Generalized autoencoder-based fault detection method for traction systems with performance degradation

Fault diagnosis of traction systems is important for the safety operation of high-speed trains.Long-term operation of the trains will degrade the performance of systems,which decreases the fault detection accuracy.To solve this problem,this paper proposes a fault detection method developed by a Generalized Autoencoder(GAE)for systems with performance degradation.The advantage of this method is that it can accurately detect faults when the traction system of high-speed trains is affected by performance degradation.Regardless of the probability distribution,it can handle any data,and the GAE has extremely high sensitivity in anomaly detection.Finally,the effectiveness of this method is verified through the Traction Drive Control System(TDCS)platform.At different performance degradation levels,our method’s experimental results are superior to traditional methods.

Recent Advances in the Rapid Detection and Performance Evaluation Methods of Detergent Additives for Gasoline

Although detergent additives for gasoline have been widely commercialized,their formulas are often kept confidential and there is still no standardized method for quickly detecting the main active ingredients and evaluating their effectiveness,which makes their regulation difficult.An overview of the current state of the development and application of detergent additives for gasoline in China and other regions,as well as a review of the rapid detection and performance evaluation methods available for analyzing detergent additives are given herein.The review focuses on the convenience,cost,efficiency,and feasibility of on-site detection and the evaluation of various methods,and also looks into future research directions,such as detecting and evaluating detergent additives in ethanol gasoline and with advanced engine technologies.

Design and prototyping of the readout electronics for the transition radiation detector in the high energy cosmic radiation detection facility

The high energy cosmic-radiation detection(HERD)facility is planned to launch in 2027 and scheduled to be installed on the China Space Station.It serves as a dark matter particle detector,a cosmic ray instrument,and an observatory for high-energy gamma rays.A transition radiation detector placed on one of its lateral sides serves dual purpose,(ⅰ)calibrating HERD's electromagnetic calorimeter in the TeV energy range,and(ⅱ)serving as an independent detector for high-energy gamma rays.In this paper,the prototype readout electronics design of the transition radiation detector is demonstrated,which aims to accurately measure the charge of the anodes using the SAMPA application specific integrated circuit chip.The electronic performance of the prototype system is evaluated in terms of noise,linearity,and resolution.Through the presented design,each electronic channel can achieve a dynamic range of 0–100 fC,the RMS noise level not exceeding 0.15 fC,and the integral nonlinearity was<0.2%.To further verify the readout electronic performance,a joint test with the detector was carried out,and the results show that the prototype system can satisfy the requirements of the detector's scientific goals.

Target acquisition performance in the presence of JPEG image compression

This paper presents an investigation on the effect of JPEG compression on the similarity between the target image and the background,where the similarity is further used to determine the degree of clutter in the image.Four new clutter metrics based on image quality assessment are introduced,among which the Haar wavelet-based perceptual similarity index,known as HaarPSI,provides the best target acquisition prediction results.It is shown that the similarity between the target and the background at the boundary between visually lossless and visually lossy compression does not change significantly compared to the case when an uncompressed image is used.In future work,through subjective tests,it is necessary to check whether this presence of compression at the threshold of just noticeable differences will affect the human target acquisition performance.Similarity values are compared with the results of subjective tests of the well-known target Search_2 database,where the degree of agreement between objective and subjective scores,measured through linear correlation,reached a value of 90%.

Detection performance and inversion processing of logging-while-drilling extra-deep azimuthal resistivity measurements

We present systematic investigations on the physics,detection performance and inversion of logging-while-drilling extradeep azimuthal resistivity measurements(EDARM).First,the definitions of EDRAM measurements are discussed,followed by the derivation of the attenuation and phase-shift geometrical factors to illustrate the relative contributions of formation units to the observed signals.Then,a new definition of detection depth,which considers the uncertainty of inversion results caused by the data noise,is proposed to quantify the detection capability of ED ARM.Finally,the B ayesian theory associated with Markov chain Monte Carlo sampling is introduced for fast processing of EDARM data.Numerical results show that ED ARM is capable of detecting the azimuth and distance of remote bed boundaries,and the detection capability increases with increasing spacing and resistivity contrast.The EDARM tool can accommodate a large range of formation resistivity and is able to provide the resistivity anisotropy at arbitrary relative dipping angles.In addition,multiple bed boundaries and reservoir images near the borehole are readily obtained by using the Bayesian inversion.

