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Research on Plant Species Identification Based on Improved Convolutional Neural Network 认领 引用
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作者 Chuangchuang Yuan Tonghai Liu +2 位作者 Shuang Song Fangyu Gao Rui Zhang 《Phyton-International Journal of Experimental Botany》 SCIE 2023年第4期1037-1058,共22页
Plant species recognition is an important research area in image recognition in recent years.However,the existing plant species recognition methods have low recognition accuracy and do not meet professional requiremen... Plant species recognition is an important research area in image recognition in recent years.However,the existing plant species recognition methods have low recognition accuracy and do not meet professional requirements in terms of recognition accuracy.Therefore,ShuffleNetV2 was improved by combining the current hot concern mechanism,convolution kernel size adjustment,convolution tailoring,and CSP technology to improve the accuracy and reduce the amount of computation in this study.Six convolutional neural network models with sufficient trainable parameters were designed for differentiation learning.The SGD algorithm is used to optimize the training process to avoid overfitting or falling into the local optimum.In this paper,a conventional plant image dataset TJAU10 collected by cell phones in a natural context was constructed,containing 3000 images of 10 plant species on the campus of Tianjin Agricultural University.Finally,the improved model is compared with the baseline version of the model,which achieves better results in terms of improving accuracy and reducing the computational effort.The recognition accuracy tested on the TJAU10 dataset reaches up to 98.3%,and the recognition precision reaches up to 93.6%,which is 5.1%better than the original model and reduces the computational effort by about 31%compared with the original model.In addition,the experimental results were evaluated using metrics such as the confusion matrix,which can meet the requirements of professionals for the accurate identification of plant species. 展开更多
关键词 Deep learning convolutional neural network plant identification model improvement
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Object Recognition Algorithm Based on an Improved Convolutional Neural Network 认领 引用 被引量：1
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作者 Zheyi Fan Yu Song Wei Li 《Journal of Beijing Institute of Technology》 EI CAS 2020年第2期139-145,共7页
In order to accomplish the task of object recognition in natural scenes,a new object recognition algorithm based on an improved convolutional neural network(CNN)is proposed.First,candidate object windows are extracted... In order to accomplish the task of object recognition in natural scenes,a new object recognition algorithm based on an improved convolutional neural network(CNN)is proposed.First,candidate object windows are extracted from the original image.Then,candidate object windows are input into the improved CNN model to obtain deep features.Finally,the deep features are input into the Softmax and the confidence scores of classes are obtained.The candidate object window with the highest confidence score is selected as the object recognition result.Based on AlexNet,Inception V1 is introduced into the improved CNN and the fully connected layer is replaced by the average pooling layer,which widens the network and deepens the network at the same time.Experimental results show that the improved object recognition algorithm can obtain better recognition results in multiple natural scene images,and has a higher degree of accuracy than the classical algorithms in the field of object recognition. 展开更多
