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Amplitude-Phase control a*algorithm based on fuzzy PI against vibration fluctuation of ultrasonic surgical instruments

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APPLIED ACOUSTICS 2025年 231卷

作者： Wang, Linwei Yan, Ming Liu, Yu Wang, Dan Shenyang Univ Technol Sch Mech Engn Shenyang 110870 Peoples R China Northeastern Univ Sch Mech Engn & Automat Shenyang 110819 Peoples R China Shenyang Inst Technol Sch Mech Engn & Automat Shenyang 113122 Peoples R China

To address the issue of resonance frequency shifts, output amplitude reduction, and vibration instability in piezoelectric transducers (PT) during the cutting process of ultrasonic surgical instruments due to temperature changes and load fluctuations, resulting in decreased cutting quality and efficiency, severely impacting surgical outcomes, this paper proposes an amplitude-phase control a*algorithm based on fuzzy PI against vibration fluctuations of ultrasonic surgical instruments. This a*algorithm adopts a dual-loop control design: the frequency control loop locks the impedance phase of the transducer to zero through the phase control method, achieving rapid resonance frequency tracking;the amplitude control loop maintains a constant input current through the amplitude control method, ensuring stable amplitude output. In complex surgical environments, this dual-loop control design is more reliable and practical compared to a single control method. To enhance the control a*algorithm's adaptability and robustness to load and temperature changes, a fuzzy PI controller is used instead of a traditional PI controller. Simulation results show that when the load is large, merely using a resonance frequency tracking a*algorithm cannot ensure stable vibration amplitude of the piezoelectric transducer. Compared with the classic PI control, the a*algorithm based on fuzzy PI control can quickly and stably track the resonance frequency and maintain constant amplitude under different loads, exhibiting stronger robustness and adaptability. Experiments validate the effectiveness of this a*algorithm in practical applications, demonstrating that within a reasonable working pressure range (not exceeding 5 N), the control a*algorithm is robust, maintaining good cutting ability and vibration stability of the ultrasonic surgical instrument. The method proposed in this paper is of significant importance for ensuring the vibration stability of ultrasonic surgical instruments, improving surgical outcom

关键词： Amplitude-phase control a*algorithm Resonant frequency tracking Constant amplitude control Ultrasonic surgical instruments Fuzzy PI

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An Auto-Adjusting a*algorithm to Enhance Indoor Localization Accuracy: A Real-Time Experimental Analysis

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IEEE ACCESS 2025年 13卷 29739-29753页

作者： Alfahad, Othman A. Saied, Husham Malaekah, Emad Rashdi, Ahmed Al Emam, Mohammed Bakouri, Mohsen Prince Sultan Mil Coll Hlth Sci Dept Biomed Technol Dhahran 34313 Saudi Arabia King Salman Ctr Disabil Res Riyadh 11614 Saudi Arabia Qassim Univ Coll Appl Med Sci Dept Radiol Technol Buraydah 51452 Saudi Arabia Majmaah Univ Coll Appl Med Sci Dept Med Equipment Technol Al Majmaah 11952 Saudi Arabia Fezzan Univ Coll Arts Dept Phys Traghen City Libya

Indoor localization systems (ILS) have become essential tools to address the challenge of locating and tracking items and individuals, such as children, the elderly, and patients with Alzheimer's or dementia. In this study, our aim is to develop an auto-adjusting a*algorithm to select coefficients based on the current automatically Received Signal Strength Indicator (RSSI) class. The method adopted by the Internet of Things (IoT) model integrated with Bluetooth Low Energy (BLE) to achieve this objective. The system is designed to track lost items and individuals using a wearable central unit (mini-Raspberry Pi) as a controller and BLE nodes as peripheral devices. The developed system includes Bluetooth beacons, data aggregation, storage, and a web interface for real-time tracking and visualization. The RSSI foot printing method is adopted to detect a specific zone within indoor environments. A web-based application has also been developed to enable monitoring and management of the designed system. The study was evaluated in a real-time experimental environment (with fixed and auto-adjust coefficients) to explore the challenges of accurately determining indoor locations in five rooms. The proposed method initially succeeded in reducing the error caused by fixed coefficients and RSSI by 28.03%. The results demonstrated that the auto-tuning a*algorithm with dynamic coefficients was able to improve the accuracy of positioning by dynamically adjusting RSSI coefficients;this study successfully reduced the average absolute percentage error of indoor localization by 8% and decreased the maximum localization error to 2.01 meters.

