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Area Variation Based Color Snake Algorithm for Moving Object Tracking 认领 引用
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作者 Shoum-ik ROYCHOUDHURY Young-joon HAN 《Journal of Measurement Science and Instrumentation》 CAS 2010年第1期46-49,共4页
A snake algorithm has been known that it has a strong point in extracting the exact contour of an object. But it is apt to be influenced by scattered edges around the control points. Since the shape of a moving object... A snake algorithm has been known that it has a strong point in extracting the exact contour of an object. But it is apt to be influenced by scattered edges around the control points. Since the shape of a moving object in 2D image changes a lot due to its rotation and translation in the 3D space, the conventional algorithm that takes into account slowly moving objects cannot provide an appropriate solution. To utilize the advantages of the snake algorithm while minimizing the drawbacks, this paper proposes the area variation based color snake algorithm for moving object tracking. The proposed algorithm includes a new energy term which is used for preserving the shape of an object between two consecutive images. The proposed one can also segment precisely interesting objects on complex image since it is based on color information. Experiment results show that the proposed algorithm is very effective in various environments. 展开更多
关键词 color snake algorithm area variation moving object tracking snake energy segmentation
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Rebound of Region of Interest (RROI), a New Kernel-Based Algorithm for Video Object Tracking Applications 认领 引用
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作者 Andres Alarcon Ramirez Mohamed Chouikha 《Journal of Signal and Information Processing》 2014年第4期97-103,共7页
This paper presents a new kernel-based algorithm for video object tracking called rebound of region of interest (RROI). The novel algorithm uses a rectangle-shaped section as region of interest (ROI) to represent and ... This paper presents a new kernel-based algorithm for video object tracking called rebound of region of interest (RROI). The novel algorithm uses a rectangle-shaped section as region of interest (ROI) to represent and track specific objects in videos. The proposed algorithm is constituted by two stages. The first stage seeks to determine the direction of the object’s motion by analyzing the changing regions around the object being tracked between two consecutive frames. Once the direction of the object’s motion has been predicted, it is initialized an iterative process that seeks to minimize a function of dissimilarity in order to find the location of the object being tracked in the next frame. The main advantage of the proposed algorithm is that, unlike existing kernel-based methods, it is immune to highly cluttered conditions. The results obtained by the proposed algorithm show that the tracking process was successfully carried out for a set of color videos with different challenging conditions such as occlusion, illumination changes, cluttered conditions, and object scale changes. 展开更多
