Detection Method Research Articles

Experimental Study on Corrosion of Stainless Steel in Low Temperature Multi effect Seawater Desalination

Starting from the corrosion mechanism, this paper analyzes the characteristics of various types of stainless steel and selects the best performance composite plate composite plate stainless steel. Analyze and select the most suitable corrosion detection method based on specific practical multi working conditions, discuss the interference factors that affect metal corrosion during experimental simulation, and the advantages of newly developed sheet metal. The new development of composite board panels, with the substrate and composite materials applying their respective capabilities for MED, will bring breakthrough progress to the scientific research and engineering application of composite boards.

A general-purpose pointer meter reading detection method based on adaptive feature fusion and data augmentation

Pointer meters continue to serve as the primary monitoring instruments within substations. To solve the issue of poor model accuracy and low reading accuracy when reading multiple types of pointer meters, this paper proposes a universal cascaded convolutional neural network for accurate reading of various types of pointer instruments. The proposed detection method consists of two stages: instrument digital positioning and instrument pointer fitting. In the digital(figure of a number) positioning stage, an instrument data augmentation method for “large background and small target” is proposed to improve the model's sensitivity to digital position and reduce the interference of redundant background information to the model. In addition, an adaptive feature fusion pyramid structure is proposed for more effective feature fusion and enhanced fuzzy small target recognition. Compared with the baseline model, the method introduced in this study realizes a 0.47 % improvement in target detection accuracy. In our proposed dataset, the average recognition accuracy for the digital position location phase is 98.65 %,the average reading errors in square instruments, circular instruments, and instruments with uneven scales are 0.76 %, 0.73 %, and 0.67 % respectively. The proposed universal detection method in this paper achieves ultra-high precision and can be applied to reading pointer instruments of different types and scales. Compared with existing pointer meter reading methods, the proposed detection method showcases superior simplicity and effectiveness. Furthermore, it showcases a reduced measurement error, thereby bolstering its practical utility in industrial contexts.

A New Bioactive Thiazolidinone-based Azo Dye for Naked-eye Colorimetric Detection of Cyanide Ions

Abstract: A new bioactive azo dye embedding a thiazolidinone heterocyclic core was designed and synthesized for antibacterial application and colorimetric sensing of cyanide ion (CNˉ) in organic solutions. The structure of the prepared dye was elucidated from its 1H NMR, FT-IR and UV-Vis spectral data. It proved to be a fast and sensitive colorogenic sensor for detection of CNˉ. Spectroscopic studies were carried out to investigate the effect of different CNˉ concentrations on the detection efficiency. Moreover, the studies revealed no significant competition or influence of other anions on the sensitivity of CNˉ detection by the synthesized dye. A Job's plot indicated a 1:1 stoichiometry of the dye and CNˉ in their colorful complex. Further development of the method for naked-eye detection of CNˉ in low-concentration aqueous solutions was achieved by using the cellulose papers painted with the dye. The as-prepared testing paper allowed CNˉ sensing in concentrations as low as 2 μM. Evaluation of the dye for antibacterial activities using the well diffusion technique displayed that its inhibitory activity is at least as good as and in some cases superior to ampicillin against the bacterial strains employed in the zone assay. In response to CN¯, the dye changes color from yellow to reddish brown which accounts for its significant interactions with CNˉ and affords a naked-eye sensing method without resorting to any spectroscopic instrumentation.

Magneto-acoustic Fusion Sensing for Surface and Subsurface Defect Differentiation in the Ferromagnetic Material

The conventional periodic permanent magnet (PPM) shear horizontal (SH0) guided wave electromagnetic acoustic transducer (EMAT) has a good detection capability for defects on both the surface and subsurface of the material. However, it is difficult to distinguish between surface and subsurface defects in the material using only the amplitude of the defect echo. In this paper, SH0 guided wave is excited using the SH0 guided wave EMAT with PPM configuration, which is used to achieve the detection and evaluation of defects on the surface and subsurface of ferromagnetic metal; on the other hand, the local magnetic field perturbation (MFP) effect of the PPM configuration on ferromagnetic material is fully exploited to achieve the identification of surface defects by picking up the MFP of the material at the defects. The proposed magneto-acoustic fusion effect sensor has an improved spatial magnetic field at both the mechanical construction and the magnetic sensor compared to the traditional magnetic flux leakage (MFL) detection method. Both simulation and experiment show that the proposed magneto- acoustic fusion effect sensing method based on the PPM configuration is capable of detecting the defects on the surface and subsurface of metal material, while the MFP effect in the fusion sensor can be used to help differentiate between the surface and subsurface defects in the material.

