Rolling bearing performance degradation evaluation by VMD and embedding selection-based NPE

In order to improve the incipient fault sensitivity and stability of degradation index in the rolling bearing performance degradation evaluation process,an embedding selection-based neighborhood preserving embedding(ESNPE)method is proposed.Firstly,the acquired vibration signals are decomposed by variational mode decomposition(VMD),and the singular value and relative energy of each intrinsic mode function(IMF)are extracted to form a high-dimensional feature set.Then,the NPE manifold learning method is used to extract the embedded features in the feature space.Considering the problem that useful embedding information is easily suppressed in NPE,an embedding selection strategy is built based on the Spearman correlation coefficient.The effectiveness of embeddings is measured by the coefficient absolute value,and useful embeddings are preserved in the early stage of bearing degradation by using the first-order difference method.Finally,the degradation index is established using the support vector data description(SVDD)model and bearing performance degradation evaluation is achieved.The proposed method was tested with the whole life experiment data of a rolling bearing,and the result was compared with the feature extraction methods of traditional principal component analysis(PCA)and NPE.The results show that the proposed method is superior in improving the incipient fault sensitivity and stability of the degradation index.

基于WD-MNPE-CVA的带钢热轧全流程动态过程监测方法

过程监测技术是保障复杂工业全流程安全、高质、高效运行的有效手段.考虑带钢热轧过程的“非线性、多模态、动态性”等特征,本文提出一种基于改进的加权差分邻域保持嵌入–规范变量分析(WD-MNPE-CVA)的带钢热轧全流程动态过程监测方法.首先,针对过程数据的非线性、多模态特性,采用加权差分方法进行数据预处理;其次,基于过程的机理知识进行流程划分,并开发了一种改进的邻域保持嵌入算法,基于样本点之间的欧氏距离和余弦距离,获得每个样本点更准确的邻域关系,进而基于规范变量分析建立每个子流程的局部动态监测模型;最后,采用贝叶斯推理建立全局的动态过程监测模型,通过带钢热轧实际过程故障数据验证了该方法的有效性.

A Linked List Encryption Scheme for Image Steganography without Embedding

Information steganography has received more and more attention from scholars nowadays,especially in the area of image steganography,which uses image content to transmit information and makes the existence of secret information undetectable.To enhance concealment and security,the Steganography without Embedding(SWE)method has proven effective in avoiding image distortion resulting from cover modification.In this paper,a novel encrypted communication scheme for image SWE is proposed.It reconstructs the image into a multi-linked list structure consisting of numerous nodes,where each pixel is transformed into a single node with data and pointer domains.By employing a special addressing algorithm,the optimal linked list corresponding to the secret information can be identified.The receiver can restore the secretmessage fromthe received image using only the list header position information.The scheme is based on the concept of coverless steganography,eliminating the need for any modifications to the cover image.It boasts high concealment and security,along with a complete message restoration rate,making it resistant to steganalysis.Furthermore,this paper proposes linked-list construction schemeswithin theproposedframework,which caneffectively resist a variety of attacks,includingnoise attacks and image compression,demonstrating a certain degree of robustness.To validate the proposed framework,practical tests and comparisons are conducted using multiple datasets.The results affirm the framework’s commendable performance in terms of message reduction rate,hidden writing capacity,and robustness against diverse attacks.

Enhancing visual security: An image encryption scheme based on parallel compressive sensing and edge detection embedding

A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete wavelet transform.Then, the coefficient matrix is scrambled and compressed to obtain a size-reduced image using the Fisher–Yates shuffle and parallel compressive sensing. Subsequently, to increase the security of the proposed algorithm, the compressed image is re-encrypted through permutation and diffusion to obtain a noise-like secret image. Finally, an adaptive embedding method based on edge detection for different carrier images is proposed to generate a visually meaningful cipher image. To improve the plaintext sensitivity of the algorithm, the counter mode is combined with the hash function to generate keys for chaotic systems. Additionally, an effective permutation method is designed to scramble the pixels of the compressed image in the re-encryption stage. The simulation results and analyses demonstrate that the proposed algorithm performs well in terms of visual security and decryption quality.

