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Impulse feature extraction method for machinery fault detection using fusion sparse coding and online dictionary learning 认领 引用 被引量：7
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作者 Deng Sen Jing Bo +2 位作者 Sheng Sheng Huang Yifeng Zhou Hongliang 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2015年第2期488-498,共11页
Impulse components in vibration signals are important fault features of complex machines. Sparse coding （SC） algorithm has been introduced as an impulse feature extraction method, but it could not guarantee a satisf... Impulse components in vibration signals are important fault features of complex machines. Sparse coding （SC） algorithm has been introduced as an impulse feature extraction method, but it could not guarantee a satisfactory performance in processing vibration signals with heavy background noises. In this paper, a method based on fusion sparse coding （FSC） and online dictionary learning is proposed to extract impulses efficiently. Firstly, fusion scheme of different sparse coding algorithms is presented to ensure higher reconstruction accuracy. Then, an improved online dictionary learning method using FSC scheme is established to obtain redundant dictionary and it can capture specific features of training samples and reconstruct the sparse approximation of vibration signals. Simulation shows that this method has a good performance in solving sparse coefficients and training redundant dictionary compared with other methods. Lastly, the proposed method is further applied to processing aircraft engine rotor vibration signals. Compared with other feature extraction approaches, our method can extract impulse features accurately and efficiently from heavy noisy vibration signal, which has significant supports for machinery fault detection and diagnosis. 展开更多
关键词 Dictionary learning Fault detection Impulse feature extraction Information fusion Sparse coding
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Learning compact binary code based on multiple heterogeneous features 认领 引用
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作者 左欣 罗立民 +1 位作者 沈继锋 于化龙 《Journal of Southeast University(English Edition)》 EI CAS 2013年第4期372-378,共7页
A novel hashing method based on multiple heterogeneous features is proposed to improve the accuracy of the image retrieval system. First, it leverages the imbalanced distribution of the similar and dissimilar samples ... A novel hashing method based on multiple heterogeneous features is proposed to improve the accuracy of the image retrieval system. First, it leverages the imbalanced distribution of the similar and dissimilar samples in the feature space to boost the performance of each weak classifier in the asymmetric boosting framework. Then, the weak classifier based on a novel linear discriminate analysis （LDA） algorithm which is learned from the subspace of heterogeneous features is integrated into the framework. Finally, the proposed method deals with each bit of the code sequentially, which utilizes the samples misclassified in each round in order to learn compact and balanced code. The heterogeneous information from different modalities can be effectively complementary to each other, which leads to much higher performance. The experimental results based on the two public benchmarks demonstrate that this method is superior to many of the state- of-the-art methods. In conclusion, the performance of the retrieval system can be improved with the help of multiple heterogeneous features and the compact hash codes which can be learned by the imbalanced learning method. 展开更多
