Network Monitoring Data Research Articles

Hydrogeochemistry of the shallow aquifer in the western Po Plain (Piedmont, Italy): spatial and temporal variability

ABSTRACT This paper describes the spatial and temporal variability of the hydrogeochemistry of shallow aquifer in the western Po Plains, located in Piedmont (northwestern Italy), using groundwater monitoring network data for the main chemical–physical parameters. Hydrochemical maps for the 2015–2020 period were created to identify the main natural and anthropogenic factors responsible for the remarkable spatial variability. Temporal variations in the 2000–2020 period were defined to show the existence of variabilities that were previously not investigated. The spatial and temporal elaborations show significant and various variabilities. The hydrochemical maps confirm the existence of several natural and anthropogenic factors, including the lithological compositions of aquifers, agricultural practices and pressure from urban centres. The temporal variations suggest different resilience capacities of the aquifers to the several impacting factors. The availability of this knowledge is crucial to create the basis for groundwater protection.

OPC UA Application Study in Oil and Gas Pipeline Network Monitoring Data Forwarding

INTRODUCTION： With the continuous development of oil and gas pipeline network monitoring and control technology, the need for data transmission and communication is becoming more and more prominent. In this context, OPC UA has attracted wide attention. This study aims to explore the application of OPC UA in data forwarding for oil and gas pipeline network monitoring in order to improve the efficiency, reliability and security of data transmission.PURPOSE: The purpose of this study is to evaluate the applicability of OPC UA in oil and gas pipeline network monitoring and to verify its performance in data forwarding through empirical studies. By gaining an in-depth understanding of the characteristics of OPC UA, it aims to provide a more advanced and efficient monitoring data transfer solution for the oil and gas industry.METHOD: The study adopts a combination of field monitoring and laboratory simulation. First, the essential characteristics and requirements of monitoring data in oil and gas pipeline networks were collected. Subsequently, a monitoring system with OPC UA as the communication protocol was established and field tested. In the laboratory environment, data transmission scenarios under different working conditions were simulated, and the performance of OPC UA under different conditions was analyzed.RESULT: The field monitoring results show that the data transmission efficiency is significantly improved by using OPC UA as the communication protocol for data forwarding in oil and gas pipeline network monitoring. Meanwhile, the system performs well in different environments with high reliability and security. The laboratory simulation results further verify the stability and adaptability of OPC UA under complex working conditions.CONCLUSION: OPC UA is an effective communication protocol that can meet the data transmission requirements for oil and gas pipeline network monitoring. Its efficient, reliable, and secure characteristics make it an ideal choice for improving the communication performance of monitoring systems in the oil and gas industry. The empirical results of this study provide reliable technical support for the oil and gas industry in the field of data transmission and a vital reference for the optimization and upgrading of monitoring systems in the future.

해양환경모니터링 네트워크 자료를 이용한 동해연안 수온, 용존산소 농도의 계절과 연직 분포차이 검정

해양환경모니터링 네트워크 자료를 이용한 동해연안 수온, 용존산소 농도의 계절과 연직 분포차이 검정

Distributed neural tensor completion for network monitoring data recovery

Network monitoring data is usually incomplete, accurate and fast recovery of missing data is of great significance for practical applications. The tensor-based nonlinear methods have attracted recent attentions with their capability of capturing complex interactions among data for more accurate recovery. However, the training process of existing methods is often time-consuming due to massive data and unreasonable network resource allocation. Thus motivated, we propose a distributed neural tensor completion method, named D-NORM, which simultaneously optimizes both recovery accuracy and time. Specifically, D-NORM adopts two schemes to solve the resulting optimization problem. First, we design a parameter-efficient multi-layer architecture with convolutional neural network to learn nonlinear correlations among data. Second, we reformulate the initial model as an equivalent set function optimization problem under a matroid base constraint. After constructing an approximate supermodular function to substitute the objective set function with provable upper bound, we propose an approximation algorithm based on the two-stage search procedure with theoretical performance guarantee to rationally allocate computing resources and efficiently recover missing data. Extensive experiments conducted on real-world datasets validate the superiority of D-NORM in both efficiency and effectiveness.

