Monitoring Data Processing Research Articles

General Paradigm of Edge-Based Internet of Things Data Mining for Geohazard Prevention.

Geological hazards (geohazards) are geological processes or phenomena formed under external-induced factors causing losses to human life and property. Geohazards are sudden, cause great harm, and have broad ranges of influence, which bring considerable challenges to geohazard prevention. Monitoring and early warning are the most common strategies to prevent geohazards. With the development of the internet of things (IoT), IoT-based monitoring devices provide rich and fine data, making geohazard monitoring and early warning more accurate and effective. IoT-based monitoring data can be transmitted to a cloud center for processing to provide credible data references for geohazard early warning. However, the massive numbers of IoT devices occupy most resources of the cloud center, which increases the data processing delay. Moreover, limited bandwidth restricts the transmission of large amounts of geohazard monitoring data. Thus, in some cases, cloud computing is not able to meet the real-time requirements of geohazard early warning. Edge computing technology processes data closer to the data source than to the cloud center, which provides the opportunity for the rapid processing of monitoring data. This article presents the general paradigm of edge-based IoT data mining for geohazard prevention, especially monitoring and early warning. The paradigm mainly includes data acquisition, data mining and analysis, and data interpretation. Moreover, a real case is used to illustrate the details of the presented general paradigm. Finally, this article discusses several key problems for the general paradigm of edge-based IoT data mining for geohazard prevention.

Risk-based centralized data monitoring of clinical trials at the time of COVID-19 pandemic

ObjectivesCOVID-19 pandemic caused several alarming challenges for clinical trials. On-site source data verification (SDV) in the multicenter clinical trial became difficult due to travel ban and social distancing. For multicenter clinical trials, centralized data monitoring is an efficient and cost-effective method of data monitoring. Centralized data monitoring reduces the risk of COVID-19 infections and provides additional capabilities compared to on-site monitoring. The key steps for on-site monitoring include identifying key risk factors and thresholds for the risk factors, developing a monitoring plan, following up the risk factors, and providing a management plan to mitigate the risk. MethodsFor analysis purposes, we simulated data similar to our clinical trial data. We classified the data monitoring process into two groups, such as the Supervised analysis process, to follow each patient remotely by creating a dashboard and an Unsupervised analysis process to identify data discrepancy, data error, or data fraud. We conducted several risk-based statistical analysis techniques to avoid on-site source data verification to reduce time and cost, followed up with each patient remotely to maintain social distancing, and created a centralized data monitoring dashboard to ensure patient safety and maintain the data quality. ConclusionData monitoring in clinical trials is a mandatory process. A risk-based centralized data review process is cost-effective and helpful to ignore on-site data monitoring at the time of the pandemic. We summarized how different statistical methods could be implemented and explained in SAS to identify various data error or fabrication issues in multicenter clinical trials.

A Dynamic Pyramid Tilling Method for Traffic Data Stream Based on Flink

Traffic guidance, traffic management and emergency vehicle traffic all require keeping abreast of traffic status. Intelligent Transportation Systems (ITS) is highly expected to provide real-time traffic condition information service. To achieve this, the capability of handling dynamic data stream collected from multi traffic monitoring sources and serving the public with information timely is essential for ITS. With the wide spread of Internet of Things technology, not only the amount, but also the spatial and temporal resolutions of real-time traffic data have explosive growth, thereby enhancing the difficulty of real-time traffic data processing in ITS. Web pyramid map tiles is wide accepted for massive spatial data service, and the latency of tile generation significantly reduces the timeliness of information transmission and the reliability of services. A Flink-based method for dynamic pyramid tile generation and updating is proposed here. Take advantages of combining grid indexes, employing data partition and window selection mechanisms, and applying iterative computational characteristics for resampling, the distributed dynamic pyramid map tile generation algorithm (DPTG), can quickly visualize real-time spatial traffic data with digital map tiles. Taking the national highway road data from China as an example, the experimental results show that the Flink-based DPTG method has high efficiency and scalability in both batch processing and stream processing mode, which highlights the capability of the proposed method to support real-time traffic monitoring data processing for timely large-scale public service in ITS.

