Method For Real-time Monitoring Research Articles

Hierarchical explicit–implicit combined sensing-based real-time monitoring method for the service performance of complex equipment

PurposeReal-time monitoring of the critical physical fields of core components in complex equipment is of great significance as it can predict potential failures, provide reasonable preventive maintenance strategies and thereby ensure the service performance of the equipment. This research aims to propose a hierarchical explicit–implicit combined sensing-based real-time monitoring method to achieve the sensing of critical physical field information of core components in complex equipment.Design/methodology/approachSensor deployable and non-deployable areas are divided based on the dynamic and static constraints in actual service. An integrated method of measurement point layout and performance evaluation is used to optimize sensor placement, and an association mapping between information in non-deployable and deployable areas is established, achieving hierarchical explicit–implicit combined sensing of key sensor information for core components. Finally, the critical physical fields of core components are reconstructed and visualized.FindingsThe proposed method is applied to the spindle system of CNC machine tools, and the result shows that this method can effectively monitor the spindle system temperature field.Originality/valueThis research provides an effective method for monitoring the service performance of complex equipment, especially considering the dynamic and static constraints during the service process and detecting critical information in non-deployable areas.

Health monitoring of automotive clutch system through feature fusion and application of tree-based classifiers

Clutch systems are crucial components of automotive systems, essential for transferring engine power to the wheels through gear shifts. A failed clutch can halt gear shifts and power transmission, rendering a vehicle immobile. Effective fault diagnosis is vital for ensuring reliability, preventing breakdowns, and addressing root causes promptly. Consequently, condition monitoring of the clutch is essential for maintaining system reliability and minimizing unwanted breakdowns. This paper presents an innovative condition-monitoring technique derived from vibration analysis to detect faults in the clutch system. The study involves extracting statistical, histogram, and autoregressive moving average features from acquired vibration signals. The J48 decision tree algorithm is utilized to select the most significant features, which are then classified using tree-based classifiers across three load conditions (no load, 5 kg, and 10 kg) and six clutch conditions. The experimental results demonstrate that the feature fusion strategy significantly enhances classification accuracy. The obtained results state that the classification accuracies for no load, 5 kg load, and 10 kg load were 98.33%, 100.00%, and 99.16%, respectively, while applying feature fusion strategies. This study highlights the effectiveness of feature fusion in improving fault diagnosis accuracy for clutch systems, presenting a robust method for real-time condition monitoring in automotive applications.

Error and Traffic State Analysis of Floating Car Data

Abstract. Floating car technology is widely applied in traffic information collection and has become a common method for real-time and dynamic monitoring of urban traffic conditions. Using floating car data from Shanghai as the research object, this paper analyzes the positioning error of floating cars and finds that most errors are within 15 meters. The error is significantly affected by vehicle speed but less by time, and the error decreases as the road grade increases. Speed and travel time ratio are used as parameters to assess the spatiotemporal traffic state, exploring the spatial distribution and temporal evolution of speed, as well as the spatiotemporal distribution of traffic congestion. In the spatial distribution of speed, clustering analysis is used to deduce the temporal distribution pattern of speed on the same road segment. Based on this, corresponding traffic management measures can be applied to different road segments and at different times.

Construction error control method of large-span spatial structures based on digital twin

In the process of construction of large-span spatial structures, there are many control elements, which need to carry on the dual control of internal force and shape. The traditional control method cannot measure the construction deviation completely and accurately, and there is a lag in information feedback. In this paper, spatio-temporal information is integrated into digital twin and a construction error control method is proposed for large-span spatial structures. By analyzing the fusion mechanism of spatio-temporal information and digital twin, a closed-loop control system for structural errors before, during and after construction is formed. Driven by the control architecture, the method of structure simulation analysis and real-time monitoring during construction is proposed, and the functional relationship between cable force and cable length is established. Based on point cloud data, construction error optimization detection method is proposed for structure formation state. The nonlinear relation between elevation change rate and cable force deviation is formed and the control measures of formation state error are obtained. The experimental model of cable truss structure is taken as an example to apply and verify the theoretical method. The construction error control twin platform is formed in the experiment. Driven by the platform, the twinning analysis and real-time monitoring of the construction process are carried out to control the error of the structure formation. The results show that this method integrates spatio-temporal information into digital twin to realize data integration processing. The optimized error detection method can save 30% time cost and effectively control the construction error within 3%. The integration of spatio-temporal information and digital twin provides theoretical and algorithm support for the intelligent management and control of large-span spatial structure construction.

