Research on multitask scheduling systems in factory environments is a popular topic in the field of intelligent manufacturing. Existing research mainly focuses on the optimization of automated guided vehicle (AGV) pat...
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Research on multitask scheduling systems in factory environments is a popular topic in the field of intelligent manufacturing. Existing research mainly focuses on the optimization of automated guided vehicle (AGV) path planning and scheduling, emphasizing on the minimization of conflicts and deadlocks, multi-objective task scheduling, and metaheuristic a*algorithm optimization, while ignoring path stability and real-time path planning in dynamic environments. Therefore, this paper aims to address these issues to better handle dynamic changes in actual operating environments. This paper establishes a mathematical model with the optimization objective of minimizing the overall running time of material distribution tasks and proposes an improved ant colony a*algorithm to optimize the model. First, the concept of prior time is introduced to improve the traditional ant colony a*algorithm. The path of the ongoing task is introduced with a time calculation, and the occupancy time window of each grid point on the path is calculated. Based on this, the initial pheromone distribution on subsequent paths is altered dynamically, which accelerates the convergence of the ants to a collision-free path. Second, in the pheromone update stage, the method of calculating the pheromone increment in the traditional ant colony a*algorithm is modified. The original distance influence factor is changed to a time influence factor, which ensures that all tasks still have the minimum running time when calculating a collision-free path. Finally, through 30 sets of simulation experiments on material distribution tasks, it is shown that the proposed a*algorithm shortens the total running time by 15.14%, 12.87%, and 10.59% compared to two ant colony a*algorithms and one strategic multi-AGV scheduling a*algorithm, respectively, thus verifying the effectiveness of the proposed method.
Optimizing the fractional-order PID (FOPID) controller using metaheuristic a*algorithms has gained significant popularity across various engineering domains. This paper introduces a novel approach by employing the artif...
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Optimizing the fractional-order PID (FOPID) controller using metaheuristic a*algorithms has gained significant popularity across various engineering domains. This paper introduces a novel approach by employing the artificial hummingbird a*algorithm (AHA), an innovative optimization technique inspired by the unique flight and foraging behaviors of hummingbirds, to fine-tune the FOPID controller for the automatic voltage regulator (AVR) system in synchronous generators, a critical component in maintaining voltage stability. The proposed method is rigorously tested using MATLAB/Simulink simulations under challenging conditions, including nonsmoothed higher-order dynamics of the control plant, parameter variations, time delays, and nonlinearities. The effectiveness of the AHA-based FOPID control strategy on the AVR system is comprehensively evaluated through extensive tests and analyses, focusing on aspects such as transient response, robustness, stability, and trajectory tracking. Moreover, a comparative assessment against established optimization a*algorithms, namely particle swarm optimization (PSO), genetic a*algorithm (GA), gray wolf optimizer (GWO), and artificial bee colony (ABC) is conducted. The results demonstrate the superiority of the proposed AHA-based FOPID control strategy, which significantly increases convergence speed. This is evidenced by a 25% faster rise time and a 45.74% shorter settling time compared to the GA-FOPID controller, the closest in performance for these metrics. Additionally, the AHA-based FOPID controller achieves a 92% reduction in steady-state oscillations compared to the ABC-FOPID controller, the nearest competitor in this aspect. These improvements highlight the AHA-based FOPID controller's superior efficiency and rapid response in achieving optimal performance. Hence, the proposed method shows remarkable success in enhancing stability and robustness, making it highly suitable for the design of practical high-performance applications.
To address the issues of low search efficiency, excessive node expansion, and the presence of redundant nodes in the traditional A* a*algorithm, this article proposes an improved A* a*algorithm for mobile robot path plann...
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To address the issues of low search efficiency, excessive node expansion, and the presence of redundant nodes in the traditional A* a*algorithm, this article proposes an improved A* a*algorithm for mobile robot path planning. Firstly, a multi-neighborhood hybrid search method is introduced, optimizing the traditional eight-neighborhood and twenty-four-neighborhood into a new sixteen-neighborhood. The choice between eight-neighborhood search and sixteen-neighborhood search is determined based on the presence of obstacles in the eight-neighborhood around the current node, effectively enhancing the search efficiency of the a*algorithm and reducing the number of nodes expanded during the search process. Subsequently, unnecessary nodes are eliminated based on the positional relationship between the current node and the target node, according to neighborhood direction search rules, further decreasing the number of expanded nodes. Additionally, improvements to the bidirectional search mechanism along with the incorporation of dynamic weight coefficients further enhance the search efficiency of the a*algorithm. Furthermore, a strategy for extracting key nodes is employed to effectively remove useless turn points, thus resolving the issue of redundant nodes. Finally, simulation experiments demonstrate that the proposed improved A* a*algorithm outperforms the traditional A* a*algorithm in terms of search speed, number of expanded nodes, and path length, validating the effectiveness of the proposed method.
In this paper, we present an adaptive intelligent reflecting surfaces (IRS) adjusting a*algorithm designed to enhance the communication quality of secondary users (SUs) within heterogeneous cognitive radio networks (CRN...
