Traditional Detection Methods Research Articles

A Novel Electromagnetic Wavelength Measurement Method Based on Photoacoustic Effect and Photoacoustic Response Characteristics of Nanomaterials

This study proposes a differential wavelength measurement method based on the electromagnetic-induced photoacoustic effect. The differential method involves irradiating the sample with multiple wavelengths and utilizing differences in absorption characteristics across different materials to calculate and measure the excitation light wavelengths. Compared to traditional detection methods, this approach combines the unique properties of electromagnetic-induced photoacoustic effect, offering high sensitivity and a wider detection range from microwave to light. Furthermore, the system is structurally simple and stable, suitable for non-destructive testing of various materials, including wavelength-sensitive biological tissues. The experimental results demonstrate that combined with Polymers Benzodithiophene Triazole–Quinoxaline (PBTQ) and Single-Walled Carbon Nanotubes (SWCNTs) as absorbing media, this technique provides a rapid and cost-effective means of wavelength measurement, achieving an uncertainty of approximately 2.33 nm within the range of 680–800 nm, and it can be used for wavelength/frequency measurement of various electromagnetic waves.

Rapid identification of moxa wool storage period based on hyperspectral imaging technology and machine learning

Moxa wool (MW), derived from the dried leaves of Artemisiaargyi, plays a significant role in traditional Chinese medicine. However, the quality of MW varies with its storage period, impacting its therapeutic efficacy. Traditional methods for quality detection are limited and destructive. To address this, we propose a non-destructive detection method using hyperspectral imaging technology and machine learning algorithms to accurately identify the storage period of MW. Nevertheless, hyperspectral data poses challenges due to its high dimensionality and redundancy, leading to increased computational complexity. To overcome this, we employed principal component analysis (PCA), competitive adaptive reweighted sampling (CARS), and successive projection algorithm (SPA) for data dimensionality reduction and wavelength selection. The results demonstrate that these techniques significantly enhance the accuracy of MW storage year identification. For Nanyang MW, the CARS+SVM model achieved the highest accuracy rates of 99.8% in the visible-near-infrared (VNIR) range and 99.55% in the shortwave infrared (SWIR) range. Similarly, for Qichun MW, the SPA+SVM model achieved identification accuracies of 99.78% and 99.47% in the VNIR and SWIR ranges, respectively. This research provides valuable insights into the rapid detection of MW quality by indication of storage years and presents a novel approach for quality control of MW in the field of traditional Chinese medicine. The combination of hyperspectral imaging and machine learning offers a promising solution for efficient and accurate MW identification, contributing to the advancement of traditional medicine practices.

Center-concave nanosheets of core-shell WO3@Prussian blue based handheld microchip-devices for ultrasensitive lysine determination

Lysine is one of the essential amino acids for human body, and its imbalance is a major cause to anemia, aging process, leukemia cell proliferation and tumor growth. Therefore, its monitoring is dominative to the prevention the disease progress and guidance to the clinical treatment. However, traditional in-hospital detection methods, such as colorimetry and fluorometric, often suffer the disadvantages of high cost and long time-consuming. These drawbacks show a difficulty in the home-in and dairy monitoring for the lysine regulation in body. In this study, we have proposed an ultrasensitive microchip-based portable device to achieve the onsite and precise determination of lysine within only 10 s. This microchip was functionalized through constructing a center-concave nanosheet of core-shell WO3@Prussian blue (WO3@PB) to remarkably strengthen the generation and transfer of the detection signal. In this special architecture, the core WO3 nanosheet can be exposed at the center region of this nanocomposite to effectively promote the enzymatic oxidation, while the PB shell enables to strongly reduce the H2O2 produced by the enzymatic reaction. Under above synergetic effects, a handheld device was designed to support the plug-and-play microchip, which performed an outstanding accuracy for the lysine detection in blood.