关键词 Video Object Tracking Cluttered Conditions Kernel-Based Algorithm
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Multiple Object Tracking through Background Learning 认领 引用 被引量：1
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作者 Deependra Sharma Zainul Abdin Jaffery 《Computer Systems Science & Engineering》 SCIE EI 2023年第1期191-204,共14页
This paper discusses about the new approach of multiple object track-ing relative to background information.The concept of multiple object tracking through background learning is based upon the theory of relativity,th... This paper discusses about the new approach of multiple object track-ing relative to background information.The concept of multiple object tracking through background learning is based upon the theory of relativity,that involves a frame of reference in spatial domain to localize and/or track any object.Thefield of multiple object tracking has seen a lot of research,but researchers have considered the background as redundant.However,in object tracking,the back-ground plays a vital role and leads to definite improvement in the overall process of tracking.In the present work an algorithm is proposed for the multiple object tracking through background learning.The learning framework is based on graph embedding approach for localizing multiple objects.The graph utilizes the inher-ent capabilities of depth modelling that assist in prior to track occlusion avoidance among multiple objects.The proposed algorithm has been compared with the recent work available in literature on numerous performance evaluation measures.It is observed that our proposed algorithm gives better performance. 展开更多
关键词 Object tracking image processing background learning graph embedding algorithm computer vision
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Methods and Means for Small Dynamic Objects Recognition and Tracking 认领 引用 被引量：1
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作者 Dmytro Kushnir 《Computers, Materials & Continua》 SCIE EI 2022年第11期3649-3665,共17页
A literature analysis has shown that object search,recognition,and tracking systems are becoming increasingly popular.However,such systems do not achieve high practical results in analyzing small moving living objects... A literature analysis has shown that object search,recognition,and tracking systems are becoming increasingly popular.However,such systems do not achieve high practical results in analyzing small moving living objects ranging from 8 to 14 mm.This article examines methods and tools for recognizing and tracking the class of small moving objects,such as ants.To fulfill those aims,a customized You Only Look Once Ants Recognition(YOLO_AR)Convolutional Neural Network(CNN)has been trained to recognize Messor Structor ants in the laboratory using the LabelImg object marker tool.The proposed model is an extension of the You Only Look Once v4(Yolov4)512×512 model with an additional Self Regularized Non–Monotonic(Mish)activation function.Additionally,the scalable solution for continuous object recognizing and tracking was implemented.This solution is based on the