Identification of partial differential equations from noisy data with integrated knowledge discovery and embedding using evolutionary neural networks

Identification of underlying partial differential equations(PDEs)for complex systems remains a formidable challenge.In the present study,a robust PDE identification method is proposed,demonstrating the ability to extract accurate governing equations under noisy conditions without prior knowledge.Specifically,the proposed method combines gene expression programming,one type of evolutionary algorithm capable of generating unseen terms based solely on basic operators and functional terms,with symbolic regression neural networks.These networks are designed to represent explicit functional expressions and optimize them with data gradients.In particular,the specifically designed neural networks can be easily transformed to physical constraints for the training data,embedding the discovered PDEs to further optimize the metadata used for iterative PDE identification.The proposed method has been tested in four canonical PDE cases,validating its effectiveness without preliminary information and confirming its suitability for practical applications across various noise levels.

Embedding-based approximate query for knowledge graph

To solve the low efficiency of approximate queries caused by the large sizes of the knowledge graphs in the real world,an embedding-based approximate query method is proposed.First,the nodes in the query graph are classified according to the degrees of approximation required for different types of nodes.This classification transforms the query problem into three constraints,from which approximate information is extracted.Second,candidates are generated by calculating the similarity between embeddings.Finally,a deep neural network model is designed,incorporating a loss function based on the high-dimensional ellipsoidal diffusion distance.This model identifies the distance between nodes using their embeddings and constructs a score function.k nodes are returned as the query results.The results show that the proposed method can return both exact results and approximate matching results.On datasets DBLP(DataBase systems and Logic Programming)and FUA-S(Flight USA Airports-Sparse),this method exhibits superior performance in terms of precision and recall,returning results in 0.10 and 0.03 s,respectively.This indicates greater efficiency compared to PathSim and other comparative methods.

TCM-HIN2Vec:A strategy for uncovering biological basis of heart qi deficiency pattern based on network embedding and transcriptomic experiment

Objective:To elucidate the biological basis of the heart qi deficiency(HQD)pattern,an in-depth understanding of which is essential for improving clinical herbal therapy.Methods: We predicted and characterized HQD pattern genes using the new strategy,TCM-HIN2Vec,which involves heterogeneous network embedding and transcriptomic experiments.First,a heterogeneous network of traditional Chinese medicine(TCM)patterns was constructed using public databases.Next,we predicted HQD pattern genes using a heterogeneous network-embedding algorithm.We then analyzed the functional characteristics of HQD pattern genes using gene enrichment analysis and examined gene expression levels using RNA-seq.Finally,we identified TCM herbs that demonstrated enriched interactions with HQD pattern genes via herbal enrichment analysis.Results: Our TCM-HIN2Vec strategy revealed that candidate genes associated with HQD pattern were significantly enriched in energy metabolism,signal transduction pathways,and immune processes.Moreover,we found that these candidate genes were significantly differentially expressed in the transcriptional profile of mice model with heart failure with a qi deficiency pattern.Furthermore,herbal enrichment analysis identified TCM herbs that demonstrated enriched interactions with the top 10 candidate genes and could potentially serve as drug candidates for treating HQD.Conclusion: Our results suggested that TCM-HIN2Vec is capable of not only accurately identifying HQD pattern genes,but also deciphering the basis of HQD pattern.Furthermore our finding indicated that TCM-HIN2Vec may be further expanded to develop other patterns,leading to a new approach aimed at elucidating general TCM patterns and developing precision medicine.

Insider threat detection approach for tobacco industry based on heterogeneous graph embedding

In the tobacco industry,insider employee attack is a thorny problem that is difficult to detect.To solve this issue,this paper proposes an insider threat detection method based on heterogeneous graph embedding.First,the interrelationships between logs are fully considered,and log entries are converted into heterogeneous graphs based on these relationships.Second,the heterogeneous graph embedding is adopted and each log entry is represented as a low-dimensional feature vector.Then,normal logs and malicious logs are classified into different clusters by clustering algorithm to identify malicious logs.Finally,the effectiveness and superiority of the method is verified through experiments on the CERT dataset.The experimental results show that this method has better performance compared to some baseline methods.

