Optimization of pv cells/modules parameters using a modified quasi-oppositional logistic chaotic rao-1 (QOLCR) algorithm.

Since the behavior of photovoltaic (PV) modules under different operational conditions is highly nonlinear, predicting the performance of PV systems in industrial applications is becoming a major challenge issue. Moreover, the most important information required to configure an optimal PV system is unavailable in all manufacturer's datasheets. In this context, a novel method is recommended to optimize PV cells/module parameters with the ability to correctly characterize the I-V and P-V curves of different PV models. In the present article, a chaotic map is incorporated in the so-called quasi-oppositional Rao-1 algorithm to improve its efficiency, and the resulting algorithm is named quasi-oppositional logistic chaotic Rao-1 (QOLCR) algorithm. Numerical results indicate that the QOLCR algorithm has presented very good performance in terms of accuracy and robustness. The idea is to minimize the root mean square error (RMSE) between the estimated and the actual data. Simulation results in the single diode model give an RMSE of value [Formula: see text], and in the double diode model, an RMSE of value [Formula: see text] has been reached as the minimum value among the other compared optimization methods. Hence, the QOLCR approach also converges faster than the basic Rao-1 algorithm and its other variants. Moreover, the modified QO Rao-1 algorithm shows its perfectness and could be involved as tools for optimal designing of PV systems.

In vitro estimation of fast and slow wave parameters of thin trabecular bone using space-alternating generalized expectation-maximization algorithm.

In testing cancellous bone using ultrasound, two types of longitudinal Biot's waves are observed in the received signal. These are known as fast and slow waves and their appearance depend on the alignment of bone trabeculae in the propagation path and the thickness of the specimen under test (SUT). They can be used as an effective tool for the diagnosis of osteoporosis because wave propagation behavior depends on the bone structure. However, the identification of these waves in the received signal can be difficult to achieve. In this study, ultrasonic wave propagation in a 4mm thick bovine cancellous bone in the direction parallel to the trabecular alignment is considered. The observed Biot's fast and slow longitudinal waves are superimposed; which makes it difficult to extract any information from the received signal. These two waves can be separated using the space alternating generalized expectation maximization (SAGE) algorithm. The latter has been used mainly in speech processing. In this new approach, parameters such as, arrival time, center frequency, bandwidth, amplitude, phase and velocity of each wave are estimated. The B-Scan images and its associated A-scans obtained through simulations using Biot's finite-difference time-domain (FDTD) method are validated experimentally using a thin bone sample obtained from the femoral-head of a 30 months old bovine.