Differentiable architecture search (DARTS) suffers from over-parameterized supernetworks that make architecture search difficult to perform in complex detection scenarios. In this paper, a shareable attention-mask for object detection architecture search (SAM-Det) is proposed, which is designed to address the high computational cost and poor performance caused by search over-parameterization. To reduce the search cost, a series of convolutional candidate operations is merged by using a shareable attention mask to represent the operations as a single convolution with shared weights. Moreover, to reduce the interference of redundant features on the search, the features are shuffled in groups to learn the attentional importance, thus selecting the important channels to be transferred into the search space. The experimental results show that SAM-Det is able to obtain network architectures with better detection performance and lower search cost in different detection scenarios compared to other Search methods.

Industrial fault diagnosis increasingly benefits from Large Models (LMs), which can handle diverse data sources. Within this context, multimodal approaches, which integrate inputs like vibration, process, and video data, boost accuracy but often assume all modalities are available, which rarely holds in practice due to sensor failures, deployment limits, etc. While unimodal methods are more practical, they often lack sufficient information for complex scenarios. The key challenge is how to effectively fuse multimodal data and transfer that knowledge to enhance unimodal performance when some modalities are missing. This paper proposes an adaptive multimodal fault diagnosis framework that enables bidirectional enhancement between multimodal and unimodal representations. For model construction, a Cross-Fusion Channel Attention (CFCA) module is introduced to align features across modalities, and then a Shared Temporal Attention (STA) module captures sequential dependencies and facilitates representation sharing. A multi-head diagnosis structure is finally used to jointly supervise both multimodal and unimodal branches. For parameter estimation, we develop a similarity-aware and uncertainty-guided gradient modulation strategy to adaptively balance multimodal and unimodal learning, ensuring stable optimization and knowledge transfer. Experiments on the real-world industrial PRONTO dataset demonstrate that our method achieves superior performance and adaptability across various scenarios with varying modality availability.

Detection Transformers (DETRs) have achieved notable performance and outperformed traditional deep neural network models in object detection. However, the self-attention of encoder tokens incurs significant computational overhead. To improve computational efficiency and maintain detection precision simultaneously, we propose an efficient detection transformer model based on learnable query queuing and encoder self-distillation, QD-DETR. Specifically, we first design a query queuing mechanism to select object-relevant queries with the guidance of supervising signals and spatial features. Second, we construct the query representation variational information bottleneck module to optimize the query queuing via mutual information regularization. Lastly, we develop an encoder feature self-distillation method to compensate for the information loss by directly distilling the encoder output token sequences. We conducted experiments on multiple datasets and verified the accuracy and efficiency of QD-DETR in comparison with mainstream detection models. The experiment results demonstrate that QD-DETR reduces computational complexity by 46% and increases inference speed by 69% while maintaining high accuracy in complex object detection scenarios.

The existence of stochastic sampling phenomena in wastewater treatment processes (WWTPs) breaks the assumption that the existing control strategies use periodic data, and the operational constraints of equipment and the requirements for effluent water quality impose constraints on the system's input and output. These factors collectively increase the difficulty of achieving stable control of dissolved oxygen concentration (DOC). To solve these problems, a data-driven model predictive control (DDMPC) strategy is proposed to achieve stable control of constrained WWTPs with stochastic sampling intervals. First, a DDMPC framework is designed, which involves designing the objective function based on the mathematical expectation of the predicted output and considering system input and output constraints. In this framework, the problem of stochastic data acquisition caused by stochastic sampling can be solved, and the stable operation of the system can be ensured under constraints. Second, a data-driven multimodel prediction structure is constructed based on the stochastic characteristics of the sampling intervals. Specifically, fuzzy neural networks (FNNs) that match possible sampling intervals are established, thereby providing predictive outputs for the control process at the corresponding sampling instants. Third, a controller solving algorithm based on the generalized multiplier method is proposed, in which the constrained optimization problem within the model-predictive control (MPC) framework is reformulated by incorporating system constraints into the objective function as penalty functions to obtain the optimal control input that satisfies the constraints. Finally, the stability of the proposed DDMPC strategy is demonstrated, and its effectiveness is verified through the simulations on the benchmark simulation model No. 1 (BSM1). The results show that the proposed DDMPC strategy can achieve stable control of DOC in constrained WWTPs with stochastic sampling intervals.

Train control systems are critical for ensuring railway operational safety. With the advancement of railway intelligentization strategies and the rapid development of information technology, the train control system is evolving from closed to open systems. In this context, the security of the key management system(KMS) has become a core research direction, as the train-ground communication faces the risk of unauthorized access due to the use of open channels. Currently deployed KMS in industrial settings predominantly adopt offline architectures, which exhibit vulnerabilities to physical tampering during key storage and computation processes. Meanwhile, there is a general lack of research on the adaptation of mainstream online key management technologies (such as Hardware Security Modules (HSM), quantum key distribution(QKD), and Trusted Computing(TC)) in the context of rail train control systems. To address these challenges, this paper proposes an enhanced scheme based on an online key management mechanism. By integrating TC technology and optimizing the key distribution strategy, the scheme provides high-security protection throughout the full key lifecycle (generation, distribution, storage, and destruction). Experimental results demonstrate that, compared to traditional offline systems, the proposed solution significantly improves overall system security, resilience against attacks, and key update efficiency, thereby establishing a robust foundation for constructing highly secure modern railway train control systems.

