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Tomato Growth Height Prediction Method by Phenotypic Feature Extraction Using Multi-modal Data
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作者 GONG Yu WANG Ling +3 位作者 ZHAO Rongqiang YOU Haibo ZHOU Mo LIU Jie 《智慧农业(中英文)》 2025年第1期97-110,共14页
[Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-base... [Objective]Accurate prediction of tomato growth height is crucial for optimizing production environments in smart farming.However,current prediction methods predominantly rely on empirical,mechanistic,or learning-based models that utilize either images data or environmental data.These methods fail to fully leverage multi-modal data to capture the diverse aspects of plant growth comprehensively.[Methods]To address this limitation,a two-stage phenotypic feature extraction(PFE)model based on deep learning algorithm of recurrent neural network(RNN)and long short-term memory(LSTM)was developed.The model integrated environment and plant information to provide a holistic understanding of the growth process,emploied phenotypic and temporal feature extractors to comprehensively capture both types of features,enabled a deeper understanding of the interaction between tomato plants and their environment,ultimately leading to highly accurate predictions of growth height.[Results and Discussions]The experimental results showed the model's ef‐fectiveness:When predicting the next two days based on the past five days,the PFE-based RNN and LSTM models achieved mean absolute percentage error(MAPE)of 0.81%and 0.40%,respectively,which were significantly lower than the 8.00%MAPE of the large language model(LLM)and 6.72%MAPE of the Transformer-based model.In longer-term predictions,the 10-day prediction for 4 days ahead and the 30-day prediction for 12 days ahead,the PFE-RNN model continued to outperform the other two baseline models,with MAPE of 2.66%and 14.05%,respectively.[Conclusions]The proposed method,which leverages phenotypic-temporal collaboration,shows great potential for intelligent,data-driven management of tomato cultivation,making it a promising approach for enhancing the efficiency and precision of smart tomato planting management. 展开更多
关键词 tomato growth prediction deep learning phenotypic feature extraction multi-modal data recurrent neural net‐work long short-term memory large language model
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Pulse-to-pulse periodic signal sorting features and feature extraction in radar emitter pulse sequences 被引量:5
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作者 Qiang Guo Zhenshen Qu Changhong Wang 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2010年第3期382-389,共8页
A novel class of periodically changing features hidden in radar pulse sequence environment,named G features,is proposed.Combining fractal theory and Hilbert-Huang transform,the features are extracted using changing ch... A novel class of periodically changing features hidden in radar pulse sequence environment,named G features,is proposed.Combining fractal theory and Hilbert-Huang transform,the features are extracted using changing characteristics of pulse parameters in radar emitter signals.The features can be applied in modern complex electronic warfare environment to address the issue of signal sorting when radar emitter pulse signal parameters severely or even completely overlap.Experiment results show that the proposed feature class and feature extraction method can discriminate periodically changing pulse sequence signal sorting features from radar pulse signal flow with complex variant features,therefore provide a new methodology for signal sorting. 展开更多
关键词 signal sorting fractal geometry Hilbert-Huang transform(HHT) G feature extraction.
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Feature evaluation and extraction based on neural network in analog circuit fault diagnosis 被引量:16
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作者 Yuan Haiying Chen Guangju Xie Yongle 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2007年第2期434-437,共4页
Choosing the right characteristic parameter is the key to fault diagnosis in analog circuit. The feature evaluation and extraction methods based on neural network are presented. Parameter evaluation of circuit feature... Choosing the right characteristic parameter is the key to fault diagnosis in analog circuit. The feature evaluation and extraction methods based on neural network are presented. Parameter evaluation of circuit features is realized by training results from neural network; the superior nonlinear mapping capability is competent for extracting fault features which are normalized and compressed subsequently. The complex classification problem on fault pattern recognition in analog circuit is transferred into feature processing stage by feature extraction based on neural network effectively, which improves the diagnosis efficiency. A fault diagnosis illustration validated this method. 展开更多
关键词 Fault diagnosis feature extraction Analog circuit Neural network Principal component analysis.
