Robust Damage Detection and Localization Under Complex Environmental Conditions Using Singular Value Decomposition-based Feature Extraction and One-dimensional Convolutional Neural Network

Ultrasonic guided wave is an attractive monitoring technique for large-scale structures but is vulnerable to changes in environmental and operational conditions(EOC),which are inevitable in the normal inspection of civil and mechanical structures.This paper thus presents a robust guided wave-based method for damage detection and localization under complex environmental conditions by singular value decomposition-based feature extraction and one-dimensional convolutional neural network(1D-CNN).After singular value decomposition-based feature extraction processing,a temporal robust damage index(TRDI)is extracted,and the effect of EOCs is well removed.Hence,even for the signals with a very large temperature-varying range and low signal-to-noise ratios(SNRs),the final damage detection and localization accuracy retain perfect 100%.Verifications are conducted on two different experimental datasets.The first dataset consists of guided wave signals collected from a thin aluminum plate with artificial noises,and the second is a publicly available experimental dataset of guided wave signals acquired on a composite plate with a temperature ranging from 20℃to 60℃.It is demonstrated that the proposed method can detect and localize the damage accurately and rapidly,showing great potential for application in complex and unknown EOC.

Detection value of Th17 related indexes and platelet activation indexes in patients with liver cancer

Objective:To study the detection value of Th17 related indexes and platelet activation indexes in the patients with liver cancer.Methods: A total of 59 patients with liver cancer in our hospital from July 2015 to June 2016 were selected as the observation group, 59 healthy persons of the same ages with physical examination were selected as the control group, then the serum Th17 related indexes and platelet activation indexes levels of two groups were detected and compared, then the serum Th17 related indexes and platelet activation indexes levels of observation group with different stages and types of liver cancer were compared too. Results:The serum Th17 related indexes and platelet activation indexes levels of observation group were all higher than those of control group, the serum Th17 related indexes and platelet activation indexes levels of observation group with different stages and types of liver cancer had obvious differences (allP<0.05).Conclusions: The Th17 related indexes and platelet activation indexes of patients with liver cancer show higher expression state, and the expression levels of patients with different stages and types of liver cancer have obvious differences too, so the clinical detection value of those indexes in the patients with liver cancer are higher.

Clinical Value of Hepatitis B Virus RNA Detection in Patients with Chronic Hepatitis B Infection

Objective:To study the clinical value of hepatitis B virus pregenomic RNA(HBV-pgRNA)detection in the treatment of hepatitis B.Methods:60 patients with hepatitis B were included in the study.Serum HBV-pgRNA and HBV DNA levels in different phases of infection and during treatment were detected,and serum hepatitis B surface antigen(HbsAg)titer was detected by chemiluminescent immunoassay.DNA was extracted from liver biopsy tissue,and covalently closed circular DNA was detected to predict the therapeutic value in patients.Results:At the initial stage of treatment,the level of HBV-pgRNA in phase I,II,III,and IV showed a gradual decrease.Comparing the levels of HBV-pgRNA before and after treatment,we found that the level of HBV-pgRNA was significantly lower after treatment(P<0.05).Among the indicators for predicting HBsAg seroconversion,the accuracy of HBV-pgRNA level was 85.0%(51/60).Conclusion:The clinical value of HBV-pgRNA detection in the treatment of hepatitis B is high.

Cut-off Values of Diagnostic Indices to Detect Iron Deficiency in Chinese Breast-fed Infants

Iron deficiency anemia is one of the most prevalent nutritional deficiency worldwide. The commonly used cut-off values for identifying iron deficiency are extrapolated from older children and may not be suitable for infants. Therefore, our study aimed to establish appropriate cut-off values for the evaluation of iron status in Chinese infants. Pregnant women who delivered at 〉37 gestational weeks with normal iron status were recruited. Later, infants with normal birth weight and who were breastfed in the first 4 months were selected. Blood samples were collected to assess hemoglobin, serum ferritin, soluble transferrin receptor, mean corpuscular volume and free erythrocyte protoporphyrin. Cut-offs of all iron indices were determined as the limit of 95% confidence interval.

