Digital Twins, which are virtual representations of physical systems mirroring their behavior, enable real-time monitoring, analysis, and optimization. Understanding and identifying the temporal dependencies included in the multivariate time series d...
Data on various air-quality metrics are collected repeatedly by numerous monitoring stations worldwide to closely assess the severity of pollution. Particle pollution from fine particulate matter PM2.5 is one such measure used as an indicator of whet...
Fault diagnosis of bearings in rotating machinery is a critical task. Vibration signals are a valuable source of information, but they can be complex and noisy. A transformer model can capture distant relationships, which makes it a promising solutio...
Industrial Control Systems (ICSs) are critical infrastructure for maintaining social and economic stability, but they face increasing security threats that require robust anomaly detection mechanisms. Anomaly detection in ICS, based on sensor data, i...
Extending earlier research on forecasting recessions with financial variables, I examine the importance of additional financial variables and temporal dependence for recession prediction. I show that both additional financial variables, in particular...
Changes in ocean temperature over time have important implications for marine ecosystems and global climate change. Marine temperature changes with time and has the features of closeness, period, and trend. This paper analyzes the temporal dependence...
Cardiac arrhythmias, especially premature beats and atrial fibrillation, pose substantial clinical risks and detection hurdles. While deep learning has shown promise for automated arrhythmia diagnosis, single-model architectures often lack sufficient...
The development of location-based services facilitates the use of location data for detecting urban events. Currently, most studies based on location data model the pattern of an urban dynamic and then extract the anomalies, which deviate significant...
Providing interpretable explanations can notably enhance users’ confidence and satisfaction with regard to recommender systems. Counterfactual explanations demonstrate remarkable performance in the realm of explainable sequential recommendation...
The purpose of this study is to investigate whether spatial-temporal dependence models can improve the prediction performance of short-term freight volume forecasts in inland ports. To evaluate the effectiveness of spatial-temporal dependence forecas...
Nowadays, a noteworthy temporal alteration of traditional hydrological patterns is being observed, producing a higher variability and more unpredictable extreme events worldwide. This is largely due to global warming, which is generating a growing un...
Face the issues of rural youth outflow, increase of aging population, and rural decline, this research explored the spatiotemporal evolution characteristics and influencing factors of the rural human-land relationship using the quantification index o...
Aiming at the multi-depot heterogeneous vehicle routing problem under the time-dependent road network and soft time window, considering vehicle fixed cost, time window penalty cost and vehicle transportation cost, an optimization model of time-depend...
The Global Navigation Satellite System (GNSS) has been widely used in every area of our daily life to provide accurate Positioning, Navigation, and Timing (PNT) services. However, due to the multipatch effect and an obstructed view of the satellite,...
Yield and quality estimations provide vital information to fruit growers, yet require accurate monitoring throughout the growing season. To this end, the temporal dependency of fruit yield and quality estimations through spectral vegetation indices w...
With the escalating global cyber threats, Distributed Denial of Service (DDoS) attacks have become one of the most disruptive and prevalent network attacks. Traditional DDoS detection systems face significant challenges due to the unpredictable natur...
In this study, an inverse conjugate heat transfer problem is examined to estimate temporally and spatially the dependent unknown surface heat flux using thermography techniques with a thermal camera in a three-dimensional domain. Thermography techniq...
Reliable environmental perception capabilities are a prerequisite for achieving autonomous driving. Cameras and LiDAR are sensitive to illumination and weather conditions, while millimeter-wave radar avoids these issues. Existing models rely heavily...
Text-dependent speaker verification (TD-SV), which verifies speaker identity using predefined phrases, has gained attention as a reliable contactless biometric authentication method for smart devices, internet of things (IoT), and real-time applicati...
Wind power forecasting is a critical technology for promoting the effective integration of wind energy. To enhance the accuracy of wind power predictions, this paper introduces a novel wind power prediction model that considers the evolving relations...
The oil and gas industry has shifted paradigms after seeing the drastic decrease in oil prices since 2015. Companies are now focused as much on cost reduction as much as production maximization to drive profitable operations. This aspect is more prev...
Long-term prediction of physical systems on irregular unstructured meshes is extremely challenging due to the spatial complexityof meshes and the dynamic changes over time; namely, spatial dependence and temporal dependence. Recently, graph-based nex...
A method of modeling the time of object transition between given pairs of cameras based on the Gaussian Mixture Model (GMM) is proposed in this article. Temporal dependencies modeling is a part of object re-identification based on the multi-camera ex...
Runoff forecasting is important for water resource management. Although deep learning models have substantially improved the accuracy of runoff prediction, the temporal and feature dependencies between rainfall–runoff time series elements have...
Traffic flow forecasting, as one of the important components of intelligent transport systems (ITS), plays an indispensable role in a wide range of applications such as traffic management and city planning. However, complex spatial dependencies and d...
