Fast Detection Method Research Articles

Analysis of Remote Islanding Detection Methods for Distributed Resources

Penetration of distributed generation in the power grid is increasing. This situation leads to new possibilities but also new issues, such as anti-islanding protection. During a loss-of-mains or islanding situation, distributed generation (DG) units remain feeding a part of the electrical network without connection to the main power system. Currently, DG units have to be disconnected from the grid during islanding as soon as possible, due to potential risks for repair crews and components, poor power quality in the island and reclosing problems. A fast and reliable islanding detection method could avoid mentioned issues. Among the many existing methods, remote schemes could meet these requirements. Hence, this paper presents the operating principles of remote islanding detection methods, as well as the current state of development of communication-based remote islanding detection methods to be applied in distributed power generation systems.

A sensitive gold nanoparticle-based lateral flow immunoassay for quantitative on-site detection of Salmonella in foods

Salmonella contamination is a leading cause of foodborne infection outbreaks worldwide. Therefore, detecting this bacterium is crucial for public health. Conventional detection methods, while specific and sensitive, are costly, time-consuming, labor-intensive, and require skilled analysts, impacting various industries, particularly the food supply chain. Lateral Flow Immunoassays (LFA) present a promising solution to address the need for a fast, user-friendly, sensitive, and specific detection method. This method overcomes various challenges by providing quick results and being easy to handle, cost-effective, and portable. This study aimed at developing a gold nanoparticle-based lateral flow immunoassay for Salmonella detection in food. Gold nanoparticles coated with anti-Salmonella polyclonal antibodies were characterized using UV–Vis, Raman, and Fourier-transform infrared spectroscopy, in addition to dynamic light scattering, X-ray diffraction, and transmission electron microscopy. Sandwich-type lateral flow immunoassay strips were then constructed and the test optimized for factors like optical density, antibody concentration in the detection zone, incubation time, blocking solutions, and result reading time. The test's selectivity was confirmed by examining its efficiency in detecting Escherichia coli and Staphylococcus aureus, while specificity was assessed against different Salmonella serovars. The developed biosensor displayed superior selectivity, specificity, and sensitivity compared to existing commercial tests. It had a detection limit of 103 CFU·mL−1, providing results in just 15 min after inserting the strip into the sample. Moreover, it successfully detected the presence of Salmonella in contaminated chicken, black pepper, milk, chocolate, and egg samples. This device offers a promising alternative for screening Salmonella in food, potentially enhancing food safety.

Reconstruction Filters Improving the Spatial Resolution and Signal-to-Noise Ratio of Surface Plasmon Resonance Microscopy.

Benefitting from high sensitivity, real-time, and label-free imaging, surface plasmon resonance microscopy (SPRM) has become a powerful tool for dynamic detection of nanoparticles. However, the evanescent propagation of surface plasmon polaritons (SPPs) induces interference between scattered and launched SPPs, which deteriorates the spatial resolution and signal-to-noise ratio (SNR). Due to the simplicity and fast processing, image reconstruction based on deconvolution has shown the feasibility of improving the spatial resolution of SPRM imaging. Retrieving the particle scattering from SPRM interference imaging by filters is crucial for reconstruction. In this work, we illustrate the effect of filters extracting SPP scattering of nanoparticles with different sizes and shapes for reconstruction. The results indicate that the optimum filters are determined by the material of nanoparticles instead of particle sizes. The reconstruction of single Au and PS nanospheres as well as Ag nanowires with optimum filters is achieved. The reconstructed spatial resolution is improved to 254 nm, and the SNR is increased by 8.1 times. Our research improves the quality of SPRM imaging and provides a reliable method for fast detection of particles with diverse sizes and shapes.