关键词 object recognition selective search algorithm improved convolutional neural network(CNN)
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Feature Selection Using Tree Model and Classification Through Convolutional Neural Network for Structural Damage Detection 认领 引用 被引量：1
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作者 Zihan Jin Jiqiao Zhang +3 位作者 Qianpeng He Silang Zhu Tianlong Ouyang Gongfa Chen 《Acta Mechanica Solida Sinica》 SCIE EI CSCD 2024年第3期498-518,共21页
Structural damage detection(SDD)remains highly challenging,due to the difficulty in selecting the optimal damage features from a vast amount of information.In this study,a tree model-based method using decision tree a... Structural damage detection(SDD)remains highly challenging,due to the difficulty in selecting the optimal damage features from a vast amount of information.In this study,a tree model-based method using decision tree and random forest was employed for feature selection of vibration response signals in SDD.Signal datasets were obtained by numerical experiments and vibration experiments,respectively.Dataset features extracted using this method were input into a convolutional neural network to determine the location of structural damage.Results indicated a 5%to 10%improvement in detection accuracy compared to using original datasets without feature selection,demonstrating the feasibility of this method.The proposed method,based on tree model and classification,addresses the issue of extracting effective information from numerous vibration response signals in structural health monitoring. 展开更多
关键词 Feature selection Structural damage detection Decision tree Random forest Convolutional neural network
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A Novel Forgery Detection in Image Frames of the Videos Using Enhanced Convolutional Neural Network in Face Images 认领 引用 被引量：2
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作者 S.Velliangiri J.Premalatha 《Computer Modeling in Engineering & Sciences》 SCIE EI 2020年第11期625-645,共21页
Different devices in the recent era generated a vast amount of digital video.Generally,it has been seen in recent years that people are forging the video to use it as proof of evidence in the court of justice.Many kin... Different devices in the recent era generated a vast amount of digital video.Generally,it has been seen in recent years that people are forging the video to use it as proof of evidence in the court of justice.Many kinds of researches on forensic detection have been presented,and it provides less accuracy.This paper proposed a novel forgery detection technique in image frames of the videos using enhanced Convolutional Neural Network(CNN).In the initial stage,the input video is taken as of the dataset and then converts the videos into image frames.Next,perform pre-sampling using the Adaptive Rood Pattern Search(ARPS)algorithm intended for reducing the useless frames.In the next stage,perform preprocessing for enhancing the image frames.Then,face detection is done as of the image utilizing the Viola-Jones algorithm.Finally,the improved Crow Search Algorithm(ICSA)has been used to select the extorted features and inputted to the Enhanced Convolutional Neural Network(ECNN)classifier for detecting the forged image frames.The experimental outcome of the proposed system has achieved 97.21%accuracy compared to other existing methods. 展开更多
关键词 Adaptive Rood Pattern Search(ARPS) Improved Crow Search Algorithm(ICSA) Enhanced Convolutional Neural Network(ECNN) Viola Jones algorithm Speeded Up Robust Feature(SURF)
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Forest fire smoke recognition based on convolutional neural network 认领 引用 被引量：6