Real-time modeling of transient crustal deformation through the quantification of uncertainty deduced from GNSS data

We propose a new method for real-time uncertainty monitoring of earthquake and volcano source models using data from the global navigation satellite system (GNSS) and explore its application concerning observation station placement. The Geospatial Information Authority of Japan operates two main types of GNSS earth observation network system (GEONET) coordinates for crustal deformation monitoring on different time scales: post-processing analysis values and real-time GEONET analysis system for rapid deformation monitoring (REGARD). REGARD uses the Markov Chain Monte Carlo (MCMC) method termed real-time automatic uncertainty estimation of a coseismic single rectangular model using GNSS data (RUNE) for single rectangular fault model estimation to handle uncertainty. Thus far, no GNSS monitoring system can automatically detect transient crustal deformation events, such as volcanic activity and earthquake swarms, on timescales of a day or less. We extended RUNE and developed a core program for a new monitoring system for earthquake and volcanic source models and their uncertainties. Our program achieved automatic and stable MCMC utilization for rectangular fault, dike, Mogi, and spheroid models by increasing the computational speed, improving search efficiency, and adjusting hyperparameters. The program automatically determines the standard deviation of the likelihood function assuming a normal distribution with weights for each observation station. The calculation time was within 15 s for 1 × 106 samples on a standard 1U server. We assessed the reliability of the developed method using synthetic and observed GNSS data from the 2015 Sakurajima volcanic event. The results were consistent with the assumed model and previous studies and indicated an advantage in automatically quantifying uncertainty in a short computation time. Based on MCMC samples, we developed a new visualization algorithm to indicate areas on a map in which the number of observation stations should be expanded. We assessed the reliability using data from the 2023 Noto Peninsula earthquake [Mj 6.5]. The results indicate that the algorithm is helpful in studying the placement of stations. The above model extensions and their application are essential to achieve a rapid quantitative understanding of disaster events near urban areas and for utilizing this information in emergency response activities.Graphical

Research on fatigue life of carbon fiber reinforced polymer strengthened single-sided butt welded joints utilizing resin pre-coating for strong adhesive bonding

Single-sided butt welding is a common connection method used in box beam structures such as excavator booms. Single-sided butt weld joints are prone to fatigue failure since the structures are subjected to cyclic loads over extended periods. The purpose of this study is to improve the fatigue performance of single-sided butt welded joints. Carbon fiber reinforced polymer (CFRP) strengthened single-sided butt welded joint specimens with permanent backing plates were prepared by pre-coating method, and constant-amplitude tension–tension fatigue tests were carried out on the specimens. The effects of welding groove parameters and CFRP reinforcement on fatigue life were studied. The test results show that the CFRP-strengthened specimens without blunt edges and with a groove angle of 40° show the highest average fatigue life. The single-sided bonding of 10 layers of CFRP sheets effectively inhibits the propagation of fatigue cracks and significantly improves the fatigue life of welded joints. In the CFRP/welded joint composite structure, the strain monitoring results show that the peak strain of the interface is the largest in the middle position, and the peak strain rises sharply in the rapid crack propagation stage, which indicates that the real-time interface strain peak can reflect the fatigue crack propagation. It provides an early warning method for real-time monitoring of fatigue life failure of welded structures in practical engineering.

Monitoring of Bacillus spore-forming dynamics through flow cytometry.

