Search Results (22)

With the exponential growth in transactions and exchanges carried out via the Internet, the risks of the falsification and distortion of information are multiplying, encouraged by widespread access to the virtual world. In this context, digital image watermarking has emerged as an essential solution for protecting digital content by enhancing its durability and resistance to manipulation. However, no current digital watermarking technology offers complete protection against all forms of attack, with each method often limited to specific applications. This field has recently benefited from the integration of deep learning techniques, which have brought significant advances in information security. This article explores the implementation of digital watermarking in embedded systems, addressing the challenges posed by resource constraints such as memory, computing power, and energy consumption. We propose optimization techniques, including frequency domain methods and the use of lightweight deep learning models, to enhance the robustness and resilience of embedded systems. The experimental results validate the effectiveness of these approaches for enhanced image protection, opening new prospects for the development of information security technologies adapted to embedded environments. Full article

Newborns cry intensely, and most parents struggle to understand the reason behind their crying, as the baby cannot verbally express their needs. This makes it challenging for parents to know if their child has a need or a health issue. An embedded solution based on a Raspberry Pi is presented to address this problem. The module analyzes audio techniques to capture, analyze, classify, and remotely monitor a baby’s cries. These techniques rely on prosodic and cepstral features, such as MFCC coefficients. They can differentiate the reason behind a baby’s cry, such as hunger, stomach pain, or discomfort. A machine learning model was trained to anticipate the reason based on audio features. The embedded system includes a microphone to capture real-time cries and a display screen to show the anticipated reason. In addition, the system sends the collected data to a web server for storage, enabling remote monitoring and more detailed data analysis. A cell phone application has also been developed to notify parents in real time of why their baby is crying. This application enables parents to adapt quickly and efficiently to their infant’s needs, even when they are not around. Full article

The accurate prediction of temperature in Permanent Magnet Synchronous Motors (PMSMs) has always been essential for monitoring performance and enabling predictive maintenance in the industrial sector. This study examines the efficiency of a set of artificial neural network (ANN) models, namely Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN), in predicting the Permanent Magnet Temperature. A comparative evaluation study is conducted using common performance indicators, including root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²), to assess the predictive accuracy of each model. The intent is to identify the most favorable model that balances high accuracy with low computational cost. Full article

Dual-active-bridge (DAB) DC–DC converters are of great interest for DC–DC conversion in battery electric vehicle (BEV) powertrain applications. There are two versions of DAB DC–DC converters: single-phase (1p) and three-phase (3p) architectures. Many studies have compared these architectures, selecting the 3p topology as the most efficient. However, there is a gap in the literature when comparing both architectures when single-phase-shift (SPS) modulation is not used to drive the converter. The aim of this study was to compare 1p and 3p DAB DC–DC converters driven by optimal modulation techniques appropriate for BEV powertrain applications. Mathematical loss models were derived for both architectures, and their performances were compared. A case study of a 100 kW converter was considered as an example to visualize the overall efficiency of the converter for each layout. The 1p DAB DC–DC converter architecture outperformed the 3p layout in both its Y–Y and D–D transformer configurations. The higher performance efficiency, lower number of components, and reduced design complexity make the 1p DAB DC–DC converter topology a favorable choice for BEV powertrain applications. Full article

Phosphorus is a vital nutrient essential for plant development and numerous biological functions. It is primarily obtained from phosphate rock through a process known as beneficiation. However, the declining reserves of high-grade phosphate rock, combined with the uneven global distribution of phosphorus and the environmental impacts associated with its extraction and use, highlight the need for a more efficient management of this critical resource. Increasingly, alternative sources of phosphorus, such as extraction from waste materials, are being explored. This study aims to assess the feasibility of recovering phosphorus from coarse rejects produced during phosphate beneficiation at a phosphate washing plant. Before conducting laboratory preparation and reverse flotation tests, the sample underwent initial laboratory examination and analysis. The sample was found to contain low-grade apatite minerals with a phosphorus pentoxide (P₂O₅) content ranging from 19% to 20%. Additionally, carbonate and quartz were identified as the primary accompanying minerals. Flotation experiments yielded a phosphorus recovery rate of 29% P₂O₅, with a carbonation rate of 1.6. Although this recovery rate is slightly below the commercial phosphate standard of 30% P₂O₅, it represents a significant improvement and demonstrates potential for further optimization to meet industry requirements. Consequently, these coarse discarded rejects could serve as a supplementary source of phosphorus in the future. Full article