Stress corrosion phenomenon of casing in CO2 medium and magnetic memory detection

Due to being in an acidic CO2 environment and high-pressure underground, the stress corrosion phenomenon on the inner wall of the CCUS wellbore is more pronounced, with the possibility of wall thickness reduction and crack propagation, which affects the integrity and storage reliability of the wellbore. This article conducted indoor experiments on stress corrosion of oil casing, and characterized the stress corrosion through magnetic memory detection technology. Through experiments, the stress corrosion morphology of specimens under different concentrations, stresses, and defects was obtained. The microstructure of the stress corrosion site was observed using SEM electron microscopy, and the stress corrosion situation of the material in CO2 was observed, laying a foundation for magnetic memory detection; A stress corrosion detection method under CO2 environment was proposed, and the magnetic memory detection method was used to measure that the more severe the stress corrosion, the more obvious the magnetic signal mutation; The shape and morphology of different defects vary, but the main factors affecting magnetic memory signals are concentration and loading force. This article verifies the feasibility of magnetic memory in stress corrosion detection through experiments, and characterizes the stress corrosion situation using magnetic memory signals, which is of great significance for non-destructive testing of wellbore stress corrosion in CO2 storage.

Validation and optimization of the loop-mediated isothermal amplification (LAMP) technique for rapid detection of wheat stripe mosaic virus, a wheat-infecting pathogen

BackgroundWheat stripe mosaic virus (WhSMV) is a significant wheat pathogen that causes substantial yield losses in Brazil and other countries. Although several detection methods are available, reliable and efficient tools for on-site WhSMV detection are currently lacking. In this study, a Loop-Mediated Isothermal Amplification (LAMP) method was developed for rapid and reliable field detection of WhSMV. We designed WhSMV-specific primers for the LAMP assay and optimized reaction conditions for increased sensitivity and specificity using infected plant samples. ResultsWe have developed a diagnostic method utilizing the Loop-Mediated Isothermal Amplification (LAMP) technique capable of rapidly and reliably detecting WhSMV. The LAMP assay has been optimized to enhance sensitivity, specificity, and cost-effectiveness. ConclusionThe LAMP assay described here represents a valuable tool for early WhSMV detection, serving to mitigate the adverse economic and social impacts of this viral pathogen. By enabling swift and accurate identification, this assay can significantly improve the sustainability of cereal production systems, safeguarding crop yields against the detrimental effects of WhSMV.

A novel method of detecting malware on Android mobile devices with explainable artificial intelligence

The increasing prevalence of malware targeting android mobile devices has raised significant concerns regarding user privacy and security. In response, effective methods for malware classification and detection are crucial to protect users from malicious applications. This paper presents an approach that leverages deep learning techniques and explainable artificial intelligence (XAI) for android mobile malware classification and detection. Convolutional neural networks (CNNs) are deep learning model that has shown impressive performance in several application areas, including image and text classification. In the context of android mobile malware, CNNs have shown promising results in capturing intricate patterns and features inherent in malware samples. By training these models on large datasets of benign and malicious applications, accurate classification can be achieved. To enhance transparency and interpretability, XAI techniques are integrated into the classification process. These techniques provide insights into the decision-making process of the deep learning models, enabling the identification of critical features and characteristics that contribute to the classification results. This research, by combining deep learning and XAI methods, presents a fresh strategy for identifying and categorizing Android malware. This research paper will focus on a fascinating CNN-based malware categorization technique.