A chaotic hierarchical encryption/watermark embedding scheme for multi-medical images based on row-column confusion and closed-loop bi-directional diffusion

Security during remote transmission has been an important concern for researchers in recent years.In this paper,a hierarchical encryption multi-image encryption scheme for people with different security levels is designed,and a multiimage encryption(MIE)algorithm with row and column confusion and closed-loop bi-directional diffusion is adopted in the paper.While ensuring secure communication of medical image information,people with different security levels have different levels of decryption keys,and differentiated visual effects can be obtained by using the strong sensitivity of chaotic keys.The highest security level can obtain decrypted images without watermarks,and at the same time,patient information and copyright attribution can be verified by obtaining watermark images.The experimental results show that the scheme is sufficiently secure as an MIE scheme with visualized differences and the encryption and decryption efficiency is significantly improved compared to other works.

Thin-Shell Wormholes Admitting Conformal Motions in Spacetimes of Embedding Class One

This paper discusses the feasibility of thin-shell wormholes in spacetimes of embedding class one admitting a one-parameter group of conformal motions. It is shown that the surface energy density σis positive, while the surface pressure is negative, resulting in , thereby signaling a violation of the null energy condition, a necessary condition for holding a wormhole open. For a Morris-Thorne wormhole, matter that violates the null energy condition is referred to as “exotic”. For the thin-shell wormholes in this paper, however, the violation has a physical explanation since it is a direct consequence of the embedding theory in conjunction with the assumption of conformal symmetry. These properties avoid the need to hypothesize the existence of the highly problematical exotic matter.

嵌入式物联网融合通讯协议实践教学设计

为使学生能够融会贯通嵌入式物联网分散在各门专业课的知识,本文提出一个以通讯协议为纽带的综合实践课程设计方法。该方法应用Zigbee无线协议,串口协议和网络协议,整合现有的专业课知识。通过设计基于硬件和软件的实践项目,同时以嵌入式物联网的典型问题为导向,引导学生加深理解专业知识。实践的结果表明,采用该教学方法的嵌入式物联网综合实践课程能够在一定程度上提高学生对专业知识的综合理解和应用。

移动机器人嵌入式控制系统关键技术分析

本文聚焦于工业自动化领域中的一种移动机器人,通过深入分析其功能需求和工作环境,提出并实现了一套基于ARM9微处理器和专用运动控制芯片的嵌入式控制系统解决方案,以满足实际工业生产中的应用需求。

基于嵌入式平台的深度学习人脸识别关键技术研究

为了进一步加大人脸识别技术的普及与扩宽应用,人脸识别算法部署到计算资源有限的嵌入式设备成为热门的问题。因此,通过优化深度学习模型、压缩网络结构和提升嵌入式设备的计算效率,可以实现快速、准确的人脸识别,为信息安全和智能化应用提供有力支持。

高价值专利知识网络嵌入与企业高质量发展:创业行为的调节作用

本文基于社会网络理论,选取CNIPA数据库中拥有高价值专利的288家上市公司为研究对象,探究高价值专利知识网络嵌入对企业高质量发展的影响以及创业行为在其中的调节作用。主要结论为:高价值专利知识网络关系嵌入、结构嵌入均正向影响企业高质量发展;组合创业行为、连续创业行为均正向调节高价值专利知识网络关系嵌入、结构嵌入与高质量发展之间的正向关系;国际创业行为增强高价值专利知识网络关系嵌入和企业高质量发展间的正向关系,但对高价值专利知识网络结构嵌入与企业高质量发展间的正相关关系不存在调节作用。此外,高价值专利知识网络嵌入对企业高质量发展的影响存在显著的企业所有制和行业竞争程度异质性。研究结论对于企业积极培育高价值专利、构建以高价值专利为核心的知识网络、实现企业高质量发展具有一定指导作用。

基于小波包能量谱和NPE的模拟电路故障诊断

提出采用小波包能量谱和邻域保持嵌入作为预处理实行特征提取的模拟电路故障诊断方法。该方法对采集到的故障响应信号进行小波包分解,将不同频带内的能量作为故障特征值,然后利用邻域保持嵌入算法进一步提取故障特征,最后将所得到的最优故障特征输入支持向量机进行故障诊断。仿真结果表明,提出的故障特征提取方法能很好地反映故障响应信号的本质特征,不仅表现出了比其他特征提取方法更好的性能,而且最后的故障诊断中也获得了令人满意的结果。