The plate counting method is a traditional and widely accepted technique for live cell counting, often employed for Bacillus enumeration and spore forming rate calculations. However, this method requires at least 12 h to generate results, making it unsuitable for real-time monitoring of bacterial growth status and spore transformation rate. Bacillus thuringiensis crystals, produced during sporulation, are widely used as microbial pesticides, with high demand for industrial scale production. Variations in cultivation conditions and harvest timing during large-scale pore production of Bacillus thuringiensis significantly affect spore forming rate, impacting crystallization yield. Nevertheless, there is a lack of real-time monitoring methods for spore conversion rate. Flow cytometry (FCM), a well-established technique for single-cell analysis in eukaryotic cells, has been successfully applied in bacterial detection in environmental and food samples. In this study, we introduced a rapid flow cytometry-based method for determining spore forming rate of Bacillus thuringiensis, with two nucleic acid dyes, SYTO24 and LDS751. The method enables dynamic monitoring of spore, vegetative cell, and viable but non-culturable/dead cell proportions during the whole cultivation process, and spore forming rate could be gained within 30 min. Data of spore forming rate by FCM method is consistent with that by plate counting method, offering a faster and more efficient approach for assessing sporulation status in industrial Bacillus thuringiensis microbial pesticide production.

Detecting marine heatwaves below the sea surface globally using dynamics-guided statistical learning

Extreme warm water events, known as marine heatwaves, cause a variety of adverse impacts on the marine ecosystem. They are occurring more and more frequently across the global ocean. Yet monitoring marine heatwaves below the sea surface is still challenging due to the sparsity of in situ temperature observations. Here, we propose a statistical learning method guided by ocean dynamics and optimal prediction theory, to detect subsurface marine heatwaves based on the observable sea surface temperature and sea surface height. This dynamics-guided statistical learning method shows good skills in detecting subsurface marine heatwaves in the oceanic epipelagic zone over many parts of the global ocean. It outperforms both the classical ordinary least square regression and popular deep learning methods that do not effectively exploit ocean dynamics, with clear dynamical interpretation for its outperformance. Our study provides a useful statistical learning method for near real-time monitoring of subsurface marine heatwaves at a global scale and highlights the importance of exploiting ocean dynamics for enhancing the efficiency and interpretability of statistical learning.

Neural-driven viscosity modeling and fatigue optimization for cycloidal gear systems

This study presents a comprehensive approach to enhancing RV reducer performance. A novel RAM viscosity-pressure model is developed, combining traditional stability with neural network precision. Surface micro-morphological analysis is integrated with contact fatigue modeling, revealing critical insights into surface characteristics and their impact on fatigue. A neural proxy-based optimization strategy is employed to identify optimal operational parameters, significantly reducing fatigue risks. Additionally, a new method for real-time transmission ratio monitoring is introduced, validating the model's effectiveness and offering a robust framework for improving the reliability and lifespan of RV reducers in industrial applications.

Real-time monitoring and protection strategies for dense granular flow spallation target in Accelerator-Driven System

An innovative concept of a high-power, gravity-driven, dense granular spallation target has been integrated into the prototype design of the China initiative Accelerator-Driven System (CiADS), which is envisioned as a promising nuclear demonstration facility aimed at converting long-lived fission products and minor actinides into short-lived isotopes. This spallation target system, bridging the proton beam accelerator and the subcritical reactor, plays a crucial role in ensuring safety control and facilitating dynamic operational strategies. In this study, we have developed a system design for an online detector array, incorporating several neutron fission chambers and thermocouples installed in the gap between the spallation target and the subcritical reactor to monitor operations. Furthermore, we have devised real-time monitoring and protection methods to tackle the challenges of measuring beam position and the blockage condition associated with granular flow, considering the distribution of neutron flux and heat deposition under abnormal irradiation conditions. Monte Carlo simulations have demonstrated the applicability and feasibility of the control system for the dense granular spallation target in the accelerator-driven system. The numerical results confirm that the proposed control system meets the requirements for stable measurement with acceptable accuracy.