Multiple sensors are widely used in the monitoring of the degradation process and prediction of the remaining useful lifetime (RUL) of units in complex engineering systems. However, ensuring the prognostic performance with only a few units available remains difficult. Under a few-shot scenario, the discordant observations that exist in sensor data introduce considerable uncertainty into the degradation model, which leads to an empirical loss far from the expected loss. On the other hand, the learned degradation model tends to be overfitted on the limited available units and results in a biased model parameter distribution, which limits the model generalization capability on unseen units. To address these issues, this paper proposes a restricted-learning network with observation credibility inference (OCI) for few-shot degradation modeling. We initially introduce the OCI to figure out discordant observations from sensor data. Then, OCI is incorporated into restrictive learning through the deletion of discordant observations from sensor data, which enforces a prior distribution constraint on degradation model parameters to prevent overfitting. Finally, a posterior augmented classifier is learned to estimate health status based on the posterior sensor paths, and the RUL can be predicted subsequently. A case study that uses the degradation dataset of aircraft engines demonstrates the superiority of the proposed method over benchmark methods under few-shot scenarios.Note to Practitioners—This paper aims to develop a few-shot degradation modeling method for conducting status monitoring and RUL prediction. Specifically, the developed method addresses two challenging issues in practice: 1) How to figure out discordant observations exist in sensor data; 2) How to prevent overfitting issues under few-shot scenarios. To implement this method, four steps are included as follows: First, collect multiple sensor data and failure time of historical units. Second, construct the degradation model network, and train the network with restricted parameter distribution after deleting discordant observations. Third, construct and learn the classifier for predicting the probability of failure. Fourth, estimate the degradation status of in-service units, and predict the RUL based on the classifier. The proposed method is expected to be able to characterize various degradation processes and be applied to the degradation modeling of engineering systems with limited data available.

Deep learning-driven object detection models are capable of accurately identifying and localizing objects. However, small objects contain limited information relative to global features, resulting in the fact that detection models often do not learn small object features adequately. To enhance the precision in detecting small objects, we propose a bi-directional and triangular circulation fusion neural network (BTFN). First, to selectively strengthen the position features of small objects, we propose a feature circulation extraction module composed of a bi-directional triangular densely nested convolutional network (BTF), thus achieving repetitive multi-layer feature fusion. Second, to fill up the semantic gaps between different scales of features, we design a mixed dual attention module (MDA) in the bi-directional triangular densely nested network. Third, to mitigate the lost information in the neural networks with deep layers as well as improve the inference time, we design a re-parameterization bi-directional composite feature fusion module (Rep-BFM) that fuses the features of multiple scales. The proposed model is evaluated extensively on the MS COCO, Tsinghua-Tencent 100k, and Haier dismantled parts of used home appliances datasets. The experiment results show that the proposed model improves the AP on MS COCO by 4%, especially the APS of small objects is improved by 7.7% compared with SOTA models.

Feature pyramids are widely adopted in visual detection models for capturing multiscale features of objects. However, the utilization of feature pyramids in practical object detection tasks is prone to complex background interference, resulting in suboptimal capture of discriminative multiscale foreground semantic features. In this article, a foreground capture feature pyramid network (FCFPN) for multiscale object detection is proposed, to address the problem of inadequate feature learning in complex backgrounds. FCFPN consists of a foreground dual attention (FDA) module and a pathway aggregation (PA) structure. Specifically, the FDA mechanism activates top-down foreground channel responses and lateral spatial foreground location features, so that channel and spatial foreground features are adequately captured. Then, the PA module adaptively learns the fusion weights of multiscale features at different levels of the feature pyramid, which enhances the complementarity of semantic information between different levels of the foreground feature maps. Since the fusion weights are learned adaptively based on different pyramid levels, the detection model accordingly retains the gained information of feature sizes and suppresses the conflicting information. The evaluations on public datasets and the self-built complex background dataset demonstrate that the detection average precision (AP) and the feature learning performance of the proposed method are superior compared with other FPNs, which proves the effectiveness of the proposed FCFPN.

Distributed learning-based high-dimensional temporal modeling for the Industrial Internet of Things (IIoT) has become a prevailing trend. However, traditional distributed learning inefficiently extracts information by straightforward architects, resulting in low modeling accuracy and high communication costs. We propose a distributed hierarchical temporal graph learning (DHTGL) approach. In terminal equipment, we construct an adaptive hierarchical dilation convolutional network to dynamically capture spatiotemporal features by adjusting the dilation factor at each layer. Next, we construct adaptive graphs according to the connection similarity between dimensions to capture implicit connections. In the edge device, we design a node-edge graph distance calculation based on Gromov-Wasserstein distance to group feature graphs and construct representative cluster feature graphs. Edge devices upload cluster feature graphs to reduce communication costs while minimizing information loss. In the central server, we incorporate graph attention networks into graph neural networks for edge updating in training models on clustered feature graphs. Experiments using public IIoT datasets and the self-built IIoT platform demonstrate the effectiveness of DHTGL in comparison with common distributed learning approaches. The results confirm that DHTGL consumes fewer communications while achieving higher accuracies.

