Search Results (512)

With the rapid proliferation of real-time digital communication, particularly in multimedia applications, securing transmitted image data has become a vital concern. While chaotic systems have shown strong potential for cryptographic use, most existing approaches rely on low-dimensional, integer-order architectures, limiting their complexity and resistance to attacks. Advances in fractional calculus and memristive technologies offer new avenues for enhancing security through more complex and tunable dynamics. However, the practical deployment of high-dimensional fractional-order memristive chaotic systems in hardware remains underexplored. This study addresses this gap by presenting a secure image transmission system implemented on a field-programmable gate array (FPGA) using a universal high-dimensional memristive chaotic topology with arbitrary-order dynamics. The design leverages four- and five-dimensional hyperchaotic oscillators, analyzed through bifurcation diagrams and Lyapunov exponents. To enable efficient hardware realization, the chaotic dynamics are approximated using the explicit fractional-order Runge–Kutta (EFORK) method with the Caputo fractional derivative, implemented in VHDL. Deployed on the Xilinx Artix-7 AC701 platform, synchronized master–slave chaotic generators drive a multi-stage stream cipher. This encryption process supports both RGB and grayscale images. Evaluation shows strong cryptographic properties: correlation of $-6.1081\times {10}^{-5}$, entropy of 7.9991, NPCR of 99.9776%, UACI of 33.4154%, and a key space of $2^{1344}$, confirming high security and robustness. Full article

Rainbow table attacks utilize a time-memory trade-off to efficiently crack passwords by employing precomputed tables containing chains of passwords and hash values. Generating these tables is computationally intensive, and several researchers have proposed utilizing parallel computing to speed up the generation process. This paper introduces a modification to the traditional master-slave parallelization model using the MPI framework, where, unlike previous approaches, the generation of starting points is decentralized, allowing each process to generate its own tasks independently. This design is proposed to reduce communication overhead and improve the efficiency of rainbow table generation. We reduced the number of inter-process communications by letting each process generate chains independently. We conducted three experiments to evaluate the performance of the parallel rainbow tables generation algorithm for four cryptographic hash functions: NTLMv2, MD5, SHA-256 and SHA-512. The first experiment assessed parallel performance, showing near-linear speedup and 95–99% efficiency across varying numbers of nodes. The second experiment evaluated scalability by increasing the number of processed chains from 100 to 100,000, revealing that higher workloads significantly impacted execution time, with SHA-512 being the most computationally intensive. The third experiment evaluated the effect of chain length on execution time, confirming that longer chains increase computational cost, with SHA-512 consistently requiring the most resources. The proposed approach offers an efficient and practical solution to the computational challenges of rainbow tables generation. The findings of this research can benefit key stakeholders, including cybersecurity professionals, ethical hackers, digital forensics experts and researchers in cryptography, by providing an efficient method for generating rainbow tables to analyze password security. Full article

To address the obstacle-avoidance path-planning requirements of dual-arm robots operating in complex environments, such as chemical laboratories and biomedical workstations, this paper proposes ODSN-RRT (optimization-direction-step-node RRT), an efficient planner based on rapidly-exploring random trees (RRT). ODSN-RRT integrates three key optimization strategies. First, a two-stage sampling-direction strategy employs goal-directed growth until collision, followed by hybrid random-goal expansion. Second, a dynamic safety step-size strategy adapts each extension based on obstacle size and approach angle, enhancing collision detection reliability and search efficiency. Third, an expansion-node optimization strategy generates multiple candidates, selects the best by Euclidean distance to the goal, and employs backtracking to escape local minima, improving path quality while retaining probabilistic completeness. For collision checking in the dual-arm workspace (self and environment), a cylindrical-spherical bounding-volume model simplifies queries to line-line and line-sphere distance calculations, significantly lowering computational overhead. Redundant waypoints are pruned using adaptive segmental interpolation for smoother trajectories. Finally, a master-slave planning scheme decomposes the 14-DOF problem into two 7-DOF sub-problems. Simulations and experiments demonstrate that ODSN-RRT rapidly generates collision-free, high-quality trajectories, confirming its effectiveness and practical applicability. Full article

A Cournot triopoly is a type of oligopoly market involving three firms that produce and sell homogeneous or similar products without cooperating with one another. In Cournot models, firms’ decisions about production levels play a crucial role in determining overall market output. Compared to duopoly models, oligopolies with more than two firms have received relatively less attention in the literature. Nevertheless, triopoly models are more reflective of real-world market conditions, even though analyzing their dynamics remains a complex challenge. A reaction–diffusion system of PDEs generalizing a nonlinear triopoly model describing a master–slave Cournot game is introduced. The effect of diffusion on the stability of Nash equilibrium is investigated. Self-diffusion alone cannot induce Turing pattern formation. In fact, linear stability analysis shows that cross-diffusion is the key mechanism for the formation of spatial patterns. The conditions for the onset of cross-diffusion-driven instability are obtained via linear stability analysis, and the formation of several Turing patterns is investigated through numerical simulations. Full article

