Real Data Processing Research Articles

A Joint Estimation Method of the Channel Phase Error and Motion Error for Distributed SAR on a Single Airborne Platform Based on a Time-Domain Correlation Method

Distributed synthetic aperture radar (SAR) is a system in which transmitting or receiving arrays are distributed on multiple platforms or at different locations on one platform. Distributed SAR can be used for high-resolution wide-swath (HRWS) imaging. The typical platform used for distributed SAR is a satellite constellation, which has long baselines and an ideal trajectory. Instead of satellite constellations, this paper focuses on distributed SAR on a single airborne platform, for which the channel error and motion error are coupled. Furthermore, the traditional channel error estimation methods are invalid. Thus, based on the time-domain correlation method (TDCM), this article proposes a joint estimation method of the channel phase error and motion error for the distributed SAR on a single airborne platform. Firstly, a channel error and motion error coupled phase error model of the distributed SAR is constructed. A joint estimation method of the channel phase error and motion error is then proposed. Finally, a simulation and real data processing are provided to demonstrate the effectiveness of the proposed method.

The unsupervised pattern recognition for the ECG signal features detection

Smart devices and cloud-based technologies allow continuous non-invasive bio-signal recordings in daily life activities. There are many programs and applications that allow monitoring health condition and sharing that information with other people. However, bio-signals that are recorded in real nonstationary conditions are highly contaminated with noise that depends on various activities. Improper signal processing algorithms may lead to a faulty diagnosis or inaccurate decision-making results. Movement contaminated bio-signals require adaptive filtering and feature extraction algorithms because low frequency trends are mostly unstable, and the noise may cause higher impulses than the signal itself. That is why ordinary ECG signal parameters extraction algorithms fail in real time signal processing. In this research signals were filtered using two different methods: Butterworth filter for the high frequency noise reduction and BEADS algorithm for the low frequency noise removal. A new ECG feature extraction algorithm was proposed that is based on the unsupervised MTEO algorithm together with additional local extremum search. The proposed algorithm was compared with other methods on the MIT-BIH database. The additional comparison was made with Pan-Tompkins and k-TEO algorithms on ECG signals that were recorded in movement. Several examples are presented that show how each algorithm performs during training sessions with various intensity levels. The suggested method performed in linear time complexity that made it sufficient for the real time data processing. The obtained ECG parameter values could be used for diagnostics and fatigue recognition in health monitoring processes.

Comparison of Different Approaches to the Creation of a Mathematical Model of Melt Temperature in an LD Converter

In the steel production process in the LD converter, it is important to have information about the melt temperature. The temperature and chemical composition of the steel are important parameters in this process in terms of its completion. During the process, continuous measurement of the melt temperature and thus also information about the end of the process are missing. This paper describes three approaches to creating a mathematical model of melt temperature. The first approach is a regression model, which predicts an immeasurable melt temperature based on other directly measured process variables. The second approach to creating a mathematical model is based on the machine learning method. Simple and efficient learning algorithms characterize the machine learning methods. We used support vector regression (SVR) method and the adaptive neuro-fuzzy inference system (ANFIS) to create a mathematical model of the melt temperature. The third approach is the deterministic approach, which is based on the decomposition of the process and its heat balance. The mathematical models that were compiled based on the mentioned approaches were verified and compared to real process data.

Quality-Discriminative Localization of Multisensor Signals for Root Cause Analysis

