Reconstruction-based unsupervised detection methods have demonstrated strong generalization capabilities in the field of tablet anomaly detection, but there are still problems such as poor reconstruction effect and inaccurate positioning of abnormal...
Defect detection is crucial in quality control for fabric production. Deep-learning-based unsupervised reconstruction methods have been recognized universally to address the scarcity of fabric defect samples, high costs of labeling, and insufficient...
Anomaly detection methods play a crucial role in automated quality control within modern manufacturing systems. In this context, unsupervised methods are increasingly favored due to their independence from large-scale labeled datasets. However, exist...
Column semantic-type detection is a crucial task for data integration and schema matching, particularly when dealing with large volumes of unlabeled tabular data. Existing methods often rely on supervised learning models, which require extensive labe...
A crucial area of study in data mining is outlier detection, particularly in the areas of network security, credit card fraud detection, industrial flaw detection, etc. Existing outlier detection algorithms, which can be divided into supervised metho...
Video anomaly detection is widely applied in modern society, which is achieved by sensors such as surveillance cameras. This paper learns anomalies by exploiting videos under the fully unsupervised setting. To avoid massive computation caused by back...
It is critical to use scientific methods to track the performance degradation of in-service buildings over time and avoid accidents. In recent years, both supervised and unsupervised learning methods have yielded positive results in structural health...
The existing unsupervised change detection methods using full-polarimetric synthetic aperture radar (PolSAR) do not use all the polarimetric information, and the results are subject to the influence of noise. In order to solve these problems, a novel...
With the rapid expansion of high-speed rail infrastructure, maintaining the structural integrity of insulators is critical to operational safety. However, conventional defect detection techniques typically rely on extensive labeled datasets, struggle...
The early detection of cancerous lesions is a challenging task given the cancer biology and the variability in tissue characteristics, thus rendering medical image analysis tedious and time-inefficient. In the past, conventional computer-aided diagno...
In complex marine environments, underwater magnetic anomaly detection is challenging because target magnetic anomaly signals are typically weak and easily overwhelmed by background magnetic noise. Although deep learning-based methods have significant...
Container technology is currently one of the mainstream technologies in the field of cloud computing, yet its adoption in resource-constrained, latency-sensitive edge environments introduces unique security challenges. While existing system call-base...
After a landslide, swift and precise identification of the affected area is paramount for facilitating urgent rescue operations and damage assessments. This is particularly vital for land use planners and policymakers, enabling them to efficiently ad...
Data-driven chatter detection techniques avoid complex physical modeling and provide the basis for industrial applications of cutting process monitoring. Among them, feature extraction is the key step of chatter detection, which can compensate for th...
As synthetic aperture radar (SAR) is playing an increasingly important role in Earth observations, many new methods and technologies have been proposed for change detection using multi-temporal SAR images. Especially with the development of deep lear...
In recent years, multi-temporal imagery from spaceborne sensors has provided a fast and practical means for surveying and assessing changes in terrain surfaces. Owing to the all-weather imaging capability, polarimetric synthetic aperture radar (PolSA...
Over the service life of several decades, structural damage detection is crucial for ensuring the safety and durability of engineering structures. However, existing methods often overlook the spatiotemporal coupling in multi-sensor data, hindering th...
Existing industrial image anomaly detection techniques predominantly utilize codecs based on convolutional neural networks (CNNs). However, traditional convolutional autoencoders are limited to local features, struggling to assimilate global feature...
Structural damage detection using unsupervised learning methods has been a trending topic in the structural health monitoring (SHM) research community during the past decades. In the context of SHM, unsupervised learning methods rely only on data acq...
With recent advancements in artificial intelligence (AI) and next-generation communication technologies, the demand for Internet-based applications and intelligent digital services is increasing, leading to a significant rise in cyber-attacks such as...
Change detection (CD) methods using synthetic aperture radar (SAR) data have received significant attention in the field of remote sensing Earth observation, which mainly involves knowledge-driven and data-driven approaches. Knowledge-driven CD metho...
The integrity and stability of railway fasteners are of vital importance to railway safety. To address the challenges of limited anomaly samples, irregular defect geometries, and complex operational conditions in rail fastener anomaly detection, this...
The traditional unsupervised change detection methods based on the pixel level can only detect the changes between two different times with same sensor, and the results are easily affected by speckle noise. In this paper, a novel method is proposed t...
The stable operation of electrical equipment is critical for industrial safety, yet traditional anomaly detection methods often suffer from limitations, such as high resource demands, dependency on expert knowledge, and lack of real-world capabilitie...
The problem of the prompt detection of early-stage hollow worn wheels in railway vehicles via on-board random vibration measurements under normal operation and varying speeds is investigated. This is achieved based on two unsupervised statistical tim...