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In this paper, we present an adaptive intelligent reflecting surfaces (IRS) adjusting a*algorithm designed to enhance the communication quality of secondary users (SUs) within heterogeneous cognitive radio networks (CRNs), using a cross-layer analysis approach. Initially, a Markov model is established based on queue analysis of SUs' buffer. Subsequently, to optimize the diagonal reflection coefficient matrix of the IRS, we derive the key objectives of the established multi-objective optimization problem, including potential throughput, data packet rejection rate, and data packet queue length of SUs, into closed-form expressions. Thereafter, the optimal solution guides the dynamic pre-adjustment of IRS by the station. Simulation results verify the superior performance of the proposed a*algorithm, particularly in terms of spectral efficiency and bit error rate, compared to existing methods.
The path planning a*algorithm not only ensures that the mobile robot can avoid obstacles to reach the target point at a safe speed but also ensures that the mobile robot can quickly adapt to the complex changing environ...
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The path planning a*algorithm not only ensures that the mobile robot can avoid obstacles to reach the target point at a safe speed but also ensures that the mobile robot can quickly adapt to the complex changing environment. In this paper, the existing path planning a*algorithms of mobile robots are analyzed, and then the fusion path planning a*algorithm is studied. The main work is summarized as follows: A* a*algorithm is used to complete global path planning and path smoothing, the basic principle of dynamic window method (DWA) is studied, and the dynamic constraints of mobile robots are discussed. The shortcomings of the dynamic window method, i.e., that the dynamic window method does not have the ability to self-learn and self-adapt in the dynamic and unknown environment, are analyzed through simulation experiments. In addition, by studying the basic principle of deep reinforcement learning, the essence and characteristics of DWA a*algorithm and DQN a*algorithm are analyzed, which provides ideas for the fusion path planning a*algorithm based on DQN and DWA. Finally, to cope with the complex and changeable environment and improve the real-time obstacle avoidance ability of mobile robots, a fusion path planning a*algorithm based on DQN and DWA is proposed. First, the dynamic window method is used to limit the driving of the mobile robot directly to the velocity space. Then, a deep Q network is designed and trained to approximate the state-action value function of the mobile robot, then dynamically interact with the environment to adjust the robot's moving trajectory in real time, and finally, find the optimal path for the robot. The simulation results show that the fusion path planning a*algorithm proposed in this paper ensures that the mobile robot has strong generalization ability and robustness under the complex, variable, and dynamic unknown environment. Compared with the existing DWA and DQN a*algorithms, the proposed fusion path planning a*algorithm achieves better path planning
Every year, the global production of marine debris reaches a staggering 400 million tons, and the amount of garbage continues to increase. Among this, about 14 million tons will eventually flow into the oceans, posing...
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Every year, the global production of marine debris reaches a staggering 400 million tons, and the amount of garbage continues to increase. Among this, about 14 million tons will eventually flow into the oceans, posing a significant threat to marine ecosystems. Apart from endangering marine ecology, this waste will ultimately come back to affect humans. To address the increasingly severe issue of marine debris, our team independently developed a surface garbage cleaning robot, DaYu No. 1, and designed a path planning a*algorithm specifically for DaYu No. 1, called DyNav. DaYu No. 1 aims to increase garbage cleaning efficiency and adaptability. The traditional A (& lowast;) a*algorithm performs well in static environments, but the ocean is a dynamic environment where marine debris constantly shifts due to ocean currents or tides, making it extremely challenging for robots to collect marine debris. Therefore, our proposed DyNav a*algorithm can adaptively adjust paths to cope with environmental changes. We proposed DyNav, a new path planning a*algorithm that takes ocean currents into account, and the DaYu No. 1 robot with YOLOv7 and IMA a*algorithms built in. These will make cleaning up marine debris much more efficient and flexible. Compared to traditional path planning a*algorithms, DyNav demonstrates superior performance. We have validated the effectiveness of the DyNav a*algorithm through practical experiments. The results show that in dynamic environments, the robot can clean surface debris more quickly and flexibly while avoiding obstacles. This not only contributes to environmental protection, but also improves cleaning efficiency, offering an innovative solution for water environment management.
Anomaly detection in Internet of Things (IOT) network traffic involves identifying abnormal patterns or behaviors, enabling early detection of potential security threats or system malfunctions in the IOT ecosystem. Io...
