Search Results (5)

The quest for an intelligence compliance system to solve power stability problems in real-time with high predictive accuracy, and efficiency has led to the discovery of deep learning (DL) techniques. This paper investigates the potency of several artificial neural network (ANN) techniques in assessing the steady-state stability of a power system. The new voltage stability pointer (NVSP) was employed to parameterize and reduce the input data to the neural network algorithms to predict the proximity of power systems to voltage instability. In this study, we consider five neural network algorithms viz. feedforward neural network (FFNN), cascade-forward neural network (CFNN), layer recurrent neural network (LRNN), linear layer neural network (LLNN), and Elman neural network (ENN). The evaluation is based on the predictability and accuracy of these techniques for dynamic stability in power systems. The neural network algorithms were trained to mimic the NVSP dataset using a Levenberg-Marquardt (LM) model. Similarly, the performance analyses of the neural network techniques were deduced from the regression learner algorithm (RLA) using a root-mean-squared error (rmse) and response plot graph. The effectiveness of these NN algorithms was demonstrated on the IEEE 30-bus system and the Nigerian power system. The simulation results show that the FFNN and the CFNN possess a relatively better performance in terms of accuracy and efficiency for the considered power networks. Full article

Network latency is a crucial factor affecting the quality of communications networks due to the irregularity of vehicular traffic. To address the problem of performance degradation or instability caused by latency in vehicular networks, this paper proposes a time delay prediction algorithm, in which digital twin technology is employed to obtain a large quantity of actual time delay data for vehicular networks and to verify autocorrelation. Subsequently, to meet the prediction conditions of the ARMA time series model, two neural networks, i.e., Radial basis function (RBF) and Elman networks, were employed to construct a time delay prediction model. The experimental results show that the average relative error of the RBF is 7.6%, whereas that of the Elman-NN is 14.2%. This indicates that the RBF has a better prediction performance, and a better real-time performance than the Elman-NN. Full article

Unconsolidated earthen surface materials can retain heavy metals originating from different sources. These metals are dangerous to humans as well as the immediate environment. This danger leads to the need to assess various geochemical conditions of the materials. In this study, the assessment of topsoil materials’ contamination with heavy metals (HMs) was conducted. The material’s representative spatial samples were taken from various sources: agricultural, industrial, and residential areas. The materials include topsoil, eolian deposits, and other unconsolidated earthen materials. The samples were analyzed using the ICP-OES. The obtained results based on the experimental procedure indicated that the average levels of the heavy metals were: As (1.21 ± 0.69 mg/kg), Ba (110.62 ± 262 mg/kg), Hg (0.08 ± 0.18 mg/kg), Pb (6.34 ± 14.55 mg/kg), Ni (8.95 ± 5.66 mg/kg), V (9.98 ± 6.08 mg/kg), Cd (1.18 ± 4.33 mg/kg), Cr (31.79 ± 37.9 mg/kg), Cu (6.76 ± 12.54 mg/kg), and Zn (23.44 ± 84.43 mg/kg). Subsequently, chemometrics modeling and a prediction of Cr concentration (mg/kg) were performed using three different modeling techniques, including two artificial intelligence (AI) techniques, namely, generalized neural network (GRNN) and Elman neural network (Elm NN) models, as well as a classical multivariate statistical technique (MST). The results indicated that the AI-based models have a superior ability in estimating the Cr concentration (mg/kg) than MST, whereby GRNN can enhance the performance of MST up to 94.6% in the validation step. The concentration levels of most metals were found to be within the acceptable range. The findings indicate that AI-based models are cost-effective and efficient tools for trace metal estimations from soil. Full article

To eliminate the noise and temperature drift in an Micro-Electro-Mechanical Systems (MEMS) gyroscope’s output signal for improving measurement accuracy, a parallel processing model based on Multi-objective particle swarm optimization based on variational modal decomposition-time-frequency peak filter (MOVMD–TFPF) and Beetle antennae search algorithm- Elman neural network (BAS–Elman NN) is established. Firstly, variational mode decomposition (VMD) is optimized by multi-objective particle swarm optimization (MOPSO); then, the best decomposition parameters [k_best,a_best] can be obtained. Secondly, the gyroscope output signals are decomposed by VMD optimized by MOPSO (MOVMD); then, the intrinsic mode functions (IMFs) obtained after decomposition are classified into a noise segment, mixed segment, and drift segment by sample entropy (SE). According to the idea of a parallel model, the noise segment can be discarded directly, the mixed segment is denoised by time-frequency peak filtering (TFPF), and the drift segment is compensated at the same time. In the compensation part, the beetle antennae search algorithm (BAS) is adopted to optimize the network parameters of the Elman neural network (Elman NN). Subsequently, the double-input/single-output temperature compensation model based on the BAS-Elman NN is established to compensate the drift segment, and these processed segments are reconstructed to form the final gyroscope output signal. Experimental results demonstrate the superiority of this parallel processing model; the angle random walk of the compensated gyroscope output is decreased from 0.531076 to 5.22502 × 10⁻³°/h/√Hz, and its bias stability is decreased from 32.7364°/h to 0.140403°/h, respectively. Full article

Being reliant on Air Conditioning (AC) throughout the majority of the year, desert countries with extremely hot weather conditions such as Qatar are facing challenges in lowering weariness cost due to AC On-Off switching while maintaining an adequate level of comfort under a wide-range of ambient temperature variations. To address these challenges, this paper investigates an optimal On-Off control strategy to improve the AC utilization process. To overcome complexities of online optimization, a Elman Neural Networks (NN)-based estimator is proposed to estimate real values of the outdoor temperature, and make off-line optimization available. By looking up the optimum values solved from an off-line optimization scheme, the proposed control solutions can adaptively regulate the indoor temperature regardless of outdoor temperature variations. In addition, a cost function of multiple objectives, which consider both Coefficient of Performance (COP), and AC compressor weariness due to On-Off switching, is designed for the optimization target of minimum cost. Unlike conventional On-Off control methodologies, the proposed On-Off control technique can respond adaptively to match large-range (up to 20 ${}^{\circ }$ C) ambient temperature variations while overcoming the drawbacks of long-time online optimization due to heavy computational load. Finally, the Elman NN based outdoor temperature estimator is validated with an acceptable accuracy and various validations for AC control optimization under Qatar’s real outdoor temperature conditions, which include three hot seasons, are conducted and analyzed. The results demonstrate the effectiveness and robustness of the proposed optimal On-Off control solution. Full article