Impact-Echo for Crack Detection in Concrete with Artificial Intelligence based on Supervised Deep Learning

Aging concrete infrastructure such as bridges and tunnels need to be appropriately maintained in their service life because these structures must be employed with ensured safety level. Inspection and maintenance with reasonable measures are typically operated for the reinforced concrete before severe damage and failure are developed. Especially, the crack propagation in concrete can significantly affect the structural integrity, durability and overall performance of structural elements. Effective maintenance strategies are required for material and structural assessment with detection and repair of cracks to prevent further deterioration. Identification of cracks in concrete is effectively operated to extend the service life of structures for mitigating the potential safety hazards and minimize the repair costs. In order to detect the cracks in the concrete structure member, non-destructive testing (NDT) system can be often introduced to satisfy the technical issues in terms of damage evaluation and estimation of repair condition. Currently, various NDT methods have been utilized for quantitative analysis of internal damage/defects using elastic wave method such as impact-echo method, focusing on reflection of P-wave in concrete as wave propagation behavior. In experimental laboratory tests, concrete specimens are usually prepared with interior damages (cracks) which are simulated as embedded plastic plate or styrofoam. In this study, starch- type polysaccharide sheet is used to form the simulated cracks in concrete specimen. This study aims at applying impact-echo method with AI (Artificial Intelligence) to identify the internal crack information based on the waveform characteristics obtained from the elastic wave propagation behavior. The waveform results are converted to frequency spectrum by FFT analysis. Consequently, it is found in this study that AI, which is employed by supervised deep learning models, successfully evaluates the data and displays the probability of crack existence in concrete. Since FFT is a powerful algorithm that quickly converts a signal from the time domain to the frequency domain, it gives efficient analysis to the impact echo method for crack detection with the application of AI system.

A benchmark of health insurance fraud detection using machine learning techniques

Health insurance fraud is a complex problem that also has a significant financial impact. Recently, with the availability of large volumes of data and the evolution of computing power, machine learning techniques have become the preferred method for fraud detection. However, the main difficulty facing researchers in this field is the lack of real data sets and the absence of reliable fraud labels. Most published studies use aggregated provider-level or simulated data to test fraud detection algorithms, which may not deliver accurate results. The present study aims to provide a more accurate assessment of fraud detection methods by using real detailed health insurance claims data to compare six of the most common supervised classification algorithms including neural networks and the use of two categorical feature preparation methods. The study was conducted under the guidance of insurance experts, who provided the fraud label inference rules and reviewed the results. A comprehensive description of the benchmarking process and an interpretation of the results are provided in this paper. The results show that supervised classification can be used effectively to detect health insurance fraud, improving detection accuracy by a factor of 4.2 (84% recall for a positive rate of 20%).<p> </p>

Xác định acetamiprid bằng phương pháp quang học sử dụng dung dịch nano vàng được ổn định bằng Polydiallyldimethylammoni clorua

On this research, the development of a simple, cost-efective and highly sensitive spectrophotometric method for acetamiprid detection based on the localized surface plasmon resonance of polydiallyldimethylammonium chloridestabilized gold nanoparticles. By naked eyes, one can detect acetamiprid by the visual change of gold solution color from red to purple, corresponding to the shift of maximum absorbance wavelength in the UV-Vis spectrum. Quantification of acetamiprid is possible through a linear relationship between the maximum absorbance wavelength and the acetamiprid concentrations in the range of 8×10-8 -2×10-6 M. Under the optimum conditions including pH 5, NaCl concentration of 0.02 M and incubation time 20 min, the present method has high precision (RSD < 5%), very low limit of detection (LOD) of 2.7×10-8 M, limit of quantification (LOQ) of 8.2×10-8 M. This method has been successfully applied to determine acetamiprid in several pesticide samples and vegetable samples. The measurement results of acetamiprid in the pesticide samples of the research method with the HPLC-UV method showed a difference of < 2%, while for the vegetable sample, the difference between the research method and the LC-MS/method method/ MS < 5% shows that the method has high accuracy and reliability.

