The streamflow of the upper reaches of the Yangtze River exhibits different timing and periodicity characteristics in different quarters and months of the year, which makes it difficult to predict. Existing sliding window-based methods usually use a...
Since data stream anomaly detection algorithms based on sliding windows are sensitive to the abnormal deviation of individual interference data, this paper presents a sliding nest window chart anomaly detection based on the data stream (SNWCAD-DS) by...
In recent years, machine learning, a popular artificial intelligence technique, has been successfully applied to monthly runoff forecasting. Monthly runoff autoregressive forecasting using machine learning models generally uses a sliding window algor...
With the explosive growth of the amount of information in social networks, the recommendation system, as an application of social networks, has attracted widespread attention in recent years on how to obtain user-interested content in massive data. A...
In an asynchronous data stream, the data items may be out of order with respect to their original timestamps. This paper studies the space complexity required by a data structure to maintain such a data stream so that it can approximate the set of fr...
This paper presents a comprehensive approach to detect violent events in videos by combining CrimeNet, a Vision Transformer (ViT) model with structured neural learning and adversarial regularization, with an adaptive threshold sliding window model ba...
There are problems associated with facial expression recognition (FER), such as facial occlusion and head pose variations. These two problems lead to incomplete facial information in images, making feature extraction extremely difficult. Most current...
This paper proposes a transformer temperature early warning method based on an adaptive sliding window and stacking ensemble learning algorithm, aiming to improve the accuracy and robustness of temperature prediction. The transformer temperature earl...
Recently, deep learning methods, for example, convolutional neural networks (CNNs), have achieved high performance in hyperspectral image (HSI) classification. The limited training samples of HSI images make it hard to use deep learning methods with...
Diversity maximization is a fundamental problem with broad applications in data summarization, web search, and recommender systems. Given a set X of n elements, the problem asks for a subset S of
With the current increasing volume and dimensionality of data, traditional data classification algorithms are unable to satisfy the demands of practical classification applications of data streams. To deal with noise and concept drift in data streams...
Step detection for smartphones plays an important role in the pedestrian dead reckoning (PDR) for indoor positioning. Aiming at the problem of low step detection accuracy of smartphones in complex unconstrained states in PDR, smartphone-based unconst...
In this paper, a new sliding window-based joint sparse representation (SWJSR) anomaly detector for hyperspectral data is proposed. The main contribution of this paper is to improve the judgments about the probability of anomaly presence in signals us...
In order to detect outliers in temperature time series data for improving data quality and decision-making quality related to design and operation, we proposed an algorithm based on sliding window prediction. Firstly, the time series are segmented ba...
In industrial process fault monitoring, it is very important to collect accurate data, but in the actual process, there are often various noises that are difficult to eliminate in the collected data due to sensor accuracy, measurement errors, or huma...
Landmark generation is an essential component in landmark-based visual place recognition. In this paper, we present a simple yet effective method, called multi-scale sliding window (MSW), for landmark generation in order to improve the performance of...
Outliers are often present in large datasets of water quality monitoring time series data. A method of combining the sliding window technique with Dixon detection criterion for the automatic detection of outliers in time series data is limited by the...
This paper proposes a Nested Sliding Window (NSW) method based on the correlation between pixel vectors, which can extract spatial information from the hyperspectral image (HSI) and reconstruct the original data. In the NSW method, the neighbourhood...
To verify the performance of the high-resolution fully polarimetric synthetic aperture radar (SAR) sensor carried by the Xinzhou 60 remote-sensing aircraft, we used corner reflectors to calibrate the acquired data. The target mechanism in high-resolu...
Background: In brain–computer interfaces (BCIs), transformer-based models have found extensive application in motor imagery (MI)-based EEG signal recognition. However, for subject-independent EEG recognition, these models face challenges: low s...
Deep space exploration has risen in interest among scientists in recent years, with soft landings being one of the most straightforward ways to acquire knowledge about the Moon. In general, landing mission success depends on the selection of landing...
One of the predominant technologies for multi-source navigation in vehicles involves the fusion of GNSS/IMU/ODO through a factor graph. To address issues such as the asynchronous sampling frequencies between the IMU and ODO, as well as diminished acc...
Generalized autoregressive conditional heteroskedasticity (GARCH) is one of the most popular models for time-series forecasting. The GARCH model uses a maximum likelihood method for parameter estimation. For the likelihood method to work, there shoul...
Ensuring the accuracy of flow measurement is crucial to promoting high-quality cigarette production. In order to monitor the working status of flowmeters, this paper proposes an anomaly detection method based on the sliding-window recursive Lasso (Le...
To address the performance degradation of the conventional linear frequency modulation signal ranging method in the presence of impulse noise, this paper proposes a novel technique that integrates a sliding-window tracking differentiator (TD) with th...