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作者 Xiaofang Sun Liping Sun Yinglai Huang 《Journal of Forestry Research》 SCIE CAS CSCD 2021年第5期1921-1927,共7页
Traditional fire smoke detection methods mostly rely on manual algorithm extraction and sensor detection;however,these methods are slow and expensive to achieve discrimination.We proposed an improved convolutional neu... Traditional fire smoke detection methods mostly rely on manual algorithm extraction and sensor detection;however,these methods are slow and expensive to achieve discrimination.We proposed an improved convolutional neural network(CNN)to achieve fast analysis.The improved CNN can be used to liberate manpower.The network does not require complicated manual feature extraction to identify forest fire smoke.First,to alleviate the computational pressure and speed up the discrimination efficiency,kernel principal component analysis was performed on the experimental data set.To improve the robustness of the CNN and to avoid overfitting,optimization strategies were applied in multi-convolution kernels and batch normalization to improve loss functions.The experimental analysis shows that the CNN proposed in this study can learn the feature information automatically for smoke images in the early stages of fire automatically with a high recognition rate.As a result,the improved CNN enriches the theory of smoke discrimination in the early stages of a forest fire. 展开更多
关键词 Forest fire smoke Convolutional neural network Image classification Kernel principal component analysis
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An improved YOLOv8 apple leaf disease detection algorithm 认领 引用
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作者 Xinyu PEI Wei YUAN +1 位作者 Yuexiu ZHANG Lianjun SONG 《Optoelectronics Letters》 EI 2026年第5期314-320,共7页
This paper suggests an improved you only look once version 8n(YOLOv8n)algorithm for apple leaf disease detection,abbreviated as ALWB-YOLOv8n.The model is comprised of four essential components.Initially,arbitrary kern... This paper suggests an improved you only look once version 8n(YOLOv8n)algorithm for apple leaf disease detection,abbreviated as ALWB-YOLOv8n.The model is comprised of four essential components.Initially,arbitrary kernel convolution(AKConv)replaces the convolution module,which significantly decreases both the model’s parameter count and its overall size.Secondly,the large selective kernel network(LSKNet)attention mechanism is added in the Backbone,which can dynamically adjust the spatial sensory domain,and experiments have proved that this method is extremely advantageous for small target detection.Third,a weighted bi-directional feature pyramid network is introduced,which enables the model to achieve multi-scale feature fusion and is more concise and faster.Finally,wise intersection over union(WIoU)is used to replace complete intersection over union(CIoU)in YOLOv8,and the idea of focal loss is introduced,which effectively solves the detection problems in cases such as apple leaves occluding each other and blurred boundaries of diseased leaves.The improved algorithm exhibits superior performance compared to other common object detection algorithms.Compared with YOLOv8n,the improved algorithm achieves 2.3%improvement in precision,3.8%improvement in recall,and 2.5%and 2.7%improvement in mAP0.5 and mAP0.5:0.95,respectively.Compared with YOLOv8n,the improved model reduces the number of parameters and size of the model and realizes real-time monitoring with a frames per second(FPS)of 50.5. 展开更多
关键词 large selective kernel network arbitrary kernel convolution large selective kernel network lsknet attention kernel convolution akconv replaces weighted bi directional feature pyramid network adjust spatial sensory domainand improved YOLOv n algorithm apple leaf disease detection
