Search Results (25)

To address the issues of uneven initial distribution and limited search accuracy with the traditional divergent quantum-inspired differential search (DCS) algorithm, a hybrid multi-strategy variant, termed DQDCS, is proposed. This improved version overcomes these limitations by integrating the refined set strategy and clustering process for population initialization, along with the double Q-learning model to balance exploration and exploitation This enhanced version replaces the conventional pseudo-random initialization with a refined set generated through a clustering process, thereby significantly improving population diversity. A novel position update mechanism is introduced based on the original equation, enabling individuals to effectively escape from local optima during the iteration process. Additionally, the table reinforcement learning model (double Q-learning model) is integrated into the original algorithm to balance the probabilities between exploration and exploitation, thereby accelerating the convergence towards the global optimum. The effectiveness of each enhancement is validated through ablation studies, and the Wilcoxon rank-sum test is employed to assess the statistical significance of performance differences between DQDCS and other classical algorithms. Benchmark simulations are conducted using the CEC2019 and CEC2022 test functions, as well as two well-known constrained engineering design problems. The comparison includes both recent state-of-the-art algorithms and improved optimization methods. Simulation results demonstrate that the incorporation of the refined set and clustering process, along with the table reinforcement learning model (double Q-learning model) mechanism, leads to superior convergence speed and higher optimization precision. Full article

A new text matching model based on dynamic gated sparse attention feature distillation with a faithful semantic preservation strategy is proposed to address the fact that text matching models are susceptible to interference from weakly relevant information and that they find it difficult to obtain key features that are faithful to the original semantics, resulting in a decrease in accuracy. Compared to the traditional attention mechanism, with its high computational complexity and difficulty in discarding weakly relevant features, this study designs a new dynamic gated sparse attention feature distillation method based on dynamic gated sparse attention, aiming to obtain key features. Weakly relevant features are obtained through the synergy of dynamic gated sparse attention, a gradient inversion layer, a SoftMax function, and projection theorem literacy. Among these, sparse attention enhances weakly correlated feature capture through multimodal dynamic fusion with adaptive compression. Then, the projection theorem is used to identify and discard the noisy features in the hidden layer information to obtain the key features. This feature distillation strategy, in which the semantic information of the original text is decomposed into key features and noise features, forms an orthogonal decomposition symmetry in the semantic space. A new variety of faithful semantic preservation strategies is designed to make the key features faithful to the original semantic information. This strategy introduces an interval loss function and calculates the angle between the key features and the original hidden layer information with the help of cosine similarity in order to ensure that the features reflect the semantics of the original text. This can further update the iterative key features and thus improve the accuracy. The strategy builds a feature fidelity verification mechanism with a symmetric core of bidirectional considerations of semantic accuracy and correspondence to the original text. The experimental results show that the accuracies are 89.10% and 95.01% in the English datasets MRPC and Scitail, respectively; 87.8% in the Chinese dataset PAWX; and 80.32% and 80.27% in the Ant Gold dataset, respectively. Meanwhile, the accuracies in the KUAKE-QTR dataset and Macro-F1 are 70.10% and 68.08%, respectively, which are better than other methods. Full article

This paper presents the Mobility-Aware Client Selection (MACS) strategy, developed to address the challenges associated with client mobility in Federated Learning (FL). FL enables decentralized machine learning by allowing collaborative model training without sharing raw data, preserving privacy. However, client mobility and limited resources in IoT environments pose significant challenges to the efficiency and reliability of FL. MACS is designed to maximize client participation while ensuring timely updates under computational and communication constraints. The proposed approach incorporates a Mobility Prediction Model to forecast client connectivity and resource availability and a Resource-Aware Client Evaluation mechanism to assess eligibility based on predicted latencies. MACS optimizes client selection, improves convergence rates, and enhances overall system performance by employing these predictive capabilities and a dynamic resource allocation strategy. The evaluation includes comparisons with advanced baselines such as Reinforcement Learning-based FL (RL-based) and Deep Learning-based FL (DL-based), in addition to Static and Random selection methods. For the CIFAR dataset, MACS achieved a final accuracy of 95%, outperforming Static selection (85%), Random selection (80%), RL-based FL (90%), and DL-based FL (93%). Similarly, for the MNIST dataset, MACS reached 98% accuracy, surpassing Static selection (92%), Random selection (88%), RL-based FL (94%), and DL-based FL (96%). Additionally, MACS consistently required fewer iterations to achieve target accuracy levels, demonstrating its efficiency in dynamic IoT environments. This strategy provides a scalable and adaptable solution for sustainable federated learning across diverse IoT applications, including smart cities, healthcare, and industrial automation. Full article