Detectable Adhesives: Nondestructive Detection of Adhesion

AbstractMetal‐to‐metal adhesives are vital across diverse sectors including automotive, aerospace, and industrial assembly. However, traditional adhesion detection methods are mainly based on the fracture mechanism, posing challenges for nondestructive detection. Here, a nondestructive method that leverages electrical signals to monitor the adhesion state of metal interfaces in real‐time is presented. The approach utilizes detectable adhesives (DA) synthesized from poly(ionic liquid) (PIL), which not only exhibit robust adhesion properties (4.9 MPa) but also function as ionic conductors. By correlating changes in capacitance and resistance with the adhesive state, the method allows for in situ monitoring of the curing process, prediction of adhesion strength, and early detection of potential failures. This dual‐sensing capability, combining electrical and mechanical aspects, enhances understanding of adhesive behavior in diverse conditions, presenting a fresh approach for digitally transforming adhesive technologies.

Electricity theft detection model based on the CEEMDAN-CNN-ViT

Abstract Most of the traditional power theft detection methods construct the model directly on the basis of the original power sequences, and do not simultaneously consider the long-period dependencies in the long-time sequences and the local connectivity features between periods, which limits the deep excavation of the behavioral laws of power users. In order to further improve the accuracy of power theft detection, this paper proposes a high-precision power theft detection model that integrates local anomaly filtering, energy consumption decomposition, and multi-feature fusion strategies. First, local anomaly filtering is used to eliminate local anomalies in the normalized energy consumption data to avoid the misleading effect of short-term abnormal behavior on the model. Then, the energy consumption decomposition based on CEEMDAN selects specific frequency band data that can accurately characterize the pattern of power theft users to improve the accuracy of power theft detection. Next, the long-time periodic features in the two-dimensional data and the short-time local features in the one-dimensional sequences are integrated by multi-feature fusion to enhance the adaptability of the model. The results show that the proposed model can effectively improve the detection accuracy, detection completeness, F1 score, and accuracy compared with the existing methods.

An improved assessment of forest disturbance using a novel approach of combining a Gaussian mixture model with an EM algorithm

Forests have experienced unprecedented decline and mortality beyond their historical range in past decades, which is attributed to disturbances like drought, fire, insects and disease. Traditional disturbance detection methods that typically employ a time series perspective to identify discrete disturbance events within continuous tree growth signals and were mainly designed for gap-scale often fail to identify disturbances across populations. To more accurately identify growth suppression and release clusters based on the perspective of forest population dynamics, here we applied a novel method of detecting forest disturbances using a Gaussian mixture model with an expectation maximisation algorithm (DGE), to fit annual distributions of growth indicators, i.e., tree-ring index. We further compared our novel approach of DGE with five traditional methods based on two sets of real tree-ring data and simulated tree-ring data. The results show an improvement of accuracy (35.5 %–48.1 %), sensitivity (36.6 %–58.1 %), precision (21.2 %–51.5 %) and specificity (11.1 %–20.6 %) for the average of two real radial growth datasets and an improvement of accuracy (7.6 %–13.9 %), sensitivity (70 %), precision (100 %) and specificity (9.1 %–9.7 %) for the simulation radial growth dataset, indicating that our DGE approach performs much better than the traditional methods. This study contributes to an improved strategy and understanding for sustainable forest management in the context of climate warming, demonstrating that our DGE approach can be better applied to global forest disturbance detection under global change.

Prediction method of loess landslides based on faster R-CNN and WACM

Due to the complexity of the environment and geological conditions in which the loess slope is located, there are many challenges in the accuracy and prediction of loess landslide detection. Therefore, this study introduces a fast convolutional neural network model to solve the problems of traditional detection methods in terms of technology, cost, and detection accuracy, and to achieve real-time detection of the morphology of loess landslides. A weight absorption coupling model is constructed to address the uniform moisture content in loess with hidden dangers. Combined with instability probability, the probability of shallow loess landslides is predicted. The results showed that the mAP value of the Faster R-CNN algorithm using the ResNet125 network exceeded 90%, which was 46.23% and 32.01% higher than the algorithm models using ResNet50 and VGG16, respectively. The proposed model performed fractal analysis on four different loess particle samples, with correlation coefficients R2 above 0.9. The difference between the predicted and actual moisture content of upper and surface loess was within 11%. Compared with existing methods, the research and construction of a loess landslide detection and probability prediction model has greatly improved reliability and accuracy, which is of great significance for predicting the probability of different loess landslides.

