Mohamed Elleuch

Synchronverter-Based Emulation and Control of HVDC Transmission

This paper presents a new control strategy for high voltage direct current (HVDC) transmission based on the synchronverter concept: the sending-end rectifier controls emulate a synchronous motor (SM), and the receiving end inverter emulates a synchronous generator (SG). The two converters connected with a DC line provide what is called a synchronverter HVDC (SHVDC). The structure of the SHVDC is firstly analyzed. It is shown that the droop and voltage regulations included in the SHVDC structure are necessary and sufficient to well define the behavior of SHVDC. The standard parameters of the SG cannot be directly used for this structure. A specific tuning method of these parameters is proposed in order to satisfy the usual HVDC control requirements. The new tuning method is compared with the standard vector control in terms of local performances and fault critical clearing time (CCT) in the neighboring zone of the link. The test network is a 4-machine power system with parallel HVDC/AC transmission. The results indicate the contribution of the proposed controller to enhance the stability margin of the neighbor AC zone of the link.

A New Design Based-SVM of the CNN Classifier Architecture with Dropout for Offline Arabic Handwritten Recognition

In this paper we explore a new model focused on integrating two classifiers; Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for offline Arabic handwriting recognition (OAHR) on which the dropout technique was applied. The suggested system altered the trainable classifier of the CNN by the SVM classifier. A convolutional network is beneficial for extracting features information and SVM functions as a recognizer. It was found that this model both automatically extracts features from the raw images and performs classification. Additionally, we protected our model against over-fitting due to the powerful performance of dropout. In this work, the recognition on the handwritten Arabic characters was evaluated; the training and test sets were taken from the HACDB and IFN/ENIT databases. Simulation results proved that the new design based-SVM of the CNN classifier architecture with dropout performs significantly more efficiently than CNN based-SVM model without dropout and the standard CNN classifier. The performance of our model is compared with character recognition accuracies gained from state-of-the-art Arabic Optical Character Recognition, producing favorable results.

Comparison between OptiSlip and Fixed Speed wind energy conversion systems

A comparison of the performances between the OptiSlip pitch regulated wind system (Vestas V39-600) and a fixed speed stall regulated wind system of the same power. This comparison will relate to the quality of the power generated by each of the two systems and their stabilities following a grid fault.

Improving halva quality with dietary fibres of sesame seed coats and date pulp, enriched with emulsifier

Supplementation of halva with waste products of manufacturing, for example defatted sesame seed coats (testae) and date fibre concentrate, can improve its nutritional and organoleptic qualities. These constituents provide high fibre content and technological potential for retaining water and fat. Standard halva supplemented with date fibre concentrate, defatted sesame testae and emulsifier was evaluated for oil separation, texture and colour changes, sensory qualities and acceptability to a taste panel. Addition of both fibres with an emulsifier, improved emulsion stability and increased the hardness of halva significantly. The functional properties of sesame testae and date fibres promote nutrition and health, supplying polyphenol antioxidants and laxative benefits.

Sesame (Sesamum indicum L.) Seeds in Food, Nutrition, and Health

Publisher Summary This chapter outlines the nutritional and health benefits of sesame seeds. Sesame seeds have been used as a health food for disease prevention in Asian countries for several thousand years. They significantly increase plasma g-tocopherol and enhance vitamin E activity, which are believed to prevent human aging-related diseases such as cancer and heart disease. Culinary use of sesame seed includes the decoration of bread and cookies, to produce paste added to certain dishes, and in desserts such as sweetened tahin. Sesame oil is a cooking and salad oil. Nutritionally, sesame seeds are rich in oil with high levels of unsaturated fatty acids, mainly oleic and linoleic, protein, specially high levels of methionine, and micronutrients such as minerals, lignans, tocopherol, and phytosterol. Studies have shown that sesame oil can inhibit human colon cancer growth in vitro, lower blood pressure, decrease lipid peroxidation, and increase antioxidant status in hypertensive patients. In vitro and animal studies have shown that sesame seed is a rich source of mammalian lignan precursors, which may have protective effects against hormone-related diseases such as breast cancer. Sesamin, a major lignan of sesame seeds, exerts multiple functions, such as an antihypertensive effect, and cholesterol, lipid-lowering, and anticancer activities. It also induces growth inhibition in human cancer cells by regulating cyclin D1 protein expression in various kinds of human tumor cells. Sesame seed may induce allergenic symptoms such as urticaria/angioedema, allergic rhinitis, asthma, and even anaphylaxis.