Data privacy has become a critical concern in today's data-driven world. Interval privacy emerges as a promising safeguard, representing private values as intervals. Traditional interval analysis methods, however, often rely on critical assumptions that are questionable in practice. To address this gap, we propose a novel paradigm for analyzing interval-valued data generated by the interval privacy mechanism. Our contributions are two-fold: First, we innovatively model intervals as random objects in a metric space and use the Hausdorff distance to quantify their dissimilarity without imposing restrictive assumptions. Second, as an application of our paradigm, we develop an interval-to-interval regression method named Hausdorff distance-based regression (HDBR), extending multivariate linear regression to metric spaces. The HDBR method estimates regression coefficients by minimizing the Hausdorff distance between the observed and estimated intervals. Simulation studies demonstrate the effectiveness and robustness of our proposed approach compared to mainstream competitors. We also provide a real data example to illustrate how to perform regression analysis within the interval privacy framework, and the results further validate the superiority of the HDBR method.

In wastewater treatment process (WWTP), abnormal data seriously reduce data quality rendering the application techniques impractical. The implementation of abnormal data detection is challenging due to the nonlinear nature of WWTP. Typically, constructing an accurate anomaly detector requires large amounts of labeled data, which is difficult in practice. Thus, a self-supervised memory enhanced deep clustering method (SMEL) is proposed to detect abnormal data without using any labels. First, a self-supervised deep clustering network, combining stacked autoencoders and the clustering algorithm, leverages unlabeled data to extract nonlinear features and capture the normal pattern. Second, an adaptive weight objective function, jointly optimizing the reconstruct error and clustering error, is designed to obtain a robust clustering structure. Third, double memory enhanced modules, consisting of a centroid memory and a score memory, are presented to enhance training stability and detection accuracy. Finally, experiments on three WWTP datasets show that SMEL achieves the highest detection accuracy.

Missing values frequently appearing in the wastewater treatment process are automatically replaced by zero to ensure the implementation of downstream applications. These meaningless zero values bias data distribution and decrease data quality. However, the existing imputation methods treat all values equally without considering the existence of meaningless zero values, affecting the performances of imputation and downstream models. Thus, an attention-guided position-sensitive multiple imputation (APMI) method is proposed. First, a position-sensitive localization attention module selectively focuses on the most informative values, enhancing the ability for observed data utilization. Second, a masked attention multiple imputation module focuses on the observed values and fuses multiple candidate estimations as the final result to improve imputation performance. Third, a joint optimization objective function is designed to ensure the consistency of localization and imputation tasks. The extensive experimental results show that the proposed APMI outperforms existing method imputation performance under different missing rates.

Cyber Physical Systems (CPS) are networked systems of cyber (computation and communication) and physical (sensors and actuators) components that interact in a feedback loop with the possible help of human intervention, interaction and utilization. These ...

Cybersecurity breaches are common anomalies for distributed cyber-physical systems (CPS). However, the cyber security breach classification is still a difficult problem, even using cutting-edge artificial intelligence (AI) approaches. In this article, we study a multi-class classification problem in cyber security for attack detection. A challenging multi-node data-censoring case is considered. In such a case, data within each data center/node cannot be shared while the local data is incomplete. Particularly, local nodes contain only a part of the multiple classes. In order to train a global multi-class classifier without sharing the raw data across all nodes, we design a multi-node multi-class classification ensemble approach which is the main result of our study. By gathering the estimated parameters of the binary classifiers and data densities from each local node, the missing information for each local node is completed to build the global multi-class classifier. Numerical experiments are given to validate the effectiveness of the proposed approach under the multi-node data-censoring case. Under such a case, we even show the out-performance of the proposed approach over the full-data approach.

Cyber-Physical Systems (CPS) has emerged as a paradigm that connects cyber and physical worlds, which provides unprecedented opportunities to realize intelligent applications such as smart home, smart cities, and smart manufacturing. However, CPS faces a great number of information security challenges (e.g., attacks) due to the integration of CPS as well as the human behaviors and interactions. Therefore, accurate and real-time attack detection and identification are essential to ensure information security and reliability of CPS. In this paper, we propose a novel integrated learning method that accurately detects an attack of a CPS system and then identifies the attack type in real time. Specifically, we consider a One-Class Support Vector Machine (OCSVM) model that only relies on the data from the normal state for training to achieve a real-time and effective detection of a CPS system state (i.e., normal or under-attack). If the system is detected to be under-attack, we then develop a Pairwise Self-supervised Long Short-Term Memory (PSLSTM) approach to identify the attack type, which aims to accurately distinguish the known attack types and discover unknown new attacks. Lastly, experimental results show the proposed method achieves promising performances compared with conventional and state-of-the-art learning-based benchmarks.

