Monitoring Data Processing Research Articles

ASSESSMENT OF TEMPORAL TRENDS OF MALIGNANT NEOPLASMS INCIDENCE USING MULTIVARIABLE STATISTICAL ANALYSIS

The article presents the results of the assessment of the trends of malignant neoplasms incidence in Russian regions using multivariable statistics. The study was performed using cluster analysis and panel data models for the data on cancer incidence for all 78 Russian regions for the period from 2005 to 2015. RStudio package was used for all calculations. Data on all independent variables were collected from the official state statistics. Cluster analysis and modeling methods taking into account the panel structure of data makes it possible to study associations between environmental indicators and the incidence of cancers or so-called oncoepidemiological process. The proposed method of multivariable analysis of medical and statistical indicators contributes to the field of data processing for medical and environmental monitoring.

Fault-tolerant flexible lossless cluster compression method for monitoring data in smart grid

Big data in smart grid dispatch monitoring systems is susceptible to interference from processing delays and slow response times. Hence, a new fault-tolerant flexible lossless cluster compression method is proposed. This paper presents the Five-tuples (S, D, O, T, M) model, and builds a monitoring data processing platform based on hive. By deploying the dispatch host and monitoring servers under the cloud computing environment, where data nodes are respectively transformed by Deflate, Gzip, BZip2 and LZO lossless compression method. Taking the power dispatch automation system of Long-hai line as example, experimental results show that the cluster lossless compression ratio of BZip2 is greater than 81%; when data records reach twelve million, the compression ratio can be further improved to certain extent by using RCFile storage hive format, which has significant flexible features. Therefore, the new method proposed in this paper can improve the flexibility and fault-tolerant ability of big monitoring data processing in smart grid.

Joint Attention Mechanism for Person Re-Identification

Although person re-identification (ReID) has drawn increasing research attention due to its potential to address the problem of analysis and processing of massive monitoring data, it is very challenging to learn discriminative information when the people in the images are occluded, in large pose variations or from different perspectives. To address this problem, we propose a novel joint attention person ReID (JA-ReID) architecture. The idea is to learn two complementary feature representations by combining a soft pixel-level attention mechanism and a hard region-level attention mechanism. The soft pixel-level attention mechanism learns a discriminative embedding for the fine-grained information by exploring the salient parts in the feature maps. The hard region-level attention mechanism conducts uniform partitions on the convolutional feature maps for learning local features. We have achieved competitive results in three popular benchmarks, including Market1501, DukeMTMC-reID, and CUHK03. The experimental results verify the adaptability of the joint attention mechanism to non-rigid deformation of the human body, which can effectively improve the accuracy of ReID.

Некоторые статистические характеристики содержания тригалогенметанов в питьевой воде инфильтрационного водозабора

The article deals with statistical processing of monitoring data for the content of trihalomethanes in drinking water of one of the infiltration Ufa water intakes. Using the methods of mathematical statistics, the following parameters were revealed: the distribution density of the total content of trihalomethanes in drinking water, the likelihood of occurrence of adverse events associated with the formation of trihalomethanes as a result of the chlorination of drinking water. As a result of the analysis, it was found that the most common concentrations of the total content of trihalomethanes lie in the range 5.8-8.1 μg / dm3. It was found that the distribution density of the total concentration of trihalomethanes is described by the power dependence ρ (TGM) = 0.348 × Q-2.18 (confidence level R2 = 0.94). It was concluded that the situation at the selected infiltration water intake can be considered favorable, since with a probability of 99%, the total concentration of THM does not exceed 18.03 μg / dm3 with an available maximum concentration of 100 μg / dm3.

РЕАЛИЗАЦИЯ АЛГОРИТМОВ НА ОСНОВЕ АНСАМБЛЕЙ СОПРЯЖЕННЫХ ФУНКЦИЙ ДЛЯ ОБРАБОТКИ ДАННЫХ МОНИТОРИНГА

On the basis of the approach with the use of ensembles of solutions of adjoint equations, it is possible to solve a wide range of inverse modeling problems in a uniform way with the processing of monitoring data of various types, including is situ measurement data and image-type data. The corresponding computational system is implemented within the object-oriented approach. The article provides a brief description of the main components of the system developed for solving inverse problems for advection-diffusion-reaction models. Such problems arise, for example, when studying the processes of transport and transformation of impurities in the atmosphere and the processes of development of the living systems.

