Shengli Xie's research while affiliated with GuangDong University of Technology and other places

Publications (365)

Article
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To minimize the effect of optical crosstalk-generated noise (crosstalk), we present a deep learning approach to precisely estimate the full-field displacements for depth-resolved wavelength-scanning interferometry (DRWSI). A deep convolution neural network, where the transformer block is introduced to effectively capture higher-order features of th...
Preprint
Wavelength scanning interferometry (WSI) is a promising tomographic imaging technique. However, the depth resolution is fundamentally limited by a narrow wavelength scanning range, which brings challenges to the frequency extraction for depth information. In this work, we proposed a parameter estimation (PE) paradigm via sinewaves separation, which...
Article
As the technology of autonomous vehicle develops, online hailing autonomous taxi system is regarded as one of the most popular public transportation services in the future. Studies related to demand forecasting, ride matching, path planning, relocation, and pricing strategy for shared online hailing and autonomous taxi services have emerged in rece...
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Tensor completion is a fundamental tool for incomplete data analysis, where the goal is to predict missing entries from partial observations. However, existing methods often make the explicit or implicit assumption that the observed entries are noise-free to provide a theoretical guarantee of exact recovery of missing entries, which is quite restri...
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Intensity saturation is a challenging problem in structured light 3D shape measurement. Most of the existing methods achieve high dynamic range (HDR) measurement by sacrificing measurement speed, making them limited in high-speed dynamic applications. This letter proposed a generic efficient saturation-induced phase error correction method for HDR...
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The non-uniform motion-induced error reduction in dynamic fringe projection profilometry is complex and challenging. Recently, deep learning (DL) has been successfully applied to many complex optical problems with strong nonlinearity and exhibits excellent performance. Inspired by this, a deep learning-based method is developed for non-uniform moti...
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Phase-sensitive optical coherence elastography (PhS-OCE) is a novel functional imaging modality capable of mapping strain fields inside semi-transparent materials. In this work, an off-axis PhS-OCE was further developed to measure strain field and Poisson’s ratio simultaneously. Based on the intrinsic equations of continuum mechanics, the relations...
Preprint
With the gradual popularization of self-driving, it is becoming increasingly important for vehicles to smartly make the right driving decisions and autonomously obey traffic rules by correctly recognizing traffic signs. However, for machine learning-based traffic sign recognition on the Internet of Vehicles (IoV), a large amount of traffic sign dat...
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This paper presents an iterative learning control approach to achieve precise tracking control for a class of repeatable parabolic multiple‐input–multiple‐output (MIMO) partial differential equations (PDEs) with time‐varying delays over a finite time interval. Feedback control is utilised in the iterative learning control system to improve the conv...
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We propose an Otsu-Kmeans gravity-based center extraction method for multi-spot images captured by the microlens array imaging system. We develop a 4-connected-region-based region of interest location method to recognize and segment light spot areas. We design an Otsu-Kmeans gravity-based threshold selection algorithm to realize the multi-spot cent...
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This paper addresses the neuroadaptive inverse optimal consensus problem of uncertain nonlinear multiagent systems (MASs) subject to actuator and sensor faults simultaneously. Unlike traditional adaptive dynamic programming methods, the proposed control mechanism minimizes a meaningful loss function without solving the Hamilton-Jacobi-Bellman equat...
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Symmetric nonnegative tensor factorization (SNTF) is an important tool for clustering analysis. To date, most of algorithms for SNTF are based on multiplicative update rules, which have many attractive properties, e.g., they are often simple to implement and can enforce nonnegativity without extra projection steps. However, the existing multiplicat...
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Battery swapping stations (BSSs) and charging stations (CSs), which provide electric vehicle battery refueling services, are important participants in the electricity and carbon markets. Battery swapping stations (BSSs) and charging stations (CSs), which provide electric vehicle battery refueling services, are important participants in the electric...
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Tensor-ring (TR) decomposition is a powerful tool for exploiting the low-rank property of multiway data and has been demonstrated great potential in a variety of important applications. In this article, non-negative TR (NTR) decomposition and graph-regularized NTR (GNTR) decomposition are proposed. The former equips TR decomposition with the abilit...