Challenges and advances in glioblastoma targeted therapy: the promise of drug repurposing and biomarker exploration.

Glioblastoma remains the most prevalent and aggressive primary malignant brain tumor in adults, characterized by limited treatment options and a poor prognosis. Previous drug repurposing efforts have yielded only marginal survival benefits, particularly those involving inhibitors targeting receptor tyrosine kinase and cyclin-dependent kinase-retinoblastoma pathways. This limited efficacy is likely due to several critical challenges, including the tumor's molecular heterogeneity, the dynamic evolution of its genetic profile, and the restrictive nature of the blood-brain barrier that impedes effective drug delivery. Emerging diagnostic tools, such as circulating tumor DNA and extracellular vesicles, offer promising non-invasive methods for real-time tumor monitoring, potentially enabling the application of targeted therapies to more selected patient populations. Moreover, innovative drug delivery strategies, including focused ultrasound, implantable drug-delivery systems, and engineered nanoparticles, hold potential for enhancing the bioavailability and therapeutic efficacy of treatments.

Real-Time Visualization of HIV-1 RNA Detection Using Loop-Mediated Isothermal Amplification-Enabled Particle Diffusometry.

Isothermal nucleic acid amplification tests, NAATs, such as reverse transcription-loop-mediated isothermal amplification (RT-LAMP), offer promising capabilities to perform real-time semiquantitative detection of viral pathogens. These tests provide rapid results, utilize simple instrumentation for single-temperature reactions, support efficient user workflows, and are suitable for field use. Herein, we present a novel and robust method for real-time monitoring of HIV-1 RNA RT-LAMP utilizing a novel implementation of particle diffusometry (PD), a diffusivity quantification technique using fluorescent particles, to quantify viral concentration in nuclease-free water. We monitor changes in particle diffusion dynamics of 400 nm fluorescently labeled particles throughout the RT-LAMP of HIV-1 RNA in nuclease-free water, enabling measurement within 20 min and detection of concentrations as low as 25 virus particles per μL. Moreover, in a single-blind study, we demonstrate semiquantitative detection by accurately determining the initial concentration of an unknown HIV-1 RNA within a 10% absolute error margin. These results highlight the potential of real-time PD readout for quantifying HIV-1 RNA via RT-LAMP, offering promise for viral load monitoring of HIV and other chronic infections.

Pest Detection Based on Lightweight Locality-Aware Faster R-CNN

Accurate and timely monitoring of pests is an effective way to minimize the negative effects of pests in agriculture. Since deep learning-based methods have achieved good performance in object detection, they have been successfully applied for pest detection and monitoring. However, the current pest detection methods fail to balance the relationship between computational cost and model accuracy. Therefore, this paper proposes a lightweight, locality-aware faster R-CNN (LLA-RCNN) method for effective pest detection and real-time monitoring. The proposed model uses MobileNetV3 to replace the original backbone, reduce the computational complexity, and compress the size of the model to speed up pest detection. The coordinate attention (CA) blocks are utilized to enhance the locality information for highlighting the objects under complex backgrounds. Furthermore, the generalized intersection over union (GIoU) loss function and region of interest align (RoI Align) technology are used to improve pest detection accuracy. The experimental results on different types of datasets validate that the proposed model not only significantly reduces the number of parameters and floating-point operations (FLOPs), but also achieves better performance than some popular pest detection methods. This demonstrates strong generalization capabilities and provides a feasible method for pest detection on resource-constrained devices.