ENVIRONMENTAL ASSESSMENT OF SMALL RIVERS OF IRPIN RIVER BASIN BY EKOSYSTEM PRINCIPLE

Основна мета даної роботи: Дослідження екологічного стану малих річок басейну р. Ірпінь в межах Київської області. Встановлення основних причин незадовільного екологічного стану досліджуваних об’єктів, серед яких р. Нивка, р.Козка та р.Унава. Методи: При досліджені застосовувався екосистемно-басейновий підхід, статистична обробка власних даних та даних моніторингу державних установ. Здійснено систематизацію та сформовано банк даних гідрохімічних показників р. Ірпінь за десятирічний період. Результати: За результатами досліджень, у всіх притоках відмічено перевищення норм гранично допустимих концентрацій забруднюючих речовин, що є основним дестабілізуючим фактором для гідроекосистеми р. Ірпінь. Висновки про екологічний стан р. Ірпінь було виконано на основі динаміки зміни індексу якості води у восьми контрольних точках. Для кількісної екологічної характеристики навантаження на гідроекосистему басейну р. Ірпінь було розраховано індекс техногенного впливу в контрольних створах. Обговорення: Отримані результати обумовлюють необхідність подальшого дослідження структурно-функціональних особливостей розвитку гідроекосистеми р. Ірпінь у просторі і часі. Оцінка впливу антропогенних факторів на стан гідроекосистеми дозволить скорегувати природоохоронну діяльність стосовно покращення екологічного стану досліджуваного басейну.

Real-time monitoring rapid ground subsidence using GNSS and Vondrak filter

Human activities such as coal mining may cause rapid ground subsidence, which will damage severely on human-built structures such as coal transport railway or buildings, and even kill lives. In coal-mining areas, ground subsidence happens continuously, and its amplitude may be up to 10 cm per day. In order to assure the safety of coal-mining areas, it’s necessary to monitor the ground subsidence timely and precisely. This paper presents a continuously operating real-time global navigation satellite system ground subsidence monitoring system, which consists of hardware, data processing algorithms and software, communication link and peripheral equipment. A particular architecture was designed for field operation. For the data processing, the Vondrak filter is proposed to process the monitoring data. We operated the proposed monitoring system on the coal-mining area and verified the performance during the mining period from early July to December 2017. The monitoring results show that the proposed system has an accuracy of 5 mm for ground subsidence monitoring on the basis of precise leveling data that were simultaneously observed. The proposed method can meet the accuracy requirement of ground subsidence monitoring, and it can provide continuous subsidence information in real time, which cannot be achieved by the traditional leveling surveying method. The monitoring system and data processing method can be applied to the monitoring of ground subsidence in subsidence area as well as geological disasters such as landslides.

A Cyber-Physical Production System Framework of Smart CNC Machining Monitoring System

Computer numerical control (CNC) machine tools are now moving toward high precision, high speed, and complex functional machining. Machining monitoring system is important to achieve intelligent control of machining process to produce complex geometric features and precision parts. Traditional NC machining monitoring system is limited in data features, low in adaptability, and slow in transmission efficiency, and hard to achieve intelligent online assessment. To overcome these difficulties, this study proposes a smart monitoring system for CNC machining based on Cyber-Physical Production System (CPPS) framework. It is built on the CNC machine tool physical and virtual modeling, process monitoring, and big data analytics, and then synergized into a system through a distributed network. Under the CPPS framework, the smart monitoring system is divided into control layer, network layer, and decision layer. The function of each layer and the key technologies of the CPPS involved are discussed. Case studies of machining process monitoring are studied in the applications.

Avoiding amino acid depletion in a complex medium results in improved Escherichia coli BW25113 growth.

We studied Escherichia coli BW25113 growth in a complex medium with emphasis on amino acid consumption. The aim was to profile amino acid utilization in acid-hydrolysed casein and a defined nutrient-rich medium and based on these measurements modify the medium for better growth performance. Amino acid depletions in both media caused apparent biomass growth stops that prolonged growth duration. Obtained amino acid consumption values enabled a new defined medium to be formulated, where no growth stops were observed, the specific growth rate was constant, and the provided substrates were fully utilized. Similarly, we modified the acid-hydrolysed casein medium by adding pure amino acids that removed the apparent biomass growth stops. Key to our results was the combination of growth medium analysis and process monitoring data, specifically oxygen partial pressure and produced carbon dioxide that were used to track growth changes. Our findings showed the deficiencies of the nutrient-rich medium and how rational medium design, based on consumption values, removed these shortcomings. The resulting balanced medium gives a high specific growth rate and is suitable for studying E. coli physiology at fast growth.

