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.

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.

Correlation Analysis of Turbidity and Total Phosphorus in Water Quality Monitoring Data

At present,water pollution has become an important factor affecting and restricting national and regional economic development.Total phosphorus is one of the main sources of water pollution and eutrophication,so the prediction of total phosphorus in water quality has good research significance.This paper selects the total phosphorus and turbidity data for analysis by crawling the data of the water quality monitoring platform.By constructing the attribute object mapping relationship,the correlation between the two indicators was analyzed and used to predict the future data.Firstly,the monthly mean and daily mean concentrations of total phosphorus and turbidity outliers were calculated after cleaning,and the correlation between them was analyzed.Secondly,the correlation coefficients of different times and frequencies were used to predict the values for the next five days,and the data trend was predicted by python visualization.Finally,the real value was compared with the predicted value data,and the results showed that the correlation between total phosphorus and turbidity was useful in predicting the water quality.

Fatigue Safety Assessment of Concrete Continuous Rigid Frame Bridge Based on Rain Flow Counting Method and Health Monitoring Data

The fatigue of concrete structures will gradually appear after being subjected to alternating loads for a long time,and the accidents caused by fatigue failure of bridge structures also appear from time to time.Aiming at the problem of degradation of long-span continuous rigid frame bridges due to fatigue and environmental effects,this paper suggests a method to analyze the fatigue degradation mechanism of this type of bridge,which combines long-term in-site monitoring data collected by the health monitoring system(HMS)and fatigue theory.In the paper,the authors mainly carry out the research work in the following aspects:First of all,a long-span continuous rigid frame bridge installed with HMS is used as an example,and a large amount of health monitoring data have been acquired,which can provide efficient information for fatigue in terms of equivalent stress range and cumulative number of stress cycles;next,for calculating the cumulative fatigue damage of the bridge structure,fatigue stress spectrum got by rain flow counting method,S-N curves and damage criteria are used for fatigue damage analysis.Moreover,it was considered a linear accumulation damage through the Palmgren-Miner rule for the counting of stress cycles.The health monitoring data are adopted to obtain fatigue stress data and the rain flow counting method is used to count the amplitude varying fatigue stress.The proposed fatigue reliability approach in the paper can estimate the fatigue damage degree and its evolution law of bridge structures well,and also can help bridge engineers do the assessment of future service duration.

A STUDY OF SOIL CONSERVATION MONITORING INFORMATION SYSTEM BASED ON REMOTELY SENSED DATA FOR A CATCHMENT ON THE LOESS PLATEAU

The Soil Conservation Monitorins Information System (SCMIS) presented in this paper is oriented to soil erosion control, resources exploitation, utilization, planning and management for a small watershed (about 10 sq. km.) on the Loess Plateau. It sums up Remote sensing (RS), Geographical Information System (GIS) and Expert System (ES) and consists of a integrated system. As a basic level information system of Loess Plateau, its perfection and psreading will bring about a great advance in resources exploitation and management of Loess Plateau.

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.

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.

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.

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.

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.

Design and Implementation of a Photovoltaic Data Acquisition System for Some Meteorological Variables

In this paper we have developed a data logging and monitoring system, we validated the system by comparing the result from it with the existing one and found that the system performs slightly better than the existing work in the same area. This implies that the data logger and monitoring system is good and can be used to monitor solar energy variables even at the comfort of our homes. We fitted a model to the generated data and found that the meteorological variables considered accounted for 99.88% of the power output in the rainy seasons while 0.12% of the variation was not explained due to other factors. Solar panels inclined at an angle of 5° (Tilt) and facing South Pole perform optimally.

Dynamic Control System of Automatic Monitoring Equipment for Waste Gas Pollution Source

In order to improve the quality of automatic monitoring data of pollution sources and apply the automatic monitoring data to verify the environmental tax,Shandong Province took the lead in adopting the Internet of Things technology and drawing on the successful experience of air automatic monitoring stations and surface water automatic monitoring stations in management,and developed a dynamic management and control system for automatic monitoring equipment of pollution sources to improve and strengthen the quality audit of automatic monitoring data,further improve the quality of automatic monitoring data and better provide a basis for environmental management and decision making.The system realizes the simultaneous monitoring of monitoring data,running state and parameters of the automatic monitoring equipment,eliminates the phenomenon of falsification by modifying equipment parameters,and judges the validity of the collected data by acquiring the working state of the equipment remotely and randomly.After the actual operation test of the Department of Ecological Environment of Shandong Province,the system is proved to have the characteristics of practicality,real time and high efficiency,and be able to make up for low frequency and narrow coverage of manual inspection,with good application prospect in the field of environment and pollution source monitoring.

Design of a Web Crawler for Water Quality Monitoring Data and Data Visualization

Many countries are paying more and more attention to the protection of water resources at present,and how to protect water resources has received extensive attention from society.Water quality monitoring is the key work to water resources protection.How to efficiently collect and analyze water quality monitoring data is an important aspect of water resources protection.In this paper,python programming tools and regular expressions were used to design a web crawler for the acquisition of water quality monitoring data from Global Freshwater Quality Database(GEMStat)sites,and the multi-thread parallelism was added to improve the efficiency in the process of downloading and parsing.In order to analyze and process the crawled water quality data,Pandas and Pyecharts are used to visualize the water quality data to show the intrinsic correlation and spatiotemporal relationship of the data.