Real-Time Fraud Detection Using Machine Learning

Credit card fraud remains a significant challenge, with financial losses and consumer protection at stake. This study addresses the need for practical, real-time fraud detection methodologies. Using a Kaggle credit card dataset, I tackle class imbalance using the Synthetic Minority Oversampling Technique (SMOTE) to enhance modeling efficiency. I compare several machine learning algorithms, including Logistic Regression, Linear Discriminant Analysis, K-nearest Neighbors, Classification and Regression Tree, Naive Bayes, Support Vector, Random Forest, XGBoost, and Light Gradient-Boosting Machine to classify transactions as fraud or genuine. Rigorous evaluation metrics, such as AUC, PRAUC, F1, KS, Recall, and Precision, identify the Random Forest as the best performer in detecting fraudulent activities. The Random Forest model successfully identifies approximately 92% of transactions scoring 90 and above as fraudulent, equating to a detection rate of over 70% for all fraudulent transactions in the test dataset. Moreover, the model captures more than half of the fraud in each bin of the test dataset. SHAP values provide model explainability, with the SHAP summary plot highlighting the global importance of individual features, such as “V12” and “V14”. SHAP force plots offer local interpretability, revealing the impact of specific features on individual predictions. This study demonstrates the potential of machine learning, particularly the Random Forest model, for real-time credit card fraud detection, offering a promising approach to mitigate financial losses and protect consumers.

Multi-parameter gene expression profiling of peripheral blood for early detection of hepatocellular carcinoma

AIM In our previous study, we have built a nine-gene(GPC3, HGF, ANXA1, FOS, SPAG9, HSPA1 B, CXCR4, PFN1, and CALR) expression detection system based on the Ge XP system. Based on peripheral blood and Ge XP, we aimed to analyze the results of genes expression by different multi-parameter analysis methods and build a diagnostic model to classify hepatocellular carcinoma(HCC) patients and healthy people.METHODS Logistic regression analysis, discriminant analysis, classification tree analysis, and artificial neural network were used for the multi-parameter gene expression analysis method. One hundred and three patients with early HCC and 54 age-matched healthy normal controls were used to build a diagnostic model. Fiftytwo patients with early HCC and 34 healthy people were used for validation. The area under the curve, sensitivity, and specificity were used as diagnostic indicators.RESULTS Artificial neural network of the total nine genes had the best diagnostic value, and the AUC, sensitivity, and specificity were 0.943, 98%, and 85%, respectively. At last, 52 HCC patients and 34 healthy normal controls were used for validation. The sensitivity and specificity were 96% and 86%, respectively.CONCLUSION Multi-parameter analysis methods may increase the diagnostic value compared to single factor analysis and they may be a trend of the clinical diagnosis in the future.

Novel detection method for infrared small targets using weighted information entropy

This paper presents a method for detecting the small infrared target under complex background. An algorithm, named local mutation weighted information entropy （LMWIE）, is proposed to suppress background. Then, the grey value of targets is enhanced by calculating the local energy. Image segmentation based on the adaptive threshold is used to solve the problems that the grey value of noise is enhanced with the grey value improvement of targets. Experimental results show that compared with the adaptive Butterworth high-pass filter method, the proposed algorithm is more effective and faster for the infrared small target detection.

Dynamic unbalance detection of cardan shaft in high-speed train based on EMD-SVD-NHT

Contrary to the aliasing defect between the adjacent intrinsic model functions(IMFs) existing in empirical model decomposition(EMD), a new method of detecting dynamic unbalance with cardan shaft in high-speed train was proposed by applying the combination between EMD, Hankel matrix, singular value decomposition(SVD) and normalized Hilbert transform(NHT). The vibration signals of gimbal installed base were decomposed through EMD to get different IMFs. The Hankel matrix constructed through the single IMF was orthogonally executed through SVD. The critical singular values were selected to reconstruct vibration signs on the basis of the key stack of singular values. Instantaneous frequencys(IFs) of reconstructed vibration signs were applied to detect dynamic unbalance with shaft and eliminated clutter spectrum caused by the aliasing defect between the adjacent IMFs, which highlighted the failure characteristics. The method was verified by test data in the unbalance condition of dynamic cardan shaft. The results show that the method effectively detects the fault vibration characteristics caused by cardan shaft dynamic unbalance and extracts the nature vibration features. With comparison to the traditional EMD-NHT, clarity and failure characterization force are significantly improved.

