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Using Multi-Scale Convolution Fusion and Memory-Augmented Adversarial Autoencoder to Detect Diverse Anomalies in Multivariate Time Series

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Tsinghua science and technology 2025年第1期30卷 234-246页

作者： Zefei Ning Hao Miao Zhuolun Jiang Li Wang College of Computer Science and Technology(College of Data Science) Taiyuan University of TechnologyJinzhong 030600China

Time series anomaly detection is an important task in many applications,and deep learning based time series anomaly detection has made great ***,due to complex device interactions,time series exhibit diverse abnormal signal shapes,subtle anomalies,and imbalanced abnormal instances,which make anomaly detection in time series still a *** and analysis of multivariate time series can help uncover their intrinsic spatio-temporal characteristics,and contribute to the discovery of complex and subtle *** this paper,we propose a novel approach named Multi-scale Convolution Fusion and Memory-augmented Adversarial AutoEncoder(MCFMAAE)for multivariate time series anomaly *** is an encoder-decoder-based framework with four main ***-scale convolution fusion module fuses multi-sensor signals and captures various scales of temporal ***-attention-based encoder adopts the multi-head attention mechanism for sequence modeling to capture global context *** module is introduced to explore the internal structure of normal samples,capturing it into the latent space,and thus remembering the typical ***,the decoder is used to reconstruct the signals,and then a process is coming to calculate the anomaly ***,an additional discriminator is added to the model,which enhances the representation ability of autoencoder and avoids *** on public datasets demonstrate that MCFMAAE improves the performance compared to other state-of-the-art methods,which provides an effective solution for multivariate time series anomaly detection.

关键词： multivariate time series anomaly detection AutoEncoder(AE) multi-scale fusion

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Deeply‐Recursive Attention Network for video steganography

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CAAI Transactions on Intelligence technology 2023年第4期8卷 1507-1523页

作者： Jiabao Cui Liangli Zheng Yunlong Yu Yining Lin Huajian Ni Xin Xu Zhongfei Zhang College of Computer Science and Technology Zhejiang UniversityHangzhouChina School of Software Technology Zhejiang UniversityNingboChina College of Information Science and Electronic Engineering Zhejiang UniversityHangzhouChina Shanghai SUPREMIND Technology Co. Ltd.ShanghaiChina College of Intelligence Science and Technology National University of Defense TechnologyChangshaChina Department of Computer Science Binghamton UniversityBinghamtonNew YorkUSA

Video steganography plays an important role in secret communication that conceals a secret video in a cover video by perturbing the value of pixels in the cover *** is the first and foremost requirement of any steganographic *** by the fact that human eyes perceive pixel perturbation differently in different video areas,a novel effective and efficient Deeply‐Recursive Attention Network(DRANet)for video steganography to find suitable areas for information hiding via modelling spatio‐temporal attention is *** DRANet mainly contains two important components,a Non‐Local Self‐Attention(NLSA)block and a Non‐Local Co‐Attention(NLCA)***,the NLSA block can select the cover frame areas which are suitable for hiding by computing the correlations among inter‐and intra‐cover *** NLCA block aims to effectively produce the enhanced representations of the secret frames to enhance the robustness of the model and alleviate the influence of different areas in the secret ***,the DRANet reduces the model parameters by performing similar operations on the different frames within an input video *** results show the proposed DRANet achieves better performance with fewer parameters than the state‐of‐the‐art competitors.