Root cause analysis (RCA) methods for effectively identifying the critical causes of abnormal processes have attracted attention because manufacturing processes have grown in scale and complexity. However, the existing methods for building automatic RCA models suffer from the disadvantage of typically requiring expert knowledge. In addition, without a dataset representing the causal relationship of multivariate processes, it is difficult to provide useful information for RCA. Although data-driven RCA methods have been proposed, most are based on classification models. Given that product quality is defined as a continuous variable in many manufacturing industries, classification models are limited in deriving root causes affecting the product quality level. In this article, we propose a regression model-based RCA method, which we call quality-discriminative localization, consisting of a convolutional neural network (CNN)-based activation mapping of multisensor signal data. In our proposed method, the CNN predicts the product quality of a continuous variable. Activation mapping then extracts causal maps that highlight significant sensor signals for each product. To identify the root causes, we generate a root cause map from the weighted sum of quality and causal maps. We consider root causes as locations of abnormal processes and processing times from localized activation scores on the root cause map. We experimentally demonstrate the usefulness of the proposed method with simulated data and real process data from a steel manufacturing process. Our results show that the proposed method successfully identifies root causes with distinct sensor signal patterns.

Anomaly detection of industrial state quantity time-Series data based on correlation and long short-term memory

ABSTRACT Anomaly detection of multi-dimensional time-series data is a key research area, and the analysis of control, switching, and other state signals (i.e., industrial state quantity time series) is of particular importance to the operational sciences. When only the limited values of industrial state quantities are taken in the discrete set, there is no continuous change trend, making it difficult to achieve good results when applying analogue anomaly detection methods directly. In this study, assuming a correlation between the time series of state and analogue quantities in industrial systems, a model for anomaly detection in state quantity time-series data is built through a correlation supported by long short-term memory, and the model is verified using real physical process data. These results demonstrate that the proposed method is superior to extant industrial time-series models. Thus far, no studies focusing on the anomaly detection of two-state quantity time-series outliers have been performed. We believe that the research problem addressed herein and the proposed method contribute an interesting design methodology for the anomaly detection of time-series data in IIoT.

A Simulation Method of Two-Dimensional Sea-Surface Current Field for Trajectory Crossing Spaceborne SAR

The trajectory-crossing method is an important and effective method for spaceborne SAR (synthetic aperture radar) to detect a two-dimensional current field. However, in practical applications, there are few spaceborne SAR data that meet the requirements of close acquisition time and overlapping trajectories, which makes it difficult to comprehensively analyze the impact of different systems and environmental parameters on current measurement results from real data processing. With the proposal of a SAR constellation plan in the future, the data resources will be constantly enriched, and the trajectory-crossing method will be widely used. It is, thus, necessary to lay some theoretical foundation at present. A two-dimensional sea-surface-current-field simulation method for trajectory crossing spaceborne SAR is proposed in this paper. Based on the principles of the trajectory-crossing method, the proposed method can realize the simulation of two-dimensional sea-surface-current field under given spaceborne SAR system and environmental parameters. In this paper, the simulation process of this method is given, and the simulation experiment is performed. Compared with the current measurement results of SAR data, the simulation experiment shows that the current velocity error is less than 0.03 m/s and the direction error is less than 10 degrees, which proves the reliability of the proposed simulation method. The proposed method lays a foundation for analyzing the influence of various parameters in the application of the trajectory-crossing method.

Postopek modeliranja v okviru množičnega vrednotenja nepremičnin

The mass valuation of real estate in Slovenia is based on valuation models, which are made based on realised real estate market data and illustrate the functioning of the real estate market. The system is based on valuation models that simulate the behaviour of the real estate market and allow a statistically reliable assessment of market value. Models are formed using statistical methods of processing real estate market data. This paper presents the quality verification process and preparation of real estate market data. These data are used to design evaluation models or modelling, which consists of time adjustment of prices or rents, zoning, levelling, calibration, and analysis of quality parameters. Finally, the critical information of the modelling process made in the context of real estate mass valuation in Slovenia is summarised.

