Cheng Lian

Wuhan University of Technology | WHUT · School of Automation

Evaluation of uncertainties associated with landslide displacement prediction is essential for improving the reliability of landslide early warning systems. An efficient probabilistic forecasting method for the construction of prediction intervals (PIs) using bootstrap and kernel-based extreme learning machine (ELM) is proposed. To overcome the dra...

In this paper, we propose a new approach to establish a landslide displacement forecasting model based on artificial neural networks (ANNs) with random hidden weights. To quantify the uncertainty associated with the predictions, a framework for probabilistic forecasting of landslide displacement is developed. The aim of this paper is to construct p...

Landslide prediction is always the emphasis of landslide research. Using global positioning system GPS technologies to monitor the superficial displacements of landslide is a very useful and direct method in landslide evolution analysis. In this paper, an EEMD–ELM model [ensemble empirical mode decomposition (EEMD) based extreme learning machine (E...

An accurate prediction of landslide displacement is challenging and of great interest to governments and researchers. In order to reduce the risk of selecting the types of influencing factors and artificial neural networks (ANNs), a multiple ANNs switched prediction method is proposed for landslide displacement forecasting. In the first stage, a se...

In this paper, an M–EEMD–ELM model (modified ensemble empirical mode decomposition (EEMD)-based extreme learning machine (ELM) ensemble learning paradigm) is proposed for landslide displacement prediction. The nonlinear original surface displacement deformation monitoring time series of landslide is first decomposed into a limited number of intrins...

When encountering the distribution shift between the source (training) and target (test) domains, domain adaptation attempts to adjust the classifiers to be capable of dealing with different domains. Previous domain adaptation research has achieved a lot of success both in theory and practice under the assumption that all the examples in the source...

Multimodal physiological signals play a pivotal role in drivers’ perception of work stress. However, the scarcity of labels and the multitude of modalities render the utilization of physiological signals for driving cognitive alertness detection challenging. We thus propose a multimodal physiological signal detection model based on self-supervised...

The development of intelligent transportation technology has provided a significant impetus for autonomous driving technology. Currently, autonomous vehicles based on Model Predictive Control (MPC) employ motion control strategies based on sampling time, which fail to fully utilize the spatial information of obstacles. To address this issue, this p...

In recent years, self-supervised learning-based models have been widely used for electrocardiogram (ECG) representation learning. However, most of the models utilize contrastive learning that strongly depend on data augmentation. In this paper, we propose a masked self-supervised learning model based on multiview information bottleneck principle. O...

Driver fatigue is a critical factor that lead to traffic accidents with a high fatality rate. Electroencephalogram (EEG) is one of the most reliable indicators to objectively assess fatigue status, but recognizing fatigue driving status from it is still an essential and challenging problem. In this paper, we propose a multiscale global prompt Trans...

Multi-view learning (MVL) has gained great success in integrating information from multiple perspectives of a dataset to improve downstream task performance. To make MVL methods more practical in an open-ended environment, this paper investigates a novel paradigm called multi-view class incremental learning (MVCIL), where a single model incremental...

The 12-lead electrocardiogram (ECG) is a common method used to diagnose cardiovascular diseases. Recently, ECG classification using deep neural networks has been more accurate and efficient than traditional methods. Most ECG classification methods usually connect the 12-lead ECG into a matrix and then input this matrix into a deep neural network. W...

In the study of hyperspectral image classification based on machine learning theory and techniques, the problems related to the high dimensionality of the images and the scarcity of training samples are widely discussed as two main issues that limit the performance of the data-driven classifiers. These two issues are closely interrelated, but are u...

Many deaths are caused by heart disease. A phonocardiogram (PCG) reflects the general rule of heart movement, so the analysis of heart sound signals is particularly important. In this paper, we propose a new deep neural network termed DsaNet, which is mainly constructed by depthwise separable convolution and the attention module. DsaNet can directl...

Landslide displacement prediction is a challenging and important subject in landslide research. To improve the prediction accuracy of and reduce disasters caused by landslides, we propose a selective ensemble deep bidirectional Random Vector Functional Link Network (sedb-RVFLN) for landslide displacement prediction in which each independent hidden...

Unsupervised domain adaptation aims to learn a classification model for the target domain without any labeled samples by transferring the knowledge from the source domain with sufficient labeled samples. The source and the target domains usually share the same label space but are with different data distributions. In this paper, we consider a more...

A phonocardiogram (PCG) is a plot of high-fidelity recording of the sounds of the heart obtained using an electronic stethoscope that is highly valuable in clinical medicine. It can help cardiologists diagnose cardiovascular diseases quickly and accurately. In this paper, we propose a multi-view deep network for the classification of PCG signals th...

Zero-shot learning casts light on lacking unseen class data by transferring knowledge from seen classes via a joint semantic space. However, the distributions of samples from seen and unseen classes are usually imbalanced. Many zero-shot learning methods fail to obtain satisfactory results in the generalized zero-shot learning task, where seen and...

Recognition of obstacle type based on visual sensors is important for navigation by unmanned surface vehicles (USV), including path planning, obstacle avoidance, and reactive control. Conventional detection techniques may fail to distinguish obstacles that are similar in visual appearance in a cluttered environment. This work proposes a novel obsta...

Convolutional neural network has achieved remarkable success, and has excellent local feature extraction ability. Similarly, Transformer has been developed markedly in recent years, achieving excellent representation capabilities in terms of global features, which has aroused heated discussions. In terms of multivariate time series classification,...

