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.

Multi Attribute Case Based Privacy-preserving for Healthcare Transactional Data Using Cryptography

Medical data mining has become an essential task in healthcare sector to secure the personal and medical data of patients using privacy policy.In this background,several authentication and accessibility issues emerge with an inten-tion to protect the sensitive details of the patients over getting published in open domain.To solve this problem,Multi Attribute Case based Privacy Preservation(MACPP)technique is proposed in this study to enhance the security of privacy-preserving data.Private information can be any attribute information which is categorized as sensitive logs in a patient’s records.The semantic relation between transactional patient records and access rights is estimated based on the mean average value to distinguish sensitive and non-sensitive information.In addition to this,crypto hidden policy is also applied here to encrypt the sensitive data through symmetric standard key log verification that protects the personalized sensitive information.Further,linear integrity verification provides authentication rights to verify the data,improves the performance of privacy preserving techni-que against intruders and assures high security in healthcare setting.

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.

The Method of Data Access for Nuclear Instrument Based on Linked List

A new method of data access which can effectively resolve the problem of high speed and real time reading data of nuclear instrument in small storage space is introduced. This method applies the data storage mode of “linked list” to the system of Micro Control Unit (MCU), and realizes the pointer access of nuclear data on the small storage space of MCU. Experimental results show that this method can solve some problems of traditional data storage method, which has the advantages of simple program design, stable performance, accurate data, strong repeatability, saving storage space and so on.

A Solution of Field Data Acquisition and Instrument Rebuild

The problems of the present management information system are introduced. A solution based on field-bus is proposed to overcome the problem. A issue how the old instruments is applied to the new system is discussed, and a rebuild method of the old instruments with LED or LCD to fit the solution is provided.

A 3-LEAD 24-HOUR HOLTER SYSTEM BASED ON CASSETTE TAPE DATA RECORDING TECHNOLOGY

Holter is a kind of widely-used clinical instrument to monitor and diagnoseheart diseases.At present,some kinds of Holter record analogous ECG signal di-rectly.But their complicated recording devices lead to their high price.Other kindsintroduce semiconductor memory.But their limited capacity makes them quite diffi-cult to record 24-hour ECG signal.The 24-hour Holter system based on digital stor-

BC-PC-Share:Blockchain-Based Patient-Centric Data Sharing Scheme for PHRs in Cloud Computing

Sharing of personal health records(PHR)in cloud computing is an essential functionality in the healthcare system.However,how to securely,efficiently and flexibly share PHRs data of the patient in a multi-receiver setting has not been well addressed.For instance,since the trust domain of the cloud server is not identical to the data owner or data user,the semi-trust cloud service provider may intentionally destroy or tamper shared PHRs data of user or only transform partial ciphertext of the shared PHRs or even return wrong computation results to save its storage and computation resource,to pursue maximum economic interest or other malicious purposes.Thus,the PHRs data storing or sharing via the cloud server should be performed with consistency and integrity verification.Fortunately,the emergence of blockchain technology provides new ideas and prospects for ensuring the consistency and integrity of shared PHRs data.To this end,in this work,we leverage the consortiumblockchain technology to enhance the trustworthiness of each participant and propose a blockchain-based patient-centric data sharing scheme for PHRs in cloud computing(BC-PC-Share).Different from the state-of-art schemes,our proposal can achieve the following desired properties:(1)Realizing patient-centric PHRs sharing with a public verification function,i.e.,which can ensure that the returned shared data is consistent with the requested shared data and the integrity of the shared data is not compromised.(2)Supporting scalable and fine-grained access control and sharing of PHRs data with multiple domain users,such as hospitals,medical research institutes,and medical insurance companies.(3)Achieving efficient user decryption by leveraging the transformation key technique and efficient user revocation by introducing time-controlled access.The security analysis and simulation experiment demonstrate that the proposed BC-PC-Share scheme is a feasible and promising solution for PHRs data sharing via consortium blockchain.

Acoustic and Vibration Environment Prediction Technology of Instrument Cabin Based on Multi-Source Data

Noise characteristic is one of the important factors to be considered during the design of a launch vehicle system.In this paper,the acceptance conditions for the external noise environment of the instrument cabin are given based on multi-source data,including the measured data of the launch vehicle lift-off noise,the mechanical environment of the launch vehicle equipment,and the external noise environment of the instrument cabin deduced from empirical formula.Then an acoustic and vibration transfer model is established based on the response data of the instrument equipment used to conduct measurements in the noise test reverberation room.By using an external noise environment and a transfer model,the response of the instrument and equipment for the acceptance condition can be obtained.The acoustic and vibration prediction technology introduced in this paper can provide technical support in the environmental prediction analysis of heavy launch vehicles for the future.

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.