Optimization and validation of a micro–QuEChERS method for phthalates detection in small samples of cetacean blubber

In this study, an innovative method was developed to detect and quantify phthalates in fresh cetacean blubber. An adaptation of the ammonium formate QuEChERS method was used and adapted as a micro-extraction for small quantities of samples. Significantly, this technique utilized minimal quantities of reagents and salts, with the additional implementation of rigorous Quality Assurance/Quality Control protocols to further reduce background contamination. To ensure the reliability of this method, comprehensive validation procedures were conducted, with a specific focus on two widely studied cetacean species: the common bottlenose dolphin (Tursiops truncatus) and the short-finned pilot whale (Globicephala macrorhynchus). Determination coefficients (R2) for matrix-matched calibration were >0.93 with limits of quantifications (LOQ) of the method in the range of 5-10 ng/g. Mean recovery values were between 40 and 100%. This novel methodology holds particular relevance for environmental research studies, offering the capability to detect emerging contaminants with minimal sample requirements. This aspect is particularly valuable in investigations that involve free-ranging animals and rely on biopsy sampling. It allows for the assessment of contaminant levels in healthy individuals within wild populations, enhancing our understanding of ecological impacts and potential conservation measures.• A micro-extraction adaptation of the ammonium formate QuEChERS method was developed and applied to a small quantity of fresh cetacean blubber to detect phthalates.

Alkanediol–hexafluoroisopropanol amphiphilic supramolecular solvent: Fabrication, characterization, and application potential for doping control of hormones and metabolic modulators in human urine

A novel amphiphilic supramolecular solvent (SUPRAS) was developed by hexafluoroisopropanol (HFIP)-induced self-assembly of 1,2-hexanediol in an aqueous matrix. Its application potential in multiclass determination was assessed by extracting from urine 10 prohibited hormones and metabolic modulators (log P from 2.38 to 7.57) listed by the World Anti-Doping Agency (WADA). This SUPRAS is stably formed across a wide pH range (2–12) and is unaffected by salt concentrations (<10 % g mL−1 of NaCl), and it has a higher density than water. The dehydration and hydrogen bonding ability of HFIP, as well as hydrophobic interactions between alkanediols, dominates the formation of the SUPRAS. The nanostructure of the SUPRAS, composed of 1,2-hexanediol, HFIP, and a high content of water (>25 %, w/w), forms large spherical micellar aggregates (10–100 μm) that exhibit amphiphilic properties. A 1,2-hexanediol-HFIP SUPRAS-based liquid-phase microextraction (LPME) method for detection of selected prohibited drugs in urine coupled with liquid chromatography–tandem mass spectrometry (LC-MS/MS) was established, optimized, and validated. The method showed fast extraction time (30 s of vortex) and low solvent consumption (160 μL of organic solvent). Under the optimal conditions (7 % g mL−1 of 1,2-hexanediol, 10 % v/v of HFIP), the SUPRAS achieved high extraction efficiencies (>80 %) and an enrichment factor of 6.0, with minimal matrix effects for all analytes due to its high amphiphilicity and large surface area. The method demonstrated excellent linearity (R ≥ 0.9909), with method limits of detection ranging from 0.018 to 0.18 ng mL−1 and method limits of quantitation ranging from 0.060 to 0.60 ng mL−1, which are well below the minimum required performance levels set by authorities. Furthermore, the intra- and inter-day relative recoveries for spiked human urine samples varied in the ranges of 90.3 %–121 % and 88.1–119 %, respectively, with relative standard deviations less than 13 % and 19 %, respectively. This SUPRAS-based LPME method proves to be simple, highly efficient, and eco-friendly, and it has promising potential for multiclass determination in doping control.

Digital Quantitative Analysis of the Relationship Between Periodontitis and Porphyromonas gingivalis

Porphyromonas gingivalis (P. gingivalis) constitutes an essential part of the subgingival dental plaque biofilm, serving as a significant factor in the development of periodontitis. Therefore, establishing a rapid and highly sensitive detection method for P. gingivalis is crucial to effectively manage periodontitis and its associated complications. In this study, droplet digital PCR (ddPCR) technology was employed for the detection of P. gingivalis, with a detection limit of 101 CFU/mL, exhibiting a 10-fold higher sensitivity compared to qPCR (with a sensitivity of 102 CFU/mL). Furthermore, no cross-reactivity was observed with four other bacterial species. In comparison to real-time quantitative PCR, ddPCR demonstrated enhanced sensitivity in detecting P. gingivalis at lower concentrations in 16 simulated samples, indicating its applicability for rapid detection of P. gingivalis.

