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A privacy-preserving vehicle trajectory clustering framework

Frontiers of Information Technology & Electronic Engineering 2024年第7期25卷 988-1002页

作者： Ran TIAN Pulun GAO Yanxing LIU College of Computer Science&Engineering Northwest Normal UniversityLanzhou 730070China

As one of the essential tools for spatio‒temporal traffic data mining,vehicle trajectory clustering is widely used to mine the behavior patterns of ***,uploading original vehicle trajectory data to the server and clustering carry the risk of privacy ***,one of the current challenges is determining how to perform vehicle trajectory clustering while protecting user *** propose a privacy-preserving vehicle trajectory clustering framework and construct a vehicle trajectory clustering model(IKV)based on the variational autoencoder(VAE)and an improved K-means *** the framework,the client calculates the hidden variables of the vehicle trajectory and uploads the variables to the server;the server uses the hidden variables for clustering analysis and delivers the analysis results to the *** IKV’workflow is as follows:first,we train the VAE with historical vehicle trajectory data(when VAE’s decoder can approximate the original data,the encoder is deployed to the edge computing device);second,the edge device transmits the hidden variables to the server;finally,clustering is performed using improved K-means,which prevents the leakage of the vehicle *** is compared to numerous clustering methods on three *** the nine performance comparison experiments,IKV achieves optimal or sub-optimal performance in six of the ***,in the nine sensitivity analysis experiments,IKV not only demonstrates significant stability in seven experiments but also shows good robustness to hyperparameter *** results validate that the framework proposed in this paper is not only suitable for privacy-conscious production environments,such as carpooling tasks,but also adapts to clustering tasks of different magnitudes due to the low sensitivity to the number of cluster centers.

关键词： Privacy protection variational autoencoder Improved K-means Vehicle trajectory clustering

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Anomaly Detection for Injection Molding Machines Using Probabilistic Deep Learning

Anomaly Detection for Injection Molding Machines Using Proba...

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作者： Ketonen, Vili Aalto University

学位级别：硕士

In manufacturing industries, monitoring the complicated devices often necessitates automated methods that can leverage the multivariate time series data produced by the machines. However, analyzing this data can be challenging due to varying noise levels in the data and possible nonlinear relations between the process variables, requiring appropriate tools to deal with such properties. This thesis proposes a deep learning-based approach to detect anomalies and interpret their root causes from multivariate time series data, which can be applied in a near real-time setting. The proposed approach extends an existing model from the literature, which employs a variational autoencoder architecture and recurrent neural networks to capture both stochasticity and temporal relations of the data. The anomaly detection and root cause interpretation performance of the proposed method is compared against five baseline algorithms previously proposed in the literature using real-world data collected from plastic injection molding machines and artificially generated multivariate time series data. The results of this thesis show that the proposed method performs well on the evaluated multivariate time series datasets, mostly outperforming the baseline methods. Additionally, the approach had the best performance among the selected methods on providing root cause interpretation of the detected anomalies. The experiments conducted in this thesis suggest that deep learning-based algorithms are beneficial for anomaly detection in scenarios where the problem is too complicated for traditional methods, and enough training data is available. However, the amount of real-world injection molding machine data used in the experiments is relatively small, and therefore further experiments should be performed with larger datasets to obtain more generalizable results.

关键词： anomaly detection multivariate time series machine learning variational autoencoder recurrent neural network injection molding

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Shear behavior of SiCf/SiC interface under the thermo-chemo-mechanical influence and machine-learning-based interfacial microstructure design

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MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING 2023年第5期31卷 055005-055005页

作者： Chen, Shaohua Xu, Nuo Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn Nanjing 210016 Peoples R China Harbin Inst Technol Sch Mat Sci & Engn Harbin 150001 Peoples R China