基于轻量级YOLO-v4模型的变电站数字仪表检测识别

为了在变电站实际场景中准确获取数字仪表读数,智能管控变电站的安全风险,同时推动变电站智能化发展,以实际场景中变电站数字仪表作为研究对象,综合考虑实时性及准确度等,提出一种基于轻量级YOLOv4模型的变电站数字仪表检测识别方法.首先,通过从鄂尔多斯变电站实际拍摄变电站数字仪表图像数据,使用Albumentations框架对数字仪表图像进行数据扩充,构建变电站数字仪表目标检测数据集;然后,以YOLO-v4网络为基础,结合注意力机制构建一个有效通道注意(efficient channel attention,ECA)改进的深度可分离卷积模块(ECA-bneck-m);最后,提出一个轻量级YOLO-v4模型,进行模型大小与性能的对比实验.实验结果表明:本文方法可以在几乎不损失检测准确度的情况下,将整个模型存储大小压缩为原先的1/5,同时将模型推理速度从24.0帧/s提升至36.9帧/s,其实时性能够满足实际变电站检测识别的工程需要.

Stand-Alone Patient Reception and Referral System with Health Data Management

The COVID-19 pandemic has exposed vulnerabilities within our healthcare structures. Healthcare facilities are often faced with staff shortages and work overloads, which can have an impact on the collection of health data and constants essential for early diagnosis. In order to minimize the risk of error and optimize data collection, we have developed a robot incorporating artificial intelligence. This robot has been designed to automate and collect health data and constants in a contactless way, while at the same time verifying the conditions for correct measurements, such as the absence of hats and shoes. Furthermore, this health information needs to be transmitted to services for processing. Thus, this article addresses the aspect of reception and collection of health data and constants through various modules: for taking height, temperature and weight, as well as the module for entering patient identification data. The article also deals with orientation, presenting a module for selecting the patient’s destination department. This data is then routed via a wireless network and an application integrated into the doctors’ tablets. This application will enable efficient queue management by classifying patients according to their order of arrival. The system’s infrastructure is easily deployable, taking advantage of the healthcare facility’s local wireless network, and includes encryption mechanisms to reinforce the security of data circulating over the network. In short, this innovative system will offer an autonomous, contactless method for collecting vital constants such as size, mass, and temperature. What’s more, it will facilitate the flow of data, including identification information, across a network, simplifying the implementation of this solution within healthcare facilities.

智慧供热系统数据运行核查方法研究

高质量数据是智慧供热系统数据分析的前提和分析结论可靠性的保障,计量器具的良好工作状态是获得高质量数据的基础。本文讨论了计量器具的检定、校准及运行核查的区别,研究了智慧供热系统计量器具在线运行核查方法。研究表明:流量计根据其实际安装工作条件,采用安装条件修正系数可解决直管段长度不够带来的测量误差问题;采用基准表法对使用的流量计进行技术性能核查,可正确判别单台流量计的工作状态;示踪法是解决大口径贸易结算表数据质量问题的有效方法。本研究可为及时发现工作异常的计量器具、确保测量数据的准确性及可靠性提供理论指导及技术支撑。

AVEVA Instrumentation Engineer在仪表系统设计中的应用研究

本文分析AVEVA Instrumentation软件功能模块Engineer在仪表系统设计中的应用。重点探讨借助AVEVA Instrumentation软件实现跨专业、跨软件的数据交互开发及仪表专业清单文件、数据表的模板定制。

政策工具视角下我国地方政府公共数据治理政策研究

[目的/意义]公共数据对于经济发展以及社会服务具有重要意义,聚焦我国省域公共数据治理的政策工具问题,有助于公共数据政策设计的完善以及相关工作的开展。[方法/过程]基于马—萨理论模型构建公共数据“政策工具—政策执行”二维分析框架,运用文本内容分析法并借助NVivo质性分析软件对我国31个省级行政区(不含港澳台地区)的公共数据政策进行编码和量化分析,考察我国地方政府在公共数据治理中的决策逻辑、执行策略及其运行特征。[结果/结论]我国省级公共数据政策地域发展不均衡,存在环境型政策工具过溢、需求型政策工具应用不足、政策工具组合结构欠佳,以及政策执行目标定位不足、内部执行要素不够协调、外部环境衔接不强等问题,未来应该加强区域协调发展,优化工具结构,明确价值定位,规范执行程序,强化衔接影响。

基于古今医案云平台探究中医治疗失眠的辨证思路及用药规律研究

目的运用古今医案云平台整理并分析中医治疗失眠的辨证思路及用药规律。方法收集古今医案云平台(V2.3.8)名医医案库中名老中医及其书籍中的医案数据,以古今医案云平台(V2.3.8)对用药频次、药味药性归经、舌象脉象等进行统计分析、聚类分析、复杂网络分析。结果共纳入医案794个,涉及中药477味,主要有柴胡、白芍、甘草、酸枣仁、川芎、远志、赤芍、当归、合欢皮、茯苓、郁金、栀子。中药药性以平为主;药味多属甘、苦、辛;主要归肝经。高频中药系统聚类分析聚为4组,第一组:酸枣仁、茯苓;第二组:远志、合欢皮、郁金;第三组:白芍、栀子;第四组:柴胡、川芎、赤芍。药物关联分析得到:柴胡与白芍、柴胡与赤芍的关联性最强,其次是柴胡与川芎。复杂网络分析得到7组中医证候,从高到底依次为:肝阳上亢证,痰热扰心证,肝火扰心证,气滞血瘀证,心脾两虚证,肝肾阴虚证,肝郁气滞证。结论中医治疗失眠以清热助眠为法,平调阴阳为纲,多使用清热安神之品,值得临床参考运用。

