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Transfer learning for estimating occupancy and recognizing activities in smart buildings

BUILDING AND ENVIRONMENT 2022年 217卷 1-22页

作者： Dridi, Jawher Amayri, Manar Bouguila, Nizar Concordia Univ Concordia Inst Informat Syst Engn CIISE Montreal PQ Canada Grenoble Inst Technol G SCOP Lab Grenoble France

Activities Recognition (AR) and Occupancy Estimation (OE) are vital to many smart systems that work on providing good services in smart buildings. Many applications, such as energy management need information like activities and occupancy to provide good assistance. Most of the previous research about AR and OE focused on applying supervised machine learning methods. Researchers train a model and evaluate it using data collected from the same environment (Domain). A model trained in a specific domain will not generalize well in other domains. Creating a trained model to every environment is not feasible due to the lack of data. Collecting sufficient data can be time consuming and infeasible in some cases. Computational power can be a challenge for researchers by increasing the training time due to the lack of the required computing resources. Using traditional machine learning methods, the obtained performance may be unsatisfactory, and can not lead to optimal solutions. For all these reasons, we need a solution that helps us overcome the stated problem and obtain models with acceptable results. In this work, we present and discuss different transfer learning methods that help us transfer knowledge from a source domain to a target domain. The goal is to reuse as much as possible information from the source domain to enhance the performance of the model at the target domain. This type of approaches will solve the problems mentioned before such as the lack of data and will provide us with good results due to the use of knowledge from multiple source domains. We tested five Transfer learning (TL) approaches: a principal component analysis (PCA)-like method that creates a transformation like the PCA transformation and apply it to the data to create new common domain, a PCA based method that creates common domain using PCA, a PCA-SMOTE method that balances the data and creates common domain, a basic method based on a simple matching between similar features from source a

关键词： Activities recognition Occupancy estimation Transfer learning Smart buildings PCA SMOTE sparse coding

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sparse coding-BASED TOPIC MODEL FOR REMOTE SENSING IMAGE SEGMENTATION

SPARSE CODING-BASED TOPIC MODEL FOR REMOTE SENSING IMAGE SEG...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Shi, Jun Jiang, Zhiguo Feng, Hao Ma, Yibing Beijing Univ Aeronaut & Astronaut Image Proc Ctr Sch Astronaut Beijing 100191 Peoples R China Beijing Key Lab Digital Media Beijing 100191 Peoples R China

ISBN: (纸本)9781479911141

Land cover segmentation can be viewed as topic assignment that the pixels are grouped into homogeneous regions according to different semantic topics in topic model. In this paper, we propose a novel topic model based on sparse coding for segmenting different kinds of land covers. Different from conventional topic models which generally assume each local feature descriptor is related to only one visual word of the codebook, our method utilizes sparse coding to characterize the potential correlation between the descriptor and multiple words. Therefore each descriptor can be represented by a small set of words. Furthermore, in this paper probabilistic Latent Semantic Analysis (pLSA) is applied to learn the latent relation among word, topic and document due to its simplicity and low computational cost. Experimental results on remote sensing image segmentation demonstrate the excellent superiority of our method over k-means clustering and conventional pLSA model.

关键词： remote sensing sparse coding pLSA land cover segmentation

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sparse coding-BASED CO-SALIENT OBJECT DETECTION WITH APPLICATION TO VIDEO ABSTRACTION 12

SPARSE CODING-BASED CO-SALIENT OBJECT DETECTION WITH APPLICA...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Chen, Duan-Yu Lin, Chuan-Yu Yang, Nien-Tzu Yu, Jen-Yu Yuan Ze Univ Dept Elect Engn Taoyuan Taiwan Ind Technol Res Inst Info & Comm Res Labs Hsinchu Taiwan

ISBN: (纸本)9781479902606

Automatic abstraction is important for video retrieval and browsing in a semantic manner. Detecting the focus of interest such as co-occurring objects in video frames automatically can benefit the tedious manual labelling process. However, detecting the co-occurring objects that is visually salient in video sequences is a challenging task. In this paper, in order to detect co-salient video objects efficiently, we first use the preattentive scheme to locate the co-salient regions in video frames and then measure the similarity between salient regions based on KL-divergence. In addition, to update preattentive patch set for co-salient objects, sparse coding is used for dictionary learning and further discrimination among co-salient objects. Finally a set of primary co-salient objects can be found across all video frames using our proposed filtering scheme. As a result, a video sequence can be automatically parsed based on the detection of co-occurring video objects. Our experiment results show that the proposed co-salient video objects modeling achieves high precision value about 85% and reveals its robustness and feasibility in videos.