关键词 hashing code linear discriminate analysis asymmetric boosting heterogeneous feature
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Multi-Index Image Retrieval Hash Algorithm Based on Multi-View Feature Coding 认领 引用
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作者 Rong Duan Junshan Tan +3 位作者 Jiaohua Qin Xuyu Xiang Yun Tan N.eal NXiong 《Computers, Materials & Continua》 SCIE EI 2020年第12期2335-2350,共16页
In recent years,with the massive growth of image data,how to match the image required by users quickly and efficiently becomes a challenge.Compared with single-view feature,multi-view feature is more accurate to descr... In recent years,with the massive growth of image data,how to match the image required by users quickly and efficiently becomes a challenge.Compared with single-view feature,multi-view feature is more accurate to describe image information.The advantages of hash method in reducing data storage and improving efficiency also make us study how to effectively apply to large-scale image retrieval.In this paper,a hash algorithm of multi-index image retrieval based on multi-view feature coding is proposed.By learning the data correlation between different views,this algorithm uses multi-view data with deeper level image semantics to achieve better retrieval results.This algorithm uses a quantitative hash method to generate binary sequences,and uses the hash code generated by the association features to construct database inverted index files,so as to reduce the memory burden and promote the efficient matching.In order to reduce the matching error of hash code and ensure the retrieval accuracy,this algorithm uses inverted multi-index structure instead of single-index structure.Compared with other advanced image retrieval method,this method has better retrieval performance. 展开更多
关键词 Hashing multi-view feature large-scale image retrieval feature coding feature matching
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A SEMI-OPEN-LOOP CODING MODE SELECTION ALGORITHM BASED ON EFM AND SELECTED AMR-WB+FEATURES 认领 引用
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作者 Hong Ying Zhao Shenghui Kuang Jingming 《Journal of Electronics(China)》 2009年第2期274-278,共5页
To solve the problems of the AMR-WB+(Extended Adaptive Multi-Rate-WideBand)semi-open-loop coding mode selection algorithm,features for ACELP(Algebraic Code Excited Linear Prediction)and TCX(Transform Coded eXcitation)... To solve the problems of the AMR-WB+(Extended Adaptive Multi-Rate-WideBand)semi-open-loop coding mode selection algorithm,features for ACELP(Algebraic Code Excited Linear Prediction)and TCX(Transform Coded eXcitation)classification are investigated.11 classifying features in the AMR-WB+codec are selected and 2 novel classifying features,i.e.,EFM(Energy Flatness Measurement)and stdEFM(standard deviation of EFM),are proposed.Consequently,a novel semi-open-loop mode selection algorithm based on EFM and selected AMR-WB+features is proposed.The results of classifying test and listening test show that the performance of the novel algorithm is much better than that of the AMR-WB+semi-open-loop coding mode selection algorithm. 展开更多
