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RRT*-GSQ:A hybrid sampling path planning algorithm for complex orchard scenarios 认领 引用
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作者 ZHU Qingzhen ZHAO Jiamuyang +1 位作者 DAI Xu YU Yang 《农业工程学报》 EI CAS CSCD 北大核心 2026年第3期13-25,共13页
Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT*),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narr... Traditional sampling-based path planning algorithms,such as the rapidly-exploring random tree star(RRT*),encounter critical limitations in unstructured orchard environments,including low sampling efficiency in narrow passages,slow convergence,and high computational costs.To address these challenges,this paper proposes a novel hybrid global path planning algorithm integrating Gaussian sampling and quadtree optimization(RRT*-GSQ).This methodology aims to enhance path planning by synergistically combining a Gaussian mixture sampling strategy to improve node generation in critical regions,an adaptive step-size and direction optimization mechanism for enhanced obstacle avoidance,a Quadtree-AABB collision detection framework to lower computational complexity,and a dynamic iteration control strategy for more efficient convergence.In obstacle-free and obstructed scenarios,compared with the conventional RRT*,the proposed algorithm reduced the number of node evaluations by 67.57%and 62.72%,and decreased the search time by 79.72%and 78.52%,respectively.In path tracking tests,the proposed algorithm achieved substantial reductions in RMSE of the final path compared to the conventional RRT*.Specifically,the lateral RMSE was reduced by 41.5%in obstacle-free environments and 59.3%in obstructed environments,while the longitudinal RMSE was reduced by 57.2%and 58.5%,respectively.Furthermore,the maximum absolute errors in both lateral and longitudinal directions were constrained within 0.75 m.Field validation experiments in an operational orchard confirmed the algorithm's practical effectiveness,showing reductions in the mean tracking error of 47.6%(obstacle-free)and 58.3%(with obstructed),alongside a 5.1%and 7.2%shortening of the path length compared to the baseline method.The proposed algorithm effectively enhances path planning efficiency and navigation accuracy for robots,presenting a superior solution for high-precision autonomous navigation of agricultural robots in orchard environments and holding significant value for engineering applications. 展开更多
关键词 robot path planning orchard improved RRT*algorithm Gaussian sampling autonomous navigation
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An improved Alpha-shape algorithm for extracting section contours of the super-high steel bridge tower using point clouds 认领 引用
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作者 ZHANG Yiming ZHAO Tianhao +2 位作者 LIAO Ruixuan LI Haoqing WANG Hao 《Journal of Southeast University(English Edition)》 EI CAS 2026年第1期26-35,共10页
The virtual preassembly of super-high steel bridge towers faces a challenge in the efficient and precise extraction of complex cross-sectional features.Factors such as fabrication errors,gravity-induced deformations,a... The virtual preassembly of super-high steel bridge towers faces a challenge in the efficient and precise extraction of complex cross-sectional features.Factors such as fabrication errors,gravity-induced deformations,and temperature fluctuations can compromise the accuracy of contour extraction.To address these limitations,an improved Alpha-shape-based point cloud contour extraction method is proposed.The proposed approach uses a hierarchical strategy to process three-dimensional laser scanning point clouds.The processed data are then subjected to curvatureadaptive voxel filtering to reduce acquisition noise.In addition,an enhanced iterative closest point(ICP)variant with correspondence validation accurately aligns the discrete point cloud segments.The proposed curvature-responsive Alpha-shape framework enables multiscale contour delineation through topology-adaptive threshold modulation,which resolves boundary ambiguities in geometrically complex cross-sections.The method was experimentally validated using field-acquired measurement datasets from the Zhangjinggao Yangtze River Bridge tower segments,confirming its capability to reconstruct noncanonical cross-sectional geometries.Three contour extraction methods,including Poisson reconstruction,the conventional Alpha-shape algorithm,and random sample consensus with ICP(RANSAC-ICP),were compared to evaluate the performance of the proposed Alpha-shape algorithm.The results demonstrate that the proposed method achieves superior contour extraction accuracy and data reduction efficiency,highlighting its effectiveness in contour extraction tasks. 展开更多
关键词 super-high steel bridge tower point cloud contour extraction improved Alpha-shape algorithm
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SSA*-PDWA:A Hierarchical Path Planning Framework with Enhanced A*Algorithm and Dynamic Window Approach for Mobile Robots 认领 引用
