Farzane Mahdikhani; Mohammadreza Hassannejad Bibalan
Abstract
This paper proposes a novel thresholding method for oil slick detection from synthetic aperture radar (SAR)images using modified Otsu and Bradley’s approaches. The existence of oil sources in the seas causeshydrocarbon stains to appear on the surface of the seas and as a result, it leads to a decrease ...
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This paper proposes a novel thresholding method for oil slick detection from synthetic aperture radar (SAR)images using modified Otsu and Bradley’s approaches. The existence of oil sources in the seas causeshydrocarbon stains to appear on the surface of the seas and as a result, it leads to a decrease in the qualityof these waters. Oil slicks are distinguished from the sea surface through the utilization of a combinedOtsu-Bradley’s quantization technique, logical operators, and averaging the input image, while categorizing the classes based on the geometrical, textural, and radiometric properties of the images. We aim to enhance the identification of oil spills by utilizing remote sensing techniques, SAR satellite imagery processing, thresholding methods, and extracting geometric and textural features. We performed the classification process several times, and KNN classification method revealed an accuracy of 94.9%. Furthermore, KNN achieved a precision of 92.4%, so we repeated the classification using two selected features, area and entropy to reach a precision of 96.36%.
Loghman Asadpor
Abstract
A miniaturized quadruple-band antenna with a spiral-shaped patch is proposed to cover wireless system bands including GSM, Wi-Fi/WLAN (IEEE 802.11b/g/n), Extended UMTS (IEEE 802.11y), WiMAX and the Wi-Fi/WLAN2 (IEEE 802.11a/h/j). The antenna design features a CPW-fed configuration with a spiral-shaped ...
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A miniaturized quadruple-band antenna with a spiral-shaped patch is proposed to cover wireless system bands including GSM, Wi-Fi/WLAN (IEEE 802.11b/g/n), Extended UMTS (IEEE 802.11y), WiMAX and the Wi-Fi/WLAN2 (IEEE 802.11a/h/j). The antenna design features a CPW-fed configuration with a spiral-shaped patch, vertical slot, and open-end slits in a rectangular ground plane. In the design of this antenna, the POES structure is used to improve the characteristics of the antenna. A prototype antenna was constructed and tested, demonstrating operation across four frequency bands ranging from 1.71 GHz to 7.1 GHz. The antenna exhibits circular polarization, a monopole-like radiation pattern, and good gain over its operating bands,making it suitable for GSM, Extended UMTS, WiMAX, and Wi-Fi/WLAN applications. The antennas overall dimension is 27×25 mm2.
Sajad Balali Dehkordi; Saeed Nasri; Sina Dami
Abstract
Anomaly detection in diverse domains is confronted with the challenges posed by the increasing volume, velocity, and complexity of data. This paper presents a comprehensive review of recent advancements and research trends in anomaly detection across various domains, including high-dimensional big data, ...
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Anomaly detection in diverse domains is confronted with the challenges posed by the increasing volume, velocity, and complexity of data. This paper presents a comprehensive review of recent advancements and research trends in anomaly detection across various domains, including high-dimensional big data, sensor systems, information and communication technology, IoT data, energy consumption, and real-time networks amidst cyber-attacks. Through a systematic analysis of recent literature, this review synthesizes key findings, methodologies, and challenges, providing insights into current strategies and future directions for anomaly detection technology. The reviewed papers highlight the importance of addressing domain-specific challenges, fostering interdisciplinary collaboration, and advancing methodological innovation to develop robust, scalable, and effective anomaly detection solutions capable of meeting the evolving demands of today’s data-driven world.
Aymen Kadhim Mohaisen
Abstract
Areas with high or unstable electricity costs implement microgrids (MGs), making them economically viable. They also provide backup power during grid outages, reducing peak demand charges and promoting grid independence. Lowering electricity costs within an MG can lead to increased economic activity ...
