基于SSAE-ResNet的入侵检测模型的研究

针对无线局域网流量冗余特征多、安全日益突出、网络入侵检测存在误报和漏报的问题,构建了堆叠稀疏编码器(Stacked Sparse Auto-Encoder,SSAE)和一维残差网络(ResNet)入侵检测模型,以在无线局域网数据集AWID为数据样本,首先对海量高维数据进行数据预处理,将数据处理为模型能够适应的数据类型,然后设计了自编码器和残差网络组合模型。为了避免模型过拟合以及对特征的提取不完全,在自编码器中添加了正则项,并设计堆叠自编码器进行特征提取,将提取到的特征作为分类器的输入,分类器采用改进的一维ResNet设计,使流量数据无需转换为图像,节省数据转换图像的时间。通过实验对比表明,上述模型具有较好检测的效果,运行稳定,从而表明该模型的有效性。

基于cGAN-SAE的室内定位指纹生成方法

针对室内定位中指纹采集成本高、构建数据集难等问题,提出了一种基于条件稀疏自编码生成对抗网络的室内定位指纹生成方法。该方法通过增加自编码器隐藏层和输出层,增强了特征提取能力,引导生成器学习并生成指纹数据的关键特征。利用指纹选择算法筛选出最相关的指纹数据,扩充至指纹数据库中,并用于训练卷积长短时记忆网络模型以进行在线效果评估。实验结果表明,条件稀疏自编码生成对抗网络在不增加采集样本的情况下,提高了多栋多层建筑室内定位的精度。与原始条件生成对抗网络模型相比,在UJIIndoorLoc数据集上的预测中,定位误差降低了6%;在实际应用中,定位误差降低了14%。

有限元模拟和神经网络相结合的喷丸处理SAE9254钢疲劳寿命预测

采用ABAQUS有限元软件建立基于Python脚本的随机多弹丸喷丸模型,对不同弹丸直径、不同弹丸速度和不同喷丸覆盖率下喷丸处理后悬架弹簧用SAE9254钢的残余应力分布和表面粗糙度进行预测,并与试验结果进行对比;基于有限元模拟结果结合神经网络模型对试验钢的疲劳寿命进行预测,并进行试验验证。结果表明:模拟得到SAE9254钢的残余应力沿深度方向的变化曲线与试验结果吻合较好,最大残余压应力的相对误差约为14.77%,表面粗糙度的相对误差约为3.18%,建立的随机多弹丸喷丸模型能够准确地预测SAE9254钢喷丸后的残余应力分布及表面粗糙度。采用有限元模拟与神经网络相结合的方法得到的疲劳寿命预测值和试验值的平均相对误差为6.85%,该方法可以准确地预测SAE9254钢的疲劳寿命。

Masked Autoencoders as Single Object Tracking Learners

Significant advancements have beenwitnessed in visual tracking applications leveragingViT in recent years,mainly due to the formidablemodeling capabilities of Vision Transformer(ViT).However,the strong performance of such trackers heavily relies on ViT models pretrained for long periods,limitingmore flexible model designs for tracking tasks.To address this issue,we propose an efficient unsupervised ViT pretraining method for the tracking task based on masked autoencoders,called TrackMAE.During pretraining,we employ two shared-parameter ViTs,serving as the appearance encoder and motion encoder,respectively.The appearance encoder encodes randomly masked image data,while the motion encoder encodes randomly masked pairs of video frames.Subsequently,an appearance decoder and a motion decoder separately reconstruct the original image data and video frame data at the pixel level.In this way,ViT learns to understand both the appearance of images and the motion between video frames simultaneously.Experimental results demonstrate that ViT-Base and ViT-Large models,pretrained with TrackMAE and combined with a simple tracking head,achieve state-of-the-art(SOTA)performance without additional design.Moreover,compared to the currently popular MAE pretraining methods,TrackMAE consumes only 1/5 of the training time,which will facilitate the customization of diverse models for tracking.For instance,we additionally customize a lightweight ViT-XS,which achieves SOTA efficient tracking performance.