Personalized feature extraction for manufacturing process signature characterization and anomaly detection

Manufacturing process signatures reflect the process stability and anomalies that potentially lead to detrimental effects on the manufactured outcomes. Sensing technologies, especially in-situ image sensors, are widely used to capture process signatures for diagnostics and prognostics. This imaging data is crucial evidence for process signature characterization and monitoring. A critical aspect of process signature analysis is identifying the unique patterns in an image that differ from the generic behavior of the manufacturing process in order to detect anomalies. It is equivalent to separating the “unique features” and process-wise (or phase-wise) “shared features” from the same image and recognizing the transient anomaly, i.e., recognizing the outlier “unique features”. In state-of-the-art literature, image-based process signature analysis relies on conventional feature extraction procedures, which limit the “view” of information to each image and cannot decouple the shared and unique features. Consequently, the features extracted are less interpretable, and the anomaly detection method cannot distinguish the abnormality in the current process signature from the process-wise evolution. Targeting this limitation, this study proposes personalized feature extraction (PFE) to decouple process-wise shared features and transient unique features from a sensor image and further develops process signature characterization and anomaly detection strategies. The PFE algorithm is designed for heterogeneous data with shared features. Supervised and unsupervised anomaly detection strategies are developed upon PFE features to remove the shared features from a process signature and examine the unique features for abnormality. The proposed method is demonstrated on two datasets (i) selected data from the 2018 AM Benchmark Test Series from the National Institute of Standards and Technology (NIST), and (ii) thermal measurements in additive manufacturing of a thin-walled structure of Ti–6Al–4V. The results highlight the power of personalized modeling in extracting features from manufacturing imaging data.

Multiple faults detection in doubly-fed induction generator wind turbine using artificial neural network

The development of fault detection methods in wind turbine (WT), especially for single fault detection, is continuously increasing. However, the rapid growth of fault detection in WT leads to another challenge where multiple faults can occur. The single fault detection method in WT is no longer reliable, especially when multiple faults occur simultaneously. Therefore, multiple faults detection in doubly-fed induction generators (DFIG) WT was proposed using an artificial neural networks (ANN) model. These multiple faults include internal and external stator faults happening simultaneously. Internal stator faults cover inter-turn short circuit faults and open circuit faults, while external stator faults cover loss of excitation and external short circuit faults. The performance of the developed multiple faults detection model was measured using accuracy and the root mean square error (RMSE) value. The results show that the developed model performs well with high accuracy and a low RMSE value. Thus, the developed model can accurately detect the coexistence of multiple faults in DFIG WT.

A method for calculating fall risk parameters from discrete stride time series regardless of sensor placement

BackgroundTo complement traditional clinical fall risk assessments, research is oriented towards adding real-life gait-related fall risk parameters (FRP) using inertial sensors fixed to a specific body position. While fixing the sensor position can facilitate data processing, it can reduce user compliance. A newly proposed step detection method, Smartstep, has been proven to be robust against sensor position and real-life challenges. Moreover, FRP based on step variability calculated from stride times (Standard deviation (SD), Coefficient of Variance (Cov), fractal exponent, and sample entropy of stride duration) proved to be useful to prospectively predict the fall risk. Research questionsTo evaluate whether Smartstep is convenient for calculating FRP from different sensor placements. Methods29 elderly performed a 6-minute walking test with IMU placed on the waist and the wrist. FRP were computed from step-time estimated from Smartstep and compared to those obtained from foot-mounted inertial sensors: precision and recall of the step detection, Root mean square error (RMSE) and Intraclass Correlation Coefficient (ICC) of stride durations, and limits of agreement of FRP. ResultsThe step detection precision and recall were respectively 99.5% and 95.9% for the waist position, and 99.4% and 95.7% for the wrist position. The ICC and RMSE of stride duration were 0.91 and 54 ms respectively for both the waist and the hand position. The limits of agreement of Cov, SD, fractal exponent, and sample entropy of stride duration are respectively 2.15%, 25 ms, 0.3, 0.5 for the waist and 1.6%, 16 ms, 0.23, 0.4 for the hand. SignificanceRobust against the elderly's gait and different body locations, especially the wrist, this method can open doors toward ambulatory measurements of steps, and calculation of different discrete stride-related falling risk indicators.