Cued search algorithm with uncertain detection performance for phased array radars

A cued search algorithm with uncertain detection performance is proposed for phased array radars. Firstly, a target search model based on the information gain criterion is presented with known detection performance, and the statistical characteristic of the detection probability is calculated by using the fluctuant model of the target radar cross section （RCS）. Secondly, when the detection probability is completely unknown, its probability density function is modeled with a beta distribution, and its posterior probability distribution with the radar observation is derived based on the Bayesian theory. Finally simulation results show that the cued search algorithm with a known RCS fluctuant model can achieve the best performance, and the algorithm with the detection probability modeled as a beta distribution is better than that with a random selected detection probability because the model parameters can be updated by the radar observation to approach to the real value of the detection probability.

Rapid Determination of Dopamine and Its Metabolites During in vivo Cerebral Microdialysis by Routine High Performance Liquid Chromatography With Electrochemical Detection

To determine dopamine and its metabolites during in vivo cerebral microdialysis by routine high performance liquid chromatography with electrochemical detection. Methods Microdialysis probes were placed into the right striatum of Wistar rat brains and perfused with Ringer＇s solution at a rate of 1.5 pL/min. A reverse phase HPLC with electrochemistry was used to assay DA, DOPAC, and HVA after cerebral microdialysates were collected every 20 minutes from awake and freely moving rats. In order to identify the reliability of this method, its selectivity, linear range, precision and accuracy were tested and the contents of DA, DOPAC, and HVA in rat microdialysates were determined. Results The standard curve was in good linear at the concentration ranging from 74 nmol/L to 1.5 pmol/L for DOPAC （r^2= 0.9996）, from 66 nmol/L to 1.3 gmol/L for DA （r^2=l.0000） and from 69 nmol/L to 1.4 pmol/L for HVA （r^2=0.9992）. The recovery of DOPAC （0.30, 0.77, 1.49 gmol/L）, DA （0,26, 0.69, 1.32 gmol/L）, and HVA （0.27, 0.71, 1.37 gmol/L） was 82.00±1.70%, 104.00±4.00%, 98.70±3.10%; 92.30± 1.50%, 105.30±2.30%, 108.00±2.00%; 80.00±7.80%, 107.69±8.00%, and 108.66±3.10%, respectively at each concentration. Their intra-day RSD was 3.3%, 3.4%, and 2.5%, and inter-day RSD was 4.2%, 2.3%, and 5.6%, respectively. The mean extracellular concentrations of DOPAC, DA, and HVA in rat brain microdialysates were 10.7, 2.4, and 9.2 gmol/L （n=6）, respectively. Conclusion The findings of our study suggested that the simple, accurate and stable method can be applied to basic researches of diseases related to monoamines neurotransmitters by cerebral microdialysis in rats.

Determination of neomycin in water samples by high performance anion chromatography with pulsed amperometric detection

A simple, fast and reliable method, using high performance anion chromatography with pulsed amperometric detection, had been developed for the analysis of neomycin in water samples. The elution and separation were carried out with an isocratic mobile phase, containing 10 mmol/L NaOH. The influence of the concentration and pH of the mobile phase on the separation and detection was investigated. A quadruple-potential waveform used for the detection was optimized. The detection limit of neomycin was down to 0.027 μg/mL. The linearity of neomycin calibration curve ranged from 0.050 to 0.505 μg/mL with correlation coefficient of 0.9997. R.S.D. (n = 11) was 4.0%.

Trace Determination of Tamoxifen in Biological Fluids Using Hollow Fiber Liquid-Phase Microextraction Followed by High-Performance Liquid Chromatography-Ultraviolet Detection