体育器材SAE1008低碳钢高速磨削砂轮工艺优化

利用CBN砂轮高速磨削处理技术,对体育器材用SAE1008钢开展单因素测试,对比了不同磨削工艺下表层残余应力变化特征,并分析了相关影响因素。研究结果表明:当砂轮线速度到达60m/s时,获得了较大切向力,残余压应力相对其它工艺条件明显减小。当砂轮进给速增大后,形成线性降低的残余压应力,获得了更大比磨削能和更小残余压应力。位于(0.5~0.6)mm/min范围内呈现拉应力状态,应力作用造成的层深介于(100~150)μm,形成了更高磨削温度。230/270砂轮相对其它砂轮在磨削阶段形成更小表面残余应力,沿深度方向也产生了残余拉应力。该研究为设计减小残余拉应力以及改善钢材工件表面组织结构完整性的高速磨削技术提供了一定的参考价值。

面向矿井提升机制动系统的SAE故障诊断方法

为了更充分地利用矿井提升机在运转过程中的监测数据,对制动系统进行精确诊断,提出了一种基于稀疏自动编码器(SAE)的故障诊断方法。通过模拟故障试验,获取故障数据,经标准化处理后生成训练集和测试集。并加入Dropout正则化方法对故障诊断模型进行了优化,根据训练结果采用梯度下降法优化模型参数。最后使用测试数据集对优化前后的诊断模型进行对比试验。结果表明,文中提出的提升机故障诊断方法,减少了过拟合现象,降低了获取标签数据的工作量,故障类型的平均分类精度能够达到96%。此方法使用提升机的监测数据,减少人为的影响,可以对矿井提升机的故障进行准确诊断。

A Trust Evaluation Mechanism Based on Autoencoder Clustering Algorithm for Edge Device Access of IoT

First,we propose a cross-domain authentication architecture based on trust evaluation mechanism,including registration,certificate issuance,and cross-domain authentication processes.A direct trust evaluation mechanism based on the time decay factor is proposed,taking into account the influence of historical interaction records.We weight the time attenuation factor to each historical interaction record for updating and got the new historical record data.We refer to the beta distribution to enhance the flexibility and adaptability of the direct trust assessment model to better capture time trends in the historical record.Then we propose an autoencoder-based trust clustering algorithm.We perform feature extraction based on autoencoders.Kullback leibler(KL)divergence is used to calculate the reconstruction error.When constructing a convolutional autoencoder,we introduce convolutional neural networks to improve training efficiency and introduce sparse constraints into the hidden layer of the autoencoder.The sparse penalty term in the loss function measures the difference through the KL divergence.Trust clustering is performed based on the density based spatial clustering of applications with noise(DBSCAN)clustering algorithm.During the clustering process,edge nodes have a variety of trustworthy attribute characteristics.We assign different attribute weights according to the relative importance of each attribute in the clustering process,and a larger weight means that the attribute occupies a greater weight in the calculation of distance.Finally,we introduced adaptive weights to calculate comprehensive trust evaluation.Simulation experiments prove that our trust evaluation mechanism has excellent reliability and accuracy.

Trusted Encrypted Traffic Intrusion Detection Method Based on Federated Learning and Autoencoder

With the rapid development of the Internet,network security and data privacy are increasingly valued.Although classical Network Intrusion Detection System(NIDS)based on Deep Learning(DL)models can provide good detection accuracy,but collecting samples for centralized training brings the huge risk of data privacy leakage.Furthermore,the training of supervised deep learning models requires a large number of labeled samples,which is usually cumbersome.The“black-box”problem also makes the DL models of NIDS untrustworthy.In this paper,we propose a trusted Federated Learning(FL)Traffic IDS method called FL-TIDS to address the above-mentioned problems.In FL-TIDS,we design an unsupervised intrusion detection model based on autoencoders that alleviates the reliance on marked samples.At the same time,we use FL for model training to protect data privacy.In addition,we design an improved SHAP interpretable method based on chi-square test to perform interpretable analysis of the trained model.We conducted several experiments to evaluate the proposed FL-TIDS.We first determine experimentally the structure and the number of neurons of the unsupervised AE model.Secondly,we evaluated the proposed method using the UNSW-NB15 and CICIDS2017 datasets.The exper-imental results show that the unsupervised AE model has better performance than the other 7 intrusion detection models in terms of precision,recall and f1-score.Then,federated learning is used to train the intrusion detection model.The experimental results indicate that the model is more accurate than the local learning model.Finally,we use an improved SHAP explainability method based on Chi-square test to analyze the explainability.The analysis results show that the identification characteristics of the model are consistent with the attack characteristics,and the model is reliable.

