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Indoor environment data time-series reconstruction using autoencoder neural networks

BUILDING AND ENVIRONMENT 2021年 191卷 107623-107623页

作者： Liguori, Antonio Markovic, Romana Dam, Thi Thu Ha Frisch, Jerome van Treeck, Christoph Causone, Francesco Rhein Westfal TH Aachen E3D Inst Energy Efficiency & Sustainable Bldg Mathieustr 30 D-52074 Aachen Germany Karlsruhe Inst Technol Bldg Sci Grp Englerstr 7 D-76131 Karlsruhe Germany Politecn Milan Dept Energy Via Lambruschini 4 I-20156 Milan Italy

As the number of installed meters in buildings increases, there is a growing number of data time-series that could be used to develop data-driven models to support and optimize building operation. However, building data sets are often characterized by errors and missing values, which are considered, by the recent research, among the main limiting factors on the performance of the proposed models. Motivated by the need to address the problem of missing data in building operation, this work presents a data-driven approach to fill these gaps. In this study, three different autoencoder neural networks are trained to reconstruct missing short-term indoor environment data time-series in a data set collected in an office building in Aachen, Germany. This consisted of a four year-long monitoring campaign in and between the years 2014 and 2017, of 84 different rooms. The models are applicable for different time-series obtained from room automation, such as indoor air temperature, relative humidity and CO2 data streams. The results prove that the proposed methods outperform classic numerical approaches and they result in reconstructing the corresponding variables with average RMSEs of 0.42 degrees C, 1.30 % and 78.41 ppm, respectively.

关键词： Indoor environment data time-series Machine learning Data analytics autoencoder Neural networks

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FEEDBACK TURBO autoencoder

FEEDBACK TURBO AUTOENCODER

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jiang, Yihan Kim, Hyeji Asnani, Himanshu Oh, Sewoong Kalman, Sreeram Viswanath, Pramod Univ Washington Seattle WA 98195 USA Samsung AI Res Mountain View CA USA Tata Inst Fundamental Res Mumbai Maharashtra India Univ Illinois Champaign IL USA

ISBN: (纸本)9781509066315

Designing channel codes is one of the core research areas for modern communication systems. Canonical channel codes asymptotically achieve near-capacity performance under large block length regime for additive white gaussian noise channels. However, this achieved success does not generalize to many channels. Channels with output feedback, proposed by Shannon, is one of such channels where practical codes have been unknown for several decades. Recently it has been demonstrated that deep learning based code outperforms the state-of-the-art codes for channels with output feedback. While the success is promising and inspiring, there are a few major challenges that need to be addressed. Firstly, the channel assumes a feedback with a unit step delay, which is not very practical. Second is the lack of generalization to larger block lengths. In this work, we propose Feedback Auto Turbo Encoder (FTAE) which harmoniously combines interleaver and iterative decoding with CNN architectures and demonstrate the blocklength gain and improved performance in the block feedback setting.

关键词： Information Theory Channel Coding Deep Learning Feedback Channel autoencoder

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An autoencoder Based Model for Detecting Fraudulent Credit Card Transaction

An Autoencoder Based Model for Detecting Fraudulent Credit C...

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International Conference on Computational Intelligence and Data Science (ICCIDS)

作者： Misra, Sumit Thakur, Soumyadeep Ghosh, Manosij Saha, Sanjoy Kumar Jadavpur Univ Dept Comp Sci & Engn Kolkata India

With the rapid growth in credit card based financial transactions, it has become important to identify the fraudulent ones. In this work, a two stage model is proposed to identify such fraudulent transactions. To make a fraud detection system trustworthy, both miss in fraud detection and false alarms are to minimized Understanding and learning the complex associations among the transaction attributes is a major problem. To address this issue, at the first stage of the proposed model an autoencoder is used to transform the transaction attributes to a feature vector of lower dimension. The feature vector thus obtained is used as the input to a classifier at the second stage. Experiment is done on a benchmarked dataset. It is observed that in terms of F1-measure, proposed two stage model performs better than the systems relying on only classifier and other autoencoder based systems. (C) 2020 The Authors. Published by Elsevier B.V.

关键词： Fraud Detection Financial Transaction autoencoder

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Laughter synthesis: A comparison between Variational autoencoder and autoencoder 5

Laughter synthesis: A comparison between Variational autoenc...