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Intrusion Detection System Using a Distributed Ensemble Design Based Convolutional Neural Network in Fog Computing 认领 引用
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作者 Aiming Wu Shanshan Tu +3 位作者 Muhammad Wagas Yongjie Yang Yihe Zhang Xuetao Bai 《Journal of Information Hiding and Privacy Protection》 2022年第1期25-39,共15页
With the rapid development of the Internet of Things(IoT),all kinds of data are increasing exponentially.Data storage and computing on cloud servers are increasingly restricted by hardware.This has prompted the develo... With the rapid development of the Internet of Things(IoT),all kinds of data are increasing exponentially.Data storage and computing on cloud servers are increasingly restricted by hardware.This has prompted the development of fog computing.Fog computing is to place the calculation and storage of data at the edge of the network,so that the entire Internet of Things system can run more efficiently.The main function of fog computing is to reduce the burden of cloud servers.By placing fog nodes in the IoT network,the data in the IoT devices can be transferred to the fog nodes for storage and calculation.Many of the information collected by IoT devices are malicious traffic,which contains a large number of malicious attacks.Because IoT devices do not have strong computing power and the ability to detect malicious traffic,we need to deploy a system to detect malicious attacks on the fog node.In response to this situation,we propose an intrusion detection system based on distributed ensemble design.The system mainly uses Convolutional Neural Network(CNN)as the first-level learner.In the second level,the random forest will finally classify the prediction results obtained in the first level.This paper uses the UNSW-NB15 dataset to evaluate the performance of the model.Experimental results show that the model has good detection performance for most attacks. 展开更多
关键词 Intrusion detection system fog computing convolutional neural network feature selection
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Improved lightweight road damage detection based on YOLOv5 认领 引用 被引量：2
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作者 LIU Chang SUN Yu +2 位作者 CHEN Jin YANG Jing WANG Fengchao 《Optoelectronics Letters》 EI 2025年第5期314-320,共7页
There is a problem of real-time detection difficulty in road surface damage detection. This paper proposes an improved lightweight model based on you only look once version 5(YOLOv5). Firstly, this paper fully utilize... There is a problem of real-time detection difficulty in road surface damage detection. This paper proposes an improved lightweight model based on you only look once version 5(YOLOv5). Firstly, this paper fully utilized the convolutional neural network(CNN) + ghosting bottleneck(G_bneck) architecture to reduce redundant feature maps. Afterwards, we upgraded the original upsampling algorithm to content-aware reassembly of features(CARAFE) and increased the receptive field. Finally, we replaced the spatial pyramid pooling fast(SPPF) module with the basic receptive field block(Basic RFB) pooling module and added dilated convolution. After comparative experiments, we can see that the number of parameters and model size of the improved algorithm in this paper have been reduced by nearly half compared to the YOLOv5s. The frame rate per second(FPS) has been increased by 3.25 times. The mean average precision(m AP@0.5: 0.95) has increased by 8%—17% compared to other lightweight algorithms. 展开更多