Change vector analysis in posterior probability space (CVAPS) is an effective change detection (CD) framework that does not require sound radiometric correction and is robust against accumulated classification errors. Based on training samples within...
Power theft and other abnormal power consumption behaviors seriously affect the safety, reliability, and stability of the power grid system. The traditional abnormal power consumption detection methods have complex models and low accuracy. In this pa...
Recently, object detectors based on deep learning have become widely used for vehicle detection and contributed to drastic improvement in performance measures. However, deep learning requires much training data, and detection performance notably degr...
The widespread application of drones in complex environments imposes higher demands on flight safety. However, traditional anomaly detection methods often rely on mathematical models or large amounts of labeled samples, which makes them ill suited to...
Videos represent the primary source of information for surveillance applications. Video material is often available in large quantities but in most cases it contains little or no annotation for supervised learning. This article reviews the state-of-t...
Time series anomaly detection in streaming environments faces persistent challenges due to concept drift, which gradually degrades model reliability. In this paper, we propose Anomaly Detection with Drift-Aware Ensemble-based Incremental Learning (AD...
This paper presents the first results of sea ice detection using the data of Ka- and Ku-band radars at low incidence angles. A classification method based on an unsupervised K-means approach is applied to the arrays of the data for the Arctic and Ant...
This paper investigates the use of an unsupervised hybrid statistical–local outlier factor algorithm to detect anomalies in time-series flight data. Flight data analysis is an activity carried out by airlines primarily as a means of improving t...
Industrial quality control is an important task. Most of the existing vision-based unsupervised industrial anomaly detection and segmentation methods require that the training set only consists of normal samples, which is difficult to ensure in pract...
Effectively monitoring the operational status of reactor coolant pumps (RCPs) is crucial for enhancing the safety and stability of nuclear power operations. To address the challenges of limited interpretability and suboptimal detection performance in...
The efficient fault detection (FD) of traction control systems (TCSs) is crucial for ensuring the safe operation of high-speed trains. Transient faults (TFs) can arise due to prolonged operation and harsh environmental conditions, often being masked...
It is difficult to detect ports in polarimetric SAR images due to the complicated components, morphology, and coastal environment. This paper proposes an unsupervised port detection method by extracting the water of the port based on three-component...
Artificial intelligence (AI) technologies have been widely applied to the automated detection of pipeline leaks. However, traditional AI methods still face significant challenges in effectively detecting the complete leak process. Furthermore, the de...
Social media serves as a platform for netizens to stay informed and express their opinions through the Internet. Currently, the social media discourse environment faces a significant security threat—offensive comments. A group of users posts co...
The paper focuses on the importance of prompt and efficient process fault detection in contemporary manufacturing industries, where product quality and safety protocols are critical. The study compares the efficiencies of two techniques for process f...
Most photovoltaic (PV) plants conduct operation and maintenance (O&M) by periodical inspection and cleaning. Such O&M is costly and inefficient. It fails to detect system faults in time, thus causing heavy loss. To ensure their operations are...
In biomedical signal processing, we often face the problem of artifacts that distort the original signals. This concerns also sleep recordings, such as EEG. Artifacts may severely affect or even make impossible visual inspection, as well as automatic...
The task of unsupervised anomalous sound detection (ASD) is challenging for detecting anomalous sounds from a large audio database without any annotated anomalous training data. Many unsupervised methods were proposed, but previous works have confirm...
Unsupervised anomaly detection in medical imaging aims to automatically identify potential lesions that deviate from normal patterns in multimodal medical images without requiring annotations of abnormal samples, and is of great clinical value for ea...
Ship detection in low-quality Synthetic Aperture Radar (SAR) images poses a persistent challenge. Noise signals in complex environments disrupt imaging conditions, hindering SAR systems from acquiring precise target information, thereby significantly...
Banana Fusarium wilt (BFW) is a devastating disease with no effective cure methods. Timely and effective detection of the disease and evaluation of its spreading trend will help farmers in making right decisions on plantation management. The main pur...
Object-based change detection (OBCD) has recently been receiving increasing attention as a result of rapid improvements in the resolution of remote sensing data. However, some OBCD issues relating to the segmentation of high-resolution images remain...
In a physical microgrid system, equipment failures, manual misbehavior of equipment, and power quality can be affected by intentional cyberattacks, made more dangerous by the widespread use of established communication networks via sensors. This pape...
Detecting anomalies in time series data is important in a variety of fields, including system monitoring, healthcare and cybersecurity. While the abundance of available methods makes it difficult to choose the most appropriate method for a given appl...
From the breakdown of the Kaplan rotor of a hydrogenerator unit and the monitored data collected during its operation before such a failure, this work presents a post-occurrence data analysis in which a previously developed hybrid method based on uns...
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