Research on Enhanced Contraband Dataset ACXray Based on ETL

To address the shortage of public datasets for customs X-ray images of contraband and the difficulties in deploying trained models in engineering applications,a method has been proposed that employs the Extract-Transform-Load(ETL)approach to create an X-ray dataset of contraband items.Initially,X-ray scatter image data is collected and cleaned.Using Kafka message queues and the Elasticsearch(ES)distributed search engine,the data is transmitted in real-time to cloud servers.Subsequently,contraband data is annotated using a combination of neural networks and manual methods to improve annotation efficiency and implemented mean hash algorithm for quick image retrieval.The method of integrating targets with backgrounds has enhanced the X-ray contraband image data,increasing the number of positive samples.Finally,an Airport Customs X-ray dataset(ACXray)compatible with customs business scenarios has been constructed,featuring an increased number of positive contraband samples.Experimental tests using three datasets to train the Mask Region-based Convolutional Neural Network(Mask R-CNN)algorithm and tested on 400 real customs images revealed that the recognition accuracy of algorithms trained with Security Inspection X-ray(SIXray)and Occluded Prohibited Items X-ray(OPIXray)decreased by 16.3%and 15.1%,respectively,while the ACXray dataset trained algorithm’s accuracy was almost unaffected.This indicates that the ACXray dataset-trained algorithm possesses strong generalization capabilities and is more suitable for customs detection scenarios.

CRYSTAL STRUCTURE AND X-RAY POWDER DIFFRACTION DATA FOR RE COMPOUND HoNiSb

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Rietveld Refinement and X-ray Powder Diffraction Data of GdAlSi

The X-ray powder diffraction data of the compound GdAlSi was studied by means of X-ray diffraction technique and refined by Rietveld method. The compound GdAlSi has tetragonal α-ThSi_2-type structure, space group I4_1/amd (No.141), Z=4, the lattice parameters a=041234 (1) nm, c=1.44202(1) nm. The Smith and Snyder figure of merit [5] F_N is F_ 30=2521(36). The R-factors of Rietveld refinement are R_p=0.098 and R_ wp=0.128. The X-ray powder diffraction data are given. The field dependence of the magnetization measured at room temperature and the temperature variation of the inverse magnetic susceptibility of the compound GdAlSi were also presented.

DATA PROCESSING OF UNIVERSAL QUANTITATIVE METHOD IN STANDARDLESS X-RAY PHASE ANALYSIS

Accuracy of coeffcient A_(isp) is related to the reference phase chosen during analysis. The cri- terion of choosing reference phase which may minimize the error of A_(isp) was deduced. The optimum results could be obtained by using the method of least squares if the number of sam- pies for analysis is more than the phase in samples. The procedure presented here is satisfacto- ryfor ordinary phase analysis.

Observation and Analysis of VLF Nocturnal Multimode Interference Phenomenon based on Waveguide Mode Theory

Very low frequency(VLF)signals are propagated between the ground-ionosphere.Multimode interference will cause the phase to show oscillatory changes with distance while propagating at night,leading to abnormalities in the received VLF signal.This study uses the VLF signal received in Qingdao City,Shandong Province,from the Russian Alpha navigation system to explore the multimode interference problem of VLF signal propagation.The characteristics of the effect of multimode interference phenomena on the phase are analyzed according to the variation of the phase of the VLF signal.However,the phase of VLF signals will also be affected by the X-ray and energetic particles that are released during the eruption of solar flares,therefore the two phenomena are studied in this work.It is concluded that the X-ray will not affect the phase of VLF signals at night,but the energetic particles will affect the phase change,and the influence of energetic particles should be excluded in the study of multimode interference phenomena.Using VLF signals for navigation positioning in degraded or unavailable GPS conditions is of great practical significance for VLF navigation systems as it can avoid the influence of multimode interference and improve positioning accuracy.