OpenDatacam system,with extended Object Tracking modules that allow for tracking and counting objects that have crossed the custom boundary line.During the study,the methods of the alignment algorithm for finding the trajectory of moving objects were modified.I discovered that the Hungarian algorithm showed better results in tracking small objects than the K–D dimensional tree(k-d tree)matching algorithm used in OpenDataCam.Remarkably,such an algorithm showed better results with the implemented YOLO_AR model due to the lack of False Positives(FP).Therefore,I provided a new tracker module with a Hungarian matching algorithm verified on the Multiple Object Tracking(MOT)benchmark.Furthermore,additional customization parameters for object recognition and tracking results parsing and filtering were added,like boundary angle threshold(BAT)and past frames trajectory prediction(PFTP).Experimental tests confirmed the results of the study on a mobile device.During the experiment,parameters such as the quality of recognition and tracking of moving objects,the PFTP and BAT,and the configuration parameters of the neural network and boundary line model were analyzed.The results showed an increased tracking accuracy with the proposed methods by 50%.The study results confirmed the relevance of the topic and the effectiveness of the implemented methods and tools. 展开更多
关键词 Object detection artificial intelligence object tracking object counting small movable objects ants tracking ants recognition YOLO_AR Yolov4 Hungarian algorithm k-d tree algorithm MOT benchmark image labeling movement prediction
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Up-Sampled Cross-Correlation Based Object Tracking & Vibration Measurement in Agriculture Tractor System 认领 引用
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作者 R.Ganesan G.Sankaranarayanan +1 位作者 M.Pradeep Kumar V.K.Bupesh Raja 《Intelligent Automation & Soft Computing》 SCIE 2023年第4期667-681,共15页
This research introduces a challenge in integrating and cleaning the data,which is a crucial task in object matching.While the object is detected and then measured,the vibration at different light intensities may influ... This research introduces a challenge in integrating and cleaning the data,which is a crucial task in object matching.While the object is detected and then measured,the vibration at different light intensities may influence the durability and reliability of mechanical systems or structures and cause problems such as damage,abnormal stopping,and disaster.Recent research failed to improve the accuracy rate and the computation time in tracking an object and in the vibration measurement.To solve all these problems,this proposed research simplifies the scaling factor determination by assigning a known real-world dimension to a predetermined portion of the image.A novel white color sticker of the known dimensions marked with a color dot is pasted on the surface of an object for the best result in the template matching using the Improved Up-Sampled Cross-Correlation(UCC)algorithm.The vibration measurement is calculated using the Finite-Difference Algorithm(FDA),a machine vision