The mechanical behavior of composite interface can be influenced by multiple factors, including the morphological roughness, the structure of coating interphase, and the temperature. Here, high-throughput molecular dynamics (MD) simulations are carried out to investigate the entangled effects of these factors on the shear stiffness G, the friction coefficient mu, the debonding strain is an element of(d) and stress T-d, of SiCf/SiC interface. We find that G is maximized by small roughness and high temperature for the optimal chemical bonding effect;mu and.d are maximized by large roughness and low temperature, taking advantage of the mechanical interlocking effect while avoiding cusp softening;T-d demonstrates two local maxima which result from the competition between chemical bonding and mechanical interlocking. Provided the MD simulation results, a variational autoencoder (VAE) model is proposed to design the microstructure of SiCf/SiC interface for desired shear properties. According to the validations, the VAE-predicted interfacial configuration demonstrates highly similar shear properties to the reference one, justifying its potential for the microstructure design of composite interface. The results of this work can be employed to facilitate the development of SiCf/SiC composite by taking advantage of the synergistic effects of multiple designable factors.

关键词： interface interphase interfacial microstructures shear behavior molecular dynamics variational autoencoder

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Crystal structure prediction in a continuous representative space

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COMPUTATIONAL MATERIALS SCIENCE 2021年 194卷 110436-110436页

作者： Lee, In-Ho Chang, K. J. Korea Res Inst Stand & Sci Daejeon 34113 South Korea Korea Adv Inst Sci & Technol Dept Phys Daejeon 34141 South Korea

Here we report a method of finding multiple crystal structures similar to the known crystal structures of materials on database through machine learning. The radial distribution function is used to represent the general characteristics of the known crystal structures, and then the variational autoencoder is employed to generate a set of representative crystal replicas defined in a two-dimensional optimal continuous space. For given chemical compositions and crystal volume, we generate random crystal structures using constraints for crystal symmetry and atomic positions and directly compare their radial distribution functions with those of the known and/or replicated crystals. For selected crystal structures, energy minimization is subsequently performed through firstprinciples electronic structure calculations. This approach enables us to predict a set of new low-energy crystal structures using only the information on the radial distribution functions of the known structures.

关键词： Crystal structure Radial distribution function variational autoencoder

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Design subspace learning: Structural design space exploration using performance-conditioned generative modeling

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AUTOMATION IN CONSTRUCTION 2021年 127卷 103664-103664页

作者： Danhaive, Renaud Mueller, Caitlin T. MIT Bldg Technol Program Dept Architecture 77 Massachusetts Ave Cambridge MA 02139 USA

Designers increasingly rely on parametric design studies to explore and improve structural concepts based on quantifiable metrics, generally either by generating design variations manually or using optimization methods. Unfortunately, both of these approaches have important shortcomings: effectively searching a large design space manually is infeasible, and design optimization overlooks qualitative aspects important in architectural and structural design. There is a need for methods that take advantage of computing intelligence to augment a designer's creativity while guiding-not forcing-their search for better-performing solutions. This research addresses this need by integrating conditional variational autoencoders in a performance-driven design exploration framework. First, a sampling algorithm generates a dataset of meaningful design options from an unwieldy design space. Second, a performance-conditioned variational autoencoder with a low-dimensional latent space is trained using the collected data. This latent space is intuitive to explore by designers even as it offers a diversity of high-performing design options.

关键词： Deep generative modeling Latent space Latent variable variational autoencoder Design space Computational design

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Machine learning methods for generating high dimensional discrete datasets

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WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY 2022年第2期12卷 e1450-e1450页

作者： Manco, Giuseppe Ritacco, Ettore Rullo, Antonino Sacca, Domenico Serra, Edoardo IICAR CNR Calabria Italy Univ Calabria DIMES Dept I-87036 Calabria Italy Boise State Univ Comp Sci Dept Boise ID 83725 USA

