Adaptive Threshold Research Articles

Automated inspection approach for OCA particles in multi-layered cover glass of display module assembly

Abstract CG (Cover Glass) is a critical component of electronic screens, and its manufacturing quality is closely relevant to the display effect. To satisfy the requirement of quality control, a machine vision system for real-time inspection of particles in the OCA (Optically Clear Adhesive) layer of CG is presented in this paper. With a brief description of the optical logic of particle imaging and the design of the vision system, our emphasis is put on the post-processing image analysis. To align particles regions in the CG under multi-mode imaging, a spatial alignment calibration algorithm is proposed with perspective distortion correction to calculate the triaxial offset. Then, a CLAHE+PM filtering is adopted to enhance the contrast of the particle. Furthermore, a Meanshift method combined with adaptive local thresholding is proposed to extract the contours of tiny particles. Finally, to distinguish between multiple layers of particles in the CG and detect OCA particles, a combination of fast background reconstruction and Averaged Stochastic Gradient Descent-Support Vector Machine is used. According to in-line experiments and tests, our system can find out a majority of the OCA particles with a PR (over-detection rate) of 1.31% and a PM (miss detection rate) of 0.33% for over 10 000 CG samples.

Using machine vision algorithms for characterizing gas-liquid slug flows in vertical pipes

Slug flow, characterized by the distinctive interfacial structures of Taylor bubbles surrounded by liquid films and bridged by aerated liquid slugs, is a dynamically complex two-phase flow pattern exist in many oil and gas, and energy systems. Accurate and precise quantification of such complex flow behaviour is essential for optimal design, safe operation, and reliable modelling of these systems. Existing image-based measurement techniques mostly rely on offline image processing algorithms and are often limited to a narrow set of flow characteristics primarily focusing on Taylor bubbles. Such constraints not only impede real-time flow monitoring and regulation but also leave liquid slug characteristics unmeasured, resulting in an inability to accurately determine the flow characteristics and extract instantaneous void fraction signals. Present study examined the performance of adaptive thresholding (AT) and background subtraction (BS) algorithms in capturing slug flow characteristics. It was found that while the former excels in Taylor bubbles detection and the latter in small bubbles identification, neither individually addresses the accurate measurement of both flow structures' characteristics. This observation, along with the mentioned restrictions of existing algorithms are the main reason for developing the present combined machine vision-based algorithm. While unlocking the ability to extract instantaneous void fraction signals, this new approach facilitates online measurement of a wide range of key flow characteristics, including Taylor bubble length, velocity, void fraction, and surrounding liquid film thickness; liquid slug length and void fraction; and slug unit length, void fraction, and frequency. Parallel to the high-speed imaging, time-series void fraction data was collected using two capacitance sensors installed alongside the imaging area on the pipe, providing benchmark data essential for the validation of the new algorithm's accuracy. The comparisons demonstrated a high degree of accuracy and precision for the combined algorithm. Quantitatively, the new algorithm measured key unit cell characteristics with RMS errors ranging from 2 to 10 %, while the BS and AT algorithms exhibited wider RMS error ranges of 8–46 % and 2–53 %, respectively. This underscores the new algorithm's potential as a transformative tool for slug flow analysis.

Missile Fault Detection and Localization Based on HBOS and Hierarchical Signed Directed Graph

The rudder surfaces and lifting surfaces of a missile are utilized to acquire aerodynamic forces and moments, adjust the missile’s attitude, and achieve precise strike missions. However, the harsh flying conditions of missiles make the rudder surfaces and lifting surfaces susceptible to faults. In practical scenarios, there is often a scarcity of fault data, and sometimes, it is even difficult to obtain such data. Currently, data-driven fault detection and localization methods heavily rely on fault data, posing challenges for their applicability. To address this issue, this paper proposes an HBOS (Histogram-Based Outlier Score) online fault-detection method based on statistical distribution. This method generates a fault-detection model by fitting the probability distribution of normal data and incorporates an adaptive threshold to achieve real-time fault detection. Furthermore, this paper abstracts the interrelationships between the missile’s flight states and the propagation mechanism of faults into a hierarchical directed graph model. By utilizing bilateral adaptive thresholds, it captures the first fault features of each sub-node and determines the fault propagation effectiveness of each layer node based on the compatibility path principle, thus establishing a fault inference and localization model. The results of semi-physical simulation experiments demonstrate that the proposed algorithm is independent of fault data and exhibits high real-time performance. In multiple sets of simulated tests with randomly parameterized deviations, the fault-detection accuracy exceeds 98% with a false-alarm rate of no more than 0.31%. The fault-localization algorithm achieves an accuracy rate of no less than 97.91%.