Reverse flotation is a commonly used method for separating carbonate minerals from apatite, but its application to phosphate beneficiation coarse rejects, which are low in P₂O₅, is often costly due to the high collector dosages used. This study aimed to explore alternative techniques for preconcentration before flotation to improve recovery rates and reduce costs. Our investigation focused on dense medium separation and acid leaching. Dense medium separation, conducted at a cut-off density of 2.76, yielded a preconcentrate with 27% P₂O₅ and a recovery rate of 90%. The feed material had an initial P₂O₅ content of 20.52% and a particle size range of +40 µm to −4 mm. In contrast, acid leaching, employing an 8% acetic acid solution over 35 min, yielded a concentrate with 29.11% P₂O₅, an LOI of 8.99%, and a recovery rate of 100% from an ore fraction [400–200 µm] with an initial P₂O₅ content of 22.82% and an LOI of 15.78%. Furthermore, integrating flotation and leaching resulted in a concentrate with 32.27% P₂O₅ and a recovery rate of 98.38%. These findings suggest that combining acid leaching with flotation can enhance P₂O₅ recovery and reduce processing costs for low-grade phosphate ores. Full article

This paper presents a comprehensive and comparative study of solar energy forecasting in Morocco, utilizing four machine learning algorithms: Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), recurrent neural networks (RNNs), and artificial neural networks (ANNs). The study is conducted using a smart metering device designed for a photovoltaic system at an industrial site in Benguerir, Morocco. The smart metering device collects energy usage data from a submeter and transmits it to the cloud via an ESP-32 card, enhancing monitoring, efficiency, and energy utilization. Our methodology includes an analysis of solar resources, considering factors such as location, temperature, and irradiance levels, with PVSYST simulation software version 7.2, employed to evaluate system performance under varying conditions. Additionally, a data logger is developed to monitor solar panel energy production, securely storing data in the cloud while accurately measuring key parameters and transmitting them using reliable communication protocols. An intuitive web interface is also created for data visualization and analysis. The research demonstrates a holistic approach to smart metering devices for photovoltaic systems, contributing to sustainable energy utilization, smart grid development, and environmental conservation in Morocco. The performance analysis indicates that ANNs are the most effective predictive model for solar energy forecasting in similar scenarios, demonstrating the lowest RMSE and MAE values, along with the highest R² value. Full article

This article presents an innovative approach to analyzing and extracting electrocardiogram (ECG) signals from the abdomen and thorax of pregnant women, with the primary goal of isolating fetal ECG (fECG) and maternal ECG (mECG) signals. To resolve the difficulties related to the low amplitude of the fECG, various noise sources during signal acquisition, and the overlapping of R waves, we developed a new method for extracting ECG signals using blind source separation techniques. This method is based on independent component analysis algorithms to detect and accurately extract fECG and mECG signals from abdomen and thorax data. To validate our approach, we carried out experiments using a real and reliable database for the evaluation of fECG extraction algorithms. Moreover, to demonstrate real-time applicability, we implemented our method in an embedded card linked to electronic modules that measure blood oxygen saturation (SpO2) and body temperature, as well as the transmission of data to a web server. This enables us to present all information related to the fetus and its mother in a mobile application to assist doctors in diagnosing the fetus’s condition. Our results demonstrate the effectiveness of our approach in isolating fECG and mECG signals under difficult conditions and also calculating different heart rates (fBPM and mBPM), which offers promising prospects for improving fetal monitoring and maternal healthcare during pregnancy. Full article