Inverse Calculation and Regularization Process for the Solar Aspect System(SAS) of HXI Payload on ASO-S Spacecraft

For the ASO-S/HXI payload, the accuracy of the flare reconstruction is reliant on important factors such as the alignment of the dual grating and the precise measurement of observation orientation. To guarantee optimal functionality of the instrument throughout its life cycle, the Solar Aspect System (SAS) is imperative to ensure that measurements are accurate and reliable. This is achieved by capturing the target motion and utilizing a physical model-based inversion algorithm. However, the SAS optical system’s inversion model is a typical ill-posed inverse problem due to its optical parameters, which results in small target sampling errors triggering unacceptable shifts in the solution. To enhance inversion accuracy and make it more robust against observation errors, we suggest dividing the inversion operation into two stages based on the SAS spot motion model. First, the as-rigid-aspossible (ARAP) transformation algorithm calculates the relative rotations and an intermediate variable between the substrates. Second, we solve an inversion linear equation for the relative translation of the substrates, the offset of the optical axes, and the observation orientation. To address the ill-posed challenge, the Tikhonov method grounded on the discrepancy criterion and the maximum a posteriori (MAP) method founded on the Bayesian framework are utilized. The simulation results exhibit that the ARAP method achieves a solution with a rotational error of roughly±3 5 (1/2-quantile);both regularization techniques are successful in enhancing the stability of the solution, the variance of error in the MAP method is even smaller—it achieves a translational error of approximately±18μm (1/2-quantile) in comparison to the Tikhonov method’s error of around±24μm (1/2-quantile). Furthermore, the SAS practical application data indicates the method’s usability in this study. Lastly, this paper discusses the intrinsic interconnections between the regularization methods.

The diagnostic rules of peripheral lung cancer preliminary study based on data mining technique

Objective： To discuss the clinical and imaging diagnostic rules of peripheral lung cancer by data mining technique, and to explore new ideas in the diagnosis of peripheral lung cancer, and to obtain early-stage technology and knowledge support of computer-aided detecting （CAD）. Methods： 58 cases of peripheral lung cancer confirmed by clinical pathology were collected. The data were imported into the database after the standardization of the clinical and CT findings attributes were identified. The data was studied comparatively based on Association Rules （AR） of the knowledge discovery process and the Rough Set （RS） reduction algorithm and Genetic Algorithm（GA） of the generic data analysis tool （ROSETTA）, respectively. Results： The genetic classification algorithm of ROSETTA generates 5 000 or so diagnosis rules. The RS reduction algorithm of Johnson＇s Algorithm generates 51 diagnosis rules and the AR algorithm generates 123 diagnosis rules. Three data mining methods basically consider gender, age, cough, location, lobulation sign, shape, ground-glass density attributes as the main basis for the diagnosis of peripheral lung cancer. Conclusion： These diagnosis rules for peripheral lung cancer with three data mining technology is same as clinical diagnostic rules, and these rules also can be used to build the knowledge base of expert system. This study demonstrated the potential values of data mining technology in clinical imaging diagnosis and differential diagnosis.

A simulation package—SASAL for X-ray and EUV spectroscopy of astrophysical and laboratory plasmas

For the laboratory astrophysics community, those spectroscopic modeling codes extensively used in astronomy, e.g. Chianti, AtomDB, Cloudy and Xstar, cannot be directly applied to analyzing laboratory measurements due to their discrepancies from astrophysical cases. For example, plasma from an electron beam ion trap has an electron energy distribution that follows a Gaussian profile, instead of a Maxwellian one. The laboratory miniature for a compact object produced by a laser-driven implo- sion shows a departure from equilibrium, that often occurs in celestial objects, so we setup a spectral analysis system for astrophysical and laboratory （SASAL） plasmas to act as a bridge between them, which benefits the laboratory astrophysical community.

X-RAY POWDER DIFFRACTION STUDY FOR RE COMPOUND GdNiSn

The compound GdNiSn has been studied by X-ray powder diffraction technique.The crystal structure and the X-ray diffraction data for this compound at room temperature are reported.The compound GdNiSn is orthorhombic with lattice parameters a=7.2044(1)A,b=7.6895(6)A,c=4.4772(4)A,space group Pna2_(1) and 4 formula units of GdNiSn in unit cell.The Smith and Snyder figure of index F_(30) for this compound is 35(0.015,59).