Lightweight and fast visual detection method for 3C assembly

In the context of 3C assembly scenarios, characterized by numerous semi-flexible, heterogeneous, and small slender targets, traditional target detection algorithms face significant challenges such as low accuracy, weak generalization, large model sizes, and slow inference speeds. To address these issues, this study introduces an enhanced method based on the YOLOv5 model, named YOLOv5-GTB. This method integrates Bidirectional Feature Pyramid Network (BiFPN), Ghost lightweight convolution, Vision Transformer (ViT), and adaptive activation function technologies, aiming to improve the accuracy and speed of target detection. Our approach utilizes Ghost convolution to construct a Ghost bottleneck layer, optimizing the feature extraction network while significantly reducing computational costs and enhancing the convolutional neural network’s feature extraction capabilities. Additionally, the Cross-Stage Partial Network (CSPNet) architecture is employed to effectively segment the data flow of the input feature map layer, thereby improving the efficiency of gradient processing. Furthermore, we introduce a fusion structure of CNN and Transformer, leveraging the strengths of convolutional neural networks in local feature extraction and the ViT’s capability in long-range information capture, thereby further enhancing the overall network’s feature extraction performance. Regarding feature fusion, considering the limitations of traditional top-down unidirectional information flow in effectively merging features with both location and semantic information, BiFPN is incorporated into the YOLOv5-GTB. This enhances the fusion of feature layers extracted from both the end of the backbone network and the first module’s fused feature layer, thus improving detection accuracy. Ablation and comparative experiments conducted on the 3C assembly scenario target detection dataset demonstrate the significant advantages of the YOLOv5-GTB model in terms of accuracy and speed. Ultimately, the application of this model to the 3C assembly platform successfully achieves rapid and accurate target recognition in this scenario.

Enhancing smart grid security: A novel approach for efficient attack detection using SMART framework

Smart Grids are constructed using control, networking, and advanced computing technologies. Malicious attacks are still a possibility for the grid, although there are now insufficiently fast and accurate detection methods. Cybercrimes affecting the security of the smart grid include the compromise of vital client data by attackers, the spread of viruses, cybersecurity mistakes, and vulnerabilities in distributed systems. In order to provide security in a smart grid context, a SMART (Smart Attack detectoR Technique) framework is developed in this paper. The attacker first sends the request to the risk identification, which will identify the request's original source. Risk estimation calculates the degree of risk and gives processing decisions. Following that, word embedding is used to complete the pre-processing. Information will be prohibited if the process is found to be too vulnerable according to CNN-BiLSTM categorization criteria. The data will be sent to the smart grid automatically if the procedure is low vulnerable and there is no assault. A MATLAB simulator is used to implement the suggested approach. In comparison to the presently used CNN-LSTM [10], SCADA [13], and GOOSE [14] approaches, which have success rates of 19.09 %, 32.22 %, and 57.47 %, respectively, the experimental findings indicate that the suggested SMART strategy has a 99 % success rate, which is very high.

Research on non-destructive and rapid detection technology of foxtail millet moisture content based on capacitance method and Logistic-SSA-ELM modelling.

Moisture content testing of agricultural products is critical for quality control, processing efficiency and storage management. Testing foxtail millet moisture content ensures stable foxtail millet quality and helps farmers determine the best time to harvest. A differential capacitance moisture content detection device was designed based on STM32 and PCAP01 capacitance digital converter chip. The capacitance method combined with the back-propagation(BP) algorithm and the extreme learning machine(ELM) algorithm was chosen to construct an analytical model for foxtail millet moisture content, temperature, and volume duty cycle. This work performs capacitance measurements on foxtail millet with different moisture contents, temperatures, and proportions of the measured substance occupying the detection area (that is, the volumetric duty cycle). On this foundation, the sparrow search algorithm (SSA) is used to optimize the BP and ELM models. However, SSA may encounter problems such as falling into local optimization solutions due to the reduction of population diversity in the late iterations. As a consequence, Logistic algorithm is introduced to optimize SSA, making it more appropriate for solving specific problems. Upon comparative analysis, the model predicted using the Logistic-SSA-ELM algorithm was more accurate. The results indicate that the predicted values of prediction set coefficient of determination (RP), prediction set root mean square error (RMSEP) and prediction set ratio performance deviation (RPDP) were 0.7016, 3.7150 and 1.4035, respectively. This algorithm has excellent prediction performance and can be used as a model for detection of foxtail millet moisture content. In view of the important role of foxtail millet moisture content detection in acquisition and storage, it is particularly important to study a nondestructive and fast online real-time detection method. The designed capacitive sensor with differential structure has well stabilization and high accuracy, which can be further studied in depth and gradually move towards the general trend of agricultural development of smart agriculture and precision agriculture.

