Detection of Turbulence Anomalies Using a Symbolic Classifier Algorithm in Airborne Quick Access Record(QAR)Data Analysis

As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry,it has become imperative to monitor and mitigate these threats to ensure civil aviation safety.The eddy dissipation rate(EDR)has been established as the standard metric for quantifying turbulence in civil aviation.This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder(QAR)data.The detection of atmospheric turbulence is approached as an anomaly detection problem.Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events.Moreover,comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available.In summary,the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data,comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms.This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards.

Data Masking for Chinese Electronic Medical Records with Named Entity Recognition

With the rapid development of information technology,the electronifi-cation of medical records has gradually become a trend.In China,the population base is huge and the supporting medical institutions are numerous,so this reality drives the conversion of paper medical records to electronic medical records.Electronic medical records are the basis for establishing a smart hospital and an important guarantee for achieving medical intelligence,and the massive amount of electronic medical record data is also an important data set for conducting research in the medical field.However,electronic medical records contain a large amount of private patient information,which must be desensitized before they are used as open resources.Therefore,to solve the above problems,data masking for Chinese electronic medical records with named entity recognition is proposed in this paper.Firstly,the text is vectorized to satisfy the required format of the model input.Secondly,since the input sentences may have a long or short length and the relationship between sentences in context is not negligible.To this end,a neural network model for named entity recognition based on bidirectional long short-term memory(BiLSTM)with conditional random fields(CRF)is constructed.Finally,the data masking operation is performed based on the named entity recog-nition results,mainly using regular expression filtering encryption and principal component analysis(PCA)word vector compression and replacement.In addi-tion,comparison experiments with the hidden markov model(HMM)model,LSTM-CRF model,and BiLSTM model are conducted in this paper.The experi-mental results show that the method used in this paper achieves 92.72%Accuracy,92.30%Recall,and 92.51%F1_score,which has higher accuracy compared with other models.

Medi-Block Record Secure Data Sharing in Healthcare System:Issues,Solutions and Challenges

With the advancements in the era of artificial intelligence,blockchain,cloud computing,and big data,there is a need for secure,decentralized medical record storage and retrieval systems.While cloud storage solves storage issues,it is challenging to realize secure sharing of records over the network.Medi-block record in the healthcare system has brought a new digitalization method for patients’medical records.This centralized technology provides a symmetrical process between the hospital and doctors when patients urgently need to go to a different or nearby hospital.It enables electronic medical records to be available with the correct authentication and restricts access to medical data retrieval.Medi-block record is the consumer-centered healthcare data system that brings reliable and transparent datasets for the medical record.This study presents an extensive review of proposed solutions aiming to protect the privacy and integrity of medical data by securing data sharing for Medi-block records.It also aims to propose a comprehensive investigation of the recent advances in different methods of securing data sharing,such as using Blockchain technology,Access Control,Privacy-Preserving,Proxy Re-Encryption,and Service-On-Chain approach.Finally,we highlight the open issues and identify the challenges regarding secure data sharing for Medi-block records in the healthcare systems.

High-speed broadband data acquisition system based on FPGA

A field-programmable gate array(FPGA)based high-speed broadband data acquisition system is designed.The system has a dual channel simultaneous acquisition function.The maximum sampling rate is 500 MSa/s and bandwidth is200 MHz,which solves the large bandwidth,high-speed signal acquisition and processing problems.At present,the data acquisition system is successfully used in broadband receiver test systems.

If real-time route and high-speed data is important,GoTa is vital.

GoTaTM from ZTE is the world’s first CDMA-based system. Now, ZTE proudly introduces its third-generation digital trunking system featuring a centralized dispatch,

If real-time route and high-speed data is important, GoTa is vital.

GoTa TM from ZTE is the world’s first CDMA-based system. Now, ZTE proudly introduces its third-generation digital trunking system featuring

If real-time route and high-speed data is important, GoTa is vital.

Go Tafrom ZTE is the world’s first CDMA-based system. Now, ZTE proudly introduces its third-generation digital trunking system featuring a centralized dispatch,

A power-quality monitoring and assessment system for high-speed railways based on train-network-data center integration

In recent years,with the rapid development of high-speed railways(HSRs),power interruptions or disturbances in traction power supply systems have become increasingly dangerous.However,it is often impossible to detect these faults immediately through single-point monitoring or collecting data after accidents.To coordinate the power quality data of both traction power supply systems(TPSSs)and high-speed trains(HSTs),a monitoring and assessing system is proposed to access the power quality issues on HSRs.By integrating train monitoring,traction substation monitoring and data center,this monitoring system not only realizes the real-time monitoring of operational behaviors for both TPSSs and HSTs,but also conducts a comprehensive assessment of operational quality for train-network systems.Based on a large number of monitoring data,the field measurements show that this real-time monitoring system is effective for monitoring and evaluating a traction-network system.