12362 Pathophysiology From Oxidative Stress Effectively Monitored In A Pre-clinical Model Of Type 2 Diabetes (T2D) With Point-of-care Microscale Magnetic Resonance (µNMR)

Abstract Disclosure: A.T. Alves: ; Co-Founder. ; Self. ; LarmorBio. S.S. Thamarath: ; Co-Founder. ; Self. ; LarmorBio. S. Fjordside: Employee; Self; Novo Nordisk. S. Sassower, MS.EE: ; Co-Founder. ; Self. ; LarmorBio. J. Han: None. L. Bouchard: Advisory Board Member; Self; LarmorBio. R. Rohr, BS.EE: ; Co-Founder, CEO. ; Self. ; LarmorBio. A.P. Chambers: Employee; Self; Novo Nordisk. The declining age of onset of T2D is a vital factor influencing its prospective burden. An early manifestation of the disease leads to an extended duration, which amplifies and accelerates the risk of micro and macrovascular complications. Oxidative stress is a major driver in the pathogenesis of T2D. Early detection and prevention of oxidative stress overload can improve and potentially modify the long-term harmful outcomes of metabolic diseases, including chronic hyperglycaemia.A novel µNMR assay was developed to monitor oxidative stress from minimally invasive plasma samples at the point of care. This approach is highly sensitive to redox changes in the blood microenvironment, including increased ferric iron (Fe3+), protein oxidation, and lipid peroxidation[1]. We hypothesized that oxidative stress would increase significantly faster in the disease group, enabling disease progression monitoring throughout the study and near real-time subject risk stratification based on the core pathophysiology of the diabetic phenotype.This 15-week longitudinal study (6th - 21st week of age) quantified oxidative stress in db/db and db/+ mice (n = 15 per group), accompanied by HbA1c, blood glucose (BG), and body weight (BW). The assay detailed separation (P ≤ 0.01) between the db/db and db/+ mice from the onset of the diabetic phenotype of db/db at 10 weeks of age (mean HbA1c = 6.5%). The oxidative stress monitored continued to increase in the subsequent weeks, where the HbA1c of db/db mice reached maximum (mean HbA1c = 8.8%). The definition of good (HbA1c ≤ 8) and poor (HbA1c > 8) glycaemic control was defined where sub-stratification (P ≤ 0.05) was achieved. Furthermore, the assay highlights a positive correlation with BW (ρ+/db = 0.52, P ≤ 0.0001, ρdb/db = 0.57, P ≤ 0.0001). In the two groups of mice, a positive correlation of oxidative stress with HbA1c (ρ = 0.58, P ≤ 0.0001) and BG (ρ = 0.47, P ≤ 0.0001) was reported. During the 15-week study, the control db/+ group exhibited a 13% mean increase in oxidative stress. In contrast, the db/db group demonstrated a cumulative 20% increase. Notably, considering the diabetic phenotype of the db/db mice, we quantified oxidative stress attributed to aging at 13%.In view of changing diabetes demographics, a multimodal approach of targeted sub-phenotype treatment combined with advanced metabolic monitoring may provide holistic, clinically relevant information to reduce the micro and macrovascular burden of diabetes. In summary, the µNMR assay is an effective method for real-time monitoring of oxidative stress assessment in metabolic disease research and opens new possibilities for cardiometabolic risk assessment and targeted interventions.(1) Peng, W.K. et.al., npj Aging Mech Dis 2020, 6, 11 Presentation: 6/1/2024

Assessment of fish health status for waterless transportation based on image features and deep learning models

In the current aquaculture industry, the detection of physiological stress indicators in fish during waterless transportation is considered to be an effective method to monitor their health status. However, commonly used detection methods are invasive and interfere with the accuracy of detection results. In order to solve this problem, this study takes grouper as an example, combines multi-sensor physiological stress indicators monitoring, and designs a health status assessment and classification model based on image features and deep learning models, which inputs real-time fish image feature information data acquired during low-temperature waterless transportation into the optimised CNN-BiGRU classification model based on whale optimisation algorithm (WOA) proposed in this study, so as to achieve an accurate assessment of the health status of the fish. By inputting the image feature datasets based on time series and classified into the WOA-CNN-BiGRU, BiGRU, and GRU classification models for training, validation, and testing, respectively, the effectiveness of the classification performance of the models was verified by using accuracy, precision, recall and F1-value composite scores as the metrics. It was found that the WOA-CNN-BiGRU model had excellent classification performance, followed by the BiGRU and GRU models, especially in classifying the health status assessment of small size grouper. This study proposes an efficient and inexpensive method for real-time monitoring and accurate assessment of the health status of fish during waterless transportation, and also provides a reference for vigour monitoring and health assessment of other similar aquatic products.