A Fast and Robust Open-Circuit Fault Detection Method for Voltage-Source-Inverter with Integrated High-Frequency Sensor

A Fast and Robust Open-Circuit Fault Detection Method for Voltage-Source-Inverter with Integrated High-Frequency Sensor

A Fast Progressive Ship Detection Method for Very Large Full-Scene SAR Images

A Fast Progressive Ship Detection Method for Very Large Full-Scene SAR Images

A Fast Iris Liveness Detection for Embedded Systems using Textural Feature Level Fusion Algorithm

Iris recognition is a widely used biometric authentication technique due to its high accuracy and uniqueness. However, iris recognition systems are susceptible to attacks using fake or synthetic iris images, causing a serious security threat. To address this issue, this paper presents a fast iris liveness detection method specifically designed for embedded systems. The proposed method utilizes a textural feature level fusion algorithm using Local Binary Pattern (LBP) and Gray-Level Co-Occurrence Matrix (GLCM) to distinguish between live and printed iris images. LBP captures texture information, while GLCM characterizes the statistical properties of the iris images. By combining these complementary features, the proposed method enhances the discrimination capability and robustness against presentation attacks. Furthermore, to enable real-time and efficient implementation, the proposed liveness detection is optimized and implemented for embedded systems. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed method in accurately detecting iris liveness. The proposed fast iris liveness detection is implemented and optimized on C++ which can be complied and deployed in various embedded devices for iris recognition systems on real-world applications, such as access control, biometric authentication, and surveillance systems.

Accurate and Fast Primitive Detection Method for 3D Point Cloud Data

Accurate and Fast Primitive Detection Method for 3D Point Cloud Data

DLOAM: Real-time and Robust LiDAR SLAM System Based on CNN in Dynamic Urban Environments

Dynamic object detection, state estimation, and map-building are crucial for autonomous robot systems and intelligent transportation applications in urban scenarios. Most current LiDAR Simultaneous Localization and Mapping (SLAM) systems operate on the assumption that the observed environment is static. However, the overall accuracy and robustness of a SLAM system can be compromised by dynamic objects in the environment. Aiming at the problem of inaccurate odometry estimation and wrong mapping caused by the existing LiDAR SLAM method which cannot detect the dynamic objects, we study the SLAM problem of robots and unmanned vehicles equipped with LiDAR traveling in the dynamic urban scenes. We propose a fast LiDAR-only model-free dynamic objects detection method, which uses the spatial and temporal information of point cloud through a convolutional neural network (CNN), and the detection accuracy is improved by 35 use spatial information. We further integrate it into a state-of-the-art LiDAR SLAM framework to improve the SLAM performance. Firstly, the range image constructed by LiDAR point cloud is used for ground extraction and non-ground point clustering. Then, the motion of objects in the scene is estimated by the difference between adjacent frames, and the segmented objects are further divided into dynamic objects and static objects by their motion features. After that, the stable feature points are extracted from the static objects. Finally, the pose transformation of adjacent frames is solved by matching feature point pairs. We evaluated the accuracy and robustness of our system on datasets with different challenging dynamic environments, and the results show our system has significant improvements in accuracy and robustness of odometry and mapping, while still maintain real-time performance, which is sufficient for autonomous robot systems and intelligent transportation applications in urban scenarios.

A method for fast detection of wind farms from remote sensing images using deep learning and geospatial analysis

A method for fast detection of wind farms from remote sensing images using deep learning and geospatial analysis

A novel amidine-based fluorescent probe TPE-4+ for rapid detection of anionic surfactant sodium dodecyl sulfate

An accurate, fast, and simple surfactant detection method is of great significance for monitoring surfactants pollution. Sodium dodecyl sulfate (SDS) is one of the most commonly used anionic surfactants and has been listed as an important monitoring pollutant for surfactant residues. Herein, a novel fluorescent probe named TPE-4+ with four amidines as the recognition functional group and tetraphenylethene as the fluorophore was fabricated. Due to the special intramolecular environment, the probe showed selectively identification towards SDS which made an aggregation induced fluorescence enhencement. Under the optimum conditions, the fluorescence enhencement of TPE-4+ is linearly related to the concentration of SDS in the range of 5.0–60.0 μM with limit of detection (LOD) of 0.010 μM and limit of quantification (LOQ) of 0.034 μM. Relative to the reported methods, the probe in our work showed better selectivity and sensitivity. The proposed method was successfully applied for the SDS determination of disinfecting bowls.