A Modified Generative Adversarial Network for Fault Diagnosis in High-Speed Train Components with Imbalanced and Heterogeneous Monitoring Data

Data-driven methods are widely considered for fault diagnosis in complex systems.However,in practice,the between-class imbalance due to limited faulty samples may deteriorate their classification performance.To address this issue,synthetic minority methods for enhancing data have been proved to be effective in many applications.Generative adversarial networks(GANs),capable of automatic features extraction,can also be adopted for augmenting the faulty samples.However,the monitoring data of a complex system may include not only continuous signals but also discrete/categorical signals.Since the current GAN methods still have some challenges in handling such heterogeneous monitoring data,a Mixed Dual Discriminator GAN(noted as M-D2GAN)is proposed in this work.In order to render the expanded fault samples more aligned with the real situation and improve the accuracy and robustness of the fault diagnosis model,different types of variables are generated in different ways,including floating-point,integer,categorical,and hierarchical.For effectively considering the class imbalance problem,proper modifications are made to the GAN model,where a normal class discriminator is added.A practical case study concerning the braking system of a high-speed train is carried out to verify the effectiveness of the proposed framework.Compared to the classic GAN,the proposed framework achieves better results with respect to F-measure and G-mean metrics.

An Optimal Feed Interpolator Based on G^2 Continuous Bézier Curves for High-Speed Machining of Linear Tool Path

A numerical control (NC) tool path of digital CAD model is widely generated as a set of short line segments in machining. However, there are three shortcomings in the linear tool path, such as discontinuities of tangency and curvature, huge number of line segments, and short lengths of line segments. These disadvantages hinder the development of high speed machining. To smooth the linear tool path and improve machining efficiency of short line segments, this paper presents an optimal feed interpolator based on G^2 continuous Bézier curves for the linear tool path. First, the areas suitable for fitting are screened out based on the geometric characteristics of continuous short segments (CSSs). CSSs in every area are compressed and fitted into a G^2 Continuous Bézier curve by using the least square method. Then a series of cubic Bézier curves are generated. However, the junction between adjacent Bézier curves is only G^0 continuous. By adjusting the control points and inserting Bézier transition curves between adjacent Bézier curves, the G^2 continuous tool path is constructed. The fitting error is estimated by the second-order Taylor formula. Without iteration, the fitting algorithm can be implemented in real-time environment. Second, the optimal feed interpolator considering the comprehensive constraints (such as the chord error constraint, the maximum normal acceleration, servo capacity of each axis, etc.) is proposed. Simulation and experiment are conducted. The results shows that the proposed method can generate smooth path, decrease the amount of segments and reduce machining time for machining of linear tool path. The proposed research provides an effective method for high-speed machining of complex 2-D/3-D profiles described by short line segments.

Digital Continuity Guarantee Approach of Electronic Record Based on Data Quality Theory

Since the British National Archive put forward the concept of the digital continuity in 2007,several developed countries have worked out their digital continuity action plan.However,the technologies of the digital continuity guarantee are still lacked.At first,this paper analyzes the requirements of digital continuity guarantee for electronic record based on data quality theory,then points out the necessity of data quality guarantee for electronic record.Moreover,we convert the digital continuity guarantee of electronic record to ensure the consistency,completeness and timeliness of electronic record,and construct the first technology framework of the digital continuity guarantee for electronic record.Finally,the temporal functional dependencies technology is utilized to build the first integration method to insure the consistency,completeness and timeliness of electronic record.

Constructing Large Scale Cohort for Clinical Study on Heart Failure with Electronic Health Record in Regional Healthcare Platform:Challenges and Strategies in Data Reuse

Regional healthcare platforms collect clinical data from hospitals in specific areas for the purpose of healthcare management.It is a common requirement to reuse the data for clinical research.However,we have to face challenges like the inconsistence of terminology in electronic health records (EHR) and the complexities in data quality and data formats in regional healthcare platform.In this paper,we propose methodology and process on constructing large scale cohorts which forms the basis of causality and comparative effectiveness relationship in epidemiology.We firstly constructed a Chinese terminology knowledge graph to deal with the diversity of vocabularies on regional platform.Secondly,we built special disease case repositories (i.e.,heart failure repository) that utilize the graph to search the related patients and to normalize the data.Based on the requirements of the clinical research which aimed to explore the effectiveness of taking statin on 180-days readmission in patients with heart failure,we built a large-scale retrospective cohort with 29647 cases of heart failure patients from the heart failure repository.After the propensity score matching,the study group (n=6346) and the control group (n=6346) with parallel clinical characteristics were acquired.Logistic regression analysis showed that taking statins had a negative correlation with 180-days readmission in heart failure patients.This paper presents the workflow and application example of big data mining based on regional EHR data.

HOTCAP:a new software package for high-speed oscilloscopebased three-dimensional bunch charge and position measurement

A software package to be used in high-speed oscilloscope-basedthree-dimensionalbunch-by-bunch charge and position measurement is presented.The software package takes the pick-up electrode signal waveform recorded by the high-speed oscilloscope as input,and it calculates and outputs the bunch-by-bunch charge and position.In addition to enabling a three-dimensional observation of the motion of each passing bunch on all beam position monitor pick-up electrodes,it offers many additional features such as injection analysis,bunch response function reconstruction,and turn-by-turn beam analysis.The software package has an easy-to-understand graphical user interface and convenient interactive operation,which has been verified on the Windows 10 system.

