This study introduces a novel, practical approach for designing a hierarchical online anomaly detection system for industrial cyber-physical systems. The proposed method utilizes the Hierarchical Temporal Memory (HTM) unsupervised learning algorithm,...
Time series prediction is an effective tool for marine scientific research. The Hierarchical Temporal Memory (HTM) model has advantages over traditional recurrent neural network (RNN)-based models due to its online learning and prediction capabilitie...
With the development of intrusion detection, a number of the intelligence algorithms (e.g., artificial neural networks) are introduced to enhance the performance of the intrusion detection systems. However, many intelligence algorithms should be trai...
Extracting geoscientific dataset names from the literature is crucial for building a literature–data association network, which can help readers access the data quickly through the Internet. However, the existing named-entity extraction methods...
Short-term load forecasting is a key capability for smart-grid operation, but real smart-meter streams are affected by missing values, communication noise, and non-stationary consumption patterns. This paper studies forecasting using raw smart-meter...
Gait recognition and understanding systems have shown a wide-ranging application prospect. However, their use of unstructured data from image and video has affected their performance, e.g., they are easily influenced by multi-views, occlusion, clothe...
Despite the pivotal role of unmanned aerial vehicles (UAVs) in intelligent inspection tasks, existing video instance segmentation methods struggle with irregular deforming targets, leading to inconsistent segmentation results due to ineffective featu...
Over the years, and with the emergence of various technological innovations, the relevance of automatic learning methods has increased exponentially, and they now play a key role in society. More specifically, Deep Learning (DL), with the ability to...
The interest in video anomaly detection systems that can detect different types of anomalies, such as violent behaviours in surveillance videos, has gained traction in recent years. The current approaches employ deep learning to perform anomaly detec...
As a software framework, Hierarchical Temporal Memory (HTM) has been developed to perform the brain’s neocortical functions, such as spatial and temporal pooling. However, it should be realized with hardware not software not only to mimic the n...
Simultaneous Localization and Mapping (SLAM) is a fundamental problem in the field of robotics, enabling autonomous robots to navigate and create maps of unknown environments. Nevertheless, the SLAM methods that use cameras face problems in maintaini...
Anomaly detection is an important research direction, which takes the real-time information system from different sensors and conditional information sources into consideration. Based on this, we can detect possible anomalies expected of the devices...
Farming is a fundamental factor driving economic development in most regions of the world. As in agricultural activity, labor has always been hazardous and can result in injury or even death. This perception encourages farmers to use proper tools, re...
We propose a new nature- and neuro-science-inspired algorithm for spatiotemporal learning and prediction based on sequential recall and vector symbolic architecture. A key novelty is the learning of spatial and temporal patterns as decoupled concepts...
Traffic flow prediction plays a crucial role in the development of smart cities. However, existing studies face challenges in effectively capturing spatio-temporal contexts, handling hierarchical temporal features, and understanding spatial heterogen...
Fractional operators, fractal geometry, and localization frequently appear together in descriptions of wave transport, although they correspond to distinct physical mechanisms. Here, we introduce a graph-wave framework that allows their respective ro...
The rapid advancement of deepfake generation technologies presents significant challenges to the verification of digital video authenticity. These time-dependent artifacts are difficult to detect using conventional frame-based detection approaches. T...
Weakly supervised video anomaly detection (WSVAD) aims to identify unusual events using only video-level labels. However, current methods face several key challenges, including ineffective modelling of complex temporal dependencies, indistinct featur...
Multi-modal Magnetic Resonance Imaging (MRI) provides complementary information for clinical diagnosis, yet its large-scale storage, privacy sensitivity, and annotation cost pose significant challenges. Inspired by biological vision systems, which in...
Various studies have focused on feature extraction methods for automatic patent classification in recent years. However, most of these approaches are based on the knowledge from experts in related domains. Here we propose a hierarchical feature extra...
Temporal knowledge graphs play an increasingly prominent role in scenarios such as social networks, finance, and smart cities. As such, research on temporal knowledge graphs continues to deepen. In particular, research on temporal knowledge graph rea...
Accurate multi-step traffic-flow forecasting requires dynamic spatial modeling and adaptation to heterogeneous temporal and node-level patterns. This study proposes FAMoE-ST, a hierarchically frozen attention network with hybrid-memory experts. Histo...
Video prediction which maps a sequence of past video frames into realistic future video frames is a challenging task because it is difficult to generate realistic frames and model the coherent relationship between consecutive video frames. In this pa...
Remote sensing enables the analysis of landscape dynamics; however, catastrophic disturbances create new surface conditions that are not adequately captured by retrospectively defined land-cover classes. This study addresses the challenge of temporal...
The recently proposed spacecraft three-dimensional (3D) structure recovery method based on optical images and LIDAR has enhanced the working distance of a spacecraft’s 3D perception system. However, the existing methods ignore the richness of t...