关键词 Speech/Audio Semi-open-loop coding mode selection Features selection Energy Flat-ness Measurement(EFM)
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Integrated Multi-featured Android Malicious Code Detection 认领 引用
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作者 Qing Yu Hui Zhao 《国际计算机前沿大会会议论文集》 EI 2019年第1期215-216,共2页
To solve the problem that using a single feature cannot play the role of multiple features of Android application in malicious code detection, an Android malicious code detection mechanism is proposed based on integra... To solve the problem that using a single feature cannot play the role of multiple features of Android application in malicious code detection, an Android malicious code detection mechanism is proposed based on integrated learning on the basis of dynamic and static detection. Considering three types of Android behavior characteristics, a three-layer hybrid algorithm was proposed. And it combined the malicious code detection based on digital signature to improve the detection efficiency. The digital signature of the known malicious code was extracted to form a malicious sample library. The authority that can reflect Android malicious behavior, API call and the running system call features were also extracted. An expandable hybrid discriminant algorithm was designed for the above three types of features. The algorithm was tested with machine learning method by constructing the optimal classifier suitable for the above features. Finally, the Android malicious code detection system was designed and implemented based on the multi-layer hybrid algorithm. The experimental results show that the system performs Android malicious code detection based on the combination of signature and dynamic and static features. Compared with other related work, the system has better performance in execution efficiency and detection rate. 展开更多
关键词 Malicious code Feature Optimal algorithm
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Feature Representation for Facial Expression Recognition Based on FACS and LBP 认领 引用 被引量：15
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作者 Li Wang Rui-Feng Li +1 位作者 Ke Wang Jian Chen 《International Journal of Automation and computing》 CSCD 2014年第5期459-468,共10页
In expression recognition, feature representation is critical for successful recognition since it contains distinctive information of expressions. In this paper, a new approach for representing facial expression featu... In expression recognition, feature representation is critical for successful recognition since it contains distinctive information of expressions. In this paper, a new approach for representing facial expression features is proposed with its objective to describe features in an effective and efficient way in order to improve the recognition performance. The method combines the facial action coding system(FACS) and 'uniform' local binary patterns(LBP) to represent facial expression features from coarse to fine. The facial feature regions are extracted by active shape models(ASM) based on FACS to obtain the gray-level texture. Then, LBP is used to represent expression features for enhancing the discriminant. A facial expression recognition system is developed based on this feature extraction method by using K nearest neighborhood(K-NN) classifier to recognize facial expressions. Finally, experiments are carried out to evaluate this feature extraction method. The significance of removing the unrelated facial regions and enhancing the discrimination ability of expression features in the recognition process is indicated by the results, in addition to its convenience. 展开更多