For prognostics and health management of industrial systems, machine remaining useful life (RUL) prediction is an essential task. While deep learning-based methods have achieved great successes in RUL prediction tasks, large-scale neural networks are still difficult to deploy on edge devices owing to the constraints of memory capacity and computing power. In this article, we propose a lightweight and adaptive knowledge distillation (KD) framework to alleviate this problem. First, multiple teacher models are compressed into a student model through KD to improve the industrial prediction accuracy. Second, a dynamic exiting method is studied to enable an adaptive inference on the distilled student model. Finally, we develop a reparameterization scheme to further lessen the student network. Experiments on two turbofan engine degradation datasets and a bearing degradation dataset demonstrate that our method significantly outperforms the state-of-the-art KD methods and enables the distilled model with an adaptive inference ability.

We propose a novel seismic signal processing approach to efficiently and effectively attenuate seismic random noises. The proposed approach is a generalized seismic noise attenuation solution that can be applied to typical denoising operators. Our work has two main contributions. First, conventional filtering operators “regularize” the denoised results through the operator design. However, as seismic data have strong nonstationarity, it is inevitable to remove certain signal components. The resampling mechanism alleviates the signal loss. Second, the resampling operation does not require a lot of parameter tuning, which improves the denoising efficiency. Using the proposed approach, compared with existing denoising operators, the intrinsic seismic signal components are better recovered since random noise has been suppressed. Synthetic example and field data applications quantitatively and qualitatively demonstrate excellent performances of the proposed approach.

Microseismic event picking is one of the key steps in seismic processing and imaging. Manually picking is a widely used way to pick the microseismic events, which is time-consuming. The standard short-term average/long-term average (STA/LTA) is a traditional method to pick the microseismic first arrivals, which would lead to inaccurate first-arrival picks in case of low signal-to-noise ratio (SNR). We developed a workflow to automatically pick the microseismic first arrivals by using the feature pyramid networks (FPNs). To train the proposed model, we first randomly select part of the microseismic traces and manually pick the time index of the first arrivals. Next, we segment every selected trace into two parts based on the time index of the manual picking and then assign each part a label. Afterward, we train the proposed fine-tuning FPN model by using the training data and the corresponding labels. It should be noticed that we proposed a loss function, named the point-aware loss, for solving the microseismic first-arrival picking issue. Finally, we predict the microseismic first arrivals by using the well-trained fine-tuning FPN model. The numerical examples demonstrate that our proposed model successfully identifies the microseismic first arrivals. The microseismic first arrivals predicted by using our proposed model are more robust and more accurate than those obtained by using the STA/LTA and the encoder–decoder network.

Seismic instantaneous frequency (IF), as one of the instantaneous attributes, is widely used for seismic interpretation and stratigraphy analysis. The Hilbert transform (HT)-based complex analysis approaches are commonly used to extract seismic IF, which are sensitive to kinds of noise contained in field data. Although the normalized HT (NHT) improves the antinoise property of HT by normalizing the original trace, the HT-based methods are a global operator that is not suitable for the local analysis. For example, IF calculated by using the HT-based method is unstable when meeting strong seismic events. In this letter, we propose a workflow to extract local IF (LIF) and then apply it to describe superposed sands. Note that the proposed workflow extracts a stable IF result even when processing a seismic trace with strong events. To demonstrate the effectiveness of the proposed workflow, we apply it to both synthetic and field data. Compared with results from HT and NHT, the proposed workflow provides a stable IF extraction and offers potentials in precisely highlighting superposed sands.

Delineating seismic faults is one of the main steps in seismic structure interpretation. Recently, deep learning (DL) models are used to automatic seismic fault interpretation. For the DL-based models, there are two widely used techniques, which can enhance the model performance, that is, data augmentation (DA) and ensemble learning (EL). Qualitatively and quantificationally analyzing the performances of these two techniques is a rarely studied domain. In this study, we make detailed comparisons between the DL models using DA and EL. For the DL model with DA, we first build a holistically nested Unet (HUnet) model by adopting the holistically nested module to the widely used Unet model. Then, we train a HUnet model by using the original and its augmented synthetic datasets (HUnet-D model for short). Besides, we train a Unet model in the same way as a comparison (Unet-D model for short). On the other hand, for the DL model with EL, we first obtain several individual HUnet models separately trained by only using a type of the augmented datasets for each time. Next, we propose a data-driven EL model to integrate these HUnet models. Specially, we propose an adjoint-net module for the EL model to extract the multi-scale features from seismic data, which benefits for checking and fine-tuning the fusing results. Finally, we qualitatively and quantificationally evaluate these DL models (Unet-D, HUnet-D, and EL-HUnet) using the synthetic validation dataset. Moreover, we apply these models to 3-D field data volumes for automatic fault interpretation. Compared with the coherence attribute, Unet-D and HUnet-D models, we find that the EL-HUnet model achieves the comparable model performance for effectively enhancing the precision and continuity of the detected faults.