Cognitive computing and 3D facial tracking method to explore the ethical implication associated with the detection of fraudulent system in online examination

Following the Covid-19 pandemic, the rapid spread of online education and tests demanded the implementation of cheating detection tools to ensure academic integrity. While advances in technology such as face recognition, face expression recognition, head posture analysis, eye gaze tracking, network data traffic analysis, and IP spoofing detection have shown promising results in detecting fraudulent behavior, their integration raises ethical concerns that must be carefully considered. This work presents a cognitive computing strategy for investigating the ethical implications of using cheating detection systems in online tests. This study attempts to examine the potential impact on students’ privacy, fairness, and trust in the examination process by employing cognitive computing, which models human cognitive capacities. A thorough literature review is used in the process to uncover existing ethical norms and regulatory frameworks linked to online assessments and cheating detection. Soft computing approaches are also used to evaluate the effectiveness and dependability of the aforementioned cheating detection strategies. The study looks into how far facial recognition and expression analysis can go in terms of privacy, as well as the possibility of bias in head posture analysis and eye gaze tracking algorithms. Furthermore, it investigates the ethical implications of monitoring network data traffic and detecting IP spoofing, with a focus on data security and user permission. The cognitive computing model, based on the analysis, presents a comprehensive framework for ethical decision-making when installing cheating detection technologies. The findings of this study contribute to the continuing discussion about the ethical concerns of using modern technologies to identify cheating in online exams. It provides educational institutions and policymakers with practical ideas for striking a balance between academic integrity and protecting students’ rights and dignity. By emphasizing ethical issues, this study aims to ensure that the implementation of cheating detection systems adheres to values of fairness, transparency, and privacy protection, promoting a trusting and supportive online learning environment for all parties involved.

Passive-tracer modelling at super-resolution with Weather Research and Forecasting – Advanced Research WRF (WRF-ARW) to assess mass-balance schemes

Abstract. Super-resolution atmospheric modelling can be used to interpret and optimize environmental observations during top-down emission rate retrieval campaigns (e.g. aircraft-based) by providing complementary data that closely correspond to real-world atmospheric pollution transport and dispersion conditions. For this work, super-resolution model simulations with large-eddy-simulation sub-grid-scale parameterization were developed and implemented using WRF-ARW (Weather Research and Forecasting - Advanced Research WRF). We demonstrate a series of best practices for improved (realistic) modelling of atmospheric pollutant dispersion at super-resolutions. These include careful considerations for grid quality over complex terrain, sub-grid turbulence parameterization at the scale of large eddies, and ensuring local and global tracer mass conservation. The study objective was to resolve small dynamical processes inclusive of spatio-temporal scales of high-speed (e.g. 100 m s−1) airborne measurements. This was achieved by downscaling of reanalysis data from 31.25 km to 50 m through multi-domain model nesting in the horizontal and grid-refining in the vertical. Further, WRF dynamical-solver source code was modified to simulate the release of passive tracers within the finest-resolution domain. Different meteorological case studies and several tracer source emission scenarios were considered. Model-generated fields were evaluated against observational data (surface monitoring network and aircraft campaign data) and also in terms of tracer mass conservation. Results indicated agreement between modelled and observed values within 5 ∘C for temperature, 1 %–25 % for relative humidity, and 1–2 standard deviations for wind fields. Model performance in terms of (global and local) tracer mass conservation was within 2 % to 5 % of model input emissions. We found that, to ensure mass conservation within the modelling domain, tracers should be released on a regular-resolution grid (vertical and horizontal). Further, using our super-resolution modelling products, we investigated emission rate estimations based on flux calculation and mass-balancing. Our results indicate that retrievals under weak advection conditions (horizontal wind speeds < 5 m s−1) are not reliable due to weak correlation between the source emission rate and the downwind tracer mass flux. In this work we demonstrate the development of accurate super-resolution model simulations useful for planning, interpreting, and optimizing top-down retrievals, and we discuss favourable conditions (e.g. meteorological) for reliable mass-balance emission rate estimations.

