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A novel non-intrusive load monitoring technique using semi-supervised deep learning framework for smart grid

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Building Simulation 2024年第3期17卷 441-457页

作者： Mohammad Kaosain Akbar Manar Amayri Nizar Bouguila Concordia institute for information Systems Engineering Concordia UniversityMontrealCanada

Non-intrusive load monitoring(NILM)is a technique which extracts individual appliance consumption and operation state change information from the aggregate power consumption made by a single residential or commercial unit.NILM plays a pivotal role in modernizing building energy management by disaggregating total energy consumption into individual appliance-level insights.This enables informed decision-making,energy optimization,and cost reduction.However,NILM encounters substantial challenges like signal noise,data availability,and data privacy concerns,necessitating advanced algorithms and robust methodologies to ensure accurate and secure energy disaggregation in real-world scenarios.Deep learning techniques have recently shown some promising results in NILM research,but training these neural networks requires significant labeled data.Obtaining initial sets of labeled data for the research by installing smart meters at the end of consumers’appliances is laborious and expensive and exposes users to severe privacy risks.It is also important to mention that most NILM research uses empirical observations instead of proper mathematical approaches to obtain the threshold value for determining appliance operation states(On/Off)from their respective energy consumption value.This paper proposes a novel semi-supervised multilabel deep learning technique based on temporal convolutional network(TCN)and long short-term memory(LSTM)for classifying appliance operation states from labeled and unlabeled data.The two thresholding techniques,namely Middle-Point Thresholding and Variance-Sensitive Thresholding,which are needed to derive the threshold values for determining appliance operation states,are also compared thoroughly.The superiority of the proposed model,along with finding the appliance states through the Middle-Point Thresholding method,is demonstrated through 15%improved overall improved F1micro score and almost 26%improved Hamming loss,F1 and Specificity score for the performance of individual appliance when compared

关键词： semi-supervised learning non-intrusive load monitoring middle-point thresholding deep learning TCN LSTM

Self-supervised recalibration network for person re-identification

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Defence Technology（防务技术） 2024年第1期31卷 163-178页

作者： Shaoqi Hou Zhiming Wang Zhihua Dong Ye Li Zhiguo Wang Guangqiang Yin Xinzhong Wang Shenzhen institute of information Technology Shenzhen518172China University of Electronic Science and Technology of China Chengdu611731China Kash institute of Electronics and information Industry Kashi844099China

The attention mechanism can extract salient features in images,which has been proved to be effective in improving the performance of person re-identification(Re-ID).However,most of the existing attention modules have the following two shortcomings:On the one hand,they mostly use global average pooling to generate context descriptors,without highlighting the guiding role of salient information on descriptor generation,resulting in insufficient ability of the final generated attention mask representation;On the other hand,the design of most attention modules is complicated,which greatly increases the computational cost of the model.To solve these problems,this paper proposes an attention module called self-supervised recalibration(SR)block,which introduces both global and local information through adaptive weighted fusion to generate a more refined attention mask.In particular,a special"Squeeze-Excitation"(SE)unit is designed in the SR block to further process the generated intermediate masks,both for nonlinearizations of the features and for constraint of the resulting computation by controlling the number of channels.Furthermore,we combine the most commonly used Res Net-50 to construct the instantiation model of the SR block,and verify its effectiveness on multiple Re-ID datasets,especially the mean Average Precision(m AP)on the Occluded-Duke dataset exceeds the state-of-the-art(SOTA)algorithm by 4.49%.

关键词： Person re-identification Attention mechanism Global information Local information Adaptive weighted fusion

Robustness of interdependent networks with weak dependency links and reinforced nodes

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Science China(information Sciences) 2024年第2期67卷 329-330页

作者： Qian LI Hongtao YU Weitao HAN Ruiyang HUANG Shaomei LI Jianpeng ZHANG institute of information Technology PLA Strategic Support Force Information Engineering University

Interdependent networks are more vulnerable than single,non-interacting networks under cascading failure [1–3]. Percolation theory provides a quantifiable theoretical framework to quantify and evaluate the robustness of networked systems. Most existing models [1–3] on the robustness of interdependent networks mainly focused on the giant connected component(GCC) to explore the structural robustness from the macro perspective,

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Track-before-detect for bistatic radar based on pseudo-spectrum