The applicability of hollow fiber liquid-phase microextraction (HF-LPME) combined with high-performance liquid chromatography-ultraviolet detection (HPLC-UV) was evaluated for the extraction and determination of tamoxifen (TAM) in biological fluids including human urine and plasma. The drug was extracted from a 15 mL aqueous sample (source phase;SP) into an organic phase impregnated in the pores of the hollow fiber (membrane phase;MP) followed by the back-extraction into a second aqueous solution (receiving phase;RP) located in the lumen of the hollow fiber. The effects of several factors such as the nature of organic solvent, compositions of SP and RP solutions, extraction time, ionic strength and stirring rate on the extraction efficiency were examined and optimized. An enrichment factor of 360 along with substantial sample clean up was obtained under the optimized conditions. The calibration curve showed linearity in the range of 1 - 500 ng?mL–1 and the limit of detection was found to be 0.5 ng?mL–1 in aqueous medium. A reasonable relative recovery (≥89%) and satisfactory intra-assay (3.7% - 4.2%, n = 3) and inter-assay (7.5% - 7.8%, n = 3) precision illustrated good performance of the analytical procedure in spiked human urine and plasma samples.

MCMOD:The Multi-Category Large-Scale Dataset for Maritime Object Detection

The marine environment is becoming increasingly complex due tothe various marine vehicles,and the diversity of maritime objects poses a challengeto marine environmental governance.Maritime object detection technologyplays an important role in this segment.In the field of computer vision,there is no sufficiently comprehensive public dataset for maritime objects inthe contrast to the automotive application domain.The existing maritimedatasets either have no bounding boxes(which are made for object classification)or cover limited varieties of maritime objects.To fulfil the vacancy,this paper proposed the Multi-Category Large-Scale Dataset for MaritimeObject Detection(MCMOD)which is collected by 3 onshore video camerasthat capture data under various environmental conditions such as fog,rain,evening,etc.The whole dataset consists of 16,166 labelled images alongwith 98,590 maritime objects which are classified into 10 classes.Comparedwith the existing maritime datasets,MCMOD contains a relatively balancedquantity of objects of different sizes(in the view).To evaluate MCMOD,this paper applied several state-of-the-art object detection approaches fromcomputer vision research on it and compared their performances.Moreover,a comparison between MCMOD and an existing maritime dataset was conducted.Experimental results indicate that the proposed dataset classifies moretypes of maritime objects and covers more small-scale objects,which canfacilitate the trained detectors to recognize more types of maritime objects anddetect maritime objects over a relatively long distance.The obtained resultsalso showthat the adopted approaches need to be further improved to enhancetheir capabilities in the maritime domain.

High resolution radar target adaptive detector and performance assessment

The high resolution radar target detection is addressed in the non-Gaussian clutter. An adaptive detector is derived for range-spread target based on a novel covariance matrix estimator. It is proved that the new detector is constant false alarm rate （CFAR） to both of the clutter covariance matrix structure and power level theoretically for match cases. The simulation results show that the new detector is almost CFAR for mismatch cases, and it outperforms the existing adaptive detector based on the sample covariance matrix. It also shows that the detection performance improves, as the number of pulses, the number of secondary data or the clutter spike increases. In addition, the derived detector is robust to different subsets, estimated clutter group sizes and correlations of clutter. Importantly, the number of iterations for practical application is just one.

Efficacy of ctDNA methylation combined with traditional detection modality to detect liver cancer among high-risk patients:A multicenter diagnostic trial

Objective:Circulating tumor DNA(ctDNA)and alpha-fetoprotein(AFP)plus ultrasound(US)have been considered to have high diagnostic accuracy for cancer detection,however,the efficacy of ctDNA methylation combined with the traditional detection modality of liver cancer has not been tested in a Chinese independent cohort.Methods:The high-risk individuals aged between 35 and 70 years who were diagnosed with liver cirrhosis or had moderate and severe fatty liver were eligible for inclusion.All participants were invited to receive a traditional examination[referring to AFP plus US],and ctDNA methylation,respectively.The sensitivity and specificity of different diagnostic tools were calculated.The logistic regression model was applied to estimate the area under the curve(AUC),which was further validated by 10-fold internal cross-validation.Results:A total of 1,205 individuals were recruited in our study,and 39 participants were diagnosed with liver cancer.The sensitivity of AFP,US,US plus AFP,and the combination of US,AFP,and ctDNA methylation was33.33%,56.41%,66.67%,and 87.18%,respectively.The corresponding specificity of AFP,US,US plus AFP,and the combination of all modalities was 98.20%,99.31%,97.68%,and 97.68%,respectively.The AUCs of AFP,US,US plus AFP,and the combination of AFP,US,and ctDNA methylation were 65.77%,77.86%,82.18%,and92.43%,respectively.The internally validated AUCs of AFP,US,US plus AFP,and the combination of AFP,US,and ctDNA methylation were 67.57%,83.26%,86.54%,and 93.35%,respectively.Conclusions:The ctDNA methylation is a good complementary to AFP and US for the detection of liver cancer.