Using artificial neural networks for GNSS observations analysis and displacement prediction of suspension highway bridge

Bridges are playing a major role in the socio-economic development of any country over the world. Suspension highway bridges are one of the most sensitive structures to various external influences and loads. Therefore, the need for structural monitoring system, maintenance, and deformation prediction for these structures is important and vital. One of the main objectives of monitoring the structural deformation is predicting the deformation values, which will help to avoid sudden failure and accidents in the future. Artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS) have proven successful solution in many engineering applications and problems. This paper investigates an integrated monitoring system using GNSS observations for studying the deformation behavior and displacement prediction for suspension highway bridge, taking into consideration the effect of wind, temperature, humidity and traffic loads during the operational and short-term measurements. Due to the complexity of the mathematical processing of large GNSS monitoring data for obtaining reliable results, adequate model of several alternatives should be chosen. One of the main objectives of this paper is to investigate the optimum predictive model for analysis of GNSS observations and displacement prediction. Several models are applied and compared for prediction of suspension bridge displacement for both kinematic and dynamic models. The resulting predicted displacement values by applying artificial neural networks (ANNs) and ANFIS provide a significant improvement for predicting the structure deformation values for suspension highway bridges from GNSS observations.

Monitoring and Analysis of Distributed New Energy Resources Based on the Internet of Things

In order to accurately monitor and analyze new energy resources in real time, the analysis and design of a distributed new energy resource monitoring device based on the Internet of Things cloud platform is carried out, and a Hadoop distributed new energy system based on the Internet of Things cloud computing is designed. It analyzes the computing architecture of parallel collection and distributed storage of new energy resource monitoring data, analyzes the infrastructure system of the distributed system, and builds the big data processing architecture and computing service architecture for the electricity information collection system based on the Hadoop cluster. Tasks are processed in parallel to improve the computing efficiency of electricity consumption big data; at the same time, in order to solve the problem of insufficient storage space caused by the sharp increase in the amount of data in the electricity consumption information collection system and the difficulty of data interaction at all levels, a distributed database is developed. The modular design of the parallel data collection system of HBase and the data warehouse tool Hive, and the distributed data calculation design based on the MapReduce method have improved the efficiency of data collection and the unified storage of heterogeneous data. The research provides reference for realizing an efficient and safe new energy resource monitoring data processing system.

Optimal Bibd Extended Design Based Neighbour Discovery in Asynchronous Wsn

For asynchronous Wireless Sensor Networks (WSNs), this research suggests an ideal Balanced Incomplete Block Design (BIBD) extended design based neighbour finding technique. The algorithm takes use of the characteristics of BIBD extended designs to build a series of transmission plans that guarantee effective neighbour detection with little energy consumption and delay. Simulations are used to test the proposed algorithm, and the results show that it significantly outperforms previous approaches in terms of energy usage, latency, and scalability.
 The value of this study is in the innovative neighbour finding algorithm that it proposes, which overcomes the drawbacks of existing approaches and provides a scalable and energy-efficient solution for large-scale WSNs. The suggested technique may be used in a variety of settings, including environmental monitoring, industrial automation, and healthcare, where accurate and timely data gathering and processing depend on efficient neighbour detection.
 The suggested algorithm is implemented step by step in the technique section along with the relevant equations and tables. The simulation results for sample input data are shown in the results section along with pertinent tables and graphs. The results are outlined in the conclusion section, which also emphasises the importance of the suggested method.
 Overall, the suggested technique provides a viable option for rapid neighbour detection in WSNs, potentially having repercussions for a broad variety of applications.

Mitigating damage on heritage structures by continuous conservation using thermal real-time monitoring. Case study of Ziri Wall, city of Granada, Spain

Climate change and anthropogenic causes represent a multidisciplinary problem in urban environments, specifically in the destruction of historical elements of enormous value. Although conventional preservation methods are based on comprehensive conservation measures and periodic inspection, these urban elements sometimes lie in a state of neglect due to lack of means to carry out continuous monitoring. The present work develops a replicable and easy-to-apply multi-analytical methodology, which proposes the use of standard monitoring to automatically diagnose the elements of heritage buildings. Similar approaches are becoming popular in the setting of Structural Health Monitoring, and their application in the field of environmental monitoring is one of the valuable outputs of this work. Diagnosis is based on the development of an inverse characterisation model of the thermal response in a known state of conservation through standard monitoring. This model is calibrated using real measurements. The methodology involves characterising energy response to environmental excitations measured against what is deemed to be a baseline state of conservation. Also, the monitoring system and data processing required is defined and explained in detail. Anomalies detected in the comparison of the baseline to subsequent real-time measurements would highlight the need to bring scheduled on-site inspections forward with an affordable cost. This benchmarking using the estimation of baseline and real measurements is the main result of the methodology. Finally, the methodology developed was validated with an exhaustive study conducted on the Ziri Wall at Granada, Spain, in both summer and winter. The findings revealed a good fit between calculated and experimental data, with mean absolute errors of under 1 °C in both seasons. The proposal can be applied in other fields such as preventive maintenance.