РАСЧЕТ АПРИОРНЫХ СТАТИЧЕСКИХ ПОПРАВОК В МОВ С УЧЕТОМ ПОЛОЖЕНИЯ ЗАРЯДА ОТНОСИТЕЛЬНО ПОДОШВЫ ЗОНЫ МАЛЫХ СКОРОСТЕЙ

While processing ground-based seismic operations by the reflection method using an explosive excitation source, the standard method of compensation technique for the inhomogeneity of the low velocity layer (LVL) is based on the use of blastholes parameters: shot depth, TSP and velocity of underlying stratum, determined by the apparent velocity of refracted wave. The necessary condition for the applicability of this method is the arrangement of the charge below the base of the LVL. Therefore, to substatiate this method, it is necessary to evaluate the position of the charge relative to the roof of the LWL for each source. If the necessary condition for calculating statics computations is not met, it is necessary to solve the problem of estimating the LVL’s thickness and velocity. The presented work contains the problem of analyzing the position of the charge relative to the LVL’s position and estimating the layer parameters which is solving on the basis of joint analysis of the parameters of wells and wave refracted on the LVL base. The solution of the problem is illustrated by the example of real data processing.

GPR Application for Non-Rigid Road Pavement Condition Assessment

Abstract The main aim of the article is to demonstrate the effectiveness of the use of ground penetrating radars to assess various objects using the example of assessing the current state of highways. The article uses the software developed by the authors and the corresponding mathematical models. The analysis of the results obtained is based on mathematical models that have proven their effectiveness and is time-tested. It should be emphasised that the main problem in assessing roads with non-rigid pavement is associated with a change in the main parameters of the layers – thickness and dielectric constant. Previously, we proposed a scheme for the layer-by-layer determination of the values of the relative permittivity and then - the subsequent determination of the thickness of each layer, starting from the top layer and ending with the base. The paper presents the results of experiments actually carried out by the authors with various GPRs, which not only have different values of the central frequency, but also have significant design differences. In addition, the results of processing real data using the software developed by the authors are presented. As a result, an improved method of signal calibration has been proposed, which makes it possible to increase the reliability of assessing the thickness of road surfaces, as well as other objects.

Back Projection Algorithm for Multi-Receiver Synthetic Aperture Sonar Based on Two Interpolators

The back projection (BP) algorithm is characterized by its high performance for multi-receiver synthetic aperture sonar (SAS). For this reason, it is usually used to evaluate the imaging performance of Fourier-domain methods. However, this algorithm suffers from a large computation load, and the imaging efficiency is seriously lowered. In order to improve the imaging performance, this paper proposes focusing the multi-receiver SAS data using the BP algorithm based on two interpolators, including the linear interpolation and nearest-neighbor interpolation. The former interpolation is used to decrease the interpolation error based on adjacent sampled data; the latter estimates the data at the desired moment by assigning the data value of the nearest sample as estimated data. Then, the imaging performance of the presented method is discussed in detail based on simulations and real-data processing. With the presented method, the imaging performance can be improved without a loss of efficiency compared to nearest-neighbor interpolation without an upsampling operation. In comparison with the traditional BP algorithm, the presented method can be used to improve the imaging efficiency without any loss of performance.

Are the statistical tests the best way to deal with the biomarker selection problem?

Statistical tests are a powerful set of tools when applied correctly, but unfortunately the extended misuse of them has caused great concern. Among many other applications, they are used in the detection of biomarkers so as to use the resulting p-values as a reference with which the candidate biomarkers are ranked. Although statistical tests can be used to rank, they have not been designed for that use. Moreover, there is no need to compute any p-value to build a ranking of candidate biomarkers. Those two facts raise the question of whether or not alternative methods which are not based on the computation of statistical tests that match or improve their performances can be proposed. In this paper, we propose two alternative methods to statistical tests. In addition, we propose an evaluation framework to assess both statistical tests and alternative methods in terms of both the performance and the reproducibility. The results indicate that there are alternative methods that can match or surpass methods based on statistical tests in terms of the reproducibility when processing real data, while maintaining a similar performance when dealing with synthetic data. The main conclusion is that there is room for the proposal of such alternative methods.