A novel spike detection model for dynamic stress monitoring of bogie frame

The fatigue evaluation of the bogie frame is an important part of the structural health monitoring of the vehicle. During the dynamic stress monitoring, some signal spikes, which are much larger than the normal fluctuation range due to the interference of the complex electromagnetic environment, affect the accuracy of the structural damage assessment and need to be accurately detected and replaced. Aiming at the drawbacks of traditional detection methods that are overly dependent on engineering experience and not universal, a novel spike detection model is proposed in this paper. By the process of data transformation, spike region features are effectively separated. Based on the isolation forest algorithm, the normalized anomaly score of each point is calculated, and the threshold is determined adaptively. The spike detection rate and damage sensitivity are proposed as the evaluation indices of the detection effect of the method. The results show that the spike detection rate is improved by 7.86% on average, and the damage sensitivity is improved by 15.59% on average. The spike detection model in this paper is significantly improved compared to the existing methods.

Generative AI in Fintech: Advancing Risk Assessment and Fraud Detection in Digital Payment Technologies

This article explores the transformative potential of artificial intelligence (AI), particularly generative AI, in enhancing risk assessment and fraud detection within digital payment technologies. As financial fraud evolves in sophistication, traditional detection methods often fall short. We propose a novel approach leveraging generative AI to create synthetic data that mimics real-world scenarios, thereby training more comprehensive and adaptive fraud detection models. This article addresses limitations in historical datasets, improves the accuracy of predictive models, and reduces false positives. Furthermore, we demonstrate how this approach enables proactive detection of emerging fraud trends, allowing fintech firms to stay ahead in the ongoing arms race against fraudsters. Our findings suggest that integrating generative AI into fraud detection systems can significantly enhance the security and reliability of digital payment platforms, potentially revolutionizing risk management in the fintech industry. This article contributes to the growing body of knowledge on AI applications in financial security and provides a foundation for future developments in this critical area.

Morphological dictionary learning based sparse classification for small electric motor state recognition under unbalanced samples

Accurately recognizing sound states in the production line of small electric motors is of great importance for manufacturers to carry out quick repairs and ensure high quality deliveries. Since the number of normal samples is much larger than the number of abnormal samples in practice, resulting in unbalanced data, which poses huge challenges to traditional detection methods. To overcome these difficulties, this study presents a morphological dictionary learning-based sparse classification (MDL-SC) combined with audio data augmentation method for small electric motor state recognition under unbalanced samples. Firstly, audio data augmentation methods such as adding background noise, pitch shifting, time stretching and combined augmentation are investigated for augmenting the number and diversity of samples. Secondly, morphological dictionary learning is proposed for characterizing transient sounds of small electric motors and enhancing the discriminative feature learning capability of the dictionary. Finally, the minimum reconstruction error strategy is relied upon to establish automatic recognition of small electric motor states. Three small motor datasets with unbalanced ratios are established in the experiments to verify the effectiveness of the proposed MDL-SC, which has higher recognition accuracy under unbalanced conditions compared with traditional dictionary learning based sparse classification (DL-SC), k-nearest neighbors, support vector machines and convolutional neural networks. This study can provide some theoretical implications for the later development of online detection of small electric motors or other types of electric motors.

Corrosion degree detection of ferromagnetic materials using a hybrid machine learning approach and self-magnetic flux leakage technology

Corrosion of ferromagnetic materials can lead to performance degradation or even failure in engineering structures. Therefore, detecting the degree of corrosion in these materials is crucial. Traditional detection methods are primarily manual and lack precision, highlighting the need for developing automated, high-precision detection techniques. In this paper, we developed a novel self-magnetic flux leakage (SMFL) scanner. We enhanced the Marine Predator Algorithm (MPA) by integrating Chaos Opposition (CO) and Group Learning (GL), resulting in the COGL-MPA-XGB model. This model optimizes Extreme Gradient Boosting parameters for accurate corrosion prediction, achieving a 96% coefficient of determination (R2) and a mean error of 1.87. We tested the method on 170 rod-shaped steel samples with varying degrees of corrosion, evaluating its effectiveness through ablation experiments and comparisons with three other machine learning models. The results show that the combined SMFL and hybrid model strategy reduces the maximum detection error by 73.7% and offers a new perspective for quantitative corrosion detection.