Lithology interpretation is important for understanding subsurface properties. Yet, the common manual well log interpretation is usually with low efficiency and bad consistency. Therefore, the automatic well log interpretation tools based on machine learning and deep learning have been developed. Although the state-of-the-art sophisticated models can show fine interpretation performance with acceptable accuracies, “blind” tests do not always exhibit satisfactory results because of the complexity of lithology interpretation with respect to subsurface rock properties and the data-labeling quality. To solve this generalization challenge, we propose to leverage the parameterized quantum circuits in the deep-learning model. The quantum computing takes advantages of the superposition and entanglement quantum systems, which could potentially endow the generalization power or capability to the deep-learning model. Using the proposed quantum-enhanced deep-learning (QEDL) model, we have tested the model performance on field well log data from different wells. Compared with the classic fine convolutional neural network (CNN) model and the long short-term memory (LSTM) model, the proposed QEDL model achieves comparable model performance with a clearly improved generalization power for interpreting both thin and thick lithology layers. In addition, because of the quantum circuit structure, the QEDL model needs much fewer model parameters than LSTM and CNN models, i.e., the QEDL parameter number in our study can be approximately 75% less than that of LSTM and 89% less than that of CNN.

Ground roll, a source-generated surface wave, is a main type of coherent noise in a land seismic survey. Low-frequency and high-amplitude ground roll often overlays valid reflection events, resulting in obscuring seismic reflections. Ground-roll attenuation is an essential step for seismic data processing, which is based on the accurate separation of the ground roll and reflections without damaging their morphological characteristics. In this study, we effectively separate and suppress ground roll in shot gathers through a proposed workflow. We first identify the major components of the ground roll adopting the multichannel variational mode decomposition (MVMD), which shows significant improvements compared to the conventional single-channel VMD. Ground roll can be identified on the decomposed band-limited intrinsic mode functions (IMFs). Moreover, we propose an adaptive criterion to determine the number of decomposed IMFs. Due to the narrowly concentrated frequency components with the multichannel continuity constraint from MVMD, ground roll is mainly contained in low-frequency IMFs, which benefits the accurate ground-roll suppression. Next, we separate ground roll and reflections on the selected low-frequency IMFs through a curvelet based block-coordinate relaxation method. Afterward, we can obtain a filtered gather by removing the separated ground roll from the original shot gather. Finally, we apply the proposed workflow to synthetic and field gathers to testify its validity and effectiveness for simultaneously attenuating ground roll and preserving valid seismic reflector information.

Edge computing is emerging as a new infrastructure for Internet-of-Things (IoT) networks by placing computation and analytics near to where data are generated. This article presents a novel data analytics framework for edge computing. The framework is based on a new decentralized algorithm, which enables all the nodes to obtain the global optimal model without sharing raw data. The resulting scheme executes in a hybrid mode: local IoT nodes send computed information to edge nodes. The edge nodes cooperate with each other by exchanging analytics with their neighbors only. The presenting approach is analyzed and evaluated on various applications and the experimental results demonstrate the effectiveness of the proposed methodology in providing fast data analytics to edge computing infrastructure.

With the dramatic growth and complexity of seismic data, manual seismic facies analysis has become a significant challenge. Machine learning and deep learning (DL) models have been widely adopted to assist geophysical interpretations in recent years. Although acceptable results can be obtained, the uninterpretable nature of DL (which also has a nickname “alchemy”) does not improve the geological or geophysical understandings on the relationships between the observations and background sciences. This article proposes a noble interpretable DL model based on 3-D (spatial-spectral) attention maps of seismic facies features. Besides regular data-augmentation techniques, the high-resolution spectral analysis technique is employed to generate multispectral seismic inputs. We propose a trainable soft attention mechanism-based deep dilated convolutional neural network (ADDCNN) to improve the automatic seismic facies analysis. Furthermore, the dilated convolution operation in the ADDCNN generates accurate and high-resolution results in an efficient way. With the attention mechanism, not only the facies-segmentation accuracy is improved but also the subtle relations between the geological depositions and the seismic spectral responses are revealed by the spatial-spectral attention maps. Experiments are conducted, where all major metrics, such as classification accuracy, computational efficiency, and optimization performance, are improved while the model complexity is reduced.

In this article, a systematic assessment of cyber-physical security on the energy management system for connected and automated electric vehicles is proposed, which, to our knowledge, has not been attempted before. The generalized methodology of impact analysis of cyber attacks is developed, including novel evaluation metrics from the perspectives of steady state and transient performance of the energy management system and innovative index-based resilience and security criteria. Specifically, we propose a security criterion in terms of dynamic performance, comfortability, and energy, which are the most critical metrics to evaluate the performance of an electronic control unit (ECU). If an attack does not impact these metrics, it perhaps can be negligible. Based on the statistical results and the proposed evaluation metrics, the impact of cyber attacks on ECU is analyzed comprehensively. The conclusions can serve as guidelines for attack detection, diagnosis, and countermeasures.

Temporal data are continuously collected in a wide range of domains. The increasing availability of such data has led to significant developments of time series analysis. Time series classification, as an essential task in time series analysis, aims to assign a set of temporal sequences to different categories. Among various approaches for time series classification, the distance metric learning based ones, such as the virtual sequence metric learning (VSML), have attracted increased attention due to their remarkable performance. In VSML, virtual sequences attract samples from different classes to facilitate time series classification. However, the existing VSML methods simply employ fixed virtual sequences, which might not be optimal for the subsequent classification tasks. To address this issue, in this paper, we propose a novel time series classification method named Discriminative Virtual Sequence Learning (DVSL). Following the unified framework of sequence metric learning, our DVSL method jointly learns a set of discriminative virtual sequences that help separate time series samples in a feature space, and optimizes the temporal alignment by dynamic time warping. Extensive experiments on 15 UCR time series datasets demonstrate the efficiency of DVSL, compared with several representative baselines.