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Improved method for the feature extraction of laser scanner using genetic clustering 被引量:6
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作者 Yu Jinxia Cai Zixing Duan Zhuohua 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2008年第2期280-285,共6页
Feature extraction of range images provided by ranging sensor is a key issue of pattern recognition. To automatically extract the environmental feature sensed by a 2D ranging sensor laser scanner, an improved method b... Feature extraction of range images provided by ranging sensor is a key issue of pattern recognition. To automatically extract the environmental feature sensed by a 2D ranging sensor laser scanner, an improved method based on genetic clustering VGA-clustering is presented. By integrating the spatial neighbouring information of range data into fuzzy clustering algorithm, a weighted fuzzy clustering algorithm (WFCA) instead of standard clustering algorithm is introduced to realize feature extraction of laser scanner. Aimed at the unknown clustering number in advance, several validation index functions are used to estimate the validity of different clustering algorithms and one validation index is selected as the fitness function of genetic algorithm so as to determine the accurate clustering number automatically. At the same time, an improved genetic algorithm IVGA on the basis of VGA is proposed to solve the local optimum of clustering algorithm, which is implemented by increasing the population diversity and improving the genetic operators of elitist rule to enhance the local search capacity and to quicken the convergence speed. By the comparison with other algorithms, the effectiveness of the algorithm introduced is demonstrated. 展开更多
关键词 laser scanner feature extraction weighted fuzzy clustering validation index genetic algorithm.
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Adaptive WNN aerodynamic modeling based on subset KPCA feature extraction 被引量:4
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作者 孟月波 邹建华 +1 位作者 甘旭升 刘光辉 《Journal of Central South University》 SCIE EI CAS 2013年第4期931-941,共11页
In order to accurately describe the dynamic characteristics of flight vehicles through aerodynamic modeling, an adaptive wavelet neural network (AWNN) aerodynamic modeling method is proposed, based on subset kernel pr... In order to accurately describe the dynamic characteristics of flight vehicles through aerodynamic modeling, an adaptive wavelet neural network (AWNN) aerodynamic modeling method is proposed, based on subset kernel principal components analysis (SKPCA) feature extraction. Firstly, by fuzzy C-means clustering, some samples are selected from the training sample set to constitute a sample subset. Then, the obtained samples subset is used to execute SKPCA for extracting basic features of the training samples. Finally, using the extracted basic features, the AWNN aerodynamic model is established. The experimental results show that, in 50 times repetitive modeling, the modeling ability of the method proposed is better than that of other six methods. It only needs about half the modeling time of KPCA-AWNN under a close prediction accuracy, and can easily determine the model parameters. This enables it to be effective and feasible to construct the aerodynamic modeling for flight vehicles. 展开更多
关键词 WAVELET neural network fuzzy C-means clustering kernel principal components analysis feature extraction aerodynamic modeling
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A novel feature extraction method for ship-radiated noise 被引量:6
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作者 Hong Yang Lu-lu Li +1 位作者 Guo-hui Li Qian-ru Guan 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2022年第4期604-617,共14页
