Modelling of a pressure regulator

With the increase of gas consumption and the expansion of the associated distribution network, a research program was set up to study the stability and to simulate the main characteristics of the dynamic behaviour of any type of pressure regulator. The modelling of a pressure regulating station is based on hydraulic, mechanical and valve models. The library of models is validated on one type of pressure regulator and simulations are in good agreement with measurements. The study showed that the operating conditions and installation requirements have the greatest influence on the stability of the pressure regulator. From measurements and simulations, the amplitudes of the downstream pressure are particularly sensitive to the size of the downstream volume and to upstream pressure.     

Solvability and asymptotic behavior of generalized Riccatiequations arising in indefinite stochastic LQ controls

The optimal control problem in a finite time horizon with an indefinite quadratic cost function for a linear system subject to multiplicative noise on both the state and control can be solved via a constrained matrix differential Riccati equation. In this paper, we provide general necessary and sufficient conditions for the solvability of this generalized differential Riccati equation. Furthermore, its asymptotic behavior is investigated along with its connection to the generalized algebraic Riccati equation associated with the linear quadratic control problem in finite time horizon. Examples are presented to illustrate the results established     

A multi-biometric iris recognition system based on a deep learning approach

Multimodal biometric systems have been widely applied in many real-world applications due to its ability to deal with a number of significant limitations of unimodal biometric systems, including sensitivity to noise, population coverage, intra-class variability, non-universality, and vulnerability to spoofing. In this paper, an efficient and real-time multimodal biometric system is proposed based on building deep learning representations for images of both the right and left irises of a person, and fusing the results obtained using a ranking-level fusion method. The trained deep learning system proposed is called IrisConvNet whose architecture is based on a combination of Convolutional Neural Network (CNN) and Softmax classifier to extract discriminative features from the input image without any domain knowledge where the input image represents the localized iris region and then classify it into one of N classes. In this work, a discriminative CNN training scheme based on a combination of back-propagation algorithm and mini-batch AdaGrad optimization method is proposed for weights updating and learning rate adaptation, respectively. In addition, other training strategies (e.g., dropout method, data augmentation) are also proposed in order to evaluate different CNN architectures. The performance of the proposed system is tested on three public datasets collected under different conditions: SDUMLA-HMT, CASIA-Iris-V3 Interval and IITD iris databases. The results obtained from the proposed system outperform other state-of-the-art of approaches (e.g., Wavelet transform, Scattering transform, Local Binary Pattern and PCA) by achieving a Rank-1 identification rate of 100% on all the employed databases and a recognition time less than one second per person.     

Ultrasonic Monitoring of Material Degradation in FRP Composites

This research uses laser ultrasonic techniques to monitor a (directly measurable) ultrasonic property—frequency-dependent Rayleigh wave velocity (material dispersion)—and then relates changes in this acoustic property to changes in the material's properties (such as stiffness) that characterize damage. The subject material system is a thick, glass-reinforced, vinylester (thermosetting) fiber-reinforced polymer (FRP) composite. Laser ultrasonics is an ideal methodology to monitor changes in the Rayleigh phase (or group) velocity of this material because of its high fidelity, broad bandwidth, point source∕receiver, and noncontact nature. The experimental procedure consists of measuring a series of transient elastic waveforms in a thick FRP specimen and then operating on these waveforms with the 2D fast Fourier transform to develop the dispersion relationship for that specimen. Material degradation (damage) is introduced into these specimens with environmental aging, and the dispersion curves are used to quantitatively track changes in material properties as a function of degradation.     

Twisted polymers: Structural reorganization in macromolecular network through metal ion coordination

The alkylation of flexible styrene–divinylbenzene copolymer, with 5-chloromethyl-8-hydroxyquinoline in the “impregnated” dry state in the presence of aluminum chloride in nitrobenzene, is accompanied by structural reorganization. The polymer external shape is twisted and the internal structure constringes or expands, according to the impregnation method. The process of the “twist” polymer formation can be explained by the coordination of aluminum ions by the oxine ligand concurrently with the alkylation.     

Fault-Tolerant Rate-Monotonic Scheduling

Due to the critical nature of the tasks in hard real-time systems, it is essential that faults be tolerated. In this paper, we present a scheme which can be used to tolerate faults during the execution of preemptive real-time tasks. We describe a recovery scheme which can be used to re-execute tasks in the event of single and multiple transient faults and discuss conditions that must be met by any such recovery scheme. We then extend the original Rate Monotonic Scheduling (RMS) scheme and the exact characterization of RMS to provide tolerance for single and multiple transient faults. We derive schedulability bounds for sets of real-time tasks given the desired level of fault tolerance for each task or subset of tasks. Finally, we analyze and compare those bounds with existing bounds for non-fault-tolerant and other variations of RMS.     

High dimension dynamic programming model for water resources expansion projects

An optimization model for High Dimension Dynamic Programming (HDDP) was developed to determine the optimal size of water resources projects within a planning period. The model uses Objective Space Dynamic Programming (OSDP) technique to determine the size of the projects and a Mixed Integer Programming (MIP) formulation to overcome the ‘inner’ and ‘outer’ problems of OSDP and to check for the global optimality of the solution. The model is applied to determine the optimal capacity of proposed desalination plants needed to satisfy a number of demand points from different cities, during a planning period of 20 years in Egypt. The model’s speed towards the optimal solution depends on the objective space bounds and search method for these bounds. The integration of OSDP and MIP is shown to be an efficient approach for solving optimization problems. Results show that the model is well suited for solving large-scale water resources expansion problems.