关键词： Accuracy Location awareness Internet of Things Received signal strength indicator Heuristic a*algorithms Object recognition Monitoring Meters Medical services Main-secondary ILP BLE RSSI IoT auto-adjusting a*algorithm distance estimation

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Fuzzy Adaptive Sliding-Mode Control Optimized by an Arithmetic Optimization a*algorithm for a Quadrotor Drone With Chaotic Nonlinear Dynamics

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OPTIMAL CONTROL APPLICATIONS & METHODS 2025年第2期46卷 634-646页

作者： Mahmoodabadi, M. J. Ibrahim, M. Yousef Sirjan Univ Technol Dept Mech Engn Sirjan Iran Federat Univ Australia Sch Engn & Informat Technol Churchill Vic Australia

An optimal fuzzy adaptive sliding mode controller (OFASMC) is introduced in the present paper for stabilizing a quadrotor drone with chaotic and nonlinear dynamics. At first, control efforts related to the motor torques of the regarded quadrotor drone are determined by the sliding mode procedure as the main stabilizer. Next, using the gradient descent formulation as well as the chain derivative rule, the control gains are adapted through the system states. Then, human knowledge-based fuzzy systems are appropriately designed to set the control parameters for achieving more accurate results. The output errors and control efforts are minimized through the optimum values found for the effective coefficients of the controller by applying the Arithmetic Optimization a*algorithm (AOA). Simulation results clearly illustrate the effectiveness of the introduced strategy to stabilize the quadrotor drone system with and without external disturbances. Block diagram of theproposed optimal fuzzy adaptive sliding mode control for the quadrotor drone. image

关键词： adaptive control arithmetic optimization a*algorithm fuzzy system nonlinear quadrotor drone optimal control sliding mode control

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A Novel Foot Placement Planning of Biped Robots Based on A* a*algorithm and Kinematic Manipulability

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INTERNATIONAL JOURNAL OF HUMANOID ROBOTICS 2025年第2期22卷

作者： Shahbazy, Meysam Fard, Behnam Miripour Univ Guilan Fac Mech Engn Rasht Iran

In motion planning of biped robots, the place where the swing foot lands on the ground is crucial. Stability, energy consumption, smoothness of movement as well as naturalness of motion can be depending on the selected foot placement. In this paper, a novel method based on the A* a*algorithm and kinematic manipulability for foot placement planning is presented. In this method, by calculating the magnitude and direction of the maximum manipulability for the candidate foot placements, one is selected that leads to the most dexterous and agile walking while maintaining stability. The advantage of this approach is avoiding singularities and being ready to create a higher potential for foot velocities. Therefore, the possibility of performing maneuvers to maintain walking stability would be enhanced. The mentioned a*algorithm has been successfully implemented on a simulated three-dimensional biped robot.

关键词： Biped robots foot placement planning A* a*algorithm manipulability

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Optimal prescribed-time attitude tracking for multiple spacecraft systems based on adaptive critic a*algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2025年第4期239卷 374-387页

作者： Du, Runle Zhou, Zhi-Gang Zhou, Di Liu, Jiaqi Shi, Xiao-Ning Hou, Shuai Natl Key Lab Sci & Technol Test Phys & Numer Math Beijing Peoples R China Jiangsu Univ Sci & Technol Coll Automat Zhenjiang Peoples R China Harbin Inst Technol Sch Astronaut Dept Control Sci & Engn Harbin Peoples R China Rocket Force Univ Engn 2 Tongxin Rd Xian 710025 Peoples R China

In this paper, an adaptive critic-based approximate optimal control scheme is constructed for the attitude coordinated tracking problem with prescribed performance requirements and input saturation constraint. Since the virtual leader's information is not available for some followers, a predetermined-time distributed observer is deployed for each follower to reconstruct the leader's information. Subsequently, an error transformation procedure is imported to reformulate the prescribed-time attitude coordination issue with prescribed performance constraint as the stabilization one of an unconstraint error system. To confront the input saturation constraint, a nonquadratic function is included in the cost function and in this case the described issue is formulated as a series of nonlinear optimal control problems. The critic architecture is built to approximately solve the corresponding Hamilton-Jacobi-Bellman (HJB) equation, i.e., only critic neural network (NN) is utilized to reconstruct the value function and control policy for each follower. The effectiveness and advantage is validated through numerical examples.