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Areas with high or unstable electricity costs implement microgrids (MGs), making them economically viable. They also provide backup power during grid outages, reducing peak demand charges and promoting grid independence. Lowering electricity costs within an MG can lead to increased economic activity and improved quality of life. MGs improve power quality and play an important role in renewable energy (RE) systems. MGs can help reduce carbon emissions by integrating renewable energy sources into the electrical grid and eliminating the need for fossil fuel electricity generation. Therefore, minimizing electricity prices in an MG is essential for ensuring affordability, sustainability, and reliability, as well as promoting the widespread adoption of MG technology. This paper uses the linear programming optimization (LPO) method as an energy management system (EMS) to manage the energy and power sharing between the MG components, which are solar photovoltaic (PV), battery energy storage system (BESS), and load. Moreover, we tested theproposed system PV/BESS under real solar irradiance and residential load profiles using MATLAB/Simulink software. We subjected the MGs to tests under different weather conditions, specifically clear and cloudy days, to evaluate the proposed system. The results show that the proposed technique provides the lowest price of electricity on clear and cloudy days when compared to the base case. The LPO-based EMS reduced the daily grid cost from 904.1$ to 580$, and it provides a cost savings of 45% when the grid usage is 3400 kWh. Finally, the suggested EMS reduces the grid’s cost from 700$ to 370$ under cloudy day conditions, saving 80% of the cost.
Saadat Boulanouar; Fengal Boualem
Abstract
The solar panel or solar cell is one of the most important components of the solar system that produces electrical energy with high efficiency compatible with electrical loads, but any defect in this cell can cause its efficiency to decrease. The objective of this work is to establish a fault diagnosis ...
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The solar panel or solar cell is one of the most important components of the solar system that produces electrical energy with high efficiency compatible with electrical loads, but any defect in this cell can cause its efficiency to decrease. The objective of this work is to establish a fault diagnosis method that can be implemented in a real structure. These faults are diagnosed and located by implementing an algorithm based on the measured values of the solar panel using an intelligent recursive least squares approach. Our objective is to contribute to the diagnosis of faults in photovoltaic systems based on fuzzy logic in a recurrent manner. The integration of recursive least squares (RLS) with fuzzy logic are essential to improve system efficiency and reliability. This approach enables rapid identification and resolution of faults, helping to avoid energy losses, reduce downtime, and support proactive maintenance. It guarantees the optimal functioning of solar panels, maximizing energy production and improving return on investment. Quantitatively, this method achieves high diagnostic accuracy (over 90%), reduces error rates by up to 30% under dynamic conditions, andprovides real-time fault detection with minimal latency. The combination of RLS and fuzzy logic improves fault diagnosis by effectively handling uncertainties and handling ambiguous situations better than traditional methods.
Mahtab Vaezi; Mehdi Nasri; Farhad Azimifar; Mahdi Mosleh
Abstract
Emotions play an important role in our daily activities, decision-making, and artificial intelligence needs to identify emotions to interact constructively with its audience. In this paper, an intelligent method for two-dimensional emotion recognition is proposed. The ECG signal available in the DREAMER ...
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Emotions play an important role in our daily activities, decision-making, and artificial intelligence needs to identify emotions to interact constructively with its audience. In this paper, an intelligent method for two-dimensional emotion recognition is proposed. The ECG signal available in the DREAMER database has been used to recognize emotions because of the high correlation of this signal with emotions and easy recording. First step for valence and arousal recognition, the ECG signal is entered into the deep learning network, which is a combination of CNN and LSTM. CNN performs feature extraction and LSTM performs data classification. The attention mechanism aims to optimize the weights and improve the performance of the network, overseeing the proposed deep learning network. Using the proposed method, valence and emanation were identified with 95% and 94% accuracy, respectively. The proposed hybrid network is very suitable for high-dimensional data, and the use of the attention mechanism helps to improve the performance of the network by preventing overfit and getting stuck in local optimal.
Sy Yi Sim; Zeng Feihu; Wang Zhiwen; Lai Mingrui
Abstract
In this study, reduced graphene oxide (rGO) was synthesized by reducing graphene oxide (GO) using Vitamin C (VC) as a reducing agent, to create a conductive electrode material. The structural and property effects of VC/GO mass ratios, reaction temperature and time on rGO were investigated in detail. ...