To improve the feature extraction of ship-radiated noise in a complex ocean environment,a novel feature extraction method for ship-radiated noise based on complete ensemble empirical mode decomposition with adaptive s... To improve the feature extraction of ship-radiated noise in a complex ocean environment,a novel feature extraction method for ship-radiated noise based on complete ensemble empirical mode decomposition with adaptive selective noise(CEEMDASN) and refined composite multiscale fluctuation-based dispersion entropy(RCMFDE) is proposed.CEEMDASN is proposed in this paper which takes into account the high frequency intermittent components when decomposing the signal.In addition,RCMFDE is also proposed in this paper which refines the preprocessing process of the original signal based on composite multi-scale theory.Firstly,the original signal is decomposed into several intrinsic mode functions(IMFs)by CEEMDASN.Energy distribution ratio(EDR) and average energy distribution ratio(AEDR) of all IMF components are calculated.Then,the IMF with the minimum difference between EDR and AEDR(MEDR)is selected as characteristic IMF.The RCMFDE of characteristic IMF is estimated as the feature vectors of ship-radiated noise.Finally,these feature vectors are sent to self-organizing map(SOM) for classifying and identifying.The proposed method is applied to the feature extraction of ship-radiated noise.The result shows its effectiveness and universality. 展开更多
关键词 Complete ensemble empirical mode decomposition with adaptive noise Ship-radiated noise feature extraction Classification and recognition
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A novel signal feature extraction technology based on empirical wavelet transform and reverse dispersion entropy 被引量:4
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作者 Yu-xing Li Shang-bin Jiao Xiang Gao 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2021年第5期1625-1635,共11页
Feature extraction is an important part of signal processing,which is significant for signal detection,classification,and recognition.The nonlinear dynamic analysis method can extract the nonlinear characteristics of ... Feature extraction is an important part of signal processing,which is significant for signal detection,classification,and recognition.The nonlinear dynamic analysis method can extract the nonlinear characteristics of signals and is widely used in different fields.Reverse dispersion entropy(RDE)proposed by us recently,as a nonlinear dynamic analysis method,has the advantages of fast computing speed and strong anti-noise ability,which is more suitable for measuring the complexity of signal than traditional permutation entropy(PE)and dispersion entropy(DE).Empirical wavelet transform(EWT),based on the theory of wavelet analysis,can decompose a complex non-stationary signal into a number of empirical wavelet functions(EWFs)with compact support set spectrum,which has better decomposition performance than empirical mode decomposition(EMD)and its improved algorithms.Considering the advantages of RDE and EWT,on the one hand,we introduce EWT into the field of underwater acoustic signal processing and fault diagnosis to improve the signal decomposition accuracy;on the other hand,we use RDE as the features of EWFs to improve the signal separability and stability.Finally,we propose a novel signal feature extraction technology based on EWT and RDE in this paper.Experimental results show that the proposed feature extraction technology can effectively extract the complexity features of actual signals.Moreover,it also has higher distinguishing ability for different types of signals than five latest feature extraction technologies. 展开更多
关键词 feature extraction Empirical mode decomposition Empirical wavelet transform Permutation entropy Reverse dispersion entropy