关键词 road surface damage detection convolutional neural network feature maps convolutional neural network cnn lightweight model yolov improved lightweight model spatial pyram
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A Low-Power 12-Bit SAR ADC for Analog Convolutional Kernel of Mixed-Signal CNN Accelerator 认领 引用
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作者 Jungyeon Lee Malik Summair Asghar HyungWon Kim 《Computers, Materials & Continua》 SCIE EI 2023年第5期4357-4375,共19页
As deep learning techniques such as Convolutional Neural Networks(CNNs)are widely adopted,the complexity of CNNs is rapidly increasing due to the growing demand for CNN accelerator system-on-chip(SoC).Although convent... As deep learning techniques such as Convolutional Neural Networks(CNNs)are widely adopted,the complexity of CNNs is rapidly increasing due to the growing demand for CNN accelerator system-on-chip(SoC).Although conventional CNN accelerators can reduce the computational time of learning and inference tasks,they tend to occupy large chip areas due to many multiply-and-accumulate(MAC)operators when implemented in complex digital circuits,incurring excessive power consumption.To overcome these drawbacks,this work implements an analog convolutional filter consisting of an analog multiply-and-accumulate arithmetic circuit along with an analog-to-digital converter(ADC).This paper introduces the architecture of an analog convolutional kernel comprised of low-power ultra-small circuits for neural network accelerator chips.ADC is an essential component of the analog convolutional kernel used to convert the analog convolutional result to digital values to be stored in memory.This work presents the implementation of a highly low-power and area-efficient 12-bit Successive Approximation Register(SAR)ADC.Unlink most other SAR-ADCs with differential structure;the proposed ADC employs a single-ended capacitor array to support the preceding single-ended max-pooling circuit along with minimal power consumption.The SARADCimplementation also introduces a unique circuit that reduces kick-back noise to increase performance.It was implemented in a test chip using a 55 nm CMOS process.It demonstrates that the proposed ADC reduces Kick-back noise by 40%and consequently improves the ADC’s resolution by about 10%while providing a near rail-to-rail dynamic rangewith significantly lower power consumption than conventional ADCs.The ADC test chip shows a chip size of 4600μm2with a power consumption of 6.6μW while providing an signal-to-noise-and-distortion ratio(SNDR)of 68.45 dB,corresponding to an effective number of bits(ENOB)of 11.07 bits. 展开更多
关键词 Convolution neural networks split-capacitor-based digital-toanalog converter(DAC) SAR analog-to-digital converter artificial intelligence system-on-chip analog convolutional kernel
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Nonparametric Statistical Feature Scaling Based Quadratic Regressive Convolution Deep Neural Network for Software Fault Prediction 认领 引用
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作者 Sureka Sivavelu Venkatesh Palanisamy 《Computers, Materials & Continua》 SCIE EI 2024年第3期3469-3487,共19页
The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software w... The development of defect prediction plays a significant role in improving software quality. Such predictions are used to identify defective modules before the testing and to minimize the time and cost. The software with defects negatively impacts operational costs and finally affects customer satisfaction. Numerous approaches exist to predict software defects. However, the timely and accurate software bugs are the major challenging issues. To improve the timely and accurate software defect prediction, a