Correlation Between Daily PM10, Nitrogen Dioxide, and Ozone Measurements with the Stringency Index in 15 Different Districts of a Big Metropolis.

Studies have investigated the effects of lockdowns on air quality around the world and found that fine particulate matter and nitrogen dioxide concentrations decreased due to reduced human activity, while ozone concentrations increased. In this study, we aimed to evaluate the correlation between daily stringency index values of our country and daily PM10, nitrogen dioxide, and ozone measurements in different districts of Istanbul between March 1, 2020, and February 28, 2022. Ministry of Environment and Urbanization and National Air Quality Monitoring Network data on Istanbul air quality monitoring stations were used. The analysis included 15 stations that can monitor at least 75% of the days in a year. PM10, nitrogen dioxide, and ozone were the main pollutants analyzed. There was negative correlation between daily PM10 measurements and daily stringency index values in 3 stations; there was positive correlation in 6 stations. Between daily stringency index values and daily nitrogen dioxide measurements, there was a negative correlation in 3 stations and a positive correlation in 1 station. The daily measurements of 1 station showed a negative correlation with the daily values of stringency index for both PM10 and nitrogen dioxide. In 1 station, while PM10 measures were negatively correlated with stringency index, nitrogen dioxide measurements were positively correlated. This study showed that pandemic limitations could not improve Istanbul's air quality everywhere. For adequate evaluation of impact of the limitations on air quality, it may be more relevant to study the socioeconomic infrastructure of each living area, the sociospatial inequality, industrial employment, the number of households, the density of employee class, and so on with all influencing factors that could have contributed to these various changes.

Changes in Ozone Pollution Trend Characteristics and Sensitivity in Jinan from 2015 to 2020

The surface ozone (O3) spatiotemporal distribution, variations, and its causes in Ji'nan from 2015 to 2020 were revealed based on the air quality monitoring network data and satellite retrievals from the Ozone Monitoring Instrument (OMI). The results showed that the ozone concentration in Ji'nan gradually increased from 2015 to 2020. The annual 90th percentile of the daily maximum 8-h average (MDA8) O3(namely the annual evaluation value) and the MDA8 O3(April-September) increased by 4.8 μg·(m3·a)-1 and 3.8 μg·(m3·a)-1, respectively. The trend of the ozone levels in the high-concentration range increased faster than that in the low-concentration range. The MDA8 in June increased by 7.4 μg·(m3·a)-1, and the rate range of increases was 2.6-3.9 μg·(m3·a)-1 in the cool seasons (December-February); thus, the O3 control in winter cannot be ignored. It is apparent from the diurnal variations in ozone from 2015 to 2020 in April-September that the average ozone levels have risen in recent years. The growth rate in the daytime was higher than that at night. The capacity of photochemical production has been increasing, especially in recent years. Additionally, it is noteworthy that the peak time for ozone levels occurred approximately 1-2 h earlier. The disparity of ozone concentrations among different stations gradually decreased in recent years. Compared with that in 2015, the range of areas with high O3 concentrations in 2019-2020 was further expanded. The significant positive trends in MDA8-90th and MDA8 (April-September) were observed in 16.1% and 22.6% of the monitoring sites in Ji'nan (P<0.05), most of which were located in urban areas and the suburbs close to urban areas. The temporal and spatial changes in ozone in Jinan had been affected by the changes in VOCs and NOx emissions since 2015. Satellite remote sensing data from 2015 to 2020 revealed that the NO2 tropospheric columns (April-September) showed reductions of 20.6%, with a decreasing rate of 0.3×1015 mole·(cm2·a)-1, especially in the urban areas and suburbs. The detected variation trends of tropospheric HCHO were weak and insignificant, which suggested that the decrease in NOx emissions was much greater than the decrease in VOCs emissions, and the gap had become more obvious in the urban areas. With responses to precursor emissions, the chemical sensitivity of O3 formation had been changing. The VOCs-limited regimes continuously decreased, and the mixed NOx/VOCs-sensitive regimes and NOx-limited regimes increased. In general, such an extremely inappropriate control ratio of ozone precursor NOx/VOCs led to an overall trend of slow increasing fluctuations of O3 in Ji'nan. The findings clearly indicate that the reduction of VOCs in Ji'nan was far from sufficient, and strengthening the current control of VOCs emissions is an effective measure to control the growth trend of O3 pollution in Ji'nan in the near future, especially in urban and surrounding suburban areas.