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In this study, reduced graphene oxide (rGO) was synthesized by reducing graphene oxide (GO) using Vitamin C (VC) as a reducing agent, to create a conductive electrode material. The structural and property effects of VC/GO mass ratios, reaction temperature and time on rGO were investigated in detail. Structural characterization and conductivity assessments of both GO and rGO were conducted using powder X-ray diffraction (XRD), Fourier-transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), Brunauer-Emmett-Teller (BET) surface area analysis, and a four-probe conductivity tester. The results showed that VC as a reducing agent effectively reduces the functional groups on the GO surface, forming C=C bonds, while also increasing the d-spacing of rGO. The increase of reaction temperature promotes the ionization and decomposition of VC, thereby improving the reaction efficiency. The synthesized rGO exhibited a porous network with an irregular structure formed by interconnected, wrinkled nanosheets. Optimal conditions wereobserved when the VC/GO mass ratio was 1:1, the reaction temperature was 100 °C, and the reaction time was 3 hours. Under these conditions, the synthesized rGO material achieved a resistivity of 1.82 Ω·cm and a resistance value of 2.75 Ω, positioning it as an excellent electrode material.
Prasad Kulkarni; Lakshmanrao S. Paragond; Shivashankar Hiremath
Abstract
The steadily increasing acceptance of battery technology has created numerous opportunities for identifying new technologies and methods to improve the performance and safety of batteries used in various applications, including electric vehicles and digital devices. The current study focuses on the interaction ...
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The steadily increasing acceptance of battery technology has created numerous opportunities for identifying new technologies and methods to improve the performance and safety of batteries used in various applications, including electric vehicles and digital devices. The current study focuses on the interaction of hardware and software for recording and monitoring battery pack data. The battery management system uses IoT technology, a microcontroller, and sensors to collect voltage and temperature data from battery cells. The individual cell voltage reached approximately 4.2 volts and achieved a full charge of 99%, which was measured locally and displayed remotely on a mobile dashboard via an IoT server. The cells are charged in parallel, and the entire charging process for all cells is completed in about 10 minutes. The battery pack temperature was continuously monitored during charging and discharging, assisting in mitigating risks and improving battery lifespan through proper data. The battery management system’s ability to monitor charging and discharging cycles and their performance allows for corrective actions and informed decision-making to ensure safe operation. Validation, testing, and demonstration of the effectiveness of the IoT-based hybrid-powered battery management system revealed its ability to detect battery performance issues and exchange data for disciplinary action. This creates a safe environment for the use of battery management systems in a variety of battery operations.
Nilakshi Maruti Mule; Dipti Durgesh Patil
Abstract
A proficient real-time decision support system has the potential to reduce the daily probability of acute exacerbation and loss of control for those suffering from chronic obstructive pulmonary disease (COPD). Applying statistical learning techniques to well-structured, medical E-nose data typically ...
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A proficient real-time decision support system has the potential to reduce the daily probability of acute exacerbation and loss of control for those suffering from chronic obstructive pulmonary disease (COPD). Applying statistical learning techniques to well-structured, medical E-nose data typically results in high accuracy. Volatile organic compounds or changes by disease processes can be measured in exhaled breath. This work elaborated on the integration of sensors into a sensor array, sampling methodologies, and an algorithm for data analysis. The clinical feasibility of the device was assessed in 40 COPD patients, 20 controls, 8 smokers, and 10 ambient air samples. The classification model utilizing Bi-Directional Long Short-Term Memory (Bi-LSTM) achieved an accuracy, sensitivity, specificity, and area under the curve of99%, with recall, precision, and F1-score of 1 for COPD classification. The gas sensor array was non-invasive, economical, and provided a quick response. Research has shown that the VOC profiles of COPD patients differ from those of healthy controls, indicating that the E-nose system may serve as a viable diagnostic tool for COPD patients.
Swati Sharma; Ikbal Ali
Abstract
The rapid proliferation of electric vehicles (EVs) has significantly escalated the strain on the public grid by exacerbating fluctuations and hindering widespread EV adoption. This paper presents a cutting-edge solution with a real-time cost optimization model tailored for AC/DC microgrid energy management. ...