ASTool: An Easy-to-Use Tool to Accurately Identify Alternative Splicing Events from Plant RNA-Seq Data

Alternative splicing (AS) is an essential co-transcriptional regulatory mechanism in eukaryotes. The accumulation of plant RNA-Seq data provides an unprecedented opportunity to investigate the global landscape of plant AS events. However, most existing AS identification tools were originally designed for animals, and their performance in plants was not rigorously benchmarked. In this work, we developed a simple and easy-to-use bioinformatics tool named ASTool for detecting AS events from plant RNA-Seq data. As an exon-based method, ASTool can detect 4 major AS types, including intron retention (IR), exon skipping (ES), alternative 5′ splice sites (A5SS), and alternative 3′ splice sites (A3SS). Compared with existing tools, ASTool revealed a favorable performance when tested in simulated RNA-Seq data, with both recall and precision values exceeding 95% in most cases. Moreover, ASTool also showed a competitive computational speed and consistent detection results with existing tools when tested in simulated or real plant RNA-Seq data. Considering that IR is the most predominant AS type in plants, ASTool allowed the detection and visualization of novel IR events based on known splice sites. To fully present the functionality of ASTool, we also provided an application example of ASTool in processing real RNA-Seq data of Arabidopsis in response to heat stress.

Analysis of semi-Markov systems with fuzzy initial data

In real operating conditions of complex systems, random changes in their possible states occur in the course of their operation. The traditional approach to describing such systems uses Markov models. However, the real non-deterministic mechanism that controls the duration of the system's stay in each of its possible states predetermines the insufficient adequacy of the models obtained in this case. This circumstance makes it expedient to consider models that are more general than Markov ones. In addition, when choosing such models, one should take into account the fundamental often manifested feature of the statistical material actually used in the processing of an array of observations, their small sample. All this, taken together, makes it relevant to study the possibility of developing less demanding, tolerant models of the behavior of complex systems. A method for the analysis of systems described under conditions of initial data uncertainty by semi-Markov models is proposed. The main approaches to the description of this uncertainty are considered: probabilistic, fuzzy, and bi-fuzzy. A procedure has been developed for determining the membership functions of fuzzy numbers based on the results of real data processing. Next, the following tasks are solved sequentially. First, the vector of stationary state probabilities of the Markov chain embedded in the semi-Markov process is found. Then, a set of expected values for the duration of the system's stay in each state before leaving it is determined, after which the required probability distribution of the system states is calculated. The proposed method has been developed to solve the problem in the case when the parameters of the membership functions of fuzzy initial data cannot be clearly estimated under conditions of a small sample

Smart teledentistry healthcare architecture for medical big data analysis using IoT-enabled environment

The current spread out in Big Data analytics and the medical Internet of Things (IoT) originated the recognition of smart health. Smart health is the integration of devices, sensors, cameras, and objects or things embedded with sensors that generate an enormous amount of data known as big data. IoT-enabled smart health management assimilates the digital traces generated by things and humans including teledentistry. The key purpose of teledentistry is to analyze the data and produce valuable insights and hidden patterns to offer services and overcome the existing challenges. This article proposes a data management design using Big Data analytics for smart teledentistry planning. The proposed scheme is the customization of the present parallel platforms to accomplish effective processing. The design offers three different modules which are the data pre-processing, data ingestion, and data processing module. The pre-processing is performed to speed up the real data processing. The recent schemes lack well-organized and effective parallel data loading and proficient communication. The Big Data storage and ingestion are achieved using optimized utility and parallel mechanisms. Optimized RDD-enabled (resilient distribution) Yet Another Resource Negotiator (YARN) based proposal is designed for resourceful cluster administration and computation of medical Big Data. The proposed data design is experimentally simulated with authentic datasets using Apache Spark 3.0 framework. The proposed architecture is compared with existing state-of-the-art proposals. The simulation results reveal the efficacy of the proposed system.