To address the performance degradation of violation action recognition models due to changing operational scenes in power grid operations, this paper proposes a Few-shot Adaptive Network (FSA-Net). The method incorporates few-shot learning into the network design by adding a parameter mapping layer to the classification network and developing a task-adaptive module to adjust the network parameters for changing scenes. A task-specific linear classifier is added after the backbone, allowing the adaptive generation of classifier weights based on the changing task scene to enhance the model’s generalizability. Additionally, the model uses a strategy of freezing the backbone network and iteratively updating only certain module parameters during training in order to minimize training costs. This approach addresses the challenge of iteratively updating difficulties in the original model, which are caused by limited image data following scene changes. In this paper, 2000 samples under power grid scenarios are used as the experimental dataset; the average recognition accuracy for violation actions is 81.77% for images after scene changes, which represents a 4.58% improvement when compared to the ResNet-50 classification network. Furthermore, the model’s training efficiency is enhanced by 40%. The experimental results show that the method enhances the performance of the violation action recognition model before and after scene changes and improves the efficiency of the iterative model by updating with a smaller sample size, lower model design cost, and lower training cost. Full article

In this article, an innovative online adaptive high-efficiency control strategy is proposed to improve the power generation efficiency of a marine micro gas turbine under partial load. Firstly, a mathematical model of the micro-gas turbine is established, and a control strategy consisting of an on-board prediction model and an online update model is proposed. To evaluate the performance changes of the gas turbine, we applied deep learning techniques to enhance the extreme learning machine (ELM) algorithm, resulting in the development of a high-precision, high-real-time deep extreme learning machine (DL_ELM) prediction model. This model effectively monitors changes in the gas turbine’s performance. Furthermore, an online time-series deep extreme learning machine with a dynamic forgetting factor (DFF_DL_OSELM) model is designed to achieve the real-time tracking of performance variations. When the DL_ELM model detects a gas turbine’s performance change, a particle swarm optimization (PSO) algorithm is employed to iteratively calculate the DFF_DL_OSELM model, determining the optimal speed control scheme to ensure the gas turbine operates at maximum efficiency. To validate the superiority of the proposed control strategy, a comparison is made with traditional high-efficiency control strategies based on polynomial fitting and BP neural networks. The results demonstrate that although all three strategies can achieve efficient operation under constant conditions, traditional strategies fail to identify and adjust to performance changes in real time, leading to decreased control performance and potential engine damage as engine characteristics degrade. In contrast, the proposed online adaptive control strategy dynamically adjusts the speed control plan based on performance degradation, ensuring that the gas turbine operates efficiently while keeping the turbine inlet and exhaust temperatures within safe limits. Full article

