Search Results (33)

Multi-robot rescue path planning (MRRPP) is critical for ensuring the rapid and effective completion of post-disaster rescue tasks. Most studies focus on minimizing the length of rescue paths, the number of robots, and rescue time, neglecting the task utility, which reflects the effect of timely emergency supplies delivery, which is also important for post-disaster rescue. In this study, we integrated multiple optimization indicators into the rescue cost and modeled the problem as a variant of the vehicle routing problem (VRP) with timeliness and battery constraints. A population-based iterative greedy algorithm with Q-learning (QPIG) is proposed to solve it. First, two problem-specific heuristic schemes are designed to generate a high-quality and diverse population. Second, a competition-oriented destruction-reconstruction mechanism is applied to improve the global search ability of the algorithm. In addition, a Q-learning-based local search strategy is developed to enhance the algorithm’s exploitation ability. Moreover, a historical information-based constructive strategy is investigated to accelerate the convergence speed of the algorithm. Finally, the proposed QPIG is validated by comparing it with five efficient algorithms on 56 instances. Experiment results show that the proposed QPIG significantly outperforms compared algorithms in terms of rescue cost and convergence speed. Full article

This paper investigates the problem of data-driven optimal preview repetitive control of linear discrete-time systems. Firstly, by integrating prior information into the preview time domain, an augmented state-space system is established. Secondly, the original output tracking problem is mathematically reconstructed and transformed into the optimization problem form of a linear quadratic tracking (LQR). Furthermore, a Q-function-based iterative algorithm is designed to dynamically calculate the optimal tracking control gain based solely on online measurable data. This method has a dual-breakthrough feature: it neither requires prior knowledge of system dynamics nor provides an initial stable controller. Finally, the superiority of the proposed scheme is verified through numerical simulation experiments. Full article

In this paper, the use of nilpotent Lie algebras as the basis for homomorphic encryption based on additive operations is explored. The $g$-setting is set up over $g{l}_n\left({\mathbb{Z}}_q\right)$) and the group $G=exp\left(g\right)$, and it is noted that the exponential and logarithm series are truncated by nilpotency in a natural way. From this, an additive symmetric conjugation scheme is constructed: given a message element $M$ and a central randomizer $U\in z\left(g\right),$ we encrypt $=Kexp\left(M+U\right)K^{-1}$ and decrypt to $M=log\left(K^{-1}CK\right)-U$. The scheme is additive in nature, with the security defined in the IND-CPA model. Integrity is ensured using an encrypt-then-MAC construction. These properties together provide both confidentiality and robustness while preserving the homomorphic functionality. The scheme realizes additive homomorphism through a truncated BCH-sum, so it is suitable for ciphertext summations. We implemented a prototype and took reproducible measurements (Python 3.11/NumPy) of the series $\{{10,10}^2,{10}^3,{10}^4,{10}^5\}$ over 10 iterations, reporting the medians and 95% confidence intervals. The graphs exhibit that the latency per operation remains constant at fixed values, and the total time scales approximately linearly with the batch size; we also report the throughput, peak memory usage, $\mid C\mid /\mid M\mid$ expansion rate, and achievable aggregation depth. The applications are federated reporting, IoT telemetry, and privacy-preserving aggregations in DBMS; the limitations include its additive nature (lacking general multiplicative homomorphism), IND-CPA (but not CCA), and side-channel resistance requirements. We place our approach in contrast to the standard FHE building blocks BFV/BGV/CKKS nd the emerging NIST PQC standards (FIPS 203/204/205), as a well-established security model with future engineering optimizations. Full article

Unmanned aerial vehicles (UAVs) are increasingly favored for emergency search and rescue operations due to their high adaptability to harsh environments and low operational costs. However, the dynamic nature of search path endpoints, influenced by target movement, limits the applicability of shortest path models between fixed points in moving target search problems. Consequently, the moving target search problem using UAVs in complex environments presents considerable challenges, constituting an NP-hard problem. The Grey Wolf Optimizer (GWO) is known for addressing such problems. However, it suffers from limitations, including premature convergence and instability. To resolve these constraints, a Grey Wolf Optimizer with variable population and strategy (GWO-VPS) is developed in this work. GWO-VPS implements a variable encoding scheme for UAV movement patterns, combining motion-based encoding with path-based encoding. The algorithm iteratively alternates between global optimization and local smoothing phases. The global optimization phase incorporates: (1) a Q-learning-based strategy selection; (2) position updates with obstacle avoidance and energy consumption reduction; and (3) adaptive exploration factor. The local smoothing phase employs four path smoothing operators and Q-learning-based strategy selection. Experimental results demonstrate that GWO-VPS outperforms both enhanced GWO variants and standard algorithms, confirming the algorithm’s effectiveness in UAV-based moving target search simulations. Full article

