Ahmed Ghorbel

A survey of control-chart pattern-recognition literature (1991-2010) based on a new conceptual classification scheme

Control Chart Pattern Recognition (CCPR) is a critical task in Statistical Process Control (SPC). Abnormal patterns exhibited in control charts can be associated with certain assignable causes adversely affecting the process stability. Abundant literature treats the detection of different Control Chart Patterns (CCPs). In fact, numerous CCPR studies have been developed according to various objectives and hypotheses. Despite the widespread literature on this topic, efforts to review and analyze research on CCPR are very limited. For this reason, this survey paper proposes a new conceptual classification scheme, based on content analysis method, to classify past and current developments in CCPR research. More than 120 papers published on CCPR studies within 1991-2010 were classified and analyzed. Major findings of this survey include the following. (1) The most popular CCPR studies deal with independently and identically distributed process data. (2) Some recent studies on identification of mean shifts or/and variance shifts of a multivariate process are based on innovative techniques. (3) The percentage of studies that address concurrent pattern identification is increasing. (4) The majority of the reviewed articles use Artificial Neural Network (ANN) approach. Feature-based techniques, in particular wavelet-denoise, are investigated for improving the recognition performance of ANN. For the same reason, there is a general trend followed by many authors who propose hybrid, modular and integrated ANN recognizer designs combined with decision tree learning, particle swarm optimization, etc. (5) There are two main categories of performance criteria used to evaluate CCPR approaches: statistical criteria that are related to two conventional Average Run Length (ARL) measures, and recognition-accuracy criteria, which are not based on these ARL measures. The most applied criteria are recognition-accuracy criteria, mainly for ANN-based approaches. Performance criteria which are related to ARL measures are insufficient and inappropriate in the case of concurrent pattern identification. Finally, this paper briefly discusses some future research directions and our perspectives.

Optimization of a supply portfolio in the context of supply chain risk management: literature review

The aim of this paper is to review the literature in the field of supplier selection under supply chain risk management. Collected papers from 2003 to 2014 are analyzed and classified, first, according to the characteristics of the problem they deal with, secondly, according to the approach they propose, and thirdly, according to the techniques they use. The papers have been grouped into five categories: the first group relates to quantitative approaches to supplier selection, the second concerns qualitative approaches, the third consists of hybrid approaches that blend two or more different approaches together, the fourth relates to simulation approaches and the last group to artificial intelligence. The techniques used in each category are outlined. The different approaches and their associated techniques are analyzed and some recommendations are made on improving their efficiency and performance. This paper is thus a systematic scope review of journal articles and conference papers issued during this period. It brings together a collection of 124 papers on the topic of supplier selection under supply chain risk management.

A comparative study of GOM, uLBP, VLC and fractional Eigenfaces for face recognition

This paper compares four methods of feature extraction: Fractional Eigenfaces and Vander Lugt Correlator as global methods, and Gabor Ordinal Measures and Uniform Local Binary Pattern as local ones. We evaluate the four methods on the standard FERET probe data sets in order to study the robustness of these techniques against illumination variation, facial expression variation and aging. The Gabor ordinal measures as a combination of Gabor filters and ordinal measures outperforms the others methods on the four test sets in terms of recognition rate.

Vibration Analysis of a Nonlinear Drivetrain System in the Presence of Acyclism

Drivetrain vibrations are a great concern in the automotive industry, once they are related to many Noise, Vibration and Harshness (NVH) phenomena. An automobile drivetrain system generally consists of the following main components: engine, clutch, gearbox, disk brake, and transmission shafts. This paper represents a nonlinear dynamic model for each drivetrain components in the presence of the acyclism phenomena (cyclostationary regim). This model is simulated by 18 degrees of freedom. The governing nonlinear time-varying motion equation formulated is resolved by the analytic Runge–Kutta method. The dynamic responses of the clutch and the single-stage helical gear reducer are investigated in the idle engine regime. The results are presented in the time and time-frequency domain by using the Wigner-Ville distribution. The dynamic behavior study of the system, comes to confirm the significant influence of the engine excitation (torque and speed fluctuation), particularly in the case of the gasoline engine acyclism condition.

The effect of the similarity measures and the interpolation techniques on fractional eigenfaces algorithm

Face recognition system is considered as a smart technique for authentication. It guarantees security, stability and variability. It was used in a wide variety of applications like control of access, surveillance, passport and credit cards. Many algorithms were proposed in order to improve the recognition rate. One of these techniques is the fractional Eigenfaces, which combines the Eigenfaces algorithm and the theory of the fractional covariance matrix. In this paper, we highlight the influence of the interpolation and the similarity measurement methods on the efficiency of the fractional Eigenfaces algorithm. Experimental results are evaluated with three image databases: ORL, YALE and UMIST.