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The rapid proliferation of electric vehicles (EVs) has significantly escalated the strain on the public grid by exacerbating fluctuations and hindering widespread EV adoption. This paper presents a cutting-edge solution with a real-time cost optimization model tailored for AC/DC microgrid energy management. Leveraging a unique hybridization of particle-swarm optimization (PSO) and grey wolf optimization (GWO), our approach dynamically orchestrates energy flow and EV charging schedules. The model has been developed using MATLAB 2022a.Thus, a non-linear stochastic mathematical programming model optimizes EV charging and distributed energy resources (DERs) generation costs. We scrutinize our model across medium scale microgrid IEEE- 37 Node systems—via real-time digital simulator (RTDS). Our multi-level control strategy ensures both immediate response to disturbances and long-term optimization, maintaining microgrid stability. Through meticulous real-time monitoring and control, our hybrid PSO-GWO algorithm delivers superior performance, slashing costs by $152.47 for medium scale microgrid while reducing execution time by 0.81 seconds ascompared to other metaheuristic algorithms. About 36.85% of the load is absorbed by EVs, with surplus power fed back to the main grid. This comprehensive approach not only enhances the cost-effectiveness but also fosters energy efficiency, affirming the efficacy of hybrid PSO-GWO in real-time microgrid management.
Ghazanfar Shahgholiyan; Mohammadreza Moradian; Mohammad Amin Honarvar
Abstract
The microgrid acts as a single controllable system, and by generating flexible power, it ensures the reliability of the electric grid. The use of microgrid helps to solve environmental pollution problems. The microgrid control operation is different from the grid-connected mode. Based on the nature of ...
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The microgrid acts as a single controllable system, and by generating flexible power, it ensures the reliability of the electric grid. The use of microgrid helps to solve environmental pollution problems. The microgrid control operation is different from the grid-connected mode. Based on the nature of bus voltage, microgrids are divided into two categories: DC microgrid and AC microgrid. Several studies have been conducted in the field of microgrid application and exploitation, but their expansion still faces various challenges. DC microgrids are more reliable than ac microgrids. The purpose of this article is to review the documentation and provide a short review on the application of the droop method to control parallel converters in direct current microgrids. Droop control method is a popular and well-known technique for sharing load power, which is widely used in DC microgrids. In this control strategy, the reference voltage of each source is determined based on the nominal output voltage, output current and loss factor, where the power sharing rateis determined by increasing the loss. The various advantages of direct current microgrids will cause them to be used more in the near future, so this review is for researchers as a preliminary research to study the development of direct current microgrids based on the application of droop control methods. And improving it can be useful.
Seyed Mohammad Zare; Majid Ebnali Heidari; Mohammad Reza Shayesteh; Aliakbar Ebnali Heidari; Maryam Nayeri
Abstract
This article introduces the development and enhancement of a side-coupled chalcogenide-based 2D PhC nanobeam cavity in the mid-IR spectral range. The structure configuration consists of 2D PhC nanobeam and side-coupled bending bus waveguide for efficient light coupling. Through numerical simulation and ...
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This article introduces the development and enhancement of a side-coupled chalcogenide-based 2D PhC nanobeam cavity in the mid-IR spectral range. The structure configuration consists of 2D PhC nanobeam and side-coupled bending bus waveguide for efficient light coupling. Through numerical simulation and optimization, we optimized three key parameters: the number of mirror holes, the radius of the central hole of the nanobeam cavity, and the optimized coupling gap size, improving the quality factor of the primary mode of the cavity. The cavities exhibit high Q factors and low modal volumes, making them attractive for various applications, including laser, sensing, nonlinear optics, optical trapping, and quantum technologies. Regarding applications, this allows us to compare optical devices with each other.
Cempaka Amalin Mahadzir; Ahmad Fateh Mohamad Nor; Siti Amely Jumaat; Noor Syahirah Ahmad Safawi
Abstract
This paper focuses on utilizing an Artificial Neural Network (ANN) to predict photovoltaic (PV) panel output power. Since solar power output is fluctuating and depends on climatic, geographical and temporal factors, precise prediction requires the implementation of computational approaches. The aim of ...