关键词 Local binary patterns (LBP) facial expression recognition active shape models (ASM) facial action coding system (FACS) feature representation
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Flotation bubble seed image filling algorithm based on boundary point features 认领 引用 被引量：3
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作者 Zhu Hong Zhang Guoying +1 位作者 Liu Guanzhou Sun Qi 《International Journal of Mining Science and Technology》 EI CAS 2012年第3期289-293,共5页
Bubble seed image filling is an important prerequisite for the image segmentation of flotation bubble that can be used to improve flotation automatic control.These common image filling algorithms in dealing with compl... Bubble seed image filling is an important prerequisite for the image segmentation of flotation bubble that can be used to improve flotation automatic control.These common image filling algorithms in dealing with complex bubble image exists under-filling and over-filling problems.A new filling algorithm based on boundary point feature and scan lines(PFSL)is proposed in the paper.The filling a|gorithm describes these boundary points of image objects by means of chain codes.The features of each boundary point,including convex points,concave points,left points and right points,are defined by the point's entrancing chain code and leaving chain code.The algorithm firstly finds out all double-matched boundary points based on the features of boundary points,and fill image objects by these double-matched boundary points on scan lines.Experimental results of bubble seed image filling show that under-filling and over-filling problem can be eliminated by the proposed algorithm. 展开更多
关键词 Flotation bubble Filling Chain code Point feature Scan line
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Digital signature systems based on smart card and fingerprint feature 认领 引用 被引量：3
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作者 You Lin Xu Maozhi Zheng Zhiming 《Journal of Systems Engineering and Electronics》 SCIE EI 2007年第4期825-834,共10页
Two signature systems based on smart cards and fingerprint features are proposed. In one signature system, the cryptographic key is stored in the smart card and is only accessible when the signer＇s extracted fingerpr... Two signature systems based on smart cards and fingerprint features are proposed. In one signature system, the cryptographic key is stored in the smart card and is only accessible when the signer＇s extracted fingerprint features match his stored template. To resist being tampered on public channel, the user＇s message and the signed message are encrypted by the signer＇s public key and the user＇s public key, respectively. In the other signature system, the keys are generated by combining the signer＇s fingerprint features, check bits, and a rememberable key, and there are no matching process and keys stored on the smart card. Additionally, there is generally more than one public key in this system, that is, there exist some pseudo public keys except a real one. 展开更多
关键词 digital signature fingerprint feature error-correcting code cryptographic key smart card