Region of Interest (ROI) extraction is a crucial step in an automatic finger vein recognition system. The aim of ROI extraction is to decide which part of the image is suitable for finger vein feature extraction. This paper proposes a finger vein ROI...
This paper presents a systematic methodology for identifying optimal scaling regions in segment-based box-counting fractal dimension calculations through a three-phase algorithmic framework combining grid offset optimization, boundary artifact detect...
Unreliability of the data streams generated by RFID readers is among the primary factors which limit the widespread adoption of the RFID technology. RFID data cleaning is, therefore, an essential task in the RFID middleware systems in order to reduce...
The fusion of visual and inertial measurements for motion tracking has become prevalent in the robotic community, due to its complementary sensing characteristics, low cost, and small space requirements. This fusion task is known as the vision-aided...
Reference signal reconstruction serves as a crucial technique for suppressing multipath interference and noise in the reference channel of passive radar. Aiming at the challenge of detecting Low-Slow-Small (LSS) targets using Digital Terrestrial Mult...
To address high uncertainty, dynamic evolution, and limited information in emergency decision-making for major sudden disasters, this paper proposes a sliding-window game-theoretic method with four reference points for emergency response selection. F...
An efficient and secure computation framework based on the sliding-window attention mechanism and sliding loss function was proposed to address challenges in temporal and spatial feature modeling for multimodal data processing. The framework aims to...
Polymerization products are indispensable for our daily life, and the relevant modeling process plays a vital role in improving product quality. However, the model identification of the related process is a difficult point in industry due multivariat...
The stochastic nature of renewable energy sources, especially solar PV output, has created uncertainties for the power sector. It threatens the stability of the power system and results in an inability to match power consumption and production. This...
The spatial distribution of night-time lights (NTL) provides a new perspective for studying the range and influence of human activities. However, most studies employing NTL time series are based on monthly or annual composite data, and time series st...
Unmanned pavement construction is of great significance in China, and the primary issue to be solved is how to identify the boundaries of the Pavement Construction Area (PCA). In this paper, we present a simple yet effective method, named the Bidirec...
Accurately predicting the capacity of lithium-ion batteries is crucial for improving battery reliability and preventing potential incidents. Current prediction models for predicting lithium-ion battery capacity fluctuations encounter challenges like...
Tropospheric delay products play a critical role in achieving high-precision positioning in Precise Point Positioning Real-Time Kinematic (PPP-RTK) applications. The short-term prediction of these products remains a significant challenge that warrant...
Acoustic perception of the automotive environment has the potential to advance driving potentials with enhanced safety. The challenge arises when these acoustic perception systems need to perform under resource and power constraints on edge devices....
To address the issue of reduced accuracy or even divergence in micro-electro-mechanical inertial navigation systems’/global navigation satellite systems’ (MINSs’/GNSSs’) integrated navigation systems caused by small amplitude...
Convolutional Neural Networks (CNNs) have demonstrated high accuracy in applications such as object detection, classification, and image processing. However, convolutional layers account for the majority of computations within CNNs. Typically, these...
Water level data obtained from telemetry stations typically contains large number of outliers. Anomaly detection and a data imputation are necessary steps in a data monitoring system. Anomaly data can be detected if its values lie outside of a normal...
In response to the increasing demand for high-precision navigation of satellites operating in the cislunar space, this study introduces an onboard orbit determination algorithm considering both convergence and computational efficiency, referred to as...
Large volumes of automatic identification system (AIS) data provide new ideas and methods for ship data mining and navigation behavior pattern analysis. However, large volumes of big data have low unit values, resulting in the need for large-scale co...
Water quality monitoring is critical for public health, ecology, and economic sustainability, but traditional methods are limited by temporal-spatial coverage and cost, failing to meet real-time assessment needs. Deep learning for water quality predi...
Predicting customer churn is essential for telecommunications companies to maintain profitability. However, training models on historical models leads to performance degradation when they are applied to future conditions—a phenomenon known as c...
In smart cities, relief items distribution is a complex task due to the factors such as incomplete information, unpredictable exact demand, lack of resources, and causality levels, to name a few. With the development of Internet of Things (IoT) techn...
Eucalyptus plantations are expanding rapidly in southern China owing to their short rotation periods and high wood yields. Determining the plantation dynamics of eucalyptus plantations facilitates accurate operational planning, maximizes benefits, an...
Top-coal structure detection is an important basis for realizing effective mining in fully mechanized cave faces. However, the top-coal structure is very complex and often contains multi-layer gangues, which seriously influence the level of effective...
The high-speed rotor electric drive system in control moment gyroscopes (CMGs) is essential for precise spacecraft attitude control. Rigorous testing of this system is critical for ensuring reliability and longevity throughout orbital missions. Howev...
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