Discrimination of background events in the PolarLight X-ray polarimeter

PolarLight is a space-borne X-ray polarimeter that measures the X-ray polarization via electron tracking in an ionization chamber.It is a collimated instrument and thus suffers from the background on the whole detector plane.The majority of background events are induced by high energy charged particles and show ionization morphologies distinct from those produced by X-rays of interest.Comparing on-source and off-source observations,we find that the two datasets display different distributions on image properties.The boundaries between the source and background distributions are obtained and can be used for background discrimination.Such a means can remove over 70%of the background events measured with PolarLight.This approaches the theoretical upper limit of the background fraction that is removable and justifies its effectiveness.For observations with the Crab nebula,the background contamination decreases from 25%to 8%after discrimination,indicative of a polarimetric sensitivity of around 0.2 Crab for PolarLight.This work also provides insights into future X-ray polarimetric telescopes.

Development of the Data Processing and Analysis System Framework for ICF Experiments

An idea is presented about the development of a data processing and analysis system for ICF experiments, which is based on an object oriented framework. The design and preliminary implementation of the data processing and analysis framework based on the ROOT system have been completed. Software for unfolding soft X-ray spectra has been developed to test the functions of this framework.

Real-Time Anomaly Detection in Packaged Food X-Ray Images Using Supervised Learning

Physical contamination of food occurs when it comes into contact with foreign objects.Foreign objects can be introduced to food at any time during food delivery and packaging and can cause serious concerns such as broken teeth or choking.Therefore,a preventive method that can detect and remove foreign objects in advance is required.Several studies have attempted to detect defective products using deep learning networks.Because it is difficult to obtain foreign object-containing food data from industry,most studies on industrial anomaly detection have used unsupervised learning methods.This paper proposes a new method for real-time anomaly detection in packaged food products using a supervised learning network.In this study,a realistic X-ray image training dataset was constructed by augmenting foreign objects with normal product images in a cut-paste manner.Based on the augmented training dataset,we trained YOLOv4,a real-time object detection network,and detected foreign objects in the test data.We evaluated this method on images of pasta,snacks,pistachios,and red beans under the same conditions.The results show that the normal and defective products were classified with an accuracy of at least 94%for all packaged foods.For detecting foreign objects that are typically difficult to detect using the unsupervised learning and traditional methods,the proposed method achieved high-performance realtime anomaly detection.In addition,to eliminate the loss in high-resolution X-ray images,the false positive rate and accuracy could be lowered to 5%with patch-based training and a new post-processing algorithm.

Measuring the orbital periods of low mass X-ray binaries in the X-ray band

A low mass X-ray binary （LMXB） contains either a neutron star or a black hole accreting materials from its low mass companion star. It is one of the primary astrophysical sources for studying stellar-mass compact objects and accreting phe- nomena. As with other binary systems, the most important parameter of an LMXB is the orbital period, which allows us to learn about the nature of the binary system and constrain the properties of the system＇s components, including the compact ob- ject. As a result, measuring the orbital periods of LMXBs is essential for investigating these systems even though fewer than half of them have known orbital periods. This article introduces the different methods for measuring the orbital periods in the X-ray band and reviews their application to various types of LMXBs, such as eclipsing and dipping sources, as well as pulsar LMXBs.

Multi-Label Chest X-Ray Classification via Deep Learning

In this era of pandemic, the future of healthcare industry has never been more exciting. Artificial intelligence and machine learning (AI & ML) present opportunities to develop solutions that cater for very specific needs within the industry. Deep learning in healthcare had become incredibly powerful for supporting clinics and in transforming patient care in general. Deep learning is increasingly being applied for the detection of clinically important features in the images beyond what can be perceived by the naked human eye. Chest X-ray images are one of the most common clinical method for diagnosing a number of diseases such as pneumonia, lung cancer and many other abnormalities like lesions and fractures. Proper diagnosis of a disease from X-ray images is often challenging task for even expert radiologists and there is a growing need for computerized support systems due to the large amount of information encoded in X-Ray images. The goal of this paper is to develop a lightweight solution to detect 14 different chest conditions from an X ray image. Given an X-ray image as input, our classifier outputs a label vector indicating which of 14 disease classes does the image fall into. Along with the image features, we are also going to use non-image features available in the data such as X-ray view type, age, gender etc. The original study conducted Stanford ML Group is our base line. Original study focuses on predicting 5 diseases. Our aim is to improve upon previous work, expand prediction to 14 diseases and provide insight for future chest radiography research.

