Towards Cache-Assisted Hierarchical Detection for Real-Time Health Data Monitoring in IoHT

Real-time health data monitoring is pivotal for bolstering road services’safety,intelligence,and efficiency within the Internet of Health Things(IoHT)framework.Yet,delays in data retrieval can markedly hinder the efficacy of big data awareness detection systems.We advocate for a collaborative caching approach involving edge devices and cloud networks to combat this.This strategy is devised to streamline the data retrieval path,subsequently diminishing network strain.Crafting an adept cache processing scheme poses its own set of challenges,especially given the transient nature of monitoring data and the imperative for swift data transmission,intertwined with resource allocation tactics.This paper unveils a novel mobile healthcare solution that harnesses the power of our collaborative caching approach,facilitating nuanced health monitoring via edge devices.The system capitalizes on cloud computing for intricate health data analytics,especially in pinpointing health anomalies.Given the dynamic locational shifts and possible connection disruptions,we have architected a hierarchical detection system,particularly during crises.This system caches data efficiently and incorporates a detection utility to assess data freshness and potential lag in response times.Furthermore,we introduce the Cache-Assisted Real-Time Detection(CARD)model,crafted to optimize utility.Addressing the inherent complexity of the NP-hard CARD model,we have championed a greedy algorithm as a solution.Simulations reveal that our collaborative caching technique markedly elevates the Cache Hit Ratio(CHR)and data freshness,outshining its contemporaneous benchmark algorithms.The empirical results underscore the strength and efficiency of our innovative IoHT-based health monitoring solution.To encapsulate,this paper tackles the nuances of real-time health data monitoring in the IoHT landscape,presenting a joint edge-cloud caching strategy paired with a hierarchical detection system.Our methodology yields enhanced cache efficiency and data freshness.The corroborative numerical data accentuates the feasibility and relevance of our model,casting a beacon for the future trajectory of real-time health data monitoring systems.

Optimization of PV/Diesel Production through Data Monitoring

The study PV/diesel system is a stand-alone microgrid powered by the PV/diesel combination without production storage. The study focused on optimising PV/diesel production by monitoring data. It also referred to a comparison of sensitive factors in PV/diesel production. This study implemented structural and non-structural factors of the said system. A literature search was conducted to determine the factors involved. So, factors such as system autonomy, energy quality, system stability and data monitoring were considered for the study. Thus, after a detailed presentation of the data monitoring, a comparison based on the method, Analysis of Failure Modes, their Effects and Criticalities (FMEA) was carried out. At the end of the comparison, a hierarchy of parameters in the exploitation of the energy production of autonomous microgrids was made. From its results, it emerges a good consideration of the factor “data monitoring” in the management of the system studied. The results obtained confirm the importance of data monitoring for a better optimization of energy production. A monitoring program or procedure has been developed according to the originality that the present study has identified. The study also made it possible to evaluate the performance of data monitoring for the energy production of photovoltaic systems in general and hybrid PV/diesel systems in particular.

Data monitoring system of technical diagnosis system for EAST

Technical diagnosis system(TDS)is an important subsystem to monitor status parameters of EAST (experimental advanced superconducting tokamak).The upgraded TDS data monitoring system is comprised of management floor,monitoring floor and field floor.Security protection,malfunction record and analysis are designed to make the system stable,robust and friendly.During the past EAST campaigns,the data monitoring system has been operated reliably and stably.The signal conditioning system and software architecture are described.

Standard Operating Procedures for Chinese Medicine Data Monitoring Committees of Clinical Studies

Although there is guidance from different regulatory agencies,there are opportunities to bring greater consistency and stronger applicability to address the practical issues of establishing and operating a data monitoring committee(DMC)for clinical studies of Chinese medicine.We names it as a Chinese Medicine Data Monitoring Committee(CMDMC).A panel composed of clinical and statistical experts shared their experience and thoughts on the important aspects of CMDMCs.Subsequently,a community standard on CMDMCs(T/CACM 1323-2019)was issued by the China Association of Chinese Medicine on September 12,2019.This paper summarizes the key content of this standard to help the sponsors of clinical studies establish and operate CMDMCs,which will further develop the scientific integrity and quality of clinical studies.