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Strain field characterization for evaluating the mechanical properties of polymethyl methacrylate (PMMA) is highly significant. This study establishes a method based on phase-contrast optical coherence tomography for the through-thickness strain field measurement of PMMA sheets, which utilizes two methods of probing light passing through the specim...
Preprint
Tensor completion is a fundamental tool for incomplete data analysis, where the goal is to predict missing entries from partial observations. However, existing methods often make the explicit or implicit assumption that the observed entries are noise-free to provide a theoretical guarantee of exact recovery of missing entries, which is quite restri...
Article
Multiview dictionary learning (DL) is attracting attention in multiview clustering due to the efficient feature learning ability. However, most existing multiview DL algorithms are facing problems in fully utilizing consistent and complementary information simultaneously in the multiview data and learning the most precise representation for multivi...
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This article presents a robust feedback compensator design approach for semilinear parabolic distributed parameter systems (DPSs) with external disturbances via mobile actuators and sensors. An performance constraint is introduced to deal with the external disturbances from the model and measurement noise. Two types of feedback compensators are des...
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Multi-energy multi-microgrid (MMG) networks are considered as a promising form of energy systems that can integrate various energy resources and improve energy utilization efficiency. Carbon emission limitation, regarded as a significant factor in energy management, has received increasing attention in recent years. By taking into account both econ...
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This paper presents a novel iterative learning feedback control method for linear parabolic distributed parameter systems with multiple collocated piecewise observation. Multiple actuators and sensors distributed at the same position of the spatial domain are utilized to perform collocated piecewise control and measurement operations. The advantage...
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To address the problems of slow acquisition speed and low accuracy faced by existing grid search-based satellite acquisition methods in complex scenarios, this study proposes a high accuracy and fast satellite signal acquisition method based on blind source separation. The proposed method first adopts wavelet threshold denoising to reduce the noise...
Preprint
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Distinguishing the importance of views has proven to be quite helpful for semi-supervised multi-view learning models. However, existing strategies cannot take advantage of semi-supervised information, only distinguishing the importance of views from a data feature perspective, which is often influenced by low-quality views then leading to poor perf...
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Tensor robust principal component analysis (TRPCA) is a fundamental model in machine learning and computer vision. Recently, tensor train (TT) decomposition has been verified effective to capture the global low-rank correlation for tensor recovery tasks. However, due to the large-scale tensor data in real-world applications, previous TRPCA models o...
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Incomplete multiview clustering (IMC) has attracted considerable attention as it can flexibly fuse the multiview information when part of the view samples are unobserved. Considering that the main challenge of IMC is the unobserved samples causing the information loss, in this paper, we propose a novel IMC model to complete the unobserved samples,...
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Underdetermined blind source separation of speech mixtures is a challenging issue in the classical “Cocktail-party” problem. Recently, there has been attention to use dictionary learning to solve this problem. In this paper, we build a novel framework to solve the underdetermined blind separation of speech mixtures as a sparse signal recovery probl...
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In this letter, a deep-learning-based approach is proposed for estimating the strain field distributions in phase-sensitive optical coherence elastography (PhS-OCE). The method first uses the simulated wrapped phase maps and corresponding phase-gradient maps to train the strain estimation convolution neural network (CNN) and then employs the traine...
Article
With the rapid development of Internet of Things (IoT), digital twin is emerging as one of the most promising technologies to connect physical components with digital space for better optimization of physical systems. However, the limited wireless resource and security concerns impede the deployment of digital twin in IoT. In this paper, we exploit...
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The f-wave extraction (FE) is essential for analysis of atrial fibrillations. However, the state-of-the-art FE methods are model-based, and they cannot well adapt to the QRST complexes with high morphological variabilities which often appear in clinical electrocardiogram (ECG). Recently, the encoder-decoder based deep learning networks have been su...
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Complex industrial process data often exhibit nonlinear static and dynamic characteristics. Traditional deep learning methods like stacked autoencoder (SAE) have excellent nonlinear static feature learning capabilities, but they ignore the dynamic correlation existing in process data. Feature learning based on manifold learning using neighborhood s...