A Nondestructive and Rapid Method for the Determination of Triamterene Content by Transmission Raman Spectroscopy Combined with Chemometrics

Background: Triamterene is a potassium-conserving diuretic mainly used in the treatment of edematous diseases. Although there are some methods to measure the content of triamterene, the existing methods have the characteristics of sample destructiveness and low flux, making it difficult to meet the needs of online monitoring. In recent years, transmission Raman spectroscopy (TRS), as a new technology for the determination of drug content, has emerged as being nondestructive and rapid and provides a new method for the determination of triamterene content. Objective: In this study, we used transmission Raman spectroscopy combined with partial least squares (PLS) approaches to establish a six concentration levels model for measuring the content of triamterene. The model was applied to determine whether tablets are commercially available. Method: Firstly, TRS was used to collect the spectra of the principal components and mixed excipients of triamterene, and the feasibility test was carried out. Secondly, six concentration levels were determined by the design of experimental (DOE), and hand-made tablets were prepared to obtain corresponding spectra. The content prediction model was established by the PLS method, and the content of triamterene was determined by high-performance liquid chromatography (HPLC) to correct the model. Finally, the model was applied to the determination of triamterene in commercially available tablets. Results: The results showed that the established model was successfully applied to the determination of triamterene. The values of RMSEC (0.0089783) and RMSECV (0.0097241) of the final model were very low and close to each other. The relative error of 12 hand-made tablets predicted by this model was less than 5% compared with the results determined by HPLC. In addition, in the process of applying the model to the determination of the content of commercially available tablets, the accuracy of the model can be significantly improved by adding the spectrum of commercially available tablets. The corrected model was used to determine the content of triamterene in two commercially available tablets. The results showed that the relative error was less than 5%. Conclusion: We described a new strategy to analyze the content of active pharmaceutical ingredients (API) in triamterene tablets by TRS with PLS. The established model has the advantage of non-destructive and rapid quantitation, which can provide a new method for real-time monitoring of production lines and promote the development of process analytical technology (PAT).

Electrochemical DNA hybridization signal amplification system using methylene blue and ascorbic acid

This study presents a method for real-time monitoring of heterogeneous DNA hybridization using chronoamperometry (CA). The electrochemical assay is based on methylene blue (MB)-labeled ssDNA target (tMB) hybridization with a complementary ssDNA capture probe (p) chemisorbed to a gold electrode through the Au-S linkage. Real-time monitoring of nucleic acid binding is enabled by introducing ascorbic acid (AA) during CA measurements at oxidative potential. After hybridization, the newly formed MB-labeled double stranded DNA (ptMB) oxidized AA and, as a result, shuttled the electrons directly to the electrode. AA-ptMB electrocatalytic cycle allows amplifying the signal from hybridized MB-labeled DNA through oxidation of AA. The process consists three particular processes: hybridization of tMB to the surface probe, reaction between AA and ptMB, and (c) the electron transfer from ptMB to the electrode. We have demonstrated that system is limited by the AA-ptMB redox reaction (bimolecular constant, k = 8 ± 0.1 M–1 s–1). AA-ptMB cycle is sequence-specific, thus fully developed current saturation curves can be used to calculate hybridization parameters and evaluate binding kinetics under various conditions. In addition, evaluating the initial hybridization rate allows quantifying the concentration of labeled ssDNA (tMB) in several minutes. This study provides the first report of a simple electrocatalytic cycle between MB-labeled DNA and a reducer AA for monitoring DNA hybridization in real time.

Gold Nanowire Sponge Electrochemistry for Permeable Wearable Sweat Analysis Comfortably and Wirelessly.