Türkiye’nin bir sanayi şehrinde konut ısıtmasının hava kirliliğine katkısı

In this study, the contribution of fuels (coal + natural gas) used for residential heating in Kayseri city center to air pollution was investigated. For this reason, the amount of emissions released to the atmosphere was calculated by using the natural gas and coal data used in the residences in the 5 (five) year period between 2015-2019 in the province of Kayseri. In addition, the percentage contribution of both residential heating and industrial point sources to air pollution in terms of emissions was compared. The relationship between annual average emission amounts and National Air Quality Monitoring Network data was also revealed. During the 5 years period, the total amount of emissions originating from the fuels used in residences and industries were 6171 tons for PM, 9049 tons for SO2, and 14179 tons for NOx. It has been evaluated that the decrease in PM10 concentrations and the increase in NOX concentrations from 2015 to 2019 may be related to the increase in natural gas use in the city. When evaluated in terms of emission amounts; it has been revealed that PM and SO2 are mostly released from domestic sources, while NOXs are from industrial sources.

Information model of power distribution IoT terminal for high-rise building electrical fire monitoring

Fires caused by electrical reasons such as short circuits and leakage in low-voltage distribution network lines account for a relatively large proportion of the high-rise building fires. Building an electrical fire monitoring system for high-rise buildings based on the Internet of Things (IoTs) technology can monitor electrical circuits in real-time and reduce electrical fires. Aiming at the interconnection and intercommunication of the information exchange of the IoT terminals in the electrical fire monitoring system of high-rise buildings, this paper firstly analyzes the functional communication requirements of the electrical fire monitoring system of high-rise buildings, and combined with the requirements of functional communication, the information model required for electrical quantity collection and non-electricity collection functions is studied based on IEC 61850, which is conducive to the fusion and application of low-voltage distribution network monitoring data and existing distribution automation system data. At the same time, the location information logical node is established by supplementing, which realizes the determination of the spatial position of the terminal and the sensor, the location of the fire source can be determined by analyzing and calculating according to the spatial location information and the spatiotemporal sequence of the detection information. Finally, the information modeling of different types of monitoring terminal equipment is carried out and the configuration suggestion based on SCL language is proposed. By realizing the integration of low-voltage terminal unit (LTU) into the system and the configuration of LTU, the standard system of IEC 61850 can be actually operated. A monitoring terminal based on the proposed information model has been developed and applied to a building in Wuhan.

Data Compensation with Gaussian Processes Regression: Application in Smart Building’s Sensor Network

Data play an essential role in the optimal control of smart buildings’ operation, especially in building energy-management for the target of nearly zero buildings. The building monitoring system is in charge of collecting and managing building data. However, device imperfections and failures of the monitoring system are likely to produce low-quality data, such as data loss and inconsistent data, which then seriously affect the control quality of the buildings. This paper proposes a new approach based on Gaussian process regression for data-quality monitoring and sensor network data compensation in smart buildings. The proposed method is proven to effectively detect and compensate for low-quality data thanks to the application of data analysis to the energy management monitoring system of a building model in Viet Nam. The research results provide a good opportunity to improve the efficiency of building energy-management systems and support the development of low-cost smart buildings.