systemfitted with a macro lens sensor that is capable of capturing the image at a closer range,which does not affect the quality of displacement measurement from the video frames.Thefield test was conducted on the TAFE(Tractors and Farm Equipment Limited)tractor parts,and the percentage of error was recorded between 30%and 50%at very low vibration values close to zero,whereas it was recorded between 5%and 10%error in most high-accelerations,the essential range for vibration analysis.Finally,the suggested system is more suitable for measuring the vibration of stationary machinery having low frequency ranges.The use of a macro lens enables to capture of image frames at very close-ups.A 30%to 50%error percentage has been reported when the vibration amplitude is very small.Therefore,this study is not suitable for Nano vibration analysis. 展开更多
关键词 Vibration measurement object tracking up-sampled cross-correlation finite difference algorithm template matching macro lens machine vision
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Real-Time Front Vehicle Detection Algorithm Based on Local Feature Tracking Method 认领 引用 被引量：1
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作者 Jae-hyoung YU Young-joon HAN Hern-soo HAHN 《Journal of Measurement Science and Instrumentation》 CAS 2011年第3期244-246,共3页
This paper proposes an algorithm that extracts features of back side of the vehicle and detects the front vehicle in real-time by local feature tracking of vehicle in the continuous images.The features in back side of... This paper proposes an algorithm that extracts features of back side of the vehicle and detects the front vehicle in real-time by local feature tracking of vehicle in the continuous images.The features in back side of the vehicle are vertical and horizontal edges,shadow and symmetry.By comparing local features using the fixed window size,the features in the continuous images are tracked.A robust and fast Haarlike mask is used for detecting vertical and horizontal edges,and shadow is extracted by histogram equalization,and the sliding window method is used to compare both side templates of the detected candidates for extracting symmetry.The features for tracking are vertical edges,and histogram is used to compare location of the peak and magnitude of the edges.The method using local feature tracking in the continuous images is more robust for detecting vehicle than the method using single image,and the proposed algorithm is evaluated by continuous images obtained on the expressway and downtown.And it can be performed on real-time through applying it to the embedded system. 展开更多
关键词 vehicle detection object tracking real-time algorithm Haarlike edge detection
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基于DeepSORT和ByteTrack的多目标跟踪研究 认领 引用
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作者 吕兴振 《计算机应用文摘》 2026年第11期97-99,103,共3页
针对动态环境下多目标跟踪鲁棒性不足、目标易丢失等问题,文章对比研究了两种多目标跟踪算法(DeepSORT与ByteTrack).首先,分析了高密度目标交互、频繁遮挡及复杂背景条件下多目标跟踪面临的主要问题;其次,介绍了DeepSORT和ByteTrack的... 针对动态环境下多目标跟踪鲁棒性不足、目标易丢失等问题,文章对比研究了两种多目标跟踪算法(DeepSORT与ByteTrack).首先,分析了高密度目标交互、频繁遮挡及复杂背景条件下多目标跟踪面临的主要问题;其次,介绍了DeepSORT和ByteTrack的基本原理及实验数据;再次,基于MOT16与MOT17公开数据集开展实验,并采用多目标跟踪准确率(MOTA)、多目标跟踪精度(MOTP)及身份识别准确率(IDF1)等指标对算法性能进行评价.实验结果表明,两种算法均具有较好的跟踪性能,在测试集上实现了MOTA>75%,MOTP>90%和IDF1>80%的目标. 展开更多