In this paper, we introduce a novel real-time and contact-free sensor system, Helena, that can be mounted on a bed frame to continuously monitor sleep activities (entry/exit of bed, movement, and posture changes), vital signs (heart rate and respiration rate), and falls from bed in a real-time and pervasive computing manner. The smart sensor senses bed vibrations generated by body movements to characterize sleep activities and vital signs based on advanced signal processing and machine learning methods. The device can provide information about sleep patterns, generate real-time results, and support continuous sleep assessment and health tracking. The novel method for detecting falls from bed has not been attempted before and represents a life-changing for high-risk communities, such as seniors. Comprehensive tests and validations were conducted to evaluate system performances using FDA approved and wearable devices. Our system has an accuracy of 99.5% detecting on-bed (entries), 99.73% detecting off-bed (exits), 97.92% detecting movements on the bed, 92.08% detecting posture changes, and 97% detecting falls from bed. The system estimation of heart rate (HR) ranged ±2.41 beats-per-minute compared to Apple Watch Series 4, while the respiration rate (RR) ranged ±0.89 respiration-per-minute compared to an FDA oximeter and a metronome.

Internet of Things (IoT) enables extensive connections between cyber and physical "things". Nevertheless, the streaming data among IoT sensors bring "big data" issues, for example, large data volumes, data redundancy, lack of scalability and so on. Under "big data" circumstances, IoT system monitoring becomes a challenge. Furthermore, cyberattacks which threaten IoT security are hard to be detected. In this paper, we propose an online distributed IoT security monitoring algorithm (ODIS). An advanced influential point selection operation extracts important information from multidimensional time series data across distributed sensor nodes based on the spatial and temporal data dependence structure. Then, an accurate data structure model is constructed to capture the IoT system behaviors. Next, hypothesis testing is carried out to quantify the uncertainty of the monitoring tasks. Besides, the distributed system architecture solves the scalability issue. Using a real sensor network testbed, we commit cyberattacks to an IoT system with different patterns and strengths. The proposed ODIS algorithm demonstrates promising detection and monitoring performances.

To better describe features of nonstationary seismic signals, mode decomposition-based approaches are widely used for seismic processing and analysis, such as empirical mode decomposition (EMD) and empirical wavelet transform (EWT). EWT builds an adaptive filter bank and then decomposes a nonstationary seismic trace into several intrinsic mode functions (IMFs), which has been applied for analyzing the nonstationary seismic signal. In this letter, we propose an enhanced EWT (EEWT) using Hankel transform (HT), which is an integral transform whose kernels are Bessel functions. Compared with sinusoidal functions of Fourier transform (FT), Bessel functions are more effective for describing features of nonstationary signals. Moreover, HT obtains a more compact spectrum than FT for wideband and nonstationary signal analysis, which contributes to the detection of spectral segmentation. To demonstrate the effectiveness of the proposed algorithm, we apply it to both synthetic and field data. Compared with the results provided by EWT, EEWT provides a time-frequency spectrum with higher resolution and offers potentials in precisely highlighting reservoirs.

To better reveal time-varying spectral components of nonstationary seismic signals, time-frequency analysis (TFA) has been widely applied in seismic processing and analysis. In this letter, we propose an advanced seismic TFA method based on an optimal spectral mode separation and an adaptive wavelet bank design. The proposed adaptive mode separation-based wavelet transform (AMSWT) generates a superior time-frequency resolution. In addition, because the wavelet bank is adaptively built on the intrinsic spectral modes, the ability to accurately characterize geophysical structures has been significantly improved. To demonstrate the effectiveness of the proposed AMSWT method, we apply it on both synthetic and field data. Compared with the results from continuous wavelet transform (CWT), empirical mode decomposition (EMD), variational mode decomposition (VMD), and empirical wavelet transform (EWT), AMSWT provides a higher resolution and offers potentials in precisely highlighting stratigraphy boundaries.

In[1], the grant number in the first footnote for the National Postdoctoral Program for Innovative Talents should be BX20190279.

With the dramatic growth of seismic data volume, efficient and accurate seismic source location has become a significant challenge to seismologists. Recently, time reversal imaging (TRI) has been widely applied in automatic seismic source location for its robustness and accuracy, but its wave-equation-based implementation is usually computationally expensive. To achieve an efficient in situ and real-time source location, the emerging sensor network is a good option. In this article, we propose a simplified Gaussian beam TRI (SGTRI) method to implement the seismic source location in a distributed sensor network. Gaussian beam (GB) is a high-frequency asymptotic solution of the wave equation, which can help reduce the computation costs of the wavefield extrapolation in conventional TRI. Traditionally, the GB construction for reflection seismic imaging covers the entire subsurface space. However, for certain source localization, only limited areas contribute. Thus, we propose a beamforming-technique-based simplified GB construction to further boost efficiency. Then, we propose an imaging condition for the SGTRI to construct the final source location map. Using synthetic experiments, we demonstrate the accuracy, robustness, and efficiency of the proposed method compared with conventional TRI. In the end, a field application also shows promising results.