novel technique called Nonparametric Statistical feature scaled QuAdratic regressive convolution Deep nEural Network (SQADEN) is introduced. The proposed SQADEN technique mainly includes two major processes namely metric or feature selection and classification. First, the SQADEN uses the nonparametric statistical Torgerson–Gower scaling technique for identifying the relevant software metrics by measuring the similarity using the dice coefficient. The feature selection process is used to minimize the time complexity of software fault prediction. With the selected metrics, software fault perdition with the help of the Quadratic Censored regressive convolution deep neural network-based classification. The deep learning classifier analyzes the training and testing samples using the contingency correlation coefficient. The softstep activation function is used to provide the final fault prediction results. To minimize the error, the Nelder–Mead method is applied to solve non-linear least-squares problems. Finally, accurate classification results with a minimum error are obtained at the output layer. Experimental evaluation is carried out with different quantitative metrics such as accuracy, precision, recall, F-measure, and time complexity. The analyzed results demonstrate the superior performance of our proposed SQADEN technique with maximum accuracy, sensitivity and specificity by 3%, 3%, 2% and 3% and minimum time and space by 13% and 15% when compared with the two state-of-the-art methods. 展开更多
关键词 Software defect prediction feature selection nonparametric statistical Torgerson-Gower scaling technique quadratic censored regressive convolution deep neural network softstep activation function nelder-mead method
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基于轻量化CNN与PINN的四电平变流器开路故障在线诊断方法 认领 引用 被引量：2
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作者 赵筛筛 陈剑飞 +1 位作者 何怡刚 张朝龙 《高电压技术》 EI CAS CSCD 北大核心 2026年第3期1233-1243,I0042-I0045,共11页
针对机器学习方法难以实现电力电子变流器高精度在线开路故障(open-circuit fault,OCF)诊断的问题,提出一种快速、有效的电力电子变流器OCF在线诊断方法。首先,所提方法在传统卷积神经网络基础上引入轻量化层,实现电力电子变流器输出电... 针对机器学习方法难以实现电力电子变流器高精度在线开路故障(open-circuit fault,OCF)诊断的问题,提出一种快速、有效的电力电子变流器OCF在线诊断方法。首先,所提方法在传统卷积神经网络基础上引入轻量化层,实现电力电子变流器输出电流的去噪、压缩与多尺度特征提取;其次,通过注意力机制自适应调整物理信息网络中物理损失函数权重,建立高精度电力电子变流器OCF诊断模型。其中,物理损失函数通过引入总谐波失真物理量构建。最后,搭建了四电平变流器OCF在线诊断平台开展实验验证,结果表明所提方法诊断准确率高于99.26%,最大诊断误差低于5%,在线诊断时间约为13 ms。 展开更多
关键词 在线故障诊断 改进卷积神经网络 物理信息神经网络 自适应注意力机制 四电平变流器
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基于CNN-BiLSTM-SSA的锅炉再热器壁温预测模型 认领 引用 被引量：1
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作者 徐世明 何至谦 +6 位作者 彭献永 商忠宝 范景玮 王俊略 曲舒杨 刘洋 周怀春 《动力工程学报》 CAS CSCD 北大核心 2026年第1期121-130,共10页
针对锅炉高温再热器壁温动态特点,提出了一种基于稀疏自注意力(SSA)、卷积神经网络(CNN)及双向长短期记忆神经网络(BiLSTM)相融合的再热器壁温软测量模型。首先,采用核主成分分析(KPCA)算法对原始候选变量进行筛选降维,选择前26个主成... 针对锅炉高温再热器壁温动态特点,提出了一种基于稀疏自注意力(SSA)、卷积神经网络(CNN)及双向长短期记忆神经网络(BiLSTM)相融合的再热器壁温软测量模型。首先,采用核主成分分析(KPCA)算法对原始候选变量进行筛选降维,选择前26个主成分变量作为模型的最终输入。其次,考虑利用CNN捕捉局部相关性,BiLSTM学习数据的长期序列依赖性的优势,使用卷积神经网络-双向长短期记忆神经网络(CNN-BiLSTM)捕捉时序数据中的短期和长期依赖关系,引入稀疏自注意力SSA机制,通过为不同特征部分分配自适应权重,从而增强CNN-BiLSTM模型的特征提取与建模能力,最后利用在役1000 MW超超临界锅炉的历史数据进行仿真实验。结果表明:CNN-BiLSTM-SSA模型在高温再热器壁温预测中的均方根误差(RMSE)、平均绝对误差(MAE)及平均绝对百分比误差(MAPE)分别为4.92℃、3.81℃和0.6241%,相应的指标均优于CNN、LSTM、BiLSTM、CNN-LSTM和CNN-BiLSTM模型。 展开更多
关键词 再热器壁温软测量 深度学习 卷积神经网络 长短期记忆网络 注意力机制 核主成分分析 CNN-BiLSTM
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基于改进物理信息神经网络的轴流泵流场重构方法研究 认领 引用 被引量：1
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作者 刘康 刘兴宁 +4 位作者 孙勇 刘良 贾贺 曾涛 张耀飞 《人民黄河》 CAS 北大核心 2026年第3期157-162,共6页