Bulk Handling Facility Modeling and Simulation for Safeguards Analysis

The Separation and Safeguards Performance Model (SSPM) uses MATLAB/Simulink to provide a tool for safeguards analysis of bulk handling nuclear processing facilities. Models of aqueous and electrochemical reprocessing, enrichment, fuel fabrication, and molten salt reactor facilities have been developed to date. These models are used for designing the overall safeguards system, examining new safeguards approaches, virtually testing new measurement instrumentation, and analyzing diversion scenarios. The key metrics generated by the models include overall measurement uncertainty and detection probability for various material diversion or facility misuse scenarios. Safeguards modeling allows for rapid and cost-effective analysis for Safeguards by Design. The models are currently being used to explore alternative safeguards approaches, including more reliance on process monitoring data to reduce the need for destructive analysis that adds considerable burden to international safeguards. Machine learning techniques are being applied, but these techniques need large amounts of data for training and testing the algorithms. The SSPM can provide that training data. This paper will describe the SSPM and its use for applying both traditional nuclear material accountancy and newer machine learning options.

New Automated Work Flows Enhance Formation Evaluation

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 187040, “Dynamic Data Analysis With New Automated Work Flows for Enhanced Formation Evaluation,” by M.A. Proett, SPE, S.M. Ma, SPE, N.M. Al-Musharfi, SPE, and M. Berkane, SPE, Saudi Aramco, prepared for the 2017 SPE Annual Technical Conference and Exhibition, San Antonio, Texas, USA, 9–11 October. The paper has not been peer reviewed. Petrophysical work flows are designed primarily to process static data for traditional openhole logs, which can provide estimates of porosity, saturation, lithology, and mineralogy. However, these estimates normally have a high degree of uncertainty and formation testing and sampling (FTS) data often are required for reservoir-condition calibration. This paper bridges the gap between operational petrophysicists and FTS specialists, introducing an automated work flow by which petrophysicists can conduct FTS jobs. Introduction Wireline formation testers (WFTs) were commercialized in the late 1950s; drilling formation testers (DFTs) were introduced more recently. The primary benefits for FTS by means of wireline (e.g., WFT) or drillpipe (e.g., DFT) tools always has been a link between openhole log static volumetric formation evaluation and dynamic reservoir properties such as reservoir pressure, mobility, and fluid-sample composition, with many more applications under development using advanced testing tools and methods. However, FTS always has faced challenges with the integration of formation-tester data into the petrophysical work flow, limiting the ability to take full advantage of this valuable source of dynamic data. Current work flows normally involve having an FTS specialist evaluate openhole log analyses, plan testing and sampling jobs, monitor data acquisition and quality-control (QC) results, and report on the final data interpretation. Thus, an FTS specialist should hold a wide spectrum of expertise, including FTS-tool use and data analysis and openhole logging and log analysis, as well as reservoir engineering and reservoir dynamics. Consequently, few petrophysicists are truly qualified to be FTS specialists. Therefore, automating FTS work flows is desirable. Automated FTS Work Flow In recent years, new automated methods have been introduced to speed up FTS data delivery. In automated QC data processing, real-time pressure tests are given a rating on the basis of criteria such as pressure stability, temperature stability, drawdown mobility, radius of investigation, and supercharging. More recently, methods have been published that can identify test sequences automatically. Similar methods have been implemented in at least one commercial software offering that demonstrates the benefit of automating the tedious process of selecting testing events manually. By combining the automated QC with the test-event selections, the analysis of data can be automated for real-time monitoring and post-job data processing objectively. With the testing data being processed automatically and the valid or best results being selected from each test sequence, automatically identifying reservoir fluid-flow behaviors is possible.