Maintaining Consistency Based on Timely Updating Records List in Workflow Data

In order to settle the problem of workflow data consis-tency under the distributed environment, an invalidation strategy based-on timely updating record list is put forward. The strategy adopting the method of updating the records list and the recovery mechanism of updating message proves the classical invalidation strategy. When the request cycle of duplication is too long, the strategy uses the method of updating the records list to pause for sending updating message; when the long cycle duplication is requested again, it uses the recovery mechanism to resume the updating message. This strategy not only ensures the consistency of the workflow data, but also reduces the unnecessary network traffic. From theoretical comparison with those common strategies, the unnecessary network traffic of this strategy is fewer and more stable. The simulation results validate this conclusion.

Data Processing of Fault Recorder in Power System

This article studies the fault recorder in power system and introduces the Comtrade format. Andituses C++ programming to read recorded fault data and adopts Fourier analysis and symmetrical component method to filter and extract fundamental waves. Finally the effectiveness of the data processing method introduced in this paper is verified by CAAP software.

Statistical delay distribution analysis on high-speed railway trains

The focus of this study is to explore the statis-tical distribution models of high-speed railway (HSR) train delays. Based on actual HSR operational data, the delay causes and their classification, delay frequency, number of affected trains, and space–time delay distributions are discussed. Eleven types of delay events are classified, and a detailed analysis of delay distribution for each classifica-tion is presented. Models of delay probability delay prob-ability distribution for each cause are proposed. Different distribution functions, including the lognormal, exponen-tial, gamma, uniform, logistic, and normal distribution, were selected to estimate and model delay patterns. The most appropriate distribution, which can approximate the delay duration corresponding to each cause, is derived. Subsequently, the Kolmogorov–Smirnov (K–S) test was used to test the goodness of fit of different train delay distribution models and the associated parameter values. The test results show that the distribution of the test data is consistent with that of the selected models. The fitting distribution models show the execution effect of the timetable and help in finding out the potential conflicts in real-time train operations.

A fault prediction method for catenary of high-speed rails based on meteorological conditions

Fault frequency of catenary is related to meteo-rological conditions. In this work, based on the historical data, catenary fault frequency and weather-related fault rate are introduced to analyse the correlation between catenary faults and meteorological conditions, and further the effect of meteorological conditions on catenary oper-ation. Moreover, machine learning is used for catenary fault prediction. As with the single decision tree, only a small number of training samples can be classified cor-rectly by each weak classifier, the AdaBoost algorithm is adopted to adjust the weights of misclassified samples and weak classifiers, and train multiple weak classifiers. Finally, the weak classifiers are combined to construct a strong classifier, with which the final prediction result is obtained. In order to validate the prediction method, an example is provided based on the historical data from a railway bureau of China. The result shows that the mapping relation between meteorological conditions and catenary faults can be established accurately by AdaBoost algorithm. The AdaBoost algorithm can accurately predict a catenary fault if the meteorological conditions are provided.

Polarization readout analysis for multilevel phase change recording by crystallization degree modulation

Four different states of Si15Sb85 and Ge2Sb2Te5 phase change memory thin films are obtained by crystallization degree modulation through laser initialization at different powers or annealing at different temperatures. The polarization characteristics of these two four-level phase change recording media are analyzed systematically. A simple and effective readout scheme is then proposed, and the readout signal is numerically simulated. The results show that a high-contrast polarization readout can be obtained in an extensive wavelength range for the four-level phase change recording media using common phase change materials. This study will help in-depth understanding of the physical mechanisms and provide technical approaches to multilevel phase change recording.

Guarantee Mechanism of Data Continuity for Electronic Record Based on Linked Data

In the field of electronic record management,especially in the current big data environment,data continuity has become a new topic that is as important as security and needs to be studied.This paper decomposes the data continuity guarantee of electronic record into a set of data protection requirements consisting of data relevance,traceability and comprehensibility,and proposes to use the associated data technology to provide an integrated guarantee mechanism to meet the above three requirements.

Test Calibration of the Paleoclimatic Proxy Data with Chinese Historical Records

古气候代理的刻度目前是在古气候的学习的关键问题之一。气候的历史的记录片记录对在 climatological 意义标明日期和代理数据的气候的含意上的刻度合适。用中国历史的记录片记录改正 Delingha 树戒指降水系列上的测试刻度在 14011950 广告在 44 个极端干燥盒子之中显示出那， 42 个盒子(或 95.5%) 是可信的。因此 Delingha 表示戒指的降水的长系列是高度可靠的。用历史的记录基于 Zhanjiang Huguangyan 沉积验证季风紧张代理数据的另一测试显示马尔·蒂湖的年满足季风紧张在完全对面到在 800900 广告的严厉冬季的历史的描绘文件的年的指明系列的冬季。作为结果，严肃的疑问关于这个 paleo 季风代理系列的气候的含意被提起。引证张， D. ， 2011：与中国历史的记录测试 paleoclimatic 代理数据的刻度。副词。Clim。变化物件， 2 (1 ) ， doi：10.3724/SP .J.1248.2011.00038。