关键词 DeepSORT ByteTrack 多目标跟踪 目标关联算法
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Ant colony optimization for bearings-only maneuvering target tracking in sensors network 认领 引用
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作者 Benlian XU Zhiquan WANG Zhengyi WU 《控制理论与应用（英文版）》 2007年第3期301-306,共6页
In this paper, the problem of bearings-only maneuvering target tracking in sensors network is investigated. Two objectives are proposed and optimized by the ant colony optimization （ACO）, then two kinds of node sear... In this paper, the problem of bearings-only maneuvering target tracking in sensors network is investigated. Two objectives are proposed and optimized by the ant colony optimization （ACO）, then two kinds of node searching strategies of the ACO algorithm are presented. On the basis of the nodes determined by the ACO algorithm, the interacting multiple models extended Kalman filter （IMMEKF） for the multi-sensor bearings-only maneuvering target tracking is introduced. Simulation results indicate that the proposed ACO algorithm performs better than the Closest Nodes method. Furthermore, the Strategy 2 of the two given strategies is preferred in terms of the requirement of real time. 展开更多
关键词 Ant colony algorithm Multi-objective optimization Maneuvering target tracking Bearings-only
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Occlusion Robust Low-Contrast Sperm Tracking Using Switchable Weight Particle Filtering 认领 引用
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作者 Mohammadreza Ravanfar Leila Azinfar +1 位作者 Mohammad Hassan Moradi Reza Fazel-Rezai 《Advances in Sexual Medicine》 2014年第3期42-54,共13页
Sperm motility analysis has a particular place in male fertility diagnosis. Computerized sperm tracking has an important role in extracting sperm trajectory and measuring sperm’s dynamic features. Due to free movemen... Sperm motility analysis has a particular place in male fertility diagnosis. Computerized sperm tracking has an important role in extracting sperm trajectory and measuring sperm’s dynamic features. Due to free movements of sperms in three dimensions, occlusion has remained a challenging problem in this area. This paper aims to present a robust single sperm tracking method being able to handle misdetections in sperm occlusion scenes. In this paper, a robust method of segmentation was utilized to provide the required measurements for a switchable weight particle filtering which was designed for single sperm tracking. In each frame, the target sperm was categorized in one of these three stages: before occlusion, occlusion, and after occlusion where the occlusion had been detected based on sperm’s physical characteristics. Depending on the target sperm stage, particles were weighted differently. In order to evaluate the algorithm, two groups of samples were studied where an expert had selected a single sperm of each sample to track manually and automatically. In the first group, the sperms with no occlusion along their trajectories were tracked to depict the general compatibility of the algorithm with sperm tracking. In the second group, the algorithm was applied on the sperms which had at least one occlusion during their path. The algorithm showed an accuracy of 95% on the first group and 86.66% on the second group which illustrate the robustness of the algorithm against occlusion. 展开更多
