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Joint load prediction of multiple buildings using multi-task learning with selected-shared-private mechanism

ENERGY AND BUILDINGS 2023年第1期293卷

作者： Xiao, Jiang-Wen Cao, Minghui Fang, Hongliang Wang, Jinsong Wang, Yan-Wu Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Case Western Reserve Univ Case Sch Engn Cleveland OH 44106 USA

Short-term load forecasting for residential buildings is of great significance in ensuring the safe and economic operation of the power grid. However, most existing prediction methods focus solely on the temporal characteristics of individual buildings and ignore the spatial correlation between different buildings in a neighborhood. To tackle this problem and realize joint prediction for multiple buildings, a novel multi-task learning model with a selected-shared-private mechanism is proposed in this paper. Firstly, the temporal and spatial characteristics hidden in electricity consumption patterns are analyzed and represented by auto -correlation and cross-correlation, respectively. Then, a correlation-oriented combination strategy is proposed to build input feature set for the prediction model, and the temporal convolutional network is adopted to extract features. Furthermore, a novel selected-shared-private mechanism is designed for multi-task learning to improve the prediction accuracy, which can selectively utilize information from related tasks while learning private features. The proposed model is compared with other methods on the public dataset, and the results demonstrate that the proposed model can make satisfactory joint predictions for multiple buildings with more than 35% accuracy improvement over the support vector machine.

关键词： Multiple residential buildings Short-term load prediction Multi-task learning Selected-shared-private mechanism

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Iron oxide magnetic nanoparticles based low-field MR thermometry

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NANOTECHNOLOGY 2020年第34期31卷 345101-345101页

作者： Zhang, Yapeng Guo, Silin Zhang, Pu Zhong, Jing Liu, Wenzhong Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China TU Braunschweig Inst Elect Measurement Sci & Fundamental Elect En Braunschweig Germany

This paper reports on a highly accurate approach of magnetic resonance (MR) thermometry using iron oxide magnetic nanoparticles (MNPs) as temperature sensors. An empirical model for the description of the temperature dependentR(2)relaxation rate is proposed by taking into account the temperature sensitivity of the MNP magnetization. The temperature sensitivity of the MNP magnetization (eta) and the temperature sensitivity of theR(2)relaxation rate (kappa) are simulated with the proposed empirical models to investigate their dependence on the magnetic field and the particle size. Simulation results show the existence of optimal magnetic fieldsH(o eta)andH(o kappa)that maximize the temperature sensitivities eta and kappa. Furthermore, simulations and experiments demonstrate that the optimal magnetic fieldH(o eta)(H-o kappa) decreases with increasing the particle size. Experiments on temperature dependentR(2)relaxation rate are performed at different magnetic fields for MNP samples with different iron concentrations. Experimental results show that the proposed MR thermometry using MNPs as temperature sensors allows a temperature estimation accuracy of about 0.05 degrees C. We believe that the achieved approach of highly accurate MR thermometry is of great interest and significance to biomedicine and biology.

关键词： MR thermometry iron oxide magnetic nanoparticles low-field NMR R(2)relaxation rate

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nD-CS: A circularly shifting chaotic map generation method

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CHAOS SOLITONS & FRACTALS 2024年 181卷

作者： Wu, Zihua Zhang, Yinxing Bao, Han Lan, Rushi Hua, Zhongyun Harbin Inst Technol Sch Comp Sci & Technol Shenzhen 518055 Guangdong Peoples R China Changzhou Univ Sch Microelect & Control Engn Changzhou 213164 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Peoples R China