Evolutionary Algorithm Based Feature Subset Selection for Students Academic Performance Analysis

Educational Data Mining(EDM)is an emergent discipline that concen-trates on the design of self-learning and adaptive approaches.Higher education institutions have started to utilize analytical tools to improve students’grades and retention.Prediction of students’performance is a difficult process owing to the massive quantity of educational data.Therefore,Artificial Intelligence(AI)techniques can be used for educational data mining in a big data environ-ment.At the same time,in EDM,the feature selection process becomes necessary in creation of feature subsets.Since the feature selection performance affects the predictive performance of any model,it is important to elaborately investigate the outcome of students’performance model related to the feature selection techni-ques.With this motivation,this paper presents a new Metaheuristic Optimiza-tion-based Feature Subset Selection with an Optimal Deep Learning model(MOFSS-ODL)for predicting students’performance.In addition,the proposed model uses an isolation forest-based outlier detection approach to eliminate the existence of outliers.Besides,the Chaotic Monarch Butterfly Optimization Algo-rithm(CBOA)is used for the selection of highly related features with low com-plexity and high performance.Then,a sailfish optimizer with stacked sparse autoencoder(SFO-SSAE)approach is utilized for the classification of educational data.The MOFSS-ODL model is tested against a benchmark student’s perfor-mance data set from the UCI repository.A wide-ranging simulation analysis por-trayed the improved predictive performance of the MOFSS-ODL technique over recent approaches in terms of different measures.Compared to other methods,experimental results prove that the proposed(MOFSS-ODL)classification model does a great job of predicting students’academic progress,with an accuracy of 96.49%.

Intrusion Detection Using Federated Learning for Computing

The integration of clusters,grids,clouds,edges and other computing platforms result in contemporary technology of jungle computing.This novel technique has the aptitude to tackle high performance computation systems and it manages the usage of all computing platforms at a time.Federated learning is a collaborative machine learning approach without centralized training data.The proposed system effectively detects the intrusion attack without human intervention and subsequently detects anomalous deviations in device communication behavior,potentially caused by malicious adversaries and it can emerge with new and unknown attacks.The main objective is to learn overall behavior of an intruder while performing attacks to the assumed target service.Moreover,the updated system model is send to the centralized server in jungle computing,to detect their pattern.Federated learning greatly helps the machine to study the type of attack from each device and this technique paves a way to complete dominion over all malicious behaviors.In our proposed work,we have implemented an intrusion detection system that has high accuracy,low False Positive Rate(FPR)scalable,and versatile for the jungle computing environment.The execution time taken to complete a round is less than two seconds,with an accuracy rate of 96%.

Automatic detection and assessment of crack development in ultra-high performance concrete in the spatial and Fourier domains

Automatic detection and assessment of surface cracks are beneficial for understanding the mechanical performance of ultra-high performance concrete(UHPC).This study detects crack evolution using a novel dynamic mode decomposition(DMD)method.In this method,the sparse matrix‘determined’from images is used to reconstruct the foreground that contains cracks,and the global threshold method is adopted to extract the crack patterns.The application of the DMD method to the three-point bending test demonstrates the efficiency in inspecting cracks with high accuracy.Accordingly,the geometric features,including the area and its projection in two major directions,are evaluated over time.The relationship between the geometric properties of cracks and load-displacement curves of UHPC is discussed.Due to the irregular shape of cracks in the spatial domain,the cracks are then transformed into the Fourier domain to assess their development.Results indicate that crack patterns in the Fourier domain exhibit a distinct concentration around a central position.Moreover,the power spectral density of cracks exhibits an increasing trend over time.The investigation into crack evolution in both the spatial and Fourier domains contributes significantly to elucidating the mechanical behavior of UHPC.