DETECTION OF RHODAMINE VIA SURFACE ENHANCED RAMAN SPECTROSCOPY UTILISING AG NANOWIRES

Rhodamine, which is extensively used as a synthetic dye in food industry, is regarded as an illegal additive by European Food Safety Authority because of its carcinogenic and toxicological properties. Since it’s a colourless material at low concentrations such as 10-7 M, its detection via spectroscopical methods is very challenging and crucial in terms of food safety issues. Surface Enhanced Raman Spectroscopy (SERS) provides a fast and cheap method for detection of such molecules at ultra-low concentrations. It is based on the principle of boosting Raman signals, which have low intensity by nature, by utilising metal nanoparticles in order to enhance Raman signals by creating hot-spots. In this study Ag nanowires were synthesized, their crystal structure is characterized via XRD analysis, their surface morphology and radius are determined via SEM images and EDS analysis was performed for determining their chemical composition. Afterwards, rhodamine solutions which were prepared at 10-4 – 10-7 M was dropcasted onto Ag nanowire solutions. After selecting three characteristic Raman peaks belonging to the Rhodamine molecule, which are located at 612, 1189 and 1362 cm-1, detection of Rhodamine was performed at these ultra low concentrations. As a result, one can conclude that, Ag nanowires can be utilized as possible SERS substrates for detection of Rhodamine at low concentrations by exhibiting significant reproducibility, stability and recyclability.

A colorimetric and fluorescent dual-mode sensor based on bifunctional G-quadruplex-hemin complex for the determination of Pb2+

Due to the threat of lead pollution to health, environmental and food safety, developing simple and fast detection methods is highly required. Whereas, traditional single-mode probe suffers from limited application scenario. In this study, a colorimetric and fluorometric dual-mode probe for Pb2+ determination was constructed by using bifunctional G-quadruplex-hemin complex. In this dual-mode probe, enzyme strand and substrate strand of 8–17 DNAzyme are labeled with G-quadruplex-hemin complex and fluorophore, respectively. In the absence of Pb2+, the self-assembly of enzyme strand and substrate strand inhibits intrinsic mimic peroxidase of G-quadruplex-hemin complex by base-pairing, which also quench the fluorescence via in proximity effect. When the DNAzyme is activated by Pb2+, the quenched fluorescence is restored as well as the inherent peroxidase mimetic activity, leading to dual signal output. Under optimal conditions, this dual-mode probe exhibit a good linear relationship between logarithm of Pb2+ concentration and signal difference within the range from 1.5 nM to 20 nM and 0.5 nM to 10 nM for colorimetric and fluorescence mode, respectively. The detection limits for the corresponding mode were estimated to be 1.29 nM and 0.16 nM, respectively. This dual-mode probe also successfully applied for the spiked river water assay with satisfactory recovery in the range of 93.2 %–115.3 %. This work paves a new way for DNAzyme based dual-mode probe construction.

A method for accurately extracting power lines and identifying potential intrusion risks from urban laser scanning data

Timely and accurate monitoring of power line safety is a key step in ensuring urban production and daily life. The rapid development of vehicle-borne mobile laser scanning (MLS) technology has provided an effective solution for intelligent maintenance of power grids. This paper proposes a comprehensive method for accurate extraction, missing completion, and intrusion risk detection of urban power lines. Firstly, local linear geometric features are described using principal component analysis (PCA) covariance matrix, and outliers are accurately separated using a joint Bezier curve distance threshold. Secondly, the statistical inference function estimation method is employed to accurately simulate the complete topology of power lines. Finally, a fast intrusion risk detection method based on the separating axis theorem (SAT) for boundary box neighboring objects is developed. Experimental results demonstrate that our method achieves a recall rate and precision rate of 98.28 % and 98.71 %, respectively, for power line extraction. The root mean square error (RMSE) accuracy and coordinate residual of catenary line fitting are both below 0.04 m, indicating strong noise resistance and robustness. Compared to mainstream geometric extraction methods, our approach exhibits superior performance.