Hyperspectral Anomaly Detection Based on Spectral Similarity Variability Feature.

In the traditional method for hyperspectral anomaly detection, spectral feature mapping is used to map hyperspectral data to a high-level feature space to make features more easily distinguishable between different features. However, the uncertainty in the mapping direction makes the mapped features ineffective in distinguishing anomalous targets from the background. To address this problem, a hyperspectral anomaly detection algorithm based on the spectral similarity variability feature (SSVF) is proposed. First, the high-dimensional similar neighborhoods are fused into similar features using AE networks, and then the SSVF are obtained using residual autoencoder. Finally, the final detection of SSVF was obtained using Reed and Xiaoli (RX) detectors. Compared with other comparison algorithms with the highest accuracy, the overall detection accuracy (AUCODP) of the SSVFRX algorithm is increased by 0.2106. The experimental results show that SSVF has great advantages in both highlighting anomalous targets and improving separability between different ground objects.

A novel methodology for anomaly detection in smart home networks via Fractional Stochastic Gradient Descent

With the rising integration of IoT devices within smart home environments, securing these interconnected systems against unauthorized access and cyber threats has become increasingly critical. This study introduces a novel methodology utilizing an advanced Artificial Neural Network (ANN) frame-work to enhance attack and anomaly detection capabilities within smart home networks. The objective is to develop a robust model that outperforms traditional detection methods and provides high accuracy and low false positive rates in identifying potential security threats. The research employs a pioneering approach to train the ANN by incorporating a Fractional Stochastic Gradient Descent optimizer grounded in Grunwald–Letnikov fractional calculus. This method was chosen for its potential to refine learning processes and improve detection accuracy over standard optimizers. The evaluation of the model was performed using the DS2OS traffic traces dataset, applying precision, sensitivity (recall), and specificity metrics to assess performance. The proposed model demonstrated exceptional performance with an accuracy of 0.9951. It significantly surpassed traditional methods like Logistic Regression and Support Vector Machines. The precision achieved was high, indicating a low rate of false positives, while the sensitivity and specificity values underscore the model’s ability to identify both typical and unconventional behaviours within the network accurately. This study introduces a robust and efficient ANN-based methodology for enhancing security in smart home IoT networks. Using a fractional stochastic gradient descent optimizer has proven effective in improving the model’s accuracy and reliability in detecting anomalies and attacks. The findings suggest significant implications for the future of IoT security, highlighting the potential for broader applications of fractional calculus in machine learning to enhance cybersecurity measures in various domains.

Qualitative discrimination and quantitative prediction of salt in aqueous solution based on near-infrared spectroscopy

Freshwater resources have been gradually salinized in recent years, dramatically impacting the ecosystem and human health. Therefore, it is necessary to detect the salinity of freshwater resources. However, traditional detection methods make it difficult to check the type and concentration of salt quickly and accurately in solution. This paper uses a portable near-infrared spectrometer to qualitatively discriminate and quantitatively predict the salt in the solution. The study was carried out by adding ten salts of NaCl, KCl, MgCl2, CaCl2, Na2CO3, K2CO3, CaCO3, Na2SO4, K2SO4, MgSO4 to 2 milliliters of deionized water to prepare a single salt solution (0.02% - 1.00%) totaling 100 sets. It was found that the Support vector machine (SVM) model was only effective in discriminating the class of salt anions in the solution. The Partial least squares-discriminant analysis (PLS-DA) model, on the other hand, can effectively discriminate the classes of salt in solution, and the accuracies of the optimal model prediction set and the interactive validation set are 98.86% and 99.66%, respectively. Furthermore, the Partial least squares regression (PLSR) models can accurately predict the concentration of NaCl, KCl, MgCl2, CaCl2, Na2CO3, K2CO3, CaCO3, Na2SO4, K2SO4, MgSO4 salt solutions. The coefficients of determination R2 of their model interactive validation sets were 0.99, 0.99, 0.99, 0.97, 0.99, 0.99, 0.98, 0.99, 0.98, and 0.98, respectively. This study shows that NIRS can achieve rapid and accurate qualitative and quantitative detection of salts in solution, which provides technical support for the utilization of safe water resources.