Electrochemistry-based wearable and wireless sweat analysis is emerging as a promising noninvasive method for real-time health monitoring by tracking chemical and biological markers without the need for invasive blood sampling. It offers the potential to remotely monitor human sweat conditions in relation to metabolic health, stress, and electrolyte balance, which have implications for athletes, patients with chronic conditions, and individuals for the early detection and management of health issues. The state-of-the-art mainstream technology is dominated by the concept of a wearable microfluidic chip, typically based on elastomeric PDMS. While outstanding sensing performance can be realized, the design suffers from the poor permeability of PDMS, which could cause skin redness or irritation. Here, we introduce an omnidirectionally permeable, deformable, and wearable sweat analysis system based on gold nanowire sponges. We demonstrate the concept of all-in-one soft sponge electrochemistry, where the working, reference, and counter electrodes and electrolytes are all integrated within the sponge matrix. The intrinsic porosity of sponge in conjunction with vertically aligned gold nanowire electrodes gives rise to a high electrochemically active surface area of ∼67 cm2. Remarkably, this all-in-one sponge-based electrochemical system exhibited stable performance under a pressure of 10 kPa and 300% omnidirectional strain. The gold sponge biosensing electrodes could be sandwiched between two biocompatible sweat pads, which can serve as natural sweat collection and outflow layers. This naturally biocompatible and permeable platform can be integrated with wireless communication circuits, leading to a wireless sweat analysis system for the real-time monitoring of glucose, lactate, and pH during exercise.

Snapshot computed tomographic microscopic imaging spectrometer and its video-level tracking of poisonous Microcystis aeruginosa cells in mixed algae

Conventional microscopic spectral imaging suffers from extended scanning times across wavelength or spatial dimension. To improve capabilities of dynamic microscopic spectral imaging, we developed a snapshot computed tomographic microscopic imaging spectrometer (CTMIS) based on the zeroth and first orders dispersive diffraction of a two-dimensional grating. Utilizing the CTMIS-UNET reconstruction algorithm, we can reconstruct a spectral cube (541x541x26) for each frame of micro spectral imaging video. Experimental results demonstrate a sub-4 μm spatial resolution achievable through a 20x objective lens and a spectral resolution better than 10 nm among 450–700 nm, while maintaining spectral cosine similarities exceeding 0.9989 when comparing reconstructed spectra with ground truth data. Spectral imaging videos of four species of algae and mixed algae were captured under 10 ms exposure time using the CTMIS system. Leveraging the self-developed UNET-SI26 algae recognition network, precise identification and tracking of four types of algae and poisonous microcysts aeruginosa in mixed algae were conducted. The pixel-level recognition accuracy exceeds 95 %, while the accuracy for counting the numbers of different types of cells surpasses 85 %, offering an efficient and accurate spectral imaging method for real-time monitoring and early warning of harmful algae at the cellular level.

The real-time shadow detection of the PV module by computer vision based on histogram matching and gamma transformation method

Solar energy plays an important role in renewable energy generation, with the advantages of low pollution, easy installation, and relatively easy access. However, photovoltaic (PV) modules are susceptible to cause localized shading from external factors such as leaves in the canopy, surrounding buildings, etc., which would affect power generation efficiency and even pose safety risks. Existing methods cannot perform well in real-time conditions. This paper proposes a real-time shading monitoring method for the PV module based on computer vision. The gamma transform and histogram matching were adopted to enhance key features and adjust the global gamut strength distribution in the image of the PV module; then the gray-level slicing method finished the segmentation to detect the shadow from the video. All processing can be realized in the real-time monitor camera and the detection results can be displayed on the HMI in PC with high efficiency and low cost. According to tests in the practical complex environment, the method can have enough detection performance and high real-time performance with an accuracy of 0.98, and the F0.5 and F2 values are 0.87 and 0.85, respectively. The metrics of the proposed method are higher than those of the existing Canny detection method, the Random Forest detection method, and the CNN detection method. In addition, the average time required by the proposed method to process a frame is 0.721 s. In addition, the average time required by the method to process an image frame is 0.721 s, which has good real-time performance.