This exploratory study examines how complex non-linear television narratives support viewers’ cognitive processing through embedded compensatory mechanisms. Using Season 1 of the Netflix series Dark as a case study, we conducted a quantitative...
Wear state prediction based on oil monitoring technology enables the early identification of potential wear and failure risks of friction pairs, facilitating optimized equipment maintenance and extended service life. However, the complexity of lubric...
Serverless computing has emerged as a prominent research focus in cloud computing because it provides infrastructure-transparent development and elastic resource management. However, this computing paradigm still faces the inherent challenge of cold...
In recent years, the increasing adoption of High-Performance Computing (HPC) clusters in scientific research and engineering has exposed challenges such as resource imbalance, node idleness, and overload, which hinder scheduling efficiency. Accurate...
The rapid and accurate forecasting of two-phase flow in porous media is a critical challenge in oil field development, exerting a substantial impact on optimization and decision-making processes. Although the Convolutional Long Short-Term Memory (Con...
Reservoir construction has profoundly altered natural runoff evolution in river basins. Dynamic conflicts among multi-objective operational strategies—such as flood control, water supply, and ecological compensation—across varying tempora...
Coastal ship tracking is used in many applications, such as autonomous navigation, maritime rescue, and environmental monitoring. Many general object-tracking methods based on deep learning have been explored for ship tracking, but they often fail to...
Internet of things (IoT) nodes are deployed in large-scale automated monitoring applications to capture the massive amount of data from various locations in a time-series manner. The captured data are affected due to several factors such as device ma...
Integrating on-device learning into autonomous systems requires neural network frameworks that achieve both high energy efficiency and low latency. While spiking neural networks (SNNs) provide a promising event-driven paradigm, implementing hardware-...
The rapid advancement of unmanned aerial systems in various civilian roles necessitates improved safety measures during their operation. A key aspect of enhancing safety is effective collision avoidance, which is based on conflict detection and is gr...
Cryptocurrency markets exhibit persistent temporal dependence and multifractal scaling behavior, yet these properties remain only partially incorporated into existing deep learning architectures. This study proposes the Fractional Attention-Driven LS...
Neuromorphic vision sensors detect changes in luminosity taking inspiration from mammalian retina and providing a stream of events with high temporal resolution, also known as Dynamic Vision Sensors (DVS). This continuous stream of events can be used...
This study presents a novel spatio-temporal detection framework for identifying False Data Injection (FDI) attacks in DC microgrid systems from the perspective of cyber–physical symmetry. While modern DC microgrids benefit from increasingly sop...
Manufacturing workshops operate in dynamic and complex environments, where multiple orders are processed simultaneously through interdependent stages. This complexity makes it challenging to accurately predict the remaining completion time of ongoing...
The swift advancement of communication and information technologies has transformed urban infrastructures into smart cities. Traditional assessment methods face challenges in capturing the complex interdependencies and temporal dynamics inherent in t...
Conventional educational assessments enforce a rigid and symmetrical framework of identical question sequences upon a learner population inherently defined by asymmetry in cognitive capabilities and knowledge profiles. This mismatch results in ineffi...
To address the limitations of static-obstacle-based route planning in forest fire missions, this study develops a three-dimensional route-planning method for fixed-wing unmanned aerial vehicles (UAVs) that accounts for time-varying fire and smoke thr...
Rotor motor fault diagnosis in Unmanned Aerial Vehicles (UAVs) presents significant challenges under variable speeds. Recent advances in deep learning offer promising solutions. To address challenges in extracting spatial, temporal, and hierarchical...
The spatial-temporal distribution of underwater sound speed plays a critical role in determining the propagation mode of underwater acoustic signals. Therefore, rapid estimation and prediction of sound speed distribution are imperative for facilitati...
Accurate and rapid fault diagnosis is paramount to stabilizing proton exchange membrane fuel cells (PEMFC). To achieve this, this study proposes a novel fault diagnosis method that integrates a convolutional neural network (CNN), a bi-directional lon...
Complex event processing (CEP) systems have gained significant importance in various domains, such as finance, logistics, and security, where the real-time analysis of event streams is crucial. However, as the volume and complexity of event data cont...
With the increasing deployment of multi-unmanned aerial vehicle (multi-UAV) systems in dynamic environments, the problem of efficient cooperative path planning has emerged as a critical challenge requiring urgent solutions. To address this issue, thi...
This study proposes a novel framework integrating long short-term memory (LSTM) networks with Bayesian optimization (BO) to address process–device co-optimization challenges in trench-gate metal–oxide–semiconductor field-effect tran...
Automatic modulation classification (AMC) is the premise for signal detection and demodulation applications, especially in non-cooperative communication scenarios. It has been a popular topic for decades and has gained significant progress with the d...
As a major daily task for the popularization of artificial intelligence technology, more and more attention has been paid to the scientific research of mental state electroencephalogram (EEG) in recent years. To retain the spatial information of EEG...
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