Considering the dynamic RCS characteristics of stealthy UAVs, we proposed a stealthy UAV path planning algorithm based on the Double Deep Q-Network (DDQN). By introducing the reinforcement learning model that can interact with the environment, the stealth UAV adjusts the path planning strategy through the rewards obtained from the environment to design the optimal path in real-time. Specifically, by considering the effect of RCS from different angles on the detection probability of the air defense radar, the stealth UAV realizes the iterative optimization of the path planning scheme to improve the reliability of the penetration path. Under the guidance of a goal-directed composite reward function proposed, the convergence speed of the stealth UAV path planning algorithm is improved. The simulation results show that the stealth UAV can reach the target position with the optimal path while avoiding the threat zone. Full article

In crab pond environments, obstacles such as long aerobic pipelines, aerators, and ground cages are usually sparsely distributed. Automatic feeding boats can navigate while avoiding obstacles and execute feeding tasks along planned paths, thus improving feeding quality and operational efficiency. In large-scale crab pond farming, a single feeding operation often fails to achieve the complete coverage of the bait casting task due to the limited boat load. Therefore, this study proposes a multi-voyage path planning scheme for feeding boats. Firstly, a complete coverage path planning algorithm is proposed based on an improved genetic algorithm to achieve the complete coverage of the bait casting task. Secondly, to address the issue of an insufficient bait loading capacity in complete coverage operations, which requires the feeding boat to return to the loading wharf several times to replenish bait, a multi-voyage path planning algorithm is proposed. The return point of the feeding operation is predicted by the algorithm. Subsequently, the improved Q-Learning algorithm (I-QLA) is proposed to plan the optimal multi-voyage return paths by increasing the exploration of the diagonal direction, refining the reward mechanism and dynamically adjusting the exploration rate. The simulation results show that compared with the traditional genetic algorithm, the repetition rate, path length, and the number of 90° turns of the complete coverage path planned by the improved genetic algorithm are reduced by 59.62%, 1.27%, and 28%, respectively. Compared with the traditional Q-Learning algorithm, average path length, average number of turns, average training time, and average number of iterations planned by the I-QLA are reduced by 20.84%, 74.19%, 48.27%, and 45.08%, respectively. The crab pond experimental results show that compared with the Q-Learning algorithm, the path length, turning times, and energy consumption of the I-QLA algorithm are reduced by 29.7%, 77.8%, and 39.6%, respectively. This multi-voyage method enables efficient, low-energy, and precise feeding for crab farming. Full article

This paper proposes a novel heart rate detection scheme to address key challenges in millimeter-wave radar-based vital sign monitoring, including weak signals, various types of interference, and the demand for high-precision and super-resolution frequency estimation under practical computational constraints. First, we propose a multi-dimensional coherent accumulation (MDCA) method to enhance the signal-to-noise ratio (SNR) by fully utilizing both spatial information from multiple receiving channels and temporal information from adjacent range bins. Additionally, we are the first to apply the fast iterative interpolated beamforming (FIIB) algorithm to radar-based heart rate detection, enabling super-resolution frequency estimation with low computational complexity. Compared to the traditional fast Fourier transform (FFT) method, the FIIB achieves an improvement of 1.08 beats per minute (bpm). A reordering strategy is also introduced to mitigate potential misjudgments by FIIB. Key parameters of FIIB, including the number of frequency components L and the number of iterations Q, are analyzed and recommended. Dozens of subjects were recruited for experiments, and the root mean square error (RMSE) of heart rate estimation was less than 1.12 bpm on average at a distance of 1 m. Extensive experiments validate the high accuracy and robust performance of the proposed framework in heart rate estimation. Full article

In this study, we focus on solving the nonlinear time-fractional Hirota–Satsuma coupled Korteweg–de Vries (KdV) and modified Korteweg–de Vries (MKdV) equations, using the Yang transform iterative method (YTIM). This method combines the Yang transform with a new iterative scheme to construct reliable and efficient solutions. Readers can understand the procedures clearly, since the implementation of Yang transform directly transforms fractional derivative sections into algebraic terms in the given problems. The new iterative scheme is applied to generate series solutions for the provided problems. The fractional derivatives are considered in the Caputo sense. To validate the proposed approach, two numerical examples are analysed and compared with exact solutions, as well as with the results obtained from the fractional reduced differential transform method (FRDTM) and the q-homotopy analysis transform method (q-HATM). The comparisons, presented through both tables and graphical illustrations, confirm the enhanced accuracy and reliability of the proposed method. Moreover, the effect of varying the fractional order is explored, demonstrating convergence of the solution as the order approaches an integer value. Importantly, the time-fractional Hirota–Satsuma coupled KdV and modified Korteweg–de Vries (MKdV) equations investigated in this work are not only of theoretical and computational interest but also possess significant implications for achieving global sustainability goals. Specifically, these equations contribute to the Sustainable Development Goal (SDG) “Life Below Water” by offering advanced modelling capabilities for understanding wave propagation and ocean dynamics, thus supporting marine ecosystem research and management. It is also relevant to SDG “Climate Action” as it aids in the simulation of environmental phenomena crucial to climate change analysis and mitigation. Additionally, the development and application of innovative mathematical modelling techniques align with “Industry, Innovation, and Infrastructure” promoting advanced computational tools for use in ocean engineering, environmental monitoring, and other infrastructure-related domains. Therefore, the proposed method not only advances mathematical and numerical analysis but also fosters interdisciplinary contributions toward sustainable development. Full article