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5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

作者： Mansouri, Nadia Lachiri, Zied Tunis El Manar Univ Signal Image & Informat Technol Res Lab LR SITI Natl Engn Sch Tunis ENIT Tunis Tunisia

ISBN: (数字)9781728175133

ISBN: (纸本)9781728175133

Laughter is one of the most famous non verbal sounds that human produce since birth, it conveys messages about our emotional state. These characteristics make it an important sound that should be studied in order to improve the human-machine interactions. In this paper we investigate the audio laughter generation process from its acoustic features. This suggested process is considered as an analysis-transformation-synthesis benchmark based on unsupervised dimensionality reduction techniques: The standard autoencoder (AE) and the variational autoencoder (VAE). Therefore, the laughter synthesis methodology consists of transforming the extracted high-dimensional log magnitude spectrogram into a low-dimensional latent vector. This latent vector contains the most valuable information used to reconstruct a synthetic magnitude spectrogram that will be passed through a specific vocoder to generate the laughter waveform. We systematically, exploit the VAE to create new sound (speech-laugh) based on the interpolation process. To evaluate the performance of these models two evaluation metrics were conducted: objective and subjective evaluations.

关键词： Laughter audio generation variational autoencoder autoencoder interpolation objective and subjective evaluation

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Multi-view Subspace Adaptive Learning via autoencoder and Attention 27th

Multi-view Subspace Adaptive Learning via Autoencoder and At...

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27th International Conference on Neural Information Processing

作者： Liu, Jian-wei Xie, Hao-jie Lu, Run-kun Luo, Xiong-lin China Univ Petr Coll Informat Sci & Engn Dept Automat Beijing Campus Cup Beijing 102249 Peoples R China

ISBN: (纸本)9783030638320;9783030638337

Multi-view learning can cover all features of data samples more comprehensively, so multi-view learning has attracted widespread attention. Traditional subspace clustering methods, such as sparse subspace clustering (SSC) and low-ranking subspace clustering (LRSC), cluster the affinity matrix for a single view, thus ignoring the problem of fusion between views. In our article, we propose a new Multiview Subspace Adaptive Learning based on Attention and autoencoder (MSALAA). This method combines a deep autoencoder and a method for aligning the self-representations of various views in Multi-view Low-Rank Sparse Subspace Clustering (MLRSSC), which can not only increase the capability to non-linearity fitting, but also can meets the principles of consistency and complementarity of multi-view learning. We empirically observe significant improvement over existing baseline methods on six real-life datasets.

关键词： Multi-view learning Subspace self-representation autoencoder Attention Spectral clustering

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A SYNDROME-BASED autoencoder FOR POINT CLOUD GEOMETRY COMPRESSION

A SYNDROME-BASED AUTOENCODER FOR POINT CLOUD GEOMETRY COMPRE...

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IEEE International Conference on Image Processing (ICIP)

作者： Milani, Simone Univ Padua Dept Informat Engn Padua Italy

ISBN: (纸本)9781728163956

Point cloud compression has been extensively-investigated in the past twenty years to find effective solutions that reduce the coded bit stream and permits adapting the coded bit rate to different scenarios. Despite these efforts, predictive strategies have so far performed poorly because of the low correlation level of the input data and the flexibility requirements, which imply minimizing the decoding dependences. The current paper proposes a convolutional autoencoder that applies the principles of Distributed Source Coding (DSC) to the deep representations of voxelized point cloud geometry data. The hidden variables, called syndromes, enable reconstructing the coded point cloud geometry from different reference data. The proposed strategy overcomes the state-of-the-art solutions in terms of flexibility and rate-distortion performance.

关键词： point cloud compression geometry distributed source coding autoencoder UNet

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Anomaly detection during milk processing by autoencoder neural network based on near-infrared spectroscopy

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JOURNAL OF FOOD ENGINEERING 2021年 299卷 110510-110510页

作者： Vasafi, Pegah Sadeghi Paquet-Durand, Olivier Brettschneider, Kim Hinrichs, Joerg Hitzmann, Bernd Univ Hohenheim Dept Proc Analyt & Cereal Sci 150i Garbenstr 23 D-70599 Stuttgart Germany

Anomaly detection during milk processing (such as changes in fat or temperature, added water or cleaning solution) can assure a satisfactory final product quality, including compositional and hygienic characteristics, as well as adulteration with water. The use of near-infrared (NIR) spectroscopy for change detection in complex dairy matrix is discussed. The autoencoder neural network plays fundamental role in anomaly detection. To evaluate this capability, the raw spectra obtained from NIR as well as first derivative and combination of both were analysed. An autoencoder was trained by 1.5% fat UHT-milk (measured at 5 degrees C) and applied to detect possible changes happening during the milk processing. The trained autoencoder using first derivative spectra was capable to detect 5% added water and 9% cleaning solution in the milk. Also, with the combination spectra, it was able to recognize a difference of 0.1% in fat concentration. In addition, both procedures were able to detect different production methods (specific procedure of suppliers such as homogenization level or pressure) and difference of 10 degrees C in the temperature. It can be concluded, that using an autoencoder neural network in combination with near-infrared spectroscopy is a reliable method to monitor the milk processing. By doing so, abnormal changes can be detected early, controlling the process becomes easier and the quality and safety of the product is guaranteed.

