C. L. Philip Chen

Institute of Electrical and Electronics Engineers | IEEE · IEEE Systems, Man, and Cybernetics Society

In this article, a proactive control strategy is developed for robots interacting with humans by integrating the estimation of human partner’s motion intention. A human control model is used and a least square-based observer is employed to estimate the human control input without a force sensor. Using the estimate of the human intention, a neural n...

This article investigates the fixed-time attitude control problem of quadrotor unmanned aerial vehicles (QUAVs) subject to stochastic disturbances. A fixed-time stability criterion for stochastic systems, which can compress the upper bound of the convergence time compared with the fixed-time stable Lyapunov theorem commonly used in the existing wor...

Imbalanced data poses a substantial challenge to conventional classification methods, which often disproportionately favor samples from the majority class. To mitigate this issue, various oversampling techniques have been deployed, but opportunities for optimizing data distributions remain underexplored. By exploiting the ability of metric learning...

Due to the advantages of fast training speed and competitive performance, Broad Learning System (BLS) has been widely used for classification tasks across various domains. However, the random weight generation mechanism in BLS makes the model unstable, and the performance of BLS may be limited when dealing with some complex datasets. On the other h...

Recently, rule-based classification on multivariate time series (MTS) data has gained lots of attention, which could improve the interpretability of classification. However, state-of-the-art approaches suffer from three major issues. 1) few existing studies consider temporal relations among features in a rule, which could not adequately express the...

This paper concentrates on asynchronous thruster fault detection for unmanned marine vehicles (UMVs) under multiple cyber attacks, external disturbances and thruster fault. A novel model of multiple attacks is constructed using an asynchronous switched method by considering aperiodic denial-of-service (DoS) attacks and stochastic false data injecti...

We propose a compressive yet effective mesh representation, Blocked and Patchified Tokenization (BPT), facilitating the generation of meshes exceeding 8k faces. BPT compresses mesh sequences by employing block-wise indexing and patch aggregation, reducing their length by approximately 75\% compared to the original sequences. This compression milest...

Feature selection is a highly regarded research area in the field of data mining, as it significantly enhances the efficiency and performance of high-dimensional data analysis by eliminating redundant and irrelevant features. Despite the ease of data acquisition, labeling data remains a laborious and expensive task. To leverage the abundance of unl...

This paper studies the predefined-time adaptive control problem for a class of strict-feedback nonlinear systems with parametric uncertainties and disturbances. A new practical predefined time stable (PPTS) criterion is first presented as the theoretical basis of predefined time control design, and then a new nonlinear command filter is designed. B...

Deception attacks are a big danger to the security and durability of multi-agent systems (MASs). This article introduces a new technique to tackle the problem of deception attacks in nonlinear multi-agent systems (NMASs). The system can be attacked through sensors and actuators, which may lead to incomplete system data transmission and render the s...

Multi-label classification is a challenging problem that has attracted significant attention from researchers, particularly in the domain of image and text attribute annotation. However, multi-label datasets are prone to serious intra-class and inter-class imbalance problems, which can significantly degrade the classification performance. To addres...

Graph-based methods have demonstrated exceptional performance in semi-supervised classification. However, existing graph-based methods typically construct either a predefined graph in the original space or an adaptive graph within the output space, which often limits their ability to fully utilize prior information and capture the optimal intrinsic...

Broad learning system (BLS) is an effective neural network requiring no deep architecture, however it is somehow fragile to noisy data. The previous robust broad models directly map features from the raw data, which inevitably learn useless or even harmful features for data representation when the inputs are corrupted by noise and outliers. To addr...

This paper presents an adaptive fixed-time command-filtered fuzzy controller design method of nonlinear stochastic systems subject to saturating input. Fuzzy logic systems (FLSs) can be utilized to fit the uncertainties. Inverse function theory is first introduced to tackle the design obstacle that exists in the input terminal, that is, saturating...

Imbalanced data biases the classifier towards the majority class. Accompanied with high-dimensional characteristics, classification performance is further degraded. Existing researches for skewed data mainly involve resampling, cost-sensitive learning, and classifier ensemble. However, these approaches have some limitations: 1) resampling suffers f...

This study examined the problemof event-triggered formation control for nonlinear multiagent systems (MASs) with unmeasured states. First, by applying fuzzy logic systems (FLSs), the identification of unknown nonlinearities could be achieved. To save communication resources, we introduce an event-triggered mechanism. And use the triggered output si...

Although multimodal physiological data from the central and peripheral nervous systems can objectively respond to human emotional states, the individual differences caused by non-stationary and low signal-to-noise properties bring several challenges to cross-subject emotion recognition tasks. Many previous studies usually focused on learning high c...