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This paper focuses on utilizing an Artificial Neural Network (ANN) to predict photovoltaic (PV) panel output power. Since solar power output is fluctuating and depends on climatic, geographical and temporal factors, precise prediction requires the implementation of computational approaches. The aim of this research is to develop ANN algorithms that anticipate solar power output and enhance the structure of them by incorporating the derating factor due to dirt (kdirt) into account. The effectiveness and dependability of the ANN are determined using MATLAB software. By comparing the Mean Squared Error (MSE) of four different values of derating factor due to dirt which are 0.8, 0.88, 0.9 and 0.98 in ANN predictions comprehend with 4 input layers and 10 hidden layers. Direct data input is obtained through a photovoltaic solar panel at University Tun Hussein Onn Malaysia (UTHM). Comparative analysis also has been carried out after theresults has been obtained from the mathematical equations. The daily solar power output predictions are effectively achieved by the deployed ANN. As the result, the optimal kdirt has been selected which is 0.8 based on its ability to produce the most accurate ANN predictions than the other values of kdirt.
Hakima Rahem; Rabah Mellah; Massinissa Zidane; Abdelhakim Saim
Abstract
In this paper, an intelligent adaptive controller is designed to handle the transmission time delay problem in a complex and nonlinear bilateral teleoperation system with force feedback. The combination of the inference capacities of fuzzy logic reasoning and the learning capacities of neural networks, ...
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In this paper, an intelligent adaptive controller is designed to handle the transmission time delay problem in a complex and nonlinear bilateral teleoperation system with force feedback. The combination of the inference capacities of fuzzy logic reasoning and the learning capacities of neural networks, enable the formation of an adaptive and robust neuro-fuzzy controller ANFIS (Adaptive Neuro-Fuzzy Inference System) which adapts to variations in the dynamics of the system by the automatic adjustment of the parameters of the fuzzy rules and the membership functions using a learning algorithm, thus ensuring compensation of the negative effects of transmission delay in the closed loop system. Thanks to the speed and power of the microprocessor of the Arduino Due board and the functionalities of Matlab-Simulink, a real experimental platform of master-slave teleoperation system with transmission delay and force feedback is designed. In order to demonstrate the efficiency, adaptability and advantages of ANFIS controller, several comparative experiments are carried out using different controllers (ANFIS, conventional PI and PI regulator with the modified wave variable method PI+MWVM). The experimental results clearly show the high system performance achieved with the ANFIScontroller.
Muhammad Najwan Hamidi; Dahaman Ishak
Abstract
Multilevel inverters (MLI) play a pivotal role in diverse applications, notably solar power generation, owing to their capacity for generating high-quality, multiple-level output voltages with reduced total harmonic distortions (THD) in comparison to traditional inverters. Nevertheless, as MLI levels ...
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Multilevel inverters (MLI) play a pivotal role in diverse applications, notably solar power generation, owing to their capacity for generating high-quality, multiple-level output voltages with reduced total harmonic distortions (THD) in comparison to traditional inverters. Nevertheless, as MLI levels increase, challenges arise, impacting implementation feasibility due to heightened computational demands for pulse width modulation (PWM) generation. Researchers have inclined towards low-frequency, pre-calculated switching signal methods, although these tend to induce higher output harmonics, particularly at lower levels. This study focuses on studying the significance of employing the particle swarm optimization (PSO) technique to solve selective harmonic elimination (SHE) equations (SHE-PSO) in MLI applications, evaluating its impact on THD at various output levels (3L, 5L, and 7L) within a cascaded H-bridge MLI. Results are compared with simpler methods, namely sine-based calculation (SBC) and Newton Raphson-based SHE (SHE-NR). Thefindings illustrate that SHE-PSO effectively minimizes lower-order harmonics to as low as 0%, outperforming SBC and NR. However, in terms of THD, SHE-PSO proves advantageous only at 5L, with SHE-NR exhibiting superior performance at other levels. This study concludes that the reduction of lower-order harmonics in MLI does not necessarily translate to an overall improvement in THD, particularly at higher levels.