Evaluation of Lesion Burden in Pediatric Patients with Multiple Sclerosis by Computer Aided Algorithm and Comparison with Standard Detection Methods

Background: The aim of this retrospective study was to assess the lesion burden in pediatric patients with multiple sclerosis (pMS) using a computer-assisted algorithm, specifically the VolBrain program. The study aimed to compare the performance of this automated tool with traditional detection methods performed by neuroimaging analysts, providing valuable insights into the potential of automated tools for lesion quantification in pMS. Materials and Methods: The study cohort consisted of 20 PMS patients, aged 10-18 years, registered at Atatürk University Research Hospital. Lesion assessment was performed using the VolBrain program (by an anatomist) and standard detection methods (by a neuroradiologist) using T2 SPACE dark matter sequences. Statistical analysis included Wilcoxon and Pearson correlation tests, and the study adhered to ethical considerations and standardised magnetic resonance imaging (MRI) protocols. Results: In this study, pMS patients aged 10-18 years, the cohort consisted of 60% females (n=12) and 40% males (n=8). The mean age for females was 15.67±1.969 and for males 14.50±2.20 years (p=0.24). Plaque count analysis showed a statistically significant difference between radiologist and VolBrain assessment in all pMS patients (p=0.021). Significant differences were also observed in female pMS patients (p=0.034) but not in males (p=0.362). Correlations between radiologist and VolBrain assessments showed significant associations in both female and male patients, with strong correlations observed for plaque number, lesion burden and Expanded Disability Status Scale (EDSS) scores (p<0.01). Conclusions: This study demonstrates the potential of the VolBrain programme in assessing lesion burden in pMS patients. The observed correlations with traditional methods and clinical parameters support the concurrent validity of VolBrain and emphasise its potential clinical relevance. Incorporating automated tools into routine clinical practice could improve the accuracy of lesion quantification and thus contribute to improved monitoring and management of pMS.

USSC-YOLO: Enhanced Multi-Scale Road Crack Object Detection Algorithm for UAV Image.

Road crack detection is of paramount importance for ensuring vehicular traffic safety, and implementing traditional detection methods for cracks inevitably impedes the optimal functioning of traffic. In light of the above, we propose a USSC-YOLO-based target detection algorithm for unmanned aerial vehicle (UAV) road cracks based on machine vision. The algorithm aims to achieve the high-precision detection of road cracks at all scale levels. Compared with the original YOLOv5s, the main improvements to USSC-YOLO are the ShuffleNet V2 block, the coordinate attention (CA) mechanism, and the Swin Transformer. First, to address the problem of large network computational spending, we replace the backbone network of YOLOv5s with ShuffleNet V2 blocks, reducing computational overhead significantly. Next, to reduce the problems caused by the complex background interference, we introduce the CA attention mechanism into the backbone network, which reduces the missed and false detection rate. Finally, we integrate the Swin Transformer block at the end of the neck to enhance the detection accuracy for small target cracks. Experimental results on our self-constructed UAV near-far scene road crack i(UNFSRCI) dataset demonstrate that our model reduces the giga floating-point operations per second (GFLOPs) compared to YOLOv5s while achieving a 6.3% increase in mAP@50 and a 12% improvement in mAP@ [50:95]. This indicates that the model remains lightweight meanwhile providing excellent detection performance. In future work, we will assess road safety conditions based on these detection results to prioritize maintenance sequences for crack targets and facilitate further intelligent management.

