Multiple bottlenecks sorting criterion at initial sequence in solving permutation flow shop scheduling problem

This paper proposes a heuristic that introduces the application of bottleneck-based concept at the beginning of an initial sequence determination with the objective of makespan minimization. Earlier studies found that the scheduling activity become complicated when dealing with machine, m greater than 2, known as non-deterministic polynomial-time hardness (NP-hard). To date, the Nawaz-Enscore-Ham (NEH) algorithm is still recognized as the best heuristic in solving makespan problem in scheduling environment. Thus, this study treated the NEH heuristic as the highest ranking and most suitable heuristic for evaluation purpose since it is the best performing heuristic in makespan minimization. This study used the bottleneck-based approach to identify the critical processing machine which led to high completion time. In this study, an experiment involving machines (m =4) and n-job (n = 6, 10, 15, 20) was simulated in Microsoft Excel Simple Programming to solve the permutation flowshop scheduling problem. The overall computational results demonstrated that the bottleneck machine M4 performed the best in minimizing the makespan for all data set of problems

Compressive sampling for accelerometer signals in structural health monitoring

In structural health monitoring (SHM) of civil structures, data compression is often needed to reduce the cost of data transfer and storage, because of the large volumes of sensor data generated from the monitoring system. The traditional framework for data compression is to first sample the full signal and, then to compress it. Recently, a new data compression method named compressive sampling (CS) that can acquire the data directly in compressed form by using special sensors has been presented. In this article, the potential of CS for data compression of vibration data is investigated using simulation of the CS sensor algorithm. For reconstruction of the signal, both wavelet and Fourier orthogonal bases are examined. The acceleration data collected from the SHM system of Shandong Binzhou Yellow River Highway Bridge is used to analyze the data compression ability of CS. For comparison, both the wavelet-based and Huffman coding methods are employed to compress the data. The results show that the values of compression ratios achieved using CS are not high, because the vibration data used in SHM of civil structures are not naturally sparse in the chosen bases

Bayesian wavelet de-noising with the caravan prior

According to both domain expert knowledge and empirical evidence, wavelet coefficients of real signals tend to exhibit clustering patterns, in that they contain connected regions of coefficients of similar magnitude (large or small). A wavelet de-noising approach that takes into account such a feature of the signal may in practice outperform other, more vanilla methods, both in terms of the estimation error and visual appearance of the estimates. Motivated by this observation, we present a Bayesian approach to wavelet de-noising, where dependencies between neighbouring wavelet coefficients are a priori modelled via a Markov chain-based prior, that we term the caravan prior. Posterior computations in our method are performed via the Gibbs sampler. Using representative synthetic and real data examples, we conduct a detailed comparison of our approach with a benchmark empirical Bayes de-noising method (due to Johnstone and Silverman). We show that the caravan prior fares well and is therefore a useful addition to the wavelet de-noising toolbox.Comment: 32 pages, 15 figures, 4 table

N-body simulations with two-orders-of-magnitude higher performance using wavelets

Noise is a problem of major concern for N-body simulations of structure formation in the early Universe, of galaxies and plasmas. Here for the first time we use wavelets to remove noise from N-body simulations of disc galaxies, and show that they become equivalent to simulations with two orders of magnitude more particles. We expect a comparable improvement in performance for cosmological and plasma simulations. Our wavelet code will be described in a following paper, and will then be available on request.Comment: Mon. Not. R. Astron. Soc., in press. The interested reader is strongly recommended to ignore the low-resolution Fig. 3 (and Fig. 4), and to download the full-resolution paper (700 kb) from http://www.oso.chalmers.se/~romeo/Paper_VI.ps.g

A wavelet add-on code for new-generation N-body simulations and data de-noising (JOFILUREN)

Wavelets are a new and powerful mathematical tool, whose most celebrated applications are data compression and de-noising. In Paper I (Romeo, Horellou & Bergh 2003, astro-ph/0302343), we have shown that wavelets can be used for removing noise efficiently from cosmological, galaxy and plasma N-body simulations. The expected two-orders-of-magnitude higher performance means, in terms of the well-known Moore's law, an advance of more than one decade in the future. In this paper, we describe a wavelet add-on code designed for such an application. Our code can be included in common grid-based N-body codes, is written in Fortran, is portable and available on request from the first author. The code can also be applied for removing noise from standard data, such as signals and images.Comment: Mon. Not. R. Astron. Soc., in press. The interested reader is strongly recommended to ignore the low-resolution Figs 10 and 11, and to download the full-resolution paper (800 kb) from http://www.oso.chalmers.se/~romeo/Paper_VII.ps.g