토양 내 오염물질 농도 예측을 위한 베이지안 벌점 스플라인

For the diagnosis of soil contamination, it is necessary to accurately understand the distribution of pollutants in the soil of the survey site. However, since it is practically impossible to investigate all observable points in the survey site, the concentration of pollutants in the soil of the survey site is predicted through a spatial prediction model based on data obtained from some selected points in the survey site. However, due to the nature of the soil data, the size of the data is often insufficient, which can greatly reduce the accuracy of spatial prediction. Therefore, in this study, to solve this problem, we propose a Bayesian penalized spline model that predicts the concentration of contaminants in the soil of the survey site by using the soil quality monitoring network data provided by the Ministry of Environment(MOE) in Korea as prior information. In addition, in order to evaluate the performance of the proposed model, RMSE, MAE, MAPE were used to compare the prediction accuracy by data size with several comparative models. As a result of the performance evaluation, it was confirmed that the performance of the proposed model showed better performance than the comparative models. In particular, the smaller the data size, the better the performance of the proposed model compared to the comparative models. Therefore, the use of the proposed model can be considered when trying to understand the distribution of pollutants in the soil at the initial stage of soil survey, where soil data for the survey site is relatively scarce.

SOFI dataset: Symptom-fault relationship for IP-network

This article describes a dataset of network performance and well-known faults. It is a dataset of symptom-fault causal relationships for an IP-based network. This information is periodically gathered from edge nodes of an emulated enterprise network. The dataset contains about 649 h of network monitoring data, 10 of which refer to artificially induced faults. Those faults are impact failures as faulty cables, fiber cuts, high link utilization, line card failure, link down, misconfigurations, protocol issues, and distribution layer going down. Data were methodically structured by an authoring tool. It is an original dataset created on purpose because of no availability of this dataset type. It is of interest to researchers in data mining, fault diagnosis, and overall network management.

Evaluation of current earthquake activity on the Ganos Fault: MONGAN network test analysis

The Ganos Fault MONGAN earthquake monitoring network data presents significant opportunities and challenges for earthquake detection, location and magnitude calculations, source mechanism solutions, and discovery of fault zone waves. This study consists mostly of primary data analysis and seismological evaluations. While stations located in a local area allow the discovery of earthquakes at micro earthquake level, they create significant difficulties in determining the source parameters of these earthquakes. Extracting small earthquakes from continuous data shows that special strategies need to be developed. Network data revealed the presence of many earthquakes with magnitude M

Comparative analysis of algorithms for estimating traffic parameters in corporate multiservice communication networks

Adequate management of modern corporate communication networks is possible if many control procedures function in near real time. In this case, the processed network monitoring data must have accurate characteristics sufficient for making objective management decisions. This fully applies to the data monitoring of network traffic parameters, which determines the relevance of the proposed work. The proposed in the paper algorithm for on-line estimation of traffic parameters in corporate multiservice communication networks is based on the concept of conditional nonlinear Pareto-optimal filtering V. C. Pugachev. Its essence is that the estimation of traffic parameters is performed in two stages - in the first stage, we evaluate the forecast values of parameters, and in the second, with the next observations of random sequences, we make adjustments to their values. Traffic parameter values forecasts are constructed in a small-sized sliding window, and the adjustment is implemented on the basis of pseudo-gradient procedures whose parameters are adjusted using a fuzzy control algorithm based on the Takagi-Sugeno method. The proposed algorithm belongs to the class of adaptive algorithms with prior learning. The maximum value of the average relative error of estimation of traffic parameters was less than 8.2%, which is a sufficient value for the implementation of operational network management tasks. At the same time, the actual scientific and technical task is to analyze the comparison of the characteristics of the developed adaptive algorithm with the characteristics of the optimal algorithms, the characteristics of which are the maximum achievable. Translated with www.DeepL.com/Translator (free version). The results of a comparison of the proposed method with the optimal Coleman filtration (OKF) are presented.