IMMERSIVE VISUAL EXPERIENCE FOR WAYFINDING ANALYSIS

Abstract. The research presents a new digital approach to wayfinding analysis in immersive reality, integrated with biosensors for the interpretation of data surveyed. The three-dimensional model of the actual state of the Hall of Perugia Railway Station was realized, considering and detecting the present signage, and it was developed a specific algorithm for a multi-platform graphics engine, able to register an interaction mediated by HMD devices. Wayfinding experiences in immersive reality are developed guaranteeing to the user to move freely to complete the missions assigned. The algorithm in this phase runs in the background and records the movements of users and their pupil, saving them in a special file. In the second phase, instead, the real data processing and the subsequent representation of the collected information take place. Starting from its current state, the Station hall is transformed inserting different elements, derived by the aim of developing four scenarios, to analyse different users’ response in wayfinding. A three-dimensional heat map of visual attentions is created and the different scenarios can be valued for their impact in the user wayfinding, the results are then confronted with the neuro-analysis obtained by EEG instruments and biosensors. In this way, it is possible to value the impact of the environment and spatial orientation cues, by quantifying and qualifying the impact of the project choices.

Application of Long Short-Term Memory (LSTM) on the Prediction of Rainfall-Runoff in Karst Area

In actual engineering fields, the bearing capacity of a rock is closely related to the pore water pressure in the rock. Studies have shown that the pore water in the rock has a great relationship with the change in runoff. Thus, it has crucial meaning to accurately evaluate and quantitate the property of the rainfall–runoff, and many traditional classic models are proposed to study the characteristic of rainfall–runoff. While considering the high uncertainty and randomness of the rainfall–runoff property, more and more artificial neural networks (ANN) are used for the rainfall–runoff modeling as well as other fields. Among them, the long short-term memory (LSTM), which can be trained for sequence generation by processing real data sequences one step at a time and has good prediction results in other engineering fields, is adopted in this study to investigate the changes of rainfall–runoff values and make a prediction. In order to ensure the accuracy of the trained model, the cross-validation method is used in this study. The training data set is divided into 12 parts. The monthly forecast results from 2014 to 2015 show that the model can well reflect the peaks and troughs. In a recent study, the relationship between the rainfall–runoff and discharge are commonly based on the current measured data, while the prediction results are adopted to analyze the relation of these parameters, and considering that the existing methods have fuzzy relationship between runoff and discharge, which leads to a high risk of forecasting and dispatching. A method of modeling analysis and parameter estimation of hydrological runoff and discharge relationship based on machine learning is designed. From the experimental results, the average risk of this method is 61.23%, which is 15.104% and 13.397% less than that of the other two existing methods, respectively. It proves that the method of hydrological runoff relationship modeling and parameter estimation integrated with machine learning has better practical application effect.

Sliding Mode Observer Design for decentralized multi-phase flow estimation

Robust flow measurement in multi-phase systems has extensive applications in understanding, design, and operation of complex environmental, energy and industrial processes. The nonlinearity and spatiotemporal variability of the interactions between different flow phases makes the multi-phase flow measurement a challenging task. Two Sliding Mode Observer (SMO) schemes are proposed in this study for the state estimation of a decentralized multi-phase flow measurement system. The developed observers are shown to be theoretically valid and numerically applicable for a real-life case study data. The multi-phase flow system considered in this paper can be described as two interconnected sub-systems including fluid and gas sub-systems, and two scenarios are considered in the design of the observers. The first scenario considers the interconnections as bounded disturbance (SMOD), while the second scenario considers the interconnections as an uncertainty (SMOU). Hence, the Sliding Mode Observers are adopted to mitigate the effects of disturbance in the system and uncertainties in the parameters. Numerical simulations are conducted using MATLAB and dynamic HYSYS simultaneously, using the data obtained from field-based multi-phase flow measurements. The results demonstrate the appropriateness and robustness of the proposed Sliding Mode Observer (SMO) for estimation of the multi-phase fluid specifications including the density, velocity, and the volume phases fraction in each subsystem. The analysis of the results highlights that the proposed model is computationally efficient with fast transient response, accurate tracking capability of the real process data, and very low steady-state error. This study shows that choosing an appropriate Lyapunov-Krasovsky function results in the asymptotic stability of the decentralized system and improves the performance of the proposed observers. Uncertainty analysis is conducted on the velocity estimation results obtained from the Sliding Observers. Overall, SMOU method shown better performance with RMSE of 0.24%, while RMSE of 0.46% was achieved for the SMOD. Comparison of the numerical results with the field-based flow measurement, as a benchmark, shows that although uncertainty in SMOU is approximately half of the uncertainty in SMOD, state estimation for both schemes was achieved in a finite time with high order of precision. It was shown that both observers developed in this study are well capable of estimating the multi-phase flow variables and states.

