Design of the automation system for the chemical water treatment plant of the oil refinery in Santiago de Cuba

Production processes in modern industry demand higher levels of quality and efficiency in their products. The “Hermanos Díaz” Oil Refinery Company of Santiago de Cuba, a fundamental pillar in the economic and social development of the eastern part of the country, has a chemical water treatment plant responsible for supplying processed water to the industry’s boilers. The current state of this plant supports the lack of optimal physical-chemical conditions in the water it delivers and, therefore, the gradual deterioration of the boilers. This work conceives an automation solution for the dosing, precipitation, and clarification processes of the chemical water treatment plant. Control systems were designed based on instrumentation proposals, enabling reliable measurements and practical actions. In addition, an algorithm of supervision and automatic control using a programmable programmable logic controller (PLC) is presented, making the plant capable of delivering a product in optimal conditions. Images were designed for local and remote process control using a human-machine interface (HMI) panel and a supervisory control and data acquisition (SCADA) system. Finally, an automation architecture with a decentralized periphery is proposed to ensure safety and accuracy in the system’s decision-making through communication protocols

Indonesian continuous speech recognition optimization with convolution bidirectional long short-term memory architecture

Speech recognition can be defined as converting voice signals into text or lines of words by using algorithms implemented in computer programs. There are several types of speech recognition, including recognition for isolated word speech, continuous speech, spontaneous speech, and conversational speech. Research on continuous speech recognition, especially in Indonesian, has been developed using both stochastic methods such as Hidden Markov model (HMM) and deep learning methods. Currently, deep learning approaches are more widely used in speech recognition applications. This research optimizes Indonesian speech recognition by adding convolution layers to the bidirectional long short-term memory (Bi-LSTM) architecture. The goal of this research is to find the best architecture so that better Indonesian continuous speech recognition results can be obtained. The dataset used in this research was created by the intelligent systems research group in the Department of Informatics at Universitas Diponegoro. All speakers who participated in this dataset came from five ethnic groups in Indonesia, representing the dialects of their respective ethnic groups. The research results show that by adding a convolution layer to the Bi-LSTM architecture, speech recognition performance increases significantly with an average word error rate (WER) reduction of 15.56% compared to using only the Bi-LSTM architecture

Advanced crop yield prediction using machine learning and deep learning: a comprehensive review

The advancement of machine learning (ML) and deep learning (DL) techniques has significantly improved crop yield prediction, making it more accurate and reliable. In this review, the implementation of ML and DL algorithms for crop yield prediction is thoroughly investigated, focusing on their crucial role in enhancing crop productivity. Along with ML and DL algorithms examine, the review analyses the use of remote sensing technologies, such as satellite and drone data, in providing high-resolution inputs essential for accurate yield predictions. The study identifies the state of art algorithms, most used features, data sources and evaluation metrics, providing a comparison of ML and DL. The findings indicate that DL models are more effective with large datasets, while ML models remain robust for smaller datasets. The future directions are proposed to develop the generalised models for different crops and regions. The review aims to assist researchers by summarising state of art techniques and identifying the present

Jacobian approximation of the Sum-Alpha stopping criterion

This article will report the development of new application of the SumAlpha stopping criterion to the case of log – maximum a posterioru LogMAP turbo decoding. It shows how to adapt Sum-Alphas quantities when using the Log-MAP algorithm and how to deduce a good decision threshold. We apply a logarithm to the quantity Sum-Alpha which is evaluated by the same Jacobian logarithm of the Log-MAP algorithm. We call this new adaptation Jacobian Approximation of Sum-Alpha (JASA) criterion. The simulation results demonstrate that the JASA criterion achieves comparable performance (in terms of bit error rate (BER) and frame error rate (FER)) to the Sum-Alpha and cross-entropy (CE) criteria, with the same average number of iterations

Customer segmentation in e-commerce: K-means vs hierarchical clustering

Customer segmentation is important for e-commerce companies to understand and target different customers. The primary focus of this work is the application and comparison of K-means clustering and hierarchical clustering, unsupervised machine learning techniques, in customer segmentation for e-commerce platforms. Clustering leverages customer search behavior, reflecting brand preferences, and identifying distinct customer segments. The proposed work explores the K-means algorithm and hierarchical clustering. It uses them to classify customers in a standard e-commerce customer dataset, mainly focused on frequently searched brands. Both techniques are compared based on silhouette scores and cluster visualizations. K-means clustering yielded well-separated segments compared to hierarchical clustering. Then, using the K-means algorithm, customers are classified into different segments based on brand search patterns. Further, targeted marketing strategies are discussed for each segment. Results show three customer segments: high searchers-low buyers, loyal customers, and moderate engagers. The proposed work provides valuable insights into customers that could be used for developing targeted marketing campaigns, product recommendations, and customer engagement strategies to enhance the conversion rate, customer satisfaction, and, in turn, the growth of an e-commerce platform

Dual-band MIMO antenna for wideband THz communication in future 6G applications

This paper presents an industrial and innovation dual-band multiple-input multiple-output (MIMO) antenna designed for terahertz (THz) frequencies to enhance future sixth-generation (6G) communication systems. The antenna utilizes a polyimide substrate with a thickness of 12 µm, a dielectric constant of 3.5 and a tangent loss of 0.0027. Both the patch and the ground plane are constructed from copper, ensuring robust performance. The antenna achieves resonance at 5.45 THz with a gain of 14 dB and a bandwidth of 0.7 THz and at 6.34 THz with a gain of 14.44 dB and a bandwidth of 1.77 THz. Additionally, it demonstrates a minor peak at 7.4 THz and a maximum efficiency of 95.87%. The transmission coefficient shows an isolation of -31.01 dB, indicating excellent separation between antenna elements. Key MIMO performance metrics, containing the envelope correlation coefficient (ECC), diversity gain (DG), mean effective gain (MEG), total active reflection coefficient (TARC), and channel capacity loss (CCL), were analyzed, displaying optimum performance. An analogous circuit was designed and simulated in advanced design system (ADS) to validate these discoveries, creating comparable reflection coefficients to those attained from computer simulation technology (CST) simulations. These findings approve the antenna’s possible for THz-band 6G wireless communication applications