基于固定效应回归分析的省级公共数据政策对政府数据开放绩效的影响研究

对公共数据政策与政府数据开放绩效的关系进行实证研究,了解公共数据政策工具对政府数据开放绩效的作用效果。基于政策工具和数据生态系统理论,分析省级公共数据政策影响政府数据开放绩效的机理,提出本文的研究假设和模型,然后通过对公共数据政策的编码和量化,计算公共数据供给型、环境型和需求型政策工具的效力,得到模型核心自变量的数据,最后构建面板数据,对公共数据政策工具效力与开放数林指数进行固定效应回归分析,从而对研究假设进行检验。研究发现,省级公共数据政策整体对政府数据开放绩效存在一定的正向影响,其供给型和需求型政策工具对政府数据开放绩效的提高具有促进作用,环境型政策工具对政府数据开放绩效的提高具有抑制作用,且不同政策工具对同一绩效指标的影响大小存在差异。

基于QAR数据的民机高高原进近着陆风险评估方法

民机高高原进近着陆是高原飞行高风险阶段。为有效实施高高原进近着陆风险识别和等级判据,提出基于熵权可变模糊识别的长短时记忆网络与深度神经网络(LSTM-DNN)相融合的深度学习风险评估方法。基于快速存取记录器(QAR)记录的高高原飞行数据,借鉴民机飞行品质监控(FOQA)咨询通告和行业QAR监控标准,结合指标重要度分析与Delphi专家调查,提取着陆时航向变化大、航迹低、610~305 m进近时下降率大、接地时垂直加速度及153~15 m进近时下降率大5个关键监控项目作为民机高高原进近着陆风险评估指标。为克服评估指标权重主观性偏差,应用熵权法确定评估指标权重,基于可变模糊识别方法构建风险等级隶属函数,建立基于LSTM-DNN的民机高高原进近着陆风险评估模型。以成都—拉萨进近着陆航段为例,提取QAR数据,对该风险评估模型进行训练与测试,并与Logistic多元回归、支持向量机(SVM)等评估方法进行比较,结果表明:所提方法平均准确率达到94.18%,最高可达94.79%,验证了方法的客观有效性。

在线数据记录和扰动观测器相结合的柔性机械臂复合学习控制

本文针对多输入多输出的柔性机械臂动力学模型,提出了一种基于神经网络估计和扰动观测器的复合学习控制策略.首先,通过奇异摄动分析将系统解耦为快慢子系统.然后,针对慢变刚性子系统的动力学模型,基于在线数据记录模型构造新型预测误差,结合跟踪误差设计自适应控制律;针对快变柔性子系统采用滑模控制抑制弹性振动.在此基础上,构建了扰动观测器实时估计复合扰动信号,并纳入在线数据记录模型作为前馈补偿.最后,基于Lyapunov稳定性分析可证系统误差信号一致终值有界,仿真算例验证了所提策略的有效性和优越性.

基于QAR着陆数据的跑道状况等级评估方法

跑道状况等级是飞机安全着陆的一个关键信息。为了进一步提高跑道状况评估的准确性和及时性,提出一种利用飞行快速存取记录器(Quick Access Recorder, QAR)数据快速评估跑道状况等级的方法。首先,根据飞行数据特征从繁多的数据中提取模型所需输入参数;其次,建立基于QAR数据建立飞机采用自动刹车着陆的动力学模型,并计算不同跑道状况等级对应的理论着陆距离;最后,将实际着陆距离与不同的跑道状况等级对应的理论着陆距离作对比,确定跑道状况等级。利用波音738和空客A320的QAR着陆数据分析某机场的跑道状况等级,并与该机场的雪情通告进行对比,验证了模型的准确性。研究成果可用于未来航班的着陆距离预测,为飞行员选择合适的刹车等级提供参考。

基于PLC+4G的病案库房远程巡查机器人研发

研发一款远程视频巡查库房中病案霉变痕迹、虫害痕迹及库房渗水痕迹的机器人。机器人采用四轮行走形式,2个后轮由2台步进电机分别驱动。机器人上安装垂直升降丝杆,其滑块上安装水平伸缩丝杆,一台筒形病案检查摄像机安装在水平丝杆滑块上,2根丝杆均由步进电机驱动,另有2台360°球形摄像机保证机器人行走无盲区。设计用户手机端HMI,点击HMI上按钮,通过物联网-4G-4G网关数据链向机器人上的PLC发出指令,控制机器人把病案检查摄像机移动到检查位置。3台摄像机视频通过另一条4G-物联网数据链传到用户电脑屏幕显示。实验验证远程控制系统及信号传输数据链切实可行,可在电脑上远程视频察看库房病案存储状况,确保病案安全。