This paper investigated a new speed regulator using an adaptive transitioning policy iteration learning technique for the variable reluctance motor (VRM) drive. A transitioning strategy is used in this unique scheme to handle the nonlinear behavior of the VRM by using a series of learning centers, each of which is an individual local learning controller at linear operational location that grows throughout the system’s nonlinear domain. This improved control technique based on an adaptive dynamic programming algorithm is developed to derive the prime solution of the infinite horizon linear quadratic tracker (LQT) issue for an unidentified dynamical configuration with a VRM drive. By formulating a policy iteration algorithm for VRM applications, the speed of the motor shows inside the machine model, and therefore the local centers are directly affected by the speed. Hence, when the speed of the rotor changes, the parameters of the local centers grid would be updated and tuned. Additionally, a multivariate transition algorithm has been adopted to provide a seamless transition between the Q-centers. Finally, simulation and experimental results are presented to confirm the suggested control scheme’s efficacy. Full article

In Mobile Crowdsensing (MCS), sensing tasks have different impacts and contributions to the whole system or specific targets, so the importance of the tasks is different. Since resources for performing tasks are usually limited, prioritizing the allocation of resources to more important tasks can ensure that key data or information can be collected promptly and accurately, thus improving overall efficiency and performance. Therefore, it is very important to consider the importance of tasks in the task selection and allocation of MCS. In this paper, a task queue is established, the importance of tasks, the ability of participants to perform tasks, and the stability of the task queue are considered, and a novel task selection and allocation scheme (TSAS) in the MCS system is designed. This scheme introduces the Lyapunov optimization method, which can be used to dynamically keep the task queue stable, balance the execution ability of participants and the system load, and perform more important tasks in different system states, even when the participants are limited. In addition, the Double Deep Q-Network (DDQN) method is introduced to improve on the traditional solution of the Lyapunov optimization problem, so this scheme has a certain predictive ability and foresight on the impact of future system states. This paper also proposes action-masking and iterative training methods for the MCS system, which can accelerate the training process of the neural network in the DDQN and improve the training effect. Experiments show that the TSAS based on the Lyapunov optimization method and DDQN performs better than other algorithms, considering the long-term stability of the queue, the number and importance of tasks to be executed, and the congestion degree of tasks. Full article

The paper concerns a nonlinear second-order system of coupled PDEs, having the principal part in divergence form and subject to in-homogeneous dynamic boundary conditions, for both $\theta (t,x)$ and $\phi (t,x)$. Two main topics are addressed here, as follows. First, under a certain hypothesis on the input data, $f_1$, $f_2$, $w_1$, $w_2$, $\alpha$, $\xi$, ${\theta }_0$, ${\alpha }_0$, ${\phi }_0$, and ${\xi }_0$, we prove the well-posedness of a solution $\theta ,\alpha ,\phi ,\xi$, which is $\left(\theta (t,x),\alpha (t,x)\right)\in W_p^{1,2}\left(Q\right)\times W_p^{1,2}(\Sigma )$, $\left(\phi (t,x),\xi (t,x)\right)\in W_{\nu }^{1,2}\left(Q\right)\times W_p^{1,2}(\Sigma )$, $\nu =min\{q,\mu \}$. According to the new formulation of the problem, we extend the previous results, allowing the new mathematical model to be even more complete to describe the diversity of physical phenomena to which it can be applied: interface problems, image analysis, epidemics, etc. The main goal of the present paper is to develop an iterative scheme of fractional-step type in order to approximate the unique solution to the nonlinear second-order system. The convergence result is established for the new numerical method, and on the basis of this approach, a conceptual algorithm, alg-frac_sec-ord_u+varphi_dbc, is elaborated. The benefit brought by such a method consists of simplifying the computations so that the time required to approximate the solutions decreases significantly. Some conclusions are given as well as new research topics for the future. Full article