An approach for automatization of geotechnical monitoring in cryolithozone

The article presents an analysis of publications in the field of research devoted to the problems associated with the construction of buildings and structures in areas of permafrost soils, which belong to the cryolithozone. At the same time, the analysis of the research works made it possible to conclude that currently the data obtained because of monitoring, as a rule, are processed manually. In the article the process of geotechnical monitoring is presented in the form of a generalized diagram, from which a sufficiently large array of data is collected during the process of geotechnical monitoring. According to this information graphs of changes in the movement of the building foundations in time and graphs of thermometric observations of the soil massif, etc. are made. That fact justifies the need of automatization of the collection and processing of geotechnical monitoring data. The article presents an approach for automatization of geotechnical monitoring. Systematization of the geotechnical monitoring data and ensuring the operation of equipment synchronously in an automated mode will provide the timely notification of stakeholders about the identified deviations of the controlled parameters and reduce risks and avoid accidents.

Research on computer application software monitoring data processing technology based on NLP

In the process of using computer application software, the system may fail because of software defects. During the operation of the computer system, the running state of the system is also monitored. This paper mainly studies the monitoring data processing technology of computer application software based on NLP and presents a software monitoring data processing scheme based on NLP. First, the data were preprocessed by word segmentation and data cleaning. Then the named entity recognition and extraction information are accomplished by fusing CRF and rule-based methods. It can provide effective data support for system failure prediction by characterizing the software monitoring data.

Comparative Analysis of Atmospheric Air Monitoring Systems Data

Purpose. To analyze the international experience in determining the state of atmospheric air, to conduct a comparative analysis of integrated indicators of atmospheric air quality on the example of the city of Mariupol. Methods. Processing and analysis of atmospheric air monitoring data were performed by calculation, statistical and graphical methods. Results. The study of the air quality of the city of Mariupol is carried out at 2 posts of the Automated Environmental Monitoring System in the Donetsk region and 13 automatic monitoring posts (AMP) at the borders of sanitary protection zones (SPZ) of the city enterprises. As a result of data analysis, the average percentage of pollutants, sub-indices and air quality indices according to the standards of Ukraine, EEA, EPA in 2020 were calculated. The obtained results differ in quantitative values, but on the scale of air quality are similar. Using the data of the Automated Environmental Monitoring System of Donetsk region, the average monthly concentrations of pollutants were calculated according to the data of regional posts and automatic monitoring posts on the border of sanitary protection zones. To assess the quality of atmospheric air, a data set was used: suspended particles, ozone, nitrogen and sulfur dioxide, which are used to calculate global air quality indicators. There is a tendency to increase the change in the concentration of formaldehyde in the atmosphere of the city during 2017 - 2020. Conclusions. The average percentage of pollutants, sub-indices and indices of air quality in Mariupol according to the standards of Ukraine, EEA, EPA are calculated. The obtained results differ in quantitative values, but on the scale of air quality are similar.

НАУЧНО-ТЕХНИЧЕСКИЕ ПРЕДЛОЖЕНИЯ ПО ИНФОРМАЦИОННО-АНАЛИТИЧЕСКОМУ ОБЕСПЕЧЕНИЮ КОМПЛЕКСОВ РАДИОМОНИТОРИНГА

Introduction: within the framework of the scientific direction on improving the information and control systems of radio monitoring complexes, the issues of forming the structure and content of information resources that ensure the solution of the problems of collecting, processing and presenting radio monitoring data are considered. The starting point was the requirements for information management systems, the basis for the implementation of which is to find a rational structure of information resources and develop mechanisms for their interaction, filling and updating. Purpose: on the basis of an analysis of the requirements for radio monitoring complexes information and control systems, develop scientific and technical proposals for the implementation of scientific developments on the formation of their information resources that ensure the fulfillment of these requirements. Results: the target function of the system, indicators of the quality of its functioning have been determined. The article presents the results of constructing a conceptual model of a single information resource of a radio monitoring complex as a set of structural, functional and ontological models. The main structural elements are the storage of radio monitoring data coming from technical means of detection, direction finding of radio electronic means, a database of indicative descriptions of radio monitoring objects, a factual information resource, a database with the results of processing radio monitoring data, as well as directly processing algorithms. The issues of filling information resources, determining the frequency of their updating are reflected. The composition of the algorithms for operational (current) and thematic (deferred) processing of radio monitoring data is concretized, the main tasks of the control subsystems and the provision of information and control systems are detailed. The software architecture of information support of radio monitoring complexes is proposed. The application of the integrated service bus technology as a metasystem basis of information resources is shown. Conclusions are made about the applicability of the developed scientific and theoretical provisions in practice, examples of the construction of software that implement them are shown. Discussion: the research results can be used in the formation of information resources of multi-level systems and radio monitoring complexes, the information process in which involves multi-stage data processing and their presentation in real time to various consumers.