Venkatrao A. Kommuri; Kareemulla Shaik
Abstract
A global ecosystem of networked sensors, actuators, and other devices intended for data exchange and interaction is known as the Internet of Things (IoT). Password-based authentication has been a major component of IoT solutions historically, despite its numerous flaws. This survey article provides a ...
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A global ecosystem of networked sensors, actuators, and other devices intended for data exchange and interaction is known as the Internet of Things (IoT). Password-based authentication has been a major component of IoT solutions historically, despite its numerous flaws. This survey article provides a thorough analysis of the literature with an emphasis on the implementation of authentication without the use of passwords on the Internet of Things. Ensuring that authorized persons have the correct access to related IT incomes under the correct situations is the core necessity behind enterprise IoT security. Identity managing, the first line of protection in initiative security, is a key component of this project. Traditional passwordbased authentication systems are frequently regarded as “high friction,” causing users’ problems and lengthy procedures in addition to being vulnerable to different security threats. IoT businesses are investigating password less authentication techniques more frequently in an effort to improve user productivity whilepreserving strong security assurance in response to these difficulties. A comprehensive analysis of password less authentication mechanisms designed for the Internet of Things is presented in this article.
Reza Ghorbandost; Maryam Rajabzadeh Asaar; Pouya Derakhshan Barjoei
Abstract
Today, with the development of processing technologies and improvement in cloud networks, a large number of photos are transferred in networks. The need to hide data and maintain security attracted the attention of researchers. In this paper, a reversible data hiding method based on the prediction error ...
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Today, with the development of processing technologies and improvement in cloud networks, a large number of photos are transferred in networks. The need to hide data and maintain security attracted the attention of researchers. In this paper, a reversible data hiding method based on the prediction error histogram shifting is proposed in order to increase the embedding capacity and maintains the visual quality of the image as well. To reach these goals, the original image is transformed into block-wise and for each block the prediction method is figured based on the proposed method to create prediction errors. According to our prediction method, in each 4×4 block, 75% of the prediction error pixels can find the ability to embed information. The experimental results show a good acceptable embedding capacity as it is clear in a sample test image of an airplane. In this case, the embedding capacity of 206,270 bits and a Peak Signal-to-Noise Ratio (PSNR) of 50.99 dB have been reached. These results show the efficiency of our proposed method based on theembedding capacity and visual quality of the images. The outcomes of the proposed method reach better results than the main competitor methods.
El-Hadi Meftah; Abdelhalim Rabehi; Slimane Benmahmoud
Abstract
Constant false alarm rate (CFAR) processors are critical for radar reliable target detection in radar systems. Traditional CFAR designs often assume Gaussian clutter, which may not reflect real-world conditions. L´evy distributions, with heavy tails and a location parameter (δ), provide a ...
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Constant false alarm rate (CFAR) processors are critical for radar reliable target detection in radar systems. Traditional CFAR designs often assume Gaussian clutter, which may not reflect real-world conditions. L´evy distributions, with heavy tails and a location parameter (δ), provide a more accurate model for non-Gaussian and non-centered clutter in complex environments. This paper presents a comprehensive performance analysis of three widely used CFAR processors-cell-averaging (CA), greatest-of (GO), and smallest-of (SO) in homogeneous L´evy-distributed clutter with an arbitrary δ. We derive integral-form expressions for the probability of false alarm (PFA) for each processor, explicitly incorporating δ. Furthermore, we provide analytical formulations for the probability density function (PDF) of key statistics involving L´evy random variables, such as sums, minima, and maxima. Monte Carlo simulations validate the theoretical results,showing that the PFA performance improves with increasing δ, highlighting the critical impact of clutter location on CFAR detector performance. These findings offer valuable insights for designing robust CFAR detectors in non-Gaussian, non-centered clutter environments.
Ehsan Tavakoli Garmaserh; Mehran Emadi
Abstract
Accurate forecasting of electricity consumption in petrochemical industrial units is essential for optimizing energy management and ensuring operational efficiency. This study presents a novel deep learning framework that integrates advanced feature engineering and Long Short-Term Memory (LSTM) networks ...