关键词： Anomaly detection autoencoder Near-infrared spectroscopy

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Biased autoencoder for collaborative filtering with temporal signals

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 186卷 115775-115775页

作者： Dou, Runliang Arslan, Oguzhan Zhang, Ce Tianjin Univ Coll Management & Econ Tianjin 300072 Peoples R China

Recommendation systems are used in various types of online platforms and in e-commerce. Collaborative filtering (CF) is one of the most popular approaches for recommendation systems and has been widely studied in academia. In recent years, several models based on neural networks that can discover nonlinear relationships have been proposed and compared to traditional CF models. The results showed that they performed better in terms of their prediction accuracy. However, these models do not consider user bias and item bias together, and they do not include temporal signals. This paper proposes a biased autoencoder model (Biased AutoRec) for CF, which is built on the well-known AutoRec CF approach. Several approaches are also proposed to integrate temporal signals into the Biased AutoRec model to merge the power of nonlinearity and temporal signals. Experiments on several public datasets showed that the new models outperformed the AutoRec model, which outperformed the prediction accuracy of previous state-of-the-art CF models (i.e., biased matrix factorization, RBM-CF, LLORMA).

关键词： Collaborative filtering AutoRec Temporal dynamics Bias autoencoder

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Printed Circuit Board Defect Detection Using Deep Learning via A Skip-Connected Convolutional autoencoder

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SENSORS 2021年第15期21卷 4968-4968页

作者： Kim, Jungsuk Ko, Jungbeom Choi, Hojong Kim, Hyunchul Gachon Univ Dept Biomed Engn 191 Hambakmoe Ro Incheon 2199 South Korea Kumoh Natl Inst Technol Dept Medial IT Convergence Engn 350-27 Gum Daero Gumi 39253 South Korea Univ Calif Berkeley Sch Informat 102 South Hall 4600 Berkeley CA 94704 USA

As technology evolves, more components are integrated into printed circuit boards (PCBs) and the PCB layout increases. Because small defects on signal trace can cause significant damage to the system, PCB surface inspection is one of the most important quality control processes. Owing to the limitations of manual inspection, significant efforts have been made to automate the inspection by utilizing high resolution CCD or CMOS sensors. Despite the advanced sensor technology, setting the pass/fail criteria based on small failure samples has always been challenging in traditional machine vision approaches. To overcome these problems, we propose an advanced PCB inspection system based on a skip-connected convolutional autoencoder. The deep autoencoder model was trained to decode the original non-defect images from the defect images. The decoded images were then compared with the input image to identify the defect location. To overcome the small and imbalanced dataset in the early manufacturing stage, we applied appropriate image augmentation to improve the model training performance. The experimental results reveal that a simple unsupervised autoencoder model delivers promising performance, with a detection rate of up to 98% and a false pass rate below 1.7% for the test data, containing 3900 defect and non-defect images.

关键词： deep learning autoencoder detect detection PCB defeat detection printed circuit board manufacturing

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Representation learning with collaborative autoencoder for personalized recommendation

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 186卷 115825-115825页

作者： Zhu, Yi Wu, Xindong Qiang, Jipeng Yuan, Yunhao Li, Yun Yangzhou Univ Sch Informat Engn Yangzhou Jiangsu Peoples R China Hefei Univ Technol Minist Educ Key Lab Knowledge Engn Big Data Hefei Peoples R China Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei Peoples R China Mininglamp Acad Sci Mininglamp Technol Beijing Peoples R China

In the past decades, recommendation systems have provided lots of valuable personalized suggestions for the users to address the problem of information over-loaded. Collaborative Filtering (CF) is one of the most commonly applied and successful recommendation approaches, which refers to using the preferences of groups with similar interests to recommend information to other users. Recently, in addition to the traditional matrix factorization techniques, deep learning methods have been proposed to learn more abstract and higher-level representations for recommendation. However, most previous deep recommendation methods learn the higher level feature representations of users and items through an identical model structure, which ignores the different characteristics of the user-based and item-based data. In addition, the rating matrix is usually sparse which may result in a significant degradation of recommendation performance. To address these problems, we propose a representation learning method with Collaborative autoencoder for Personalized Recommendation (CAPR for short). In this method, user-based and item-based feature representations are learned by two different autoencoders for capturing different features of the data. Meanwhile, items' attributions are combined into the feature representations with semi-autoencoder for alleviating the sparsity problem. Extensive experimental results confirm the effectiveness of our proposed method compared to other state-of-the-art matrix factorization methods and deep recommendation methods.

关键词： autoencoder Personalized recommendation Semi-autoencoder Representation learning Collaborative filtering

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