A wideband microstrip antenna employing ring and hexadecagonal slots with parasitic elements for W-band applications

This article presents a monopole patch antenna, designed for operation in the W-band. The antenna is constructed on Rogers/RT 5880 dielectric material with dimensions of 3.4×4×0.16 mm³, a loss tangent of 0.0009, and a relative permittivity of 2.2. The initial design features a simple rectangular patch measuring 1.3542×1.0306 mm², powered by a microstrip line using an inset feed. To enhance the bandwidth and gain, two parasitic rectangular elements were added on both sides of the patch in addition to the incorporation of a circular ring slot with the ground plane. Further improvements in bandwidth and return loss were achieved by etching a hexadecagonal-shaped slot on the patch. Simulation results indicate that the optimized design achieves an impedance bandwidth of 28.54 GHz, ranging from 79.67 GHz to 108.21 GHz, centered at 88 GHz. The antenna also shows a maximum return loss of 59 dB and a voltage standing wave ratio (VSWR) of 1.0022. The radiation pattern is directional, with a peak gain of approximately 8.57 dBi, and a maximum directivity of about 8.6 dB, as predicted by the computer simulation technology (CST) frequency-domain solver. These advantageous characteristics make the proposed antenna a suitable choice for point-to-point transmission applications

DDoS attack detection using optimal scrutiny boosted graph convolutional and bidirectional long short-term memory

The distributed denial of service (DDoS) attack occurs when massive traffic from numerous computers is directed to a server or network, causing crashes and disrupting functionality. Such attacks often shut down websites or applications temporarily and remain among the most critical cybersecurity challenges. Detecting DDoS is difficult and must occur before mitigation. Recently, machine learning and deep learning (ML/DL) have been employed for detection; however, architectural limitations restrict their effectiveness against evolving attack methods. This paper presents a novel framework, scrutiny boosted graph convolutional–bidirectional long short-term memory and vision transformer (SBGC-BiLSTM-ViT), which integrates graph convolutional, BiLSTM, and ViT models with machine learning classifiers such as support vector machine (SVM), Naïve Bayes (NB), random forest (RF), and K-nearest neighbors (KNN). The integration enables autonomous extraction of critical features, enhancing precision in detecting and classifying DDoS attacks. To further boost performance, a Bayesian optimization algorithm (BOA) is applied for hyperparameter tuning of SBGC and ML methods. Evaluation on benchmark datasets UNSW-NB15 and CICDDoS2019 demonstrates that the proposed approach achieves higher accuracy and effectively identifies new DDoS variants, outperforming conventional methods

Visible light communication for rapid monitoring of environmental changes using thin film solar cells

This study investigates the use of visible light communication (VLC) for rapid environmental monitoring by leveraging thin film solar cells as signal receivers. VLC, which employs visible light for data transmission, presents an energy-efficient and eco-friendly approach for real-time monitoring. Thin-film solar cells, recognized for their efficiency and low-light performance, function both as environmental sensors and VLC signal receivers. We conducted experiments to evaluate the system's performance across various environmental conditions, such as light intensity and temperature changes. Our findings indicate that thin-film solar cells can swiftly and accurately detect environmental changes while maintaining a low bit error rate for VLC data. The system also shows high responsiveness to rapid light variations, making it well-suited for dynamic monitoring tasks like air quality, humidity, and forest fire detection. This research highlights VLC technology's significant potential for environmental monitoring applications requiring quick, real-time data transmission, and energy efficiency with thin-film solar cells. The integration of this technology promises to enhance environmental monitoring systems, contributing to climate change mitigation and improved environmental management, and sets the stage for developing advanced, sustainable solutions in wireless communication and ecological monitoring

Deep learning-based image super-resolution using generative adversarial networks with adaptive loss functions

This study investigates deep learning based single image super-resolution (SISR) and highlights its revolutionary potential. It emphasizes the significance of SISR, and the transition from interpolation to deep learning driven reconstruction techniques. Generative adversarial network (GAN)- based models, including super-resolution generative adversarial network (SRGAN), video super-resolution network (VSRResNet), and residual channel attention-generative adversarial network (RCA-GAN) are utilised. The proposed technique describes the loss functions of the SISR models. However, it should be noted that the conventional methods frequently fail to recover lost high-frequency details, which signify their limitations. The current visual inspections indicate that the suggested model can perform better than the others in terms of quantitative metrics and perceptual quality. The quantitative results indicate that the utilised model can achieve an average peak signal-to-noise ratio (PSNR) enhancement of X dB and an average structural similarity index (SSIM) increase of Y. A range of improvements of 7.12-23.21% and 2.75-10.00% are obtained for PSNR and SSIM, respectively. Also, the architecture deploys a total of 2,005,571 parameters, with 2,001,475 of these being trainable. These results highlight the model’s efficacy in maintaining key structures and generating visually appealing outputs, supporting its potential implications in fields demanding high-resolution imagery, such as medical imaging and satellite imagery