关键词： attitude coordination prescribed-time convergence prescribed transient and steady-state performance adaptive critic a*algorithm

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Efficient load balancing strategy for cloud computing environment with African vultures a*algorithm

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WIRELESS NETWORKS 2025年第2期31卷 1187-1203页

作者： Karuppan, A. Sandana Bhalaji, N. Sri Eshwar Coll Engn Dept Comp Sci & Business Syst Coimbatore 641202 Tamil Nadu India Rajalakshmi Inst Technol Dept Comp Sci & Engn Chennai 600124 Tamil Nadu India

Load balancing is essential in cloud computing (CC) to manage the increasing load on servers efficiently. This article proposes a load balancing strategy utilizing constraint measures to distribute the load evenly amongst the servers while minimizing power consumption. Firstly, the capacity and load of every Virtual Machine (VM) is evaluated, and tasks are assigned using the African Vultures a*algorithm (AVA) when the load exceeds a predefined threshold. This approach aim is to minimize energy consumption, makespan, and data center usage. Additionally, a load balancing method computes critical features for each VM and assesses their load, followed by calculating selection factors for tasks. Tasks with superior selection factors are assigned to VMs. The proposed Efficient Load Balancing in Cloud Computing under African Vultures a*algorithm (ELB-CC-AVA) demonstrates better performance in cloud environments, achieving lower makespan by 32.82%, 30.47%, and 25.32%, along with higher resource utilization rates of 38.22%, 40.21%, and 25.46% compared to the existing methods.

关键词： African vultures a*algorithm Cloud environment Efficient load balancing Mean response time Task scheduling Virtual machines

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Adaptive genetic a*algorithm-optimized temporal convolutional networks for high-precision ship traffic flow prediction

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EVOLVING SYSTEMS 2025年第1期16卷 1-17页

作者： Li, Yunfan Wang, Qian Kaifeng Univ Sch Finance & Econ Kaifeng 475004 Peoples R China

Timely prediction of Ship Traffic Flow (STF) is essential for managing maritime traffic and preventing congestion. However, existing deep neural network-based STF models often face challenges with hyperparameter selection and limited accuracy improvements. This study introduces a Temporal Convolutional Network (TCN) model optimized by an Adaptive Genetic a*algorithm (AGA) to address these issues. The methodology begins with comprehensive data preprocessing, using gate-line-based rules to analyze ship traffic entering and leaving ports, leveraging Automatic Identification System (AIS) data. The AGA-TCN model then employs causally dilated convolutions to capture long-term dependencies and extract frequency domain features, with the AGA dynamically optimizing TCN hyperparameters for specific prediction tasks, resulting in an end-to-end STF prediction framework. AIS data from San Francisco waters, covering the period from June 1, 2022, to December 14, 2022, was used to evaluate the model. The performance of the AGA-TCN model was compared against Particle Swarm Optimization (PSO)-TCN, standard TCN, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models. These models were chosen for comparison due to their widespread use in time-series prediction tasks, representing a variety of approaches in deep learning and optimization. The experiments demonstrate that the AGA-TCN model outperformed all these models, with improvements in RMSE, MSE, and MAPE of 54.37%, 79.18%, and 27.43%, respectively, over the standard TCN. These results underscore the robustness and high accuracy of the AGA-TCN model in STF prediction, establishing it as a superior approach for this application.