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Novel histogram descriptor for global feature extraction and description 被引量:3
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作者 张刚 马宗民 +1 位作者 邓立国 徐长明 《Journal of Central South University》 SCIE EI CAS 2010年第3期580-586,共7页
A novel histogram descriptor for global feature extraction and description was presented. Three elementary primitives for a 2×2 pixel grid were defined. The complex primitives were computed by matrix transforms. ... A novel histogram descriptor for global feature extraction and description was presented. Three elementary primitives for a 2×2 pixel grid were defined. The complex primitives were computed by matrix transforms. These primitives and equivalence class were used for an image to compute the feature image that consisted of three elementary primitives. Histogram was used for the transformed image to extract and describe the features. Furthermore, comparisons were made among the novel histogram descriptor, the gray histogram and the edge histogram with regard to feature vector dimension and retrieval performance. The experimental results show that the novel histogram can not only reduce the effect of noise and illumination change, but also compute the feature vector of lower dimension. Furthermore, the system using the novel histogram has better retrieval performance. 展开更多
关键词 feature extraction and description histogram descriptor gray histogram edge histogram
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Micro-Doppler feature extraction of micro-rotor UAV under the background of low SNR 被引量:5
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作者 HE Weikun SUN Jingbo +1 位作者 ZHANG Xinyun LIU Zhenming 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2022年第6期1127-1139,共13页
Micro-Doppler feature extraction of unmanned aerial vehicles(UAVs)is important for their identification and classification.Noise and the motion state of the UAV are the main factors that may affect feature extraction ... Micro-Doppler feature extraction of unmanned aerial vehicles(UAVs)is important for their identification and classification.Noise and the motion state of the UAV are the main factors that may affect feature extraction and estimation precision of the micro-motion parameters.The spectrum of UAV echoes is reconstructed to strengthen the micro-motion feature and reduce the influence of the noise on the condition of low signal to noise ratio(SNR).Then considering the rotor rate variance of UAV in the complex motion state,the cepstrum method is improved to extract the rotation rate of the UAV,and the blade length can be intensively estimated.The experiment results for the simulation data and measured data show that the reconstruction of the spectrum for the UAV echoes is helpful and the relative mean square root error of the rotating speed and blade length estimated by the proposed method can be improved.However,the computation complexity is higher and the heavier computation burden is required. 展开更多
关键词 micro-rotor unmanned aerial vehicle(UAV) low signal to noise ratio(SNR) MICRO-DOPPLER feature extraction parameter estimation
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Vibration-based feature extraction of determining dynamic characteristic for engine block low vibration design 被引量:2
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作者 杜宪峰 李志军 +3 位作者 毕凤荣 张俊红 王霞 邵康 《Journal of Central South University》 SCIE EI CAS 2012年第8期2238-2246,共9页