Contribution of the MERISE-Type Conceptual Data Model to the Construction of Monitoring and Evaluation Indicators of the Effectiveness of Training in Relation to the Needs of the Labor Market in the Republic of Congo

This study proposes the use of the MERISE conceptual data model to create indicators for monitoring and evaluating the effectiveness of vocational training in the Republic of Congo. The importance of MERISE for structuring and analyzing data is underlined, as it enables the measurement of the adequacy between training and the needs of the labor market. The innovation of the study lies in the adaptation of the MERISE model to the local context, the development of innovative indicators, and the integration of a participatory approach including all relevant stakeholders. Contextual adaptation and local innovation: The study suggests adapting MERISE to the specific context of the Republic of Congo, considering the local particularities of the labor market. Development of innovative indicators and new measurement tools: It proposes creating indicators to assess skills matching and employer satisfaction, which are crucial for evaluating the effectiveness of vocational training. Participatory approach and inclusion of stakeholders: The study emphasizes actively involving training centers, employers, and recruitment agencies in the evaluation process. This participatory approach ensures that the perspectives of all stakeholders are considered, leading to more relevant and practical outcomes. Using the MERISE model allows for: • Rigorous data structuring, organization, and standardization: Clearly defining entities and relationships facilitates data organization and standardization, crucial for effective data analysis. • Facilitation of monitoring, analysis, and relevant indicators: Developing both quantitative and qualitative indicators helps measure the effectiveness of training in relation to the labor market, allowing for a comprehensive evaluation. • Improved communication and common language: By providing a common language for different stakeholders, MERISE enhances communication and collaboration, ensuring that all parties have a shared understanding. The study’s approach and contribution to existing research lie in: • Structured theoretical and practical framework and holistic approach: The study offers a structured framework for data collection and analysis, covering both quantitative and qualitative aspects, thus providing a comprehensive view of the training system. • Reproducible methodology and international comparison: The proposed methodology can be replicated in other contexts, facilitating international comparison and the adoption of best practices. • Extension of knowledge and new perspective: By integrating a participatory approach and developing indicators adapted to local needs, the study extends existing research and offers new perspectives on vocational training evaluation.

Identification of Sewage Monitoring Data Based on Mathematical Statistics

In order to reduce the enormous pressure to environmental monitoring work brought by the false sewage monitoring data, Grubbs method, box plot, t test and other methods are used to make depth analysis to the data, providing a set of technological process to identify the sewage monitoring data, which is convenient and simple.

Assessment of thermal behaviour of thermo-active diaphragm walls based on monitoring data

Thermo-active diaphragm walls have proved their effectiveness in the thermal conditioning of buildings and infrastructures. However, some aspects still need to be investigated in order to tailor methods and tools for an accurate prediction of their energy and structural performance. In this perspective, some issues are addressed that concern the definition of models for the numerical analysis, in particular issues about the modelling of geometry and thermal boundary conditions. Taking advantage of a monitoring programme on a real full-scale structure, this research focuses on the assessment of heat transfer process and thermal response of diaphragm wall and soil mass on the basis of field data. Understanding of the heat transfer process contributes to the definition of the time-dependent thermal boundary conditions at the excavation side. From the analysis of thermal gradients in the wall, the condition at the excavation side is recognised as a major factor that influences the heat transfer process, governing the direction of the heat flux in different seasons of operation of the geothermal system.

An Initial Residual Stress Inference Method by Incorporating Monitoring Data and Mechanism Model

Initial residual stress is the main reason causing machining deformation of the workpiece,which has been deemed as one of the most important aspects of machining quality issues.The inference of the distribution of initial residual stress inside the blank has significant meaning for machining deformation control.Due to the principle error of existing residual stress detection methods,there are still challenges in practical applications.Aiming at the detection problem of the initial residual stress field,an initial residual stress inference method by incorporating monitoring data and mechanism model is proposed in this paper.Monitoring data during machining process is used to represent the macroscopic characterization of the unbalanced residual stress,and the finite element numerical model is used as the mechanism model so as to solve the problem that the analytic mechanism model is difficult to establish;the policy gradient approach is introduced to solve the gradient descent problem of the combination of learning model and mechanism model.Finally,the initial residual stress field is obtained through iterative calculation based on the fusing method of monitoring data and mechanism model.Verification results show that the proposed inference method of initial residual stress field can accurately and effectively reflect the machining deformation in the actual machining process.