The development of platforms and techniques for emerging Big Data and Machine Learning applications requires the availability of real-life datasets. A possible solution is to synthesize datasets that reflect patterns of real ones using a two-step approach: first, a real dataset X is analyzed to derive relevant patterns Z and, then, to use such patterns for reconstructing a new dataset X ' that preserves the main characteristics of X. This survey explores two possible approaches: (1) Constraint-based generation and (2) probabilistic generative modeling. The former is devised using inverse mining (IFM) techniques, and consists of generating a dataset satisfying given support constraints on the itemsets of an input set, that are typically the frequent ones. By contrast, for the latter approach, recent developments in probabilistic generative modeling (PGM) are explored that model the generation as a sampling process from a parametric distribution, typically encoded as neural network. The two approaches are compared by providing an overview of their instantiations for the case of discrete data and discussing their pros and cons. This article is categorized under: Fundamental Concepts of Data and Knowledge > Big Data Mining Technologies > Machine Learning Algorithmic Development > Structure Discovery

关键词： constraints-based models data generation generative adversarial networks generative models inverse frequent itemset mining synthetic dataset variational autoencoder

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Adaptive map matching based on dynamic word embeddings for indoor positioning

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NEUROCOMPUTING 2023年第1期553卷

作者： Lan, Xinyue Zhang, Lijia Xiao, Zhuoling Yan, Bo Univ Elect Sci & Technol China Sch Informat & Commun Engn 2006 Xiyuan Ave Chengdu 611731 Sichuan Peoples R China UESTC Sch Informat & Commun Engn Chengdu Peoples R China

Map matching has been widely used in various indoor localization technologies. However, conventional map matching technologies based on probabilistic models, such as particle filter (PF), still have a series of limitations, such as underutilization of map information, poor generalization, and relatively low precision. To improve the performance of PF-based map matching technique, this paper proposes MapDem, a novel map matching model fusing dynamic word embeddings and variational autoencoder (VAE) to improve matching performance significantly. The key to our approach is to extract map information using dynamic word embeddings to represent each reachable point on the map as word vectors with allowable oriented trajectory information. The same point has different representations on different trajectories so that MapDem can adaptively learn the contextual information of map for position estimation. Unlike traditional particle filters, MapDem focuses on the learning of particle sets distribution by a statistical model, variational autoencoder (VAE), followed by estimating position with combined current and previous sequence information. Extensive experiments have been conducted with 610 trajectories in three real-world scenarios. Numerical results demonstrate the adaptability of MapDem which works equally well in all three different scenarios, outperforming traditional particle filters by 18% on average.

关键词： Map matching Dynamic word embeddings Particle filter variational autoencoder

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Embedding high-dimensional Bayesian optimization via generative modeling: Parameter personalization of cardiac electrophysiological models

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MEDICAL IMAGE ANALYSIS 2020年 62卷 101670-101670页

作者： Dhamala, Jwala Bajracharya, Pradeep Arevalo, Hermenegild J. Sapp, John L. Horacek, B. Milan Wu, Katherine C. Trayanova, Natalia A. Wang, Linwei Rochester Inst Technol Rochester NY 14623 USA Johns Hopkins Univ Baltimore MD USA Dalhousie Univ Halifax NS Canada

The estimation of patient-specific tissue properties in the form of model parameters is important for personalized physiological models. Because tissue properties are spatially varying across the underlying geometrical model, it presents a significant challenge of high-dimensional (HD) optimization at the presence of limited measurement data. A common solution to reduce the dimension of the parameter space is to explicitly partition the geometrical mesh. In this paper, we present a novel concept that uses a generative variational auto-encoder (VAE) to embed HD Bayesian optimization into a low-dimensional (LD) latent space that represents the generative code of HD parameters. We further utilize VAE-encoded knowledge about the generative code to guide the exploration of the search space. The presented method is applied to estimating tissue excitability in a cardiac electrophysiological model in a range of synthetic and real-data experiments, through which we demonstrate its improved accuracy and substantially reduced computational cost in comparison to existing methods that rely on geometry-based reduction of the HD parameter space. (C) 2020 Elsevier B.V. All rights reserved.