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Chinese Journal of Aeronautics 2024年第1期37卷 236-246页

作者： Tao HAN Gongjian ZHOU School of Electronics and information Engineering Harbin Institute of TechnologyHarbin 150001China Key Laboratory of Marine Environmental Monitoring and information Processing Ministry of Industry and Information TechnologyHarbin 150001China

Track-Before-Detect(TBD) is an efficient method to detect dim targets for radars. Conventional TBD usually follows an approximate motion model of the target, which may cause an inaccurate integration of the target energy. A TBD technique on basis of pseudo-spectrum in mixed coordinates adopting an accurate motion model for bistatic radar system is developed in this paper.The predicted position in bistatic polar plane is derived according to a nonlinear function that exactly describes the constant Cartesian velocity motion. Then around the predicted position, a pseudo-spectrum is formulated and its samples are accumulated to the integration frame for energy integration. The evolution of the state and the procedure of accumulation of the target energy are derived elaborately. The superior performance of the proposed method is demonstrated by some simulations.

关键词： Weak target detection Bistatic radar Track-before-detect Pseudo-spectrum Multiframe integration

BSEC Method for Unveiling Open Clusters and its Application to Gaia DR3:83 New Clusters

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Research in Astronomy and Astrophysics 2024年第5期24卷 169-184页

作者： Zhong-Mu Li Cai-Yan Mao institute of Astronomy and information Dali UniversityDali 671003China

Open clusters(OCs) are common in the Milky Way, but most of them remain undiscovered. There are numerous techniques, including some machine-learning algorithms, available for the exploration of OCs. However, each method has its limitations and therefore, different approaches to discovering OCs hold significant values. We develop a comprehensive approach method to automatically explore the data space and identify potential OC candidates with relatively reliable membership determination. This approach combines the techniques of Hierarchical Density-Based Spatial Clustering of Applications with Noise, Gaussian mixture model, and a novel cluster member identification technique, color excess constraint. The new method exhibits efficiency in detecting OCs while ensuring precise determination of cluster memberships. Because the main feature of this technique is to add an extra constraint(EC) for the members of cluster candidates using the homogeneity of color excess,compared to typical blind search codes, it is called Blind Search-Extra Constraint(BSEC) method. It is successfully applied to the Gaia Data Release 3, and 83 new OCs are found, whose color–magnitude diagrams(CMDs) are fitted well to the isochrones. In addition, this study reports 621 new OC candidates with discernible main sequence or red giant branch. It is shown that BSEC technique can discard some false negatives of previous works, which takes about three percentage of known clusters. It shows that as an EC, the color excess(or twocolor) constraint is useful for removing fake cluster member stars from the clusters that are identified from the positions and proper motions of stars, and getting more precise CMDs, when differential reddening of member stars of a cluster is not large(e.g., ΔE(GBP-GRP) < 0.5 mag). It makes the CMDs of 15% clusters clearer(in particular for the region near turnoff) and therefore is helpful for CMD and stellar population studies. Our result suggests that the color excess constraint is more appropriate for clusters with small di

关键词： Galaxy stellar content-(Galaxy:)open clusters and associations general-stars fundamental parameters

SAT-Based Automatic Searching for Differential and Linear Trails: Applying to CRAX

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Chinese Journal of Electronics 2024年第1期33卷 72-79页

作者： Yiyi HAN Caibing WANG Zhongfeng NIU Lei HU State Key Laboratory of information Security Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences

Boolean satisfiability problem(SAT) is now widely applied in differential cryptanalysis and linear cryptanalysis for various cipher algorithms.It generated many excellent results for some ciphers,for example,Salsa20.In this research,we study the differential and linear propagations through the operations of addition,rotation and XOR(ARX),and construct the SAT models.We apply the models to CRAX to search differential trails and linear trails automatically.In this sense,our contnbution can be broadly divided into two parts.We give the bounds for differential and linear cryptanalysis of Alzette both up to 12 steps,by which we present a 3-round differential attack and a 3-round linear attack for CRAX.We construct a 4-round key-recovery attack for CRAX with time complexity289times of 4-round encryption and data complexity 225.