关键词： Spatiotemporal analysis sparse coding Video abstraction

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A deep learning framework for adaptive compressive sensing of high-speed train vibration responses

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STRUCTURAL CONTROL & HEALTH MONITORING 2022年第8期29卷 e2979-e2979页

作者： Chen, Si-Xin Ni, Yi-Qing Zhou, Lu Hong Kong Polytech Univ Hung Hom Dept Civil & Environm Engn Hong Kong Peoples R China Natl Rail Transit Electrificat & Automat Engn Tec Hong Kong Branch Hong Kong Peoples R China

Onboard monitoring plays an important role in real-time condition assessment of rail systems. However, the data amount is typically tremendous due to the high sampling rate needed and long traveling distance, especially for vibration data collected from high-speed trains (HSTs). As for fault diagnosis of mechanical systems, compressive sensing (CS) has been increasingly adopted to reduce the data amount. In comparison to rotary bearings and bolted joints in machinery that operate in relatively steady working environments, HSTs run in an open and varying environment throughout the traveling mileage, and the data amount is normally much larger, making it tricky to directly apply the classical CS methods. This study aims to bridge the gap by investigating the sparsity of HST vibration signals and CS approaches. Considering the lack of sparsity and long reconstruction time, we propose an efficient adaptive CS approach for dynamic responses of HSTs. More specifically, we unroll the iterative soft thresholding algorithm (ISTA) in a deep learning (DL) framework and configure it into a data reconstruction machine. Compared to the conventional CS methods, our approach exhibits two advantages: (i) The dictionary learning and signal reconstruction are integrated into one neural network and can be conducted in an end-to-end manner;(ii) the process is highly efficient since encapsulating ISTA in a DL framework can naturally leverage the capability of GPU. The proposed approach is validated using data collected from an in-service HST, and results show that our approach achieves superior reconstruction performance over fixed bases and redundant dictionaries.

关键词： adaptive compressive sensing deep learning high-speed train onboard monitoring sparse coding

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An Object-Oriented Visual Saliency Detection Framework Based on sparse coding Representations

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2013年第12期23卷 2009-2021页

作者： Han, Junwei He, Sheng Qian, Xiaoliang Wang, Dongyang Guo, Lei Liu, Tianming Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China Univ Georgia Dept Comp Sci Athens GA 30602 USA

Saliency detection aims at quantitatively predicting attended locations in an image. It may mimic the selection mechanism of the human vision system, which processes a small subset of a massive amount of visual input while the redundant information is ignored. Motivated by the biological evidence that the receptive fields of simple cells in V1 of the vision system are similar to sparse codes learned from natural images, this paper proposes a novel framework for saliency detection by using image sparse coding representations as features. Unlike many previous approaches dedicated to examining the local or global contrast of each individual location, this paper develops a probabilistic computational algorithm by integrating objectness likelihood with appearance rarity. In the proposed framework, image sparse coding representations are yielded through learning on a large amount of eye-fixation patches from an eye-tracking dataset. The objectness likelihood is measured by three generic cues called compactness, continuity, and center bias. The appearance rarity is inferred by using a Gaussian mixture model. The proposed paper can serve as a basis for many techniques such as image/video segmentation, retrieval, retargeting, and compression. Extensive evaluations on benchmark databases and comparisons with a number of up-to-date algorithms demonstrate its effectiveness.

关键词： Gaussian mixture models independent component analysis saliency sparse coding visual attention

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sparse coding and Gaussian Modeling of Coefficients Average for Background Subtraction

Sparse Coding and Gaussian Modeling of Coefficients Average ...

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8th International Symposium on Image and Signal Processing and Analysis (ISPA)

作者： David, Ciprian Gui, Vasile Politehn Univ Timisoara Elect & Telecommun Fac Dept Commun Timisoara Romania

ISBN: (纸本)9789531841948;9789531841870

A sparse coding based approach for background subtraction is proposed in this paper. The background model is composed from a K-SVD dictionary and a set of mean coefficients associated to each image location. Due to the use of sparse coding, our approach has a regional character. The recovered value of a pixel is obtained by reconstructing the surrounding image patch. In order to avoid problems introduced by difficult situations like dynamic backgrounds, an additional Gaussian model on the average of the coefficients set is used. A foreground confidence image results from this modeling. Two threshold will output the final background-foreground binary map. A first threshold on the confidence image selects possible foreground candidates. For these candidates we consider the reconstruction error, represented by the absolute difference between the reconstructed frame and the estimated background. A second threshold on these candidates offers the final discrimination. Our approach is tested against state-of-the-art methods. It is proved to perform better both in terms of visual comparison and quantitative measures.

关键词： background subtraction sparse coding Gaussian model learned dictionary

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Fast human action classification and VOI localization with enhanced sparse coding