Contribution Tracking Feature Selection (CTFS) Based on the Fusion of Sparse Autoencoder and Mutual Information

For data mining tasks on large-scale data,feature selection is a pivotal stage that plays an important role in removing redundant or irrelevant features while improving classifier performance.Traditional wrapper feature selection methodologies typically require extensive model training and evaluation,which cannot deliver desired outcomes within a reasonable computing time.In this paper,an innovative wrapper approach termed Contribution Tracking Feature Selection(CTFS)is proposed for feature selection of large-scale data,which can locate informative features without population-level evolution.In other words,fewer evaluations are needed for CTFS compared to other evolutionary methods.We initially introduce a refined sparse autoencoder to assess the prominence of each feature in the subsequent wrapper method.Subsequently,we utilize an enhanced wrapper feature selection technique that merges Mutual Information(MI)with individual feature contributions.Finally,a fine-tuning contribution tracking mechanism discerns informative features within the optimal feature subset,operating via a dominance accumulation mechanism.Experimental results for multiple classification performance metrics demonstrate that the proposed method effectively yields smaller feature subsets without degrading classification performance in an acceptable runtime compared to state-of-the-art algorithms across most large-scale benchmark datasets.

Abnormal State Detection in Lithium-ion Battery Using Dynamic Frequency Memory and Correlation Attention LSTM Autoencoder

This paper addresses the challenge of identifying abnormal states in Lithium-ion Battery(LiB)time series data.As the energy sector increasingly focuses on integrating distributed energy resources,Virtual Power Plants(VPP)have become a vital new framework for energy management.LiBs are key in this context,owing to their high-efficiency energy storage capabilities essential for VPP operations.However,LiBs are prone to various abnormal states like overcharging,over-discharging,and internal short circuits,which impede power transmission efficiency.Traditional methods for detecting such abnormalities in LiB are too broad and lack precision for the dynamic and irregular nature of LiB data.In response,we introduce an innovative method:a Long Short-Term Memory(LSTM)autoencoder based on Dynamic Frequency Memory and Correlation Attention(DFMCA-LSTM-AE).This unsupervised,end-to-end approach is specifically designed for dynamically monitoring abnormal states in LiB data.The method starts with a Dynamic Frequency Fourier Transform module,which dynamically captures the frequency characteristics of time series data across three scales,incorporating a memory mechanism to reduce overgeneralization of abnormal frequencies.This is followed by integrating LSTM into both the encoder and decoder,enabling the model to effectively encode and decode the temporal relationships in the time series.Empirical tests on a real-world LiB dataset demonstrate that DFMCA-LSTM-AE outperforms existing models,achieving an average Area Under the Curve(AUC)of 90.73%and an F1 score of 83.83%.These results mark significant improvements over existing models,ranging from 2.4%–45.3%for AUC and 1.6%–28.9%for F1 score,showcasing the model’s enhanced accuracy and reliability in detecting abnormal states in LiB data.

Reviewing the SAE Levels of Driving Automation and Research Gaps to Accelerate the Development of a Quantum-Safe CCAM Infrastructure

Based on a review of 28 Horizon Europe-funded CCAM projects, this paper studies the current state of Connected, Cooperative, and Automated Mobility (CCAM) and identifies significant research gaps in taxonomy, cybersecurity, Artificial Intelligence (AI) and 6G research, that hinder the advancement of a future-ready CCAM infrastructure. The research emphasizes the crucial role of infrastructure in achieving autonomous mobility, shifting focus from the current vehicle-centric approach. It critiques the SAE J3016 taxonomy for its lack of emphasis on infrastructure and proposes an updated framework with an automation level dedicated to infrastructure automation. The paper highlights the existential threats posed by Quantum Computers (QC) and AI, stressing the need for quantum-safe cybersecurity measures and an ethical, controllable AI framework proposing a decentralized Collective Artificial Super Intelligence (CASI) framework. Identifying the critical need for a cooperative approach involving Road and Transport Authorities (RTAs) to achieve 100% vehicle connectivity and robust digital infrastructure, the study outlines the European Commission’s Vision 2050 goals, aiming for zero fatalities, zero emissions, and sustainable mobility. The paper concludes by providing recommendations for future research directions to accelerate the development of a comprehensive, secure, and efficient CCAM ecosystem.