A Novel Rolling Bearing Vibration Impulsive Signals Detection Approach Based on Dictionary Learning

The localized faults of rolling bearings can be diagnosed by its vibration impulsive signals.However,it is always a challenge to extract the impulsive feature under background noise and non-stationary conditions.This paper investigates impulsive signals detection of a single-point defect rolling bearing and presents a novel data-driven detection approach based on dictionary learning.To overcome the effects harmonic and noise components,we propose an autoregressive-minimum entropy deconvolution model to separate harmonic and deconvolve the effect of the transmission path.To address the shortcomings of conventional sparse representation under the changeable operation environment,we propose an approach that combines K-clustering with singular value decomposition(K-SVD)and split-Bregman to extract impulsive components precisely.Via experiments on synthetic signals and real run-to-failure signals,the excellent performance for different impulsive signals detection verifies the effectiveness and robustness of the proposed approach.Meanwhile,a comparison with the state-of-the-art methods is illustrated,which shows that the proposed approach can provide more accurate detected impulsive signals.

Robust fault detection and diagnosis for uncertain nonlinear systems

This paper considers robust fault detection and diagnosis for input uncertain nonlinear systems. It proposes a multi-objective fault detection criterion so that the fault residual is sensitive to the fault but insensitive to the uncertainty as much as possible. Then the paper solves the proposed criterion by maximizing the smallest singular value of the transformation from faults to fault detection residuals while minimizing the largest singular value of the transformation from input uncertainty to the fault detection residuals. This method is applied to an aircraft which has a fault in the left elevator or rudder. The simulation results show the proposed method can detect the control surface failures rapidly and efficiently.

Fast recognition algorithm of underwater micro-terrain based on ultrasonic detection

An algorithm was proposed to fast recognize three types of underwater micro-terrain, i.e. the level, the gradient and the uneven. With pendulum single beam bathymeter, the hard level concrete floor, the random uneven floor and the gradient wood panel (8-) were ultrasonically detected 20 times, respectively. The results show that the algorithm is right from fact that the first clustering values of the uneven are all less than the threshold value of 60.0% that is obtained by the level and gradient samples. The algorithm based on the dynamic clustering theory can effectively eliminate the influences of the exceptional elevation values produced by the disturbances resulted from the grazing angle, the characteristic of bottom material and environmental noises, and its real-time capability is good. Thus, the algorithm provides a foundation for the next restructuring of the micro-terrain.

A ROBUST COLOR EDGE DETECTION ALGORITHM BASED ON THE QUATERNION HARDY FILTER

This paper presents a robust filter called the quaternion Hardy filter(QHF)for color image edge detection.The QHF can be capable of color edge feature enhancement and noise resistance.QHF can be used flexibly by selecting suitable parameters to handle different levels of noise.In particular,the quaternion analytic signal,which is an effective tool in color image processing,can also be produced by quaternion Hardy filtering with specific parameters.Based on the QHF and the improved Di Zenzo gradient operator,a novel color edge detection algorithm is proposed;importantly,it can be efficiently implemented by using the fast discrete quaternion Fourier transform technique.From the experimental results,we conclude that the minimum PSNR improvement rate is 2.3%and the minimum SSIM improvement rate is 30.2%on the CSEE database.The experiments demonstrate that the proposed algorithm outperforms several widely used algorithms.

Comparing the Area of Data Mining Algorithms in Network Intrusion Detection

The network-based intrusion detection has become common to evaluate machine learning algorithms. Although the KDD Cup’99 Dataset has class imbalance over different intrusion classes, still it plays a significant role to evaluate machine learning algorithms. In this work, we utilize the singular valued decomposition technique for feature dimension reduction. We further reconstruct the features form reduced features and the selected eigenvectors. The reconstruction loss is used to decide the intrusion class for a given network feature. The intrusion class having the smallest reconstruction loss is accepted as the intrusion class in the network for that sample. The proposed system yield 97.90% accuracy on KDD Cup’99 dataset for the stated task. We have also analyzed the system with individual intrusion categories separately. This analysis suggests having a system with the ensemble of multiple classifiers;therefore we also created a random forest classifier. The random forest classifier performs significantly better than the SVD based system. The random forest classifier achieves 99.99% accuracy for intrusion detection on the same training and testing data set.