关键词： High-dimensional Bayesian optimization variational autoencoder personalized modeling

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PFEMed: Few-shot medical image classification using prior guided feature enhancement

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PATTERN RECOGNITION 2023年 134卷

作者： Dai, Zhiyong Yi, Jianjun Yan, Lei Xu, Qingwen Hu, Liang Zhang, Qi Li, Jiahui Wang, Guoqiang DeepBlue Acad Sci Shanghai 200240 Peoples R China East China Univ Sci & Technol Sch Mech & Power Engn Shanghai 200237 Peoples R China Wenyao Technol Hainan 571924 Peoples R China ShanghaiTech Univ Sch Informat Sci Technol Shanghai 201210 Peoples R China Tongji Univ Shanghai 201804 Peoples R China Univ Technol Sydney Sydney Australia Univ Shanghai Sci & Technol Shanghai Peoples R China Shanghai Univ Engn Sci Sch Math Phys & Stat Shanghai 201620 Peoples R China

Deep learning-based methods have recently demonstrated outstanding performance on general image classification tasks. As optimization of these methods is dependent on a large amount of labeled data, their application in medical image classification is limited. To address this issue, we propose PFEMed, a novel few-shot classification method for medical images. To extract general and specific features from medical images, this method employs a dual-encoder structure, that is, one encoder with fixed weights pre-trained on public image classification datasets and another encoder trained on the target medical dataset. In addition, we introduce a novel prior-guided variational autoencoder (VAE) module to enhance the robustness of the target feature, which is the concatenation of the general and specific features. Then, we match the target features extracted from both the support and query medical image samples and pre-dict the category attribution of the query examples. Extensive experiments on several publicly available medical image datasets show that our method outperforms current state-of-the-art few-shot methods by a wide margin, particularly outperforming MetaMed on the Pap smear dataset by over 2.63%.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Deep learning Domain adaption Few-shot learning Medical image classification variational autoencoder

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A variational Multi-Scale Error Compensation Network for Single-Pixel Imaging

IEEE PHOTONICS JOURNAL

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IEEE PHOTONICS JOURNAL 2024年第4期16卷 1页

作者： Lin, Jian Yan, Qiurong Zou, Quan Sun, Shida Wei, Zhen Du, Hua Nanchang Univ Sch Informat Engn Nanchang 330031 Peoples R China Northern Lianchuang Commun Co Ltd Doctoral Res & Innovat Ctr Nanchang 330001 Peoples R China Northern Lianchuang Commun Co Ltd Nanchang 330001 Peoples R China

Single-pixel imaging is an advanced computational imaging technique based on compressive sensing that offers higher signal-to-noise ratio and broader application scope compared to traditional imaging techniques. However, conventional reconstruction algorithms suffer from issues such as long processing time and low reconstruction accuracy during the sampling and reconstruction processes. Deep learning-based compressed reconstruction networks can circumvent the complex iterative computations of traditional algorithms and achieve fast, high-quality reconstruction. In this paper, we propose a variational Multi-Scale Error Compensation Network (VMSE) based on variational autoencoders. VMSE designs an error compensation network to enhance the feature representation capability of the sampling reconstruction network. We employ multiple latent variables to generate error features at different scales in the intermediate layers of the error compensation network, compensating the reconstructed image. Additionally, we design a module that simultaneously learns in the spatial and frequency domains, which is used for upsampling and complementing the missing high-frequency information in the frequency domain. On the MNIST dataset, when the sampling rate is 0.025, VMSE achieved higher Peak Signal-to-Noise Ratio(PSNR) and Structural Similarity Index(SSIM) scores, especially with an SSIM score of 0.963, significantly surpassing Reconnet and DR2Net's scores of 0.930 and 0.920, respectively. This was further corroborated by practical experiments, where at low sampling rates, VMSE could reconstruct outlines more clearly compared to TVAL3.

关键词： Single pixel imaging deep learning variational autoencoder imaging reconstruction Single pixel imaging deep learning variational autoencoder imaging reconstruction

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