As the integration of renewable energy sources into microgrid operations deepens, their inherent uncertainty poses significant challenges for dispatch scheduling. This paper proposes a hybrid game-theoretic optimization strategy to address the uncertainty of renewable energy in microgrid scheduling. An energy trading framework is developed, involving integrated energy microgrids (IEMS), shared energy storage operators (ESOS), and user aggregators (UAS). A mixed game model combining master–slave and cooperative game theory is constructed in which the ESO acts as the leader by setting electricity prices to maximize its own profit, while guiding the IEMs and UAs—as followers—to optimize their respective operations. Cooperative decisions within the IEM coalition are coordinated using Nash bargaining theory. To enhance the generality of the user aggregator model, both electric vehicle (EV) users and demand response (DR) users are considered. Additionally, the model incorporates renewable energy output uncertainty through distributionally robust chance constraints (DRCCs). The resulting two-level optimization problem is solved using Karush–Kuhn–Tucker (KKT) conditions and the Alternating Direction Method of Multipliers (ADMM). Simulation results verify the effectiveness and robustness of the proposed model in enhancing operational efficiency under conditions of uncertainty. Full article

In this paper, we address the problem of intelligent operation of Distribution Static Synchronous Compensators (D-STATCOMs) in power distribution systems to reduce energy losses and CO₂ emissions while improving system operating conditions. In addition, we consider the entire set of constraints inherent in the operation of such networks in an environment with D-STATCOMs. To solve such a problem, we used three master–slave methodologies based on sequential programming methods. In the proposed methodologies, the master stage solves the problem of intelligent D-STATCOM operation using the continuous versions of the Monte Carlo (MC) method, the population-based genetic algorithm (PGA), and the Particle Swarm Optimizer (PSO). The slave stage, for its part, evaluates the solutions proposed by the algorithms to determine their impact on the objective functions and constraints representing the problem. This is accomplished by running an Hourly Power Flow (HPF) based on the method of successive approximations. As test scenarios, we employed the 33- and 69-node radial test systems, considering data on power demand and CO₂ emissions reported for the city of Medellín in Colombia (as documented in the literature). Furthermore, a test system was adapted in this work to the demand characteristics of a feeder located in the city of Talca in Chile. This adaptation involved adjusting the conductors and voltage limits to include a test system with variations in power demand due to seasonal changes throughout the year (spring, winter, autumn, and summer). Demand curves were obtained by analyzing data reported by the local network operator, i.e., Compañía General de Electricidad. To assess the robustness and performance of the proposed optimization approach, each scenario was simulated 100 times. The evaluation metrics included average solution quality, standard deviation, and repeatability. Across all scenarios, the PGA consistently outperformed the other methods tested. Specifically, in the 33-node system, the PGA achieved a 24.646% reduction in energy losses and a 0.9109% reduction in CO₂ emissions compared to the base case. In the 69-node system, reductions reached 26.0823% in energy losses and 0.9784% in CO₂ emissions compared to the base case. Notably, in the case of the Talca feeder—particularly during summer, the most demanding season—the PGA yielded the most significant improvements, reducing energy losses by 33.4902% and CO₂ emissions by 1.2805%. Additionally, an uncertainty analysis was conducted to validate the effectiveness and robustness of the proposed optimization methodology under realistic operating variability. A total of 100 randomized demand profiles for both active and reactive power were evaluated. The results demonstrated the scalability and consistent performance of the proposed strategy, confirming its effectiveness under diverse and practical operating conditions. Full article

We propose a secure and scalable file-encryption scheme for cloud systems by integrating Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD), and Advanced Encryption Standard (AES) within a distributed architecture. While prior studies have primarily focused on secure key exchange or authentication protocols (e.g., layered PQC-QKD key distribution), our scheme extends beyond key management by implementing a distributed encryption architecture that protects large-scale files through integrated PQC, QKD, and AES. To support high-throughput encryption, our proposed scheme partitions the target file into fixed-size subsets and distributes them across slave nodes, each performing parallel AES encryption using a locally reconstructed key from a PQC ciphertext. Each slave node receives a PQC ciphertext that encapsulates the AES key, along with a PQC secret key masked using QKD based on the BB84 protocol, both of which are centrally generated and managed by the master node for secure coordination. In addition, an encryption and transmission pipeline is designed to overlap I/O, encryption, and communication, thereby reducing idle time and improving resource utilization. The master node performs centralized decryption by collecting encrypted subsets, recovering the AES key, and executing decryption in parallel. Our evaluation using a real-world medical dataset shows that the proposed scheme achieves up to 2.37× speedup in end-to-end runtime and up to 8.11× speedup in encryption time over AES (Original). In addition to performance gains, our proposed scheme maintains low communication cost, stable CPU utilization across distributed nodes, and negligible overhead from quantum key management. Full article