关键词： data privacy video processing

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Autoencoder-based conditional optimal transport generative adversarial network for medical image generation

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Visual Informatics 2024年第1期8卷 15-25页

作者： Jun Wang Bohan Lei Liya Ding Xiaoyin Xu Xianfeng Gu Min Zhang School of Software Technology Zhejiang UniversityNingboZhejiangChina College of Computer Science&Technology Zhejiang UniversityHangzhouZhejiangChina Department of Pathology Sir Run Run Shaw HospitalZhejiang University School of MedicineHangzhouZhejiangChina College of Biomedical Engineering&Instrument Science Zhejiang UniversityHangzhouZhejiangChina Department of Computer Science State University of New York at Stony BrookStony BrookNYUSA

Medical image generation has recently garnered significant interest among ***,the primary generative models,such as Generative Adversarial Networks(GANs),often encounter challenges during training,including mode *** address these issues,we proposed the AECOT-GAN model(Autoencoder-based Conditional Optimal Transport Generative Adversarial Network)for the generation of medical images belonging to specific *** training process of our model comprises three fundamental *** training process of our model encompasses three fundamental ***,we employ an autoencoder model to obtain a low-dimensional manifold representation of real ***,we apply extended semi-discrete optimal transport to map Gaussian noise distribution to the latent space distribution and obtain corresponding labels *** procedure leads to the generation of new latent codes with known ***,we integrate a GAN to train the decoder further to generate medical *** evaluate the performance of the AE-COT-GAN model,we conducted experiments on two medical image datasets,namely DermaMNIST and *** model’s performance was compared with state-of-the-art generative *** show that the AE-COT-GAN model had excellent performance in generating medical ***,it effectively addressed the common issues associated with traditional GANs.

关键词： Medical image generation Mode collapse Mode mixing Optimal transport Generative adversarial networks

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An Interactive Collaborative Creation System for Shadow Puppets Based on Smooth Generative Adversarial Networks

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computers, Materials & Continua 2024年第6期79卷 4107-4126页

作者： Cheng Yang Miaojia Lou Xiaoyu Chen Zixuan Ren Department of Industrial Design Hangzhou City UniversityHangzhou310000China College of Computer Science and Technology Zhejiang UniversityHangzhou310000China

Chinese shadow puppetry has been recognized as a world intangible cultural ***,it faces substantial challenges in its preservation and advancement due to the intricate and labor-intensive nature of crafting shadow *** ensure the inheritance and development of this cultural heritage,it is imperative to enable traditional art to flourish in the digital *** paper presents an Interactive Collaborative Creation System for shadow puppets,designed to facilitate the creation of high-quality shadow puppet images with greater *** system comprises four key functions:Image contour extraction,intelligent reference recommendation,generation network,and color adjustment,all aimed at assisting users in various aspects of the creative process,including drawing,inspiration,and content ***,we propose an enhanced algorithm called Smooth Generative Adversarial Networks(SmoothGAN),which exhibits more stable gradient training and a greater capacity for generating high-resolution shadow puppet ***,we have built a new dataset comprising high-quality shadow puppet images to train the shadow puppet generation *** qualitative and quantitative experimental results demonstrate that SmoothGAN significantly improves the quality of image generation,while our system efficiently assists users in creating high-quality shadow puppet images,with a SUS scale score of *** study provides a valuable theoretical and practical reference for the digital creation of shadow puppet art.

关键词： Shadow puppets deep learning image generation co-create

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On hierarchical clustering-based identification of PWA model with model structure selection and application to automotive actuators for HiL simulation

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Soft Computing 2025年第2期29卷 543-558页

作者： Ren, Zhenxing College of Computer Science and Technology and College of Data Science Taiyuan University of Technology Shanxi Jinzhong China

The application of the electronic control unit (ECU) motivates dynamic models with high precision to simulate mechatronic systems for various analysis and design tasks like hardware-in-the-loop (HiL) simulation. Unlike traditional physical models which are established based on the research experience or the mechanics mechanism, in this study, a novel data-driven modeling approach is presented based on the piecewise affine (PWA) identification method. In this work, the highly nonlinear dynamic of automotive actuators is well approximated by the PWA model. To obtain experimental data that can accurately reflect the characteristics of actuators, a test bench was first built. On this basis, the PWA identification of automotive is composed of the data clustering, the model structure selection, and the model parameter estimation. The proposed clustering method improves the widely used balanced iterative reducing and clustering using hierarchies (BIRCH) by introducing a refinement phase for handling clusters with arbitrary shapes. The model structure selection and the parameter estimation are jointly solved by using the optimization method. The presented method is demonstrated with an academic example and an automotive throttle, and the results show that the proposed method can achieve a high model quality, which means that the normalized root mean squared error (NRMSE) is 0.03 and the absolute maximal prediction error can reach 2.43°. Compared to other models like a physical model or a fuzzy model, the improvement using the proposed model can be up to 50%. Thus, the quality of the proposed PWA model is sufficient for HiL simulation. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Hardware-in-the-loop simulation