Why wait? Let us start computing while the data is still on the wire

In this era of Big Data, computing useful and timely information from data is becoming increasingly complicated, particularly due to the ever increasing volumes of data that need to travel over the network to data centers to be stored and processed, all highly expensive operations in the long haul. This is a strong motivation to explore how to perform computing and analysis of data “on the wire”, i.e., while the data is still in transit. The nature of these computations include analysis, visualization, pattern recognition, and prediction on the streaming data. In this paper we present the idea of a framework capable of analyzing data in transit based on the principles of a service function chaining architecture. This framework can be deployed at any practical location within the network where computation on data flows is desirable. We further describe an all-virtual implementation of the framework as a worst-case scenario and present an early investigation of its capabilities with three examples — pattern recognition and data visualization on streaming Forex data, targeted advertising from clickstream data, and processing of monitoring data from solar sensors for maintenance decisions. Our results indicate that performing computations on live data flows to provide immediate perspective on the data is possible and attractive, but also that performance heavily depends on the amount and capabilities of the dedicated resources.

GNSS Data Processing and Analysis for Earthquake Disaster Prevention Monitoring

This article describes how an earthquake with highest magnitude 5.8 on the Richter scale occurred in the Gyeongju area on Sep. 12, 2016 since the first seismological observation. In addition, continuous aftershocks have occurred. There is a pressing need for proper earthquake monitoring and prevention systems. This article is intended to apprehend the dislocation and location change of Continuously Operating Reference Station (CORS) caused by earthquake by means of data on CORS of the National Geographic Information Institute (NGII) and suggest effective seismic monitoring methods. Through Precise Point Positioning (PPP) over earthquake occurrence locations, it was possible to determine the dislocation aspects of CORS for TEGN and CHSG near the seismic epicenter. The nationwide Relative Positioning processing of data for 58 CORS from Sep. 1 to Sep. 30 suggested that CORS had not transitioned coordinates due to the earthquake. For the sake of more efficient earthquake observation and monitoring, it is believed that highly frequent GNSS data acquisition and the additional installation of observation devices such as seismometers is required.

4D-SFM PHOTOGRAMMETRY FOR MONITORING SEDIMENT DYNAMICS IN A DEBRIS-FLOW CATCHMENT: SOFTWARE TESTING AND RESULTS COMPARISON

Abstract. In recent years, the combination of Structure-from-Motion (SfM) algorithms and UAV-based aerial images has revolutionised 3D topographic surveys for natural environment monitoring, offering low-cost, fast and high quality data acquisition and processing. A continuous monitoring of the morphological changes through multi-temporal (4D) SfM surveys allows, e.g., to analyse the torrent dynamic also in complex topography environment like debris-flow catchments, provided that appropriate tools and procedures are employed in the data processing steps. In this work we test two different software packages (3DF Zephyr Aerial and Agisoft Photoscan) on a dataset composed of both UAV and terrestrial images acquired on a debris-flow reach (Moscardo torrent – North-eastern Italian Alps). Unlike other papers in the literature, we evaluate the results not only on the raw point clouds generated by the Structure-from- Motion and Multi-View Stereo algorithms, but also on the Digital Terrain Models (DTMs) created after post-processing. Outcomes show differences between the DTMs that can be considered irrelevant for the geomorphological phenomena under analysis. This study confirms that SfM photogrammetry can be a valuable tool for monitoring sediment dynamics, but accurate point cloud post-processing is required to reliably localize geomorphological changes.

