PIMS:An Efficient Process Integrity Monitoring System Based on Blockchain and Trusted Computing in Cloud-Native Context

With the advantages of lightweight and high resource utilization,cloud-native technology with containers as the core is gradually becoming themainstreamtechnical architecture for information infrastructure.However,malware attacks such as Doki and Symbiote threaten the container runtime’s security.Malware initiates various types of runtime anomalies based on process form(e.g.,modifying the process of a container,and opening the external ports).Fortunately,dynamic monitoring mechanisms have proven to be a feasible solution for verifying the trusted state of containers at runtime.Nevertheless,the current routine dynamic monitoring mechanisms for baseline data protection are still based on strong security assumptions.As a result,the existing dynamicmonitoringmechanismis still not practical enough.To ensure the trustworthiness of the baseline value data and,simultaneously,to achieve the integrity verification of the monitored process,we combine blockchain and trusted computing to propose a process integrity monitoring system named IPMS.Firstly,the hardware TPM 2.0 module is applied to construct a trusted security foundation for the integrity of the process code segment due to its tamper-proof feature.Then,design a new format for storing measurement logs,easily distinguishing files with the same name in different containers from log information.Meanwhile,the baseline value data is stored on the blockchain to avoidmalicious damage.Finally,trusted computing technology is used to perform fine-grained integrity measurement and remote attestation of processes in a container,detect abnormal containers in time and control them.We have implemented a prototype system and performed extensive simulation experiments to test and analyze the functionality and performance of the PIMS.Experimental results show that PIMS can accurately and efficiently detect tampered processes with only 3.57% performance loss to the container.

GNSS receiver autonomous integrity monitoring(RAIM)algorithm based on robust estimation

Integrity is significant for safety-of-life applications. Receiver autonomous integrity monitoring（RAIM） has been developed to provide integrity service for civil aviation. At first,the conventional RAIM algorithm is only suitable for single fault detection, single GNSS constellation. However, multiple satellite failure should be considered when more than one satellite navigation system are adopted. To detect and exclude multi-fault, most current algorithms perform an iteration procedure considering all possible fault model which lead to heavy computation burden. An alternative RAIM is presented in this paper based on multiple satellite constellations（for example, GPS and Bei Dou（BDS） etc.） and robust estimation for multi-fault detection and exclusion, which can not only detect multi-failures,but also control the influences of near failure observation. Besides, the RAIM algorithm based on robust estimation is more efficient than the current RAIM algorithm for multiple constellation and multiple faults. Finally, the algorithm is tested by GPS/Bei Dou data.

GPS Vector Tracking Receivers with Rate Detector for Integrity Monitoring

In this paper,the integrity monitoring algorithm based on a Kalman filter(KF)based rate detector is employed in the vector tracking loop(VTL)of the Global Positioning System(GPS)receiver.In the VTL approach,the extended Kalman filter(EKF)simultaneously tracks the received signals and estimates the receiver’s position,velocity,etc.In contrast to the scalar tracking loop(STL)that uses the independent parallel tracking loop approach,the VTL technique uses the correlation of each satellite signal and user dynamics and thus reduces the risk of loss lock of signals.Although the VTL scheme provides several important advantages,the failure of tracking in one channel may affect the entire system and lead to loss of lock on all satellites.The integrity monitoring algorithm can be adopted for robustness enhancement.In general,the standard integrity monitoring algorithm can timely detect the step type erroneous signals.However,in the presence of ramp type slowly growing erroneous signals,detection of such type of error takes much more time since the error cannot be detected until the cumulative exceeds the specified threshold.The integrity monitoring based on the rate detector possesses good potential for resolving such problem.The test statistic based on the pseudorange residual in association with the EKF is applied for determination of whether the test statistic exceeds the allowable threshold values.The fault detection and exclusion(FDE)mechanism can then be employed to exclude the hazardous erroneous signals for the abnormal satellites to assure normal operation of GPS receivers.Feasibility of the integrity monitoring algorithm based on the EKF based rate detector will be demonstrated.Performance assessment and evaluation will be presented.

Concealed Integrity Monitoring for Wireless Sensor Networks

Nowadays, sensor networks are widely installed around the world. Typical sensors provide data for healthcare, energy management, environmental monitoring, etc. In the future sensors will become a part of critical infrastructures. In such a scenario the network operator has to monitor the integrity of the network devices, otherwise the trustworthiness of the whole system is questionable. The problem is that every integrity protocol needs a secure channel between the devices. Therefore, we will introduce a covert channel for hidden transportation of integrity monitoring messages. The covert channel enables us to hide integrity check messages embedded into regular traffic without giving potential attackers a hint on the used integrity protocol.