Efficient Grad-Cam-Based Model for COVID-19 Classification and Detection

Corona Virus(COVID-19)is a novel virus that crossed an animal-human barrier and emerged in Wuhan,China.Until now it has affected more than 119 million people.Detection of COVID-19 is a critical task and due to a large number of patients,a shortage of doctors has occurred for its detection.In this paper,a model has been suggested that not only detects the COVID-19 using X-ray and CT-Scan images but also shows the affected areas.Three classes have been defined;COVID-19,normal,and Pneumonia for X-ray images.For CT-Scan images,2 classes have been defined COVID-19 and non-COVID-19.For classi-fication purposes,pretrained models like ResNet50,VGG-16,and VGG19 have been used with some tuning.For detecting the affected areas Gradient-weighted Class Activation Mapping(GradCam)has been used.As the X-rays and ct images are taken at different intensities,so the contrast limited adaptive histogram equalization(CLAHE)has been applied to see the effect on the training of the models.As a result of these experiments,we achieved a maximum validation accuracy of 88.10%with a training accuracy of 88.48%for CT-Scan images using the ResNet50 model.While for X-ray images we achieved a maximum validation accuracy of 97.31%with a training accuracy of 95.64%using the VGG16 model.

Automatic Defect Detection in X-Ray Images Using Image Data Fusion

Automatic defect detection in X-ray images is currently a focus of much research at home and abroad. The technology requires computerized image processing, image analysis, and pattern recognition. This paper describes an image processing method for automatic defect detection using image data fusion which synthesizes several methods including edge extraction, wave profile analyses, segmentation with dynamic threshold, and weld district extraction. Test results show that defects that induce an abrupt change over a predefined extent of the image intensity can be segmented regardless of the number, location, shape or size. Thus, the method is more robust and practical than the current methods using only one method.

Timescale Analysis of Spectral Lags

A technique for timescale analysis of spectral lags performed directly in the time domain is developed. Simulation studies are made to compare the time domain technique with the Fourier frequency analysis for spectral time lags. The time domain technique is applied to studying rapid variabilities of X-ray binaries and γ-ray bursts. The results indicate that in comparison with the Fourier analysis the timescale analysis technique is more powerful for the study of spectral lags in rapid variabilities on short time scales and short duration flaring phenomena.

Crystal structural refinement for NdAlSi

The compound NdAlSi was studied using X-ray powder diffraction technique and refined by the Rietveld method. The compound NdAlSi has tetragonal α-ThSiE-type structure, space group I41/amd （No. 141）, Z = 4, and the lattice parameters a = 0.41991（1） nm, c = 1.44916（3） nm. The Smith and Snyder figure of merit FN is F30= 103.1（36）. The R-factors of Rietveld refinement are Rp= 0.113 and Rwp= 0.148, respectively. The X-ray powder diffraction data is presented in this article.

5′-Hydroxyzearalenol,a new β-resorcylic macrolide from Fusarium sp. 05ABR26

A new β-resorcylic macrolide, 5＇-hydroxyzearalenol （1）, was isolated from the culture broth of a marine-derived fungus Fusarium sp. 05ABR26. Three known compounds, zearalenone （2）, 8＇-hydroxyzearalenone （3） and zearalenol （4） were also isolated. The structure and relative stereochemistry of 1 were elucidated on the basis of spectroscopic data and single-crystal X-ray diffraction data. Compound 2 displayed potent inhibitory activity against Pyricularia oryzae with a MIC value of 6.25 μg/mL, while compound 3 was much less active; however, 1 and 4 showed no obvious activity.

An unusual timing and spectral state of a black hole microquasar XTE J1550-564

An unusual timing and spectral state of a black hole microquasar XTE J1550- 564 observed with RXTE is analyzed. Millisecond variabilities are found, which are significantly shorter than the minimum possible time scale in the light curves of black hole binaries, as suggested by Sunyaev ＆ Revnivtsev （2000）. The X-ray spectral fitting result indicates that there is an unusual soft component in the spectrum, which may be responsible for the millisecond variabilities. The millisecond variabilities as well as the unusual soft spectral component should be produced from some small, but independent active regions in the accretion disk.