A Complex Environmental Water-Level Detection Method Based on Improved YOLOv5m.

The existing methods for water-level recognition often suffer from inaccurate readings in complex environments, which limits their practicality and reliability. In this paper, we propose a novel approach that combines an improved version of the YOLOv5m model with contextual knowledge for water-level identification. We employ the adaptive threshold Canny operator and Hough transform for skew detection and correction of water-level images. The improved YOLOv5m model is employed to extract the water-level gauge from the input image, followed by refinement of the segmentation results using contextual priors. Additionally, we utilize a linear regression model to predict the water-level value based on the pixel height of the water-level gauge. Extensive experiments conducted in real-world environments encompassing daytime, nighttime, occlusion, and lighting variations demonstrate that our proposed method achieves an average error of less than 2 cm.

Application of Getis-Ord Correlation Index (Gi) for Burned Area Detection Improvement in Mediterranean Ecosystems (Southern Italy and Sardinia) Using Sentinel-2 Data

This study collects the results obtained using the Getis-Ord local spatial autocorrelation index (Gi) with the aim of improving the classification of burned area detection maps generated from spectral indices (i.e., dNBR index) derived from Sentinel-2 satellite data. Therefore, the work proposes an adaptive thresholding approach that also includes the application of a similarity index (Sorensen–Dice Similarity Index) with the aim of adaptively correcting classification errors (false-positive burned pixels) related to the spectral response of burned/unburned areas. In this way, two new indices derived from the application of the Getis-Ord local autocorrelation analysis were created to test their effectiveness. Three wildfire events were considered, two of which occurred in Southern Italy in the summer of 2017 and one in Sardinia in the summer of 2019. The accuracy assessment analysis was carried out using the CEMS (Copernicus Emergency Management Service) on-demand maps. The results show the remarkable performance of the two new indices in terms of their ability to reduce the false positives generated by dNBR. In the three sites considered, the false-positive reduction percentage was around 95–96%. The proposed approach seems to be adaptable to different vegetation contexts, and above all, it could be a useful tool for mapping burned areas to support post-fire management activities.

Кластеризация эпилептиформных разрядов в индивидуальной диагностике ранней стадии отсроченной церебральной ишемии

To detect the onset of delayed cerebral ischemia after aneurysmal hemorrhage in the subarachnoid space, a clinical data method for detecting clusters of epileptiform discharges in the data of long-term electroencephalographic monitoring of patients was proposed and tested. It is based on the identification of distributions of epileptiform discharges that are continuous in time and exceed the adaptive threshold and have a clearly expressed interhemispheric asymmetry.

A weak leak location method based on signal attenuation mechanism for energy transportation system

Traditional leak location methods based on signal saltation points make it hard to locate the position of the weak leak event in the energy transportation system. To address it, a data-driven method based on signal attenuation mechanism is proposed in this paper. First, a wavelet adaptive threshold denoising procedure is proposed to obtain the pure signal from the collected data, and the decimal-coded genetic mechanism is designed to calculate the optimal threshold of wavelet decomposition for different layers. Second, the two-stage leak location method is proposed to calculate the position of the leak point through the combination analysis of pressure data change of both ends of the pipeline. Through the presented pressure signal attenuation mechanism, the leak location result is obtained from the data changes of both ends of the pipeline, not the time difference of arrived signal. With the above signal denoising and analysis way, the proposed method has the 1% location deviation of the leak point, which is superior to leak location methods based on signal saltation points such as wavelet transform. Finally, different experiment results are studied, and the analysis results show the effectiveness of the proposed method for the leak location problem.