(Invited) Process Monitoring of 100 GaN-on-Diamond Wafers

The introduction of chemical vapor deposition (CVD) diamond as a viable substrate for GaN HEMTs [1], [2] makes GaN-on-diamond an attractive means to run higher power densities through the GaN HEMT compared to traditional GaN on host substrates. While power densities of 20 and 40W/mm have been demonstrated for pulsed devices on silicon carbide (SiC) [3], continuous powers exceeding 10W/mm will reduce the lifetime of the GaN HEMTs due to self-heating. GaN-on-diamond holds a promise of reducing the problems associated with self-heating and allows for increases in the power density of HEMT devices. To date, GaN-on-diamond has been shown to allow for higher areal power densities than GaN-on-SiC [4], [5]. The process of making GaN-on-diamond was first demonstrated more than 10 years ago [1], [2] has evolved and improved over the years from broken samples 1cm on a side with a 25 micron (800 W/mK thermal conductivity) diamond substrate to a 4” process with a 100 micron (1500 W/mK) diamond substrate. In this paper we will look at wafer level electrical comparisons of GaN-on-diamond to GaN on host substrates and at process monitoring for the 100 GaN-on-diamond wafers. The process we use to make GaN-on-diamond is to transfer an epitaxial layer of GaN from a silicon substrate to diamond. In this process we flip the GaN grown on silicon onto a sacrificial substrate and remove the growth silicon. At this point the AlGaN transition layers (TLs) which are necessary to grow low defect density GaN on silicon are etched away. The TLs while critical to the growth of low defect GaN are themselves highly defective and have poor thermal properties [6]. It is therefore advantageous both electrically and thermally to remove these layers. Once the TLs are removed we protect the GaN back side with a nucleation/adhesion layer and we grow the CVD diamond. The standard metric for GaN-on-diamond is the thermal boundary resistance (TBR) which measures how efficiently the heat can be transferred from the GaN to the diamond. With collaborators we have been able to demonstrate a drop from 135 m2K/GW to as low as 10 m2K/GW by removing the TLs and maintaining a consistent adhesion layer thickness of silicon nitride between the GaN and the diamond [7]. Process monitoring shows that we can consistently maintain a 35 +/- 5nm SiN adhesion layer between the GaN and the diamond. In this paper we will present process monitoring data of this and other parameters for the last 100 GaN wafers. Less well established are the electrical metrics for comparing GaN-on-diamond to GaN on host substrates. One important metric of electrical performance for GaN-HEMTs is the flat capacitance at high reverse bias. It allows us to characterize the presence of traps and/or doping at the GaN to host interface [8]. In our case the GaN to diamond interface. We measure this flat capacitance by taking the capacitance voltage (C-V) profile. Results show that we can take GaN-on-silicon which has a flat band capacitance of 25pF and turn it into GaN-on-diamond with a flat band capacitance of ~ 2pF Figure[1a]. This low capacitance is indicative of negligible GaN buffer and epi interface charge/doping. The capacitance of the GaN-on-diamond is now comparable to GaN-on-silicon carbide Figure [1b]. Figure 1

Structure of the Basic Software and Hardware Complex of the System for Seismometric Monitoring of the Technical Condition of Buildings and Structures

The objective of the present study is to design a basic software and hardware complex (SHC) of a seismometric monitoring system for buildings and structures recommended for serial production. To do this, the authors used modern methods and principles of designing software and hardware systems. The system is methodologically based on the engineering-seismometric method. This method uses recording of spatial vibrations of an object as a result of microseisms of natural and anthropogenic origin. Next, dynamic and elastic characteristics of the structure are determined by vibrations that make it possible to evaluate its technical condition. As a result of conducted studies, the authors defined the main technical and functional requirements for the monitoring system for a wide range of applications. A three-level structure of a modular technical condition monitoring system is proposed for discussion. This structure makes it possible to create an extensible open system in which the number of measuring channels can be easily increased by increasing the number of plugin standard modules. In addition, the system can both record seismic events, earthquakes, and perform seismometric monitoring by microseisms. In order to represent the main processes and structure of the proposed basic complex of the system in the case of its full operation, the authors developed a functional model of the system. The model is based on a data flow diagram that describes the processes of collecting, processing, storing, analyzing, and presenting seismometric monitoring data. The functioning of the proposed complex is briefly described. The input data of the complex are the vibrations of the structure recorded at observation points using three-component geophones. Then, the seismic signal recorder collects, amplifies, digitizes, and transmits data to the server. The server either records seismic events and then evaluates their impact on the structure or carries out a planned recording of microseisms in order to monitor the technical condition of the structure and stores vibration data in the corresponding files. If an earthquake is detected, the system notifies the responsible personnel. Records of microseisms are used by the data processing software to compute the statistical parameters of vibrations and complex transfer functions according to the spectra of which the operator manually selects the values of natural frequencies. Seismic monitoring data processing software evaluates elastic characteristics by a number of natural frequencies using a mathematical design model of the structure vibrations. On the basis of the analysis of the change in the obtained dynamic and elastic characteristics and taking into account the effect of external factors, the software generates information for monitoring the technical condition of the structure. These results, as well as data of the evaluation of the event impact on the structure, are the output data of the system.

