Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution

Electrocardiogram (ECG) signal is a measure of the heart’s electrical activity. Recently, ECG detection and classification have benefited from the use of computer-aided systems by cardiologists. The goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization (DTO) and Differential Evolution Algorithm (DEA) into a unified algorithm to optimize the hyperparameters of neural network (NN) for boosting the ECG classification accuracy. In addition, we proposed a new feature selection method for selecting the significant feature that can improve the overall performance. To prove the superiority of the proposed approach, several experiments were conducted to compare the results achieved by the proposed approach and other competing approaches. Moreover, statistical analysis is performed to study the significance and stability of the proposed approach using Wilcoxon and ANOVA tests. Experimental results confirmed the superiority and effectiveness of the proposed approach. The classification accuracy achieved by the proposed approach is (99.98%).     

Control schemes for stable teleoperation with communication delay based on IOS small gain theorem

The problem of stabilization of a force-reflecting telerobotic system in presence of time delay in the communication channel is addressed. We introduce an approach that is based on application of the input-to-output stability (IOS) small gain theorem for functional differential equations (FDEs). A version of the stabilization algorithm as well as its two adaptive extensions are proposed. For all these control schemes, the input-to-state stability (ISS) of the overall telerobotic system is guaranteed in the global, global practical, or semiglobal practical sense for any constant communication delay under the assumption that the environmental dynamics satisfy a weak form of finite-gain stability property. As an intermediate step, we formulate and prove a general IOS (ISS) small gain result for FDEs.     

Simultaneous reconstruction of the time-dependent Robin coefficient and heat flux in heat conduction problems

This paper aims to solve an inverse heat conduction problem in two-dimensional space under transient regime, which consists of the estimation of multiple time-dependent heat sources placed at the boundaries. Robin boundary condition (third type boundary condition) is considered at the working domain boundary. The simultaneous identification problem is formulated as a constrained minimization problem using the output least squares method with Tikhonov regularization. The properties of the continuous and discrete optimization problem are studied. Differentiability results and the adjoint problems are established. The numerical estimation is investigated using a modified conjugate gradient method. Furthermore, to verify the performance of the proposed algorithm, obtained results are compared with results obtained from the well-known finite-element software COMSOL Multiphysics under the same conditions. The numerical results show that the proposed algorithm is accurate, robust and capable of simultaneously representing the time effects on reconstructing the time-dependent Robin coefficient and heat flux.     

Optimal control of electromagnetic wave displacement with an unknown velocity of propagation

The aim of this paper is to control an electromagnetic wave that penetrates in a medium with some missing information about its physical properties. The missing value of wave velocity of propagation leads us to use averaged control notion which is recently introduced by E. Zuazua, also the boundary Dirichlet condition is unknown which requires using the notion of no-regret control introduced by J. L. Lions. In this work, we combine these two techniques where we introduce the notion of averaged no-regret control to solve our optimal control problem with missing data. The averaged no-regret control will be characterised by an optimality system.     

A survey of multidisciplinary design optimization methods in launch vehicle design

Optimal design of launch vehicles is a complex problem which requires the use of specific techniques called Multidisciplinary Design Optimization (MDO) methods. MDO methodologies are applied in various domains and are an interesting strategy to solve such an optimization problem. This paper surveys the different MDO methods and their applications to launch vehicle design. The paper is focused on the analysis of the launch vehicle design problem and brings out the advantages and the drawbacks of the main MDO methods in this specific problem. Some characteristics such as the robustness, the calculation costs, the flexibility, the convergence speed or the implementation difficulty are considered in order to determine the methods which are the most appropriate in the launch vehicle design framework. From this analysis, several ways of improvement of the MDO methods are proposed to take into account the specificities of the launch vehicle design problem in order to improve the efficiency of the optimization process.     

Joint loss pattern characterization and unequal interleaved FEC protection for robust H.264 video distribution over wireless LAN

The recently adopted H.264 standard achieves efficient video encoding and bandwidth savings. Thus, designing communication protocols and QoS control mechanisms for H.264 video distribution over wireless IP networks is a topic of intense research interest. Delivering video streams to terminals via a wireless last hop is indeed a challenging task due to the varying nature of the wireless link. While a common approach suggests exploiting the variations of the wireless channel, an alternative is to exploit characteristics of the video stream to improve the transmission. In this paper, we combine both approaches through an efficient wireless loss characterization and a low-delay unequal interleaved FEC protection. Besides deriving new QoS metrics for FEC block allocation, the wireless loss characterization is as well used to adjust the interleaving level depending on the loss correlation exhibited by the wireless channel. This novel unequal interleaved FEC (UI-FEC) protocol allows graceful video quality degradation over error-prone wireless links while minimizing the overall bandwidth consumption and the end-to-end latency.     

Adaptive iterative learning control for robot manipulators

In this paper, we propose some adaptive iterative learning control (ILC) schemes for trajectory tracking of rigid robot manipulators, with unknown parameters, performing repetitive tasks. The proposed control schemes are based upon the use of a proportional-derivative (PD) feedback structure, for which an iterative term is added to cope with the unknown parameters and disturbances. The control design is very simple in the sense that the only requirement on the PD and learning gains is the positive definiteness condition and the bounds of the robot parameters are not needed. In contrast to classical ILC schemes where the number of iterative variables is generally equal to the number of control inputs, the second controller proposed in this paper uses just two iterative variables, which is an interesting fact from a practical point of view since it contributes considerably to memory space saving in real-time implementations. We also show that it is possible to use a single iterative variable in the control scheme if some bounds of the system parameters are known. Furthermore, the resetting condition is relaxed to a certain extent for a certain class of reference trajectories. Finally, simulation results are provided to illustrate the effectiveness of the proposed controllers.     

Fuzzy classification in dynamic environments

The persistence and evolution of systems essentially depend on their adaptivity to new situations. As an expression of intelligence, adaptivity is a distinguishing quality of any system that is able to learn and to adjust itself in a flexible manner to new environmental conditions and such ability ensures self-correction over time as new events happen, new input becomes available, or new operational conditions occur. This requires self-monitoring of the performance in an ever-changing environment. The relevance of adaptivity is established in numerous domains and by versatile real-world applications. The present paper presents an incremental fuzzy rule-based system for classification purposes. Relying on fuzzy min–max neural networks, the paper explains how fuzzy rules can be continuously online generated to meet the requirements of non-stationary dynamic environments, where data arrives over long periods of time. The approach proposed to deal with an ambient intelligence application. The simulation results show its effectiveness in dealing with dynamic situations and its performance when compared with existing approaches.     

Study of the recrystallization process of AlMgSi alloys containing transition elements

The addition of small amounts of the transition elements such as Zr, Mn and Cr, which have a low solubility in the α-aluminium solid solution and thus form dispersoids, to AlMgSi alloys showed that these elements inhibit recrystallization when the alloys are pre-heated prior to deformation. The formation of coarse particles during casting is mainly due to the presence of Fe. This type of particles found, even, in solution treated samples. The particles, with a diameter exceeding 3 μm, accelerate the recrystallization as they provide good sites for nucleation of recrystallization. Precipitate free zones (PFZ's) developed around the coarse particles favour nucleation of recrystallization by subgrain growth. The presence of dispersoid particles is found to shift the recrystallization energy peak towards higher temperatures. Intermediate annealing before deformation allowed to achieve a high rate of deformation by cold rolling due to the removal of the solute from the matrix by the formation of the hardening phases. Optical and transmission electron microscopy, hardness measurements and differential scanning calorimetry (DSC) were used to study the kinetics of precipitation and recrystallization of the AlMgSi alloys.