Adaptive Detection in Real-Time Gait Analysis through the Dynamic Gait Event Identifier.

The Dynamic Gait Event Identifier (DGEI) introduces a pioneering approach for real-time gait event detection that seamlessly aligns with the needs of embedded system design and optimization. DGEI creates a new standard for gait analysis by combining software and hardware co-design with real-time data analysis, using a combination of first-order difference functions and sliding window techniques. The method is specifically designed to accurately separate and analyze key gait events such as heel strike (HS), toe-off (TO), walking start (WS), and walking pause (WP) from a continuous stream of inertial measurement unit (IMU) signals. The core innovation of DGEI is the application of its dynamic feature extraction strategies, including first-order differential integration with positive/negative windows, weighted sleep time analysis, and adaptive thresholding, which together improve its accuracy in gait segmentation. The experimental results show that the accuracy rate of HS event detection is 97.82%, and the accuracy rate of TO event detection is 99.03%, which is suitable for embedded systems. Validation on a comprehensive dataset of 1550 gait instances shows that DGEI achieves near-perfect alignment with human annotations, with a difference of less than one frame in pulse onset times in 99.2% of the cases.

Adaptive Truncation Threshold Determination for Multimode Fiber Single-Pixel Imaging

Truncated singular value decomposition (TSVD) is a popular recovery algorithm for multimode fiber single-pixel imaging (MMF-SPI), and it uses truncation thresholds to suppress noise influences. However, due to the sensitivity of MMF relative to stochastic disturbances, the threshold requires frequent re-determination as noise levels dynamically fluctuate. In response, we design an adaptive truncation threshold determination (ATTD) method for TSVD-based MMF-SPI in disturbed environments. Simulations and experiments reveal that ATTD approaches the performance of ideal clairvoyant benchmarks, and it corresponds to the best possible image recovery under certain noise levels and surpasses both traditional truncation threshold determination methods with less computation—fixed threshold and Stein’s unbiased risk estimator (SURE)—specifically under high noise levels. Moreover, target insensitivity is demonstrated via numerical simulations, and the robustness of the self-contained parameters is explored. Finally, we also compare and discuss the performance of TSVD-based MMF-SPI, which uses ATTD, and machine learning-based MMF-SPI, which uses diffusion models, to provide a comprehensive understanding of ATTD.

Biometric Vibration Signal Detection Devices for Swallowing Activity Monitoring

Swallowing is a complex neuromuscular activity regulated by the autonomic central nervous system, and impairment can lead to dysphagia, which is difficulty in swallowing. This research presents a novel approach that utilizes wireless, wearable technology for the continuous mechano-acoustic tracking of respiratory activities and swallowing. To address the challenge of accurately tracking swallowing amidst potential confounding activities or significant body movements, we employ two accelerometers. These accelerometers help distinguish between genuine swallowing events and other activities. By monitoring movements and vibrations through the skin surface, the developed device enables non-intrusive monitoring of swallowing dynamics and respiratory patterns. Our focus is on the development of both the wireless skin-interfaced device and an advanced algorithm capable of detecting swallowing dynamics in conjunction with respiratory phases. The device and algorithm demonstrate robustness in detecting respiratory patterns and swallowing instances, even in scenarios where users exhibit periodic movements due to disease or daily activities. Furthermore, peak detection using an adaptive threshold automatically adjusts to an individual’s signal strength, facilitating the detection of swallowing signals without the need for individual adjustments. This innovation has significant potential for enhancing patient training and rehabilitation programs aimed at addressing dysphagia and related respiratory issues.