基于word embedding的短文本特征扩展与分类

近几年短文本的大量涌现,给传统的自动文本分类技术带来了挑战.针对短文本特征稀疏、特征覆盖率低等特点,提出了一种基于word embedding扩展短文本特征的分类方法.word embedding是一种词的分布式表示,表示形式为低维连续的向量形式,并且好的word embedding训练模型可以编码很多语言规则和语言模式.本文利用word embedding空间分布特点和其蕴含的线性规则提出了一种新的文本特征扩展方法.结合扩展特征我们分别在谷歌搜索片段、中国日报新闻摘要两类数据集上进行了短文本分类实验,对比于仅使用词袋表示文本特征的分类方法,准确率分别提高:8.59%,7.42%.

Au改性纳米TiO_2材料对NPE-10光催化降解的活性

以钛酸四丁酯和氯金酸为原料,通过溶胶-凝胶法制备了Au掺杂的纳米TiO2光催化剂粉体,并用XRD、BET、XPS和固体紫外可见吸收光谱等技术对其晶相结构、比表面积、表面组成及紫外可见光响应范围进行了表征,对其光催化降解非离子表面活性剂壬基酚聚氧乙烯醚(NPE-10)的活性进行了考察.结果表明,掺杂的Au在纳米TiO2粉体材料中可能以两种形态存在,即以Au3+离子形式替代Ti4+进入TiO2晶格和以Au原子态形式暴露于粉体表面.前者使TiO2在480～650nm出现了更强的光吸收;后者中处于表面原子态的Au又会成为光生电子的受体,有效地避免了光生电子空穴对的复合.通过对掺杂量及处理温度的优化,在nAu3+/nTi4+=0.005,500℃煅烧的条件下可以制得具有较高的光催化活性的Au/TiO2粉体.对NPE-10的光催化氧化试验显示,日光照射4h后降解效率可以达到91.8%;而用未改性的纳米TiO2,在同样条件下,NPE-10的光催化降解效率仅能达到50.2%,商品DegussaP-25也只能达到66%.

基于Word Embedding语义相似度的字母缩略术语消歧

该文提出基于Word Embedding的歧义词多个义项语义表示方法,实现基于知识库的无监督字母缩略术语消歧。方法分两步聚类,首先采用显著相似聚类获得高置信度类簇,构造带有语义标签的文档集作为训练数据。利用该数据训练多份Word Embedding模型,以余弦相似度均值表示两个词之间的语义关系。在第二步聚类时,提出使用特征词扩展和语义线性加权来提高歧义分辨能力,提高消歧性能。该方法根据语义相似度扩展待消歧文档的特征词集合,挖掘聚类文档中缺失的语义信息,并使用语义相似度对特征词权重进行线性加权。针对25个多义缩略术语的消歧实验显示,特征词扩展使系统F值提高约4%,使用语义线性加权后F值再提高约2%,达到89.40%。

中国ICT企业应对NPE专利风险策略研究——基于事件系统理论的案例分析

NPE(Non-Practicing Entities)是一类拥有大量专利但不利用专利从事产品制造的专利运营组织,其存在饱受争议。无线未来将索尼上诉至南京中院标志着国外NPE首次将诉讼战火延烧至中国,引起各界广泛关注。客观公正地认识NPE,以恰当的策略应对外国NPE带来的专利风险,对中国ICT企业十分重要。在文献回顾的基础上,澄清有关NPE的认识并给出NPE的定义;利用事件系统理论对中国首例NPE对ICT企业专利诉讼案进行分析,构建基于事件系统理论的NPE在华专利风险评估模型;从应对NPE专利诉讼被动事件和发起专利保护主动事件两个角度,提出我国ICT企业防范和规避NPE专利风险的建议。

基于位置敏感Embedding的中文命名实体识别

在基于条件随机场的中文命名实体识别任务中,现有表示学习方法学习到的特征存在语义表示偏差,给中文命名实体识别带来噪声。针对此问题,提出了一种基于位置敏感Embedding的中文命名实体识别方法。该方法将上下文位置信息融入到现有的Embedding模型中,采用多尺度聚类方法抽取不同粒度的Embedding特征,通过条件随机场来识别中文命名实体。实验证明,该方法学习到的特征缓解了语义表示偏差,进一步提高了现有系统的性能,与传统方法相比,F值提高了2.85%。