关键词： Differential cryptanalysis Linear cryptanalysis Boolean satisfiability problem CRAX

A Theoretically Grounded Question Answering Data Set for Evaluating Machine Common Sense

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Data Intelligence 2024年第1期6卷 1-28页

作者： Henrique Santos Ke Shen Alice M.Mulvehill Mayank Kejriwal Deborah L.McGuinness Rensselaer Polytechnic institute Troy NY information Sciences institute Marina del Rey CA

Achieving machine common sense has been a longstanding problem within Artificial Intelligence.Thus far,benchmark data sets that are grounded in a theory of common sense and can be used to conduct rigorous,semantic evaluations of common sense reasoning(CSR)systems have been lacking.One expectation of the AI community is that neuro-symbolic reasoners can help bridge this gap towards more dependable systems with common sense.We propose a novel benchmark,called Theoretically Grounded common sense Reasoning(TG-CSR),modeled as a set of question answering instances,with each instance grounded in a semantic category of common sense,such as space,time,and emotions.The benchmark is few-shot i.e.,only a few training and validation examples are provided in the public release to avoid the possibility of overfitting.Results from recent evaluations suggest that TG-CSR is challenging even for state-of-the-art statistical models.Due to its semantic rigor,this benchmark can be used to evaluate the common sense reasoning capabilities of neuro-symbolic systems.

关键词： reasoning Intelligence instance

TransTM:A device-free method based on time-streaming multiscale transformer for human activity recognition

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Defence Technology（防务技术） 2024年第2期32卷 619-628页

作者： Yi Liu Weiqing Huang Shang Jiang Bobai Zhao Shuai Wang Siye Wang Yanfang Zhang institute of information Engineering Chinese Academy of Sciences School of Cyber SecurityUniversity of Chinese Academy of SciencesBeijingChina School of information Management Beijing Information Science and Technology UniversityBeijingChina

RFID-based human activity recognition(HAR)attracts attention due to its convenience,noninvasiveness,and privacy protection.Existing RFID-based HAR methods use modeling,CNN,or LSTM to extract features effectively.Still,they have shortcomings:1)requiring complex hand-crafted data cleaning processes and 2)only addressing single-person activity recognition based on specific RF signals.To solve these problems,this paper proposes a novel device-free method based on Time-streaming Multiscale Transformer called TransTM.This model leverages the Transformer's powerful data fitting capabilities to take raw RFID RSSI data as input without pre-processing.Concretely,we propose a multiscale convolutional hybrid Transformer to capture behavioral features that recognizes singlehuman activities and human-to-human interactions.Compared with existing CNN-and LSTM-based methods,the Transformer-based method has more data fitting power,generalization,and scalability.Furthermore,using RF signals,our method achieves an excellent classification effect on human behaviorbased classification tasks.Experimental results on the actual RFID datasets show that this model achieves a high average recognition accuracy(99.1%).The dataset we collected for detecting RFID-based indoor human activities will be published.

关键词： Human activity recognition RFID Transformer

A Residual Frequency Offset Estimation Method Based on Range Migration Fitting

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Journal of Beijing institute of Technology 2024年第2期33卷 103-110页

作者： Liting Zhang Hao Huan Ran Tao Xiaogang Tang School of information and Electronics Beijing Institute of Technology School of Aerospace information Space Engineering University

Recently, researchers have proposed an emitter localization method based on passive synthetic aperture. However, the unknown residual frequency offset(RFO) between the transmitter and the receiver causes the received Doppler signal to shift, which affects the localization accuracy. To solve this issue, this paper proposes a RFO estimation method based on range migration fitting. Due to the high frequency modulation slope of the linear frequency modulation(LFM)-modulation radar signal, it is not affected by RFO in range compression. Therefore, the azimuth time can be estimated by fitting the peak value position of the pulse compression in range direction.Then, the matched filters are designed under different RFOs. When the zero-Doppler time obtained by the matched filters is consistent with the estimated azimuth time, the given RFO is the real RFO between the transceivers. The simulation results show that the estimation error of azimuth distance does not exceed 20 m when the received signal duration is not less than 3 s, the pulse repetition frequency(PRF) of the transmitter radar signal is not less than 1 k Hz, the range detection is not larger than 1 000 km, and the signal noise ratio(SNR) is not less than –5 d B.

关键词： passive localization range migration fitting (RMF) residual frequency offset (RFO)