As one of the most important techniques for hyperspectral image dimensionality reduction, band selection has received considerable attention, whereas self-representation subspace clustering-based band selection algorithms have received quite a lot of...
We study the asymptotically-achievable rate region of subspace codes for wireless network coding, where receivers have different link capacities due to the access ways or the faults of the intermediate links in the network. Firstly, an outer bound of...
The notion of a linear diophantine fuzzy set as a generalization of a fuzzy set is a mathematical approach that deals with vagueness in decision-making problems. The use of reference parameters associated with validity and non-validity functions in l...
We define an ordinary single valued neutrosophic topology and obtain some of its basic properties. In addition, we introduce the concept of an ordinary single valued neutrosophic subspace. Next, we define the ordinary single valued neutrosophic neigh...
Correlation research on urban space and pedestrian–level wind (PLW) environments is helpful for improving the wind comfort in complex urban space. It could also be significant for building and urban design. Correlation research is usually carri...
For the asymmetric six-phase motor fed by a two-level six-phase voltage source inverter (VSI), when the modulation index is less than 0.5774, the motor operates in the linear region, and the harmonics in the Z1 − Z2 subspace can be suppressed t...
Electroencephalogram (EEG) signals are sensitive to the level of Mental Workload (MW). However, the random non-stationarity of EEG signals will lead to low accuracy and a poor generalization ability for cross-session MW classification. To solve this...
Because requirements for establishing spatial data for location-based services (LBS) have increased in demand together with an increase in the number of complex structures, especially in urban areas, research has revisited the limitations of data mod...
Forecasting high-dimensional multivariate time series requires models to represent dependencies across variables without making variable interactions prohibitively expensive. Full attention over variable-level tokens has a pairwise cost quadratic in...
Industrial anomaly detection is commonly performed under one-class conditions because defective samples are scarce or unavailable during training. This study presents a lightweight one-class anomaly detection framework that reduces the dependence of...
Properties of psychological variables at the mean or variance level can differ between persons and within persons across multiple time points. For example, cross-sectional findings between persons of different ages do not necessarily reflect the deve...
Pre-trained visual language models have become excellent basic models for many downstream tasks in transfer learning. However, due to the serious gap between the data scale of downstream tasks and the large-scale data used by pre-trained models, migr...
To mitigate mutual coupling and enhance the degrees of freedom (DOFs), this paper proposes a two-level large-spacing symmetric nested array (TLSSNA). Unlike conventional nested arrays, the proposed configuration does not restrict the minimum inter-el...
Indoor space is a core part of supporting indoor applications. Most of the existing indoor space models are expressed from three space scales: building, floor, and room, and the granularity is not fine enough, lacking the expression of each functiona...
Spatial spectrum estimation, also known as direction of arrival (DOA) detection, is a popular issue in many fields, including remote sensing, radar, communication, sonar, seismic exploration, radio astronomy, and biomedical engineering. MUltiple SIgn...
Subspace clustering has become widely adopted for the unsupervised analysis of hyperspectral images (HSIs). Recent model-aware deep subspace clustering methods often use a two-stage framework, involving the calculation of a self-representation matrix...
This paper proposes a novel robust latent common subspace learning (RLCSL) method by integrating low-rank and sparse constraints into a joint learning framework. Specifically, we transform the data from source and target domains into a latent common...
Discrete ill-posed problems with several right-hand sides and a common forward operator arise in multichannel imaging and in repeated or parameter-dependent experiments. A block Golub–Kahan reduction builds one solution subspace for all right-h...
Conventional adaptive beamformers usually suffer from serious performance degradation when the receive array is imperfect and unknown sporadic interferences appear. To enhance robustness against array imperfections and simultaneously suppress sporadi...
What is the boundary between a vigorous argument and a breakdown of relations? What drives a group of individuals across it? Taking Wikipedia as a test case, we use a hidden Markov model to approximate the computational structure and social grammar o...
In recent years, heavy rainfall-induced flood incidents have occurred frequently in subway stations worldwide. Flooding in complex underground facilities, such as subway stations, can result in significant casualties and property damage. Therefore, i...
One of the fundamental problems of quantum statistical physics is how an ideally isolated quantum system can ever reach thermal equilibrium behavior despite the unitary time evolution of quantum-mechanical systems. Here, we study, via explicit time e...
Hyperspectral imaging (HSI) systems often suffer from complex noise degradation during the imaging process, significantly impacting downstream applications. Deep learning-based methods, though effective, rely on impractical paired training data, whil...
Robust face clustering enjoys a wide range of applications for gate passes, surveillance systems and security analysis in embedded sensors. Nevertheless, existing algorithms have limitations in finding accurate clusters when data contain noise (e.g.,...
Since the lowest levels are symmetric under the interchange of neutrons and protons from calculations in the interacting boson approximation IBA-2 model, IBA-1 model space, in which neutron and proton degrees of freedom are not distinguished can b...
