Deep Learning Exoplanets Detection by Combining Real and Synthetic Data

Scientists and astronomers have attached Scientists and astronomers have attached great importance to the task of discovering new exoplanets, even more so if they are in the habitable zone. To date, more than 4300 exoplanets have been confirmed by NASA, using various discovery techniques, including planetary transits, in addition to the use of various databases provided by space and ground-based telescopes. This article proposes the development of a deep learning system for detecting planetary transits in Kepler Telescope lightcurves. The approach is based on related work from the literature and enhanced to validation with real lightcurves. A CNN classification model is trained from a mixture of real and synthetic data, and validated only with real data and different from those used in the training stage. The best ratio of synthetic data is determined by the perform of an optimisation technique and a sensitivity analysis. The precision, accuracy and true positive rate of the best model obtained are determined and compared with other similar works. The results demonstrate that the use of synthetic data on the training stage can improve the transit detection performance on real light curves.

A review of codebook design methods for sparse code multiple access

<span lang="EN-GB">The progressions in telecommunication beyond the 5^th generation have created a need to improve research drifts. The current 5G study has an important focus on non-orthogonal multiple access (NOMA) technology. sparse code multiple access (SCMA) is a promising technique within NOMA, enhancing the multi-user handling capability of next-generation communication. In the SCMA sphere, codebook designing and optimisation are essential research matters. This study conversed with different codebook design practises existing in the literature, analysing them for numerous parameters, including bit error rate (BER), an optimisation technique, and channel settings. From the analysis, the paper presents the efficiency of different approaches. The article also discusses the prospects and challenges of SCMA optimisation in practical implementation in various domains.</span>

Comparison Studies Between Machine Learning Optimisation Technique on Predicting Concrete Compressive Strength

In this research, a comparison study of the machine learning (ML) optimisation technique to predict the compressive strength of concrete is discussed. In previous studies, researchers focused on identifying the machine learning model by comparing, ensemble, bagging, and fusion methods in predicting the concrete strength. In this research, an ML model hyper-parameter optimisation is used to improve the prediction accuracy and performance of the model. Extreme gradient boosting (XGBoost) is used as the base model to perform the prediction, as the XGBoost has a built-in model ensemble, bagging, and boosting algorithms. Grid Search, Random Search, and Bayesian Optimisation are selected and used to optimise the hyperparameters of the XGBoost model. For this particular prediction study, the optimised models based on Random Search performed better than other optimisation methods. The Random Search optimisation method showed substantial improvements in prediction accuracy, modelling error and computation time.

A hybrid metaheuristic optimisation technique for load frequency control

This paper investigates the problem of load frequency control by the application of metaheuristic optimisation techniques. In order to analyse the effect of the system model on the performance of the algorithm, two different two-area systems are considered, modelled using the Matlab/Simulink package. The first one consists of a reheat thermal area and a hydro area while the second one is made up of identical reheat thermal areas. To take into consideration the effects of practical constraints, nonlinearities such as Generation Rate Constraint, Governor Deadband and Boiler Dynamics are introduced. The Shuffled Frog Leaping Algorithm and Teaching Learning Based Optimisation are applied followed by a proposed hybrid of both algorithms to tune Proportional-Integral-Derivative (PID) controllers for the different areas of the systems, taking into account step load changes as inputs. The aim of this proposed algorithm is to merge the qualities of the individual algorithms to provide a more efficient one, converging faster to the optimal gains of the controllers. The results obtained proved the satisfactory performances of all algorithms and superiority of the hybrid Shuffled Frog Leaping Algorithm -Teaching Learning Based Optimisation technique in controlling frequency level in both systems investigated, where the main control measures such as peak values, settling times and steady-state values have been considered. Article Highlights When considering a system of electrical mechanisms installed to produce, distribute and use electric energy, it is important to match supply with demand in order to keep frequency almost constant so as to ensure the safe and reliable use of equipment and maintain stability. The combination of two different algorithms efficiently improved the performance of the control system since the new system benefitted from the qualities of both strategies. Algorithm-based controllers provided robust and reliable frequency control and may be used to increase the quality of electrical energy under increasing and constantly changing demand.

On the optimisation efficiency for the inverse identification of constitutive model parameters

The development of full-field measurement techniques paved the way for the design of new mechanical tests. However, because these mechanical tests provide heterogeneous strain fields, no closed-form solution exists between the measured deformation fields and the constitutive parameters. Therefore, inverse identification techniques should be used to calibrate constitutive models, such as the widely known finite element model updating (FEMU) and the virtual fields method (VFM). Although these inverse identification techniques follow distinct approaches to explore full-field measurements, they all require using an optimisation technique to find the optimum set of material parameters. Nonetheless, the choice of a suitable optimisation technique lacks attention and proper research. Most studies tend to use a least-squares gradient-based optimisation technique, such as the Levenberg-Marquardt algorithm. This work analyses optimisation algorithms, gradient-based and -free algorithms, for the inverse identification of constitutive model parameters. To avoid needless implementation and take advantage of highly developed programming languages, the optimisation algorithms available in optimisation libraries are used. A FEMU based approach is considered in the calibration of a thermoelastoviscoplastic model. The material parameters governing strain hardening, temperature and strain rate are identified. Results are discussed in terms of efficiency and the robustness of the optimisation processes.