A novel scheme for processing FMCW-ladar data acquired with low sampling rate

ABSTRACT The frequency-modulated continuous wave (FMCW) ladar is a useful sensor for remote sensing applications and has been widely used in both the military and civilian fields for precise geospatial data acquisition. Typically, the FMCW-ladar signal is collected by use of the heterodyne detection through the dechirping so as to decrease the sampling rate commensurate with the bandwidth. In this manuscript, we proposed a novel scheme for processing the FMCW-ladar signal acquired through the simplified heterodyne detection, and the sampling rate was the same as that in the dechirp detection. Based on the time–frequency relation of the FMCW-ladar signal, two algorithms were proposed to post-process the echo acquired by the simplified heterodyne detection with the sub-Nyquist sampling rate. For the FMCW-ladar echo data acquired by the simplified heterodyne detection with the same sampling rate as in the traditional dechirp detection, the algorithms can achieve the unambiguous range image retaining the range resolution commensurate with the transmitted chirp bandwidth. The effectiveness of the scheme was validated by simulation and real data processing experiments. The capability of the proposed scheme provides an alternative for the FMCW-ladar system without use of the dechirp detection, which can benefit the future FMCW-ladar and microwave photonics radar applications in the remote sensing.

Off-design flow analysis of cogeneration steam turbine with real process data

This paper presents the concept of reconstruction of the existing coal-fired combined heat and power plant to comply with new European environmental policies. The existing coal-fired boiler will be replaced by two new dual pressure heat recovery steam generators, which will utilize the exhaust gas heat from two new gas turbines. The steam from the heat recovery steam generators will be fed to the existing steam turbine. After the reconstruction, the nominal turbine inlet steam mass-flow of 40 kg/s will be reduced to 30 kg/s. During periods of low heat demand, only one gas turbine and one heat recovery steam generator will be in operation and the live steam mass-flow may drop even to 12 kg/s. Prior to the reconstruction, dedicated tests of the existing steam turbine were carried out using the steam from the existing coal-fired boiler. The goal of the test was to verify the viability of operation with such an extremely low mass-flow. The results of tests show that such operation is possible but inefficient from a power generation point of view. Besides this, the turbine control algorithm needs to be accommodated to this extreme operating regime and additional measures like displacement of the extraction points and steam cooling will be required to control the temperature of the steam extractions. The novelty of this paper is using real pre-reconstruction process data for the assessment of feasibility and efficiency of the post-reconstruction operation of a combined heat and power turbine.

Examination and thermodynamic modelling of a geothermal energy power plant for sustainable development: a case study

The new design article aims to assess the thermodynamic performance modelling of the geothermal power generation plant using real process data. This advised model comprises a steam turbine, high and low-pressure organic rankine cycles, and also these plants are working by n-butane refrigerant. Moreover, with energy and exergy efficiencies, this proposed study is compared with different refrigerants. Looking at the consequences, the ORC-1's energy and exergy efficiencies are 14.49% and 19.03%, while the ORC-2's energetic and exergetic efficiencies are 10.75% and 14.69%, respectively. Analysis results specified that the energy and exergy performances of the investigated power plant are 10.33% and 39.03%, respectively.