Real time hand gesture recognition system for android devices

Hand gestures are natural and intuitive communication way for the human being to interact with his environment. They serve to designate or manipulate objects, to enhance speech, or communicate in a noisy place. They can also be a separate language. Gestures can have different meanings according to the language or culture. They can also be a way to interact with machines. The subject of our research concerns the design and development of computer vision methods for recognizing hand gestures by a mobile device. We have proposed a system based on SVM for recognizing various hand gestures. The system consists of four steps: hand segmentation, smoothing, feature extraction and classification. The idea here is to allow the smartphone to perform all necessary steps to recognize gestures without the need to connect to a computer in which a database is located to perform training process. With this system, all steps can be done by the smartphone. In this paper, for image acquisition, frontal camera of the smartphone is used. After that frames are gotten from the video, the color sampling is done which is followed by making binary representation of the hand, and then contours representing the hand were described with convex polygons to get information about fingertips and finally the input gesture was recognized using proper classifier.

Dietary Fibre Characteristics and Antioxidant Activity of Sesame Seed Coats (Testae)

The dietary fibre contained in the seed coats (testae) of sesame, by-products of the dehulling processes during the manufacture of sweetened sesame paste (halaweh), were evaluated with two assays: the AOAC enzymatic-gravimetric method and the enzymatic-chemical method. Functional properties and antioxidant activity of sesame seed coats were also determined. The total, insoluble, and soluble dietary fibre contents measured were significantly higher using the AOAC method, than with the enzymatic-chemical method. The dietary fibre contained high amounts of neutral sugars (15.11 g/100 g seed coat dry matter), insoluble uronic acids (10.52 g/100 g seed coat dry matter), and lignin (5.42 g/100 g seed coat dry matter). Physical property analyses showed a high positive correlation between particle size reduction of seed coat, water holding capacity, and oil holding capacity; however, there was a negative correlation with bulk density. Sesame testae showed a relatively high polyphenol content (9.9 mg/g of seed coat dry matter). Aqueous methanol, ethanol, and acetone extracts of seed coats yielded similar polyphenol levels (∼75 mg/g of extract), higher than those found in aqueous extracts (52.7 mg/g of extract). Aqueous organic solvent extracts possessed higher antioxidant activity than water extracts. Our results suggested that sesame seed coats can be used in the preparation of low calorie, high fibre, and antioxidant-rich foods.

DPC for three-phase inverter to improve the integration of wind turbine associated to Flywheel Energy Storage System into the grid

A Flywheel Energy Storage System (FESS) is associated to Wind Turbine (WT) to increase its penetration rate into the grid and to improve its contribution to the ancillary services. In this paper, the authors investigated a FESS associated to PMSM-wind turbine connected to the grid via a three-phase PWM inverter. The FESS is controlled by a FOC (Flux Oriented Control) strategy cascaded with an external power loop for the supervision of the power reference value. A Direct Power Control (DPC) strategy is proposed to control the grid-connected converter due to its high transient capability and its constant switching behavior.

Arabic handwritten characters recognition using Deep Belief Neural Networks

In the handwriting recognition field, the deep learning is becoming the new trend thanks to their ability to deal with unlabeled raw data especially with the huge size of raw data available nowadays. In this paper, we investigate Deep Belief Neural Network (DBNN) for Arabic handwritten character/word recognition. The proposed system takes the raw data as input and proceeds with a grasping layer-wise unsupervised learning algorithm. The approach was tested on two different databases. For the character level one, the results were promising with an error classification rate of 2.1% on the HACDB database. Unlike, the character level, the evaluation on the ADAB database to deal with word level shows an error rate which exceeds the 40%. Hence, the proposed DBNN structure is not already able to deal with high-level dimensional data and thus has to be improved.

An Improved Arabic Handwritten Recognition System using Deep Support Vector Machines

Deep learning algorithms, as a machine learning algorithms developed in recent years, have been successfully applied in various domains of computer vision, such as face recognition, object detection and image classification. These Deep algorithms aim at extracting a high representation of the data via multi-layers in a deep hierarchical structure. However, to the authors knowledge, these deep learning approaches have not been extensively studied to recognize Arabic Handwritten Script AHS. In this paper, they present a deep learning model based on Support Vector Machine SVM named Deep SVM. This model has an inherent ability to select data points crucial to classify good generalization capabilities. The deep SVM is constructed by a stack of SVMs allowing to extracting/learning automatically features from the raw images and to perform classification as well. The Multi-class SVM with an RBF kernel, as non-linear discriminative features for classification, was chosen and tested on Handwritten Arabic Characters Database HACDB. Simulation results show the effectiveness of the proposed model.