Due to more complex behavior and larger number of control parameters, high -dimensional chaotic map generation methods may provide more satisfactory performance in various practical applications compared to low -dimensional counterparts. Designing n -dimensional (nD) chaotic map generation methods for generating chaotic maps in arbitrary dimensions with desired dynamics is an interesting but challenging task. In this paper, we propose a universal framework called the nD circularly shifting chaotic map generation method (nD-CS), which utilizes existing one-dimensional chaotic maps as seed maps to generate nD chaotic maps with complex and robust behaviors. Theoretical analysis proves that when specific criteria are met by its control parameters, our nD-CS can exhibit complex and robust hyperchaotic behavior in the sense of Lyapunov exponent. To demonstrate the effectiveness of our nD-CS, we first employ it to generate three examples of three-dimensional hyperchaotic maps. The results indicate high-performance indicators of these new chaotic maps. Furthermore, we compare the nD chaotic maps generated by our nD-CS to those produced by existing methods. The results demonstrate that our chaotic maps have a better overall performance.

关键词： Chaotic map generation High-dimensional chaotic map Hyperchaotic behavior Lyapunov exponent

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Protecting Privacy of Users in Brain-Computer Interface Applications

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2019年第8期27卷 1546-1555页

作者： Agarwal, Anisha Dowsley, Rafael McKinney, Nicholas D. Wu, Dongrui Lin, Chin-Teng De Cock, Martine Nascimento, Anderson C. A. Univ Washington Tacoma Sch Engn & Technol Tacoma WA 98402 USA Amazon Seattle WA 98121 USA Aarhus Univ Dept Comp Sci DK-8200 Aarhus Denmark Bar Ilan Univ Dept Comp Sci IL-5290002 Ramat Gan Israel Breakpoint Labs Washington DC 20016 USA Huazhong Univ Sci & Technol Sch Automat Key Lab Minist Educ Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China Univ Technol Sydney Sch Software Ultimo NSW 2007 Australia Univ Ghent Dept Appl Math Comp Sci & Stat B-9000 Ghent Belgium

Machine learning (ML) is revolutionizing research and industry. Many ML applications rely on the use of large amounts of personal data for training and inference. Among the most intimate exploited data sources is electroencephalogram (EEG) data, a kind of data that is so rich with information that application developers can easily gain knowledge beyond the professed scope from unprotected EEG signals, including passwords, ATM PINs, and other intimate data. The challenge we address is how to engage in meaningful ML with EEG data while protecting the privacy of users. Hence, we propose cryptographic protocols based on secure multiparty computation (SMC) to perform linear regression over EEG signals from many users in a fully privacy-preserving(PP) fashion, i.e., such that each individual's EEG signals are not revealed to anyone else. To illustrate the potential of our secure framework, we show how it allows estimating the drowsiness of drivers from their EEG signals as would be possible in the unencrypted case, and at a very reasonable computational cost. Our solution is the first application of commodity-based SMC to EEG data, as well as the largest documented experiment of secret sharing-based SMC in general, namely, with 15 players involved in all the computations.

关键词： Secure multiparty computation cryptography machine learning linear regression driver drowsiness estimation

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Quasi-Ideal Memory System

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IEEE TRANSACTIONS ON CYBERNETICS 2015年第7期45卷 1353-1362页

作者： Sun, Junwei Shen, Yi Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Minist Educ Image Proc & Intelligent Control Key Lab Hubei 430074 Peoples R China Zhengzhou Univ Light Ind Coll Elect & Elect Engn Zhengzhou 450002 Peoples R China

The definition for ideal memory system is so strict that some physical elements cannot exist in the real world. In this paper, an ideal memory system can be extended to generate 15 different kinds of quasi-ideal memory systems, which are included in memory systems as its special cases and are different from ideal memory system. For a system to be a quasi-ideal memory system, it should show three unique fingerprints: 1) the pinched hysteretic loop of a quasi-ideal memory system must be odd symmetrical in the plane;2) the pinched hysteretic loop of a quasi-ideal memory system must be " self-crossing";and 3) the slope of tangent line for the pinched hysteresis loop must be strictly monotone in a given period.