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A spatial-construction-based fault diagnosis method is proposed to detect and locate the abnormality for unknown distributed parameter systems (DPSs). To accurately locate the abnormality, the continuous spatial basis functions (SBFs) are derived by the proposed spatial construction method from empirical data. Theoretical analysis proves that the B...
Preprint
As a distributed learning approach, federated learning trains a shared learning model over distributed datasets while preserving the training data privacy. We extend the application of federated learning to parking management and introduce FedParking in which Parking Lot Operators (PLOs) collaborate to train a long short-term memory model for parki...
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Direct-optimization-based dictionary learning has attracted increasing attention for improving computational efficiency. However, the existing direct optimization scheme can only be applied to limited dictionary learning problems, and it remains an open problem to prove that the whole sequence obtained by the algorithm converges to a critical point...
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Accurate and rapid diagnosis of COVID-19 using chest X-ray (CXR) plays an important role in large-scale screening and epidemic prevention. Unfortunately, identifying COVID-19 from the CXR images is challenging as its radiographic features have a variety of complex appearances, such as widespread ground-glass opacities and diffuse reticular-nodular...
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Tensor-ring (TR) decomposition was recently studied and applied for low-rank tensor completion due to its powerful representation ability of high-order tensors. However, most of the existing TR-based methods tend to suffer from deterioration when the selected rank is larger than the true one. To address this issue, this article proposes a new low-r...
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In the deformation monitoring based on satellite positioning, the extraction of the effective deformation signal which needs plenty of computing resources is very important. Mobile-edge computing can provide low latency and near-edge computing agility for the deformation monitoring process. In this paper, we propose an edge computing network archit...
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The product quality of injection‐molded plastic is closely related to the injection flow velocity of molten plastics. In this article, an optimal tracking control problem for the injection flow front position arising in the filling process in the injection molding machine (IMM) is considered, and an intelligent real‐time optimal control method base...
Article
As a distributed learning approach, federated learning trains a shared learning model over distributed datasets while preserving the training data privacy. We extend the application of federated learning to parking management and introduce FedParking in which Parking Lot Operators (PLOs) collaborate to train a long short-term memory model for parki...
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This paper studies optimal day-ahead scheduling of grid-connected batteries that simultaneously provide three services: 1) load shifting, 2) real-time balancing, and 3) primary frequency control (PFC). The uncertainties of load and frequency are incorporated in the cost-minimizing scheduling problem via chance constraints. The resulting chance-cons...
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This article presents an iterative learning control (ILC) approach for linear parabolic distributed parameter systems with multiple actuators and multiple sensors. The distribution functions of actuators and sensors are chosen as delta function to produce pointwise control and pointwise measurement. A P-type ILC law is proposed based on the iterati...
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Light detection and ranging (LiDAR) systems, also called laser radars, have a wide range of applications. This paper considers two problems in LiDAR data. The first problem is occlusion. A LiDAR acquires point clouds by scanning the surrounding environment with laser beams emitting from its center, and therefore an object behind another cannot be s...
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Separation of heart and lung sounds from the observated mixtures has become a hot research topic in the prevention and diagnosis of heart and lung diseases. Especially, in real clinical situations, clinicians can perform auscultation by using the bioacoustics knowledge of sounds to diagnose the diseases of heart and lung. Therefore, to have an inti...
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Nonnegative tensor ring (NTR) decomposition is a powerful tool for capturing the significant features of tensor objects while preserving the multi-linear structure of tensor data. The existing algorithms rely on frequent reshaping and permutation operations in the optimization process and use a shrinking step size or projection techniques to ensure...
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We proposed an adaptive incremental method for the cumulative strain estimation in phase-sensitive optical coherence elastography. The method firstly counts the amount of phase noise points by mapping a binary noise map. After the noise threshold value is preset, the interframe interval is adaptively adjusted in terms of the phase noise ratio. Fina...
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A method combining phase-contrast technique and spectral-domain optical coherence tomography (OCT) has been recently proposed for visualizing curing behaviors inside polymers (2020 Appl. Phys. Lett. 116 054103). Here, based on the method, a non-contact and highly-sensitive optical sensor is further developed to monitor the photocuring process of li...
