bashar s. bashar

Although theoretical models of manufacturing processes are useful for understanding physical events, it can be challenging to apply them in real-world industrial settings. When huge data are accessible, artificial intelligence approaches in the context of Industry 4.0 can offer effective answers to real production challenges. Deep learning is increasingly being used in the realm of artificial intelligence to address a variety of issues relating to information and communication technology, but it is still limited or perhaps nonexistent in the industrial sector. In this study, wire electrical discharge machining—a sophisticated machining technique primarily used for computer hardware components—is applied to effectively forecast unforeseen occurrences. By identifying hidden patterns in process signals, anomalies, such as changes in the thickness of a machined item, may be efficiently anticipated before they occur. In this study, a model for anomaly detection in the sequence of thickness change in the machined component based on transformers is suggested. Our method is able to achieve 94.32 % and 94.16 % accuracy in Z 135 and Z 15 datasets, respectively. Also, it forecasts the abnormalities inside the sequence 1.1 seconds in advance, according to our tests on a dataset that has been introduced.

To stop vision loss from glaucoma, early identification and regular screening are crucial. Convolutional neural networks (CNN) have been effectively used in recent years to diagnose glaucoma automatically from color fundus pictures. CNNs can extract distinctive characteristics directly from the fundus pictures, as opposed to the current automatic screening techniques. In this study, a CNN-based deep learning architecture is created for the categorization of normal and glaucomatous fundus pictures. In this paper, we propose a deep learning-based framework for the detection of glaucoma based on retinal images. Our proposed approach utilizes the two CNN-based models, namely Inception and DenseNet, in order to classify the input images. We also show the impact of transfer learning on the training and the validation processes and put forward an effective pipeline with lower trainable parameters for the target task. Our experiments on a collected dataset demonstrate the efficacy of the proposed model by achieving an accuracy of 93.84%, a precision of 92.83%, and a recall of 95.00%.

The frighteningly high levels of power consumption at present are caused mainly by the expanding global population and the accessibility of energy-hungry smart technologies. So far, various simulation tools, engineering- and AI-based methodologies have been utilized to anticipate power consumption effectively. While engineering approaches forecast using dynamic equations, AI-based methods forecast using historical data. The modeling of nonlinear electrical demand patterns is still lacking for durable solutions, however, the available approaches are only effective for resolving transient dependencies. Furthermore, because they are only based on historical data, the current methodologies are static in nature. In this research, we present a system based on deep learning to anticipate power consumption while accounting for long-term historical relationships. In our approach, a transformer-based model is used for the prediction of electricity demand on data collected from the regional facilities in Iraq. According to the conducted experiments, our approach claims competitive performance, achieving an error rate of 2.0 in predicting 1-day-ahead of electricity demand in the test samples.

The most commonly used variable speed wind turbine is based on doubly fed induction generator (DFIG). To control the reactive power of DFIG-based wind turbines, several methods are suggested that controls the reactive power of the DFIG with slow dynamics and considerable ripples. This paper proposes a new control method based on the adaptive reference model which controls the active and reactive powers of DFIG with high dynamics and low ripples. Given that, the proposed technique has proportional-integral (PI), selecting the proper coefficient for PI controller is significant. To overcome this problem, the grey-wolf algorithm is used to optimize the PI coefficients. The results show that the proposed method gives satisfactory performance with lower overshoots and faster dynamic response.

Due to the widespread use of antibiotics in geese, water contamination by antibiotics has become a major problem. Photocatalyst semiconductors can play an important role in removing these pollutants from the aquatic environment by using sunlight. In this work, ZnO/Co3O4 nanocomposites as a new magnetic semiconductor is introduced to remove the antibiotics azithromycin and ciprofloxacin. First, the nanocomposite is synthesized by a simple co-precipitation method. Then, the crystalline and morphological properties of the prepared nanocomposite are identified by X-ray powder diffraction (XRD) and scanning electron microscope (SEM) methods. Also, the magnetic properties of the sample are analyzed by vibrating-sample magnetometer (VSM) technique. Because the photocatalytic properties of semiconductors directly depend on their optical properties and energy gap, the optical properties of the prepared nanocomposite are fully studied by the UV method. Photocatalytic results showed that the prepared nanocomposite could significantly remove antibiotics from the waste water. The prepared nanocomposite was able to degradation 84.5 % and 71.7% of ciprofloxacin and azithromycin in 80 minutes under visible light respectively.