In response to the problem of cooperative steering control between drivers and intelligent driving systems, a master–slave Game-Based human–machine cooperative steering control framework with adaptive weight fuzzy decision-making is constructed. Firstly, within this framework, a dynamic weight approach is established. This approach takes into account the driver’s state, traffic environment risks, and the vehicle’s global control deviation to adjust the driving weights between humans and machines. Secondly, the human–machine cooperative relationship with unconscious competition is characterized as a master–slave game interaction. The cooperative steering control under the master–slave game scenario is then transformed into an optimization problem of model predictive control. Through theoretical derivation, the optimal control strategies for both parties at equilibrium in the human–machine master–slave game are obtained. Coordination of the manipulation actions of the driver and the intelligent driving system is achieved by balancing the master–slave game. Finally, different types of drivers are simulated by varying the parameters of the driver models. The effectiveness of the proposed driving weight allocation scheme was validated on the constructed simulation test platform. The results indicate that the human–machine collaborative control strategy can effectively mitigate conflicts between humans and machines. By giving due consideration to the driver’s operational intentions, this strategy reduces the driver’s workload. Under high-risk scenarios, while ensuring driving safety and providing the driver with a satisfactory experience, this strategy significantly enhances the stability of vehicle motion. Full article

In light of the current challenges posed by complex multi-agent interactions and competing interests in integrated energy systems, an economic optimization operation model is proposed. This model is based on a two-layer game comprising a one-master–many-slave structure consisting of an energy retailer, energy suppliers, and a user aggregator. Additionally, it considers energy suppliers to be engaged in a non-cooperative game. The model also incorporates a carbon trading mechanism between the energy retailer and energy suppliers, considers integrated demand response at the user level, and categorizes users in the community according to their energy use characteristics. Finally, the improved differential evolutionary algorithm combined with the CPLEX solver (v12.6) is used to solve the proposed model. The effectiveness of the proposed model in enhancing the benefits of each agent as well as reducing carbon emissions is verified through example analyses. The results demonstrate that the implementation of non-cooperative game strategies among ESs can enhance the profitability of ES1 and ES2 by 27.83% and 18.67%, respectively. Furthermore, the implementation of user classification can enhance user-level benefits by up to 39.51%. Full article

This study addresses the urgent demand for high-precision manufacturing of curved components by developing a fully servo-driven multi-axis controlled four-roll bending machine. By integrating a modular symmetric roller system design with a distributed hierarchical motion control architecture, we achieved substantial enhancements in scalability, forming stability, and machining accuracy. The mechanical system underwent static simulation optimization using SolidWorks Simulation, ensuring maximum stress in the guiding mechanism was controlled below $7.118\times {10}^3$ N/m². ABAQUS-based roll-bending dynamic simulations validated the geometric adaptability and process feasibility of the proposed mechanical configuration. A master-slave dual-core control architecture was implemented in the control system, enabling synchronized error ≤ 0.05 mm, dynamic response time ≤ 10 ms, and positioning accuracy of ±0.01 mm through collaborative control of the master controller and servo drives. Experimental validation demonstrated that the machine achieves bending errors within 1%, with an average forming error of 0.798% across various radii profiles. The arc integrity significantly outperforms conventional equipment, while residual straight edge length was reduced by 86.67%. By adopting fully servo-electric cylinder actuation and integrating a C#-developed human–machine interface with real-time feedback control, this research effectively enhances roll-bending precision, minimizes residual straight edges, and exhibits broad industrial applicability. Full article

The electrical grid is undergoing increasing integration of decentralized power sources connected to the low-voltage network. In this context, the concept of a microgrid has emerged as a system comprising small-scale energy sources, loads, and storage devices, coordinated to operate as a single controllable entity capable of functioning in either grid-connected or islanded mode. The microgrid may be organized in a centralized configuration, such as a master-slave scheme, wherein the centralized converter, i.e., the grid-forming converter (GFC), plays a pivotal role in ensuring system stability and control. This paper introduces a plug-in repetitive controller (RC) strategy tuned to even harmonic orders for application in a three-phase GFC, diverging from the conventional approach that focuses on odd harmonics. The proposed control is designed within a synchronous reference frame and is targeted at centralized AC microgrids, particularly during islanded operation. Simulation results are presented to assess the microgrid’s power flow and power quality, thereby evaluating the performance of the GFC. Additionally, the proposed control was implemented on a Texas Instruments TMS320F28335 digital signal processor and validated through hardware-in-the-loop (HIL) simulation using the Typhoon HIL 600 platform, considering multiple scenarios with both linear and nonlinear loads. The main results highlight that the RC improves voltage regulation, mitigates harmonic distortion, and increases power delivery capability, thus validating its effectiveness for GFC operation. Full article