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Cross-modal retrieval has attracted considerable attention for searching in large-scale multimedia databases because of its efficiency and effectiveness. As a powerful tool of data analysis, matrix factorization is commonly used to learn hash codes for cross-modal retrieval, but there are still many shortcomings. First, most of these methods only f...
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Semi-supervised multi-view learning methods aim to boost the learning performance by conjunction with labeled data, because the label information can enhance the discriminant ability of the learned model. Recently, non-negative matrix factorization has received widespread attention in semi-supervised multi-view learning due to its powerful ability...
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Convolutional transform learning (CTL), learning filters by minimizing the data fidelity loss function in an unsupervised way, is becoming very pervasive, resulting from keeping the best of both worlds: the benefit of unsupervised learning and the success of the convolutional neural network. There have been growing interests in developing efficient...
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Modeling high-spatial dimensional (high-D) distributed parameter systems (DPSs) is very difficult because of the spatially distributed characteristic and complex spatiotemporal coupling. In this paper, a new framework based on high-order singular vector decomposition (HOSVD) is proposed to model high-D DPS. A modified HOSVD is designed for separati...
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The thermal effect has a significant impact on the performance and durability of lithium-ion batteries. This paper proposes a systematic approach for fast modeling of the distributed battery thermal process. In the proposed method, a well-recognized time/space (T/S) separation is adopted to decompose the spatio-temporal thermal dynamics. Under the...
Article
By integrating Mobile Edge Computing (MEC) into vehicular networks, vehicular edge computing extends computing capability to the vehicular network edge and hosts services in close proximity of connected vehicles. Parked Vehicles (PVs) occupy a large portion of the global vehicle and have idle states and resources. They collaborate with the MEC serv...
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Sparse subspace clustering (SSC) method has gained considerable attention in recent decades owing to its advantages in the fields of clustering. In essence, SSC is to learn a sparse affinity matrix followed by striving for a low-dimensional representation of data. However, the SSC and its variants mainly focus on building high-quality affinity matr...
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In this paper, a hybrid-intelligent real-time optimal control approach based on deep neural networks (DNNs) is proposed to improve the autonomy and intelligence of automatic guided vehicles (AGVs) navigation control. We first formulate the motion planning problem of an AGV with static and dynamic obstacles as a nonlinear optimal control problem (OC...
Article
5G and beyond (B5G) networks significantly promote the popularity and ubiquity of drones by providing high-throughput and low-latency communication. In B5G drone networks, data sharing among drones has great potential to improve and enrich civilian and commercial applications, such as surveillance monitoring. Nevertheless, a series of security chal...
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The monitoring of temperature distribution is critical to the safety performance and cycle life of lithium-ion batteries. This paper introduces a systematic solution for real-time modeling of the battery thermal process under non-homogeneous boundary conditions. The proposed method integrates the non-homogeneity separation and the time-space (T/S)...
Preprint
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In this paper, we investigate the optimal output tracking problem for linear discrete-time systems with unknown dynamics using reinforcement learning and robust output regulation theory. This output tracking problem only allows to utilize the outputs of the reference system and the controlled system, rather than their states, and differs from most...
Article
A common approach for synchronizing the agents in a Wireless Sensor Network (WSN) is two-way timing stamps exchange mechanism. In this letter, by analyzing the change of accumulated clock offset within each communication cycle, an improved two-state discrete-time state model is proposed. A step further, two Kalman-filter-based estimators are propos...
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Rewiring is a common strategy for enhancing the controllability robustness of complex networks. In this brief, rewiring strategies including the degree-preserving strategy, underlying-topology-preserving strategy, and unconstrained-rewiring strategy, are compared and analyzed. Since measuring the true controllability-robustness values by simulation...
Article
In recent years, there has been growing concerns on the study of dictionary learning with the nonconvex sparsity-including penalty. However, how to efficiently address the dictionary learning with the nonconvex penalty is still an open problem. In this paper, we present an efficient DC-based algorithm for dictionary learning with the nonconvex smoo...