Most of the work on electroencephalogram (EEG)-based emotion recognition aims to extract the distinguishing features from high-dimensional EEG signals, ignoring the complementarity of information between EEG latent space and graph space. Furthermore, the influence of brain connectivity on emotions encompasses both physical structure and functional...

Broad learning system (BLS) has to undergo a vectorization operation before modeling image data, which makes it challenging for BLS to learn local semantic features. Thus, various convolutional-based broad learning systems (C-BLS) have been introduced to address these challenges. Regrettably, the existing C-BLS variants either lack an efficient tra...

One of the main reasons for Alzheimer’s disease (AD) is the disorder of some neural circuits. Existing methods for AD prediction have achieved great success, however, detecting abnormal neural circuits from the perspective of brain networks is still a big challenge. In this work, a novel decoupling generative adversarial network (DecGAN) is propose...

This article studies the problem of an adaptive fuzzy predefined-time tracking control approach for a type of uncertain nonstrict-feedback high-order nonlinear systems with input quantization. The considered plants contain unknown nonlinear functions, input quantization, and external disturbances. Based on the backstepping recursive technique and p...

Underwater images often suffer from various issues such as low brightness, color shift, blurred details, and noise due to light absorption and scattering caused by water and suspended particles. Previous underwater image enhancement (UIE) methods have primarily focused on spatial domain enhancement , neglecting the frequency domain information inhe...

Aiming at the course tracking control of unmanned surface vehicle (USV) under the restricted communication bandwidth at sea, this paper focuses on the fuzzy adaptive control scheme with input and state quantization. It alleviates the pressure on signal transmission in limited bandwidth and reduces the actuator actuation frequency while ensuring eff...

Single Image Super-Resolution (SISR) aims to reconstruct a high-resolution image from its corresponding low-resolution input. A common technique to enhance the reconstruction quality is Non-Local Attention (NLA), which leverages self-similar texture patterns in images. However, we have made a novel finding that challenges the prevailing wisdom. Our...

In the field of artificial intelligence, combining transformers and convolutional neural networks (CNNs) to improve performance has become a popular solution for various image restoration tasks. However, the hyperparameters related to feature levels are empirical, leading to the inevitable presence of redundant features that hinder effective image...

Deep multiview clustering provides an efficient way to analyze the data consisting of multiple modalities and features. Recently, the autoencoder (AE)-based deep multiview clustering algorithms have attracted intensive attention by virtue of their rewarding capabilities of extracting inherent features. Nevertheless, most existing methods are still...

A graph neural network (GNN) is a powerful architecture for semi-supervised learning (SSL). However, the data-driven mode of GNNs raises some challenging problems. In particular, these models suffer from the limitations of incomplete attribute learning, insufficient structure capture, and the inability to distinguish between node attribute and grap...

Robust nonlinear regression frequently arises in data analysis that is affected by outliers in various application fields such as system identification, signal processing, and machine learning. However, it is still quite challenge to design an efficient algorithm for such problems due to the nonlinearity and nonsmoothness. Previous researches usual...

Class imbalance problems pose significant challenges in the field of data mining. The skewed distribution of classes in imbalanced datasets often leads conventional classification methods to neglect the importance of minority classes, favoring the majority ones. In this paper, we propose an imbalanced complemented subspace representation model with...

Graph representation learning (GRL) focuses on abstracting critical information from raw graphs. Unfortunately, there always exist various kinds of uncertainties such as attribute noise and network topology corruption in raw graphs. Under the message passing mechanism, these uncertainties are likely to spread throughout the whole graph. Matters lik...

This article focuses on the singularity-free predefined time control design problem of the quantized nonstrict feedback (NSF) nonlinear systems. Radial basis function neural networks (NNs) are introduced to model the unknown nonlinear dynamics. With the property of the NN basis function, the algebraic loop problem posed by the NSF control structure...

Recently, Deep Neural Networks (DNNs) have been largely utilized in Collaborative Filtering (CF) to produce more accurate recommendation results due to their ability of extracting the nonlinear relationships in the user-item pairs. However, the DNNs-based models usually encounter high computational complexity, i.e., consuming very long training tim...

At present, the dynamic encoding–decoding scheme is utilized in the impulsive consensus control of multiagent systems (MASs) to solve the limited bandwidth problem. However, the unknown nonlinear dynamics and deception attacks will generate some uncertainties inevitably in the encoding–decoding, which may cause the quantizer saturation and then inf...

For a class of nontriangular structural multiagent systems, this article presents a neuro-adaptive prescribed performance consensus control scheme. By using mean value theorem to isolate the virtual variables and neural networks to approximate the ideal controller, the system model is reconstructed, based on which the virtual controllers are able t...