The State of the Art of Data Science and Engineering in Structural Health Monitoring

Structural health monitoring (SHM) is a multi-discipline field that involves the automatic sensing of structural loads and response by means of a large number of sensors and instruments, followed by a diagnosis of the structural health based on the collected data. Because an SHM system implemented into a structure automatically senses, evaluates, and warns about structural conditions in real time, massive data are a significant feature of SHM. The techniques related to massive data are referred to as data science and engineering, and include acquisition techniques, transition techniques, management techniques, and processing and mining algorithms for massive data. This paper provides a brief review of the state of the art of data science and engineering in SHM as investigated by these authors, and covers the compressive sampling-based data-acquisition algorithm, the anomaly data diagnosis approach using a deep learning algorithm, crack identification approaches using computer vision techniques, and condition assessment approaches for bridges using machine learning algorithms. Future trends are discussed in the conclusion.

A Generative Adversarial Nets Method for Monitoring Data Generation on Aircraft Engines

A sufficient sample size of monitoring data becomes a key factor for describing aircraft engines state.Generative adversarial nets(GAN)can be used to expand the sample size based on the existing state monitoring information.In the paper,a GAN model is introduced to design an algorithm for generating the monitoring data of aircraft engines.This feasibility of the method is illustrated by an example.The experimental results demonstrate that the probability density distribution of generated data after a large number of network training iterations is consistent with the probability density distribution of monitoring data.The proposed method also effectively demonstrates the generated monitoring data of aircraft engine are in a reasonable range.The method can effectively solve the problem of inaccurate performance degradation evaluation caused by the small amount of aero?engine condition monitoring data.

Analysis and application of automatic deformation monitoring data for buildings and structures of mining area

The buildings and structures of mines were monitored automatically using modern surveying technology. Through the analysis of the monitoring data, the deformation characteristics were found out from three aspects containing points, lines and regions, which play an important role in understanding the stable state of buildings and structures. The stability and deformation of monitoring points were analysed, and time-series data of monitoring points were denoised with wavelet analysis and Kalman filtering, and exponent function and periodic function were used to get the ideal deformation trend model of monitoring points. Through calculating the monitoring data obtained, analyzing the deformation trend, and cognizing the deformation regularity, it can better service mine safety production and decision-making.

Using Grey Target Theory for Power Quality Evaluation Based on Power Quality Monitoring Data

Smart grid puts forward higher requirements for power quality.Power quality evaluation can provide a decision-making basis for quality improvement.Based on power quality monitoring data,a grey target method is proposed for power quality evaluation.The grey target is composed of power quality evaluation standard and power quality monitoring data to be evaluated.Combining with the characteristics of each power quality evaluation index,the target center of the whole grey target system is found.Then,the power quality monitoring data to be evaluated and the power quality standard mode are compared and analyzed to construct the power quality grey correlation difference information space.Finally,the target center coefficient and target degree of power quality are calculated to realize the comprehensive evaluation of power quality,and the evaluation grade of power quality monitoring data to be evaluated is obtained.Compared with the evaluation results of the existing literature,the effectiveness of the proposed method is verified,which shows that grey target theory is reasonable in the comprehensive evaluation of power quality.

Application of Lightning Monitoring and Positioning Data in Investigation and Identification of Lightning Disaster in Inner Mongolia

The lightning current magnitude and other characteristics are important basic data of the lightning disaster investigation and identification. The characteristics of lightning monitoring and positioning system in Inner Mongolia were introduced and studied in the lightning accident analysis based on the lightning monitoring and positioning data of the lightning stroke accidents. The positioning error of lightning monitoring and positioning system was analyzed. The results showed that lightning current intensity and the position precision were very important data in the lightning disaster investigation. Finally, a variety of meteorological data should be applied in the lightning disaster investigation and identification.

Power Line Monitoring Data Transmission Using Wireless Sensor Network

The WSN used in power line monitoring is long chain structure, and the bottleneck near the Sink node is more obvious. In view of this, A Sink nodes’ cooperation mechanism is presented. The Sink nodes from different WSNs are adjacently deployed. Adopting multimode and spatial multiplexing network technology, the network is constructed into multi-mode-level to achieve different levels of data streaming. The network loads are shunted and the network resources are rationally utilized. Through the multi-sink nodes cooperation, the bottlenecks at the Sink node and its near several jump nodes are solved and process the competition of communication between nodes by channel adjustment. Finally, the paper analyzed the method and provided simulation experiment results. Simulation results show that the method can solve the funnel effect of the sink node, and get a good QoS.

Compliance verification and probabilistic analysis of state-wide power quality monitoring data

This paper introduces the implementation and data analysis associated with a state-wide power quality monitoring and analysis system in China. Corporation specifications on power quality monitors as well as on communication protocols are formulated for data transmission. Big data platform and related technologies are utilized for data storage and computation. Compliance verification analysis and a power quality performance assessment are conducted, and a visualization tool for result presentation is finally presented.