Plasma optical emission spectroscopy based on feedforward neural network

Optical emission spectroscopy (OES) has been widely applied to plasma etching, material processing, development of plasma equipment and technology, as well as plasma propulsion. The collisional-radiative model used in OES is affected by the deviation of fundamental data such as collision cross sections, thus leading to the error in diagnostic results. In this work, a novel method is developed based on feedforward neural network for OES. By comparing the error characteristics of the new method with those of the traditional least-square diagnostic method, it is found that the neural network diagnosis method can reduce the transmission of basic data deviation to the diagnosis results by identifying the characteristics of the spectral vector. This is confirmed by the experimental results. Finally, the mechanism of the neural network algorithm against fundamental data deviation is analyzed. This method also has a good application prospect in plasma parameter online monitoring, imaging monitoring and mass data processing.

Watershed of a small river as source of contaminants and biogenic elements

There is considered methodology of estimation of contaminants and biogenic elements intake into water objects from urban watersheds by municipal non-point runoff. We used the Volkovka River in St. Petersburg and its watershed for the research because the watershed is characterized by high degree of variety and includes all possible types of urban areas: multi-storey residential constructions, industrial areas, green spaces, cemeteries, wastelands, and even agricultural areas. The research included the monitoring data processing, the watershed analysis by GIS-technologies, and field studies.

Improved management of water resources in process industry by accounting for fluctuations of water content in feed streams and products

In this paper, a general strategy is presented for optimizing water resources in industrial activities taking into account fluctuations of water flow-streams and water contents in feed and products as well as in the utility system. Indeed, considering fluctuations can be of primary importance for several water intensive industries such as the food or the paper&pulp industry. Furthermore, whenever the water balance includes the water content of products (as is typically the case for the food industry) fluctuations in the composition of products can considerably affect the overall balance due to water being a major component of many foods. Therefore, the currently used optimization strategies that disregard water fluctuations, can lead to a severe bias in the water balance and thus overlook the potential of water recovery and reuse.Including this aspect in widely used algorithms for process monitoring (data reconciliation) and optimization might lead to a possibly prohibitive increase in the computational burden. In particular, it is shown in this paper that the resulting optimization algorithm should solve a dynamic non-convex stochastic Mixed Integer Nonlinear Programming problem.To tackle this (typically NP-hard) problem, a strategy has been developed that combines ordinal optimization for dealing with statistical information and nonlinear, non-convex deterministic algorithms.The outlined strategy has been applied to a complex process in the food industry (production of starch and starch-based products). The results seem to confirm the general validity of the algorithm developed.

Development of methods for equalizing level networks

The article is devoted to comparison of methods of calculating the approximate elevations of nodal points when adjusting leveling networks by strict and non-strict methods. Ensuring geomechanical monitoring of the mutual influence of constructed and operated objects is important in the intensive development of underground space of megacities, the construction of unique objects and structures. In the course of the research, mathematical models of level networks were developed and presented in the form of a closed loop and a set of open polygons. Analysis of modeling allows to determine the feasibility of applying the knot method by Professor V.V. Popov instead of the parametric method of adjustment. It is established that the quality of the results of strict equalization depends entirely on the quality of measurements, since deviations from the true values do not exceed the measurement error. The research has shown that it is possible to simplify the processing of monitoring data and evaluation of various configuration options for leveling networks, using algorithms for calculating approximate marks of nodal points. This makes it easier to process monitoring results and evaluate various network configuration options while ensuring the required level of measurement accuracy.

Improved modal identification using wireless continuous dynamic monitoring systems without real time synchronization

In the context of vibration based Structural Health Monitoring using automated operational modal analysis, data synchronization methods permit a reduction in the cost of wireless monitoring systems, since the use of extra hardware for perfect data synchronization can be avoided. Furthermore, the relaxation of data synchronization requirements permits a more flexible topology (higher distance between sensors, no need of external antennas for data synchronization using GPS, lower power consumption), which is particularly advantageous in the dynamic monitoring of large structures. In this paper, continuous dynamic monitoring without real-time synchronization is studied. Firstly, using numerical simulations, the effects of synchronicity faults on operational modal analysis is deeply analysed, showing that time delays have a great impact on modal identification. Secondly, a new data synchronization method based on mode shapes identified with operational modal analysis methods is proposed to improve the accuracy of modal identification. This does not need the assumption of proportional damping. Therefore, it overcomes this limitation that up to the knowledge of the authors is present in all other available methods. The proposed processing strategy is validated in the processing of monitoring data continuously collected at a long span arch bridge (Infante D. Henrique Bridge) with two wireless acceleration nodes. The full-scale application shows high time synchronization accuracy and proves that the proposed synchronization method is adequate for continuous dynamic monitoring, providing reliable modal parameters estimates with any assumptions about the damping, permitting a reduction in the monitoring costs, easier installation and power consumption reductions (very important to increase the autonomy of wireless nodes). In short, hardware costs are replaced by a smarter processing.

