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Conference on Laser 3D Manufacturing XI

作者： Ahar, Ayyoub Vandecasteele, Mathieu Booth, Brian G. De Grave, Kurt Verhees, Dries Philips, Wilfried Bey-Temsamani, Abdellatif Flanders Make B-3920 Lommel Belgium Univ Ghent Imec TELIN IPI B-3000 Leuven Belgium

ISBN: (纸本)9781510670136;9781510670129

Laser powder bed fusion is at the forefront of manufacturing metallic objects, particularly those with complex geometries or those produced in limited quantities. However, this 3D printing method is susceptible to several printing defects due to the complexities of using a high-power laser with ultra-fast actuation. Accurate online print defect detection is therefore in high demand, and this defect detection must maintain a low computational profile to enable low-latency process intervention. In this work, we propose a low-latency LPBF defect detection algorithm based on fusion of images from high-speed cameras in the visible and short-wave infrared (SWIR) spectrum ranges. First, we design an experiment to print an object while both imposing porosity defects on the print, and recording the laser's melt pool with the high-speed cameras. We then train variational autoencoders on images from both cameras to extract and fuse two sets of corresponding features. The melt pool recordings are then annotated with pore densities extracted from the printed object's CT scan. These annotations are then used to train and evaluate the ability of a fast neural network model to predict the occurrence of porosity from the fused features. We compare the prediction performance of our sensor fused model with models trained on image features from each camera separately. We observe that the SWIR imaging is sensitive to keyhole porosity while the visible-range optical camera is sensitive to lack-of-fusion porosity. By fusing features from both cameras, we are able to accurately predict both pore types, thus outperforming both single camera systems.

关键词： LPBF In-situ Monitoring Lack-of-Fusion Keyhole Porosity Additive Manufacturing SWIR variational autoencoder Sensor Fusion

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Multiple treatment effect estimation for business analytics using observational data

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COGENT ENGINEERING 2024年第1期11卷

作者： Tsuboi, Yuki Sakai, Yuta Shimizu, Ryotaro Goto, Masayuki Waseda Univ Grad Sch Creat Sci & Engn Tokyo Japan ZOZO Res Tokyo Japan Waseda Univ Sch Creat Sci & Engn Tokyo Japan

To correctly evaluate the effects of treatments, conducting randomized controlled trials (RCTs) is a reasonable approach. However, because it is generally difficult to implement RCTs for all treatments, methods to estimate the treatment effects using observational data have been actively studied and used in various decision-making processes. Observational data accumulated in business activities and elsewhere contains the results of various previously implemented treatments, and correctly estimating the effects of any given treatment without separating the impacts of other treatments is challenging. Against this background, this paper proposes a method to estimate the effects of multiple treatments of various types while considering various causal relationships. Specifically, the proposal is a variational inference method that estimates the effect of multiple treatments using four latent factors estimated from observations, making assumptions that are independent of the type and number of treatments. The proposed method makes it possible to appropriately estimate the effects of measures even in situations with complex causal relationships. In addition, in situations where measures with continuous parameters are being implemented, it is possible to estimate the effects of measures that have not been implemented in the past by treating the content of the measures as a continuous variable.

关键词： E-commerce marketing causal inference variational autoencoder machine learning business analytics treatment effect

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EASE-DR: Enhanced Sentence Embeddings for Dense Retrieval 47

EASE-DR: Enhanced Sentence Embeddings for Dense Retrieval

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47th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

作者： Zhou, Xixi Gao, Yang Jie, Xin Cai, Xiaoxu Bu, Jiajun Wang, Haishuai Zhejiang Univ Hangzhou Peoples R China

ISBN: (纸本)9798400704314

Recent neural information retrieval models using dense text representations generated by pre-trained models commonly face two issues. First, a pre-trained model (e.g., BERT) usually truncates a long document before giving its representation, which may cause the loss of some important semantic information. Second, although pre-training models like BERT have been widely used in generating sentence embeddings, a substantial body of literature has shown that the pre-training models often represent sentence embeddings in a homogeneous and narrow space, known as the problem of representation anisotropy, which hurts the quality of dense vector retrieval. In this paper, we split the query and the document in information retrieval into two sets of natural sentences and generate their sentence embeddings with BERT, the most popular pre-trained model. Before aggregating the sentence embeddings to get the entire embedding representations of the input query and document, to alleviate the usual representation degeneration problem of sentence embeddings from BERT, we sample the variational auto-encoder's latent space distribution to obtain isotropic sentence embeddings and utilize supervised contrastive learning to uniform the distribution of these sentence embeddings in the representation space. Our proposed model undergoes training optimization for both the query and the document in the abovementioned aspects. Our model performs well in evaluating three extensively researched neural information retrieval datasets.

