Longley-Rice channel model modifies the atmospheric refraction by the equivalent earth radius method, which is simple calculation but is not accurate. As it only uses the horizontal difference, but does not make use o...Longley-Rice channel model modifies the atmospheric refraction by the equivalent earth radius method, which is simple calculation but is not accurate. As it only uses the horizontal difference, but does not make use of the vertical section information, it does not agree with the actual propagation path. The atmospheric refraction error correction method of the Longley-Rice channel model has been improved. The improved method makes use of the vertical section information sufficiently and maps the distance between the receiver and transmitter to the radio wave propagation distance, It can exactly reflect the infection of propagation distance for the radio wave propagation loss. It is predicted to be more close to the experimental results by simulation in comparison with the measured data. The effectiveness of improved methods is proved by simulation.展开更多
This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satell...This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satellites. Hence, the mission planning and scheduling of AEOS is a popular research problem. This research investigates AEOS characteristics and establishes a mission planning model based on the working principle and constraints of AEOS as per analysis. To solve the scheduling issue of AEOS, several improved algorithms are developed. Simulation results suggest that these algorithms are effective.展开更多
The information of seismic response spectra is key to many problems concerned with aseismic structure and is also helpful for earthquake disaster relief if it is generated in time when earthquake happens. While curren...The information of seismic response spectra is key to many problems concerned with aseismic structure and is also helpful for earthquake disaster relief if it is generated in time when earthquake happens. While current numerical calculation methods suffer from poor precision, especially in frequency band near Nyquist frequency, we present a set of improved parameters for precision improvement. It is shown that precision of displacement and velocity response spectra are both further improved compared to current numerical algorithms. A uniform fitting formula is given for computing these parameters for damping ratio range of 0.01-0.9, quite convenient for practical application.展开更多
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
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
An optimized volt-ampere reactive(VAR)control framework is proposed for transmission-level power systems to simultaneously mitigate voltage deviations and active-power losses through coordinated control of large-scale...An optimized volt-ampere reactive(VAR)control framework is proposed for transmission-level power systems to simultaneously mitigate voltage deviations and active-power losses through coordinated control of large-scale wind/solar farms with shunt static var generators(SVGs).The model explicitly represents reactive-power regulation characteristics of doubly-fed wind turbines and PV inverters under real-time meteorological conditions,and quantifies SVG high-speed compensation capability,enabling seamless transition from localized VAR management to a globally coordinated strategy.An enhanced adaptive gain-sharing knowledge optimizer(AGSK-SD)integrates simulated annealing and diversity maintenance to autonomously tune voltage-control actions,renewable source reactive-power set-points,and SVG output.The algorithm adaptively modulates knowledge factors and ratios across search phases,performs SA-based fine-grained local exploitation,and periodically re-injects population diversity to prevent premature convergence.Comprehensive tests on IEEE 9-bus and 39-bus systems demonstrate AGSK-SD’s superiority over NSGA-II and MOPSO in hypervolume(HV),inverse generative distance(IGD),and spread metrics while maintaining acceptable computational burden.The method reduces network losses from 2.7191 to 2.15 MW(20.79%reduction)and from 15.1891 to 11.22 MW(26.16%reduction)in the 9-bus and 39-bus systems respectively.Simultaneously,the cumulative voltage-deviation index decreases from 0.0277 to 3.42×10^(−4) p.u.(98.77%reduction)in the 9-bus system,and from 0.0556 to 0.0107 p.u.(80.76%reduction)in the 39-bus system.These improvements demonstrate significant suppression of line losses and voltage fluctuations.Comparative analysis with traditional heuristic optimization algorithms confirms the superior performance of the proposed approach.展开更多
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
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
The research on optimization methods for constellation launch deployment strategies focused on the consideration of mission interval time constraints at the launch site.Firstly,a dynamic modeling of the constellation ...The research on optimization methods for constellation launch deployment strategies focused on the consideration of mission interval time constraints at the launch site.Firstly,a dynamic modeling of the constellation deployment process was established,and the relationship between the deployment window and the phase difference of the orbit insertion point,as well as the cost of phase adjustment after orbit insertion,was derived.Then,the combination of the constellation deployment position sequence was treated as a parameter,together with the sequence of satellite deployment intervals,as optimization variables,simplifying a highdimensional search problem within a wide range of dates to a finite-dimensional integer programming problem.An improved genetic algorithm with local search on deployment dates was introduced to optimize the launch deployment strategy.With the new description of the optimization variables,the total number of elements in the solution space was reduced by N orders of magnitude.Numerical simulation confirms that the proposed optimization method accelerates the convergence speed from hours to minutes.展开更多
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^(-4)s,thus meeting the requirements of real-time combat scenarios.展开更多
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
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
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
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 phosphorene/transition 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 3.662×10^(6) °/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
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
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
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
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