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2013年第2期24卷 127-136页

作者： Lu, Shiyang Zhang, Jian Wang, Zhiyong Feng, David Dagan Univ Sydney Sydney NSW 2006 Australia Univ Technol Sydney Sydney NSW 2007 Australia

sparse coding which encodes the natural visual signal into a sparse space for visual codebook generation and feature quantization, has been successfully utilized for many image classification applications. However, it has been seldom explored for many video analysis tasks. In particular, the increased complexity in characterizing the visual patterns of diverse human actions with both the spatial and temporal variations imposes more challenges to the conventional sparse coding scheme. In this paper, we propose an enhanced sparse coding scheme through learning discriminative dictionary and optimizing the local pooling strategy. Localizing when and where a specific action happens in realistic videos is another challenging task. By utilizing the sparse coding based representations of human actions, this paper further presents a novel coarse-to-fine framework to localize the Volumes of Interest (VOIs) for the actions. Firstly, local visual features are transformed into the sparse signal domain through our enhanced sparse coding scheme. Secondly, in order to avoid exhaustive scan of entire videos for the VOI localization, we extend the Spatial Pyramid Matching into temporal domain, namely Spatial Temporal Pyramid Matching, to obtain the VOI candidates. Finally, a multi-level branch-and-bound approach is developed to refine the VOI candidates. The proposed framework is also able to avoid prohibitive computations in local similarity matching (e.g., nearest neighbors voting). Experimental results on both two popular benchmark datasets (KTH and YouTube UCF) and the widely used localization dataset (MSR) demonstrate that our approach reduces computational cost significantly while maintaining comparable classification accuracy to that of the state-of-the-art methods. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Human action classification Localization sparse coding Volume of Interest (VOI)

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QMVO-SCDL: A new regression model for fMRI pain decoding using quantum-behaved sparse dictionary learning

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KNOWLEDGE-BASED SYSTEMS 2022年 252卷

作者： Anter, Ahmed M. Elnashar, Hany S. Zhang, Zhiguo Harbin Inst Technol Shenzhen Sch Comp Sci & Technol Shenzhen 518055 Peoples R China Beni Suef Univ Fac Comp & Artificial Intelligence Benisuef 62511 Egypt

The exponential growth of functional magnetic reasoning imaging (fMRI) data offers a great opportu-nity for basic and clinical research to explore functional brain activity. Nonetheless, due to the lack of effective and efficient tools for handling high-dimensional and complex fMRI data, this potential has still not been fully explored. A critical issue to be addressed is to identify clinically relevant features from high dimensional fMRI data in a fast and accurate manner. To address this problem, a new quantum-behaved multiverse optimization (QMVO) approach is proposed for fMRI dimensionality reduction and a new prediction approach based on L1-norm sparse coding and adaptive dictionary learning (SCDL) is developed to decode the whole brain fMRI data. QMVO is designed to enhance local search ability, avoid premature convergence, and increase population diversity among individuals. Further, to efficiently re-estimate parameters of the prediction model with new available data, a new SCDL sparse coding is proposed, which requires no training and needs minimal parameters to tune. The proposed QMVO-SCDL model is applied to a pain-evoked fMRI dataset to decode the pain perception level. Results show that QMVO-SCDL can decode pain levels and identify predictive fMRI patterns with high accuracy, high convergence speed, and short consumption time. Moreover, the performance of the proposed model outperforms different recent ML techniques. Therefore, the proposed model has a great promise to be a powerful tool for fMRI decoding. (C) 2022 Elsevier B.V. All rights reserved.

关键词： sparse coding Dictionary learning L1-norm basis pursuit Quantum computing Multiverse optimization fMRI

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Neural associative memories and sparse coding

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NEURAL NETWORKS 2013年 37卷 163-169页

作者： Palm, Guenther Univ Ulm Inst Neural Informat Proc D-89069 Ulm Germany

The theoretical, practical and technical development of neural associative memories during the last 40 years is described. The importance of sparse coding of associative memory patterns is pointed out. The use of associative memory networks for large scale brain modeling is also mentioned. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Associative memory sparse coding Cortical networks

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Interpretable Neural Network via Algorithm Unrolling for Mechanical Fault Diagnosis

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2022年 71卷 1页

作者： An, Botao Wang, Shibin Zhao, Zhibin Qin, Fuhua Yan, Ruqiang Chen, Xuefeng Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China

Artificial neural network (ANN) has achieved great success in mechanical fault diagnosis and has been widely used. However, traditional ANN is still opaque in terms of interpretability, making it difficult for users to understand and trust the diagnosis results. This article proposes an interpretable neural network to provide high-performance and credible mechanical fault diagnosis results. The proposed network is mainly generated by unrolling the nested iterative soft thresholding algorithm (NISTA) for a sparse coding model and it is named NISTA-Net. Therefore, the network architecture of NISTA-Net has a clear theoretical basis, and users know how it is designed. Additionally, we propose a visualization method for NISTA-Net to examine whether the network has learned meaningful features. This method helps users better understand how NISTA-Net performs classifications. These two aspects of transparency/interpretability allow NISTA-Net to be more credible when applied for mechanical fault diagnosis. We carried out simulations and two experiments of fault diagnosis to verify the performance of NISTA-Net. The results reveal that NISTA-Net can well extract the fault features of the concerned bearings and gears. As a consequence, it achieves the best performance compared with other advanced networks. Given the success of NISTA-Net, a systematic approach is finally summarized to help design interpretable fault diagnosis networks, aiming to inspire more related research.

关键词： Feature extraction Fault diagnosis Encoding Vibrations Convolutional codes Classification algorithms Ad hoc networks Algorithm unrolling interpretable neural network mechanical fault diagnosis prognostic and health management (PHM) sparse coding

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