关键词： Information Retrieval variational autoencoder Supervised Contrastive Learning

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Sound-based sleep assessment with controllable subject-dependent embedding using variational Domain Adversarial Neural Network

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INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS 2023年 1-11页

作者： Fukui, Ken-ichi Ishimaru, Shunya Kato, Takafumi Numao, Masayuki Osaka Univ Inst Sci & Ind Res SANKEN 8-1 Mihogaoka Osaka Ibaraki 5670047 Japan Osaka Univ Grad Sch Informat Sci & Technol 1-5 Yamadaoka Suita Osaka 5650871 Japan Osaka Univ Grad Sch Dent 1-8 Yamadaoka Suita Osaka 5650871 Japan

Sleep quality assessment as an indicator of daily health care plays an important role in our physiological and mental activity. Sound during sleep contains rich information on biological activities, such as body movement, snoring, and sleep bruxism. However, sound features differ depending on individual and environmental differences. In order to develop a wide-rage applicable daily sleep assessment, this paper utilizes deep learning to ease individual and environmental differences of sound features. Firstly, by variational Domain Adversarial Neural Network (VDANN) encodes sound events into latent representation, simultaneously eliminates subject-dependent features. Then, sleep pattern in the obtained latent space is trained by Long Short-Term Memory (LSTM) with associated sleep assessment of one night. We performed age group estimation from normal sleep as an objective indicator of sleep comparing to their age group. The experiment with more than 100 subjects showed that VDANN is able to extract subject independent features, and the proposed method outperforms the conventional method for age group estimation from sleep sound even for new subjects. In addition, our model is able to personalize by controlling subject-dependent embedding when after data accumulation of the subject.

关键词： Sleep quality Sound variational autoencoder Domain adaptation

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Enhancing Facial Reconstruction Using Graph Attention Networks

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IEEE ACCESS 2023年 11卷 136680-136691页

作者： Lee, Hyeong Geun Hur, Jee Sic Yoon, Yeo Chan Kim, Soo Kyun Jeju Natl Univ Dept Comp Engn Jeju 63243 South Korea Jeju Natl Univ Dept Artificial Intelligence Jeju 63243 South Korea

Traditionally, research on three-dimensional (3D) facial reconstruction has focused heavily on methods that use 3D Morphable Models (3DMMs) based on principal component analysis (PCA). Because such methods are linear, they are robust to external noise. The PCA method has limitations when restoring faces that deviate from the training data distribution, particularly when recovering fine details. By contrast, restoration methods utilizing Graph Convolution Networks (GCN) offer the advantages of non-linearity and direct regression of vertex coordinates and colors. However, GCN-based approaches can be prone to overfitting, making them less stable. This study presents a face restoration approach that aims to regress the vertex coordinates and colors of a 3D face model directly from a single wilds 2D facial image. This method demonstrates greater stability and higher accuracy compared to conventional techniques. In addition, Graph Attention Networks (GAT) enhance the restoration performance while separating the networks responsible for facial shape and color, reducing noise caused by interference between different data attributes. Through experiments, we demonstrate the most optimized network structures and training methods and demonstrate improved performance compared to existing approaches.

关键词： Graph convolution network graph attention network convolution neural network variational autoencoder facial reconstruction

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Real-time suspicious detection framework for financial data streams

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International Journal of Information Technology (Singapore) 2025年 1-17页

作者： Gadimov, Elshan Birihanu, Ermiyas Department of Data Science and Engineering Faculty of Informatics Eötvös Loránd University Budapest Hungary

