To provide more external knowledge for training self-supervised learning (SSL) algorithms, this paper proposes a maximum mean discrepancy-based SSL (MMD-SSL) algorithm, which trains a well-performing classifier by iteratively refining the classifier...
Automatic modulation classification plays a significant role in numerous military and civilian applications. Deep learning methods have attracted increasing attention and achieved remarkable success in recent years. However, few methods can generaliz...
Generative adversarial networks (GANs) have a revolutionary influence on sample generation. Maximum mean discrepancy GANs (MMD-GANs) own competitive performance when compared with other GANs. However, the loss function of MMD-GANs is an empirical est...
The analysis of chaotic time series is usually a challenging task due to its complexity. In this communication, a method of complex network construction is proposed for univariate chaotic time series, which provides a novel way to analyze time series...
Detecting sparse, small, lost persons with only a few pixels in high-resolution aerial images was, is, and remains an important and difficult mission, in which a vital role is played by accurate monitoring and intelligent co-rescuing for the search a...
With the development of modern data collection techniques, researchers often encounter high-dimensional data across various research fields. An important problem is to determine whether several groups of these high-dimensional data originate from the...
Most unsupervised domain adaptation (UDA) methods align feature distributions across different domains through adversarial learning. However, many of them require introducing an auxiliary domain alignment model, which incurs additional computational...
The capacity of Lithium-ion batteries degrades over the time, making accurate prediction of their Remaining Useful Life (RUL) crucial for maintenance and product lifespan design. However, diverse aging mechanisms, changing working conditions and cell...
Building electricity load forecasting plays an important role in building energy management, peak demand and power grid security. In the past two decades, a large number of data-driven models have been applied to building and larger-scale energy cons...
The primary challenge in unsupervised learning is training unnormalized density models and then generating similar samples. Few traditional unnormalized models know what the quality of the trained model is, as most models are evaluated by downstream...
Visible-infrared person re-identification (VIPR) has great potential for intelligent transportation systems for constructing smart cities, but it is challenging to utilize due to the huge modal discrepancy between visible and infrared images. Althoug...
Distribution mismatch caused by various resolutions, backgrounds, etc. can be easily found in multi-sensor systems. Domain adaptation attempts to reduce such domain discrepancy by means of different measurements, e.g., maximum mean discrepancy (MMD)....
Nonintrusive Load Monitoring (NILM) models often suffer from significant performance degradation when deployed across different households and datasets, primarily because of distribution discrepancies. To address this challenge, this study proposes a...
Understanding how two datasets differ can help us determine whether one dataset under-represents certain sub-populations, and provides insights into how well models will generalize across datasets. Representative points selected by a maximum mean dis...
Lamb wave-based damage detection technology shows great potential for structural integrity assessment. However, conventional damage features based damage detection methods and data-driven intelligent damage detection methods highly rely on expert kno...
The Intelligent Fault Diagnosis of rotating machinery calls for a substantial amount of training data, posing challenges in acquiring such data for damaged industrial machinery. This paper presents a novel approach for generating synthetic data using...
Recently, developing multi-UAVs to cooperatively pursue a fast-moving target has become a research hotspot in the current world. Although deep reinforcement learning (DRL) has made a lot of achievements in the UAV pursuit game, there are still some p...
Recent years have witnessed the development of the applications of machine learning technologies to well logging-based lithology identification. Most of the existing work assumes that the well loggings gathered from different wells share the same pro...
A pathological voice detection system is designed to detect pathological characteristics of vocal cords from speech. Such systems are particularly susceptible to domain mismatch where the training and testing data come from the source and target doma...
Nonparametric two-sample or homogeneity testing is a decision theoretic problem that involves identifying differences between two random variables without making parametric assumptions about their underlying distributions. The literature is old and r...
Rolling bearings are critical components in industrial machinery, and their failures can lead to equipment downtime or safety hazards, making accurate fault diagnosis vital. While data-driven intelligent methods perform well with sufficient labeled d...
This paper introduces a novel unsupervised domain adaptation (UDA) method, MeTa Discriminative Class-Wise MMD (MCWMMD), which combines meta-learning with a Class-Wise Maximum Mean Discrepancy (MMD) approach to enhance domain adaptation. Traditional M...
Generative models have gained significant attention in recent years. They are increasingly used to estimate the underlying structure of high-dimensional data and artificially generate various kinds of data similar to those from the real world. The pe...
Accurate wind power prediction is of great significance for grid stability and renewable energy integration. Addressing the challenge of effectively integrating physical mechanisms with data-driven methods in wind power prediction, this paper innovat...
Ship recognition is a fundamental and essential step in maritime activities, and it can be widely used in maritime rescue, vessel management, and other applications. However, most studies conducted in this area use synthetic aperture radar (SAR) imag...