While digital twins (DTs) have recently gained prominence as a viable option for creating reliable asset representations, many existing frameworks and architectures in the literature involve the integration of different technologies and paradigms, including the Internet of Things (IoTs), data modeling, and machine learning (ML). This complexity requires the orchestration of these different technologies, often resulting in subsystems and composition frameworks that are difficult to seamlessly align. In this paper, we present a scalable compositional framework designed for the development of a DT-based production management system (PMS) with advanced production monitoring capabilities. The conducted approach used to design the compositional framework utilizes the Factory Design and Improvement (FDI) methodology. Furthermore, the validation of our proposed framework is illustrated through a case study conducted in a phosphate screening station within the context of the mining industry. Full article

This study conducted an in-depth analysis of the performance of the largest Grid-Connected Solar Photovoltaic System in Burkina Faso from 2019 to 2021. The research utilized measured data and simulated the plant’s performance using the PVGIS database. The results revealed that the months with high solar radiation were the most energy-productive, indicating a direct correlation between solar irradiance and energy generation. During the rainy season (July and August), the PV plant exhibited the highest conversion efficiency. Conversely, the hot season (March and April) was associated with the lowest conversion efficiencies, with module temperatures reaching approximately 47 °C. Efficiency decreased from 12.29% in 2019 to 12.10% in 2021. The system’s performance ratio ranged from 80.73% in 2019 to 79.36% in 2021, while the capacity factor varied from 19.89% in 2019 to 19.33% in 2021. The final yield, measured in hours per day, was 4.89 h/d in 2019, 4.61 h/d in 2020, and 4.92 h/d in 2021. These findings highlight the deterioration in the performance of the Zagtouli PV plant over time. The study emphasizes the utility of using PVGIS-SARAH2 to forecast solar radiation and estimate energy output in PV systems. A semi-automatic cleaning system is used to clean the modules. This cleaning mechanism is inefficient because it is inconsistent. To increase the PV plant’s effectiveness, improved cleaning systems with more advanced mechanisms are required. This research, the first of its kind on the largest PV power plant connected to Burkina Faso’s national grid, serves as a valuable model for other power plants currently under construction or in the planning stages. Full article

This paper presents a systematic and comprehensive mathematical model for alkaline water electrolyzer cells, which can be used for simulation and analysis. The model accounts for factors such as gas evolution reactions, dissolution of gases in the electrolyte, bubble formation, and charge transport. It is based on a numerical two-phase model using the Euler-Euler approach, which has been validated against experimental data for various current densities. The study compares the impact of varying potassium hydroxide (KOH) concentration, separator porosity, and electrolyte flow rates on two-phase flow and bubble coverage. Therefore, the electrolyte in the cell consists of a solution of potassium hydroxide in water. The formation of gas bubbles at the electrodes decreases the electrolyte’s ionic conductivity. Additionally, the presence of these bubbles on the electrode surfaces reduces the available surface area for electrochemical reactions, leading to an increase in the overpotential at a given current density. Furthermore, this paper demonstrates how a neural network and ensembled tree model can predict hydrogen production rates in an alkaline water electrolysis process. The trained neural network accurately predicted the hydrogen production rates, indicating the potential of using neural networks for optimization and control of alkaline water electrolysis processes. The model has an average R-squared value of 0.98, indicating a good fit to the data. A new method of describing bubble transfer, “bubble diffusion,” is introduced to improve performance and reduce costs. The model is solved using COMSOL Multi physics 6.0. The machine learning models in this study were built, trained, and tested using MATLAB software R2020a. Full article

Contingency analysis plays an important role in assessing the static security of a network. Its purpose is to check whether a system can operate safely when some elements are out of service. In a real-time application, the computational time required to perform the calculation is paramount for operators to take immediate actions to prevent cascading outages. Therefore, the numerical performance of the contingency analysis is the main focus of this current research. In power flow calculation, when solving the network equations with a sparse matrix solver, most of the time is spent factorizing the Jacobian matrix. In terms of computation time, the symbolic factorization is the costliest operation in the LU (Lower-upper) factorization process. This paper proposes a novel method to perform the calculation with only one symbolic factorization using a full Newton–Raphson-based generic formulation and modular approach (GFMA). The symbolic factorization retained can be used during the iterations of any power flow contingency scenario. A computer study demonstrates that reusing the same symbolic factorization greatly reduces computation time and improves numerical performance. Power system security assessment under N-1 and N-2 contingency conditions is performed for the IEEE standard 54-bus and 108-bus to evaluate the numerical performance of the proposed method. A comparison with the conventional power flow method shows that the time required for the analysis is shortened considerably, with a minimum gain of 228%. The comparative analysis demonstrates that the proposed solution has better numerical performance for large-scale networks. Full article