ETLP: event-based three-factor local plasticity for online learning with neuromorphic hardware

Neuromorphic perception with event-based sensors, asynchronous hardware, and spiking neurons shows promise for real-time, energy-efficient inference in embedded systems. Brain-inspired computing aims to enable adaptation to changes at the edge with online learning. However, the parallel and distributed architectures of neuromorphic hardware based on co-localized compute and memory imposes locality constraints to the on-chip learning rules. We propose the event-based three-factor local plasticity (ETLP) rule that uses the pre-synaptic spike trace, the post-synaptic membrane voltage and a third factor in the form of projected labels with no error calculation, that also serve as update triggers. ETLP is applied to visual and auditory event-based pattern recognition using feedforward and recurrent spiking neural networks. Compared to back-propagation through time, eProp and DECOLLE, ETLP achieves competitive accuracy with lower computational complexity. We also show that when using local plasticity, threshold adaptation in spiking neurons and a recurrent topology are necessary to learn spatio-temporal patterns with a rich temporal structure. Finally, we provide a proof of concept hardware implementation of ETLP on FPGA to highlight the simplicity of its computational primitives and how they can be mapped into neuromorphic hardware for online learning with real-time interaction and low energy consumption.

Processes monitoring using adaptive confidence limit based on T-S fuzzy model and Luenberger observer

In hazard-sensitive processes, the monitoring upsets and malfunctions correctly is an important challenge to operation safely and enhance the performance. Conventional process monitoring frequently assumes that process data follow only one Gaussian distribution, which generates a constant confidence limit and hence produces a high number of false alarms. However, in fact, industrial processes usually include various operating modes. To ovoid this drawback, the suggested approach employs an adaptive confidence limit (ACL) when a substantial number of false alarms are created. The fundamental concept underlying this study is to extract internally several local linear sub-modes of the monitored variables. In typical operating circumstances, the Gaussian mixture model (GMM) is utilized to extract several local linear sub-modes, followed by fuzzy linearization using the Takagi-Sugeno model, thereafter a bank of Luenberger observers to construct the residual spaces. An abnormal event is detected when the squared prediction error (SPE) is too great or exceeds the adaptive threshold designed to prevent the false alarms. Furthermore, an enhanced contribution plots is effectively used to identify the defective variable.

Development of UroSAM: A Machine Learning Model to Automatically Identify Kidney Stone Composition from Endoscopic Video.

Introduction: Chemical composition analysis is important in prevention counseling for kidney stone disease. Advances in laser technology have made dusting techniques more prevalent, but this offers no consistent way to collect enough material to send for chemical analysis, leading many to forgo this test. We developed a novel machine learning (ML) model to effectively assess stone composition based on intraoperative endoscopic video data. Methods: Two endourologists performed ureteroscopy for kidney stones ≥ 10 mm. Representative videos were recorded intraoperatively. Individual frames were extracted from the videos, and the stone was outlined by human tracing. An ML model, UroSAM, was built and trained to automatically identify kidney stones in the images and predict the majority stone composition as follows: calcium oxalate monohydrate (COM), dihydrate (COD), calcium phosphate (CAP), or uric acid (UA). UroSAM was built on top of the publicly available Segment Anything Model (SAM) and incorporated a U-Net convolutional neural network (CNN). Discussion: A total of 78 ureteroscopy videos were collected; 50 were used for the model after exclusions (32 COM, 8 COD, 8 CAP, 2 UA). The ML model segmented the images with 94.77% precision. Dice coefficient (0.9135) and Intersection over Union (0.8496) confirmed good segmentation performance of the ML model. A video-wise evaluation demonstrated 60% correct classification of stone composition. Subgroup analysis showed correct classification in 84.4% of COM videos. A post hoc adaptive threshold technique was used to mitigate biasing of the model toward COM because of data imbalance; this improved the overall correct classification to 62% while improving the classification of COD, CAP, and UA videos. Conclusions: This study demonstrates the effective development of UroSAM, an ML model that precisely identifies kidney stones from natural endoscopic video data. More high-quality video data will improve the performance of the model in classifying the majority stone composition.

