Converters rely on passive filtering as a crucial element due to the high-frequency operational characteristics of power *** filtering methods involve a dual inductor-capacitor(LC)cell or an inductor-capacitor-inducto...
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Converters rely on passive filtering as a crucial element due to the high-frequency operational characteristics of power *** filtering methods involve a dual inductor-capacitor(LC)cell or an inductor-capacitor-inductor(LCL)***,capacitors are susceptible to wear-out mechanisms and failure ***,the necessity for monitoring and regular replacement adds to an elevated cost of ownership for such *** utilization of an active output power filter can be used to diminish the dimensions of the LC filter and the electrolytic dc-link capacitor,even though the inclusion of capacitors remains an indispensable part of the *** paper introduces capacitorless solid-state power filter(SSPF)for single-phase dc-ac *** proposed configuration is capable of generating a sinusoidal ac voltage without relying on *** proposed filter,composed of a planar transformer and an H-bridge converter operating at high frequency,injects voltage harmonics to attain a sinusoidal output *** design parameters of the planar transformer are incorporated,and the impact of magnetizing and leakage inductances on the operation of the SSPF is *** analysis,supported by simulation and experimental results,are provided for a design example for a single-phase *** total harmonic distortion observed in the output voltage is well below the IEEE 519 *** system operation is experimentally tested under both steady-state and dynamic conditions.A comparison with existing technology is presented,demonstrating that the proposed topology reduces the passive components used for filtering.
Early attack detection is essential to ensure the security of complex networks,especially those in critical *** is particularly crucial in networks with multi-stage attacks,where multiple nodes are connected to extern...
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Early attack detection is essential to ensure the security of complex networks,especially those in critical *** is particularly crucial in networks with multi-stage attacks,where multiple nodes are connected to external sources,through which attacks could enter and quickly spread to other network *** attack graphs(BAGs)are powerful models for security risk assessment and mitigation in complex networks,which provide the probabilistic model of attackers’behavior and attack progression in the *** attack detection techniques developed for BAGs rely on the assumption that network compromises will be detected through routine monitoring,which is unrealistic given the ever-growing complexity of *** paper derives the optimal minimum mean square error(MMSE)attack detection and monitoring policy for the most general form of *** exploiting the structure of BAGs and their partial and imperfect monitoring capacity,the proposed detection policy achieves the MMSE optimality possible only for linear-Gaussian state space models using Kalman *** adaptive resource monitoring policy is also introduced for monitoring nodes if the expected predictive error exceeds a user-defined *** and efficient matrix-form computations of the proposed policies are provided,and their high performance is demonstrated in terms of the accuracy of attack detection and the most efficient use of available resources using synthetic Bayesian attack graphs with different topologies.
This advanced paper presents a new approach to improving image steganography using the Ant Colony Optimization(ACO)*** steganography,a technique of embedding hidden information in digital photographs,should ideally ac...
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This advanced paper presents a new approach to improving image steganography using the Ant Colony Optimization(ACO)*** steganography,a technique of embedding hidden information in digital photographs,should ideally achieve the dual purposes of maximum data hiding and maintenance of the integrity of the cover media so that it is least *** contemporary methods of steganography are at best a compromise between these *** this paper,we present our approach,entitled Ant Colony Optimization(ACO)-Least Significant Bit(LSB),which attempts to optimize the capacity in steganographic *** approach makes use of a grayscale cover image to hide the confidential data with an additional bit pair per byte,both for integrity verification and the file checksumof the secret *** approach encodes confidential information into four pairs of bits and embeds it within uncompressed grayscale *** ACO algorithm uses adaptive exploration to select some pixels,maximizing the capacity of data embedding whileminimizing the degradation of visual *** evaporation is introduced through iterations to avoid stagnation in solution *** levels of pheromone are modified to reinforce successful pixel *** results obtained through the ACO-LSB method reveal that it clearly improves image steganography capabilities by providing an increase of up to 30%in the embedding capacity compared with traditional approaches;the average Peak Signal to Noise Ratio(PSNR)is 40.5 dB with a Structural Index Similarity(SSIM)of *** approach also demonstrates very high resistance to detection,cutting down the rate by 20%.Implemented in MATLAB R2023a,the model was tested against one thousand publicly available grayscale images,thus providing robust evidence of its effectiveness.
The fast growth in Internet-of-Vehicles(IoV)applications is rendering energy efficiency management of vehicular networks a highly important *** of the existing models are failing to handle the demand for energy conser...