A brain-computer interface (BCI) translates a user’s thoughts such as motor imagery (MI) into the control of external devices. However, some people, who are defined as BCI illiteracy, cannot control BCI effectively. The main characteristics of...
Variable-condition fish recognition is a type of cross-scene and cross-camera fish re-identification (re-ID) technology. Due to the difference in the domain distribution of fish images collected under different culture conditions, the available train...
Aiming to tackle the challenge of feature transfer in cross-domain fault diagnosis for rolling bearings, an enhanced domain adaptation-based intelligent fault diagnosis method is proposed. This method systematically combines multi-layer multi-core MM...
Wireless charger production is critical to energy storage, and effective fault diagnosis of bearings and gears is essential to ensure wireless charging performance with high efficiency, high tolerance to misalignment, and thermal safety. As minor fau...
The data distribution of the vibration signal under different speed conditions of the gearbox is different, which leads to reduced accuracy of fault diagnosis. In this regard, this paper proposes a deep transfer fault diagnosis algorithm combining ad...
In the Bayesian framework, the usual choice of prior in the prediction of homogeneous Poisson processes with random effects is the gamma one. Here, we propose the use of higher order maximum entropy priors. Their advantage is illustrated in a simulat...
Domain discrepancy is a key research problem in the field of deep domain adaptation. Two main strategies are used to reduce the discrepancy: the parametric method and the nonparametric method. Both methods have achieved good results in practical appl...
Deep neural networks can learn powerful representations from massive amounts of labeled data; however, their performance is unsatisfactory in the case of large samples and small labels. Transfer learning can bridge between a source domain with rich s...
Cross-project defect prediction (CPDP) is a practical solution that allows software defect prediction (SDP) to be used earlier in the software lifecycle. With the CPDP technique, the software defect predictor trained by labeled data of mature project...
In many disciplines, knowledge on the accuracy of Land Surface Temperature (LST) as an input is of great importance. One of the most efficient methods in LST evaluation is cross validation. Well-documented and validated polar satellites with a high s...
Background: The aim of this retrospective study was to conduct an in vivo evaluation of the accuracy of surgical guides obtained via 3D printing technology that were used to transfer the 3D software-planned position and axis during palatal miniscrew...
For the detection of incipient interturn short-circuit (IITSC) faults of machines without shutting them down, there are still shortcomings of insufficient incipient fault features and a high false alarm rate. This is especially the case for dual thre...
With the development of science and technology, hyperspectral image (HSI) classification has been studied in depth by researchers as one of the important means of human cognition in living environments and the exploration of surface information. Neve...
Due to their unique structural design, portal cranes have been extensively utilized in bulk cargo and container terminals. The bearing fault of their drive motors is a critical issue that significantly impacts their operational efficiency. Moreover,...
To address the problems of feature extraction, cost of obtaining labeled samples, and large differences in domain distribution in bearing fault diagnosis on variable operating conditions, an unsupervised domain-adaptive bearing fault diagnosis method...
Intelligent fault diagnosis of roller bearings is facing two important problems, one is that train and test datasets have the same distribution, and the other is the installation positions of accelerometer sensors are limited in industrial environmen...
Global Navigation Satellite System–Acoustic ranging (GNSS-A) technology can achieve centimeter-level seafloor positioning. However, the horizontal gradient of ocean sound speed limits the seafloor positioning accuracy of GNSS-A. This paper eval...
Rolling bearings are essential for modern mechanical equipment and serve in various operational environments. This paper addresses the challenge of vibration data discrepancies in bearings across different operating conditions, which often results in...
Domain adaptation techniques are crucial for addressing the discrepancies between training and testing data distributions caused by varying operational conditions in practical bearing fault diagnosis. However, transfer fault diagnosis faces significa...
The credible urban heat island (UHI) trend is crucial for assessing the effects of urbanization on climate. Land surface temperature (LST) and near surface air temperature (SAT) have been extensively used to obtain UHI intensities. However, the consi...
Remote sensing has frequently been employed to monitor extreme climatic events, especially droughts, by identifying the anomalies of vegetation activity from the regional to global scale. However, limited research has addressed the performance of rem...
Due to the mechanical equipment working under variable speed and load for a long time, the distribution of samples is different (domain shift). The general intelligent fault diagnosis method has a good diagnostic effect only on samples with the same...
There are many factors which could influence the sustainability of airlines. The main purpose of this study is to introduce a framework for a financial sustainability index and model it based on structural equation modeling (SEM) with maximum likelih...
Data-driven bearing-fault diagnosis methods have become a research hotspot recently. These methods have to meet two premises: (1) the distributions of the data to be tested and the training data are the same; (2) there are a large number of high-qual...
Temperature-index modeling is used to determine the magnitude of temperature depression in the northern Sawatch Range required to maintain steady-state mass balances of six reconstructed glaciers at their extent during the local Last Glacial Maximum...
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