DC-signal injection into the secondary
Power grid large language models (LLMs) require the efficient acquisition of dense regulatory knowledge while preserving lightweight deployment. We propose a three-stage framework for the efficient training of power grid LLMs via token-adaptive conti...
The premise for formulating effective emission control strategies is to accurately and reasonably evaluate the actual emission level of vehicles. Firstly, the active subspace method is applied to set up a low-dimensional model of the relationship bet...
In hyperspectral target detection, the spectral high-dimensionality, variability, and heterogeneity will pose great challenges to the accurate characterizations of the target and background. To alleviate the problems, we propose a Meta-pixel-driven E...
Vehicle re-identification research under surveillance cameras has yielded impressive results. However, the challenge of unmanned aerial vehicle (UAV)-based vehicle re-identification (ReID) presents a high degree of flexibility, mainly due to complica...
When performing classification tasks on high-dimensional data, traditional machine learning algorithms often fail to filter out valid information in the features adequately, leading to low levels of classification accuracy. Therefore, this paper expl...
Text-video retrieval tasks face a great challenge in the semantic gap between cross modal information. Some existing methods transform the text or video into the same subspace to measure their similarity. However, this kind of method does not conside...
In bearing fault diagnosis, machine learning methods have been proven effective on the basis of the heterogeneous features extracted from multiple domains, including deep representation features. However, comparatively little research has been perfor...
Risk assessment to quantify the danger associated with a planned trajectory is critical for micro aerial vehicles (MAVs) navigating in dynamic uncertain environments. Existing works usually perform risk assessment by reasoning the occupancy status of...
We investigate the equilibrium properties of the von Neumann entropy in a dense, dipole–dipole coupled ensemble of two-level emitters confined in a fully symmetric Dicke subspace, which additionally interact through a common thermal bath. The s...
The class of two-interacting-qubit spin–boson models with vanishing transverse fields on the spin-pair is studied. The model can be mapped exactly into two independent standard single-impurity spin–boson models where the role of the tunne...
We present strategies to derive Bell inequalities valid for systems composed of many three-level parties. This scenario is formalized by a Bell experiment with N observers, each of which performs one out of two possible three-outcome measurements on...
Thermal comfort in cities is an influential factor for citizens’ wellbeing and life quality. Urban microclimate studies have gained popularity following increasing urbanization trends and global climate change in recent years. Urban fabric and...
With the rapid development of artificial intelligence, how to take advantage of deep learning and big data to classify polarimetric synthetic aperture radar (PolSAR) imagery is a hot topic in the field of remote sensing. As a key step for PolSAR imag...
Hyperspectral images (HSIs) denoising aims at recovering noise-free images from noisy counterparts to improve image visualization. Recently, various prior knowledge has attracted much attention in HSI denoising, e.g., total variation (TV), low-rank,...
Cementitious materials in infrastructure undergo coupled chemical, mineralogical, transport and mechanical deterioration, yet development still largely follows a forward trial-and-test paradigm. This study formulates the problem as constrained robust...
The distribution of entangled quantum systems among the nodes of a network is a key task at the basis of the development of quantum technologies, e.g., quantum communication, quantum computation, etc. Many efforts have been devoted to identify strate...
The problem of timely detecting the engine faults that make engine operating parameters exceed their control limits has been well-solved. However, in practice, a fault of a diesel engine can be present with weak signatures, with the parameters fluctu...
Immunohistochemistry is a powerful technique that is widely used in biomedical research and clinics; it allows one to determine the expression levels of some proteins of interest in tissue samples using color intensity due to the expression of biomar...
As an essential reference to bridge dynamic characteristics, the identification of bridge frequencies has far-reaching consequences for the health monitoring and damage evaluation of bridges. This study proposes a uniform scheme to identify bridge fr...
Electroencephalography (EEG)-based emotion recognition is an important element in psychiatric health diagnosis for patients. However, the underlying EEG sensor signals are always non-stationary if they are sampled from different experimental sessions...
In recent years, deep learning has been widely studied for remote sensing image analysis. In this paper, we propose a method for remotely-sensed image classification by using sparse representation of deep learning features. Specifically, we use convo...
A reduced-dimension robust Capon beamforming method using Krylov subspace techniques (RDRCB) is a diagonal loading algorithm with low complexity, fast convergence and strong anti-interference ability. The diagonal loading level of RDRCB is known to b...
The severe susceptibility of qubits to environmental noise remains the primary obstacle to practical quantum computing. To overcome this, we introduce a purification-assisted quantum error-correction (QEC) framework that embeds a symmetric subspace p...
Modern grid digitalization connects communication networks, monitoring terminals, service platforms, security devices, and operational data sources. Intrusion detection in this setting requires correlating heterogeneous security data with grid-side c...
of 4