Continuum topological derivative - A novel application tool for segmentation of CT and MRI images

IntroductionComputed Tomography (CT) and Magnetic Resonance Imaging (MRI) are essential tools for unraveling anatomical and tissue properties, particularly in the head and brain. CT provides high-contrast images, particularly valuable in cases such as cerebral bleeds, and also aids in estimating cranial deformities and organ shape deviations. MRI, on the other hand, offers excellent imaging of cerebral artery regions, allowing analysis of various cerebral pathologies through different sequences. Beyond detecting common head and brain disorders, these modalities play a crucial role in identifying abnormalities in orbits, middle cerebral artery territories, brain ventricles, soft tissues, and bones. A unique aspect of brain MRI is its ability to produce multiplanar brain assessments. Both head/brain CT and MRI are invaluable for studying haemorrhage cases, with segmentation of affected areas providing detailed images for further analysis. This study explores the application of a novel mathematical technique, continuum topological derivative (CTD), for CT and MR image segmentation. MethodsThe initial stage of Continuum Topological Derivative (CTD) segmentation involves preprocessing CT and MR images due to their susceptibility to inherent noises, such as quantum mottle, and Gaussian and Rayleigh noises, respectively. In this study, we have implemented the CTD denoising algorithm to produce denoised CT/MR images, serving as ground truth for subsequent segmentation steps. Validation of the denoised CTD CT/MR images was conducted through minimal residual value computation across all case studies. Following this, segmentation of the region of interest was performed using the CTD technique, with comparisons made against Discrete Topological Derivatives (DTD), k-mean clustering and Adaptive Threshold methods. Evaluation of the proposed CTD algorithm's effectiveness in segmentation involved calculating performance metrics such as Jaccard and dice indices to assess spatial overlap of segmented images. ResultsThe CTD technique yields excellent segmentation results, not only for the delineated region of interest but also for volume-based cerebral blood areas and anomalies in the middle cerebral artery (MCA) and its territorial areas, which are substantiated through performance metrics and visual inspection by trained radiologist. This aids in determining the severity of stroke in affected patients. Additionally, a unique attempt is made to apply CTD to Electrical Impedance Tomography (EIT) images of the lungs for precise estimation of the breathing cycle. CTD successfully generates standardized images, demonstrating attenuation and density characteristics for cerebral cisterns, arteries, and ventricles. DiscussionThe denoised images obtained through CTD facilitate thorough analysis of both normal and pathological conditions, providing radiologists with enhanced capabilities to identify subtle details, particularly in areas such as abnormal cerebral artery territories, haemorrhage cases, cisterns, ventricles and arteries. Results clearly demonstrate that the combination of CTD denoising and segmentation outperforms the other three established methods in terms of both efficiency and accuracy in delineating diseased or affected areas, as evidenced by the various case studies conducted in this research. In summary, the proposed CTD method aims to delineate boundaries and contours of the region of interest, facilitating precise estimation of size and shape for accurate detection of the extent of diseased or affected areas.

Adaptive Minimum Support Threshold for Association Rule Mining

Adaptive Minimum Support Threshold for Association Rule Mining

Hybrid Adaptive Threshold Segmentation for predicting Breast Cancer

Breast cancer is a leading cause of death for women in many parts of the world. The disease is often misdiagnosed until it has progressed beyond the point of effective treatment. Therefore, early detection of the disease would aid in reducing mortality and other associated risks. Microarray gene expression data is difficult to identify and interpret, making it challenging to evaluate and choose the most relevant set of genes for use as breast cancer markers. Our research utilized Matlab 2018a and the Breast Cancer Wisconsin Diagnostic dataset to develop a mixed machine-learning model for fast breast cancer prediction. Here, we apply various machine learning techniques including random forest classification, logistic regression, support vector machine and Naive Bayes, as well as to a dataset to make predictions about their development and eventual size. The success percentage for the eXtreme Gradient Boosting classifier compared to various machine learning approaches is 99.78%. This new approach to prediction has the potential to revolutionize the detection, analysis and prognosis of breast cancer.

RSSI-WSDE: Wireless Sensing of Dynamic Events Based on RSSI.