The mast is a critical component of rotary drilling rigs, which has a cross-section consisting of a rectangular shape formed by two web plates and two flange plates. Structural optimization of the mast is necessary to address the issue of excessive weight. The shortcomings of the traditional structural optimization algorithms are summarized as follows: the optimized steel plate thickness is a non-integer, where rounding upwards may increase the cost to a certain extent, but it can ensure the safety of the structure; rounding downwards its load carrying capacity may not satisfy the requirements, and thus a novel Salp Swarm Algorithm is proposed to solve the optimization problem. First, this study improves the initialization and update strategy in the traditional Salp Swarm Algorithm. In order to obtain a solution for engineering, an innovative multi-dimensional running comparison is carried out. Secondly, the optimization model of rotary drilling rigs is established based on the division of the working conditions. The objective function of the optimization model is to minimize the weight of the mast while considering the constraints of strength, stiffness, stability, and welding process. Finally, the proposed optimization algorithm and the established optimization model are applied to optimize the design of the mast for a rotary drilling rig. The empirical results demonstrate that the weight of the mast has been reduced by 20%. In addition, the Improved Salp Swarm Algorithm exhibits higher solution quality, faster iteration capability, and extreme stability in optimizing welded box sections compared to the conventional algorithm. The example shows that the Improved Salp Swarm Algorithm is applicable to the optimization problem of box sections. Full article

The typical industrial Internet of Things (IIoT) network system relies on a real-time data upload for timely processing. However, the incidence of device heterogeneity, high network latency, or a malicious central server during transmission has a propensity for privacy leakage or loss of model accuracy. Federated learning comes in handy, as the edge server requires less time and enables local data processing to reduce the delay to the data upload. It allows neighboring edge nodes to share data while maintaining data privacy and confidentiality. However, this can be challenged by a network disruption making edge nodes or sensors go offline or experience an alteration in the learning process, thereby exposing the already transmitted model to a malicious server that eavesdrops on the channel, intercepts the model in transit, and gleans the information, evading the privacy of the model within the network. To mitigate this effect, this paper proposes asynchronous privacy-preservation federated learning for mobile edge networks in the IIoT ecosystem (APPFL-MEN) that incorporates the iteration model design update strategy (IMDUS) scheme, enabling the edge server to share more real-time model updates with online nodes and less data sharing with offline nodes, without exposing the privacy of the data to a malicious node or a hack. In addition, it adopts a double-weight modification strategy during communication between the edge node and the edge server or gateway for an enhanced model training process. Furthermore, it allows a convergence boosting process, resulting in a less error-prone, secured global model. The performance evaluation with numerical results shows good accuracy, efficiency, and lower bandwidth usage by APPFL-MEN while preserving model privacy compared to state-of-the-art methods. Full article

In China, the inversion between peak periods of wind and photovoltaic (PV) power (WPVP) generation and peak periods of electricity demand leads to a mismatch between electricity demand and supply, resulting in a significant loss of WPVP. In this context, this article proposes an improved demand response (DR) strategy to enhance the consumption of WPVP. Firstly, we use feature selection methods to screen variables related to response quantity and, based on the results, establish a response potential prediction model using random forest algorithm. Then, we design a subsidy price update formula and the subsidy price constraint conditions that consider user response characteristics and predict the response potential of users under differentiated subsidy price. Subsequently, after multiple iterations of the price update formula, the final subsidy and response potential of the user can be determined. Finally, we establish a user ranking sequence based on response potential. The case analysis shows that differentiated price strategy and response potential prediction model can address the shortcomings of existing DR strategies, enabling users to declare response quantity more reasonably and the grid to formulate subsidy price more fairly. Through an improved DR strategy, the consumption rate of WPVP has increased by 12%. Full article

This paper addresses the guidance of various flight vehicles under multiple constraints in three-dimensional space. A cooperative guidance strategy that satisfies both time and angle constraints is designed to reach a moving target. The strategy is organized into two parts: modeling and programming calculations. First, a nonlinear motion model for guidance is established and normalized, including both the vehicle and the target. Later, the arrival method is automatically determined according to the strategy and depending on the type of target. The cooperative terminal time is determined based on an augmented proportional navigation method. An improved model predictive static programming (MPSP) algorithm was designed as a means of adjusting the adaptive terminal time. Then, the algorithm was used to update the control quantity iteratively until the off-target quantity and the angle of constraints were satisfied. The simulation results showed that the strategy could enable multiple flight vehicles at different initial positions to reach the target accurately at the same time and with the ideal impact angle. The strategy boasts a high computational efficiency and is capable of being implemented in real time. Full article