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Furanocoumarin profiles and inhibitory effects on cytochrome P450 activity of whole citrus fruit

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Food Quality and Safety 2024年第4期8卷 684-694页

作者： Yujing Sun Yihua Wang Le Zhang Xingqian Ye Jingtong Guo College of Food Science and Technology Zhejiang University of TechnologyHangzhouChina Department of Food Science and Nutrition School of Biosystems Engineering and Food ScienceZhejiang UniversityHangzhouChina

Furanocoumarins in citrus fruit can cause adverse drug interactions;however,there are few reports on furanocoumarins and drug interactions in common edible citrus cultivars except *** ultra-performance liquid chromatography(UPLC)-based method for ten furanocoumarins in citrus fruit was established,and the effects on cytochrome P450 activity were *** were more abundant(2-43 times)in the peel than in the *** pulp of Xiyou,Hongyou,and Navel orange exhibited P450 inhibition,with 50% maximal inhibitory concentration(ICso)values of 0.63,0.67,and 1.02 mg/mL,*** peel of all the varieties except Huyou and Satsuma exhibited P450 ***,Baiyou,Huangjinyou,Hongyou,Ponkan,and Navel orange had IC_(50)values of 0.33,0.76,0.38,0.35,0.43,and 0.37 mg/mL,*** findings indicate that,except for Xiyou,Hongyou,and Navel orange,the consumption of the pulp of popular Chinese varieties of citrus fruit has a low risk of drug interactions,and the use of citrus products including peel may carry a significant risk of adverse drug interactions.

关键词： Citrus fruit pulp peel furanocoumarins cytochrome P450

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Detecting Ethereum Ponzi Scheme Based on Hybrid Sampling for Smart Contract

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computers, Materials & Continua 2025年第2期82卷 3111-3130页

作者： Yuanjun Qu Xiameng Si Haiyan Kang Hanlin Zhou College of Computer Science Beijing Information Science and Technology UniversityBeijing100192China

With the widespread use of blockchain technology for smart contracts and decentralized applications on the Ethereum platform, the blockchain has become a cornerstone of trust in the modern financial system. However, its anonymity has provided new ways for Ponzi schemes to commit fraud, posing significant risks to investors. Current research still has some limitations, for example, Ponzi schemes are difficult to detect in the early stages of smart contract deployment, and data imbalance is not considered. In addition, there is room for improving the detection accuracy. To address the above issues, this paper proposes LT-SPSD (LSTM-Transformer smart Ponzi schemes detection), which is a Ponzi scheme detection method that combines Long Short-Term Memory (LSTM) and Transformer considering the time-series transaction information of smart contracts as well as the global information. Based on the verified smart contract addresses, account features, and code features are extracted to construct a feature dataset, and the SMOTE-Tomek algorithm is used to deal with the imbalanced data classification problem. By comparing our method with the other four typical detection methods in the experiment, the LT-SPSD method shows significant performance improvement in precision, recall, and F1-score. The results of the experiment confirm the efficacy of the model, which has some application value in Ethereum Ponzi scheme smart contract detection.