Money laundering hides illegal money’s origin by making it seem legal. Detecting suspicious activity quickly in financial data is key to stopping fraud and money laundering. Real-time detection is popular approach for its speed and efficiently detecting illegal activities in financial institutes system. However, handling massive and distributed data streams have challenges in achieving real-time efficiency and effectiveness. Therefore proposing and developing data stream framework is needed to handle these challenges efficiently. The main goal of this study is to propose real-time suspicious detection framework for financial institutions to effectively combat money laundering. The proposed model comprises two approaches: a distributed computing architecture based on Docker container to enhance flexibility, migration capabilities, and customization, and a suspicious detection module employing the autoencoder method. To determine whether there is any suspicious activity in the system, the proposed model uses the reconstruction error. The reconstruction error is the difference between the original input data and the data reconstructed by the proposed model. To evaluate the proposed model, we used real-world data from a financial institution and synthetic data generated from the real-world data. The study demonstrates the better performance of the proposed real-time detection framework compared to traditional methods in identifying anomalous transactions. It also explores the importance and limitations of using both real-world and generated data. Our code is publicly available: https://***/Ermiyas21/Real-Time-Suspicious-Detection-Framework-for-financial-data. © The Author(s) 2025.

关键词： Anti-money laundering (AML) autoencoders Generative adversarial Network Real-time variational autoencoder

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CVML-Pose: Convolutional VAE Based Multi-Level Network for Object 3D Pose Estimation

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IEEE ACCESS 2023年 11卷 13830-13845页

作者： Zhao, Jianyu Sanderson, Edward Matuszewski, Bogdan J. J. Univ Cent Lancashire Comp Vis & Machine Learning CVML Grp Preston PR1 2HE England

Most vision-based 3D pose estimation approaches typically rely on knowledge of object's 3D model, depth measurements, and often require time-consuming iterative refinement to improve accuracy. However, these can be seen as limiting factors for broader real-life applications. The main motivation for this paper is to address these limitations. To solve this, a novel Convolutional variational Auto-Encoder based Multi-Level Network for object 3D pose estimation (CVML-Pose) method is proposed. Unlike most other methods, the proposed CVML-Pose implicitly learns an object's 3D pose from only RGB images encoded in its latent space without knowing the object's 3D model, depth information, or performing a post-refinement. CVML-Pose consists of two main modules: (i) CVML-AE representing convolutional variational autoencoder, whose role is to extract features from RGB images, (ii) Multi-Layer Perceptron and K-Nearest Neighbor regressors mapping the latent variables to object 3D pose including, respectively, rotation and translation. The proposed CVML-Pose has been evaluated on the LineMod and LineMod-Occlusion benchmark datasets. It has been shown to outperform other methods based on latent representations and achieves comparable results to the state-of-the-art, but without use of a 3D model or depth measurements. Utilizing the t-Distributed Stochastic Neighbor Embedding algorithm, the CVML-Pose latent space is shown to successfully represent objects' category and topology. This opens up a prospect of integrated estimation of pose and other attributes (possibly also including surface finish or shape variations), which, with real-time processing due to the absence of iterative refinement, can facilitate various robotic applications. Code available: https://***/JZhao12/CVML-Pose.

关键词： 3D pose estimation deep learning variational autoencoder synthetic data

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Anomaly detection on household appliances based on variational autoencoders

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SUSTAINABLE ENERGY GRIDS & NETWORKS 2022年 32卷

作者： Castangia, Marco Sappa, Riccardo Girmay, Awet Abraha Camarda, Christian Macii, Enrico Patti, Edoardo Politecn Torino Dept Control & Comp Engn I-10129 Turin Italy Politecn Torino Interuniv Dept Reg & Urban Studies & Planning I-10129 Turin Italy

Electrical anomalies in residential buildings represent a serious problem that can unpredictably change the power profiles of end-users, causing a sub-optimal energy distribution. In addition, electrical faults can cause unnoticed energy wastages and higher energy bills, or even severe damages for properties and people in the most critical cases. In this paper, we introduce a novel anomaly detection method for detecting electrical faults in household appliances based on the analysis of their power signatures with unsupervised deep learning techniques. For this purpose, we trained a variational autoencoder to reconstruct the power signatures of three commonly used devices: the dishwasher, the washing machine and the dryer. For each use case, we injected several randomly generated anomalies that simulate to our best realistic electrical faults in these devices. To demonstrate the effectiveness of our method, we compared the accuracy of the variational autoencoder with the classification performance of a one-class support vector machine (OC-SVM) trained with two manual features: the energy consumption and the duration of the appliance's operations. The variational autoencoder showed higher classification accuracy with respect to the OC-SVM, reporting an F1-score greater than 90% in all the use cases. Most importantly, the results demonstrate that deep anomaly detection methods outperform traditional algorithms based on handcrafted features, allowing to better characterize the set of normal cycles and produce more precise alerts for the monitored devices. (C) 2022 Elsevier Ltd. All rights reserved.