分布式WADGPS完备性监测技术研究

介绍了分布式WADGPS系统中监测站和用户站的监测方法,讨论了监测站和用户站的粗差探测与分离,最后应用分布式WADGPS99’陆基长距离试验和南海海上试验进行了数据处理和分析。

Multi‑level autonomous integrity monitoring method for multi‑source PNT resilient fusion navigation

For the integrity monitoring of a multi-source PNT(Positioning,Navigation,and Timing)resilient fusion navigation system,a theoretical framework of multi-level autonomous integrity monitoring is proposed.According to the mode of multi-source fusion navigation,the framework adopts the top-down logic structure and establishes the navigation source fault detection model based on the multi-combination separation residual method to detect and isolate the fault source at the system level and subsystem level.For isolated non-redundant navigation sources,the system level recovery verification model is used.For the isolated multi-redundant navigation sources,the sensor fault detection model optimized with the dimension-expanding matrix is used to detect and isolate the fault sensors,and the isolated fault sensors are verified in real-time.Finally,according to the fault detection and verification results at each level,the observed information in the fusion navigation solution is dynamically adjusted.On this basis,the integrity risk dynamic monitoring tree is established to calculate the Protection Level(PL)and evaluate the integrity of the multi-source integrated navigation system.The autonomous integrity monitoring method proposed in this paper is tested using a multi-source navigation system integrated with Inertial Navigation System(INS),Global Navigation Satellite System(GNSS),Long Baseline Location(LBL),and Ultra Short Baseline Location(USBL).The test results show that the proposed method can effectively isolate the fault source within 5 s,and can quickly detect multiple faulty sensors,ensuring that the positioning accuracy of the fusion navigation system is within 5 m,effectively improving the resilience and reliability of the multi-source fusion navigation system.

Microarrow sensor array with enhanced skin adhesion for transdermal continuous monitoring of glucose and reactive oxygen species

Conventional blood sampling for glucose detection is prone to cause pain and fails to continuously record glucose fluctuations in vivo.Continuous glucose monitoring based on implantable electrodes could induce pain and potential tissue inflammation,and the presence of reactive oxygen species(ROS)due to inflammationmay affect glucose detection.Microneedle technology is less invasive,yet microneedle adhesion with skin tissue is limited.In this work,we developed a microarrow sensor array(MASA),which provided enhanced skin surface adhesion and enabled simultaneous detection of glucose and H_(2)O_(2)(representative of ROS)in interstitial fluid in vivo.The microarrows fabricated via laser micromachining were modified with functional coating and integrated into a patch of a three-dimensional(3D)microneedle array.Due to the arrow tip mechanically interlocking with the tissue,the microarrow array could better adhere to the skin surface after penetration into skin.The MASA was demonstrated to provide continuous in vivo monitoring of glucose and H_(2)O_(2) concentrations,with the detection of H_(2)O_(2) providing a valuable reference for assessing the inflammation state.Finally,the MASA was integrated into a monitoring system using custom circuitry.This work provides a promising tool for the stable and reliable monitoring of blood glucose in diabetic patients.

Short-term displacement prediction for newly established monitoring slopes based on transfer learning

This study makes a significant progress in addressing the challenges of short-term slope displacement prediction in the Universal Landslide Monitoring Program,an unprecedented disaster mitigation program in China,where lots of newly established monitoring slopes lack sufficient historical deformation data,making it difficult to extract deformation patterns and provide effective predictions which plays a crucial role in the early warning and forecasting of landslide hazards.A slope displacement prediction method based on transfer learning is therefore proposed.Initially,the method transfers the deformation patterns learned from slopes with relatively rich deformation data by a pre-trained model based on a multi-slope integrated dataset to newly established monitoring slopes with limited or even no useful data,thus enabling rapid and efficient predictions for these slopes.Subsequently,as time goes on and monitoring data accumulates,fine-tuning of the pre-trained model for individual slopes can further improve prediction accuracy,enabling continuous optimization of prediction results.A case study indicates that,after being trained on a multi-slope integrated dataset,the TCN-Transformer model can efficiently serve as a pretrained model for displacement prediction at newly established monitoring slopes.The three-day average RMSE is significantly reduced by 34.6%compared to models trained only on individual slope data,and it also successfully predicts the majority of deformation peaks.The fine-tuned model based on accumulated data on the target newly established monitoring slope further reduced the three-day RMSE by 37.2%,demonstrating a considerable predictive accuracy.In conclusion,taking advantage of transfer learning,the proposed slope displacement prediction method effectively utilizes the available data,which enables the rapid deployment and continual refinement of displacement predictions on newly established monitoring slopes.