关键词： Pinched hysteresis loop quasi-ideal memory system self-crossing slope of tangent line

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A High-Order Disturbance Observer Based Sliding Mode Velocity control of Mobile Wheeled Inverted Pendulum Systems 12

A High-Order Disturbance Observer Based Sliding Mode Velocit...

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12th World Congress on intelligent control and Automation (WCICA)

作者： Ri, Songhyok Huang, Jian Tao, Chunjing Ri, Myonghyok Ri, Yongsok Han, Daeho Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Peoples R China Univ Sci Dept Control Sci Pyongyang North Korea Huazhong Univ Sci & Technol Sch Automat Key Lab Minist Educ Image Proc & Intelligent Control Wuhan 430074 Peoples R China Natl Res Ctr Rehabil Tech Aids R&D Dept Beijing Peoples R China Kim Chad Univ Technol Dorgan dong Automat Sch Dorgan Dong Pyongyang North Korea Univ Sci Dept Phis Pyongyang North Korea

ISBN: (纸本)9781467384148

In this paper, a novel disturbance observer (DO) for the Mobile Wheeled Inverted Pendulum (MWIP) system is proposed. A choice method of optimal gain matrices is also proposed for a given robust gain, which can improve the estimation precision of the DO. Combining the proposed DO and Sliding Mode control (SMC), a new sliding mode velocity control method is designed for the MWIP system. The convergency of the DO is proved by Lyapunov theorem. And the stability of the closed-loop system is achieved through the appropriate selection of sliding surface coefficients. The effectiveness of all proposed methods is verified by simulation results for the MWIP system.

关键词： Velocity control

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Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kuo Guo Yifan Li Hao Chen Hong-Bin Shen Yang Yang Department of Computer Science and Engineering Shanghai Jiao Tong University and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Computational Biology Department School of Computer Science Carnegie Mellon University Pittsburgh PA USA

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function labels are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific labels, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function labels at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function.

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TranPulse: Remote Photoplethysmography Estimation With Time-Varying Supervision to Disentangle Multiphysiologically Interference

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1页

作者： Shao, Hang Luo, Lei Qian, Jianjun Chen, Shuo Hu, Chuanfei Yang, Jian Nanjing Univ Sci & Technol PCA Lab Key Lab Intelligent Percept & Syst High Dimens Inf Minist Educ Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Jiangsu Key Lab Image & Video Understanding Social Nanjing 210094 Peoples R China RIKEN Ctr Adv Intelligence Project Tokyo 1030027 Japan Southeast Univ Sch Automat Key Lab Measurement & Control Complex Syst Engn Minist Educ Nanjing 211189 Peoples R China

Physical signs of the body that are too subtle to be observed by human eyes can reflect significant health indicators. Although many vision-based approaches have been devoted to recovery, they topically focus on recognizing explicit features such as colors, textures, and patches, and pay less attention to the entanglement and disentanglement among implicit biological characteristics. Meanwhile, existing deep networks for remote physiological detection are generally weak or deliberately neglectful in eliminating long-term time-varying interference and noise. To address these issues, we propose TranPulse, a novel remote estimation paradigm dedicated to video transformer-based polyphysiological disentanglement for robust heart rate (HR) prediction. Specifically, we improve existing single-stage transformer-based cardiac estimation backbones into a targeted two-stage architecture, which consists of a pair of asymmetric encoder and decoder. The encoder, which is the important module for heart waveform prediction, guides global attention to spatio-temporal enhancement and perception of periodic signals according to facial frame differences. The decoder, which is the core strategy pipeline for unwrapping time-varying disturbances, uses waveform gradient variations as the constraint for high-dimensional representations to separate spikes from multiple uncorrelated obstructions. Simultaneously, we integrate the synchronous computation of the encoder's fusion of appearance and frame difference to provide more detailed guidance for in-the-wild spatio-temporal modeling, and design a more reliable regression loss function to coordinate long-term temporal and frame-wise spatial supervision. We train, validate, and practice our proposed model on multiple publicly available datasets, where it achieves competitive performance in pulse estimation by extensive experimental results.