NETWORK INTRUSION DETECTION METHOD BASED ON RS-MSVM

A new method called RS-MSVM （Rough Set and Multi-class Support Vector Machine） is proposed for network intrusion detection. This method is based on rough set followed by MSVM for attribute reduction and classification respectively, The number of attributes of the network data used in this paper is reduced from 41 to 30 using rough set theory. The kernel function of HVDM-RBF （Heterogeneous Value Difference Metric Radial Basis Function）, based on the heterogeneous value difference metric of heterogeneous datasets, is constructed for the heterogeneous network data. HVDM-RBF and one-against-one method are applied to build MSVM. DARPA （Defense Advanced Research Projects Agency） intrusion detection evaluating data were used in the experiment. The testing results show that our method outperforms other methods mentioned in this paper on six aspects： detection accuracy, number of support vectors, false positive rate, falsc negative rate, training time and testing time.

Diagnostic efficacy of combined detection of different tumor markers for primary hepatic carcinoma

Objective:To explore the clinical diagnostic value of combined detection of different tumor markers for primary hepatic carcinoma, and to provide the reference for the clinical diagnosis. Methods: 72 patients who were diagnosed with primary hepatic carcinoma were collected as observation group, 65 patients with benign liver disease as benign liver disease group and 80 cases of health examination as healthy control group, the contents of tumor markers alpha fetoprotein(AFP), carcinoembryonic antigen(CEA), carbohydrate antigen-199(CA199), carbohydrate antigen-125(CA125) and carbohydrate antigen-153(CA153) were determined by electrochemiluminescence in all subjects, then the results of five kinds of tumor markers and the positive rates of each index between the two groups were compared, the diagnostic value of separate and combined detection of different tumor markers in primary hepatic carcinoma were analyzed.Results: The values of AFP, CA199 and CA153 in the observation group were higher than the benign liver disease group, the values of AFP, CEA, CA199, CA125 and CA153 in the observation group were higher than the control group, the values of CA199 and CA125 in the benign liver disease group were higher than the control group, the differences were statistically significant (P<0.05). The positive rates of AFP, CEA, CA199 and CA153 in the observation group were higher than the benign liver disease group, the positive rates of AFP, CEA, CA199 and CA125 in the observation group were higher than the control group, the positive rates of AFP, CEA, CA199 and CA125 in the benign liver disease group were higher than the control group, the differences were statistically significant (P<0.05). The sensitivity of combined detection of all indicators for primary hepatic carcinoma was 86.4%, specificity, correct index, misdiagnosis rate and missed diagnosis rate were 86.4%, 89.2%, 75.6%, 13.6% and 10.8% respectively, and the combined detection was higher than the correct index of each index.Conclusion: Combined detection of serum tumor markers AFP, CEA, CA199, CA125 and CA153 can improve the sensitivity and specificity of diagnosis of primary hepatic carcinoma, it has better diagnostic value for primary hepatic carcinoma.

Detection and significance of blood rheology and coagulation function index in elderly patients with type 2 diabetes mellitus complicated with cerebral infarction