A strategy for out-of-roundness damage wheels identification in railway vehicles based on sparse autoencoders

Wayside monitoring is a promising cost-effective alternative to predict damage in the rolling stock. The main goal of this work is to present an unsupervised methodology to identify out-of-roundness(OOR) damage wheels, such as wheel flats and polygonal wheels. This automatic damage identification algorithm is based on the vertical acceleration evaluated on the rails using a virtual wayside monitoring system and involves the application of a two-step procedure. The first step aims to define a confidence boundary by using(healthy) measurements evaluated on the rail constituting a baseline. The second step of the procedure involves classifying damage of predefined scenarios with different levels of severities. The proposed procedure is based on a machine learning methodology and includes the following stages:(1) data collection,(2) damage-sensitive feature extraction from the acquired responses using a neural network model, i.e., the sparse autoencoder(SAE),(3) data fusion based on the Mahalanobis distance, and(4) unsupervised feature classification by implementing outlier and cluster analysis. This procedure considers baseline responses at different speeds and rail irregularities to train the SAE model. Then, the trained SAE is capable to reconstruct test responses(not trained) allowing to compute the accumulative difference between original and reconstructed signals. The results prove the efficiency of the proposed approach in identifying the two most common types of OOR in railway wheels.

Network Intrusion Detection Model Based on Ensemble of Denoising Adversarial Autoencoder

Network security problems bring many imperceptible threats to the integrity of data and the reliability of device services,so proposing a network intrusion detection model with high reliability is of great research significance for network security.Due to the strong generalization of invalid features during training process,it is more difficult for single autoencoder intrusion detection model to obtain effective results.A network intrusion detection model based on the Ensemble of Denoising Adversarial Autoencoder(EDAAE)was proposed,which had higher accuracy and reliability compared to the traditional anomaly detection model.Using the adversarial learning idea of Adversarial Autoencoder(AAE),the discriminator module was added to the original model,and the encoder part was used as the generator.The distribution of the hidden space of the data generated by the encoder matched with the distribution of the original data.The generalization of the model to the invalid features was also reduced to improve the detection accuracy.At the same time,the denoising autoencoder and integrated operation was introduced to prevent overfitting in the adversarial learning process.Experiments on the CICIDS2018 traffic dataset showed that the proposed intrusion detection model achieves an Accuracy of 95.23%,which out performs traditional self-encoders and other existing intrusion detection models methods in terms of overall performance.

SAE1070钢在加热过程中氧化影响因素

SAE1070钢在加热过程中的氧化对其产品质量及加热炉能耗存在较大的影响.结合前人实验结果,提出了SAE1070钢坯氧化层厚度的计算公式,该公式考虑了加热温度、加热时间和氧气体积分数等炉内参数对SAE1070钢坯氧化的影响,还分析了SAE1070钢氧化层的厚度随加热温度、加热时间及氧气体积分数的变化.结果表明:当炉内烟气中氧气体积分数为3%时,SAE1070钢坯形成的氧化层厚度是炉内烟气中氧气体积分数为1%时的1.3倍.设计及实际控制加热炉时,可根据上述研究结果优化其结构及加热过程的控制策略.

Advancing Autoencoder Architectures for Enhanced Anomaly Detection in Multivariate Industrial Time Series

In the context of rapid digitization in industrial environments,how effective are advanced unsupervised learning models,particularly hybrid autoencoder models,at detecting anomalies in industrial control system(ICS)datasets?This study is crucial because it addresses the challenge of identifying rare and complex anomalous patterns in the vast amounts of time series data generated by Internet of Things(IoT)devices,which can significantly improve the reliability and safety of these systems.In this paper,we propose a hybrid autoencoder model,called ConvBiLSTMAE,which combines convolutional neural network(CNN)and bidirectional long short-term memory(BiLSTM)to more effectively train complex temporal data patterns in anomaly detection.On the hardware-in-the-loopbased extended industrial control system dataset,the ConvBiLSTM-AE model demonstrated remarkable anomaly detection performance,achieving F1 scores of 0.78 and 0.41 for the first and second datasets,respectively.The results suggest that hybrid autoencoder models are not only viable,but potentially superior alternatives for unsupervised anomaly detection in complex industrial systems,offering a promising approach to improving their reliability and safety.

Generalized autoencoder-based fault detection method for traction systems with performance degradation

Fault diagnosis of traction systems is important for the safety operation of high-speed trains.Long-term operation of the trains will degrade the performance of systems,which decreases the fault detection accuracy.To solve this problem,this paper proposes a fault detection method developed by a Generalized Autoencoder(GAE)for systems with performance degradation.The advantage of this method is that it can accurately detect faults when the traction system of high-speed trains is affected by performance degradation.Regardless of the probability distribution,it can handle any data,and the GAE has extremely high sensitivity in anomaly detection.Finally,the effectiveness of this method is verified through the Traction Drive Control System(TDCS)platform.At different performance degradation levels,our method’s experimental results are superior to traditional methods.