Receiver Autonomous Integrity Monitoring Availability and Fault Detection Capability Comparison Between BeiDou and GPS

This paper used the statistical methods of quality control to assess receiver autonomous integrity monitoring(RAIM) availability and fault detection(FD) capability of BeiDou14(Phase II with 14 satellites),BeiDou(Phase III with 35 satellites) and GPS(with 31 satellites) for the first time. The three constellations are simulated and their RAIM performances are quantified by the global, Asia-Pacific region and temporal variations respectively. RAIM availability must be determined before RAIM detection. It is proposed that RAIM availability performances from satellites and constellation geometry configuration are evaluated by the number of visible satellites(NVS, NVS > 5) and geometric dilution of precision(GDOP, GDOP < 6) together. The minimal detectable bias(MDB) and minimal detectable effect(MDE) are considered as a measure of the minimum FD capability of RAIM in the measurement level and navigation position level respectively. The analyses of simulation results testify that the average global RAIM performances for BeiDou are better than that for GPS except global RAIM holes proportion. Moreover, the Asia-Pacific RAIM performances for BeiDou are much better than that for GPS in all indexes. RAIM availability from constellation geometry configuration and RAIM minimum FD capability for BeiDou14 are better than that for GPS in Asia-Pacific region in all cases, but the BeiDou14 RAIM availability from satellites are worse than GPS's. The methods and conclusions can be used for RAIM prediction and real-time assessment of all kinds of Global Navigation Satellite Systems(GNSS) constellation.

New methods for dual constellation single receiver positioning and integrity monitoring

Navigation system integrity monitoring is crucial for mission(e.g.safety)critical applications.Receiver autonomous integrity monitoring(RAIM)based on consistency checking of redundant measurements is widely used for many applications.However,there are many challenges to the use of RAIM associated with multiple constellations and applications with very stringent requirements.This paper discusses two positioning techniques and corresponding integrity monitoring methods.The first is the use of single frequency pseudorange-based dual constellations.It employs a new cross constellation single difference scheme to benefit from the similarities while addressing the differences between the constellations.The second technique uses dual frequency carrier phase measurements from GLONASS and the global positioning system for precise point positioning.The results show significant improvements both in positioning accuracy and integrity monitoring as a result of the use of two constellations.The dual constellation positioning and integrity monitoring algorithms have the potential to be extended to multiple constellations.

Integrity Monitoring in Navigation Systems: Fault Detection and Exclusion RAIM Algorithm Implementation

The use of GPS is becoming increasingly popular for real-time navigation systems. To ensure that satellite failures are detected and excluded at the receiver is of high importance for the integrity of the satellite navigation system. The focus of this paper is to implement a fault detection and exclusion algorithm in a software GPS receiver in order to provide timely warnings to the user when it is not advisable to use the GPS system for navigation. The GPS system currently provides some basic integrity information to users via the navigation message, but it is not timely enough for safety-critical applications. RAIM is a means of providing integrity with the capability of detecting when a satellite failure or a measurement error has occurred. It is the simplest and most cost effective technique for integrity monitoring. After applying the iterative fault detection and the exclusion algorithm, a significant improvement in positioning accuracy is achieved.

Real-time integrity monitoring for a wide area precise positioning system

The wide area precise positioning system(WAPPS)is a high-precision positioning system based on a global navigation satellite system.Using a GEO satellite or a communication network,it provides users,in its service area,with real-time satellite orbit,clock,and other corrections.Users can achieve centimeter-level static positioning or decimeter-level kinematic positioning by precise point positioning.With the demands for applications of both high-precision and safety of life in real time,WAPPS is facing urgent needs to improve its service integrity.This study presents a real-time integrity monitoring approach for WAPPS.Using dual-frequency ionosphere-free corrections of GPS and BDS,along with monitor station data,related error models are established and the integrity monitoring is achieved,based on the analysis of satellite corrected residuals.In addition,satellite faults are simulated for performance verification.The results show that the algorithm can monitor both step and drift faults effectively and alert users in time.