Linking catchment structural units (CSUs) with water quality: Implications for ambient monitoring network design and data interpretation

Climate induced changes in runoff regimes and ongoing anthropogenic modification of land use and land cover (LULC) are shifting ambient water quality signals worldwide. Modulation of these signals by the physical catchment structure over different scales adds complexity to interpreting and analyzing measured data. Further bias may be introduced where monitoring networks are not representative of the structure of catchments in a given region. Here, we present a new environmental regionalization method to assess the representativeness of water quality monitoring (WQM) networks and to identify key structural drivers linked to water quality signals. Unique numerical codes were generated at the pixel level to provide wall-to-wall coverage of key Catchment Structural Units (CSUs) based on LULC, surficial geology, wetlands and slope. CSU codes were generated for all tributary (AT) catchments >20 km2 in Southern Alberta (n = 289), Canada, to determine the representativeness of an existing WQM network (54 tributary catchments) and to assess the explanatory power of CSUs with respect to water quality signals. Cluster analysis (CA) and multi-dimensional scaling (MDS) on the percent area of CSUs in the AT catchments identified six primary structural clusters in Southern Alberta. A clear gradient in catchment structure was evident progressing downstream from the Rocky Mountain headwaters through the foothills and prairie/plains region. Montane and grassland regions were found to be potentially under-represented by the current WQM program whereas catchments dominated by agriculture were likely over-represented. The disproportionate impact of specific CSU combinations on water quality was illustrated where the CA and MDS analyses indicated that even small percentages of urban areas and badland type topography results in elevated concentrations of total recoverable metals, nutrients and major ions. The application of the CSU approach in Southern Alberta demonstrates its value as an alternative method to assess and/or redesign existing WQM networks and to link water quality data to the structural composition of catchments. The general availability of the required data to generate CSUs provides universal potential for the approach to help assess other WQM programs and to contextualize data records. Applying the CSU approach when developing new ambient WQM networks can also help reduce the potential of over-monitoring similarly structured catchments as well as ensuring that all structural classes are represented by the data being generated.

Design of Centralized Heating Monitoring System Based on Wireless Sensor Networks

Current centralized heating monitoring system has realized the collection and control of working condition data in heating power stations, but there are still some shortcomings, such as the inability to collect data on the working conditions of user sides, and the inability to meet the further demand of heating enterprises for the refinement of heating network monitoring data. A wireless sensor network is a fully distributed sensor system with no central node, which can intensively deploy many sensor nodes to monitoring area through random placement, and integrates sensors, data processing and communication modules to form a self-organized network system. Therefore, in order to realize the intelligence of heating system and improve the flexibility of node data collection, the monitoring system can use wireless sensor network technology to realize wireless collection of node data, and display the collected data on a man-machine interface in real time. On the basis of research results from previous scholars, this paper expounds the research status and significance of centralized heating monitoring system design, elaborates the development background, current status and future challenges of wireless sensor network technology, introduces the methods and principles of wireless network communication protocol and heating and heat balance flow analysis, proposes a structural model of a centralized heating monitoring system based on wireless sensor networks, carries out the design of perception and convergence nodes, analyzes the layout of wireless sensor networks, explores the design scheme of centralized heating monitoring system based on wireless sensor networks, conducts the hardware and software design of the monitoring system, implements the software testing and hardware debugging of the centralized heating monitoring system, and finally discusses the relationship between data transfer related tasks and task scheduling. The study results show that the application of the centralized heating monitoring system based on the wireless sensor networks can not only more conveniently monitor, control and manage the entire heating networks, but also make full use of the centralized monitoring and quantitative management functions of the wireless sensor networks. This achieves dynamic tracking and monitoring of heating operation, real-time diagnosis of hidden dangers in heating operation, and safe, normal and energy-saving operation of the centralized heating system. The study results of this paper provide a reference for further researches on the design of centralized heating monitoring system based on wireless sensor networks.