In the era of digital communication, securing sensitive information against increasingly sophisticated cyber threats remains a critical challenge. Chaos synchronization, while promising for secure communication and control systems, faces key limitations, including high sensitivity to parameter mismatches and initial conditions, vulnerability to noise, and difficulties in maintaining stability for high-dimensional systems. This paper presents a novel secure communication system based on multi-level synchronization of three distinct chaotic systems: the Bhalekar–Gejji (BG) system, the Chen system, and a 3D chaotic oscillator. Utilizing the nonlinear active control (NAC) method, we achieve robust synchronization between master and slave systems, ensuring the stability of the error dynamics through Lyapunov theory. The proposed triple-cascade masking technique enhances security by sequentially embedding the information signal within the chaotic outputs of these systems, making decryption highly challenging without knowledge of all three systems. Numerical simulations and Simulink implementations validate the effectiveness of the synchronization and the secure communication scheme. The results demonstrate that the transmitted signal becomes unpredictable and resistant to attacks, significantly improving the security of chaos-based communication. This approach offers a promising framework for applications requiring high levels of data protection, with potential extensions to image encryption and other multimedia security domains. Full article

Effective communication among distributed energy sources (DESs) is essential for optimizing energy allocation across power sources, loads, and storage devices in integrated renewable energy and energy management systems. This paper proposes a novel communication and energy management strategy to address challenges related to communication interference and inefficiencies in energy management. The proposed strategy employs the DC bus as a communication medium, enabling module coordination via pulsed voltage signals. Controller modules regulate the bus voltage based on the energy state of the bus and control the supplementary or absorptive energy flow from slave modules to maintain voltage stability. Simultaneously, communication between the master and slave modules is achieved through pulsed voltage signals of varying pulse widths, and power sharing is realized via droop control. Experimental results demonstrate that the proposed method effectively distributes energy across different voltage levels, enabling the controller modules to precisely regulate the voltage of the slave modules under varying load conditions. Full article

Aiming at the problems that the semantic representation of information extracted by the shallow layer of the current remote sensing image scene classification network is insufficient, and that the utilization rate of primary visual features decreases with the deepening of the network layers, this paper designs a multi-scale reverse master–slave encoder network (RMSENet). It proposes a reverse cross-scale supplementation strategy for the slave encoder and a reverse cross-scale fusion strategy for the master encoder. This not only reversely supplements the high-level semantic information extracted by the slave encoder to the shallow layer of the master encoder network in a cross-scale manner but also realizes the cross-scale fusion of features at all stages of the master encoder. A multi-frequency coordinate channel attention mechanism is proposed, which captures the inter-channel interactions of input feature maps while embedding spatial position information and rich frequency information. A multi-scale wavelet self-attention mechanism is proposed, which completes lossless downsampling of input feature maps before self-attention operations. Experiments on open-source datasets RSSCN7, SIRI-WHU, and AID show that the classification accuracies of RMSENet reach 97.41%, 97.61%, and 95.9%, respectively. Compared with current mainstream deep learning models, RMSENet has lower network complexity and excellent classification accuracy. Full article

This study proposes an optimized energy management strategy for alternating current microgrids, integrating wind generation, battery energy storage systems (BESSs), and distribution static synchronous compensators (D-STATCOMs). The objective is to minimize operational costs, including grid electricity purchases (grid-connected mode), diesel generation costs (islanded mode), and maintenance expenses of distributed energy resources while ensuring voltage limits, maximum line currents, and power balance. A master–slave optimization approach is employed, where the Gray Wolf Optimizer (GWO) determines the optimal dispatch of energy resources, and successive approximations (SAs) perform power flow analysis. The methodology was validated on a 33-node microgrid, considering variable wind generation and demand profiles from a Colombian region under grid-connected and islanded conditions. To assess performance, 100 independent runs per method were conducted, comparing GWO against particle swarm optimization (PSO) and genetic algorithms (GAs). Statistical analysis confirmed that GWO achieved the lowest operational costs (USD 3299.39 in grid-connected mode and USD 11,367.76 in islanded mode), the highest solution stability (0.19% standard deviation), and superior voltage regulation. The results demonstrate that GWO with SA provides the best trade-off between cost efficiency, system stability, and computational performance, making it an optimal approach for microgrid energy management. Full article