Development of a model for the management of environmental safety of the region, taking into account of the GIS capacity

The system of space monitoring (SM) is of great importance, as a means of ensuring environmental safety. This system is based on remote sensing. The structure of SM is a distributed system. This system comprises independent data storage, system control, system of dynamic ratings, capacity and forecasting, control system, information system (IS) processing of monitoring data. As IS it is necessary to choose a geographic information system (GIS). IS monitoring refers to the problem-oriented system. These information systems include specialized databases models. All monitoring systems use sets of models, which allow building complex enterprise models. The peculiarity of the SM is the need to coordinate support of this monitoring and rate of the GIS capacity. Production Manager's decision is the impact on the object of monitoring. Results management and environmental data are received at the monitoring subsystem. Integration of SM and GIS monitoring has led to the creation of geoinformation space monitor (GISM). The operation of the system GISM is designed to provide a guaranteed result taking into account the capacity. Basis – the decision of the decision makers (DM). Therefore, an independent scientific and practical interest is the adequate mathematical model of DM.

Development of a procedure for assessing the environmental risk of the surface water status deterioration

A procedure for estimation of the risk of violation of the water body status was presented. The procedure is based on defining environmental standards of surface water quality taking into account landscape and geographical features of river basins. A database of the systems of monitoring surface water was used to assess the risk of deterioration of the aquatic ecosystem. This enables automated calculation of the environmental risk. At the first stage, it is necessary to determine environmental standards (ES) on the basis of analysis and statistical processing of long-term monitoring data on the qualitative state and hydrological regime taking into account forecasting models. At the second stage, probability of violation of ecological standards is estimated by the ratio of the number of observations of the ecological state of the water body with violation of the ES to the total number of observations. Exceeding environmental standards makes it possible to assess the risk of well-being violation in aquatic ecosystems. Assessment of the environmental risk of deterioration of Udy River and Oskil River in Kharkiv region, Ukraine, has made it possible to make the list of priority pollutants and outline the main problems for which a complex of environmental measures should be worked out. Establishment of the risk of well-being violation in the aquatic ecosystem will contribute to the implementation of a flexible system for regulating water quality taking into account ever-changing socio-economic and environmental conditions. The proposed procedure for assessing the environmental risk of deterioration of the status of aquatic ecosystems is based on the normative basis, approaches and methods of environmental assessment of surface waters adopted in Ukraine and the EU

Technologies of preliminary data processing for multidisciplinary geophysical monitoring and a case study of their application in the Kamchatka geoacoustic observation system

The problems and procedures for obtaining high quality results of multidisciplinary geophysical monitoring at the stage of preliminary data processing are considered. The case study of solving different technical problems during the long-term multidisciplinary borehole geophysical observations at the Petropavlovsk- Kamchatsky geodynamic research area is described. The algorithms for preliminary data processing and flawed data discard combining automated approaches and expert screening developed by the authors are presented. Suppression of quasi-regular disturbances is especially important in the studies of interrelations between geoacoustic, electromagnetic, and meteorological processes and seismicity because of the clear diurnal periodicity in all these processes that significantly complicates the signal extraction from the noise. The key technical, organizational, and methodical measures aimed at improving the data should be provided at the project planning stage of the measurement system. The optimal selection and reasoned application of special procedures for data preprocessing can crucially affect the research results.

Intelligent Performance Analysis with a Natural Language Interface

Abstract Performance improvement is taken as the primary goal in the asset management. Advanced data analysis is needed to efficiently integrate condition monitoring data into the operation and maintenance. Intelligent stress and condition indices have been developed for control and condition monitoring by combining generalized norms with efficient nonlinear scaling. These nonlinear scaling methodologies can also be used to handle performance measures used for management since management oriented indicators can be presented in the same scale as intelligent condition and stress indices. Performance indicators are responses of the process, machine or system to the stress contributions analyzed from process and condition monitoring data. Scaled values are directly used in intelligent temporal analysis to calculate fluctuations and trends. All these methodologies can be used in prognostics and fatigue prediction. The meanings of the variables are beneficial in extracting expert knowledge and representing information in natural language. The idea of dividing the problems into the variable specific meanings and the directions of interactions provides various improvements for performance monitoring and decision making. The integrated temporal analysis and uncertainty processing facilitates the efficient use of domain expertise. Measurements can be monitored with generalized statistical process control (GSPC) based on the same scaling functions.