To adapt to the development trend of intelligent air combat, it is necessary to research the autonomous generation of maneuvering decisions for unmanned combat aerial vehicles (UCAV). This paper presents a maneuver decision-making method for UCAV based on a hybridization of deep Q-network (DQN) and extended Kalman filtering (EKF). Firstly, a three-dimensional air combat simulation environment is constructed, and a flight motion model of UCAV is designed to meet the requirements of the simulation environment. Secondly, we evaluate the current situation of UCAV based on their state variables in air combat, for further network learning and training to obtain the optimal maneuver strategy. Finally, based on the DQN, the system state equation is constructed using the uncertain parameter values of the current network, and the observation equation of the system is constructed using the parameters of the target network. The optimal parameter estimation value of the DQN is obtained by iteratively updating the solution through EKF. Simulation experiments have shown that this autonomous maneuver decision-making method hybridizing DQN with EKF is effective and reliable, as it can eliminate the opponent and preserve its side. Full article

Intelligent manufacturing workshop uses automatic guided vehicles as an important logistics and transportation carrier, and most of the existing research adopts the intelligent manufacturing workshop layout and Automated Guided Vehicle (AGV) path step-by-step optimization, which leads to problems such as low AGV operation efficiency and inability to achieve the optimal layout. For this reason, a smart manufacturing assembly line layout optimization model considering AGV path planning with the objective of minimizing the amount of material flow and the shortest AGV path is designed for the machining shop of a discrete manufacturing enterprise of a smart manufacturing company. Firstly, the information of the current node, the next node and the target node is added to the heuristic information, and the dynamic adjustment factor is added to make the heuristic information guiding in the early stage and the pheromone guiding in the later stage of iteration; secondly, the Laplace distribution is introduced to regulate the volatilization of the pheromone in the pheromone updating of the ant colony algorithm, which speeds up the speed of convergence; the path obtained by the ant colony algorithm is subjected to the deletion of the bi-directional redundant nodes, which enhances the path smoothing degree; and finally, the improved ant colony algorithm is fused with the improved dynamic window algorithm, so as to enable the robots to arrive at the end point safely. Simulation shows that in the same map environment, the ant colony algorithm compared with the basic ant colony algorithm reduces the path length by 40% to 67% compared to the basic ant colony algorithm and reduces the path inflection points by 34% to 60%, which is more suitable for complex environments. It also verifies the feasibility and superiority of the conflict-free path optimization strategy in solving the production scheduling problem of the flexible machining operation shop. Full article

To address the problems of strong coupling and large hysteresis in the temperature control of a continuously stirred tank reactor (CSTR) process, an improved sparrow search algorithm (ISSA) is proposed to optimize the PID parameters. The improvement aims to solve the problems of population diversity reduction and easy-to-fall-into local optimal solutions when the traditional sparrow algorithm is close to the global optimum. This differs from other improved algorithms by adding a new Gauss Cauchy mutation strategy at the end of each iteration without increasing the time complexity of the algorithm. By introducing tent mapping in the sparrow algorithm to initialize the population, the population diversity and global search ability are improved; the golden partition coefficient is introduced in the explorer position update process to expand the search space and balance the relationship between search and exploitation; the Gauss Cauchy mutation strategy is used to enhance the ability of local minimum value search and jumping out of local optimum. Compared with the four existing classical algorithms, ISSA has improved the convergence speed, global search ability and the ability to jump out of local optimum. The proposed algorithm is combined with PID control to design an ISSA-PID temperature controller, which is simulated on a continuous reactor temperature model identified by modeling. The results show that the proposed method improves the transient and steady-state performance of the reactor temperature control with good control accuracy and robustness. Finally, the proposed algorithm is applied to a semi-physical experimental platform to verify its feasibility. Full article