Multiple Regression and Big Data Analysis for Predictive Emission Monitoring Systems

Predictive Emission Monitoring Systems (PEMS) offer a cost-effective and environmentally friendly alternative to Continuous Emission Monitoring Systems (CEMS) for monitoring pollution from industrial sources. Multiple regression is one of the fundamental statistical techniques to describe the relationship between dependent and independent variables. This model can be effectively used to develop a PEMS, to estimate the amount of pollution emitted by industrial sources, where the fuel composition and other process-related parameters are available. It often makes them sufficient to predict the emission discharge with acceptable accuracy. In cases where PEMS are accepted as an alternative method to CEMS, which use gas analyzers, they can provide cost savings and substantial benefits for ongoing system support and maintenance. The described mathematical concept is based on the matrix algebra representation in multiple regression involving multiple precision arithmetic techniques. Challenging numerical examples for statistical big data analysis, are investigated. Numerical examples illustrate computational accuracy and efficiency of statistical analysis due to increasing the precision level. The programming language C++ is used for mathematical model implementation. The data for research and development, including the dependent fuel and independent NOx emissions data, were obtained from CEMS software installed on a petrochemical plant.

Thermal integrity profiling of cast-in-situ piles in sand using fiber-optic distributed temperature sensing

Defects in cast-in-situ piles have an adverse impact on load transfer at the pile‒soil interface and pile bearing capacity. In recent years, thermal integrity profiling (TIP) has been developed to measure temperature profiles of cast-in-situ piles, enabling the detection of structural defects or anomalies at the early stage of construction. However, using this integrity testing method to evaluate potential defects in cast-in-situ piles requires a comprehensive understanding of the mechanism of hydration heat transfer from piles to surrounding soils. In this study, small-scale model tests were conducted in laboratory to investigate the performance of TIP in detecting pile integrity. Fiber-optic distributed temperature sensing (DTS) technology was used to monitor detailed temperature variations along model piles in sand. Additionally, sensors were installed in sand to measure water content and matric suction. An interpretation method against available DTS-based thermal profiles was proposed to reveal the potential defective regions. It shows that the temperature difference between normal and defective piles is more obvious in wet sand. In addition, there is a critical zone of water migration in sand due to the water absorption behavior of cement and temperature transfer-induced water migration in the early-age concrete setting. These findings could provide important insight into the improvement of the TIP testing method for field applications.

青少年体质健康监测政策协同的域外经验与启示

运用文献资料、逻辑分析等研究方法,围绕青少年体质健康监测政策协同展开研究,从美国的市场化与多元主体协同体系,日本的政府主导与家庭、学校、社区联合监测模式以及俄罗斯新版“劳卫制”下的政策协同与组织过程保障模式中得到启示。研究认为,青少年体质健康监测政策协同具有主体多元、政府主导、过程渐进、效果有限等时代特征,应在“社会-学校-国家”关系视角下协同推进体医融合,培育核心素养。在社会层面促进多元主体参与,跨部门推动体医融合政策协同;在学校层面以“教学-监测”双循环同向发力培育核心素养;在国家层面优化制度设计,引入“政府主导-市场监督-家长监督”全监督链条,构建管理创新机制,推动青少年体质健康监测政策协同。

飞机结构健康监测策略研究

飞机全寿命周期结构健康监测技术的应用对提高飞机在役安全性和可靠性,提升地面维护效率,延长飞机使用寿命具有巨大的价值和效益,因而得到国内外的持续关注和研究。该技术在飞机上的应用已成为衡量飞机先进性的重要指标之一,而飞机结构健康监测标准规范与策略的制定是技术开展的首要任务。分析了航空制造业先进国家结构健康监测技术相关的标准规范,明确了制定标准规范的具体要求;通过总结国外结构健康监测技术最新研究进展,梳理了结构健康监测技术框架体系;针对我国飞机结构地面试验和服役环境2大应用场景的技术特点,明晰了我国结构健康监测的技术需求,并在此基础上,提出了我国飞机结构健康监测的总体架构和可供剪裁的技术实施策略。