Prostate cancer diagnosed and staged using UV-irradiated urine samples and a paper-based analytical device

The early detection of prostate cancer (CaP), one of the most common cancers in males, improves treatment efficacy. However, current methods to detect CaP are limited by specificity and sensitivity or require an invasive and unpleasant procedure. Therefore, there is a need for a novel, fast, and noninvasive CaP detection method. This proof-of-concept study aimed to diagnose CaP from urine samples by detecting the UV-induced fluorescent clusters formed in situ that reflect a urine composition specific to CaP patients using fluorescence spectroscopy. The UV-induced clusters formed upon irradiation by UV light with a wavelength of 254 nm were detected at excitation and emission wavelengths of 400 nm and 460 nm. Fifteen patients with CaP were correctly distinguished from 5 controls. The different stages of CaP could be further determined using molecular imprinting technology. Additionally, a paper-based analytical device was developed that enabled the correct identification of stage I cancer patients by fluorescent spectroscopy detection and the naked eye. In conclusion, this novel, easy-to-operate, point-of-care assay can enable early diagnosis of CaP with high accuracy and judgement of the disease stage. The simplicity, as well as versatility of the proposed method, will enable its application also to other diseases and disorders.

Fast detection of geometric errors for three-axis machine tools with combined double-ball bars based on spatial circle detection

The geometric accuracy of the CNC machine tools determines the quality of the sculptured mechanical parts. Measuring and compensating the geometric errors of machine tools can effectively improve the machining accuracy. However, traditional measurement methods using a double-ball bar (DBB) can only detect the error along the rod length direction and thus require multiple measurements in three orthogonal planes of the machine tool. This work proposes a fast error detection method for the three-axis machine tools using combined double-ball bars (C-DBBs) based on the spatially circular detection (SCD) path. The test path of this method requires the simultaneous linkage of three linear axes of the machine tools and thus could identify 21 geometric errors of three-axis machine tool. Firstly, the measuring principle of the SCD method is introduced, and the mathematical model for detecting 21 geometric errors of the machine tool based on the SCD method is established. Then, the detection experiment based on the SCD method is carried out, and it is shown that 21 geometric errors of the three-axis machine tool are obtained. Finally, the verification experiment using a traditional DBB is conducted to validate the effectiveness of the proposed SCD method. The radial errors in three orthogonal planes measured by traditional DBB exhibit a good agreement with those predicted using 21 geometric errors obtained by the SCD method. Compared with the traditional DBB method, the proposed SCD method could detect 21 geometric errors of three-axis machine tools in a single measurement. This work provides an effective and efficient methodology for detecting all geometric errors of the three-axis machine tool.

Combined detection of SARS-CoV-2 neutralizing antibodies and specific IgG in plasma based on SERS magnetic sensor

In this study, a surface-enhanced Raman spectroscopy (SERS) magnetic sensor is established based on SERS principle and magnetic separation technology, and a highly sensitive, simple and fast method for quantitative detection of neutralizing antibodies (nABs) and specific IgG of SARS-CoV-2 in plasma is established combined with immunoassay. Two kinds of Raman nanospheres (RNPs) with different characteristic Raman shifts are used as signal sources and coupled to ACE2 and anti-IgG (FC) antibodies respectively, and magnetic beads are coupled to RBD. The competitive relationship between ACE2 and nABs, the binding relationship between specific IgG and anti-IgG (FC) antibodies are determined. The results show that the concentrations of nABs and specific IgG in the range of 10–2000 ng ml−1 are well correlated with SERS response intensity, and the recoveries are both between 90%–110%, with good precision. Bilirubin and common anticoagulants have no interference on the detection results. This method is accurate, reliable, sensitive and does not require complex pre-treatment, and is expected to be used for simultaneous detection of nABs and specific IgG in plasma of SARS-CoV-2. It has guiding significance for the development and evaluation of vaccines and the formulation of individualized vaccination schedule.

4-Mercaptobenzoic acid coated self-assembled aggregative luminescent copper nanoclusters for the detection of ethyl vanillin

Ethyl vanillin (EVA) is a food additive with strict requirements, and the addition of more than the standard will affect the human body, so the design of a fast and sensitive detection method for EVA has aroused people's interest. Here, we developed a fluorescence assay method for the sensitive detection of EVA by 4-mercaptobenzoic acid (4-MBA) coated self-assembled copper nanoclusters (4-MBA-CuNCs). Under alkaline conditions, 4-MBA-CuNCs self-assembled aggregation induces strong blue fluorescence, and its quantum yield is 5.51 %. Under very simple conditions, the fluorescence intensity of 4-MBA-CuNCs at 421 nm was reduced by EVA through static quenching and internal filtration effect. The detection method showed a good linear relationship between 0.1 and 5.1 μM, and its detection limit was 30 nM. Moreover, the preparation of 4-MBA-CuNCs could provide a simple and quick method for the detection of EVA in real samples.