关键词： Temporal convolutional networks Adaptive genetic a*algorithm Ship traffic flow Prediction

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Prediction of peak strength under triaxial compression for sandstone based on ABC-SVM a*algorithm

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 264卷

作者： Lin, Yun Guo, Zhenghai Meng, Qingbin Li, Chong Ma, Tianxing Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China China Univ Min & Technol State Key Lab Geomech & Deep Underground Engn Xuzhou 221116 Peoples R China Zhejiang Univ Ocean Coll Hangzhou 310058 Peoples R China

The peak strength is a significant parameter in rock engineering, the traditional empirical strength criteria for rocks show good agreement with test results under specific conditions. However, it is not completely accurate for a wide range of loading stress domains and uncorrelated rock types. In this research, porosity, uniaxial compressive strength (UCS) and confining pressure are selected as input variables, and the artificial bee colony (ABC) a*algorithm is used to optimize the support vector machine (SVM) model. Finally, we validate and comparatively analyze the applicability of the models based on the testing set and the comprehensive evaluation indexes (namely correlation coefficient (R2), root mean square error (RMSE) and mean absolute percentage error (MAPE)). Meanwhile, the cosine amplitude method is applied to analyze the correlation between the peak strength and the input variables. The results indicate that both SVM model and ABC-SVM model are suitable for the prediction of peak strength under triaxial compression. Additionally, the ABC-SVM model obviously has better prediction performance by comparison.

关键词： Peak strength ABC-SVM a*algorithm Empirical strength criteria Parametric sensitivity analysis Model optimization

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Improving A* a*algorithm for path planning of unmanned surface vessels in maritime rescue

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SHIPS AND OFFSHORE STRUCTURES 2025年

作者： Qin, Lei Fang, Yiming Lu, Zhikang Gao, Shuang Shaoxing Univ Sch Mech & Elect Engn Shaoxing 312000 Peoples R China Shaoxing Univ Inst Artificial Intelligence Shaoxing 312000 Peoples R China

Path-planning is the key for marine rescue using unmanned surface vessel. Planning an efficient and safe path can significantly improve the efficiency of marine rescue, and existing path-planning a*algorithms are prone to collision and grounding with islands and shallows. To address this issue, this paper proposes a method for improving A* a*algorithm. First, global key nodes were extracted based on maritime map information using image processing techniques. Then, the global key nodes that can be directly reached in a straight line and meet safety threshold were screened as child nodes of current node to participate in the search. Finally, the path was planned by introducing the path danger value into the cost function of A* a*algorithm to consider the path cost and safety. Simulation results showed that the path planned by improved A* a*algorithm is safer and smoother than other paths, and the search time and path cost are optimized.

关键词： A* a*algorithm global key nodes redefine neighbour nodes safety threshold danger value of the path

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Direct Position Determination a*algorithm Toward Coherent Signals in Low SNR Localization Scenarios

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Pu, Qiaolin Jiang, Fengyi Zhou, Mu Yu, Wenjun Chen, Min Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China

The direct position determination (DPD) a*algorithm, which combines the parameter estimation and position calculation procedures together, has attracted significant attention due to its good performance in low signal-to-noise ratio (SNR) situations. However, the high computing consumption and being only suitable for noncoherent signals make its application limited in real situations. To address these problems, we first propose to use the Kronecker theory to decompose the original signal into signal and noise subspaces, based on which a cost function is constructed, and thus, the localization is transformed into a nonconvex optimization problem. Compared with traditional decomposition a*algorithms, this processing can improve the freedom degree of linear arrays and address the challenge to identify the direct transmitting path in the case of coherent signals. Then, to solve the nonconvex optimization problem, an adaptive reverse particle swarm optimization (ARPSO) a*algorithm is proposed, in which the adaptive elite mutation (AEM) strategy is introduced into the traditional particle swarm optimization (PSO) a*algorithms to avoid obtaining the local optimal solution caused by local convergence. Moreover, we adopt a noninertial velocity (NIV) update scheme to guide the particle flight trajectories. This scheme accelerates the process of convergence, saving the computing overhead a lot. The experimental results show that our proposed decomposition a*algorithm performs well for coherent and incoherent signals, and the improved optimization a*algorithm outperforms existing a*algorithms in both precision and computing consumption.

关键词： Location awareness Direction-of-arrival estimation Cost function Signal to noise ratio Data mining Convergence Antenna arrays Accuracy Wireless fidelity Fingerprint recognition Adaptive reverse particle swarm optimization (ARPSO) a*algorithm array signal direct position determination (DPD) direction of arrival (DOA) Kronecker subspace embedding multiple signal classification (KS-MUSIC) a*algorithm

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