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Accurate forecasting of electricity consumption in petrochemical industrial units is essential for optimizing energy management and ensuring operational efficiency. This study presents a novel deep learning framework that integrates advanced feature engineering and Long Short-Term Memory (LSTM) networks to address the challenges posed by irregular seasonal trends and dynamic consumption patterns. Key innovations include the use of Fourier Transform-based feature extraction for enhanced data representation and a hybrid genetic-sparse matrix optimization technique for feature selection, ensuring high predictive performance. The proposed method effectively mitigates issues related to data irregularities through preprocessing techniques, resulting in improved accuracy and stability in both univariate and multivariate time series forecasting scenarios. Experimental evaluations using benchmark datasets demonstrate significant improvements, achieving a Root Mean Square Error (RMSE) of 0.0693 and a Mean Absolute Percentage Error (MAPE) reduction of over 15% compared to state-of-the-art methods. These results highlight the robustness and practical applicability of the proposed framework for industrial energy consumption forecasting and sustainable energy management.
Boutaina Benhmimou; Fouad Omari; Nancy Gupta; Khalid El Khadiri; Rachid Ahl Laamara; Mohamed El Bakkali
Abstract
The Present CubeSat project success rate may deter nonprofit organizations from beginning new projects, especially for first-time creators. However, since the electronic components of a CubeSat are intended to be very power-efficient and tightly placed, its size and electrical characteristics provide ...
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The Present CubeSat project success rate may deter nonprofit organizations from beginning new projects, especially for first-time creators. However, since the electronic components of a CubeSat are intended to be very power-efficient and tightly placed, its size and electrical characteristics provide a more difficult limitation. The CubeSat antennas are key parts that will need to be carefully designed since they need to be tiny, light, and deployable for bigger antennas. This study provides an extensive overview of the key characteristics of metasurface-based antennas with an emphasis on their effectiveness in CubeSat communication systems. This research work initially introduces metasurface antennas and examines how well-suited they are geometrically for various frequency bands for CubeSat spacecraft. Furthermore, a detailed analysis of these metasurface antennas’ radiating capabilities is conducted in accordance with the CubeSat configuration, links, and orbits. Additionally, over thirty X-band metasurface-based antennas are fully evaluated in terms of their suitability for CubeSats. The use of specifically designed metasurfaces has resulted in a notable increasein CubeSat antenna performance. This paper offers an emerging approach for researchers to advance the usage of metasurface-based antennas in CubeSat missions such as UM5-Ribat and UM5-EOSAT CubeSats of University Mohammed V in Rabat.
Elahe Moradi
Abstract
Power transformers (PTs) are a significant component of power grids that transmit and distribute electricity generated by renewable energy sources. Nevertheless, PTs are susceptible to faults that can cause costly outages and disruptions. Over the past decades, the technique of dissolved gas analysis ...
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Power transformers (PTs) are a significant component of power grids that transmit and distribute electricity generated by renewable energy sources. Nevertheless, PTs are susceptible to faults that can cause costly outages and disruptions. Over the past decades, the technique of dissolved gas analysis (DGA) has been extensively employed in oil-immersed transformer fault diagnosis. There are various methods to identify faults using DGA. Due to its superior accuracy compared to other techniques, the dual pentagon method (DPM) is utilized for fault diagnosis of PTs in this research. On the other hand, implementing DPM on large amounts of DGA data can be challenging. To address this issue, we proposed several data-driven, tree-based algorithms, including Decision Tree Classifier (DTC), Random Forest Classifier (RFC), eXtreme Gradient Boosting Classifier (XGBC), LightGBM (LGBM) Classifier, Adaptive Boosting (AdaBoost) Classifier, and Categorical Boosting (CatBoost) Classifier. Furthermore, four data scaling techniques have been used formore effectiveness because the dataset contains outliers. The outcomes of the data analysis and Python simulation demonstrate that the suggested approach performs better than the previous methods. From the simulation analysis, the robust Light-GBM method has achieved an accuracy of 96.08%, and MCC of 95.41%, which is higher compared to the existing techniques.
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