A soft-constrained neural network for density estimation (SC-NN-4pdf) has recently been introduced to tackle the issues arising from the application of neural networks to density estimation problems (in particular, the satisfaction of the second Kolm...
Traffic congestion remains a significant challenge in urban management, with traditional fixed-cycle traffic signal systems struggling to adapt to dynamic traffic conditions. This paper proposes an adaptive traffic signal control method based on a Gr...
To address the search-and-docking problem in multi-stage prescribed performance switching (MPPS) scenarios, this paper presents a novel compound control method for three-dimensional (3D) underwater trajectory tracking control of unmanned underwater v...
Proper monitoring of quality-related but hard-to-measure effluent variables in wastewater plants is imperative. Soft sensors, such as dynamic neural network, are widely used to predict and monitor these variables and then to optimize plant operations...
Lightweight service identification models are very important for resource-constrained distribution grid systems. To address the increasingly larger deep learning models, we provide a method for the lightweight identification of complex power services...
To address the limitations of insufficient feature representation, large model size, and high deployment cost in network traffic classification, a lightweight classification framework based on multi-teacher knowledge distillation is proposed. The fra...
Spacecraft safe proximity, as a critical component of on-orbit servicing missions, primarily encounters the following two challenges: the partial observability of the environment surrounding the service spacecraft and the necessity to evade uncertain...
This study evaluates structure-preserving neural network architectures for learning holonomically constrained mechanical dynamics in Cartesian coordinates. In contrast to methods using reduced coordinates, the full ambient phase space
Uncertainty of target motion, limited perception ability of onboard cameras, and constrained control have brought new challenges to unmanned aerial vehicle (UAV) dynamic target tracking control. In virtue of the powerful fitting ability and learning...
Network slicing enables the multiplexing of independent logical networks on the same physical network infrastructure to provide different network services for different applications. The resource allocation problem involved in network slicing is typi...
Real-time distribution system state estimation is hampered by limited observability, frequent topology changes, and measurement errors. Neural networks can capture the nonlinear characteristics of power-grid operation through a data-driven approach t...
The purpose of digital image steganalysis is to identify the signal embedded in the natural image by steganography. In the spatial domain, this embedded signal only modifies the image value of the natural image by
Developing classical three-dimensional consolidation theories considers the small-strain assumption. This small-strain assumption is inappropriate when studying the consolidation process of soft or very soft clay layers. Instead, this study derives a...
Radio frequency fingerprint identification (RFFI) based on physical-layer characteristics provides a reliable solution for secure authentication of Internet of Things (IoT) devices. Deep neural networks have demonstrated strong capability in improvin...
Microplastic pollution in riverine systems poses critical environmental challenges, yet predictive modeling remains constrained by data scarcity and the computational limitations of traditional numerical approaches. This study develops a physics-info...
As software systems increasingly combine machine learning, deep learning, and generative AI components with classical deterministic logic, the systematic detection of AI-based algorithmic elements in application code is becoming essential for softwar...
Semi-supervised clustering typically relies on both labeled and unlabeled data to guide the learning process towards the optimal data partition and to prevent falling into local minima. However, researchers’ efforts made to improve existing sem...
Poly(N-isopropylacrylamide) (PNIPAM) hydrogels are temperature-sensitive materials whose swelling is difficult to predict near the gel collapse temperature (GCT). A physics-informed neural network (PINN) was developed in which a stabilized free-energ...
The development of prostheses that accurately reproduce fine motor skills remains a key challenge for daily assistance applications. This research presents the development of a soft robotic hand prosthesis prototype inspired by the natural behavior o...
Amidst the accelerating pace of automation in sheet metal bending, the need for small-batch, multi-varietal, efficient, and adaptable production modalities has become increasingly pronounced. To address this need and to enhance the efficacy of the be...