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Anomaly detection in Internet of Things (IOT) network traffic involves identifying abnormal patterns or behaviors, enabling early detection of potential security threats or system malfunctions in the IOT ecosystem. IoT encompasses a variety of networks, addressing not only security challenges within sensor networks, the internet, and mobile communication networks but also specifically focusing on issues related to privacy protection, information management, network authentication, and access control. In this manuscript, Anomaly Detection in IoT Network Traffic using a Bidirectional 3D Quasi-Recurrent Neural Network with Coati Optimization a*algorithm (ADIOT-B3DQRNN-COA) is proposed. Initially, the input data are collected from the DS2OS Dataset. Then, the collected data is fed into pre-processing utilizing an Implicit Unscented Particle Filter (IUPF). The IUPF is used to remove the invalid data. Subsequently, the preprocessed data are sent into the Archimedes optimization a*algorithm (AOA) to select features. Seven characteristics from the DS2OS dataset are chosen using AOA. The selected features are then fed into a Bidirectional 3D Quasi-Recurrent Neural Network (Bi-3DQRNN) to classify anomaly detection in an Internet of Things network into the following categories: data probing, malicious control, malicious operation, scan, spying, incorrect configuration, DOS attack, and normal. To guarantee accurate classification of anomaly detection in IoT networks, Bi-3DQRNN generally does not express any adaptation of optimization a*algorithms for figuring out the best parameters. Hence, the Coati Optimization a*algorithm (COA) to optimize Bi-3DQRNN accurately classifies anomaly detection in the IOT network. The proposed ADIOT-B3DQRNN-COA approach is implemented in MATLAB. The performance of the proposed method was examined utilizing performance metrics like Accuracy, Computational Time, F-measure, Precision, Recall, and ROC. The proposed ADIOT-B3DQRNN-COA approach contains 32.15%,
A sparse compressed deep echo state network (SCDESN) incorporating sparse input units, compressed sampling and principal component analysis (PCA) units is proposed in this paper, and theoretically proved the sufficien...
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A sparse compressed deep echo state network (SCDESN) incorporating sparse input units, compressed sampling and principal component analysis (PCA) units is proposed in this paper, and theoretically proved the sufficient and necessary conditions to ensure the echo state characteristics of the proposed scheme. In addition, a hybrid arithmetic optimization a*algorithm based on matrix design strategy and boundary selection strategy (SVD-HAOA) was proposed to optimize the hyper-parameters of the SCDESN model, taking into account the characteristics of the SCDESN model. The steps and process for optimizing the hyper parameters of the SCDESN model using SVDHAOA were presented. Finally, the SCDESN model optimized by SVD-HAOA was experimentally and summarized on a classic benchmark dataset and two real-world chaotic time series. The outcomes of the simulation indicated that the suggested model surpassed alternative models in performance and enhanced computational efficiency while maintaining prediction accuracy. This model may serve as a viable alternative for real-world applications.
The chemical precooling variable cycle engine (CP-VCE) can operate at high Mach numbers, but the precooler does not work efficiently at low Mach numbers and low altitudes, which negatively affects the engine performan...
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The chemical precooling variable cycle engine (CP-VCE) can operate at high Mach numbers, but the precooler does not work efficiently at low Mach numbers and low altitudes, which negatively affects the engine performance. To achieve highly efficient operation of CP-VCE in the full speed domain range, this paper conducts the research of CP-VCE performance optimization method. First, the precooler simulation model, based on the epsilon-NTU method is established, and the thermodynamic framework of CP-VCE is also established. In addition, the effects of adjustable guide vanes and dual-fuel precooler on the engine performance are investigated. Finally, a performance optimization method based on the AWOA (Adaptive Adjustment Whale Optimization a*algorithm based on Weights and Search Strategy) a*algorithm is proposed to optimize the guide vane angle and fuel ratio as optimization variables for the conventional operating point and climb path point. The results show that the optimized engine has a reduced fuel consumption rate in turbofan mode and increased average thrust in turbojet mode. In this paper, the performance optimization of the CP-VCE in different modes is realized, and the improvement direction of the engine performance is given according to the relevant results.
Due to the distributed nature of federated learning, it is vulnerable to poisoning attacks during the training process. The model's resistance to poisoning attacks can be improved using robust aggregation algorith...
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Due to the distributed nature of federated learning, it is vulnerable to poisoning attacks during the training process. The model's resistance to poisoning attacks can be improved using robust aggregation a*algorithms. Current research on federated learning to resist poisoning attacks is mainly based on two settings: No trust or Byzantine robustness. However, both settings are not close enough to reality in practical scenarios. In many practical applications, some participants in federated learning are trustworthy. For example, participants who have participated in the training of this model before and performed very well, or participants with strong compliance and credibility such as governments and some national agencies participate in the training. In existing research, these trusted participants still have to accept the judgment of the aggregation node, which generates unnecessary computation, increases overhead, and does not take advantage of a trusted environment. Since there is no attack behavior on the trusted client, its training results are used to classify the trustworthiness of other untrusted clients and identify attack nodes with higher accuracy. Therefore, this paper proposes a robust federated learning a*algorithm for partially trusted environments. The proposed scheme uses the experimental results of trusted clients to judge the behavior of untrustworthy clients by the cosine similarity and the Local Outlier Factor and further identify and detect malicious clients. Experiments are performed on MNIST and CIFAR datasets. Comparison with other six aggregation a*algorithms under 30% attack scenario. And compared with the other four aggregation a*algorithms under 70% attack conditions. Our a*algorithm is more accurate than almost all of the other aggregation a*algorithms. The paper is the first to conduct robust research on federated learning in a partially trusted environment, and the proposed a*algorithm can more effectively resist poisoning attacks.
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