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Feature Extraction of Fabric Defects Based on Complex Contourlet Transform and Principal Component Analysis 认领 引用 被引量：2
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作者 吴一全 万红 叶志龙 《Journal of Donghua University(English Edition)》 EI CAS 2013年第4期282-286,共5页
To extract features of fabric defects effectively and reduce dimension of feature space,a feature extraction method of fabric defects based on complex contourlet transform （CCT） and principal component analysis （PC... To extract features of fabric defects effectively and reduce dimension of feature space,a feature extraction method of fabric defects based on complex contourlet transform （CCT） and principal component analysis （PCA） is proposed.Firstly,training samples of fabric defect images are decomposed by CCT.Secondly,PCA is applied in the obtained low-frequency component and part of highfrequency components to get a lower dimensional feature space.Finally,components of testing samples obtained by CCT are projected onto the feature space where different types of fabric defects are distinguished by the minimum Euclidean distance method.A large number of experimental results show that,compared with PCA,the method combining wavdet low-frequency component with PCA （WLPCA）,the method combining contourlet transform with PCA （CPCA）,and the method combining wavelet low-frequency and highfrequency components with PCA （WPCA）,the proposed method can extract features of common fabric defect types effectively.The recognition rate is greatly improved while the dimension is reduced. 展开更多
关键词 fabric defects feature extraction complex contourlet transform（CCT） principal component analysis（PCA）CLC number：TP391.4 TS103.7Document code：AArticle ID：1672-5220（2013）04-0282-05
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Multi-Level Feature-Based Ensemble Model for Target-Related Stance Detection 认领 引用
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作者 Shi Li Xinyan Cao Yiting Nan 《Computers, Materials & Continua》 SCIE EI 2020年第10期777-788,共12页
Stance detection is the task of attitude identification toward a standpoint.Previous work of stance detection has focused on feature extraction but ignored the fact that irrelevant features exist as noise during highe... Stance detection is the task of attitude identification toward a standpoint.Previous work of stance detection has focused on feature extraction but ignored the fact that irrelevant features exist as noise during higher-level abstracting.Moreover,because the target is not always mentioned in the text,most methods have ignored target information.In order to solve these problems,we propose a neural network ensemble method that combines the timing dependence bases on long short-term memory(LSTM)and the excellent extracting performance of convolutional neural networks(CNNs).The method can obtain multi-level features that consider both local and global features.We also introduce attention mechanisms to magnify target information-related features.Furthermore,we employ sparse coding to remove noise to obtain characteristic features.Performance was improved by using sparse coding on the basis of attention employment and feature extraction.We evaluate our approach on the SemEval-2016Task 6-A public dataset,achieving a performance that exceeds the benchmark and those of participating teams. 展开更多
关键词 Attention sparse coding multi-level features ensemble model