Conditional dependence between oil price and stock prices of renewable energy: a vine copula approach

Abstract The current paper focusses on the co-movement between oil prices and renewable energy stock markets in a multivariate framework. The vine copula approach that offers a great flexibility in conditional dependence modelling is used. More specifically, we investigate the issue of the average dependence and co-movement between oil prices (West Texas Intermediate [WTI]) and renewable energy stock prices (Wilder Hill New Energy Global Innovation Index [NEX], Wilder Hill Clean Energy Index [ECO] and S and P Global Clean Energy Index [SPGCE]) by applying the vine copula based threshold generalised autoregressive conditional heteroskedasticity (TGARCH) model. Over the period 2003–2016, empirical findings reveal significant and symmetric dependence between the considered markets. Therefore, there is symmetric tail dependence, indicating the evidence of upper and lower tail dependence. This means that movements in oil prices and renewable energy indices are coupled to the same direction. These empirical insights are of particular interest to policymakers, risk managers and investors in renewable energy sector.

Modal Analysis of the Clutch Single Spur Gear Stage System with Eccentricity Defect

Gears are an important element in a variety of industrial applications. An unexpected failure of the gear may cause significant economic losses. For that reason, fault diagnosis in gears has been the subject of intensive research. Modal analysis can be used in the fault detection of rotating machinery. It can provide natural frequencies and vibration modes which are essential information to learn about most of dynamic characteristics of the combined system. In order to investigate the dynamic behavior of a coupled clutch-gear transmission system in the presence of gear defect, a general dynamic model is developed and a numerical modal analysis technique is achieved. Several types of gear defects that can be found in the literature. In this paper, a gear eccentricity defect is introduced in the model to study their influence on the modal properties. The distributions of modal kinetic and strain energies are presented in the case without and with defect on the geared system, and a comparative study is conducted.

Efficient iris texture analysis method based on Gabor ordinal measures

With the remarkably increasing interest directed to the security dimension, the iris recognition process nis considered to stand as one of the most versatile technique critically useful for the biometric identification and nauthentication process. This is mainly due to every individual’s unique iris texture. A modestly conceived efficient napproach relevant to the feature extraction process is proposed. In the first place, iris zigzag “collarette” is nextracted from the rest of the image by means of the circular Hough transform, as it includes the most significant nregions lying in the iris texture. In the second place, the linear Hough transform is used for the eyelids’ detection npurpose while the median filter is applied for the eyelashes’ removal. Then, a special technique combining the nrichness of Gabor features and the compactness of ordinal measures is implemented for the feature extraction nprocess, so that a discriminative feature representation for every individual can be achieved. Subsequently, the nmodified Hamming distance is used for the matching process. Indeed, the advanced procedure turns out to be nreliable, as compared to some of the state-of-the-art approaches, with a recognition rate of 99.98%, 98.12%, and n95.02% on CASIAV1.0, CASIAV3.0, and IIT Delhi V1 iris databases, respectively.

An efficient iris texture analysis based on HAAR wavelet 2D Log Gabor and monogenic filter

Human iris is a perfect part of the body for biometric identification. In fact, iris patterns are unique and stable that is why two people never occur to have the same iris texture even if they are twins. In this paper, we tried to improve the Rais algorithm feature extraction method. On the one hand, we selected this algorithm thanks to its simplicity as compared to other algorithms that use complex techniques of segmentation such as snake. On the other hand, it has impressive results for some databases like CASIA V1.0 and CASIA V3.0. To enhance Rais algorithm, we suggested using the HAAR wavelet, and the combined 2D Log Gabor filter along with the monogenic filter for feature extraction. Thus, our approach achieved a trade-off between the richness of the HAAR and Gabor features and the distinctiveness of the monogenic features. Daubechies wavelet and the Histogram of Oriented Gradient (HOG) were also tested. The experimental results on the CASIA iris database V3.0 show that the proposed method, using the HAAR wavelet, the combined monogenic filter and 2D Log Gabor filter yields a recognition rate of 94.45 %.

Supplier selection under disruption risks using Stochastic Mixed Linear Programming techniques

Supplier selection is an important key of supply chain management and mainly with the presence of disruption risks. Given a set of customer orders for products, the decision maker needs to decide from which supplier to purchase custom parts required for each customer order to minimize total cost and mitigate the impact of disruption risks. Many approaches have been developed in the literature based on various formal modeling techniques. In this paper, Stochastic Mixed Linear Program (MILP) techniques are used for the selection of suppliers under risk disruption. Two set of disruption scenarios are considered: (1) scenario with independent local disruption of each supplier, and (2) scenario with local and global disruption that may result in all suppliers simultaneously. The two percentiles: Value at risk (VaR) and conditional value at risk (CVaR) are used to model the risk of supply chain disruption. It be concluded that these percentiles are capable to optimizing the supply portfolio by minimizing expects worst-case per part via calculating the value at risk of expected cost part. The extension of this study seems very interesting for the risk analysis in complex supply chains.