关键词 Sperm Tracking Particle Filtering Object Occlusion Watershed Algorithm
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基于改进YOLO 11n-Pose和Jetson Orin NX的多目标小鼠行为分析方法 认领 引用
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作者 梁秀英 刘岩 +4 位作者 何磊 赵文瑞 祝梓涵 张恩帅 杨万能 《农业机械学报》 EI CAS CSCD 北大核心 2026年第14期91-101,共11页
针对人工观察小鼠行为存在费时费力、主观性强的问题,提出了一种适用于Jetson Orin NX平台的多目标小鼠行为分析方法。经比较,YOLO 11n-Pose在多个姿态关键点模型中效果最优,精确率为94.1%、召回率为94.5%、平均精度均值为96.4%,单幅图... 针对人工观察小鼠行为存在费时费力、主观性强的问题,提出了一种适用于Jetson Orin NX平台的多目标小鼠行为分析方法。经比较,YOLO 11n-Pose在多个姿态关键点模型中效果最优,精确率为94.1%、召回率为94.5%、平均精度均值为96.4%,单幅图像推理用时36.04 ms。提出WIoU损失函数改进,精确率提升2.1个百分点,平均精度均值提升0.7个百分点。基于结构简化和量化模型并部署到Jetson Orin NX,压缩后模型的精确率、召回率和平均精度均值分别为93.93%、94.18%和95.88%,单幅图像推理用时11.01 ms,缩短69.46%。对比4个多目标跟踪模型,结果表明OC Sort的效果优于其他模型,单幅图像推理用时2.18 ms。基于OC Sort多目标跟踪模型进行改进,改进后的IDF1、MOTA和MOTP分别为83.5%、92.0%和27.6%,ID跳变比原模型减少41.18%,单幅图像推理用时2.32 ms,结果表明改进OC Sort在不影响实时性的同时对于准确性有明显提升;采用基于阈值法的小鼠行为预测,将人工值和预测值对比,结果表明蜷缩、弯曲和伸长行为判断准确率分别达到95.12%、 81.71%和80.49%;采用该方法对比青年鼠与老年鼠24 h的行为活动,结果表明青年鼠的活跃度明显高于老年鼠,青年鼠更多时候处于休息状态,而老年鼠则更多处于睡觉状态。研究表明,改进YOLO 11n-Pose和OC Sort相结合的多目标小鼠行为分析方法,在提升跟踪与关键点准确性的同时,经量化部署后可实现便携、实时的多目标小鼠行为分析。 展开更多
关键词 小鼠 改进YOLO 11n-Pose 多目标跟踪算法 Jetson Orin NX 模型轻量化 行为分析
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轨道不平顺激扰下高速磁浮列车悬浮控制参数边界及优化研究 认领 引用
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作者 王珊 徐俊起 +3 位作者 倪菲 高定刚 陈琛 林国斌 《振动工程学报》 EI CSCD 北大核心 2026年第5期1336-1344,共9页
本文针对高速磁浮列车在轨道不平顺激扰下的振动控制问题展开研究。通过在上海高速磁浮示范线进行现场测试,采用惯性基准法获取了高速磁浮的轨道不平顺。基于此,建立了考虑左右电磁铁与托臂耦合作用的最小悬浮单元动力学模型,推导了系... 本文针对高速磁浮列车在轨道不平顺激扰下的振动控制问题展开研究。通过在上海高速磁浮示范线进行现场测试,采用惯性基准法获取了高速磁浮的轨道不平顺。基于此,建立了考虑左右电磁铁与托臂耦合作用的最小悬浮单元动力学模型,推导了系统运动方程。通过劳斯稳定性判据与模态分析,明确了控制参数(加速度增益ka、速度增益kv、位置增益ks)的理论稳定边界与避免主要轨道激励频率引发共振的安全设计区域。进而,以满足频率避让和一定阻尼比要求为约束,以最小化悬浮间隙波动和托臂振动加速度为目标,采用多目标粒子群优化算法对控制参数进行协同优化。研究结果表明,为确保系统稳定并有效分离激励频率,控制增益需满足ka>0.051,kv>102,ks>6326;经优化所得参数集(ka=3.3897,kv=102.0085,ks=49996.1382)能够在典型低频(6.67 Hz)与高频(53.83 Hz)轨道激扰下,较好地平衡悬浮间隙的跟随性与车体振动的抑制效果。本研究为高速磁浮列车悬浮控制系统的参数设计与性能优化提供了理论依据和方法参考。 展开更多
关键词 高速磁浮列车 振动与控制 控制参数优化 轨道不平顺 多目标粒子群优化算法
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融合非对称卷积与辅助感知的车辆跟踪算法 认领 引用
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作者 周顺勇 周思利 +2 位作者 刘云峰 蒲虹旭 张惠琴 《计算机工程与设计》 北大核心 2026年第7期2089-2098,共10页
针对复杂交通环境中车辆检测与跟踪面临的多目标密集、遮挡频繁与小目标识别困难等挑战,提出一种检测-跟踪协同优化的改进算法。在检测阶段,在YOLO11算法中引入非对称填充瓶颈结构以增强方向感知能力,并设计深度辅助感知模块提升多尺度... 针对复杂交通环境中车辆检测与跟踪面临的多目标密集、遮挡频繁与小目标识别困难等挑战,提出一种检测-跟踪协同优化的改进算法。在检测阶段,在YOLO11算法中引入非对称填充瓶颈结构以增强方向感知能力,并设计深度辅助感知模块提升多尺度特征融合效率;在跟踪阶段,结合DeepSORT算法提出通道-方向注意力模块,增强目标特征判别性,优化遮挡场景下的跨帧匹配。在KITTI和UA-DETRAC数据集上的实验结果表明:该方法在检测指标(mAP50:93.2%,mAP50-95:72.5%)与跟踪指标(MOTA:72.43%,IDF1:75.89%)上均优于对比模型,尤其在小目标与遮挡场景中表现优异,为智能交通系统中的车辆监控提供了有效的技术方案。 展开更多
关键词 车辆检测 多目标跟踪 YOLO11算法 DeepSORT算法 非对称卷积 辅助感知 通道方向注意力
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基于水平相似度匹配机制的鱼群追踪与计数方法 认领 引用 被引量：1