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作者 Lishu Qin Yu Gao Xinyuan Lu 《Computers, Materials & Continua》 SCIE EI 2026年第4期2069-2094,共26页
With the rapid development of intelligent navigation technology,efficient and safe path planning for mobile robots has become a core requirement.To address the challenges of complex dynamic environments,this paper pro... With the rapid development of intelligent navigation technology,efficient and safe path planning for mobile robots has become a core requirement.To address the challenges of complex dynamic environments,this paper proposes an intelligent path planning framework based on grid map modeling.First,an improved Safe and Smooth A*(SSA*)algorithm is employed for global path planning.By incorporating obstacle expansion and cornerpoint optimization,the proposed SSA*enhances the safety and smoothness of the planned path.Then,a Partitioned Dynamic Window Approach(PDWA)is integrated for local planning,which is triggered when dynamic or sudden static obstacles appear,enabling real-time obstacle avoidance and path adjustment.A unified objective function is constructed,considering path length,safety,and smoothness comprehensively.Multiple simulation experiments are conducted on typical port grid maps.The results demonstrate that the improved SSA*significantly reduces the number of expanded nodes and computation time in static environmentswhile generating smoother and safer paths.Meanwhile,the PDWA exhibits strong real-time performance and robustness in dynamic scenarios,achieving shorter paths and lower planning times compared to other graph search algorithms.The proposedmethodmaintains stable performance across maps of different scales and various port scenarios,verifying its practicality and potential for wider application. 展开更多
关键词 Dynamic window approach improved A*algorithm dynamic path planning trajectory optimization
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Pedestrian detection of infrared images based on an improved FCOS algorithm 认领 引用
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作者 Fuzhen ZHU Hao HAN +1 位作者 Hengfei JIA Bing ZHU 《Optoelectronics Letters》 EI 2026年第2期105-110,共6页
The current infrared image pedestrian detectors have problems with high rates of false positives and false negatives. To solve these problems, we proposed an improved anchor-free fully convolutional one-stage object d... The current infrared image pedestrian detectors have problems with high rates of false positives and false negatives. To solve these problems, we proposed an improved anchor-free fully convolutional one-stage object detection(FCOS) algorithm. Firstly, we introduced the channel attention module squeeze excitation(SE)-Block in the FCOS backbone network, which was used to learn how to model the relative importance between different feature channels, and to achieve the weight recalibration of the features extracted from the convolution neural network, and improve the weight values that are more important for pedestrian target detection. Secondly, soft non-maximum suppression(Soft-NMS) replaced the conventional NMS within the algorithm's post-processing phase, which was used to reduce the probability of missed detection for occluded pedestrians. The experimental results show that our improved FCOS algorithm improves the average precision(AP) by 6.71% on the original dataset and 7.97% on the augmented KAIST pedestrian dataset compared with the original FCOS algorithm. Our improvements effectively meet the real-time requirements and there is no significant decrease in speed compared with the original FCOS algorithm, and decreased the false positives and false negatives for infrared image pedestrian detection. 展开更多
关键词 weight recalibration pedestrian detection infrared image pedestrian detectors channel attention module learn how model relative importance different feature channels improved FCOS algorithm infrared images Squeeze Excitation SE block
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Fault Self-Healing Cooperative Strategy of New Energy Distribution Network Based on Improved Ant Colony-Genetic Hybrid Algorithm 认领 引用 被引量：1
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作者 Fengchao Chen Aoqi Mei +2 位作者 Zheng Liu Ruhao Wu Qiwei Li 《Energy Engineering》 EI 2026年第4期247-267,共21页