In this article, a systematic and generalized methodology is originally proposed to assess the vulnerability of electric drive systems due to sensor data integrity attacks. Novel evaluation metrics from the perspectives of steady-state and transient performance of electric drive systems are established to evaluate the system condition under different attacks. By using these metrics, innovative index-based resilience and security criteria, together with the stability theorem, are proposed specifically for electric drive systems, which can then be used for cyber-attack detection and diagnosis in a more systematic manner. Then, based on the simulation results under 15 attack cases (five typical types), the qualitative attack impacts on the dynamic characteristics and the statistical damage of different cyber-attacks to the defined metrics are analyzed, which can serve as useful guidelines for attack detection, diagnosis, and countermeasures.

This paper presents a novel real-time smart system performing fall detection, location, and notification based on floor vibration data produced by fall downs. Only using floor vibration as the recognition source, the system incorporates a person identification through vibration produced by footsteps to inform who is the fallen person. Our approach operates in a real-time style, which means the system recognizes a fall immediately and can identify a person with only one or two footsteps. A collaborative in-network location method is used in which sensors collaborate with each other to recognize the person walking, and more importantly, detect if the person falls down at any moment. We also introduce a voting system among sensor nodes to improve person identification accuracy. Our system is robust to identify fall downs from other possible similar events, such as jumps, door close, and objects fall down. Such a smart system can also be connected to smart commercial devices (such as Google Home or Amazon Alexa) for emergency notifications. Our approach represents an advance in smart technology for elder people who live alone. Evaluation of the system shows that it is able to detect fall downs with an acceptance rate of 95.14% (distinguishing from other possible events), and it identifies people with one or two steps in a 97.22% (higher accuracy than other methods that use more footsteps). The fall down location error is smaller than 0.27 m, which is acceptable compared with the height of a person.

Instantaneous Heart Rate (IHR) detection is important but challenging. As a kind of biomedical signal, photoplethysmography (PPG) is a good source to extract the intrinsic IHR information for further diagnostic interpretation. However, because of the non-stationary characteristics, the traditional IHR estimation results can be unreliable, and may be contaminated with artifacts and noises. Even state-of-art time-frequency analysis techniques can not fully handle the subharmonics interferences to obtain a reliable and robust IHR estimation. In this paper, we propose a hybrid IHR estimation approach based on the non-harmonic analysis (NHA) and an advanced time-periodic transform (TPT). We propose to use a NHA model to adaptively extract intrinsic oscillatory modes from PPG data. The previously unsuppressed sub-harmonic components can be removed thanks to the extracted mono-frequency components. Then the IHR can be estimated from the left ridge on TPT. Experiment results demonstrate the advantages of the proposed approach, which is promising for PPG signal processing and analysis.

Monitoring sleep quality and status is important to learn health condition for improvement and prevent sleep apnea. A bed-mounted seismometer system is proposed to monitor the heart and respiratory rates, and body movement and posture, during the sleep. To effectively monitor sleep status, an innovative local maxima statistics based approach and an instantaneous property based method are developed to estimate heart and respiratory rates, respectively. These methods are more robust and stable compared to previous works. Besides, algorithms for body movement and posture identification are also investigated based on instantaneous properties. We incorporated these algorithms to create a novel contactless sleep monitoring system that can keep track of heart rate, respiration rate, movement patterns and posture changes on events near to the bed. Our technology includes a small, powerful and low-cost smart seismometer, that can be easily installed to a bed frame under the mattress or boxes, and a user-friendly graphic interface, that can be paired with smartphone devices and display results through the APPs notifications. A prototype system is demonstrated, showing great potentials in monitoring a person's sleep status under different conditions.

This paper presents a novel in-network person identification and fall detection system that uses floor seismic data produced by footsteps and fall downs as an only source for recognition. Compared with other existing methods, our approach is done in real-time, which means the system is able to identify a person almost immediately with only one or two footsteps. An adapted in-network localization method is proposed in which sensors collaborate among them to recognize the person walking, and most importantly, detect if the person falls down at any moment. We also introduce a voting system among sensor nodes to improve accuracy in person identification. Our system is innovative since it can be robust to identify fall downs from other possible events, like jumps, door close, objects fall down, etc. Such a smart system can also be connected to smart commercial devices (like Google Home or Amazon Alexa) for emergency notifications. Our approach represents an advance in smart technology for elder people who live alone. Evaluation of the system shows it is able to identify people with one or two steps in an average of 93.75% (higher accuracy than other methods that use more footsteps), and it detects fall downs with an acceptance rate of 95.14% (distinguishing from other possible events). The fall down localization error is smaller than 0.28 meters, which it is acceptable compared to the height of a person.