Dynamic Denoising of Tracking Sequences

©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/TIP.2008.920795In this paper, we describe an approach to the problem of simultaneously enhancing image sequences and tracking the objects of interest represented by the latter. The enhancement part of the algorithm is based on Bayesian wavelet denoising, which has been chosen due to its exceptional ability to incorporate diverse a priori information into the process of image recovery. In particular, we demonstrate that, in dynamic settings, useful statistical priors can come both from some reasonable assumptions on the properties of the image to be enhanced as well as from the images that have already been observed before the current scene. Using such priors forms the main contribution of the present paper which is the proposal of the dynamic denoising as a tool for simultaneously enhancing and tracking image sequences.Within the proposed framework, the previous observations of a dynamic scene are employed to enhance its present observation. The mechanism that allows the fusion of the information within successive image frames is Bayesian estimation, while transferring the useful information between the images is governed by a Kalman filter that is used for both prediction and estimation of the dynamics of tracked objects. Therefore, in this methodology, the processes of target tracking and image enhancement "collaborate" in an interlacing manner, rather than being applied separately. The dynamic denoising is demonstrated on several examples of SAR imagery. The results demonstrated in this paper indicate a number of advantages of the proposed dynamic denoising over "static" approaches, in which the tracking images are enhanced independently of each other

Uncovering hidden information and relations in time series data with wavelet analysis: three case studies in finance

This thesis aims to provide new insights into the importance of decomposing aggregate time series data using the Maximum Overlap Discrete Wavelet Transform. In particular, the analysis throughout this thesis involves decomposing aggregate financial time series data at hand into approximation (low-frequency) and detail (high-frequency) components. Following this, information and hidden relations can be extracted for different investment horizons, as matched with the detail components. The first study examines the ability of different GARCH models to forecast stock return volatility in eight international stock markets. The results demonstrate that de-noising the returns improves the accuracy of volatility forecasts regardless of the statistical test employed. After de-noising, the asymmetric GARCH approach tends to be preferred, although that result is not universal. Furthermore, wavelet de-noising is found to be more important at the key 99% Value-at-Risk level compared to the 95% level. The second study examines the impact of fourteen macroeconomic news announcements on the stock and bond return dynamic correlation in the U.S. from the day of the announcement up to sixteen days afterwards. Results conducted over the full sample offer very little evidence that macroeconomic news announcements affect the stock-bond return dynamic correlation. However, after controlling for the financial crisis of 2007-2008 several announcements become significant both on the announcement day and afterwards. Furthermore, the study observes that news released early in the day, i.e. before 12 pm, and in the first half of the month, exhibit a slower effect on the dynamic correlation than those released later in the month or later in the day. While several announcements exhibit significance in the 2008 crisis period, only CPI and Housing Starts show significant and consistent effects on the correlation outside the 2001, 2008 and 2011 crises periods. The final study investigates whether recent returns and the time-scaled return can predict the subsequent trading in ten stock markets. The study finds little evidence that recent returns do predict the subsequent trading, though this predictability is observed more over the long-run horizon. The study also finds a statistical relation between trading and return over the long-time investment horizons of [8-16] and [16-32] day periods. Yet, this relation is mostly a negative one, only being positive for developing countries. It also tends to be economically stronger during bull-periods

Power Quality Disturbance Detection and Classification

Power quality (PQ) monitoring is an essential service that many utilities perform for their industrial and larger commercial customers. Detecting and classifying the different electrical disturbances which can cause PQ problems is a difficult task that requires a high level of engineering knowledge. The vast majority of the disturbances are non-stationary and transitory in nature subsequently it requires advanced instruments and procedures for the examination of PQ disturbances. In this work a hybrid procedure is utilized for describing PQ disturbances utilizing wavelet transform and fuzzy logic. A no of PQ occasions are produced and decomposed utilizing wavelet decomposition algorithm of wavelet transform for exact recognition of disturbances. It is likewise watched that when the PQ disturbances are contaminated with noise the identification gets to be troublesome and the feature vectors to be separated will contain a high amount of noise which may corrupt the characterization precision. Consequently a Wavelet based denoising system is proposed in this work before feature extraction process. Two extremely distinct features basic to all PQ disturbances like Energy and Total Harmonic Distortion (THD) are separated utilizing discrete wavelet transform and is nourished as inputs to the fuzzy expert system for precise recognition and order of different PQ disturbances. The fuzzy expert system classifies the PQ disturbances as well as demonstrates whether the disturbance is unadulterated or contains harmonics. A neural network based Power Quality Disturbance (PQD) recognition framework is additionally displayed executing Multilayer Feedforward Neural Network (MFNN)