With the high proportion of new energy access,the traditional fault self-healing mechanism of the distribution network is challenged.Aiming at the demand for fast recovery of new distribution network faults,this paper... With the high proportion of new energy access,the traditional fault self-healing mechanism of the distribution network is challenged.Aiming at the demand for fast recovery of new distribution network faults,this paper proposes a fault self-healing cooperative strategy for the new energy distribution network based on an improved ant colony-genetic hybrid algorithm.Firstly,the graph theory adjacency matrix is used to characterize the topology of the distribution network,and the dynamic positioning of new energy nodes is realized.Secondly,based on the output model and load characteristic model of wind,photovoltaic,and energy storage,a two-layer cooperative self-healing model of the distribution network is constructed.The upper layer is based on the improved depth-breadth hybrid search(DFS-BFS)to divide the island,with the maximum weight load recovery and the minimum number of switching actions as the goal,combined with the load priority to dynamically restore the key load.The lower layer uses the improved ant colony-genetic hybrid algorithm to solve the fault recovery path with the minimum total power loss load and the minimum network loss as the goal,generate the optimal switching sequence,and verify the power flow constraints.Finally,the simulation results based on the IEEE 33-bus system show that the proposed method can guarantee the power supply of key loads in the distribution network with high-tech energy penetration,restore the power supply of more load nodes with the least switching operation,and effectively reduce the line loss,which verifies the effectiveness and superiority of the method. 展开更多
关键词 Fault recovery identification of topology improved ant colony-genetic hybrid algorithm distribution network self-healing
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An improved YOLOv8 apple leaf disease detection algorithm 认领 引用
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作者 Xinyu PEI Wei YUAN +1 位作者 Yuexiu ZHANG Lianjun SONG 《Optoelectronics Letters》 EI 2026年第5期314-320,共7页
This paper suggests an improved you only look once version 8n(YOLOv8n)algorithm for apple leaf disease detection,abbreviated as ALWB-YOLOv8n.The model is comprised of four essential components.Initially,arbitrary kern... This paper suggests an improved you only look once version 8n(YOLOv8n)algorithm for apple leaf disease detection,abbreviated as ALWB-YOLOv8n.The model is comprised of four essential components.Initially,arbitrary kernel convolution(AKConv)replaces the convolution module,which significantly decreases both the model’s parameter count and its overall size.Secondly,the large selective kernel network(LSKNet)attention mechanism is added in the Backbone,which can dynamically adjust the spatial sensory domain,and experiments have proved that this method is extremely advantageous for small target detection.Third,a weighted bi-directional feature pyramid network is introduced,which enables the model to achieve multi-scale feature fusion and is more concise and faster.Finally,wise intersection over union(WIoU)is used to replace complete intersection over union(CIoU)in YOLOv8,and the idea of focal loss is introduced,which effectively solves the detection problems in cases such as apple leaves occluding each other and blurred boundaries of diseased leaves.The improved algorithm exhibits superior performance compared to other common object detection algorithms.Compared with YOLOv8n,the improved algorithm achieves 2.3%improvement in precision,3.8%improvement in recall,and 2.5%and 2.7%improvement in mAP0.5 and mAP0.5:0.95,respectively.Compared with YOLOv8n,the improved model reduces the number of parameters and size of the model and realizes real-time monitoring with a frames per second(FPS)of 50.5. 展开更多
关键词 large selective kernel network arbitrary kernel convolution large selective kernel network lsknet attention kernel convolution akconv replaces weighted bi directional feature pyramid network adjust spatial sensory domainand improved YOLOv n algorithm apple leaf disease detection
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An improvement of the fast uncovering community algorithm 认领 引用 被引量：4
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作者 王莉 王将 +1 位作者 沈华伟 程学旗 《Chinese Physics B》 SCIE EI CAS CSCD 2013年第10期646-653,共8页
Community detection methods have been used in computer, sociology, physics, biology, and brain information science areas. Many methods are based on the optimization of modularity. The algorithm proposed by Blondel et ... Community detection methods have been used in computer, sociology, physics, biology, and brain information science areas. Many methods are based on the optimization of modularity. The algorithm proposed by Blondel et al. （Blondel V D, Guillaume J L, Lambiotte R and Lefebvre E 2008 J. Star. Mech. 10 10008） is one of the most widely used methods because of its good performance, especially in the big data era. In this paper we make some improvements to this algorithm in correctness and performance. By tests we see that different node orders bring different performances and different community structures. We find some node swings in different communities that influence the performance. So we design some strategies on the sweeping order of node to reduce the computing cost made by repetition swing. We introduce a new concept of overlapping degree （OV） that shows the strength of connection between nodes. Three improvement strategies are proposed that are based on constant OV, adaptive OV, and adaptive weighted OV, respectively. Experiments on synthetic datasets and real datasets are made, showing that our improved strategies can improve the performance and correctness. 展开更多