This study focuses on the problem of identifying and extracting the magnetopause boundary of the Earth’s magnetosphere using the Soft X-ray Imager (SXI) onboard the Solar Wind Magnetosphere Ionosphere Link Explorer (SMILE) mission. The SXI empl...
Floods remain among the most devastating natural disasters globally, disproportionately impacting low-resource regions where real-time flood forecasting is constrained by limited computational infrastructure and the scarcity of fine-resolution predic...
Gesture recognition acts as a key enabler for user-friendly human-computer interfaces (HCI). To bridge the human-computer barrier, numerous efforts have been devoted to designing accurate fine-grained gesture recognition systems. Recent advances in w...
This study presents a novel data-driven modeling approach employing machine learning to develop predictive “soft sensors” for real-time monitoring of ethanol and substrate levels during bioethanol fermentation processes. By utilizing read...
This paper introduces a hybrid two-stage registration framework for reconstructing three-dimensional (3D) kidney anatomy from macroscopic slices, using CT-derived models as the geometric reference standard. The approach addresses the data-scarcity an...
Automatic modulation classification (AMC) is a core task in cognitive radio and spectrum monitoring. Mainstream deep-learning-based AMC models typically contain hundreds of thousands to millions of parameters, making them difficult to deploy on resou...
Currently, artificial intelligence is facing several problems with its practical implementation in various application domains. The explainability of advanced artificial intelligence algorithms is a topic of paramount importance, and many discussions...
With the evolution of modern digital pathology, examining cancer cell tissues has paved the way to quantify subtle symptoms, for example, by means of image staining procedures using Eosin and Hematoxylin. Cancer tissues in the case of breast and lung...
Deploying deep learning (DL) models in real-world environments remains a major challenge, particularly under resource-constrained conditions where achieving both high accuracy and compact architectures is essential. While effective, Conventional prun...
A jet grout design beneath raft foundations requires simultaneous selection of column geometry while satisfying bearing resistance and settlement criteria. This study develops a physics-generated artificial neural network (ANN) surrogate for performa...
Deep learning models typically rely on large-scale datasets with accurate annotations, yet real-world applications inevitably suffer from label noise, which severely degrades generalization—particularly for lightweight neural networks with limi...
Reliable prediction of final cavern responses is required for rapid screening of solution-mined salt caverns used in large-scale underground energy storage. Final cavern volume and maximum radius jointly control storage performance and geometric safe...
The sustained growth of cloud computing and AI training workloads drives data center expansion. Optimizing their control is therefore critical for reducing operational costs. Edge real-time control is indispensable for guaranteeing thermal safety, da...
This article presents an interaction-aware motion planning framework that integrates a graph neural network (GNN) based multi-modal trajectory predictor with a model predictive control (MPC) based planner. Unlike past studies that predict a single fu...
Internal temperature distributions in black tea fermentation regulate enzymatic oxidation, heat accumulation and fermentation uniformity, but continuous three-dimensional measurements remain difficult in practical processing. We developed a CFD-prior...
Event cameras capture motion with high temporal resolution, but recovering from raw events a velocity field that also obeys the underlying flow physics remains difficult. Existing event-based methods either require ground-truth velocity or depth, whi...
White blood cell (WBC) classification plays a crucial role in hematopathology and clinical diagnostics. However, traditional methods are constrained by limited receptive fields and insufficient utilization of contextual information, which hinders cla...
Substation-level carbon-emission factors (CEFs) are operationally relevant because substations concentrate transformer losses, auxiliary consumption, and sulfur hexafluoride (SF6) leakage at the interface between transmission and distribution. Howeve...
Ancient Chinese scripts—including oracle bone carvings, bronze inscriptions, stone steles, Dunhuang scrolls, and bamboo slips—are rich in historical value but often degraded due to centuries of erosion, damage, and stylistic variability....
Driven by global carbon neutrality targets, coal-fired power generation is undergoing substantial operational changes. Modern boilers must operate more flexibly while maintaining low emissions and reliable performance. This transition has increased t...