Multi-mode process monitoring based on a novel weighted local standardization strategy and support vector data description

There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution. In order to solve these problems, a novel weighted local standardization(WLS) strategy is proposed to standardize the multimodal data, which can eliminate the multi-mode characteristics of the collected data, and normalize them into unimodal data distribution. After detailed analysis of the raised data preprocessing strategy, a new algorithm using WLS strategy with support vector data description(SVDD) is put forward to apply for multi-mode monitoring process. Unlike the strategy of building multiple local models, the developed method only contains a model without the prior knowledge of multi-mode process. To demonstrate the proposed method's validity, it is applied to a numerical example and a Tennessee Eastman(TE) process. Finally, the simulation results show that the WLS strategy is very effective to standardize multimodal data, and the WLS-SVDD monitoring method has great advantages over the traditional SVDD and PCA combined with a local standardization strategy(LNS-PCA) in multi-mode process monitoring.

Early Monitoring of Pine Wilt Disease in Pinus massioniana based on Hyperspectral Data

We selected healthy Pinus massioniana for pine wood nematode inoculation experiments to get the spectral reflectance of healthy and infected Pinus mas- sioniana in different infection stages via a ground spectrometer （ wavelength in 350 - 2 500 nm）, and analyzed the changes in chlorophyll content at various periods. The original spectral reflectance of healthy and infected P. massoniana was significantly different in the middle and late infection stages, and the reflection peak and absorption valley in visible light region and near infrared region gradually weakened and even disappeared to a straight line. There was significant correlation rela- tionship between chlorophyll content of infected plants and spectral reflectance at the wavelength of 1 405 nm, and the quantitative inversion model of chlorophyll content was correspondingly established as follows： Car = - 1.74（X1~ ）2 ＋ 4. 72X1,~ - 0. 76. Through first-order derivative spectra at the wavelength of 593 nm, combined with quantitative inversion of the corresponding chlorophyll content, we can discriminate whether P. massoniana is infected by pine lt disease or not, especially in the early stages before disease features are visible to the naked eyes it has a good quantitative monitoring effect.

Trip Purposes of Automobile Users Inference Using Multi-day Traffic Monitoring Data

Determining trip purpose is an important link to explore travel rules. In this paper,we takea utomobile users in urban areas as the research object,combine unsupervised learning and supervised learningm ethods to analyze their travel characteristics,and focus on the classification and prediction of automobileu sers’trip purposes. However,previous studies on trip purposes mainly focused on questionnaires and GPSd ata,which cannot well reflect the characteristics of automobile travel. In order to avoid the multi-dayb ehavior variability and unobservable heterogeneity of individual characteristics ignored in traditional traffic questionnaires,traffic monitoring data from the Northern District of Qingdao are used,and the K-meansc lustering method is applied to estimate the trip purposes of automobile users. Then,Adaptive Boosting(AdaBoost)and Random Forest(RF)methods are used to classify and predict trip purposes. Finally,ther esult shows:(1)the purpose of automobile users can be mainly divided into four clusters,which includeC ommuting trips,Flexible life demand travel in daytime,Evening entertainment and leisure shopping,andT axi-based trips for the first three types of purposes,respectively;(2)the Random Forest method performss ignificantly better than AdaBoost in trip purpose prediction for higher accuracy;(3)the average predictiona ccuracy of Random Forest under hyper-parameters optimization reaches96.25%,which proves the feasibilitya nd rationality of the above clustering results.

Anomaly Detection Algorithm for Stay Cable Monitoring Data Based on Data Fusion

In order to improve the accuracy and consistency of data in health monitoring system,an anomaly detection algorithm for stay cables based on data fusion is proposed.The monitoring data of Nanjing No.3 Yangtze River Bridge is used as the basis of study.Firstly,an adaptive processing framework with feedback control is established based on the concept of data fusion.The data processing contains four steps:data specification,data cleaning,data conversion and data fusion.Data processing information offers feedback to the original data system,which further gives guidance for the sensor maintenance or replacement.Subsequently,the algorithm steps based on the continuous data distortion is investigated,which integrates the inspection data and the distribution test method.Finally,a group of cable force data is utilized as an example to verify the established framework and algorithm.Experimental results show that the proposed algorithm can achieve high detection accuracy,providing a valuable reference for other monitoring data processing.