Data caching is a promising technique to alleviate the data traffic burden from the backhaul and minimize data access delay. However, the cache capacity constraint poses a significant challenge to obtaining content through the cache resource that degrades the caching performance. In this paper, we propose a novel two-tier caching mechanism for data caching on mobile user equipment (UE) and the small base station (SBS) level in ultra-dense 6G heterogeneous networks for reducing data access failure via cache resources. The two-tier caching enables users to retrieve their desired content from cache resources through device-to-device (D2D) communications from neighboring users or the serving SBS. The cache-enabled UE exploits millimeter-wave (mmWave)-based D2D communications, utilizing line-of-sight (LoS) links for high-speed data transmission to content-demanding mobile UE within a limited connection time. In the event of D2D communication failures, a dual-mode hybrid system, combining mmWave and microwave $\mathsf{\mu }$Wave technologies, is utilized to ensure effective data transmission between the SBS and UE to fulfill users’ data demands. In the proposed framework. the data transmission speed is optimized through mmWave signals in line-of-sight (LoS) conditions. In non-LoS scenarios, the system switches to $\mathsf{\mu }$Wave mode for obstacle-penetrating signal transmission. Subsequently, we propose a reinforcement learning (RL) approach to optimize cache decisions through the approximation of the Q action-value function. The proposed technique undergoes iterative learning, adapting to dynamic network conditions to enhance the content placement policy and minimize delay. Extensive simulations demonstrate the efficiency of our proposed approach in significantly reducing network delay compared with benchmark schemes. Full article

Iterative procedures have been proved as a milestone in the generation of fractals. This paper presents a novel approach for generating and visualizing fractals, specifically Mandelbrot and Julia sets, by utilizing complex polynomials of the form $Q_{\mathbb{C}}\left(p\right)=a{p}^n+mp+c$, where $n\ge 2$. It establishes escape criteria that play a vital role in generating these sets and provides escape time results using different iterative schemes. In addition, the study includes the visualization of graphical images of Julia and Mandelbrot sets, revealing distinct patterns. Furthermore, the study also explores the impact of parameters on the deviation of dynamics, color, and appearance of fractals. Full article

Quantum calculus can provide new insights into the nonlinear behaviour of functions and equations, addressing problems that may be difficult to tackle by classical calculus due to high nonlinearity. Iterative methods for solving nonlinear equations can benefit greatly from the mathematical theory and tools provided by quantum calculus, e.g., using the concept of q-derivatives, which extends beyond classical derivatives. In this paper, we develop parallel numerical root-finding algorithms that approximate all distinct roots of nonlinear equations by utilizing q-analogies of the function derivative. Furthermore, we utilize neural networks to accelerate the convergence rate by providing accurate initial guesses for our parallel schemes. The global convergence of the q-parallel numerical techniques is demonstrated using random initial approximations on selected biomedical applications, and the efficiency, stability, and consistency of the proposed hybrid numerical schemes are analyzed. Full article

With the rapid advancement of the Internet of Things (IoT), there is a global surge in network traffic. Software-Defined Networks (SDNs) provide a holistic network perspective, facilitating software-based traffic analysis, and are more suitable to handle dynamic loads than a traditional network. The standard SDN architecture control plane has been designed for a single controller or multiple distributed controllers; however, a logically centralized single controller faces severe bottleneck issues. Most proposed solutions in the literature are based on the static deployment of multiple controllers without the consideration of flow fluctuations and traffic bursts, which ultimately leads to a lack of load balancing among controllers in real time, resulting in increased network latency. Moreover, some methods addressing dynamic controller mapping in multi-controller SDNs consider load fluctuation and latency but face controller placement problems. Earlier, we proposed priority scheduling and congestion control algorithm (eSDN) and dynamic mapping of controllers for dynamic SDN (dSDN) to address this issue. However, the future growth of IoT is unpredictable and potentially exponential; to accommodate this futuristic trend, we need an intelligent solution to handle the complexity of growing heterogeneous devices and minimize network latency. Therefore, this paper continues our previous research and proposes temporal deep Q learning in the dSDN controller. A Temporal Deep Q learning Network (tDQN) serves as a self-learning reinforcement-based model. The agent in the tDQN learns to improve decision-making for switch-controller mapping through a reward–punish scheme, maximizing the goal of reducing network latency during the iterative learning process. Our approach—tDQN—effectively addresses dynamic flow mapping and latency optimization without increasing the number of optimally placed controllers. A multi-objective optimization problem for flow fluctuation is formulated to divert the traffic to the best-suited controller dynamically. Extensive simulation results with varied network scenarios and traffic show that the tDQN outperforms traditional networks, eSDNs, and dSDNs in terms of throughput, delay, jitter, packet delivery ratio, and packet loss. Full article