关键词： Frame-wise supervision heart rate (HR) remote biosignal estimation spatio-temporal modeling vision transformer (ViT) Frame-wise supervision heart rate (HR) remote biosignal estimation spatio-temporal modeling vision transformer (ViT)

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A unified feature-spatial cycle consistency fusion framework for robust image matching

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INFORMATION FUSION 2023年第1期97卷

作者： Sun, Kun Yu, Jinhong Tao, Wenbing Li, Xin Tang, Chang Qian, Yuhua China Univ Geosci Sch Comp Sci Hubei Key Lab Intelligent Geoinformat Proc Wuhan 430078 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China West Virginia Univ Lane Dept CSEE Morgantown WV 26506 USA Shanxi Univ Inst Big Data Sci & Ind Taiyuan 030006 Peoples R China Shanxi Univ Key Lab Computat Intelligence & Chinese Informat P Minist Educ Taiyuan 030006 Peoples R China

Robust image matching is a fundamental and long-standing open problem in computer vision. Conventional wisdom has exploited redundancy to improve the robustness of image matching (e.g., from pairwise to multi-image correspondence), which works well in the spatial domain. Inspired by the success of global optimization-based approaches, we propose a novel extension of cycle consistency from multi-image to multi-descriptor matching in this paper, which integrates useful information from the feature domain. More specifically, we build upon previous work of permutation synchronization and construct a novel cycle consistency model for multi-descriptor matching. The construction of cycle consistency model is based on the analogy between multi-image matching and multi-descriptor matching in a virtual universe. It allows us to formulate multi-image and multi-descriptor matching as a constrained global optimization problem. We have developed a spectral relaxation algorithm to solve this optimization problem, admitting an efficient implementation via fast singular value decomposition (SVD). To demonstrate the robustness of the proposed method named Cycle Consistency Fusion (C2F), we have evaluated it in terms of both raw matching accuracy (pairwise or multi-image) and several higher level downstream tasks such as homography and camera pose estimation. Extensive experimental results have shown that our C2F outperforms state-of-the-art methods consistently across different datasets and vision tasks.

关键词： Multi-image matching Multi-descriptor fusion Cycle consistency 3D reconstruction

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Optimizing pinning control of complex dynamical networks based on spectral properties of grounded laplacian matrices

arXiv

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arXiv 2018年

作者： Liu, Hui Xu, Xuanhong Lu, Jun-An Chen, Guanrong Zeng, Zhigang School of Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Huazhong University of Science and Technology Wuhan430074 China School of Mathematics and Statistics Wuhan University Wuhan430072 China Department of Electronic Engineering City University of Hong Kong Hong Kong Hong Kong

Pinning control of a complex network aims at forcing the states of all nodes to track an external signal by controlling a small number of nodes in the network. In this paper, an algebraic graph-theoretic condition is introduced to optimize pinning control. When individual node dynamics and coupling strength of the network are given, the effectiveness of pinning scheme can be measured by the smallest eigenvalue of the grounded Laplacian matrix obtained by deleting the rows and columns corresponding to the pinned nodes from the Laplacian matrix of the network. The larger this smallest eigenvalue, the more effective the pinning scheme. Spectral properties of the smallest eigenvalue are analyzed using the network topology information, including the spectrum of the network Laplacian matrix, the minimal degree of uncontrolled nodes, the number of edges between the controlled node set and the uncontrolled node set, etc. The identified properties are shown effective for optimizing the pinning control strategy, as demonstrated by illustrative examples. Finally, for both scale-free and small-world networks, in order to maximize their corresponding smallest eigenvalues, it is better to pin the nodes with large degrees when the percentage of pinned nodes is relatively small, while it is better to pin nodes with small degrees when the percentage is relatively large. This surprising phenomenon can be explained by one of the theorems established. Copyright © 2018, The Authors. All rights reserved.

关键词： Matrix algebra

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