Objective: To investigate the level of blood rheology and coagulation function in elderly patients with type 2 diabetes mellitus (T2DM) and cerebral infarction and its significance. Methods: A total of 81 elderly patients with T2DM and cerebral infarction were selected as the observation group, 80 cases of T2DM patients without cerebral infarction were selected as T2DM group, and 80 healthy elderly people as control group. According to the Adama classification, the patients in the observation group were divided into three groups: lacunar infarction group (n=28), small infarction group (n=39) and large infarction group (n=14). The blood rheology and coagulation function indexes levels among the groups were compared. Results: The single factor variance analysis showed that the differences of the high shear whole blood viscosity, plasma viscosity, low shear whole blood viscosity, APTT, PT, FIB and D-D levels among the control group were significant, T2DM group and observation group were statistically significant. Compared with the control group, the high shear whole blood viscosity, plasma viscosity, low shear whole blood viscosity, FIB and D-D levels in the T2DM group and observation group were significantly increased, PT and APTT were decreased sharply, and in the observation group high shear whole blood viscosity, plasma viscosity, low shear whole blood viscosity, FIB and D-D levels were significantly higher than that of T2DM group APTT, and PT were significantly lower than those of T2DM group. Lacunar infarction group, small infarction group and large infarction group with increased infarct size, with high shear whole blood viscosity, plasma viscosity, low shear whole blood viscosity, FIB and D-D levels were significantly increased, while APTT and PT were significantly decreased. Conclusion: T2DM and cerebral infarction patients with abnormal blood rheology and coagulation function, the index examination has important clinical value for cerebral infarction area evaluation.

Efficient Global Threshold Vector Outlyingness Ratio Filter for the Removal of Random Valued Impulse Noise

This research paper proposes a filter to remove Random Valued Impulse Noise (RVIN) based on Global Threshold Vector Outlyingness Ratio (GTVOR) that is applicable for real time image processing. This filter works with the algorithm that breaks the images into various decomposition levels using Discrete Wavelet Transform (DWT) and searches for the noisy pixels using the outlyingness of the pixel. This algorithm has the capability of differentiating high frequency pixels and the “noisy pixel” using the threshold as well as window adjustments. The damage and the loss of information are prevented by means of interior mining. This global threshold based algorithm uses different thresholds for different quadrants of DWT and thus helps in recovery of noisy image even if it is 90% affected. Experimental results exhibit that this method outperforms other existing methods for accurate noise detection and removal, at the same time chain of connectivity is not lost.

Target Detection in the Presence of Reverberation Based on SVD Method

Target echo detection in the presence of strong reverberation is investigated by means of singular value decomposition (SVD) method. Simulation results indicate that the method can efficiently separate the target echo from the reverberation and can improve the detection of target echo.

Removing Random-Valued Impulse Noises by a Two-Staged Nonlinear Filtering Method

Digital images are frequently contaminated by impulse noise(IN)during acquisition and transmission.The removal of this noise from images is essential for their further processing.In this paper,a two-staged nonlinear filtering algorithm is proposed for removing random-valued impulse noise(RVIN)from digital images.Noisy pixels are identified and corrected in two cascaded stages.The statistics of two subsets of nearest neighbors are employed as the criterion for detecting noisy pixels in the first stage,while directional differences are adopted as the detector criterion in the second stage.The respective adaptive median values are taken as the replacement values for noisy pixels in each stage.The performance of the proposed method was compared with that of several existing methods.The experimental results show that the performance of the suggested algorithm is superior to those of the compared methods in terms of noise removal,edge preservation,and processing time.

超声S-Detect技术在乳腺肿块诊断中的价值

目的探讨超声S-Detect技术在乳腺肿块病灶诊断中的应用价值。方法采用S-Detect技术及常规超声对62例女性患者总计85个乳腺肿块进行诊断,以术后病理结果为金标准,对比分析常规超声与S-Detect技术的诊断效能。结果低年资医师常规超声诊断效能低于S-Detect技术(P<0.05),中年资、高年资医师常规超声诊断效能与S-Detect技术无显著差异(P>0.05)。S-Detect技术与高年资医师诊断结果呈正相关(r=0.97)。S-Detect技术长轴切面和长轴垂直切面的诊断效能无显著差异(P>0.05)。中年资医师常规超声诊断直径≤20 mm乳腺肿块的效能优于S-Detect技术(P<0.05),但诊断直径>20 mm乳腺肿块的效能低于S-Detect技术(P<0.05)。结论S-Detect技术可用于乳腺良恶性肿块的鉴别诊断,其诊断效能可达到中、高年资医师BI-RADS分类的诊断水平,但在诊断直径不足20mm乳腺肿块时的效能并不十分理想。