关键词 community division algorithm improvement performance
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Efficient Adaptive Collision-Free Path Planning Algorithm for Indoor Mobile Robots:Optimization and Application 认领 引用
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作者 Xiangyang Lu Yandan Wang +1 位作者 Guangyi Zang Junqi Wang 《Journal of Beijing Institute of Technology》 EI CAS 2026年第2期205-217,共13页
An adaptive path planning algorithm was proposed,which improves upon traditional A*by integrating an improved A*algorithm with the Dynamic Window Approach(DWA).This addresses the problems of slow search speed,unsmooth... An adaptive path planning algorithm was proposed,which improves upon traditional A*by integrating an improved A*algorithm with the Dynamic Window Approach(DWA).This addresses the problems of slow search speed,unsmooth paths,and poor dynamic obstacle avoidance capability.Through an“8+5”neighborhood screening,a 16-neighborhood evaluation function,and a second-order then third-order Bézier curve optimization process,a Jetson Nano+ROS(Robot Operating System)is deployed to meet the requirements of efficient and safe navigation for fire inspection robots in complex environments.The results show that,compared with the original algorithm,the proposed algorithm reduces the average number of traversed nodes by 49.23%,the number of turns in the optimized path has decreased by approximately 28.82%,decreases curvature by 66.6%,and eliminates path tangency with obstacles.This also supports real-time obstacle avoidance with integration DWA,and outperforms traditional methods. 展开更多
关键词 adaptive path planning improved A*algorithm Bézier curve optimization fire inspection robot Jetson Nano+ROS system
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A Four-Phase Improvement of Grover's Algorithm 认领 引用 被引量：3
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作者 马博文 鲍皖苏 +3 位作者 李坦 李风光 张硕 付向群 《Chinese Physics Letters》 SCIE EI CAS CSCD 2017年第7期33-37,共5页
When applying Grover＇s algorithm to an unordered database, the probabifity of obtaining correct results usually decreases as the quantity of target increases. A four-phase improvement of Grover＇s algorithm is propos... When applying Grover＇s algorithm to an unordered database, the probabifity of obtaining correct results usually decreases as the quantity of target increases. A four-phase improvement of Grover＇s algorithm is proposed to fix the deficiency, and the unitary and the phase-matching condition are also proposed. With this improved scheme, when the proportion of target is over 1/3, the probability of obtaining correct results is greater than 97.82% with only one iteration using two phases. When the computational complexity is O（ √M/N）, the algorithm can succeed with a probability no less than 99.63%. 展开更多
关键词 A Four-Phase Improvement of Grover’s Algorithm
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Short-TermWind Power Forecast Based on STL-IAOA-iTransformer Algorithm:A Case Study in Northwest China 认领 引用 被引量：3
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作者 Zhaowei Yang Bo Yang +5 位作者 Wenqi Liu Miwei Li Jiarong Wang Lin Jiang Yiyan Sang Zhenning Pan 《Energy Engineering》 EI 2025年第2期405-430,共26页
Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart grids.Although numerous studies have employed various methods to forecast wind power,th... Accurate short-term wind power forecast technique plays a crucial role in maintaining the safety and economic efficiency of smart grids.Although numerous studies have employed various methods to forecast wind power,there remains a research gap in leveraging swarm intelligence algorithms to optimize the hyperparameters of the Transformer model for wind power prediction.To improve the accuracy of short-term wind power forecast,this paper proposes a hybrid short-term wind power forecast approach named STL-IAOA-iTransformer,which is based on seasonal and trend decomposition using LOESS(STL)and iTransformer model optimized by improved arithmetic optimization algorithm(IAOA).First,to fully extract the power data features,STL is used to decompose the original data into components with less redundant information.The extracted components as well as the weather data are then input into iTransformer for short-term wind power forecast.The final predicted short-term wind power curve is obtained by combining the predicted components.To improve the model accuracy,IAOA is employed to optimize the hyperparameters of iTransformer.The proposed approach is validated using real-generation data from different seasons and different power stations inNorthwest China,and ablation experiments have been conducted.Furthermore,to validate the superiority of the proposed approach under different wind characteristics,real power generation data fromsouthwestChina are utilized for experiments.Thecomparative results with the other six state-of-the-art prediction models in experiments show that the proposed model well fits the true value of generation series and achieves high prediction accuracy. 展开更多