The research on decision-making models of ship collision avoidance is confronted with numerous challenges. These challenges encompass inadequate consideration of complex factors, including but not limited to open water scenarios, the absence of static obstacle considerations, and insufficient attention given to avoiding collisions between manned ships and MASSs. A decision model for MASS collision avoidance is proposed to overcome these limitations by integrating the strengths of model-based and model-free methods in reinforcement learning. This model incorporates S-57 chart information, AIS data, and the Dyna framework to improve effectiveness. (1) When the MASS’s navigation task is known, a static navigation environment is built based on S-57 chart information, and the Voronoi diagram and improved A* algorithm are used to obtain the energy-saving optimal static path as the planned sea route. (2) Given the small main dimensions of an MASS, which is easily affected by wind and current factors, the motion model of an MASS is established based on the MMG model considering wind and current factors. At the same time, AIS data are used to extract the target ship (manned ship) data. (3) According to the characteristics of the actual navigation of ships at sea, the state space, action space, and reward function of the reinforcement learning algorithm are designed. The MASS collision avoidance decision model based on the Dyna-DQN model is established. Based on the DQN algorithm, the agent (MASS) and the environment interact continuously, and the actual interaction data generated are used for the iterative update of the collision avoidance strategy and the training of the environment model. Then, the environment model is used to generate a series of simulated empirical data to promote the iterative update of the strategy. Using the waters near the South China Sea as the research object for simulation verification, the navigation tasks are divided into three categories: only considering static obstacles, following the planned sea route considering static obstacles, and following the planned sea route considering both static and dynamic obstacles. The results show that through repeated simulation experiments, an MASS can complete the navigation task without colliding with static and dynamic obstacles. Therefore, the proposed method can be used in the intelligent collision avoidance module of MASSs and is an effective MASS collision avoidance method. Full article

The commercial operation of the maglev train has strict requirements for the reliability and safety of the suspension control system. However, due to a large number of unmodeled dynamics of the suspension system, it is difficult to obtain the precise mathematical model of the suspension system. After the suspension system has been operated for a long time with high load, the system model will change due to the wear, aging and failure of components, as well as the settlement of the line and track. The control performance is degraded. Therefore, this paper proposes a data-driven nonlinear iterative inversion suspension control algorithm, which can achieve high-precision tracking performance recovery control after control performance degradation without depending on the suspension system model. The control performance of the suspension system is improved by learning the measured data of the historical suspension system, and the fast convergence of the tracking error and high-precision stable suspension control are realized in the presence of unmodeled dynamics and external noise interference. Based on the historical suspension data of the maglev train suspension control system, the inverse dynamics model of the suspension system is identified by iterative inversion learning based on data drive, and the suspension control framework based on iterative inversion is designed. Then, the nonlinear input update strategy is used to realize the rapid convergence of the learning process. Finally, the simulation experiment of the maglev train suspension system and the physical experiment of the maglev system experimental platform are combined. It is verified that the proposed levitation control algorithm can achieve high-precision fast tracking performance recovery control after the system control performance degrades under noise environment. Full article

For the problem of defect inversion in magnetic flux leakage technology, a fast, globally convergent particle swarm optimization algorithm based on the finite-element forward model is introduced as an inverse iterative algorithm in this paper. Two aspects of the traditional particle swarm optimization algorithm have been improved: self-adaptive inertia weight and speed updating strategy. For the inertia weight, it can be adaptively adjusted according to the particle position. The speed update strategy mainly uses the best experience positions of other particles in a randomly selected population to realize the algorithm’s learning. At the same time, the learning factor of the position variable is designed to change with the number of iteration steps. The particle with a good position is added to jump out of the local minimum and accelerate the optimization process. Through the comparison experiment, the improved particle swarm optimization algorithm has a faster convergence speed compared with other traditional particle swarm optimization algorithms. It is more difficult for it to fall into the local minimum value and it is more easily converted to a higher precision. Full article