Heterogeneous performance analysis of the new model of CFAR detectors for partially-correlated χ~2-targets

To mitigate the deleterious effects of clutter and jammer, modern radars have adopted adaptive processing techniques such as constant false alarm rate(CFAR) detectors which are widely used to prevent clutter and noise interference from saturating the radar’s display and preventing targets from being obscured.This paper concerns with the detection analysis of the novel version of CFAR schemes(cell-averaging generalized trimmed-mean,CATM) in the presence of additional outlying targets other than the target under research. The spurious targets as well as the tested one are assumed to be fluctuating in accordance with the χ~2-model with two-degrees of freedom. In this situation, the processor performance is enclosed by the swerling models(SWI and SWII). Between these bounds, there is an important class of target fluctuation which is known as moderately fluctuating targets. The detection of this class has many practical applications. Structure of the CATM detector is described briefly. Detection performances for optimal, CAM, CA, trimmed-mean(TM) and ordered-statistic(OS) CFAR strategies have been analyzed and compared for desired probability of false alarm and determined size of the reference window. False alarm rate performance of these processors has been evaluated for different strengths of interfering signal and the effect of correlation among the target returns on the detection and false alarm performances has also been studied. Our numerical results show that, with a proper choice of trimming parameters,the novel model CAM presents an ideal detection performance outweighing that of the Neyman-Pearson detector on condition that the tested target obeys the SWII model in its fluctuation. Although the new models CAS and CAM can be treated as special cases of the CATM algorithm, their multi-target performance is modest even it has an enhancement relative to that of the classical CAcheme. Additionally, they fail to maintain the false alarm rate constant when the operating environment is of type target multiplicity. Moreover, the non-coherent integration of M pulses ameliorates the processor performance either it operates in homogeneous or multi-target environment.

A high performance waveform and a new ranging method for the proximity detector

Signal modulation is an essential design factor for proximity detectors and directly affects the system's potential performance.In order to achieve the advantages of chaotic codes bi-phase modulation(CCBPM)and linear frequency modulation(LFM) simultaneously,this paper designed a waveform which combined chaotic codes bi-phase modulation and linear frequency modulation(CCBPM-LFM) for proximity detectors.The CCBPM-LFM waveform was analyzed in the aspect of time delay resolution(TDR) and Doppler tolerance(DT) based on ambiguity function(AF).Then,a ranging method,which we called instant correlation harmonic demodulation(ICHD),was presented for the detector using the CCBPM-LFM waveform.By combining time domain instant correlation with harmonic demodulation,the ICHD solved the problem caused by combination modulation and made the most of the linear frequency modulation(LFM) harmonics and the correlation of chaotic codes.Finally,a prototype was implemented and ranging experiments were carried out.From the theoretical analysis and experimental results,the proximity detector used the CCBPM-LFM waveform has an outstanding detection performance.

A Method of Peak Detecting for Nanosecond Pulse in Optical Performance Monitoring System

In optical performance monitoring system,the analog to digital converter is needed to detect the peak of nanosecond pulse and get the signal envelope.A scheme based on a designed anti-aliasing filter and analog to digital converter is proposed to broaden the nanosecond pulse and make it easier for the analog to digital converter to catch the peak of the nanosecond pulse.The experimental results demonstrate that,with the proposed scheme,the optical performance system needs less time to get the recovered eye-diagram of high speed optical data signal,and is robust to phase mismatch in the analog to digital converter circuit.

Novel method for the determination of five carbamate pesticides in water samples by dispersive liquid-liquid microextraction combined with high performance liquid chromatography

A novel method for the determination of five carbamate pesticides （metolcarb, carbofuran, carbaryl, isoprocard and diethofencard） in water samples was developed by dispersive liquid-liquid microextraction （DLLME） coupled with high performance liquid chromatography-diode array detector （HPLC-DAD）. Some experimental parameters that influence the extraction efficiency were studied and optimized to obtain the best extraction results. Under the optimum conditions for the method, the calibration curve was linear in the concentration range from 5 to 1000 ng mL^-1 for all the five carbamate pesticides, with the correlation coefficients （r^2） varying from 0.9984 to 0.9994. Good enrichment factors were achieved ranging from 80 to 177- fold, depending on the compound. The limits of detection （LODs） （S/N = 3） were ranged from 0.1 to 0.5 ng mL^-1. The method has been successfully applied to the analysis of the pesticide residues in environmental water samples.