关键词 Short-termwind power forecast improved arithmetic optimization algorithm iTransformer algorithm SimuNPS
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Deep Learning Mixed Hyper-Parameter Optimization Based on Improved Cuckoo Search Algorithm 认领 引用
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作者 TONG Yu CHEN Rong HU Biling 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2025年第2期195-204,共10页
Deep learning algorithm is an effective data mining method and has been used in many fields to solve practical problems.However,the deep learning algorithms often contain some hyper-parameters which may be continuous,... Deep learning algorithm is an effective data mining method and has been used in many fields to solve practical problems.However,the deep learning algorithms often contain some hyper-parameters which may be continuous,integer,or mixed,and are often given based on experience but largely affect the effectiveness of activity recognition.In order to adapt to different hyper-parameter optimization problems,our improved Cuckoo Search(CS)algorithm is proposed to optimize the mixed hyper-parameters in deep learning algorithm.The algorithm optimizes the hyper-parameters in the deep learning model robustly,and intelligently selects the combination of integer type and continuous hyper-parameters that make the model optimal.Then,the mixed hyper-parameter in Convolutional Neural Network(CNN),Long-Short-Term Memory(LSTM)and CNN-LSTM are optimized based on the methodology on the smart home activity recognition datasets.Results show that the methodology can improve the performance of the deep learning model and whether we are experienced or not,we can get a better deep learning model using our method. 展开更多
关键词 improved Cuckoo Search algorithm mixed hyper-parameter optimization deep learning
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Improved coati optimization algorithm through multi-strategy integration:from theoretical design to engineering applications 认领 引用
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作者 Shuangxi LIU Ruizhe FENG +2 位作者 Yuxin WEI Wei HUANG Binbin YAN 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2025年第12期1197-1210,共14页
Optimization problems are crucial for a wide range of engineering applications,as efficient solutions lead to better performance.This study introduces an improved coati optimization algorithm(ICOA)that overcomes the p... Optimization problems are crucial for a wide range of engineering applications,as efficient solutions lead to better performance.This study introduces an improved coati optimization algorithm(ICOA)that overcomes the primary limitations of the original coati optimization algorithm(COA),notably its insufficient population diversity and propensity to become trapped in local optima.To address these issues,the ICOA integrates three innovative strategies:Latin hypercube sampling(LHS),Lévyflight,and an adaptive local search.LHS is employed to ensure a diverse initial population,thereby laying a foundation for the optimization.Lévy-flight is utilized to facilitate an efficient global search,enhancing the algorithm’s ability to explore the solution space.The adaptive local search is designed to refine solutions,enabling more precise local exploration.Together,these strategies significantly improve the population’s quality and diversity,thereby improving the algorithm’s convergence accuracy and optimization capabilities.The performance of the ICOA is tested against several established algorithms,using 12 benchmark functions.Additionally,the ICOA’s practicality and effectiveness are demonstrated through application to a real-world engineering problem,specifically the design optimization of tension/compression springs.Simulation results show that the ICOA consistently outperforms the other algorithms,providing robust solutions for a wide range of optimization problems. 展开更多
关键词 Improved coati optimization algorithm(ICOA) Latin hypercube sampling(LHS) Lévy-flight Adaptive local search Multi-strategy Engineering applications
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Design of high phase-sensitivity BlueP/TMDC heterostructure-based SPR biosensor using improved artificial bee colony algorithm 认领 引用
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作者 Chong Yue Mantong Chen +1 位作者 Yaopu Lang Qinggang Liu 《Nanotechnology and Precision Engineering》 EI CAS CSCD 2025年第2期113-122,共10页
