1) standard material layout design for airframe structures, (2) layout design of stiffener ribs for aircraft panels, (3) multi-component layout design for aerospace structural systems, (4) multi-fasteners design for assembled aircraft structures. Secondly, potential applications of topology optimization in dynamic responses design, shape preserving design, smart structures design, structural features design and additive manufacturing are introduced to provide a forward-looking perspective.     

Non-intrusive Coupling: Recent Advances and Scalable Nonlinear Domain Decomposition

This paper provides a detailed review of the global/local non-intrusive coupling algorithm. Such method allows to alter a global finite element model, without actually modifying its corresponding numerical operator. We also look into improvements of the initial algorithm (Quasi-Newton and dynamic relaxation), and provide comparisons based on several relevant test cases. Innovative examples and advanced applications of the non-intrusive coupling algorithm are provided, granting a handy framework for both researchers and engineers willing to make use of such process. Finally, a novel nonlinear domain decomposition method is derived from the global/local non-intrusive coupling strategy, without the need to use a parallel code or software. Such method being intended to large scale analysis, we show its scalability. Jointly, an efficient high level Message Passing Interface coupling framework is also proposed, granting an universal and flexible way for easy software coupling. A sample code is also given.     

3D model reconstruction with common hand-held cameras

A 3D model reconstruction workflow with hand-held cameras is developed. The exterior and interior orientation models combined with the state-of-the-art structure from motion and multi-view stereo techniques are applied to extract dense point cloud and reconstruct 3D model from digital images. An overview of the presented 3D model reconstruction methods is given. The whole procedure including tie point extraction, relative orientation, bundle block adjustment, dense point production and 3D model reconstruction is all reviewed in brief. Among them, we focus on bundle block adjustment procedure; the mathematical and technical details of bundle block adjustment are introduced and discussed. Finally, four scenes of images collected by hand-held cameras are tested in this paper. The preliminary results have shown that sub-pixel (<1 pixel) accuracy can be achieved with the proposed exterior–interior orientation models and satisfactory 3D models can be reconstructed using images collected by hand-held cameras. This work can be applied in indoor navigation, crime scene reconstruction, heritage reservation and other applications in geosciences.     

An Improved Neural Network with Random Weights Using Backtracking Search Algorithm

This paper proposes a hybrid algorithm by combining backtracking search algorithm (BSA) and a neural network with random weights (NNRWs), called BSA-NNRWs-N. BSA is utilized to optimize the hidden layer parameters of the single layer feed-forward network (SLFN) and NNRWs is used to derive the output layer weights. In addition, to avoid over-fitting on the validation set, a new cost function is proposed to replace the root mean square error (RMSE). In the new cost function, a constraint is added by considering RMSE on both training and validation sets. Experiments on classification and regression data sets show promising performance of the proposed BSA-NNRWs-N.     

A mixed integer programming approach to multi-skilled workforce scheduling

The potential benefits of using human resources efficiently in the service sector constitute an incentive for decision makers in this industry to intelligently manage the work shifts of their employees, especially those dealing directly with customers. In the long term, they should attempt to find the right balance between employing as few labor resources as possible and keeping a high level of service. In the short run (e.g., 1 week), however, contracted staff levels cannot be adjusted, and management efforts thus focus on the efficient assignment of shifts and activities to each employee. This article proposes a mixed integer program model that solves the short-term multi-skilled workforce tour scheduling problem, enabling decision makers to simultaneously design workers’ shifts and days off, assign activities to shifts and assign those to employees so as to maximize and balance coverage of a firm’s demand for on-duty staff across multiple activities. Our model is simple enough to be solved with a commercial MIP solver calibrated by default without recurring to complex methodologies, such as extended reformulations and exact and/or heuristic column generation subroutines. A wide computational testing over 1000 randomly generated instances suggests that the model’s solution times are compatible with daily use and that multi-skilling is a significant source of labor flexibility to improve coverage of labor requirements, in particular when such requirements are negatively correlated and part-time workers are a scarce resource.     

A genetic algorithm for the robust resource leveling problem

The resource leveling problem (RLP) involves the determination of a project baseline schedule that specifies the planned activity starting times while satisfying both the precedence constraints and the project deadline constraint under the objective of minimizing the variation in the resource utilization. However, uncertainty is inevitable during project execution. The baseline schedule generated by the deterministic RLP model tends to fail to achieve the desired objective when durations are uncertain. We study the robust resource leveling problem in which the activity durations are stochastic and the objective is to obtain a robust baseline schedule that minimizes the expected positive deviation of both resource utilizations and activity starting times. We present a genetic algorithm for the robust RLP. In order to demonstrate the effectiveness of our genetic algorithm, we conduct extensive computational experiments on a large number of randomly generated test instances and investigate the impact of different factors (the marginal cost of resource usage deviations, the marginal cost of activity starting time deviations, the activity duration variability, the due date, the order strength, the resource factor and the resource constrainedness).