In order to maintain vibration performances within the limits of the design, a vibration-based feature extraction method for dynamic characteristic using empirical mode decomposition (EMD) and wavelet analysis was p... In order to maintain vibration performances within the limits of the design, a vibration-based feature extraction method for dynamic characteristic using empirical mode decomposition (EMD) and wavelet analysis was proposed. The proposed method was verified experimentally and numerically by implementing the scheme on engine block. In the implementation process, the following steps were identified to be important: 1) EMD technique in order to solve the feature extraction of vibration signals; 2) Vibration measurement for the purpose of confirming the structural weak regions of engine block in experiment; 3) Finite element modeling for the purpose of determining dynamic characteristic in time region and frequency region to affirm the comparability of response character corresponding to improvement schemes; 4) Adopting a feature index oflMF for structural improvement based on EMD and wavelet analysis. The obtained results show that IMF of signal is more sensitive to response character corresponding to improvement schemes. Finally, examination of the results confirms that the proposed vibration-based feature extraction method is very robust, and focuses on the relative merits of modification and full-scale structural optimization of engine, together with the creation of new low-vibration designs. 展开更多
关键词 feature extraction dynamic characteristic finite element model empirical mode decomposition diesel engine block
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Seismic signal recognition using improved BP neural network and combined feature extraction method 被引量:1
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作者 彭朝琴 曹纯 +1 位作者 黄姣英 刘秋生 《Journal of Central South University》 SCIE EI CAS 2014年第5期1898-1906,共9页
Seismic signal is generally employed in moving target monitoring due to its robust characteristic.A recognition method for vehicle and personnel with seismic signal sensing system was proposed based on improved neural... Seismic signal is generally employed in moving target monitoring due to its robust characteristic.A recognition method for vehicle and personnel with seismic signal sensing system was proposed based on improved neural network.For analyzing the seismic signal of the moving objects,the seismic signal of person and vehicle was acquisitioned from the seismic sensor,and then feature vectors were extracted with combined methods after filter processing.Finally,these features were put into the improved BP neural network designed for effective signal classification.Compared with previous ways,it is demonstrated that the proposed system presents higher recognition accuracy and validity based on the experimental results.It also shows the effectiveness of the improved BP neural network. 展开更多
关键词 seismic signal feature extraction BP neural network signal identification
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Modified Fourier descriptor for shape feature extraction 被引量:1
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作者 张刚 马宗民 +1 位作者 牛连强 张纯明 《Journal of Central South University》 SCIE EI CAS 2012年第2期488-495,共8页
A modified Fourier descriptor was presented. Information from a local space can be used more efficiently. After the boundary pixel set of an object was computed, centroid distance approach was used to compute shape si... A modified Fourier descriptor was presented. Information from a local space can be used more efficiently. After the boundary pixel set of an object was computed, centroid distance approach was used to compute shape signature in the local space. A pair of shape signature and boundary pixel gray was used as a point in a feature space. Then, Fourier transform was used for composition of point information in the feature space so that the shape features could be computed. It is proved theoretically that the shape features from modified Fourier descriptors are invariant to translation, rotation, scaling, and change of start point. It is also testified by measuring the retrieval performance of the systems that the shape features from modified Fourier oescriptors are more discriminative than those from other Fourier descriptors. 展开更多