This paper uses an innovative improved artificial bee colony(IABC)algorithm to aid in the fabrication of a highly responsive phasemodulation surface plasmon resonance(SPR)biosensor.In this biosensor’s sensing structu... This paper uses an innovative improved artificial bee colony(IABC)algorithm to aid in the fabrication of a highly responsive phasemodulation surface plasmon resonance(SPR)biosensor.In this biosensor’s sensing structure,a double-layer Ag-Au metal film is combined with a blue phosphoreneransition metal dichalcogenide(BlueP/TMDC)hybrid structure and graphene.In the optimization function of the IABC method,the reflectivity at resonance angle is incorporated as a constraint to achieve high phase sensitivity.The performance of the Ag-Au-BlueP/TMDC-graphene heterostructure as optimized by the IABC method is compared with that of a similar structure optimized using the traditional ABC algorithm.The results indicate that optimization using the IABC method gives significantly more phase sensitivity,together with lower reflectivity,than can be achieved with the traditional ABC method.The highest phase sensitivity of 3.662×106 °/RIU is achieved with a bilayer of BlueP/WS2 and three layers of graphene.Moreover,analysis of the electric field distribution demonstrates that the optimal arrangement can be utilized for enhanced detection of small biomolecules.Thus,given the exceptional sensitivity achieved,the proposed method based on the IABC algorithm has great promise for use in the design of high-performance SPR biosensors with a variety of multilayer structures. 展开更多
关键词 SPR Phase modulation Sensitivity Improved artificial bee colony algorithm BlueP/TMDC Heterostructure
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Research on three-dimensional attack area based on improved backtracking and ALPS-GP algorithms of air-to-air missile 认领 引用
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作者 ZHANG Haodi WANG Yuhui HE Jiale 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2025年第1期292-310,共19页
In the field of calculating the attack area of air-to-air missiles in modern air combat scenarios,the limitations of existing research,including real-time calculation,accuracy efficiency trade-off,and the absence of t... In the field of calculating the attack area of air-to-air missiles in modern air combat scenarios,the limitations of existing research,including real-time calculation,accuracy efficiency trade-off,and the absence of the three-dimensional attack area model,restrict their practical applications.To address these issues,an improved backtracking algorithm is proposed to improve calculation efficiency.A significant reduction in solution time and maintenance of accuracy in the three-dimensional attack area are achieved by using the proposed algorithm.Furthermore,the age-layered population structure genetic programming(ALPS-GP)algorithm is introduced to determine an analytical polynomial model of the three-dimensional attack area,considering real-time requirements.The accuracy of the polynomial model is enhanced through the coefficient correction using an improved gradient descent algorithm.The study reveals a remarkable combination of high accuracy and efficient real-time computation,with a mean error of 91.89 m using the analytical polynomial model of the three-dimensional attack area solved in just 10-4s,thus meeting the requirements of real-time combat scenarios. 展开更多
关键词 air combat three-dimensional attack area improved backtracking algorithm age-layered population structure genetic programming(ALPS-GP) gradient descent algorithm
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Damage Detection of X-ray Image of Conveyor Belts with Steel Rope Cores Based on Improved FCOS Algorithm 认领 引用
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作者 WANG Baomin DING Hewei +1 位作者 TENG Fei LIU Hongqin 《Journal of Shanghai Jiaotong university(Science)》 EI 2025年第2期309-318,共10页
Aimed at the long and narrow geometric features and poor generalization ability of the damage detection in conveyor belts with steel rope cores using the X-ray image,a detection method of damage X-ray image is propose... Aimed at the long and narrow geometric features and poor generalization ability of the damage detection in conveyor belts with steel rope cores using the X-ray image,a detection method of damage X-ray image is proposed based on the improved fully convolutional one-stage object detection(FCOS)algorithm.The regression performance of bounding boxes was optimized by introducing the complete intersection over union loss function into the improved algorithm.The feature fusion network structure is modified by adding adaptive fusion paths to the feature fusion network structure,which makes full use of the features of accurate localization and semantics of multi-scale feature fusion networks.Finally,the network structure was trained and validated by using the X-ray image dataset of damages in conveyor belts with steel rope cores provided by a flaw detection equipment manufacturer.In addition,the data enhancement methods such as rotating,mirroring,and scaling,were employed to enrich the image dataset so that the model is adequately trained.Experimental results showed that the improved FCOS algorithm promoted the precision rate and the recall rate by 20.9%and 14.8%respectively,compared with the original algorithm.Meanwhile,compared with Fast R-CNN,Faster R-CNN,SSD,and YOLOv3,the improved FCOS algorithm has obvious advantages;detection precision rate and recall rate of the modified network reached 95.8%and 97.0%respectively.Furthermore,it demonstrated a higher detection accuracy without affecting the speed.The results of this work have some reference significance for the automatic identification and detection of steel core conveyor belt damage. 展开更多