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Robust Speech Recognition System Using Conventional and Hybrid Features of MFCC,LPCC,PLP,RASTA-PLP and Hidden Markov Model Classifier in Noisy Conditions 认领 引用 被引量：7
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作者 Veton Z.Kepuska Hussien A.Elharati 《Journal of Computer and Communications》 2015年第6期1-9,共9页
In recent years, the accuracy of speech recognition (SR) has been one of the most active areas of research. Despite that SR systems are working reasonably well in quiet conditions, they still suffer severe performance... In recent years, the accuracy of speech recognition (SR) has been one of the most active areas of research. Despite that SR systems are working reasonably well in quiet conditions, they still suffer severe performance degradation in noisy conditions or distorted channels. It is necessary to search for more robust feature extraction methods to gain better performance in adverse conditions. This paper investigates the performance of conventional and new hybrid speech feature extraction algorithms of Mel Frequency Cepstrum Coefficient (MFCC), Linear Prediction Coding Coefficient (LPCC), perceptual linear production (PLP), and RASTA-PLP in noisy conditions through using multivariate Hidden Markov Model (HMM) classifier. The behavior of the proposal system is evaluated using TIDIGIT human voice dataset corpora, recorded from 208 different adult speakers in both training and testing process. The theoretical basis for speech processing and classifier procedures were presented, and the recognition results were obtained based on word recognition rate. 展开更多
关键词 Speech Recognition Noisy Conditions Feature Extraction Mel-Frequency Cepstral Coefficients Linear Predictive Coding Coefficients Perceptual Linear Production RASTA-PLP Isolated Speech Hidden Markov Model
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Wake-Up-Word Feature Extraction on FPGA 认领 引用
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作者 Veton ZKepuska Mohamed MEljhani Brian HHight 《World Journal of Engineering and Technology》 2014年第1期1-12,共12页
Wake-Up-Word Speech Recognition task (WUW-SR) is a computationally very demand, particularly the stage of feature extraction which is decoded with corresponding Hidden Markov Models (HMMs) in the back-end stage of the... Wake-Up-Word Speech Recognition task (WUW-SR) is a computationally very demand, particularly the stage of feature extraction which is decoded with corresponding Hidden Markov Models (HMMs) in the back-end stage of the WUW-SR. The state of the art WUW-SR system is based on three different sets of features: Mel-Frequency Cepstral Coefficients (MFCC), Linear Predictive Coding Coefficients (LPC), and Enhanced Mel-Frequency Cepstral Coefficients (ENH_MFCC). In (front-end of Wake-Up-Word Speech Recognition System Design on FPGA) [1], we presented an experimental FPGA design and implementation of a novel architecture of a real-time spectrogram extraction processor that generates MFCC, LPC, and ENH_MFCC spectrograms simultaneously. In this paper, the details of converting the three sets of spectrograms 1) Mel-Frequency Cepstral Coefficients (MFCC), 2) Linear Predictive Coding Coefficients (LPC), and 3) Enhanced Mel-Frequency Cepstral Coefficients (ENH_MFCC) to their equivalent features are presented. In the WUW- SR system, the recognizer’s frontend is located at the terminal which is typically connected over a data network to remote back-end recognition (e.g., server). The WUW-SR is shown in Figure 1. The three sets of speech features are extracted at the front-end. These extracted features are then compressed and transmitted to the server via a dedicated channel, where subsequently they are decoded. 展开更多