轴流泵流场信息是其运行稳定性分析和结构优化设计的依据,受测量技术限制在运行过程中难以获取完整流场信息。为此,提出一种改进物理信息神经网络(PINN)模型,用于稀疏数据情况下重构流场。首先通过分析流场物理约束、边界约束及流场约束... 轴流泵流场信息是其运行稳定性分析和结构优化设计的依据,受测量技术限制在运行过程中难以获取完整流场信息。为此,提出一种改进物理信息神经网络(PINN)模型,用于稀疏数据情况下重构流场。首先通过分析流场物理约束、边界约束及流场约束,描述流场问题;然后引入三维卷积神经网络(3D CNN)求解流场问题;最后采用有限体积法(FVM)进行数值模拟,获取稳态流速和压力分布信息,基于网格化预处理后采样1%的流场数据进行模型训练。以某简化轴流泵管道作为测试对象,验证所提出方法。结果表明:改进PINN模型重构流场与FVM数值模拟流场对比,压力基本吻合,流速变化趋势基本相同,仅在叶轮及导叶流场区域存在细微偏差,说明所提出的方法能够在稀缺数据和复杂边界条件下准确预测三维流场。 展开更多
关键词 改进物理信息神经网络 三维卷积神经网络 流场重构 轴流泵 有限体积法
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基于VMD与MKCNN的固定式架车机齿轮箱故障分类方法 认领 引用
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作者 李愿望 付亚超 +3 位作者 贾晓宏 史时喜 张建华 宋国义 《铁道标准设计》 北大核心 2026年第4期202-210,共9页
旨在解决固定式架车机齿轮箱故障分类中因原始振动信号信噪比低、振动信号特征提取困难而导致的分类准确度不足问题。提出一种融合变分模态分解(Variational Mode Decomposition,VMD)与多核卷积神经网络(Multi-Kernel Convolutional Neu... 旨在解决固定式架车机齿轮箱故障分类中因原始振动信号信噪比低、振动信号特征提取困难而导致的分类准确度不足问题。提出一种融合变分模态分解(Variational Mode Decomposition,VMD)与多核卷积神经网络(Multi-Kernel Convolutional Neural Network,MKCNN)的新型故障分类方法。首先,利用VMD算法对原始振动信号进行分解,将其转化为多个固有模态函数(Intrinsic Mode Function,IMF),有效提取信号中的关键频率和模态信息,并通过重构IMF显著提升信号的信噪比,减少噪声对特征识别的干扰。随后,构建基于MKCNN的故障分类模型,该模型通过多核函数提取信号特征,实现特征的多尺度分析,并自动学习故障特征间的内在关联,以增强分类性能。试验结果表明,VMD信号重构方法能够有效分离出原始信号中的有用模态分量,显著提高重构信号的信噪比。与单核卷积神经网络(CNN)相比,MKCNN模型在故障分类中展现出更高的精度,故障分类模型的分类准确率提升至95%以上。 展开更多
关键词 架车机 信号重构 故障 分类 多核卷积神经网络
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基于WSET-ICNN和改进LSSVM的旋转机械故障诊断策略 认领 引用 被引量：1
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作者 仝兆景 张榕 宋静斌 《机电工程》 CAS 北大核心 2026年第3期607-618,626,共12页
针对旋转机械故障信号的非线性和非平稳特性,提出了一种融合同步提取小波变换(WSET)、改进卷积神经网络(ICNN)和改进蜜獾算法(IHBA)优化的最小二乘支持向量机(LSSVM)的旋转机械故障诊断模型。首先,利用WSET的高时频分辨率特性对原始故... 针对旋转机械故障信号的非线性和非平稳特性,提出了一种融合同步提取小波变换(WSET)、改进卷积神经网络(ICNN)和改进蜜獾算法(IHBA)优化的最小二乘支持向量机(LSSVM)的旋转机械故障诊断模型。首先,利用WSET的高时频分辨率特性对原始故障信号进行了多模态分解和时频分析,利用时频转换技术,将一维时间序列信号转换为二维时频特征图,为降低后续处理的计算复杂度,对生成的时频图像进行了降维处理;然后,将降维后的时频图像输入改进卷积神经网络中,进行了自适应深度特征提取,提取了ICNN全连接层的特征,将其作为最小二乘支持向量机的输入特征;最后,利用改进蜜獾算法优化了LSSVM的两个关键超参数,以构建最终的故障分类模型,进行了仿真验证;还在东南大学齿轮箱数据集上进行了实验和对比分析,验证了该方法的准确性。研究结果表明:WSET-IHBA-LSSVM方法对轴承故障的识别准确率为100%,对齿轮箱故障的识别准确率为99.75%;与LSSVM、蜜獾算法改进LSSVM相比,WSET-IHBA-LSSVM对轴承和齿轮箱故障的识别准确率更高,在诊断精度和稳定性方面展现出显著优势。WSET-ICNN-IHBA-LSSVM模型在轴承与齿轮箱故障诊断中具有较好的效果。 展开更多
关键词 转子机械 同步提取小波变换 时频 改进二维卷积神经网络 改进蜜獾算法 最小二乘支持向量机
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基于改进随机森林算法与多尺度卷积神经网络的频率选择表面敏捷设计 认领 引用
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作者 王义富 廖广昕 +7 位作者 李华萍 任燕飞 黄浩然 蒋伟 郑沈理 郭嘉诚 杜力 杜源 《通信学报》 EI CSCD 北大核心 2026年第1期267-278,共12页
针对传统频率选择表面(FSS)结合神经网络的设计存在预测偏差大、数据集成本高的问题,提出基于改进随机森林(RF)与多尺度卷积神经网络(MS-CNN)的FSS敏捷设计框架。改进RF通过电磁特性分裂准则与多特征交互评估,优化采样策略,构建高质量... 针对传统频率选择表面(FSS)结合神经网络的设计存在预测偏差大、数据集成本高的问题,提出基于改进随机森林(RF)与多尺度卷积神经网络(MS-CNN)的FSS敏捷设计框架。改进RF通过电磁特性分裂准则与多特征交互评估,优化采样策略,构建高质量数据集,达到均方误差(MSE)<2.0的预测精度仅需1157组样本,较传统采样减少61%;MS-CNN采用3×1、5×1、7×1多尺度卷积核提取电磁响应特征,结合频率梯度损失函数,0°/70°入射角下TE/TM双极化S21曲线预测MSE低至2.2。以MS-CNN为预测代理,结合粒子群优化(PSO)的逆向设计,输出满足25~33 GHz频段S21≥-1.5 dB、0°~70°入射角稳定、双极化适配的FSS参数,经HFSS验证达标,同时在20~28 GHz验证了模型泛化性。 展开更多
关键词 频率选择表面 随机森林算法 多尺度卷积神经网络 粒子群优化
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Ultra-short-term Photovoltaic Power Prediction Based on Improved Temporal Convolutional Network and Feature Modeling 认领 引用 被引量：1
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作者 Hao Xiao Wanting Zheng +1 位作者 Hai Zhou Wei Pei 《CSEE Journal of Power and Energy Systems》 SCIE EI CSCD 2025年第5期2024-2035,共12页