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The fast growth in Internet-of-Vehicles(IoV)applications is rendering energy efficiency management of vehicular networks a highly important *** of the existing models are failing to handle the demand for energy conservation in large-scale heterogeneous *** on Large Energy-Aware Fog(LEAF)computing,this paper proposes a new model to overcome energy-inefficient vehicular networks by simulating large-scale network *** main inspiration for this work is the ever-growing demand for energy efficiency in IoV-most particularly with the volume of generated data and connected *** proposed LEAF model enables researchers to perform simulations of thousands of streaming applications over distributed and heterogeneous *** the possible reasons is that it provides a realistic simulation environment in which compute nodes can dynamically join and leave,while different kinds of networking protocols-wired and wireless-can also be *** novelty of this work is threefold:for the first time,the LEAF model integrates online decision-making algorithms for energy-aware task placement and routing strategies that leverage power usage traces with efficiency optimization in *** existing fog computing simulators,data flows and power consumption are modeled as parameterizable mathematical equations in LEAF to ensure scalability and ease of analysis across a wide range of devices and *** results of evaluation show that LEAF can cover up to 98.75%of the distance,with devices ranging between 1 and 1000,showing significant energy-saving potential through A wide-area network(WAN)usage *** findings indicate great promise for fog computing in the future-in particular,models like LEAF for planning energy-efficient IoV infrastructures.
VPNs are vital for safeguarding communication routes in the continually changing cybersecurity ***,increasing network attack complexity and variety require increasingly advanced algorithms to recognize and categorizeV...
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VPNs are vital for safeguarding communication routes in the continually changing cybersecurity ***,increasing network attack complexity and variety require increasingly advanced algorithms to recognize and categorizeVPNnetwork *** present a novelVPNnetwork traffic flowclassificationmethod utilizing Artificial Neural Networks(ANN).This paper aims to provide a reliable system that can identify a virtual private network(VPN)traffic fromintrusion attempts,data exfiltration,and denial-of-service *** compile a broad dataset of labeled VPN traffic flows from various apps and usage ***,we create an ANN architecture that can handle encrypted communication and distinguish benign from dangerous *** effectively process and categorize encrypted packets,the neural network model has input,hidden,and output *** use advanced feature extraction approaches to improve the ANN’s classification accuracy by leveraging network traffic’s statistical and behavioral *** also use cutting-edge optimizationmethods to optimize network characteristics and *** suggested ANN-based categorization method is extensively tested and *** show the model effectively classifies VPN traffic *** also show that our ANN-based technique outperforms other approaches in precision,recall,and F1-score with 98.79%*** study improves VPN security and protects against new *** VPNtraffic flows effectively helps enterprises protect sensitive data,maintain network integrity,and respond quickly to security *** study advances network security and lays the groundwork for ANN-based cybersecurity solutions.
Cloud Computing (CC) is widely adopted in sectors like education, healthcare, and banking due to its scalability and cost-effectiveness. However, its internet-based nature exposes it to cyber threats, necessitating ad...
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Most optimization problems of practical significance are typically solved by highly configurable parameterized *** achieve the best performance on a problem instance,a trial-and-error configuration process is required...
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Most optimization problems of practical significance are typically solved by highly configurable parameterized *** achieve the best performance on a problem instance,a trial-and-error configuration process is required,which is very costly and even prohibitive for problems that are already computationally intensive,*** problems associated with machine learning *** the past decades,many studies have been conducted to accelerate the tedious configuration process by learning from a set of training *** article refers to these studies as learn to optimize and reviews the progress achieved.
Dear Editor,H∞This letter develops a new framework for the robust stability and performance conditions as well as the relevant controller synthesis with respect to uncertain robot *** often exist model uncertainties ...
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Dear Editor,H∞This letter develops a new framework for the robust stability and performance conditions as well as the relevant controller synthesis with respect to uncertain robot *** often exist model uncertainties between the nominal model and the real robot manipulator and disturbances. Hence, dealing with their effects plays a crucial role in leading to high tracking performances, as discussed in [1]–[5].
In this article the legend of Fig. 6 was presented without a reference. The legend of Fig. 6 has been changed from "The general framework for knowledge distillation involving a teacher-student relationship&q...
In recent years,developed Intrusion Detection Systems(IDSs)perform a vital function in improving security and anomaly *** effectiveness of deep learning-based methods has been proven in extracting better features and ...
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In recent years,developed Intrusion Detection Systems(IDSs)perform a vital function in improving security and anomaly *** effectiveness of deep learning-based methods has been proven in extracting better features and more accurate classification than other *** this paper,a feature extraction with convolutional neural network on Internet of Things(IoT)called FECNNIoT is designed and implemented to better detect anomalies on the ***,a binary multi-objective enhance of the Gorilla troops optimizer called BMEGTO is developed for effective feature ***,the combination of FECNNIoT and BMEGTO and KNN algorithm-based classification technique has led to the presentation of a hybrid method called *** the next step,the proposed model is implemented on two benchmark data sets,NSL-KDD and TON-IoT and tested regarding the accuracy,precision,recall,and Fl-score *** proposed CNN-BMEGTO-KNN model has reached 99.99%and 99.86%accuracy on TON-IoT and NSL-KDD datasets,*** addition,the proposed BMEGTO method can identify about 27%and 25%of the effective features of the NSL-KDD and TON-IoT datasets,respectively.
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