关键词： Smart grids Household appliance Anomaly detection Unsupervised learning variational autoencoder Deep learning

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Searching for protein variants with desired properties using deep generative models

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BMC BIOINFORMATICS 2023年第1期24卷 1-19页

作者： Li, Yan Yao, Yinying Xia, Yu Tang, Mingjing Yunnan Normal Univ Sch Informat Kunming Peoples R China Huazhong Agr Univ Natl Key Lab Crop Genet Improvement Wuhan Peoples R China Huazhong Agr Univ Natl Ctr Plant Gene Res Wuhan Peoples R China Chinese Acad Agr Sci Agr Genom Inst Shenzhen Shenzhen Branch Guangdong Lab Lingnan Modern AgrGenome Anal Lab Shenzhen Peoples R China Yunnan Normal Univ Engn Res Ctr Sustainable Dev & Utilizat Biomass E Minist Educ Kunming Peoples R China Yunnan Normal Univ Sch Life Sci Kunming Peoples R China

BackgroundProtein engineering aims to improve the functional properties of existing proteins to meet people's needs. Current deep learning-based models have captured evolutionary, functional, and biochemical features contained in amino acid sequences. However, the existing generative models need to be improved when capturing the relationship between amino acid sites on longer sequences. At the same time, the distribution of protein sequences in the homologous family has a specific positional relationship in the latent space. We want to use this relationship to search for new variants directly from the vicinity of better-performing *** improve the representation learning ability of the model for longer sequences and the similarity between the generated sequences and the original sequences, we propose a temporal variational autoencoder (T-VAE) model. T-VAE consists of an encoder and a decoder. The encoder expands the receptive field of neurons in the network structure by dilated causal convolution, thereby improving the encoding representation ability of longer sequences. The decoder decodes the sampled data into variants closely resembling the original *** to other models, the person correlation coefficient between the predicted values of protein fitness obtained by T-VAE and the truth values was higher, and the mean absolute deviation was lower. In addition, the T-VAE model has a better representation learning ability for longer sequences when comparing the encoding of protein sequences of different lengths. These results show that our model has more advantages in representation learning for longer sequences. To verify the model's generative effect, we also calculate the sequence identity between the generated data and the input data. The sequence identity obtained by T-VAE improved by 12.9% compared to the baseline model.

关键词： Protein engineering Temporal convolutional network Deep generative model variational autoencoder

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PredLife: Predicting Fine-Grained Future Activity Patterns

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IEEE TRANSACTIONS ON BIG DATA 2023年第6期9卷 1658-1669页

作者： Li, Wenjing Shi, Xiaodan Huang, Dou Shen, Xudong Chen, Jinyu Kobayashi, Hill Hiroki Zhang, Haoran Song, Xuan Shibasaki, Ryosuke Univ Tokyo Ctr Spatial Informat Sci Kashiwa Chiba 2770882 Japan Univ Tokyo Informat Technol Ctr Kashiwa Chiba 2770882 Japan Locat Mind Inc Tokyo 1010048 Japan Peking Univ Sch Urban Planning & Design Shenzhen 518055 Guangdong Peoples R China Southern Univ Sci & Technol Southern Univ Sci & Technol Univ Tokyo Joint Res Dept Comp & Engn Shenzhen 518055 Guangdong Peoples R China Malardalens Univ Sch Business Soc & Technol S-72220 Vasteras Sweden Univ Tokyo Interfac Initiat Informat Studies Tokyo 1138654 Japan Reitaku Univ Kashiwa Chiba 2770065 Japan

Activity pattern prediction is a critical part of urban computing, urban planning, intelligent transportation, and so on. Based on a dataset with more than 10 million GPS trajectory records collected by mobile sensors, this research proposed a CNN-BiLSTM-VAE-ATT-based encoder-decoder model for fine-grained individual activity sequence prediction. The model combines the long-term and short-term dependencies crosswise and also considers randomness, diversity, and uncertainty of individual activity patterns. The proposed results show higher accuracy compared to the ten baselines. The model can generate high diversity results while approximating the original activity patterns distribution. Moreover, the model also has interpretability in revealing the time dependency importance of the activity pattern prediction.

关键词： Activity pattern prediction Human mobility Big GPS data variational autoencoder LSTM

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