关键词 shape feature extraction Fourier descriptors centroid distance approach
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A Fast Feature Extraction Algorithm for Detection of Foreign Fiber in Lint Cotton within a Complex Background 被引量:3
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作者 QU Xin DING Tian-Huai 《自动化学报》 EI CSCD 北大核心 2010年第6期785-790,共6页
关键词 《自动化学报》 期刊 摘要 编辑部
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Image Feature Extraction and Matching of Augmented Solar Images in Space Weather
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作者 WANG Rui BAO Lili CAI Yanxia 《空间科学学报》 CAS CSCD 北大核心 2023年第5期840-852,共13页
Augmented solar images were used to research the adaptability of four representative image extraction and matching algorithms in space weather domain.These include the scale-invariant feature transform algorithm,speed... Augmented solar images were used to research the adaptability of four representative image extraction and matching algorithms in space weather domain.These include the scale-invariant feature transform algorithm,speeded-up robust features algorithm,binary robust invariant scalable keypoints algorithm,and oriented fast and rotated brief algorithm.The performance of these algorithms was estimated in terms of matching accuracy,feature point richness,and running time.The experiment result showed that no algorithm achieved high accuracy while keeping low running time,and all algorithms are not suitable for image feature extraction and matching of augmented solar images.To solve this problem,an improved method was proposed by using two-frame matching to utilize the accuracy advantage of the scale-invariant feature transform algorithm and the speed advantage of the oriented fast and rotated brief algorithm.Furthermore,our method and the four representative algorithms were applied to augmented solar images.Our application experiments proved that our method achieved a similar high recognition rate to the scale-invariant feature transform algorithm which is significantly higher than other algorithms.Our method also obtained a similar low running time to the oriented fast and rotated brief algorithm,which is significantly lower than other algorithms. 展开更多
关键词 Augmented reality Augmented image Image feature point extraction and matching Space weather Solar image
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Research on Feature Extraction of Composite Pseudocode Phase Modulation-Carrier Frequency Modulation Signal Based on PWD Transform
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作者 李明孜 赵惠昌 《Defence Technology(防务技术)》 SCIE EI CAS 2008年第4期281-284,共4页
The identification features of composite pseudocode phase modulation and carry frequency modulation signal include pseudocode and modulation frequency. In this paper,PWD is used to extract these features. First,the fe... The identification features of composite pseudocode phase modulation and carry frequency modulation signal include pseudocode and modulation frequency. In this paper,PWD is used to extract these features. First,the feature of pseudocode is extracted using the amplitude output of PWD and the correlation filter technology. Then the feature of frequency modulation is extracted by way of PWD analysis on the signal processed by anti-phase operation according to the extracted feature of pseudo code,i.e. position information of changed abruptly point of phase. The simulation result shows that both the features of frequency modulation and phase change position caused by the pseudocode phase modulation can be extracted effectively for SNR=3 dB. 展开更多