Results of the development of the forecast monitoring system of factors characterizing the fire hazard of the territory using satellite and ground data

Purpose. Improving of a forecast monitoring system of the fire hazard territory.
 Methods. To obtain a qualitatively new result the approach of Big Data including static (relatively slowly changing in time) data, ground information from meteorological stations and satellite data over a long period of time is applied.
 Findings. A forecast monitoring system of factors characterizing the fire hazard of the territory is developed. The system includes remote access service (providing data to users), database of forecast monitoring statistics, data processing and analysis subsystems. The structure of the system is relatively universal with the ability to complete and change both the functions themselves and the tasks to be solved. This allows considering the system as a preliminary stage of introducing modern approaches to fire hazard forecasting on the territory in the Republic of Belarus. The creation of the system is the beginning of a new forecast stage in the development of mechanisms of response to natural fires in the Republic of Belarus.
 Application field of research. The results of a forecast monitoring system of factors characterizing the fire hazard of the territory may be used in daily activities of the departments of the Ministry of Emergency Situations of the Republic of Belarus and the Ministry of Forestry of the Republic of Belarus to reduce damage due to natural fires.

Bayesian Updating of Material Balances Covariance Matrices Using Training Data

The main quantitative measure of nuclear safeguards effectiveness is nuclear material accounting (NMA), which consists of sequences of measured material balances that should be close to zero if there is no loss of special nuclear material such as Pu. NMA is essentially “accounting with measurement errors,” which arise from good, but imperfect, measurements. The covariance matrix MB of a sequence of material balances is the key quantity that determines the probability to detect loss. There is a recent push to include process monitoring (PM) data along with material balances from NMA in new schemes to monitor for material loss. PM data includes near-real-time measurements by the operator to monitor and control process operations. One concern regarding PM data is the need to estimate normal behavior of PM residuals, which requires a training period prior to ongoing testing for material loss. Assuming that a training period is used for PM data prior to its use in statistical testing for loss, that same training period could also be used for improving the estimate of MB that is used in NMA. We consider updating MB using training data with a Bayesian approach. A simulation study assesses the improvement gained with larger amounts of training data.

Effective Data Decision-Making and Transmission System Based on Mobile Health for Chronic Disease Management in the Elderly

In developing countries, the management of chronic diseases in the elderly is challenging because of limited medical resources and a large population. Hospitals cannot quickly provide treatment for chronic diseases for the elderly. This article discusses a mobile-medical-based system that uses effective data decision-making and wireless network communication to assist the elderly in implementing remote medical services and proposes a deep learning model-combined sparse autoencoder (CSAE) in the decision-making module. The model uses sparse autoencoders to process patient detection data and monitoring data collaboratively to achieve multisource data association and fusion. The prediction probability of diseases is obtained through deep neural networks and a classifier, and the best disease prediction sequence is output to realize the prediction and early warning of chronic diseases. In the experimental part, we compare CSAE with the other four machine learning classification algorithms in terms of accuracy, sensitivity, and specificity. The results prove that CSAE is superior to other algorithms in the single-disease prediction, with the accuracy and specificity exceeding 85%, and the sensitivity not lower than 75%. When predicting multiple diseases, our proposed algorithm's performance is the most stable, with an accuracy higher than other algorithms by more than 10%.

Application of an adaptive weighted estimation fusion algorithm in landslide deformation monitoring data processing

For early warning and forecast of landslide disasters, several types of sensors are used to monitor landslide deformation at different locations. In practical applications, a simple comparative analysis of the data collected at each monitoring point cannot fully use the information provided by these data. In this contribution, the use of an adaptive weighted estimation fusion algorithm is implemented to fuse the landslide deformation data collected by the Global Navigation Satellite System (GNSS) receiver and the displacement meter. The fused data are utilized for a discriminant analysis of the landslide stage. In contrast to the fusion results obtained using the original monitoring data and a back-propagation neural network, the results obtained using the adaptive weighted estimation fusion algorithm reveal that the one-sidedness of the information can be overcome using a single monitoring method and single monitoring point data. Moreover, the algorithm can provide a reliable criterion for the analysis of landslide deformation.