Accurate forecasting of wind velocity can improve the economic dispatch and safe operation of the power system. Support vector machine (SVM) has been proved to be an efficient approach for forecasting. According to th...Accurate forecasting of wind velocity can improve the economic dispatch and safe operation of the power system. Support vector machine (SVM) has been proved to be an efficient approach for forecasting. According to the analysis with support vector machine method, the drawback of determining the parameters only by experts' experience should be improved. After a detailed description of the methodology of SVM and simulated annealing, an improved algorithm was proposed for the automatic optimization of parameters using SVM method. An example has proved that the proposed method can efficiently select the parameters of the SVM method. And by optimizing the parameters, the forecasting accuracy of the max wind velocity increases by 34.45%, which indicates that the new SASVM model improves the forecasting accuracy.展开更多
The performance of linear prediction analysis of speech deteriorates rapidly under noisy environments. To tackle this issue, an improved noise-robust sparse linear prediction algorithm is proposed. First, the linear p...The performance of linear prediction analysis of speech deteriorates rapidly under noisy environments. To tackle this issue, an improved noise-robust sparse linear prediction algorithm is proposed. First, the linear prediction residual of speech is modeled as Student-t distribution, and the additive noise is incorporated explicitly to increase the robustness, thus a probabilistic model for sparse linear prediction of speech is built, Furthermore, variational Bayesian inference is utilized to approximate the intractable posterior distributions of the model parameters, and then the optimal linear prediction parameters are estimated robustly. The experimental results demonstrate the advantage of the developed algorithm in terms of several different metrics compared with the traditional algorithm and the l1 norm minimization based sparse linear prediction algorithm proposed in recent years. Finally it draws to a conclusion that the proposed algorithm is more robust to noise and is able to increase the speech quality in applications.展开更多
文摘Longley-Rice channel model modifies the atmospheric refraction by the equivalent earth radius method, which is simple calculation but is not accurate. As it only uses the horizontal difference, but does not make use of the vertical section information, it does not agree with the actual propagation path. The atmospheric refraction error correction method of the Longley-Rice channel model has been improved. The improved method makes use of the vertical section information sufficiently and maps the distance between the receiver and transmitter to the radio wave propagation distance, It can exactly reflect the infection of propagation distance for the radio wave propagation loss. It is predicted to be more close to the experimental results by simulation in comparison with the measured data. The effectiveness of improved methods is proved by simulation.
基金supported by the National Natural Science Foundation of China(7127106671171065+1 种基金71202168)the Natural Science Foundation of Heilongjiang Province(GC13D506)
文摘This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satellites. Hence, the mission planning and scheduling of AEOS is a popular research problem. This research investigates AEOS characteristics and establishes a mission planning model based on the working principle and constraints of AEOS as per analysis. To solve the scheduling issue of AEOS, several improved algorithms are developed. Simulation results suggest that these algorithms are effective.
基金supported by Science for Earthquake Resilience (XH12032)
文摘The information of seismic response spectra is key to many problems concerned with aseismic structure and is also helpful for earthquake disaster relief if it is generated in time when earthquake happens. While current numerical calculation methods suffer from poor precision, especially in frequency band near Nyquist frequency, we present a set of improved parameters for precision improvement. It is shown that precision of displacement and velocity response spectra are both further improved compared to current numerical algorithms. A uniform fitting formula is given for computing these parameters for damping ratio range of 0.01-0.9, quite convenient for practical application.
基金The National Natural Science Foundation of China(No.52338011)the Start-up Research Fund of Southeast University(No.RF1028624058)+1 种基金the Southeast University Interdisciplinary Research Program for Young Scholarsthe National Key Research and Development Program of China(No.2024YFC3014103).
文摘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.
基金supported by the Installation of OCS Distribution Network Program Control 2.0 and Other Functions for Dongguan Power Supply Bureau of Guangdong Power Grid Co.,Ltd.(No.:031900GS62220049).
文摘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.
基金supported by Yunnan Power Grid Co.,Ltd.Science and Technology Project:Research and application of key technologies for graphical-based power grid accident reconstruction and simulation(YNKJXM20240333).
文摘An optimized volt-ampere reactive(VAR)control framework is proposed for transmission-level power systems to simultaneously mitigate voltage deviations and active-power losses through coordinated control of large-scale wind/solar farms with shunt static var generators(SVGs).The model explicitly represents reactive-power regulation characteristics of doubly-fed wind turbines and PV inverters under real-time meteorological conditions,and quantifies SVG high-speed compensation capability,enabling seamless transition from localized VAR management to a globally coordinated strategy.An enhanced adaptive gain-sharing knowledge optimizer(AGSK-SD)integrates simulated annealing and diversity maintenance to autonomously tune voltage-control actions,renewable source reactive-power set-points,and SVG output.The algorithm adaptively modulates knowledge factors and ratios across search phases,performs SA-based fine-grained local exploitation,and periodically re-injects population diversity to prevent premature convergence.Comprehensive tests on IEEE 9-bus and 39-bus systems demonstrate AGSK-SD’s superiority over NSGA-II and MOPSO in hypervolume(HV),inverse generative distance(IGD),and spread metrics while maintaining acceptable computational burden.The method reduces network losses from 2.7191 to 2.15 MW(20.79%reduction)and from 15.1891 to 11.22 MW(26.16%reduction)in the 9-bus and 39-bus systems respectively.Simultaneously,the cumulative voltage-deviation index decreases from 0.0277 to 3.42×10^(−4) p.u.(98.77%reduction)in the 9-bus system,and from 0.0556 to 0.0107 p.u.(80.76%reduction)in the 39-bus system.These improvements demonstrate significant suppression of line losses and voltage fluctuations.Comparative analysis with traditional heuristic optimization algorithms confirms the superior performance of the proposed approach.