关键词 信号接收系统 信号分析 侦察 电子对抗
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基于深度学习的低光照图像增强研究综述 被引量:1
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作者 孙福艳 吕准 吕宗旺 《计算机应用研究》 北大核心 2025年第1期19-27,共9页
低光照图像增强的目的是优化在光线不足的环境中捕获的图像,提升其亮度和对比度。目前,深度学习在低光照图像增强领域已成为主要方法,因此,有必要对基于深度学习的方法进行综述。首先,将传统低光照图像增强方法进行分类,并分析与总结其... 低光照图像增强的目的是优化在光线不足的环境中捕获的图像,提升其亮度和对比度。目前,深度学习在低光照图像增强领域已成为主要方法,因此,有必要对基于深度学习的方法进行综述。首先,将传统低光照图像增强方法进行分类,并分析与总结其优缺点。接着,重点介绍基于深度学习的方法,将其分为有监督和无监督两大类,分别总结其优缺点,随后总结应用在深度学习下的损失函数。其次,对常用的数据集和评价指标进行简要总结,使用信息熵对传统方法进行量化比较,采用峰值信噪比和结构相似性对基于深度学习的方法进行客观评价。最后,总结目前方法存在的不足,并对未来的研究方向进行展望。 展开更多
关键词 低光照图像增强 深度学习 有监督 特征提取 无监督
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基于DTW算法的sEMG手势识别控制系统设计 被引量:1
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作者 韩团军 雷栋元 +1 位作者 黄朝军 卢超 《现代电子技术》 北大核心 2025年第2期131-136,共6页
人体在运动过程中会产生微弱的生物电信号,其中蕴含着大量的控制信息。为了使用生物电信号中的信息控制机械臂动作,提出一种基于DTW算法的sEMG手势识别控制系统,利用该系统对采集的原始信号进行滤波和放大。为了确定有效的sEMG,采用移... 人体在运动过程中会产生微弱的生物电信号,其中蕴含着大量的控制信息。为了使用生物电信号中的信息控制机械臂动作,提出一种基于DTW算法的sEMG手势识别控制系统,利用该系统对采集的原始信号进行滤波和放大。为了确定有效的sEMG,采用移动平均法对处理信号进行划分。使用平均绝对值从数据片段中提取有效段数据,应用DTW算法将3路表面肌电信号融合,计算样本与模型之间的相似度,实现手势识别;再将识别后的信号通过无线模块发送到控制指令,以控制机械臂的动作;最后,采用提出的算法并结合6种类型的手势分类模型创建最佳特征模型。实验测试结果表明,使用动态时间规整(DTW)算法进行手势识别的平均准确率为93.752%,6种手势的平均模型匹配率达到92%,实现了肌电信号对机械臂的精确控制。由此证明所提方法的手势识别比传统的阈值控制开关更准确。 展开更多
关键词 手势识别 DTW算法 表面肌电图(sEMG) 特征提取 机械臂 手势检测
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改进YOLOv11n的无人机小目标检测算法 被引量:4
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作者 李彬 李生林 《计算机工程与应用》 北大核心 2025年第7期96-104,共9页
为了有效应对无人机航拍中小目标检测面临的复杂背景、目标密集、目标微小化和移动端部署等挑战,对YOLOv11n模型进行了改进。使用RFCBAMConv模块改进C3k2,增强了特征提取能力。设计了膨胀特征金字塔卷积(dilated featurepyramidconvolut... 为了有效应对无人机航拍中小目标检测面临的复杂背景、目标密集、目标微小化和移动端部署等挑战,对YOLOv11n模型进行了改进。使用RFCBAMConv模块改进C3k2,增强了特征提取能力。设计了膨胀特征金字塔卷积(dilated featurepyramidconvolution,DFPC)模块,替代了原有的SPPF层。通过多尺度膨胀卷积,加强了对无人机小目标细节特征的提取。提出了一种新的特征金字塔结构,在P2层增加160×160尺寸的特征图输出,以提取小目标特征信息。该方法替代了传统通过添加P2小目标检测头的做法。引入了CSPOK模块和ContextGuidedBlock_Down(CGBD)卷积,显著提升了全局特征的提取能力和多尺度特征的融合能力。采用动态检测头(DyHead)替代了原有的检测头,提升了模型的目标检测精度。实验结果表明,改进模型在VisDrone数据集上的mAP@0.5和mAP@0.5:0.95指标分别提高了0.071和0.049。此外,在AI-TOD和SODA-A等数据集上的泛化实验也显示,改进模型在mAP@0.5上分别获得0.055和0.048的提升,充分验证了模型的有效性和泛用性。 展开更多
关键词 小目标检测 YOLOv11 特征提取 感受野注意力
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融合改进卷积神经网络和层次SVM的鸡蛋外观检测 被引量:1
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作者 姚万鹏 张凌晓 +1 位作者 赵肖峰 王飞成 《食品与机械》 北大核心 2025年第1期158-164,共7页
[目的]实现鸡蛋精细化分类和提高鸡蛋外观检测的准确率。[方法]提出一种融合改进卷积神经网络和层次SVM的鸡蛋外观检测方案。(1)采用鸡蛋机器视觉图像采集设备获取不同方位、不同外观鸡蛋图像,并运用图像增强技术扩充鸡蛋图像数据库。(2... [目的]实现鸡蛋精细化分类和提高鸡蛋外观检测的准确率。[方法]提出一种融合改进卷积神经网络和层次SVM的鸡蛋外观检测方案。(1)采用鸡蛋机器视觉图像采集设备获取不同方位、不同外观鸡蛋图像,并运用图像增强技术扩充鸡蛋图像数据库。(2)设计改进的浣熊优化算法(coati optimization algorithm,COA)和FCM聚类算法,在此基础上对卷积神经网络(convolutional neural network,CNN)模型结构和超参数进行优化,以提升CNN泛化能力。运用优化后的CNN深度学习鸡蛋图像数据库,从而实现鸡蛋外观图像特征的有效提取。(3)建立层次支持向量机鸡蛋外观分类工具,最终实现对鸡蛋外观的准确检测分类。[结果]所提鸡蛋外观检测方案的检测准确率提高了1.74%~4.31%,检测时间降低了21.68%~53.51%。[结论]所提方法能够有效实现对鸡蛋的在线实时精细化分类。 展开更多
关键词 鸡蛋外观 卷积神经网络 浣熊优化算法 支持向量机 特征提取
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基于实测不均衡小样本的配电网高阻接地故障检测方法 被引量:1
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作者 高伟 何文秀 +1 位作者 郭谋发 白浩 《高电压技术》 北大核心 2025年第3期1135-1144,I0001,共11页
为了应对实际配电网高阻接地故障信号微弱多变、数据稀缺等问题,提出一种基于实测不均衡小样本的高阻接地故障检测新方法。首先,使用基于压缩-激励网络的多头变分自编码器增殖模型,扩充小样本数据集。其次,将数据进行滤波处理后,分别提... 为了应对实际配电网高阻接地故障信号微弱多变、数据稀缺等问题,提出一种基于实测不均衡小样本的高阻接地故障检测新方法。首先,使用基于压缩-激励网络的多头变分自编码器增殖模型,扩充小样本数据集。其次,将数据进行滤波处理后,分别提取其时、频域特征。鉴于高阻故障特征微弱,增殖模型无法生成全面、有效的故障特征这一事实,进一步提出基于梯度调和机制的类别型特征提升(gradient harmonized mechanism-categorical boosting,GHM-Cat Boost)算法,引入梯度调和机制损失函数,让模型均衡易分样本和难分样本的关注度,从而解决过拟合问题。研究结果表明,数据增殖模型能够生成兼具仿真数据多样性与实测数据随机性特点的故障样本,提高了数据的可利用性。且所提GHM-Cat Boost模型的故障识别准确率可以达到97.21%,优于其对比分类器模型。通过测试和对比分析,验证了所提方案的有效性。 展开更多
关键词 配电网 高阻接地 故障检测 时频特征提取 变分自编码器 注意力机制 CatBoost
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