Wireless sensing is a crucial technology for building smart cities, playing a vital role in applications such as human monitoring, route planning, and traffic management. Analyzing the data provided by wireless sensing enables the formulation of more scientific decisions. The wireless sensing of dynamic events is a significant branch of wireless sensing. Sensing the specific times and durations of dynamic events is a challenging problem due to the dynamic event information is concealed within static environments. To effectively sense the relevant information of event occurrence, we propose a wireless sensing method for dynamic events based on RSSI, named RSSI-WSDE. RSSI-WSDE utilizes variable-length sliding windows and statistical methods to process original RSSI time series, amplifying the differences between dynamic events and static environments. Subsequently, z-score normalization is employed to enhance the comparability of the sensing effects for different dynamic events. Furthermore, by setting the adaptive threshold, the occurrence of dynamic event is sensed and the relevant information is marked on the original RSSI time series. In this study, the sensing performance of RSSI-WSDE was tested in indoor corridors and outdoor urban road environments. The wireless sensing of dynamic events, including walking, running, cycling, and driving, was conducted. The experimental results demonstrate that RSSI-WSDE can accurately sense the occurrence of dynamic events, marking the specific time and duration with millisecond-level precision. Moreover, RSSI-WSDE exhibits robust performance in wireless sensing of dynamic events in both indoor and outdoor environments.

A novel deep learning framework for rolling bearing fault diagnosis enhancement using VAE-augmented CNN model

In the context of burgeoning industrial advancement, there is an increasing trend towards the integration of intelligence and precision in mechanical equipment. Central to the functionality of such equipment is the rolling bearing, whose operational integrity significantly impacts the overall performance of the machinery. This underscores the imperative for reliable fault diagnosis mechanisms in the continuous monitoring of rolling bearing conditions within industrial production environments. Vibration signals are primarily used for fault diagnosis in mechanical equipment because they provide comprehensive information about the equipment's condition. However, fault data often contain high noise levels, high-frequency variations, and irregularities, along with a significant amount of redundant information, like duplication, overlap, and unnecessary information during signal transmission. These characteristics present considerable challenges for effective fault feature extraction and diagnosis, reducing the accuracy and reliability of traditional fault detection methods. This research introduces an innovative fault diagnosis methodology for rolling bearings using deep convolutional neural networks (CNNs) enhanced with variational autoencoders (VAEs). This deep learning approach aims to precisely identify and classify faults by extracting detailed vibration signal features. The VAE enhances noise robustness, while the CNN improves signal data expressiveness, addressing issues like gradient vanishing and explosion. The model employs the reparameterization trick for unsupervised learning of latent features and further trains with the CNN. The system incorporates adaptive threshold methods, the "3/5" strategy, and Dropout methods. The diagnosis accuracy of the VAE-CNN model for different fault types at different rotational speeds typically reaches more than 90 %, and it achieves a generally acceptable diagnosis result. Meanwhile, the VAE-CNN augmented fault diagnosis model, after experimental validation in various dimensions, can achieve more satisfactory diagnosis results for various fault types compared to several representative deep neural network models without VAE augmentation, significantly improving the accuracy and robustness of rolling bearing fault diagnosis.

Electric shock fault identification method based on DWT-AE-BPNN for residual current devices in power distribution systems

The protection dead-zone and threshold setting difficulties of the residual current devices (RCDs) in low-voltage distribution networks may lead to the misidentification of electric shock fault, resulting in severe life-threatening accidents. This paper proposes an electric shock fault identification method based on artificial intelligence for RCDs. Firstly, Mallat discrete wavelet transform (DWT) is applied to efficiently extract non-stationary electric shock feature signals from the total residual current with various noises, preventing weak non-stationary electric shock feature signals from being filtered out. Based on the average and maximum components of the signal mutation, an adaptive threshold can be determined to detect electric shock accurately, avoiding the false activation of RCDs caused by load fluctuations. Subsequently, an autoencoder (AE) is built to mine the non-linear features in which the signal of electric shock on living gradually rises and the signal of electric shock on non-living remains stable. Finally, a back propagation neural network (BPNN) is trained to classify the electric shock types from the non-linear features. The simulation and experiment have been conducted to obtain total residual current data under different conditions, and the electric shock fault real-time identification hardware platforms are developed. The accuracy of electric shock fault detection and classification can reach 100 %, which has advanced its practical applicability.

An efficient adaptive multilevel Renyi entropy thresholding method based on the energy curve with dynamic programming

An efficient adaptive multilevel Renyi entropy thresholding method based on the energy curve with dynamic programming