Bridge damage identification based on convolutional autoencoders and extreme gradient boosting trees

To enhance the accuracy and efficiency of bridge damage identification,a novel data-driven damage identification method was proposed.First,convolutional autoencoder(CAE)was used to extract key features from the acceleration signal of the bridge structure through data reconstruction.The extreme gradient boosting tree(XGBoost)was then used to perform analysis on the feature data to achieve damage detection with high accuracy and high performance.The proposed method was applied in a numerical simulation study on a three-span continuous girder and further validated experimentally on a scaled model of a cable-stayed bridge.The numerical simulation results show that the identification errors remain within 2.9%for six single-damage cases and within 3.1%for four double-damage cases.The experimental validation results demonstrate that when the tension in a single cable of the cable-stayed bridge decreases by 20%,the method accurately identifies damage at different cable locations using only sensors installed on the main girder,achieving identification accuracies above 95.8%in all cases.The proposed method shows high identification accuracy and generalization ability across various damage scenarios.

淬回火与等温淬火对SAE6150钢组织和力学性能的影响

对SAE6150钢进行淬回火和等温淬火热处理试验,分析了不同回火温度和等温温度对其组织和性能的影响。结果表明,SAE6150钢的回火组织主要由回火马氏体和残余奥氏体组成。随回火温度的升高,回火马氏体板条状形态逐渐消失,渗碳体和合金碳化物尺寸增大,数量增多。经等温淬火后,组织以贝氏体为主,随等温温度的升高,铁素体形态由细针状变为羽毛状。力学性能方面,随回火温度的升高,SAE6150钢的硬度、屈服强度和抗拉强度减小,断面收缩率和伸长率增大。对比等温淬火和回火SAE6150钢的力学性能后发现,在断面收缩率和伸长率相近的情况下,低温等温淬火试样表现出更高的硬度和强度。

A Time Series Intrusion Detection Method Based on SSAE,TCN and Bi-LSTM

In the fast-evolving landscape of digital networks,the incidence of network intrusions has escalated alarmingly.Simultaneously,the crucial role of time series data in intrusion detection remains largely underappreciated,with most systems failing to capture the time-bound nuances of network traffic.This leads to compromised detection accuracy and overlooked temporal patterns.Addressing this gap,we introduce a novel SSAE-TCN-BiLSTM(STL)model that integrates time series analysis,significantly enhancing detection capabilities.Our approach reduces feature dimensionalitywith a Stacked Sparse Autoencoder(SSAE)and extracts temporally relevant features through a Temporal Convolutional Network(TCN)and Bidirectional Long Short-term Memory Network(Bi-LSTM).By meticulously adjusting time steps,we underscore the significance of temporal data in bolstering detection accuracy.On the UNSW-NB15 dataset,ourmodel achieved an F1-score of 99.49%,Accuracy of 99.43%,Precision of 99.38%,Recall of 99.60%,and an inference time of 4.24 s.For the CICDS2017 dataset,we recorded an F1-score of 99.53%,Accuracy of 99.62%,Precision of 99.27%,Recall of 99.79%,and an inference time of 5.72 s.These findings not only confirm the STL model’s superior performance but also its operational efficiency,underpinning its significance in real-world cybersecurity scenarios where rapid response is paramount.Our contribution represents a significant advance in cybersecurity,proposing a model that excels in accuracy and adaptability to the dynamic nature of network traffic,setting a new benchmark for intrusion detection systems.

热处理对差速器齿轮用SAE4320H钢组织及性能的影响

SAE4320H往往用于耐冲击耐磨损的高端制造业零件,热轧状态或退火状态进行锻造,其热处理相关的研究较少。为探究不同热处理下组织转变和力学性能的变化,将一炼钢通过BOF-LF-VD-CC工艺生产的差速器齿轮用SAE4320H钢采用水淬、油淬、空冷和炉冷的热处理工艺进行冷却并测定力学性能。试验结果表明:淬火对齿轮用SAE4320H钢的屈服强度和抗拉强度有着明显的提升,其中水淬的屈服强度和抗拉强度最高,但塑性最差。925℃正火-880℃淬火-180℃回火后,水淬得到回火马氏体组织,油淬得到回火马氏体+回火贝氏体+少量铁素体组织,空冷得到P+B+F组织,炉冷得到P+F组织。