关键词： Blockchain smart contract detection Ponzi scheme long short-term memory hybrid sampling

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Telegraph Flux Noise Induced Beating Ramsey Fringe in Transmon Qubits

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Chinese Physics Letters 2025年第4期42卷 23-37页

作者： Zhi-Hao Wu Ling-Xiao Lei Xin-Fang Zhang Shi-Chuan Xue Shun Hu Cong Li Xiang Fu Ping-Xing Chen Kai Lu Ming-Tang Deng Jun-Jie Wu College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China College of Science National University of Defense TechnologyChangsha 410073China

Ramsey oscillations typically exhibit an exponential decay envelope due to environmental noise. However,recent experiments have observed nonmonotonic Ramsey fringes characterized by beating patterns, which deviate from the standard behavior. These beating patterns have primarily been attributed to charge-noise *** this paper, we have experimentally observed Ramsey fringe with beating pattern for transmon qubits, and traced the origin to electric instruments induced flux noise.

关键词： ramsey fringes electric instruments induced flux noise ramsey oscillations ramsey fringe transmon qubits beating patterns beating pattern telegraph flux noise

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Braille Character Segmentation Algorithm Based on Gaussian Diffusion

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computers, Materials & Continua 2024年第4期79卷 1481-1496页

作者： Zezheng Meng Zefeng Cai Jie Feng Hanjie Ma Haixiang Zhang Shaohua Li School of Computer Science and Technology Zhejiang Sci-Tech UniversityHangzhou310018China

Optical braille recognition methods typically employ existing target detection models or segmentation modelsfor the direct detection and recognition of braille characters in original braille images. However, these methodsneed improvement in accuracy and generalizability, especially in densely dotted braille image environments. Thispaper presents a two-stage braille recognition framework. The first stage is a braille dot detection algorithmbased on Gaussian diffusion, targeting Gaussian heatmaps generated by the convex dots in braille images. Thisis applied to the detection of convex dots in double-sided braille, achieving high accuracy in determining thecentral coordinates of the braille convex dots. The second stage involves constructing a braille grid using traditionalpost-processing algorithms to recognize braille character information. Experimental results demonstrate that thisframework exhibits strong robustness and effectiveness in detecting braille dots and recognizing braille charactersin complex double-sided braille image datasets. The framework achieved an F1 score of 99.89% for Braille dotdetection and 99.78% for Braille character recognition. Compared to the highest accuracy in existing methods,these represent improvements of 0.08% and 0.02%, respectively.

关键词： computer vision image recognition optical braille recognition

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Position-Aware and Subgraph Enhanced Dynamic Graph Contrastive Learning on Discrete-Time Dynamic Graph

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computers, Materials & Continua 2024年第11期81卷 2895-2909页

作者： Jian Feng Tian Liu Cailing Du College of Computer Science&Technology Xi’an University of Science and TechnologyXi’an710054China

Unsupervised learning methods such as graph contrastive learning have been used for dynamic graph represen-tation learning to eliminate the dependence of ***,existing studies neglect positional information when learning discrete snapshots,resulting in insufficient network topology *** the same time,due to the lack of appropriate data augmentation methods,it is difficult to capture the evolving patterns of the network *** address the above problems,a position-aware and subgraph enhanced dynamic graph contrastive learning method is proposed for discrete-time dynamic ***,the global snapshot is built based on the historical snapshots to express the stable pattern of the dynamic graph,and the random walk is used to obtain the position representation by learning the positional information of the ***,a new data augmentation method is carried out from the perspectives of short-term changes and long-term stable structures of dynamic ***,subgraph sampling based on snapshots and global snapshots is used to obtain two structural augmentation views,and node structures and evolving patterns are learned by combining graph neural network,gated recurrent unit,and attention ***,the quality of node representation is improved by combining the contrastive learning between different structural augmentation views and between the two representations of structure and *** results on four real datasets show that the performance of the proposed method is better than the existing unsupervised methods,and it is more competitive than the supervised learning method under a semi-supervised setting.

关键词： Dynamic graph representation learning graph contrastive learning structure representation position representation evolving pattern

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