Within the scope of this article, the problem of the formalization of physical processes in adaptive training complexes is considered on the example of virtual objects burning. Despite a fairly complete study of this process, the existing mathematica...
Semi-supervised object detection helps to monitor and manage maritime transportation effectively, saving labeling costs. Currently, many semi-supervised object detection methods use a combination of data augmentation and pseudo-label to improve model...
Organismal ageing is associated with many physiological changes, including differences in the immune system of most animals. These differences are often considered to be a key cause of age-associated diseases as well as decreased vaccine responses in...
Research has shown that an inclusive way of thinking helps to promote social interactions on and off the sports field. The most important process of accepting differences is sensitization, which is to achieve the positive change of an understanding a...
In this paper, an adaptive single neuron Proportional–Integral–Derivative (PID) controller based on the extended Kalman filter (EKF) training algorithm is proposed. The use of EKF training allows online training with faster learning and c...
Mitochondrial DNA (mtDNA) replication is thought to be an integral part of exercise-training-induced mitochondrial adaptations. Thus, mtDNA level is often used as an index of mitochondrial adaptations in training studies. We investigated the hypothes...
Mobility disability is prevalent in aging populations. While existing walking interventions improve aspects related to mobility, meaningful and sustained changes leading to preventing and reversing mobility disability have remained elusive. Split-bel...
Athletic performance is modulated by a complex interaction of physiological, environmental, and genetic factors, with regular exercise triggering molecular changes that influence gene expression and tissue adaptation. Despite growing knowledge, the u...
Modern warehouses play a fundamental role in today’s logistics, serving as strategic hubs for the reception, storage, and distribution of goods. However, training warehouse operators presents a significant challenge due to the complexity of log...
Graduate Medical Education trainees’ wellness has become an important topic in academic medicine. The Accreditation Council for Graduate Medical Education (ACGME) requires oversight, education, and resources, understanding that medical training...
In recent years, deep neural networks (DNN) have been widely used in many fields. Lots of effort has been put into training due to their numerous parameters in a deep network. Some complex optimizers with many hyperparameters have been utilized to ac...
In recent years, many countries have increased their investment in the field of humanoid robots, promoting significant technological development. This study aims to enable humanoid robots to better adapt to various complex environments, enhancing the...
Structural health monitoring (SHM) is crucial for ensuring the safety and longevity of military training aircraft, which face demanding conditions such as high maneuverability, variable loads, and extreme environments, leading to structural fatigue....
Visual speech recognition (VSR) is a challenging task that aims to interpret speech based solely on lip movements. However, although remarkable results have recently been reached in the field, this task remains an open research problem due to differe...
Deep reinforcement learning (DRL) has shown great potential in path planning tasks. However, in sparse reward environments, DRL still faces significant challenges such as low training efficiency and a tendency to converge to suboptimal policies. Trad...
Legged robots can travel through complex scenes via dynamic foothold adaptation. However, it remains a challenging task to efficiently utilize the dynamics of robots in cluttered environments and to achieve efficient navigation. We present a novel hi...
During the confinement and post-confinement period, the continuous training centers at different Catalan universities adapted the lifelong learning of professors. The present study analyzes the education of lecturers from Catalan universities before...
Retrieval-augmented generation (RAG) addresses the problem of knowledge cutoff and overcomes the inherent limitations of pre-trained language models by retrieving relevant information in real time. However, challenges related to efficiency and accura...
The unique ram air aerodynamic shape and control rope pulling course of the parafoil system make it difficult to realize its precise control. At present, the commonly used control methods of the parafoil system include proportional–integral&nda...
With age, many physiological changes occur in the human body, leading to a decline in biological functions, and those related to the locomotor system are some of the most visible. Hence, there is a particular need to provide simple and safe exercises...
Adaptive lenses offer axial scanning without mechanical translation and thus are promising to replace mechanical-movement-based axial scanning in microscopy. The scan is accomplished by sweeping the applied voltage. However, the relation between the...
Robot force control that needs to be customized for the robot structure in unstructured environments with difficult-to-tune parameters guarantees robots’ compliance and safe human–robot interaction in an increasingly expanding work enviro...
(1) Background: Change of direction (COD) is crucial for agility in team sports. Complex contrast training (CNT), alternating between heavy and light exercises, is a newer method currently gaining attention, but its effectiveness compared to others (...
For statistic space-time adaptive processing (STAP), a critical issue is estimating the clutter covariance matrix (CCM). However, sufficient training samples are difficult to obtain that satisfy the independent and identically distributed (IID) condi...
The post-Doppler adaptive matched filter (PD-AMF) with constant false alarm rate (CFAR) property was developed for adaptive detection of moving targets, which is a standardized version of the post-Doppler space–time adaptive processing (PD-STAP...