Object detection of the bornean orangutan nests using drone and YOLOv5

&lt;p class="tm6"&gt;Object detection methods when applied to ecology and conservation can help to identify and monitor endangered species and their habitats. Using drones for this purpose has become increasingly popular due to their ability to cover large areas quickly and efficiently. In this study, we aim to implement object detection using YOLOv5 to detect orangutan nests in forests. To conduct our experiment, we collect drone imagery under different conditions. We propose to use the original YOLOv5 to implement our model. The detection and monitoring of orangutan nests can help conservationists to identify critical habitats, monitor population, and design effective conservation strategies. Additionally, the use of drones can reduce the need for on-the-ground surveys, which can be time-consuming, expensive, and logistically challenging. In our study proposes a model for detecting orangutan nests in forests using a drone and the YOLOv5. Our model predicted 1,970 training images and 414 labeled orangutan nests, with a precision of 0.973, recall 0.949, accuracy mean average precision (mAP)_0.5 is 0.969, and mAP_0.5:0.95 is 0.630. The model finished 217 epochs in 58 hours and had a high object detection accuracy. The model has a 99.9% accuracy in detecting the number of orangutan nests.&lt;/p&gt;

Design and verification of q-axis perturbation based active islanding detection schemes for DG systems

The penetration of grid integrated distributed generation (DG) in the present decade, has benefited rural communities, the environment, and the power sector. These renewable power sources based DGs could eliminate the need of extensive transmission networks, especially in remote areas, reduce emissions and improve power supply reliability. A significant drawback of grid integrated DG systems is the islanding of DG units, which puts workers' safety at risk and raises the possibility of damaging electrical infrastructure. Therefore, islanding detection techniques are used to reduce the danger associated with islanded functioning of DG units. Fast detection, small non detection area and less power quality disturbance are the major requirements of any islanding detection method. To address this issue of islanding, researchers have proposed various islanding detection strategies. This paper compares various q-axis controller-based islanding identification approaches: sub-harmonic perturbation (SHP), complementary reactive power perturbation (CRPP), and even harmonic perturbation (EHP). In all three proposed methodologies, the perturbations introduced result in frequency deviations surpassing the predefined threshold values. But the time of islanding detection is least in the CRPP approach. CRPP can also drift the total harmonic distortion (THD) beyond the corresponding threshold in an appreciable way. The performance of these (Islanding detection methods) IDMs is evaluated through simulations using MATLAB-Simulink on a PV fed DG. The efficacy of the comparative analysis is ensured with necessary waveforms.

YOLOv5-T: A precise real-time detection method for maize tassels based on UAV low altitude remote sensing images

Maize tassels is an important organ of maize plant, and has a very important impact on yield prediction and variety breeding. Therefore, realizing the efficient and accurate detection of maize tassels in the natural environment is the key to obtain maize phenotypes in large quantities and accurately predict maize yield. This study constructed a dataset of tassels from different growth stages, proposed a YOLOv5-Tassel (YOLOv5-T) maize tassels detection model based on UAV remote sensing platform, combined with attention mechanism, YOLOv5_l network, spatial pyramid pooling structure and multi-scale extraction advantages of Atrous convolution. The experimental results showed that the Average Precision (AP) of the model for maize tassels detection could reach 98.70 %, and the detection speed was 42.2f/s. And the method in this paper had strong robustness to changes in light intensity and maize tassels at different growth stages. It was feasible to use YOLOv5-T to detect large areas of maize tassels in real time, which provided a useful reference for the estimation of maize yield and the selection of maize varieties.

Detection methods for Legionella pneumophila in diverse environmental conditions: A comparative study of FISH, seminested PCR and conventional culture

Detection methods for Legionella pneumophila in diverse environmental conditions: A comparative study of FISH, seminested PCR and conventional culture