关键词 conveyer belts with steel rope cores damage X-ray image image detection improved fully convo-lutional one-stage object detection(FCOS)algorithm
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Fusion Algorithm Based on Improved A*and DWA for USV Path Planning 认领 引用 被引量：2
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作者 Changyi Li Lei Yao Chao Mi 《哈尔滨工程大学学报（英文版）》 CSCD 2025年第1期224-237,共14页
The traditional A*algorithm exhibits a low efficiency in the path planning of unmanned surface vehicles(USVs).In addition,the path planned presents numerous redundant inflection waypoints,and the security is low,wh... The traditional A*algorithm exhibits a low efficiency in the path planning of unmanned surface vehicles(USVs).In addition,the path planned presents numerous redundant inflection waypoints,and the security is low,which is not conducive to the control of USV and also affects navigation safety.In this paper,these problems were addressed through the following improvements.First,the path search angle and security were comprehensively considered,and a security expansion strategy of nodes based on the 5×5 neighborhood was proposed.The A*algorithm search neighborhood was expanded from 3×3 to 5×5,and safe nodes were screened out for extension via the node security expansion strategy.This algorithm can also optimize path search angles while improving path security.Second,the distance from the current node to the target node was introduced into the heuristic function.The efficiency of the A*algorithm was improved,and the path was smoothed using the Floyd algorithm.For the dynamic adjustment of the weight to improve the efficiency of DWA,the distance from the USV to the target point was introduced into the evaluation function of the dynamic-window approach(DWA)algorithm.Finally,combined with the local target point selection strategy,the optimized DWA algorithm was performed for local path planning.The experimental results show the smooth and safe path planned by the fusion algorithm,which can successfully avoid dynamic obstacles and is effective and feasible in path planning for USVs. 展开更多
关键词 Improved A*algorithm Optimized DWA algorithm Unmanned surface vehicles Path planning Fusion algorithm
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Demodulation of Vernier-effect-based optical fiber strain sensor by using improved cross-correlation algorithm 认领 引用
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作者 LIU Bin CAO Zhi-gang +7 位作者 WANG Xing-yun LIN Zi-han CHENG Rui LIU Jun SUN Yu-han ZHENG Shu-jun ZUO Cheng LIN Ji-ping 《中国光学（中英文）》 EI CAS CSCD 北大核心 2025年第6期1463-1474,共12页
The improved cross-correlation algorithm for the strain demodulation of Vernier-effect-based optical fiber sensor(VE-OFS)is proposed in this article.The algorithm identifies the most similar spectrum to the measured o... The improved cross-correlation algorithm for the strain demodulation of Vernier-effect-based optical fiber sensor(VE-OFS)is proposed in this article.The algorithm identifies the most similar spectrum to the measured one from the database of the collected spectra by employing the cross-correlation operation,subsequently deriving the predicted value via weighted calculation.As the algorithm uses the complete information in the measured raw spectrum,more accurate results and larger measurement range can be obtained.Additionally,the improved cross-correlation algorithm also has the potential to improve the measurement speed compared to current standards due to the possibility for the collection using low sampling rate.This work presents an important algorithm towards a simpler,faster way to improve the demodulation performance of VE-OFS. 展开更多
关键词 improved cross-correlation algorithm fiber sensor vernier effect machine learning
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Energy Efficient Clustering and Sink Mobility Protocol Using Hybrid Golden Jackal and Improved Whale Optimization Algorithm for Improving Network Longevity in WSNs 认领 引用
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作者 S B Lenin R Sugumar +2 位作者 J S Adeline Johnsana N Tamilarasan R Nathiya 《China Communications》 SCIE EI CSCD 2025年第3期16-35,共20页
Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability... Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability.In this paper,Hybrid Golden Jackal,and Improved Whale Optimization Algorithm(HGJIWOA)is proposed as an effective and optimal routing protocol that guarantees efficient routing of data packets in the established between the CHs and the movable sink.This HGJIWOA included the phases of Dynamic Lens-Imaging Learning Strategy and Novel Update Rules for determining the reliable route essential for data packets broadcasting attained through fitness measure estimation-based CH selection.The process of CH selection achieved using Golden Jackal Optimization Algorithm(GJOA)completely depends on the factors of maintainability,consistency,trust,delay,and energy.The adopted GJOA algorithm play a dominant role in determining the optimal path of routing depending on the parameter of reduced delay and minimal distance.It further utilized Improved Whale Optimisation Algorithm(IWOA)for forwarding the data from chosen CHs to the BS via optimized route depending on the parameters of energy and distance.It also included a reliable route maintenance process that aids in deciding the selected route through which data need to be transmitted or re-routed.The simulation outcomes of the proposed HGJIWOA mechanism with different sensor nodes confirmed an improved mean throughput of 18.21%,sustained residual energy of 19.64%with minimized end-to-end delay of 21.82%,better than the competitive CH selection approaches. 展开更多
关键词 Cluster Heads(CHs) Golden Jackal Optimization Algorithm(GJOA) Improved Whale Optimization Algorithm(IWOA) unequal clustering
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Research on the Optimal Scheduling Model of Energy Storage Plant Based on Edge Computing and Improved Whale Optimization Algorithm 认领 引用
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作者 Zhaoyu Zeng Fuyin Ni 《Energy Engineering》 EI 2025年第3期1153-1174,共22页
Energy storage power plants are critical in balancing power supply and demand.However,the scheduling of these plants faces significant challenges,including high network transmission costs and inefficient inter-device ... Energy storage power plants are critical in balancing power supply and demand.However,the scheduling of these plants faces significant challenges,including high network transmission costs and inefficient inter-device energy utilization.To tackle these challenges,this study proposes an optimal scheduling model for energy storage power plants based on edge computing and the improved whale optimization algorithm(IWOA).The proposed model designs an edge computing framework,transferring a large share of data processing and storage tasks to the network edge.This architecture effectively reduces transmission costs by minimizing data travel time.In addition,the model considers demand response strategies and builds an objective function based on the minimization of the sum of electricity purchase cost and operation cost.The IWOA enhances the optimization process by utilizing adaptive weight adjustments and an optimal neighborhood perturbation strategy,preventing the algorithm from converging to suboptimal solutions.Experimental results demonstrate that the proposed scheduling model maximizes the flexibility of the energy storage plant,facilitating efficient charging and discharging.It successfully achieves peak shaving and valley filling for both electrical and heat loads,promoting the effective utilization of renewable energy sources.The edge-computing framework significantly reduces transmission delays between energy devices.Furthermore,IWOA outperforms traditional algorithms in optimizing the objective function. 展开更多
关键词 Energy storage plant edge computing optimal energy scheduling improved whale optimization algorithm
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Path Planning for Thermal Power Plant Fan Inspection Robot Based on Improved A*Algorithm 认领 引用 被引量：2
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作者 Wei Zhang Tingfeng Zhang 《Journal of Electronic Research and Application》 2025年第1期233-239,共7页
To improve the efficiency and accuracy of path planning for fan inspection tasks in thermal power plants,this paper proposes an intelligent inspection robot path planning scheme based on an improved A*algorithm.The... To improve the efficiency and accuracy of path planning for fan inspection tasks in thermal power plants,this paper proposes an intelligent inspection robot path planning scheme based on an improved A*algorithm.The inspection robot utilizes multiple sensors to monitor key parameters of the fans,such as vibration,noise,and bearing temperature,and upload the data to the monitoring center.The robot’s inspection path employs the improved A*algorithm,incorporating obstacle penalty terms,path reconstruction,and smoothing optimization techniques,thereby achieving optimal path planning for the inspection robot in complex environments.Simulation results demonstrate that the improved A*algorithm significantly outperforms the traditional A*algorithm in terms of total path distance,smoothness,and detour rate,effectively improving the execution efficiency of inspection tasks. 展开更多
关键词 Power plant fans Inspection robot Path planning Improved A*algorithm
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