The rehabilitation exoskeleton represents a typical human–robot system featuring complex nonlinear dynamics. This paper is devoted to proposing an adaptive impedance control strategy for a rehabilitation exoskelton. The patient’s motion i...
The classification of encrypted traffic is critical for network security, yet it faces a significant “few-shot” challenge as novel applications with scarce labeled data continuously emerge. This complexity arises from the high-dimensional...
Existing hyperspectral denoising networks typically rely on large amounts of high-quality paired noisy–clean images for training, which are often unavailable. Moreover, the noise distribution in real hyperspectral images (HSIs) is complex and v...
Wasserstein Generative Adversarial Networks (WGANs) have gained significant attention due to their theoretical foundations and effectiveness in generative modeling. However, training stability remains a major challenge, typically addressed through fi...
To improve the accuracy of lane detection in complex scenarios, an adaptive lane feature learning algorithm which can automatically learn the features of a lane in various scenarios is proposed. First, a two-stage learning network based on the YOLO v...
Accurate temperature forecasting is critical for various sectors, yet traditional methods struggle with complex atmospheric dynamics. Deep neural networks (DNNs), especially transformer-based DNNs, offer potential advantages, but face challenges with...
Background/Objectives: This study investigates the efficacy of working memory training (WMT) using the dual N-back (DNB) task on cognitive performance in young adults with Attention-Deficit/Hyperactivity Disorder (ADHD). Methods: Over the course of a...
Object detection is used widely in remote sensing image interpretation. Although most models used for object detection have achieved high detection accuracy, computational complexity and low detection speeds limit their application in real-time detec...
Path planning is an indispensable component in guiding unmanned ground vehicles (UGVs) from their initial positions to designated destinations, aiming to determine trajectories that are either optimal or near-optimal. While conventional path-planning...
With the popularity of Android and its open source, the Android platform has become an attractive target for hackers, and the detection and classification of malware has become a research hotspot. Existing malware classification methods rely on compl...
Semantic segmentation techniques for remote sensing images (RSIs) have been widely developed and applied. However, most segmentation methods depend on sufficiently annotated data for specific scenarios. When a large change occurs in the target scenes...
Autoencoders can leverage deep neural networks to jointly optimize transmitters and receivers for end-to-end communication performance. The time-varying characteristics of underwater channels due to turbulence, absorption, and scattering seriously af...
The present study discusses foods for professional athletes since sports nutrition is the foundation of effective performance. A dietary supplement complex designed for ski orienteers, swimmers, road cyclists, and biathletes is described. The composi...
The increasing complexity of aircraft systems and the growing global demand for certified maintenance personnel necessitate a fundamental shift in aviation training methodologies. This paper proposes a comprehensive digital-twin-based training ecosys...
Obesity is a complex condition characterized by abnormal and excessive fat accumulation, resulting in an increased risk for severe health problems. Skeletal muscles play a major role in movement and fat catabolism, but the insulin resistance that com...
Anomaly detection plays a vital role in ensuring product quality and operational safety across various industrial applications, from manufacturing to infrastructure monitoring. However, current methods often struggle with challenges such as limited g...
Standard convolution sliding along a fixed direction in common convolutional neural networks (CNNs) is inconsistent with the direction of aerial targets, making it difficult to effectively extract features with high-aspect-ratio and arbitrary directi...
The object detection model for synthetic aperture radar (SAR) images needs to have strong generalization ability and more stable detection performance due to the complex scattering mechanism, high sensitivity of the orientation angle, and susceptibil...
Intelligent autonomous guided vehicles (AGVs) are of huge importance in facilitating the automation of load handling in the era of Industry 4.0. AGVs heavily rely on environmental perception, such as the 6D poses of objects, in order to execute compl...
Business owners are faced with complex problems and are required to make decisions on a daily basis. The purpose of this study was to investigate complex problem solving (CPS) between experts and novices and to explore the competing theories of exper...
Simulation-based training systems are increasingly deployed to prepare learners for complex, safety-critical, and dynamic work environments. While advances in computing have enabled immersive and data-rich simulations, many systems remain optimized f...
Intelligent Heating, Ventilation, and Air Conditioning (HVAC) control using deep reinforcement learning (DRL) has recently gained a lot of attention due to its ability to optimally control the complex behavior of the HVAC system. However, more explor...
Hierarchical federated learning (HFL) has emerged as a promising paradigm for distributed machine learning over vehicular networks. Despite recent advances in vehicle selection and resource allocation, most still adopt a fixed Edge-and-Vehicle Schedu...
Optimizing learning rates (LRs) in deep learning (DL) has long been challenging. Previous solutions, such as learning rate scheduling (LRS) and adaptive learning rate (ALR) algorithms like RMSProp and Adam, added complexity by introducing new hyperpa...
Wireless communications systems are traditionally designed by independently optimising signal processing functions based on a mathematical model. Deep learning-enabled communications have demonstrated end-to-end design by jointly optimising all compo...
of 40