文摘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.
文摘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.
文摘The research on optimization methods for constellation launch deployment strategies focused on the consideration of mission interval time constraints at the launch site.Firstly,a dynamic modeling of the constellation deployment process was established,and the relationship between the deployment window and the phase difference of the orbit insertion point,as well as the cost of phase adjustment after orbit insertion,was derived.Then,the combination of the constellation deployment position sequence was treated as a parameter,together with the sequence of satellite deployment intervals,as optimization variables,simplifying a highdimensional search problem within a wide range of dates to a finite-dimensional integer programming problem.An improved genetic algorithm with local search on deployment dates was introduced to optimize the launch deployment strategy.With the new description of the optimization variables,the total number of elements in the solution space was reduced by N orders of magnitude.Numerical simulation confirms that the proposed optimization method accelerates the convergence speed from hours to minutes.
基金National Natural Science Foundation of China(62373187)Forward-looking Layout Special Projects(ILA220591A22)。
文摘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^(-4)s,thus meeting the requirements of real-time combat scenarios.
基金Supported by the Anhui Province Sports Health Information Monitoring Technology Engineering Research Center Open Project (KF2023012)。
文摘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.
基金Supported by the EDD of China(No.80912020104)the Science and Technology Commission of Shanghai Municipality(No.22ZR1427700 and No.23692106900).
文摘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.
基金supported by the Changzhou Science and Technology Support Project(CE20235045)Open Subject of Jiangsu Province Key Laboratory of Power Transmission and Distribution(2021JSSPD12)+1 种基金Talent Projects of Jiangsu University of Technology(KYY20018)Postgraduate Research&Practice Innovation Program of Jiangsu Province(SJCX23_1633).
文摘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.
基金funded by the National Natural Science Foundation of China(Grant No.52375547)the Natural Science Foundation of Chongqing,China(Grant Nos.CSTB2022NSCQ-BHX0736 and CSTB2022NSCQ-MSX1523)the Chongqing Scientific Institution Incentive Performance Guiding Special Projects(Grant No.CSTB2024JXJL-YFX0034).
文摘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 phosphorene/transition 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 3.662×10^(6) °/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.
文摘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.
基金National Natural Science Foundation of China(32301712)Natural Science Foundation of Jiangsu Province(BK20230548,BK20250876)+2 种基金Project of Faculty of Agricultural Equipment of Jiangsu University(NGXB20240203)A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD-2023-87)Open Funding Project of the Key Laboratory of Modern Agricultural Equipment and Technology(Jiangsu University),Ministry of Education(MAET202101)。
文摘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.
文摘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.
文摘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.
基金Project(71071052) supported by the National Natural Science Foundation of ChinaProject(JB2011097) supported by the Fundamental Research Funds for the Central Universities of China
文摘Accurate forecasting of wind velocity can improve the economic dispatch and safe operation of the power system. Support vector machine (SVM) has been proved to be an efficient approach for forecasting. According to the analysis with support vector machine method, the drawback of determining the parameters only by experts' experience should be improved. After a detailed description of the methodology of SVM and simulated annealing, an improved algorithm was proposed for the automatic optimization of parameters using SVM method. An example has proved that the proposed method can efficiently select the parameters of the SVM method. And by optimizing the parameters, the forecasting accuracy of the max wind velocity increases by 34.45%, which indicates that the new SASVM model improves the forecasting accuracy.
基金supported by the Natural Science Foundation of Jiangsu Province(BK2012510,BK20140074)the National Postdoctoral Foundation of China(20090461424)
文摘The performance of linear prediction analysis of speech deteriorates rapidly under noisy environments. To tackle this issue, an improved noise-robust sparse linear prediction algorithm is proposed. First, the linear prediction residual of speech is modeled as Student-t distribution, and the additive noise is incorporated explicitly to increase the robustness, thus a probabilistic model for sparse linear prediction of speech is built, Furthermore, variational Bayesian inference is utilized to approximate the intractable posterior distributions of the model parameters, and then the optimal linear prediction parameters are estimated robustly. The experimental results demonstrate the advantage of the developed algorithm in terms of several different metrics compared with the traditional algorithm and the l1 norm minimization based sparse linear prediction algorithm proposed in recent years. Finally it draws to a conclusion that the proposed algorithm is more robust to noise and is able to increase the speech quality in applications.