关键词 Speech Recognition System Feature Extraction Mel-Frequency Cepstral Coefficients Linear Predictive Coding Coefficients Enhanced Mel-Frequency Cepstral Coefficients Hidden Markov Models Field-Programmable Gate Arrays
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基于对比学习的双通道源代码漏洞检测模型 认领 引用 被引量：1
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作者 宋建华 何佳伟 张龑 《计算机科学》 CSCD 北大核心 2026年第3期424-432,共9页
随着软件漏洞日益增多,系统安全正面临着严峻的挑战。源代码漏洞检测可以在软件开发阶段及时发现软件应用中的潜在安全威胁,对保障软件应用的安全性至关重要。目前,主流的源代码漏洞检测方式为基于深度学习模型的漏洞检测方式。然而,现... 随着软件漏洞日益增多,系统安全正面临着严峻的挑战。源代码漏洞检测可以在软件开发阶段及时发现软件应用中的潜在安全威胁,对保障软件应用的安全性至关重要。目前,主流的源代码漏洞检测方式为基于深度学习模型的漏洞检测方式。然而,现有的许多深度学习模型仅依赖单一形式特征,未能充分挖掘源代码语义中的全局和局部信息,并且这些模型往往忽略了不同样本之间的差异性和相似性,导致其在处理复杂漏洞模式时表现不佳,误报率和漏报率较高。为了解决上述问题,提出了一种基于对比学习的双通道源代码漏洞检测模型。该模型使用不同通道来分别提取源代码语义中的全局特征和局部特征,并引入对比学习,使得模型能够学习不同样本之间的相似性和差异性,并以此来优化特征提取过程。实验结果表明,此模型在真实世界的漏洞数据集Devign和Reveal上的召回率、F1分数相较于基线模型显著提升。在Devign上平均提升14.65个百分点和6.30个百分点;在Reveal上平均提升31.18个百分点和22.44个百分点。 展开更多
关键词 源代码漏洞检测 双通道网络模型 对比学习 交叉注意力 特征融合
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面向机器视觉的文本提示引导的图像编码 认领 引用
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作者 黄志勐 高峰 +1 位作者 杨帆 马思伟 《电子学报》 EI CAS CSCD 北大核心 2026年第1期19-31,共13页
近年来,随着物联网(Internet of Things,IoT)、语义通信以及智慧城市等经典机器间通信(Machine to Machine,M2M)场景的快速发展,海量视觉数据在设备间的实时传输与高效处理成为了一项关键挑战。在此背景下,传统以人眼感知质量为核心的... 近年来,随着物联网(Internet of Things,IoT)、语义通信以及智慧城市等经典机器间通信(Machine to Machine,M2M)场景的快速发展,海量视觉数据在设备间的实时传输与高效处理成为了一项关键挑战。在此背景下,传统以人眼感知质量为核心的图像编码方法,因其优化目标与机器视觉任务需求存在本质差异,往往在面向机器视觉分析时出现分析精度不足的问题。为此,面向机器视觉的图像编码(Image Coding for Machine,ICM)应运而生,其核心目标是在保证下游机器视觉任务(如分类、检测、分割等)分析精度的同时,实现尽可能低的编码码率,从而更好地适配M2M场景中的带宽与存储约束。然而,现有ICM方法仍面临两大瓶颈:其一,在极低码率条件下性能急剧下降。这是由于现有方法多依赖于端到端的非线性变换提取视觉特征,未能充分挖掘和利用图像中高层语义信息的紧凑表示,导致特征编码效率不足;其二,在开放场景下的泛化能力弱。多数方法针对单一任务、单一数据集进行优化,缺乏对未知类别、跨域数据的适应能力,难以在实际动态环境中保持稳定的分析性能。为突破上述限制,本文提出一种文本提示引导的面向机器视觉图像编码框架(Text-prompted Image Coding for Machine,T-ICM)。该框架的核心思想是将图像信息解耦为语义信息与纹理信息两个互补的组成部分,其中,语义信息以结构化文本提示(如对象类别、位置描述)的形式进行表示与编码,纹理信息则通过一种任务无关的通用视觉特征进行提取与压缩。在编码端,文本提示因其高度抽象和语义紧凑的特性,可以显著降低整体码率;通用特征则通过我们提出的分组特征编码模块进行高效压缩。在解码端,文本提示不仅用于直接解析完成分类、检测等任务,更重要的是作为引导信号,通过提示编码器与掩膜解码器,动态调整重建通用特征的语义感知区域,实现特征层面的域自适应与任务适配,从而显著提升模型在开放场景下的鲁棒性。本文在多个标准数据集与任务上对T-ICM进行了全面评估。实验表明,在语义分割和实例分割等密集预测任务上,T-ICM在极低码率下仍能保持接近原始图像输入的分析精度,其性能显著优于H.266/VVC、基于深度学习的图像编码器以及现有的其他ICM方法。本研究通过将语义信息迁移至高度压缩的文本模态进行传输,并利用其引导特征重建,T-ICM在编码效率与任务性能之间实现了更优的权衡,为未来语义通信、边缘智能协同,以及自适应机器视觉系统的发展提供了新的思路与技术支撑。 展开更多
关键词 视频编码 智能编码 特征编码 面向机器视觉的特征编码 深度学习 信号处理
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融合特征提取与恢复机制的SCUNet瑞利衰落信道译码算法 认领 引用
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作者 王磊军 王宽 +3 位作者 谢晋发 彭栖栋 黎嘉文 陈荣军 《电子与信息学报》 EI CAS CSCD 北大核心 2026年第5期2144-2153,共10页
人工智能的快速发展为无线通信系统性能的优化和提升提供了新思路。针对瑞利衰落信道下常规深度神经网络(DNN)译码算法性能受限的问题,该文提出一种融合特征提取与恢复机制的SCUNet译码算法,记为SCUNetDec。该网络设计中融入了数据预处... 人工智能的快速发展为无线通信系统性能的优化和提升提供了新思路。针对瑞利衰落信道下常规深度神经网络(DNN)译码算法性能受限的问题,该文提出一种融合特征提取与恢复机制的SCUNet译码算法,记为SCUNetDec。该网络设计中融入了数据预处理、特征提取与恢复以及噪声水平图3方面机制:首先通过升维操作将一维信号映射为二维特征图,以挖掘更丰富的结构信息;继而利用特征提取与恢复模块削弱维度转换中产生的不相关干扰,从而提升译码效果;同时引入噪声水平图,使网络能够更敏锐地感知和建模信噪比的变化,进一步增强在复杂信道环境下的适应能力。仿真结果表明,SCUNetDec在瑞利衰落信道下的误码性能优于常规神经网络译码方法,接近传统最优译码算法,且同时具备更快的译码速度。 展开更多
关键词 智能译码 SCUNet 特征提取 短码 瑞利衰落信道