완주군 농촌마을 지역의 미세먼지 측정 농도와 대기오염측정망과의 비교 평가

This study aimed to evaluate the concentration of particulate matter less than 10 micrometers in diameter or less than 2.5micrometres in diameter (PM<SUB>10</SUB> and PM<SUB>2.5</SUB>) in rural villages, and thus protect the health of elderly farmers, by using the air pollution monitoring network data based on the particulate matter forecast grade. The sampling sites selected included a farm-work environment and the village entrance in the Wanju-gun village. PM<SUB>10</SUB> and PM<SUB>2.5</SUB> were measured using a portable aerosol spectrometer (PAS). When the particulate matter forecast grade was good and normal, PM<SUB>10</SUB> and PM<SUB>2.5</SUB> monitoring were performed once every two weeks. When the forecast grades were poor and very poor, PM<SUB>10</SUB> and PM<SUB>2.5</SUB> monitoring was performed daily. To compare the air pollution monitoring network data to the PM<SUB>10</SUB> and PM<SUB>2.5</SUB> levels measured at the sampling sites, the data were extracted from Air Korea. When the particulate matter forecast grade was poor, the concentration of PM<SUB>10</SUB> and PM<SUB>2.5</SUB> in the farm-work environment (80.1 ㎍/㎥, 54.1 ㎍/㎥) was the highest among all the sites (air pollution monitoring network: 78.8 ㎍/㎥, 41.4 ㎍/㎥, village entrance: 68.1 ㎍/㎥, 53.3 ㎍/㎥). Also, the concentration of PM<SUB>2.5</SUB> was significantly different between the sampling sites and the air pollution monitoring network during the sampling period (p=0.002). When the particulate matter forecast grade was poor, the sampling sites showed a higher PM<SUB>2.5</SUB>/PM<SUB>10</SUB> ratio (farm-work environment: 0.68, village entrance: 0.78) compared to the air pollution monitoring network (0.52). Based on these results, when the fine dust forecast is bad, farmers living in rural areas need to reconsider leaving their homes for farming or for any other activity. Also, it is essential to carry out particulate matter management in rural areas, including farm-work environments and residential areas.

Anomaly Detection and Localization for Process Security Based on the Multivariate Statistical Method

Anomaly detection is very important for system monitoring and security since successful execution of these engineering tasks depends on access to validated data. The localization of the variable causing the fault is very essential. Indeed, the localization of the fault is defined as the ability to determine the source of the fault on a system. Generally, the identification of faults is linked to the detection procedure implemented. Therefore, it is very important to choose the adequate fault detection model to locate fault. For nonlinear uncertain systems, the most performed fault detection method is reduced rank interval kernel principal component analysis (RRIKPCA), which enhances the computational skill by downgrading the kernel matrix dimension. We have proposed in this article a new fault localization technique for uncertain systems, named partial RRIKPCA, which combines the benefits of the RRIKPCA technique and the principle of partial localization. The principal of this method involves selecting partially reduced rank data subsets and then building more accurate models with fewer PCs and isolating faults with higher precision. The proposed fault isolation method is applied for monitoring air quality monitoring network (AIRLOR) data.

Monitoring-based assessment of environmental pollution in regions of the Russian Federation

Abstract The article assesses the environmental pollution level in urban areas of the Russian Federation regions. Environmental monitoring data were used as data sources. For each Russian region the environmental pollution level was considered as the sum of pollution indices for the basic environmental media: air, surface water and soil. It allowed ranking regions and grouped them into categories (from extremely high polluted to extremely low ones). The air pollution monitoring network in cities and industrial centres does not cover all regions of the country, leading to undetermined air pollution level in 12 of 85 regions and thus to underestimated environmental pollution level there. The paper proposes to use the monitoring network data of the snow cover chemistry for the air pollution assessment in problematic regions and more accurate calculation of the environmental pollution level in them. Based on the air monitoring data for 2018 we revealed regions with high and extremely high levels of air pollution, their list was added after analyzing the snow cover chemistry data. As a result, the total assessment of environmental pollution was recalculated upward.