Low-light remote sensing (RS) images generally feature high resolution and high spatial complexity, with continuously distributed surface features in space. This continuity in scenes leads to extensive long-range correlations in spatial domains within RS images. convolutional neural networks (CNNs), which rely on local correlations for long-distanc...

EEG emotion recognition has been hampered by the clear individual differences in the electroencephalogram (EEG). Nowadays, domain adaptation is a good way to deal with this issue because it aligns the distribution of data across subjects. However, the performance for EEG emotion recognition is limited by the existing research, which mainly focuses...

Cross-subject EEG emotion recognition suffers a major setback due to high inter-subject variability in emotional responses. Many prior studies have endeavored to alleviate the inter-subject discrepancies of EEG feature distributions, ignoring the variable EEG connectivity and prediction deviation caused by individual differences, which may cause po...

This study addresses the learning-based resilient adaptive fuzzy optimal consensus control problem for nonlinear uncertain Multiagent Systems (MASs) in the presence of intermittent Denial of Service (DoS) attacks. A key obstacle is the uncertainty in the dynamics of the followers, which makes it challenging to eliminate dependency on the identifier...

In this paper, an optimization algorithm based on deep reinforcement learning is proposed to optimize complex networks in fixed-time convergence of continuous action iteration dilemmas. The field of continuous action iterative dilemmas has long been studied, with prior research primarily emphasizing the effectiveness of strategy selection and the s...

In this study, an adaptive neural network (NN) control is proposed for nonlinear two-degree-of-freedom (2-DOF) helicopter systems considering the input constraints and global prescribed performance. First, radial basis function NN (RBFNN) is employed to estimate the unknown dynamics of the helicopter system. Second, a smooth nonaffine function is e...

Time series anomaly detection is the process of identifying anomalies within time series data. The primary challenge of this task lies in the necessity for the model to comprehend the characteristics of time-independent and abnormal data patterns. In this study, a novel algorithm called adaptive memory broad learning system (AdaMemBLS) is proposed...

The broad learning system (BLS) has recently been applied in numerous fields. However, it is mainly a supervised learning system and thus not suitable for specific practical applications with a mixture of labeled and unlabeled data. Despite a manifold regularization-based semi-supervised BLS, its performance still requires improvement, because its...

This article investigates the adaptive optimal output-feedback consensus tracking problem for nonlinear multiagent systems (MASs). Although adaptive optimal output-feedback control schemes for nonlinear systems have been developed recently, most results do not consider the two-way interaction between the state observer and its associated subsystem....

In this paper, the singularity-free predefined-time fuzzy adaptive tracking control problem is studied for non-strict feedback (NSF) nonlinear systems considering mismatched external disturbances. An innovative practical predefined-time stability (PPTS) criterion is proposed to provide the theoretical basis for subsequent control design. Compared t...

Separable nonlinear models (SNMs) serve as potent instruments for system identification, data analysis, and machine learning. However, the online identification of SNMs poses a greater challenge compared to their offline counterparts, primarily due to the dynamic nature of nonlinear systems. Traditional approaches, including the recursive Gauss–New...

This article proposes a teacher-guided peer learning approach that employs a continuous action iterated dilemma (CAID) model based on an incremental network. Traditional peer learning approaches often assume static communication relationships between students, which is not consistent with actual society, and this affects the effectiveness of peer l...

The article investigates the fixed-time dynamic surface-based adaptive composite control issue for nonlinear systems subject to unknown constants. With the help of the adaptive technology, unknown parameters for the system are approximated. Then, using the serial-parallel estimation models (SPEMs), the forecast biases and the track biases can chang...

Developing a distributed output feedback consensus tracking control scheme for nonlinear multiagent systems (MASs) with interconnected dynamics and unmeasurable states holds significant practical importance. Current approaches to this challenge often rely on fuzzy logic systems (FLSs) or neural networks (NNs) for direct compensation of interconnect...

The graph-information-based fuzzy clustering has shown promising results in various datasets. However, its performance is hindered when dealing with high-dimensional data due to challenges related to redundant information and sensitivity to the similarity matrix design. To address these limitations, this article proposes an implicit fuzzy k-means (...

Generating compact and sharply detailed 3D meshes poses a significant challenge for current 3D generative models. Different from extracting dense meshes from neural representation, some recent works try to model the native mesh distribution (i.e., a set of triangles), which generates more compact results as humans crafted. However, due to the compl...

In this paper, we improve the broad learning system (BLS) by speeding up the incremental learning for added inputs. We propose an efficient implementation for a step that is in the pseudoinverse computation of a partitioned matrix, to reduce the computational complexity. The proposed efficient implementation has two different forms for the cases of...

Imbalanced learning constitutes one of the most formidable challenges within data mining and machine learning. Despite continuous research advancement over the past decades, learning from data with an imbalanced class distribution remains a compelling research area. Imbalanced class distributions commonly constrain the practical utility of machine...