矿井微震与电法耦合监测技术

随着煤炭资源开采区域、深度及强度的增加,矿井工作面采掘扰动极易诱发矿井突水事故,严重威胁我国煤炭资源安全开采。矿井水害事故的发生主要是岩体采动破裂与矿井水渗流共同作用的结果。研究表明,微震信号的纵横波到时、幅值、频率等参量可有效表征岩体破裂位置、强度及其震源机制,地电场信号中的电阻率、激励电流及自然电位等参量可有效表征岩体渗流演化过程。透水通道形成对应微震场特征,地下水渗流对应地电场特征,因此微震电法耦合方法对矿井水害孕育发展过程具有监测预警的功能,可有效对采动破坏过程中形成的突水通道、水源位置及渗流过程等水害三要素进行精准捕捉;减少单一地球物理方法多解性,提高水害监测预警精度,对煤矿防治水意义重大。当前,微震与电法耦合监测技术在煤矿水害实时动态监测中开始得到应用,突破传统微震与电法独立采集的局限,课题组自主研发了国内外首款煤安认证的微震与电法耦合并行监测系统,该系统以微震场与地电场并行采集基站为核心,集被动源微震波场与主被动地电场为一体,通过连接矿井物联网,实现远程数据采集控制、传输及云端存储;其一体化网络服务器主机可连接多个采集基站,并将微震检波器、电法激励电极与电位测量电极进行相互隔离、并行采集,形成微震事件密度与能级分布、电阻率分布、自然电位分布等时空云图来监测预判水害三要素。微震与电法耦合监测技术仍需在微震事件智能识别与定位、地电场的电阻率三维反演以及自然电位渗流反演方面进行专项攻关;结合并行监测的微震电法大数据集,重点在微震电法数据联合反演、智能化监测预警方向开展进一步研究。微震与电法耦合监测技术,作为矿井水害的主动预警监测新方法,可为我国智能化矿井建设发挥重要作用。

中国石化油藏地球物理二十年发展与思考

油藏地球物理技术是综合应用多种地球物理资料与油藏动静态信息进行复杂油藏精细表征和动态监测的跨学科技术,对于提高油藏的储量动用程度和提高采收率具有重要的意义,是当前地球物理技术的重要发展方向。系统回顾了中国石化油藏地球物理提出的背景和过去二十年的发展历程,梳理了油藏地球物理技术系列,总结了在油藏地球物理基础研究、井中地球物理技术、多尺度资料联合反演、地球物理约束确定性建模、时移地震剩余油气预测、地震地质工程一体化、微地震油藏监测等方面的技术创新和应用效果。面对中国油气田勘探开发的深层、深水、非常规及老油田(“两深一非一老”)形势和一体化、智能化、绿色化挑战,油藏地球物理在油气产业技术致胜阶段仍然大有可为,要持续创新油藏地球物理技术,井、震、动、模一体化联合和人工智能应用,构建高水平的油藏地球物理勘探开发一体化、地质工程一体化解决方案,支撑油藏全生命周期建设。

输氢管道完整性管理技术研究进展

管道输氢被认为是当前效率最高、经济性最好的大规模输氢方式。围绕输氢管道管材的相容性、氢气的泄漏与监测、完整性管理方法与相关规范缺失等关键问题,对目前相关研究进行了归纳与总结。结果表明:典型管道钢材在高压临氢环境下产生氢致失效的机理尚未明悉,需探明临氢环境下不同等级钢材力学性能的劣化规律,并分析钢材氢脆敏感性的影响因素;输氢管道更易发生泄漏,应重点明确输氢管道及相关设备产生泄漏的具体原因,完善氢气泄漏监测技术;目前输氢管道完整性管理方法缺失,需重点考虑氢气对输氢管道完整性管理中风险评价和完整性评价的影响,完善输氢管道完整性管理方法;结合现有输氢管道规范的相关内容,为输氢管道完整性管理提供支撑,推进输氢管道完整性管理标准与规范的制定。相关结论可以为输氢管道完整性管理技术的发展提供参考。

高职“环境监测”课程模块化教学设计及实施研究——以“工业废水监测”项目为例

以“工业废水监测”项目为例,从教学整体设计、教学实施、教学评价三个方面展示了“环境监测”课程模块化教学设计思路及实施方法,以及取得的教学成效。探讨了“岗课赛证”融通、职业技能与职业精神融合培养、“三全育人”在教学实施中的具体举措。教学实践结果表明:学生的检测能力、检测素养均得到大幅提升,在学生技能竞赛和1+X证书考核中成绩优异,受到用人单位的好评。