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增量学习驱动的未知新污染物三维荧光光谱识别方法 认领 引用
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作者 胡映天 方连杰 +2 位作者 郝麦 董晓文 赵冬冬 《光电工程》 CAS CSCD 北大核心 2026年第4期73-85,共13页
针对未知新污染物监测挑战,提出了一种基于三维荧光光谱的增量学习驱动的动态特征码库构建及未知新污染物识别方法。该方法将光谱数据按激发波长的顺序重构为时序信号,利用长短期记忆网络提取具有鉴别性的特征表示,并引入增量学习机制... 针对未知新污染物监测挑战,提出了一种基于三维荧光光谱的增量学习驱动的动态特征码库构建及未知新污染物识别方法。该方法将光谱数据按激发波长的顺序重构为时序信号,利用长短期记忆网络提取具有鉴别性的特征表示,并引入增量学习机制构建动态特征码库,实现未知新污染物的自动识别与特征码入库。在河水污染模拟实验中,本方法对单一污染物的识别准确率达到93.3%,对混合污染物中所有组分均正确识别的比例达70.8%,性能优于主成分分析、平行因子分析、残差神经网络及增量学习基准方法iCaRL,表现出良好的扩展性与适应能力。 展开更多
关键词 特征码库 增量学习 三维荧光光谱 未知新污染物识别
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基于局部仿射子空间的深度特征编码的遥感场景分类 认领 引用
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作者 朱长水 袁宝华 王欢 《计算机应用与软件》 北大核心 2026年第7期164-170,共7页
为了提高深度特征鉴别能力,提出一种深度特征编码框架,被称为LASC-CNN,通过预训练卷积神经网络获取卷积层的深度特征,采用局部仿射子空间编码。LASC-CNN获得比直接从CNN提取的更具鉴别能力的深度特征。此外,LASC-CNN建立在CNN的顶部卷... 为了提高深度特征鉴别能力,提出一种深度特征编码框架,被称为LASC-CNN,通过预训练卷积神经网络获取卷积层的深度特征,采用局部仿射子空间编码。LASC-CNN获得比直接从CNN提取的更具鉴别能力的深度特征。此外,LASC-CNN建立在CNN的顶部卷积层上,其可以包含多尺度信息和任意分辨率和大小的区域。在两个公开的遥感图像数据集上的实验结果表明,与其他方法相比,LASC-CNN方法能够提高遥感图像场景分类性能。 展开更多
关键词 场景分类 深度特征 局部仿射子空间编码
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基于增强词旋转距离的JS恶意代码检测 认领 引用
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作者 徐鑫 张志宁 +3 位作者 杨景超 李立 郑玉杰 吕云山 《计算机工程与设计》 北大核心 2026年第4期1036-1044,共9页
针对现有JavaScript恶意代码检测方法在对混淆代码进行检测时会出现误报和漏报的情况,为了解决该问题,提出了AWRD-Transformer的检测方法。该方法通过将待检测代码与恶意代码的空间距离特征和待检测代码的长距离关联特征进行相结合的方... 针对现有JavaScript恶意代码检测方法在对混淆代码进行检测时会出现误报和漏报的情况,为了解决该问题,提出了AWRD-Transformer的检测方法。该方法通过将待检测代码与恶意代码的空间距离特征和待检测代码的长距离关联特征进行相结合的方式进行恶意代码检测。实验结果表明,该方法对JavaScript恶意代码检测具有较好的效果,误报率仅有0.76%,漏报率只有1.36%,F1-score达到98.93%。为了体现该方法对混淆代码的检测,在原始数据集中随机抽取一部分数据进行混淆并检测,结果表明该方法优于当前主流检测方法。 展开更多
关键词 恶意代码 混淆代码 词旋转距离 增强词旋转距离 空间距离特征 长距离关联特征 注意力机制
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基于自编码神经网络高阶特征提取的温室环境因子高维数据压缩方法 认领 引用
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作者 冷令 王琳 +3 位作者 吕金洪 李浩欣 吴伟斌 高婷 《中国农机化学报》 北大核心 2026年第1期252-257,共6页
针对温室环境数据的维度高、冗余性强,导致数据处理存在压缩比低和峰值信噪比较高的问题,提出基于自编码神经网络高阶特征提取的温室环境因子高维数据压缩方法。应用改进回归方程,填补温室环境因子数据中的缺失值,针对深度自编码神经网... 针对温室环境数据的维度高、冗余性强,导致数据处理存在压缩比低和峰值信噪比较高的问题,提出基于自编码神经网络高阶特征提取的温室环境因子高维数据压缩方法。应用改进回归方程,填补温室环境因子数据中的缺失值,针对深度自编码神经网络的内部协变量迁移现象,加入自适应平衡层,结合小批量梯度下降法,构建深度自适应平衡自编码神经网络,提取温室环境因子高阶特征,基于矢量量化思想,判断相对误差,通过实施新码书计算,获得各划分的质心,根据码书训练结果,设计高维数据压缩方法。结果表明,当数据量超过50 GB时,所设计方法的压缩比下降0.7个百分点,降幅为3.8%,整体压缩性能表现优异;峰值信噪比随着采样率变大并未大幅下降,仅降低4 dB,降幅为7.5%,压缩峰值信噪比具备更优的重建保真度。该方法具有更高的压缩比且有效降低信噪比,对提高温室管理的智能化水平具有借鉴价值。 展开更多
关键词 改进回归方程 自编码神经网络 高阶特征提取 温室环境因子 高维数据压缩
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面向图像语义通信的多尺度混合特征变换编码方法 认领 引用
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作者 王明军 杨沁衡 《计算机应用研究》 CSCD 北大核心 2026年第8期2494-2499,共6页
图像语义通信依赖深度神经网络实现端到端的语义表示与重建,其网络架构将直接影响系统性能。而现有架构仍存在局限:基于CNN的方案难以建模像素间的长程依赖,基于Transformer的方案计算开销较高而难以高效部署,两者混合架构的特征融合判... 图像语义通信依赖深度神经网络实现端到端的语义表示与重建,其网络架构将直接影响系统性能。而现有架构仍存在局限:基于CNN的方案难以建模像素间的长程依赖,基于Transformer的方案计算开销较高而难以高效部署,两者混合架构的特征融合判据相对单一,在噪声干扰下易致次优决策。为此,提出面向图像语义通信的多尺度混合特征变换(multi-scale hybrid feature transform,MHFT),构建兼顾全局语义与局部细节的双路径架构,前者在频域以较低计算开销实现全局建模,后者在空间域强化对图像纹理与边缘的表征。在此基础上,设计分阶段多证据融合策略(phased multi-evidence fusion,PMEF),基于双路径特征强度、差异与相关性分阶段构造融合证据,自适应分配路径权重,从而强化全局语义与局部纹理的协同建模,并提升在噪声信道条件下的鲁棒性。实验结果表明,在多种信道条件与带宽预算下,该方案相较主流方案在PSNR和MS-SSIM上实现一致提升,并在计算开销与端到端编解码速度上同样具有优势,体现了其有效性与应用潜力。 展开更多
关键词 语义通信 特征融合 联合信源信道编码 图像传输
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