In the era of digitalization, many technologies are evolving, namely, the Internet of Things (IoT), big data, cloud computing, artificial intelligence (IA), and digital twin (DT) which has gained significant traction in a variety of sectors, including the mining industry. The use of DT in the mining industry is driven by its potential to improve efficiency, productivity, and sustainability by monitoring performance, simulating results, and predicting errors and yield. Additionally, the increasing demand for individualized products highlights the need for effective management of the entire product lifecycle, from design to development, modeling, simulating, prototyping, maintenance and troubleshooting, commissioning, targeting the market, use, and end-of-life. However, the problem to be overcome is how to successfully integrate DT into the mining business. This paper intends to shed light on the state of art of DT case studies focusing on concept, design, and development. The DT reference architecture model in Industry 4.0 and value-lifecycle-management-enabled DT are also discussed, and a proposition of a DT multi-layered architecture framework for the mining industry is explained to inspire future case studies. Full article

After the massive integration of distributed energy resources, energy storage systems and the charging stations of electric vehicles, it has become very difficult to implement an efficient grid energy management system regarding the unmanageable behavior of the power flow within the grid, which can cause many critical problems in different grid stages, typically in the substations, such as failures, blackouts, and power transformer explosions. However, the current digital transition toward Energy 4.0 in Smart Grids allows the integration of smart solutions to substations by integrating smart sensors and implementing new control and monitoring techniques. This paper is proposing a hybrid artificial intelligence multilayer for power transformers, integrating different diagnostic algorithms, Health Index, and life-loss estimation approaches. After gathering different datasets, this paper presents an exhaustive algorithm comparative study to select the best fit models. This developed architecture for prognostic (PHM) health management is a hybrid interaction between evolutionary support vector machine, random forest, k-nearest neighbor, and linear regression-based models connected to an online monitoring system of the power transformer; these interactions are calculating the important key performance indicators which are related to alarms and a smart energy management system that gives decisions on the load management, the power factor control, and the maintenance schedule planning. Full article

Nowadays, electric vehicles attract significant attention because of the increasingly stringent exhaust emission policies all over the world. Moreover, with the fast expansion of the sustainable economy, the demand for electric vehicles is expanding. In the recent age, maintenance has seriously hampered the marketing and use of electric automobiles. As a result, the technique for maintaining electric vehicles is regarded as vital since it directly affects the security and availability for the end user and the passengers. Another key aspect of electric mobility is the integration of artificial intelligence in control, diagnostics, and prognostics. Meanwhile, a lot of research efforts are still devoted to developing and innovating electric traction systems, especially for diagnostic and prognostic purposes. Furthermore, topics covering important, current, and sustainability challenges should contain more than theoretical knowledge in high-quality education, particularly in engineering education. The purpose is to bridge the gap between the new technology and the learner’s circumstances through giving practical technical expertise and training in the sphere of overall engineering competences, to avoid non-standard, unskilled maintenance work. This article presents the first phase towards designing and developing a test bench of an electric vehicle’s powertrain used for research, learning and e-learning purposes, employing model-based systems engineering (MBSE) and systems modeling language (SysML) through the CESAM architecting and modeling framework. The aforementioned approach is used on our case study to build and present an operational viewpoint layout of the control, energy management, diagnostic, and prognostic test bench as part of the system’s initial phase of designing the system; the test bench layout proposed in this paper represents a flexible, low-cost, multidisciplinary downsized laboratory providing basic experiments related to e-mobility and covering numerous branches and study fields. Full article