Rational Identification of Lightning Derivative Disasters by Strong Convective Weather Monitoring Data in Southern China——A Case Study in Guangzhou Development Region

Analytic method and identification direction for rational identification of lightning derivative disasters by strong convective weather monitoring data in southern China were introduced. Taking identification cases of lightning disaster in Guangzhou Development Region as the background,according to the characteristics in the region that large high-precision enterprises were more,lightning derivative disasters occurred frequently in thunderstorm season,and the actual situation that time of the affected enterprise applying for lightning disaster scene identification lagged,combining Technical Specifications of Lightning Disaster Investigation（ QX / T103-2009）,qualitative analysis method of lightning derivative disaster was put forward under the weather condition of strong convection in southern China by using weather monitoring data（ Doppler sounding radar data,lightning positioning monitoring data,atmospheric electric field data,rainfall data,wind direction and force）,and was optimized by technical means（ ＂ metallographic method＂ and ＂ remanence law＂）. The research could put forward efficient and convenient analytical thinking and method for lightning derivative disaster,and further optimize accuracy and credibility of lightning disaster investigation.

Enhanced Prospect Definition Using Well and 4D Seismic Data in a Niger Delta Field

We carried out time-lapse analysis in a producing Niger Delta X-field, by first investigating the response and sensitivity of rock properties/attributes to lithology and pore fill in 3-D cross plot domain and by Gassmann’s fluid substitution modeling. Furthermore, 4-D seismic data were inverted into acoustic impedance volumes through model based inversion scheme. This served as input into a multi-attribute neural network algorithm for the extraction of rock attribute volumes based on the results of the petrophysical log analysis. Subsequently, horizon slices of rock properties/ attributes were extracted from the inverted seismic data and analyzed. In this way, we mapped hydrocarbon depleted wells in the field, and identified probable by-passed hydrocarbon zones. Thus, the integration of well and time lapse seismic (4-D) data in reservoir studies has remarkably improved information on the reservoir economic potential, and enhanced hydrocarbon recovery factor.

Comprehensive evaluation method of urban air quality statistics based on environmental monitoring data and its application

Air quality monitoring is effective for timely understanding of the current air quality status of a region or city.Currently,the huge volume of environmental monitoring data,which has reasonable real-time performance,provides strong support for in-depth analysis of air pollution characteristics and causes.However,in the era of big data,to meet current demands for fine management of the atmospheric environment,it is important to explore the characteristics and causes of air pollution from multiple aspects for comprehensive and scientific evaluation of air quality.This study reviewed and summarized air quality evaluation methods on the basis of environmental monitoring data statistics during the 13th Five-Year Plan period,and evaluated the level of air pollution in the Beijing-Tianjin-Hebei region and its surrounding areas(i.e.,the“2+26”region)during the period of the three-year action plan to fight air pollution.We suggest that air quality should be comprehensively,deeply,and scientifically evaluated from the aspects of air pollution characteristics,causes,and influences of meteorological conditions and anthropogenic emissions.It is also suggested that a threeyear moving average be introduced as one of the evaluation indexes of long-term change of pollutants.Additionally,both temporal and spatial differences should be considered when removing confounding meteorological factors.

The offline data quality monitoring of the BESIII end cap TOF system

Purpose The end cap time of flight(TOF)at Beijing spectrometer was upgraded with multi-gap resistive plate chamber technology in order to improve the particle identification capability in 2015.The offline data quality monitoring is a critical aspect of the data processing chain aiming at providing data with good quality for physics analyses.Methods An offline data quality monitoring tool for upgraded end cap TOF has been developed to provide feedback about the functioning and performance of detector hardware and data processing chain.Results Detector information and reconstructed time-of-flight characteristics of charged tracks are filled into plots using full Bhabha events reconstruction results,and then,these plots are used to assess the operational conditions of the detector and the quality of the data by the experts.Conclusion This paper is describing the design and the content of performance of the offline data quality monitoring of end cap TOF and the data quality performance achieved during last 2 years’physical data taking.

The offline data quality monitoring of the BESIII end-cap TOF system

Background The end-cap time-of-flight(TOF)at Beijing Spectrometer was upgraded with multi-gap resistive plate chamber technology in order to improve the particle identification capability in 2015.The offline data quality monitoring(ODQM)is a critical aspect of the data processing chain aiming at providing data with good quality for physics analyses.Method An ODQM tool for upgraded end-cap TOF has been developed to provide feedback about the functioning and performance of detector hardware and data processing chain.Detector information and reconstructed time-of-flight characteristics of charged tracks are filled into plots using full Bhabha events’reconstruction results,and then,these plots are used to assess the operational conditions of the detector and the quality of the data by the experts.Result This paper describes the design and the content of performance of the ODQM of end-cap TOF and the data quality performance achieved during the last 2-year physical data taking.