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作者 方斌 邹青青 +7 位作者 巨浩飞 鲍江辉 段瑞 张东旭 吕华飞 王翔 许鹏飞 段明 《计算机工程》 CAS CSCD 北大核心 2026年第3期364-375,共12页
鱼群多目标准确计数是水生态智能监测和集约化养殖产业中的重要环节,对水域生态环境智能保护和水产养殖现代化具有重要作用。现有鱼群多目标准确追踪和计数方法主要适用于鱼群外观清晰、游速缓慢和方向稳定等较理想的情况,难以有效适用... 鱼群多目标准确计数是水生态智能监测和集约化养殖产业中的重要环节,对水域生态环境智能保护和水产养殖现代化具有重要作用。现有鱼群多目标准确追踪和计数方法主要适用于鱼群外观清晰、游速缓慢和方向稳定等较理想的情况,难以有效适用于现实情况下存在的鱼群互相遮挡、游动迅速和方向多变等复杂情况。为此,结合轻量化目标检测模型YOLOv5n,提出基于水平相似度匹配机制的鱼群追踪与计数方法。将鱼群计数问题视为多目标检测与追踪问题,设计水平相似度匹配机制,并对SORT(Simple Online and Realtime Tracking)算法进行优化。通过高速水流中鱼群个体在帧与帧之间的位置关系对检测框中心点的水平距离进行限制,以有效解决SORT算法存在的目标匹配混乱问题,显著提高追踪效果。实验结果表明,所提方法在鱼群多目标追踪数据集上的性能显著优于现有追踪方法,对目标遮挡、方向变化等情况目标追踪性能提升显著,并且该方法结构简单,易于实际应用。 展开更多
关键词 多目标追踪 鱼群计数 水平相似度 SORT算法 距离交并比
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基于前序特征提示的单目标跟踪算法 认领 引用
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作者 郑颖 马国梁 +1 位作者 郭健 王梓屹 《兵工学报》 EI CAS CSCD 北大核心 2026年第6期24-40,共17页
现有单目标跟踪算法普遍依赖初始帧模板与当前帧搜索区域之间的相似性度量进行匹配,在复杂场景中易出现跟踪漂移和误匹配问题,为此提出一种基于前序特征提示的Transformer目标跟踪算法。通过广义关系建模模块对搜索区域内的目标与背景... 现有单目标跟踪算法普遍依赖初始帧模板与当前帧搜索区域之间的相似性度量进行匹配,在复杂场景中易出现跟踪漂移和误匹配问题,为此提出一种基于前序特征提示的Transformer目标跟踪算法。通过广义关系建模模块对搜索区域内的目标与背景令牌进行自适应划分,仅允许目标令牌与模板令牌交互以有效抑制相似目标及环境噪声干扰;在Transformer目标跟踪算法框架中新增加了前序特征提示模块,结合令牌划分结果与当前帧预测结果生成精确的目标蒙版,并利用前序特征提示编码器对蒙版与搜索图像的拼接结果进行联合编码;设计的前序特征提示解码器根据当前帧搜索区域特征自适应聚合编码结果生成特定提示,从而提升了跟踪算法的适应能力。采用多个基准数据集进行测试。研究结果表明:所提算法在GOT-10K数据集上平均重叠率达到76.1%,在LaSOT数据集上成功率曲线下面积为72.3%、精准度为82.4%;所提算法跟踪性能较优,能够有效解决单目标跟踪时的遮挡、形变和相似干扰等问题。 展开更多
关键词 目标跟踪 特征提示 关系建模 Transformer目标跟踪算法
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基于路况运行数据的数字轨道电车燃料电池混合动力系统参数匹配方法 认领 引用
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作者 郑殿科 綦芳 +1 位作者 张美月 燕雨 《城市轨道交通研究》 北大核心 2026年第1期121-126,141,共6页
[目的]对于数字轨道电车采用的燃料电池+超级电容混合动力电池系统,既有参数匹配方法存在估算精度低及难以多目标同时优化等短板。为精确计算车辆运行工况,进而避免极端工况给匹配结果带来的容量冗余,有必要研究基于路况运行数据的参数... [目的]对于数字轨道电车采用的燃料电池+超级电容混合动力电池系统,既有参数匹配方法存在估算精度低及难以多目标同时优化等短板。为精确计算车辆运行工况,进而避免极端工况给匹配结果带来的容量冗余,有必要研究基于路况运行数据的参数匹配方法[方法]对燃料电池系统、超级电容系统及储氢系统建立体积、质量模型,并引入车辆行驶里程等关键指标。基于实际线路速度数据,提出一种基于双移动均值滤波的工况计算方法,实现了对车辆线路工况的估算。基于估算的数据,采用多目标遗传进化算法中的NSGA-Ⅲ(非支配排序遗传算法Ⅲ),得到了动力系统配置方案的帕累托前沿,进而完成混合动力系统的参数匹配。基于某数字轨道电车的实际运行数据进行验证。[结果及结论]基于实际运行数据的计算结果表明,双移动均值滤波器结构不仅能规避直接计算所造成的误差,还能保持与实际功率曲线较高的契合度,充分说明了该滤波结构的有效性。多目标优化的帕累托前沿结果表明,动力系统体积和质量会直接影响车辆行驶里程。该参数匹配方法,能够在维持车辆正常运行的基础上有效提升车辆的行驶里程,实现对车辆动力系统的质量、体积及行驶里程的协同优化。 展开更多
关键词 数字轨道电车 燃料电池混合动力系统 系统参数匹配 多目标遗传进化算法
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多峰环境下光伏发电系统MPPT控制方法研究 认领 引用
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作者 汤永久 王冬冬 +1 位作者 詹耀晖 杜海红 《自动化仪表》 CAS 2026年第7期69-74,共6页
为消除局部阴影和多峰现象引发的跟踪振荡问题、提高光伏阵列输出功率的跟踪准确性,深入研究了多峰环境下光伏发电系统最大功率点跟踪(MPPT)控制方法。为构建最大功率点自动跟踪控制模型,以光伏阵列的输出功率最大化为目标,选取输出电... 为消除局部阴影和多峰现象引发的跟踪振荡问题、提高光伏阵列输出功率的跟踪准确性,深入研究了多峰环境下光伏发电系统最大功率点跟踪(MPPT)控制方法。为构建最大功率点自动跟踪控制模型,以光伏阵列的输出功率最大化为目标,选取输出电流作为控制变量,构建相应的目标函数。利用差分进化算法求解模型,交叉描述多峰环境中的选择过程,推动群体向优化目标进化,从而在多峰环境中有效避免陷入局部最优。根据迭代寻优过程得出跟踪控制模型最优解,实现对最大功率点的自动跟踪控制。试验结果表明,该方法能够准确跟踪光伏阵列的输出功率,对于提高光伏发电系统的能量转换效率和整体发电性能有重要作用。该方法切实可行,为解决光伏发电系统在复杂环境下的功率跟踪问题提供了有效路径,具有广阔的应用前景。 展开更多
关键词 光伏发电 差分进化算法 最大功率点跟踪控制 光伏阵列 跟踪振荡 目标函数 交叉变异
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基于空间光谱聚类和光照感知融合的高光谱目标跟踪 认领 引用