Wind power has been increasing its participation in electricity markets in many countries around the world. Due to its economical and environmental benefits, wind power generation is one of the most powerful technologies to deal with global warming a...
Accurate real-time traffic forecasting is a core technological problem against the implementation of the intelligent transportation system. However, it remains challenging considering the complex spatial and temporal dependencies among traffic flows....
Knowledge tracing, the computational modeling of student learning progression through sequential educational interactions, represents a critical component for adaptive learning systems and personalized education platforms. However, existing approache...
Traffic forecasting plays a vital role in intelligent transportation systems and is of great significance for traffic management. The main issue of traffic forecasting is how to model spatial and temporal dependence. Current state-of-the-art methods...
Stock return prediction is a pivotal yet intricate task in financial markets, challenged by volatility and multifaceted dependencies. This study proposes a hybrid model integrating long short-term memory (LSTM) networks and graph convolutional networ...
Accurate traffic flow prediction plays a critical role in intelligent transportation systems, supporting traffic management, congestion mitigation, and efficient utilization of road resources. Advances in neural network-based methods, particularly gr...
Taxi demand forecasting plays an important role in ride-hailing services. Accurate taxi demand forecasting can assist taxi companies in pre-allocating taxis, improving vehicle utilization, reducing waiting time, and alleviating traffic congestion. It...
The concept of sustainability is assumed for this research from a temporal perspective. Rivers represent natural systems with an inherent internal memory on their runoff and, by extension, to their hydrological behavior, that should be identified, ch...
Three-dimensional human pose estimation is a hot research topic in the field of computer vision. In recent years, significant progress has been made in estimating 3D human pose from monocular video, but there is still much room for improvement in thi...
Traffic flow prediction is a pivotal task in intelligent transportation systems. The primary challenge lies in accurately modeling the dynamically evolving and directional spatio-temporal dependencies inherent in road networks. Existing graph neural...
A technical challenge for workload prediction in microservice systems is how to capture both the dynamic features of workload and evolving dependencies among microservices. The existing work focused mainly on modeling dynamic features without taking...
Wind speed forecasting is an essential part of weather prediction, with significant value in economics, business, and management. Utilizing multiple meteorological variables can improve prediction accuracy, but existing methods face challenges such a...
Traffic speed prediction plays a significant role in the intelligent traffic system (ITS). However, due to the complex spatial-temporal correlations of traffic data, it is very challenging to predict traffic speed timely and accurately. The traffic s...
Data-driven approaches in machine learning are increasingly applied in economic analysis, particularly for identifying business cycle (BC) turning points. However, temporal dependence in BCs is often overlooked, leading to what we term single path an...
Event temporal relation extraction is a crucial task in natural language processing, aimed at recognizing the temporal relations between event triggers in a text. Despite extensive efforts in this area, the existing methods face two main issues. Firs...
Accurate electricity wholesale price (EWP) forecasting is crucial for advancing sustainability in the energy sector, as it supports more efficient utilization and integration of renewable energy by informing when and how it should be consumed, dispat...
In steel production, the blast furnace is a critical element. In this process, precisely controlling the temperature of the molten iron is indispensable for attaining efficient operations and high-grade products. This temperature is often indirectly...
To effectively address air pollution and enhance air quality, governments must be able to predict the air quality index with high accuracy and reliability. However, air quality prediction is subject to ambiguity and instability because of the atmosph...
The time-dependent Maxwell system in the sense of distributions is considered in the context of temporal interfaces. Just as with spatial interfaces, electromagnetic waves at temporal interfaces scatter and create a transmitted and reflected wave. A...
Predicting the health status of lithium-ion batteries is crucial for ensuring safety. The prediction process typically requires inputting multiple time series, which exhibit temporal dependencies. Existing methods for health status prediction fail to...
The strong development of distributed energy sources has become one of the most important measures for low-carbon development worldwide. With a significant quantity of photovoltaic (PV) power generation being integrated to the grid, accurate and effi...
Various real systems are being replicated in cyberspace to solve complex and difficult problems, as exemplified by the digital twin. Once such a software system is implemented, an effective computational method should be applied to the system so that...
Accurate estimation of transportation flow is a challenging task in Intelligent Transportation Systems (ITS). Transporting data with dynamic spatial-temporal dependencies elevates transportation flow forecasting to a significant issue for operational...
Decoding motor imagery electroencephalography (MI-EEG) signals presents significant challenges due to the difficulty in capturing the complex functional connectivity between channels and the temporal dependencies of EEG signals across different perio...
Remote sensing image change detection (CD) is done to identify desired significant changes between bitemporal images. Given two co-registered images taken at different times, the illumination variations and misregistration errors overwhelm the real o...
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