Accurate ultra-short-term photovoltaic(PV)power forecasting is crucial for mitigating variations caused by PV power generation and ensuring the stable and efficient operation of power grids.To capture intricate tempor... Accurate ultra-short-term photovoltaic(PV)power forecasting is crucial for mitigating variations caused by PV power generation and ensuring the stable and efficient operation of power grids.To capture intricate temporal relationships and enhance the precision of multi-step time forecast,this paper introduces an innovative approach for ultra-short-term photovoltaic(PV)power prediction,leveraging an enhanced Temporal Convolutional Neural Network(TCN)architecture and feature modeling.First,this study introduces a method employing the Spearman coefficient for meteorological feature filtration.Integrated with three-dimensional PV panel modeling,key factors influencing PV power generation are identified and prioritized.Second,the analysis of the correlation coefficient between astronomical features and PV power prediction demonstrates the theoretical substantiation for the practicality and essentiality of incorporating astronomical features.Third,an enhanced TCN model is introduced,augmenting the original TCN structure with a projection head layer to enhance its capacity for learning and expressing nonlinear features.Meanwhile,a new rolling timing network mechanism is constructed to guarantee the segmentation prediction of future long-time output sequences.Multiple experiments demonstrate the superior performance of the proposed forecasting method compared to existing models.The accuracy of PV power prediction in the next 4 hours,devoid of meteorological conditions,increases by 20.5%.Furthermore,incorporating shortwave radiation for predictions over 4 hours,2 hours,and 1 hour enhances accuracy by 11.1%,9.1%,and 8.8%,respectively. 展开更多
关键词 Astronomical feature feature modeling improved temporal convolutional neural network solar power generation ultra-short-term power generation prediction
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基于改进1DCNN-LSTM的防冲钻孔机器人钻进煤岩性状识别 认领 引用
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作者 司垒 刘扬 +5 位作者 王忠宾 顾进恒 魏东 戴剑博 李鑫 赵杨奇 《矿业科学学报》 CAS CSCD 北大核心 2026年第1期206-217,共12页
防冲钻孔机器人是高地应力矿井卸压作业的关键装备,其对钻进煤岩性状识别准确度直接影响钻孔卸压效率和卸压效果。本文针对当前煤岩钻进状态识别手段多依赖于人工经验,存在识别精度低、响应时间长、无法满足无人化钻孔卸压需求的问题,... 防冲钻孔机器人是高地应力矿井卸压作业的关键装备,其对钻进煤岩性状识别准确度直接影响钻孔卸压效率和卸压效果。本文针对当前煤岩钻进状态识别手段多依赖于人工经验,存在识别精度低、响应时间长、无法满足无人化钻孔卸压需求的问题,基于一维卷积神经网络(1DCNN)和长短时记忆网络(LSTM)并结合模拟实验提出了一种钻进过程煤岩性状识别方法。通过加入卷积块注意力机制(CBAM),提升模型识别准确率,并采用改进蜣螂优化(IDBO)算法对模型中超参数进行寻优,确定最优的网络参数组合。搭建煤岩钻进模拟试验台,制作6种典型煤岩试块,采集回转速度、回转扭矩、推进速度和推进压力等4类传感信号,开展相应的对比测试分析。结果表明:所提方法具有较高的钻进煤岩识别准确率,达到97.00%,明显优于1DCNN和1DCNN-LSTM,以及逻辑回归、支持向量机(SVM)、决策树、随机森林、K聚类、Transformer等方法。 展开更多
关键词 防冲钻孔机器人 钻进煤岩识别 一维卷积神经网络(1DCNN) 长短时记忆神经网络(LSTM) 改进蜣螂优化(IDWO)算法
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基于IBWO-VMD与CNN-BiLSTM的磁力耦合器轴承故障诊断方法 认领 引用
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作者 陈雪辉 武超凡 +3 位作者 刘伟 景甜甜 王杰 李昊 《河北工程大学学报（自然科学版）》 CAS 2026年第2期103-112,共10页
针对磁力耦合器轴承故障信号微弱、特征提取困难导致故障分类准确率低的问题,提出一种改进的白鲸优化算法(IBWO)优化变分模态分解(VMD),并结合卷积神经网络(CNN)与双向长短时记忆网络(BiLSTM)组合模型的滚动轴承故障诊断方法。首先使用... 针对磁力耦合器轴承故障信号微弱、特征提取困难导致故障分类准确率低的问题,提出一种改进的白鲸优化算法(IBWO)优化变分模态分解(VMD),并结合卷积神经网络(CNN)与双向长短时记忆网络(BiLSTM)组合模型的滚动轴承故障诊断方法。首先使用改进的白鲸优化算法寻优VMD的两个重要参数(模态数目K和惩罚因子α),然后将寻优得到的两个参数代入VMD可以获得K个模态分量(IMF),选择包络熵最小的IMF分量作为有效分量,最后将该分量输入到CNN-BiLSTM模型中进行故障诊断。分别使用凯斯西储大学以及渥太华大学公开数据集进行实验,结果表明,该模型故障识别准确率均达95%以上,证明所提出的诊断方法在识别准确率方面具有明显优势,研究结果可为磁力耦合器轴承的故障诊断提供参考。 展开更多
关键词 故障诊断 变分模态分解 改进白鲸算法 卷积神经网络 双向长短时记忆网络
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基于全局信道状态信息的中继选择算法 认领 引用
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作者 宋传旺 李玉明 +1 位作者 张海松 李恩玉 《计算机应用与软件》 北大核心 2026年第3期82-90,共9页
在移动通信中,局部的信道状态信息(Channel State Information,CSI)不能反映信道真实情况,因此,提出基于全局信道状态信息的中继选择算法,即两种中继选择方式:多中继转发—MNF(Multi Node Forward)和单中继转发—SNF(Single Node Forwa... 在移动通信中,局部的信道状态信息(Channel State Information,CSI)不能反映信道真实情况,因此,提出基于全局信道状态信息的中继选择算法,即两种中继选择方式:多中继转发—MNF(Multi Node Forward)和单中继转发—SNF(Single Node Forward)。在瑞利信道下,仿真对比分析MNF、SNF和PRS(Predictive Relay Selection)方式下中继数目和频带利用率对系统中断概率的影响,结果表明:基于全局信道状态信息的中继选择算法的中断性能优于基于局部信道状态信息的中继选择算法的中断性能。 展开更多
关键词 全局信道状态信息 中继选择 放大转发 译码转发 卷积神经网络
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