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作者 江文浩 赵东 《电光与控制》 CSCD 北大核心 2026年第6期18-24,共7页
与传统的彩色视频相比,高光谱视频由于具有良好的光谱分辨率,在目标跟踪方面表现优异。然而,目前的研究还缺乏有效的应对跟踪中产生光照变化的策略。为了解决此类问题,提出了一种基于空间光谱聚类和光照感知融合的高光谱目标跟踪(HSCIF... 与传统的彩色视频相比,高光谱视频由于具有良好的光谱分辨率,在目标跟踪方面表现优异。然而,目前的研究还缺乏有效的应对跟踪中产生光照变化的策略。为了解决此类问题,提出了一种基于空间光谱聚类和光照感知融合的高光谱目标跟踪(HSCIF)算法。首先,利用空间光谱聚类提取目标聚类的平均光谱曲线,再与高光谱图像进行光谱角距离计算实现降维,达到增强目标且抑制背景的效果;随后,通过多光照感知模块模拟光照的变化,增强跟踪器应对光照变化的能力;再将原始特征与多光照感知特征进行自适应融合,得到多光照条件下的混合特征信息;最后,将该特征送入预测头网络,得到目标的跟踪结果。实验结果表明,HSCIF具有较强的鲁棒性和准确的目标定位能力,面对光照变化挑战,在成功率上比先进的高光谱跟踪器PHTrack高了0.051。 展开更多
关键词 高光谱目标跟踪 聚类算法 光照感知 自适应融合
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基于改进SHADE的深海冗余机械臂逆解问题研究 认领 引用
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作者 巴好亮 程伟鹏 +1 位作者 张浩 叶聪 《华中科技大学学报（自然科学版）》 EI CAS CSCD 北大核心 2026年第4期73-79,87,共7页
针对冗余机械臂逆运动学求解优化问题,依据目标不同构建不同的优化目标函数,将从冗余机械臂无穷逆解确定唯一解的问题转化为单目标优化问题,有效解决冗余机械臂逆解优化与轨迹跟踪难题.通过对基于成功历史经验的差分进化算法(SHADE)的... 针对冗余机械臂逆运动学求解优化问题,依据目标不同构建不同的优化目标函数,将从冗余机械臂无穷逆解确定唯一解的问题转化为单目标优化问题,有效解决冗余机械臂逆解优化与轨迹跟踪难题.通过对基于成功历史经验的差分进化算法(SHADE)的初始化、参数设置及变异策略三方面进行改进,提出一种改进的SHADE算法.以七自由度深海水液压机械臂为研究对象,与其他主流算法进行对比试验.结果表明:该算法在逆解优化、轨迹跟踪不同应用场景的二十余次测试中,与七种主流优化算法对比,其最小适应度标准差数值最小,证明其结果最稳定,且在收敛精度和收敛速度两方面均表现最优. 展开更多
关键词 冗余机械臂 逆运动学求解 单目标优化 差分进化算法 轨迹跟踪
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考虑到发线运用的重载铁路群组计划与运行计划协同优化 认领 引用
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作者 王一博 苏璇 +2 位作者 支博旸 倪少权 吕苗苗 《铁道经济研究》 2026年第3期83-99,共17页
重载铁路群组运行模式与传统运输组织模式存在显著差异,现有研究多以既有重载铁路模式下列车独立化运行为背景,其模型难以充分刻画群组内各单元列车的协同性、整体性的独立运行特征,导致在实际应用中无法高效适应群组运行带来的复杂性... 重载铁路群组运行模式与传统运输组织模式存在显著差异,现有研究多以既有重载铁路模式下列车独立化运行为背景,其模型难以充分刻画群组内各单元列车的协同性、整体性的独立运行特征,导致在实际应用中无法高效适应群组运行带来的复杂性。群组运行模式下,车站的到发线运用、群组内单元列车的位置、停放顺序等因素相互关联,直接影响列车发车顺序、发车时刻和整体运输效率,而这些复杂因素相互作用,给重载铁路运输组织带来了新的挑战。为解决这些问题,从群组运行模式下车站到发线运用、群组计划和群组列车运行计划三者之间的相互关系出发,在已知从上一区段接入列车的时刻和车型,以及本区段内各装车站的装车需求的基础上,以最小化所有单元列车的总旅行时间以及区段始发技术站到发线的总占用时间为优化目标,构建综合考虑群组容量、装车需求、车站到发线的运用规则、列车技术作业时间、列车运行时间和列车追踪间隔时间等约束条件的多目标模型。为求解该多目标优化问题,通过设计一种改进的非支配排序遗传算法(R-NSGA-II),结合精英保留策略与参考点导向方法,经过多次迭代搜索,能够获得较为全面的Pareto前沿解,并有效权衡多个目标之间的关系。为了验证模型和算法的有效性,以神朔铁路相关数据作为算例,通过计算得出总旅行时间与车站到发线总占用时间的Pareto前沿解集。结果表明,在同一解集中的多个Pareto解中,选择分别在总旅行时间和车站到发线占用时间上表现较优的解,其中总旅行时间减少了3.5%,而车站到发线占用时间降低了22.4%,验证了所提模型和算法在决策群组运行模式下重载铁路车站到发线运用与车辆周转效率方面的可行性。为进一步研究最大群组规模对模型指标的影响,通过变动最大群组容量进行多次求解。灵敏度分析结果表明,随着规模变化,模型的优化效果受到一定制约,小规模群组运行时,列车排队等待发车的现象较为显著;而大规模群组则因等待组群的时间更长,影响运输效率。通过灵敏度分析,揭示了群组规模对模型优化结果的局限性,进一步为群组规模的合理选择提供了理论依据。 展开更多
关键词 铁路运输 重载铁路 多目标算法 到发线运用 群组计划 运行计划
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多传感器实时目标识别与追踪方法研究 认领 引用
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作者 何家钰 包长春 《黑龙江科学》 2026年第6期76-79,共4页
针对复杂环境下单一传感器在目标识别与追踪任务中存在的鲁棒性和精度不足等问题,提出一种融合二维激光雷达与深度相机的多传感器实时目标识别与追踪方法。基于深度学习网络对激光雷达点云和深度相机数据进行特征提取,实现多源信息的高... 针对复杂环境下单一传感器在目标识别与追踪任务中存在的鲁棒性和精度不足等问题,提出一种融合二维激光雷达与深度相机的多传感器实时目标识别与追踪方法。基于深度学习网络对激光雷达点云和深度相机数据进行特征提取,实现多源信息的高效表征与目标判别。设计了改进的贝叶斯置信加权融合算法,根据各传感器输出的置信度信息对目标检测结果进行自适应融合,有效抑制了噪声和异常值对融合精度的影响。为验证所提方法的有效性,搭建了系统性仿真实验平台,对比分析单一传感器、传统简单加权融合及改进贝叶斯融合方法在全局及局部高噪声和遮挡场景下的识别精度与鲁棒性。实验结果表明,本方法在均方根误差、最大偏移等关键性能指标上均优于对比方法,尤其在局部复杂区域表现出更强的自适应能力和异常点抑制效果。研究成果为多传感器环境感知、智能机器人导航及动态场景下目标识别等应用提供了理论依据和实践参考。 展开更多
关键词 二维激光雷达 深度相机 多传感器融合 贝叶斯算法 深度学习 目标识别与追踪
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