Monitoring and tracking the underground moving target have many important applications, such as homeland security and defense applications. A wireless seismic network is designed to sense the seismic wave generated by the moving target and locate the target based on Reverse Time Migration (RTM) method. A new Gaussian Beam Migration (GBM) algorithm is used to improve the traditional RTM algorithm. The GBM-RTM method is a time-frequency domain algorithm, where the complex time domain calculation is transformed to a simple frequency domain calculation. By using GBM-RTM method, we accelerate the computation and reduce the communication cost. Through the extensive simulations and experiments, we demonstrate the trajectory tracking of the moving underground targets. The stability and accuracy of our proposed localization algorithms are also evaluated.

Cyber attacks and malfunctions challenge the wide applications of Internet of Things (IoT). Since they are generally designed as embedded systems, typical auto-sustainable IoT devices usually have a limited capacity and a low processing power. Because of the limited computation resources, it is difficult to apply the traditional techniques designed for personal computers or super computers, like traffic analyzers and antivirus software. In this paper, we propose to leverage statistical learning methods to characterize the device behavior and flag deviations as anomalies. Because the system statistics, such as CPU usage cycles, disk usage, etc., can be obtained by IoT application program interfaces, the proposed framework is platform and deviceindependent. Considering IoT applications, we train multiple machine learning models to evaluate their feasibility and suitability. For the target auto-sustainable IoT devices, which operate well-planned processes, the normal system performances can be modeled accurately. Based on time series analysis methods, such as local outlier factor, cumulative sum, and the proposed adaptive online thresholding, the anomalous behaviors can be effectively detected. Comparing their performances on detecting anomalies as well as the computation sources required, we conclude that relatively simple machine learning models are more suitable for IoT security, and a data-driven anomaly detection method is preferred.

Internet of Things (IoT) are vulnerable to both cyber and physical attacks. Therefore, a cyber-physical security system against different kinds of attacks is in high demand. Traditionally, attacks are detected via monitoring system logs. However, the system logs, such as network statistics and file access records, can be forged. Furthermore, existing solutions mainly target cyber attacks. This paper proposes the first energy auditing and analytics-based IoT monitoring mechanism. To our best knowledge, this is the first attempt to detect and identify IoT cyber and physical attacks based on energy auditing. Using the energy meter readings, we develop a dual deep learning (DL) model system, which adaptively learns the system behaviors in a normal condition. Unlike the previous single DL models for energy disaggregation, we propose a disaggregation-aggregation architecture. The innovative design makes it possible to detect both cyber and physical attacks. The disaggregation model analyzes the energy consumptions of system subcomponents, e.g., CPU, network, disk, etc., to identify cyber attacks, while the aggregation model detects the physical attacks by characterizing the difference between the measured power consumption and prediction results. Using energy consumption data only, the proposed system identifies both cyber and physical attacks. The system and algorithm designs are described in detail. In the hardware simulation experiments, the proposed system exhibits promising performances.

Seismic reflectivity inversion is widely applied to improve the seismic resolution to obtain detailed underground understandings. Based on the convolution model, seismic inversion removes the wavelet effect by solving an optimization problem. Taking advantage of the sparsity property, the ℓ1-norm is commonly adopted in the regularization terms to overcome the noise/interference vulnerability observed in the lp-losses minimization. However, no one has provided a deterministic conclusion that ℓ1-norm regularization is the best choice for seismic reflectivity inversion. Instead of using an unproved fixed regularization norm, we propose an optimal seismic reflectivity inversion approach. Our method adaptively adopts an ℓp-loss-ℓq-regularization (i.e., ℓp,q-regularization) for p = 2, 0q-norm regularization. Then, the majorization-minimization and CV algorithms are briefly described. The performance of the proposed seismic inversion approach is evaluated through synthetic examples and a field example from the Bohai Bay Basin, China.

The ability to use networked seismic instruments to image subsurface civil infrastructure and activities in data-limited extreme environments is crucial for many civil and security applications. This paper presents a non-invasive smart seismic network for imaging subsurface structures using ambient noise only. An innovative in-network spatial auto-autocorrelation method is designed to image different layers of underground infrastructures at different frequency bands and finally result in a 3D image. The proposed approach is general and can characterize the near-surface sediment and the infrastructure simultaneously. The experiments demonstrate that underground utility lines affecting sediment can be imaged, and the potential of using the method for abnormal activities like leakages. An exhaustive evaluation regarding bandwidth utilization, communication cost, and system resilience were conducted to highlight the benefits of the proposed approach.

The S transform (ST) is one of the most commonly used time-frequency (TF) analysis algorithms and is commonly used in assisting reservoir characterization and hydrocarbon detection. Unfortunately, the TF spectrum obtained by the ST has a low temporal resolution at low frequencies, which lowers its ability in thin beds and channels detection. In this letter, we propose a three parameters ST (TPST) to optimize the TF resolution flexibly. To demonstrate the validity and effectiveness of the TPST, we first apply it to a synthetic data and a synthetic seismic trace and then to a filed data. Synthetic data examples show that this TPST achieves an optimized TF resolution, compared with the standard ST and modified ST with two parameters. Field data experiments illustrate that the TPST is superior to the ST in highlighting the channel edges. The lateral continuity of the frequency slice produced by the TPST is more continuous than that of the ST.