In this paper, multi-task learning is introduced into the study of landslide evolution state prediction and control. Firstly, we define two landslide evolution states and propose a method of landslide evolution state level classification prediction. Specifically, we use Gaussian mixture model (GMM) to reconstruct labeled data sets and establish a l...

Because it is very expensive to collect a large number of labeled samples to train deep neural networks in certain fields, semi-supervised learning (SSL) researcher has become increasingly important in recent years. There are many consistency regularization-based methods for solving SSL tasks, such as the \(\Pi \) model and mean teacher. In this pa...

Zero-shot learning has received great interest in visual recognition community. It aims to classify new unobserved classes based on the model learned from observed classes. Most zero-shot learning methods require pre-provided semantic attributes as the mid-level information to discover the intrinsic relationship between observed and unobserved cate...

Convolutional neural networks (CNNs) are widely used in the field of remote sensing images. However, the applications of CNNs and related techniques often ignore the properties of remote sensing data. In our study, we focus on the hyperspectral image (HSI) classification problem, and address the issue of including the very rich spectral information...

Interval prediction is an efficient approach to quantifying the uncertainties associated with landslide evolution. In this paper, a novel method, termed lower upper bound estimation (LUBE), of constructing prediction intervals (PIs) based on neural networks (NNs) is applied and extended to landslide displacement prediction. A random vector function...

Short-term wind speed prediction plays a significant role in the management of large-scale wind power plants. However, wind speed prediction is extremely complex and difficult due to the volatility and non-linearity of wind. For this purpose, a broad learning system (BLS) with ensemble and classification named BLS-EC is proposed to predict multi-st...

The research of semi-supervised learning (SSL) is of great significance because it is very expensive to collect a large quantity of data with labels in some fields. Two recent deep learning-based SSL algorithms, temporal ensembling and virtual adversarial training (VAT), have achieved state-of-the-art accuracy in some classical SSL tasks, while bot...

Given a novel class instance, the purpose of zero-shot learning (ZSL) is to learn a model to classify the instance by seen samples and semantic information transcending class boundaries. The difficulty lies in how to find a suitable space for zero-shot recognition. The previous approaches use semantic space or visual space as classification space....

Convolutional neural networks are widely used for solving image recognition and other classification problems in which the whole image is considered as a single object. In this paper, we take the pansharpening problem of remote sensing images as an example to discuss how to establish pixel-wise regression models using convolutional neural networks....

This paper proposes a new hybrid approach for constructing high-quality prediction intervals (PIs) for landslide displacements. In the first stage, we develop an improved method to optimize bootstrap-based PIs. The improved method uses part of the selected neural networks (NNs) rather than all of the NNs to construct PIs. To guarantee computational...

In conventional time series prediction techniques, uncertainty associated with predictions are usually ignored. Probabilistic predictors, on the other hand, can measure the uncertainty in predictions, to provide better supports for decision-making processes. A dynamic probabilistic predictor, named as echo state mean-variance estimation (ESMVE) mod...

Better interpretation about the contents in high-resolution remote sensing images can be obtained by using multiple features of various types. In order to process large image data sets with high feature dimensions, the very efficient algorithm of kernel extreme learning machine is employed to in our study to build image classifiers. In order to avo...

Landslide early warning systems can be implemented based on the monitoring and prediction of landslide displacements. The internal mechanisms of landslides are very complex, and precise mechanistic models of landslides are difficult to obtain; therefore, data-driven models are usually applied. From the perspective of dynamic system theory, landslid...

Time series prediction theory and methods can be applied to many practical problems, such as the early warning of landslide hazard. Most already existing time series prediction methods cannot be effectively applied on landslide displacement prediction tasks, mainly for two problems. Firstly, the underlying dynamics of landslides cannot be properly...

An efficient and accurate method for landslide displacement prediction is very important to reduce the casualties and property losses caused by this type of natural hazard. In recent years, many kinds of artificial neural networks (ANNs) have been widely applied to landslide displacement prediction. But we can't know which type of ANN is the best u...

Landslide hazard is a complex nonlinear dynamical system with uncertainty. The evolution of landslide is influenced by many factors such as tectonic, rainfall and reservoir level fluctuation. Using a time series model, total accumulative displacement of landslide can be divided into the trend component displacement and the periodic component displa...

The prediction of landslide displacement is essential for carrying out to improve the disaster warning system and reduce casualties and property losses. This study applies a novel neural network technique, extreme learning machine (ELM) with kernel function, to landslide displacement prediction problem. However, the generalization performance of EL...

In time series prediction tasks, dynamic models are less popular than static models, while they are more suitable for modeling the underlying dynamics of time series. In this paper, a novel architecture and supervised learning principle for recurrent neural networks, namely echo state networks, are adopted to build dynamic time series predictors. E...

Based on time series analysis, total accumulative displacement of landslide is divided into the trend component displacement and the periodic component displacement according to the response relation between dynamic changes of landslide displacement and inducing factors. In this paper, a novel neural network technique called the ensemble of extreme...

Considering people's self-protection behavior when coming across with diseases, we make a study of the spreading of diseases on the basis of the BA scale-free networks, using the classical SIS model. We figure out the individual self-protection behavior by reconnection of the statistic broken-edge and the dynamic broken-edge. The study proves that...