Foreign object detection in urban rail transit based on deep differentiation segmentation neural network

With the increasing scale of urban rail transit, foreign object intrusion has become a significant operational safety hazard in urban rail transit. Although the laser-based automatic foreign object detection system has advantages such as long-distance detection and insensitivity to light changes, it has drawbacks such as large blind spots and low visualization. In response to the problems existing in laser detection systems, we proposed a novel video-based deep differentiation segmentation neural network for foreign object detection. Firstly, the foreign object detection is transformed into a binary classification problem, and the foreign object is determined as the image's foreground using image segmentation principles. Secondly, build a deep segmentation network based on deep convolution. Finally, perform morphological operations and threshold judgment on the foreground segmentation image to filter out the final detection results. To improve the detection effect, we reduced the impact of airflow disturbance by sampling and calculating the average background image. At the same time, the channel attention model and spatial attention model are added to the deep differentiation neural network. Collecting real data on subway platforms for experiments shows that the proposed method has a detection accuracy of 95.8 %, which is superior to traditional detection methods and recent image segmentation neural networks.

Amperometric Highly Sensitive Phosphate Ion Sensor Based on the Electrochemically Modified Ni Electrode.

We present a study of high-performance electrochemical phosphate sensors, which are exquisitely designed and easy to operate. We innovatively utilized the insolubility of nickel phosphate and developed a new type of sensor through electrochemical methods. The experiment first used cyclic voltammetry to determine -0.4 V as the optimal electrochemical modification potential and used constant potential electrodeposition technology to form a nickel oxide layer on the surface of the nickel electrode, which serves as the active layer in response to phosphate ions. The changes in the surface structure and chemical composition of the electrode before and after modification were thoroughly characterized by scanning electron microscopy and energy scattering spectroscopy analysis. The performance evaluation of the sensor shows that the modified nickel electrode has excellent responsiveness to phosphate ions in the concentration range of 10-7 to 10-10 mol/L, with a detection lower limit of 10-10 mol/L. As the concentration decreases, a shoulder peak appears at ∼0.63 V and the current change shows a regular increase. Compared with traditional detection methods, this sensor exhibits higher stability and practicality and is suitable for the rapid identification of phosphates in real samples. In summary, this study successfully developed a fast, sensitive, and wide response range current type electrochemical phosphate sensor, which has broad application prospects in environmental monitoring, water quality analysis, and biomedical fields.

Cable Eccentricity Detection Method Based on Magnetic Field.

Amid the rapid advancement of electronic information technology, the need for cable eccentricity measurement in the industry is increasing both in China and across the globe. Current detection methods have several flaws, including high costs, insufficient accuracy, and instability. In this paper, we introduce a magnetic field-based detection method for cable eccentricity that provides high precision and cost-effectiveness. We position three pairs of magnetic field-collection modules in a circular array to gather magnetic flux density information induced by the electrified cable. We apply the law of electromagnetic induction to calculate the cable eccentricity. Our method is non-contact, preserving the cable's integrity. Our method outperforms traditional detection methods, not only in achieving greater accuracy and stability but also in significantly lowering the detection cost. Simulations and experiments show that our method's error rate under specified conditions is 0~4%, with a maximum standard deviation of 0.11, confirming its precision and stability in detecting cable eccentricity. The effectiveness of our method is influenced by two factors: lift-off value and loading current intensity. Our method presents a novel concept and a dependable strategy for the progress of cable eccentricity-detection technology.

Research on road crack segmentation based on deep convolution and transformer with multi-branch feature fusion

Abstract Efficient and precise identification of road pavement cracks contributes to better evaluation of road conditions. In practical road maintenance and safety assessment, traditional manual crack detection methods are time-consuming, physically demanding, and highly subjective. In addition, crack recognition based on image processing techniques lacks robustness. In this paper, a multi-branch feature fusion road crack segmentation network model (DTPC) based on deep convolution and transformer modules is proposed. The model is used for pixel-level segmentation of road crack images, which is a good solution to the existing needs and helps to repair dangerous cracks promptly in the follow-up work to prevent serious disasters due to crack breakage. Firstly, combine deep convolution with transformer modules to achieve precise local extraction and global contextual feature extraction. Secondly, a dual-channel attention mechanism is em-ployed to help the model better address information loss and positional offset issues. Finally, three-branch outputs are fused to obtain prediction maps that intuitively determine recognition results. The proposed model is tested for accuracy using a dedicated road pavement crack dataset. Results show that compared to mainstream models such as SegFormer, HRNet, PSPNet, and FCN, the DTPC model achieves the highest MIoU score (86.72%) and F1 score (92.49%).