Temporal link prediction is one of the most important tasks for predicting time-varying links by capturing dynamics within complex networks. But it suffers from difficulties such as vulnerability to adversarial attacks and inadaptation to distinct evolutionary patterns. In this paper, we propose a robust temporal link prediction architecture via St...

Developing a distributed bipartite optimal consensus scheme while ensuring user-predefined performance is essential in practical applications. Existing approaches to this problem typically require a complex controller structure due to adopting an identifier–actor–critic framework and prescribed performance cannot be guaranteed. In this work, an ada...

In this article, the fuzzy adaptive fixed-time bipartite output consistent tracking problem of nonlinear coopetition multi-agent systems (MASs) under a signed directed graph is studied. The fuzzy logic system (FLS) is used in the control scheme to approximate the unknown nonlinear dynamics. Under the framework of adaptive backstepping recursive des...

A fixed-time fuzzy control (FTFC) scheme is suggested for uncertain nonlinear systems with prescribed performance (PP) and event-triggered communication. First, the original system with PP constraints is transformed into an unconstrained one using a coordinate transformation. Next, fuzzy logic systems (FLSs) are introduced to estimate the uncertain...

Arc faults in domestic circuits are one of the main causes of home fires. Due to the complexity of arc faults, traditional circuit protectors cannot effectively detect series arc. In this paper, a novel broad learning system-based approach with time and frequency features (BroadNet) is proposed for the detection of series AC arc faults. In the prop...

This article investigates an adaptive neural networks (NNs) tracking control design issue for nonlinear multi‐input and multi‐output (MIMO) systems involving the sensor‐to‐controller event‐triggered mechanism (ETM). In the design, NNs are utilized to approximate the unknown nonlinear functions. A sensor‐to‐controller ETM is designed to save unneces...

The fixed-time formation control problem for uncertain nonlinear multiagent systems (MASs) with time-varying actuator failures is investigated. Actuator failures would have a huge impact on the system performance, especially when the actuator failures are time-varying, which may even cause system insecurity. In order to cope with time-varying actua...

Collaborative representation-based classification (CRC) has been extensively applied to various recognition fields due to its effectiveness and efficiency. Nevertheless, it is generally suboptimal for imbalanced learning. Previous studies have revealed that a class-imbalance distribution can lead CRC, and even most conventional classification metho...

The application of artificial intelligence technology has greatly enhanced and fortified the safety of energy pipelines, particularly in safeguarding against external threats. The predominant methods involve the integration of intelligent sensors to detect external vibration, enabling the identification of event types and locations, thereby replaci...

Representation-based methods have found widespread applications in various classification tasks. However, these methods cannot deal effectively with imbalanced data scenarios. They tend to neglect the importance of minority samples, resulting in bias toward the majority class. To address this limitation, we propose a density-based discriminative no...

In this paper, the problem of adaptive security control for a class of nonlinear multi‐agent systems (MASs) under deception attacks is studied. This scheme aims to design a new adaptive fuzzy control scheme for each subsystem of MASs to reduce the impact of network attacks (from sensor to controller) on state signals, and proposes Nussbaum function...

Images captured under low-light environments typically have poor visibility, affecting many advanced computer vision tasks. In recent years, there have been some low-light image enhancement models based on deep learning, but they have not been able to effectively mine the deep multiscale features in the image, resulting in poor generalization perfo...

The application of artificial intelligence technology has greatly enhanced and fortified the safety of energy pipelines, particularly in safeguarding against external threats. The predominant methods involve the integration of intelligent sensors to detect external vibration, enabling the identification of event types and locations, thereby replaci...

With the rapid increase of economic globalization, the significant expansion of shipping volume has resulted in shipping route congestion, causing the necessity of trajectory prediction for effective service and efficient management. While trajectory prediction can achieve a relatively high level of accuracy, the performance and generalization of p...

The study of nicotine addiction mechanism is of great significance in both nicotine withdrawal and brain science. The detection of addiction-related brain connectivity using functional magnetic resonance imaging (fMRI) is a critical step in study of this mechanism. However, it is challenging to accurately estimate addiction-related brain connectivi...

This article investigates the problem of inverse optimal fixed-time tracking for nonlinear switched systems. Given this, a control scheme is developed based on an adaptive direct fuzzy inverse optimal and fixed time. The proposed control strategy accomplishes optimal performance without solutions for the Hamiltonian function. On the other hand, thi...

In addition to stability, the system optimality has also received attention because the system is expected to achieve higher performance with lower energy consumption. In general, the conventional approach to achieve optimal control of nonlinear MIMO systems is to solve the Hamilton–Jacobi–Bellman equation directly, which is time-consuming and some...