Suitable Data Processing Research Articles

Detection resolving power of SiC Timepix3 detector to electrons, neutrons, ions and protons

Silicon Carbide is a suitable semiconductor sensor for radiation measurement and nuclear applications. It has been recently implemented as a position-sensitive and radiation imaging device coupled to the Timepix3 ASIC chip in the form of a miniaturized radiation camera MiniPIX-Timepix3 SiC. In this work we systematically evaluate the detection resolving power to different radiation species: electrons, fast neutrons, ions and protons. Experimental calibrations were made at well-defined reference fields in terms of particle type, energy and direction. The spectral-sensitive tracking response and characteristic morphology of the particle tracks are analyzed by pattern recognition algorithms. The low thickness of the radiation sensitive volume (65 μm) of the SiC sensor limits the directional tracking response and angular resolution. Three broad classes of particle-type events are resolved. The detector together with suitable data processing can be used for radiation dosimetry and particle tracking tasks in space, particle therapy, nuclear physics and nuclear reactors and particle accelerator environments.

The comparison and processing of Electromyography (EMG) signals under different actions

The experiment first investigates the working principle and the applications of Electromyography (EMG). Then, the effect of electrode location and electrode size on the EMG signal is explored by the raw data and theory. The reasons for choosing generic surface Ag/AgCl adhesive electrodes are also described. After obtaining the raw EMG signals, suitable data processing methods, such as Root Mean Square (RMS) and digital filtering are applied to generate signals that can drive the motor of a prosthetic arm. The results obtained by the two methods are also compared, where RMS can provide larger peak and mean values, and the digital filtering method can minimise the phase shift. Finally, the limitations of the EMG for prostheses are also analysed by three aspects: different flexion forces, individual finger motion and different reference locations.

Applications of geophysical methods in agriculture and their potential in Vietnam

Geophysical methods are very popular in Vietnam and have been applied for several decades in deep-earth investigations such as geological mapping, mineral resource searching, and especially oil and gas exploration. In the world, they have proven to be great tools in agriculture as well for soil characterization and monitoring thanks to their notable advantages including rapid data acquisition, large data coverage, high data density, non-destructive and inexpensive survey implementation. However, in Vietnam, the applications of geophysical methods in agriculture have received little attention probably due to the lack of suitable equipment and data processing techniques. This article gives an overview of popular geophysical methods being applied in agriculture in several countries to characterize and monitor soil properties such as moisture, salinity, density, texture, structure, porosity, etc. The main uses of each method are summarized, and relevant publications are given for reading recommendations with the aim of suggesting similar applications in Vietnam. Accordingly, Ground Penetrating Radar (GPR) and Electromagnetic Induction (EMI) are the most versatile with minimum field crew for data acquisition. They should be prioritized to try in Vietnamese agriculture. Since EMI equipment is not currently available in Vietnam, only a GPR test survey was implemented in the Agricultural Academy experimental field by the authors of Hanoi University of Mining and Geology. The preliminary result shows that the biggest challenge is to find reliable techniques to accurately infer soil properties from measured geophysical parameters, which have no explicit relationship with soil properties. Noise suppression is another problem that needs to be addressed to ensure sufficient data quality.

Design and implementation of a monitoring platform based on beidou high precision positioning technology

Traditional BDS positioning technology has limitations in accuracy, robustness, and availability in certain real-time monitoring applications. Therefore, this article designs and implements a monitoring platform based on Beidou high-precision positioning technology to solve the limitations of traditional BDS positioning technology and provide more accurate, reliable, and real-time positioning services. The research first analyzes and optimizes the positioning algorithms of the existing Beidou system to improve the accuracy of position measurement. Then apply the optimized algorithm to the system architecture of the monitoring platform to achieve high-precision positioning function. Finally, design a suitable user interface and data processing module so that users can easily obtain and analyze positioning data. Through experimental verification, this platform has achieved high accuracy and stability in position measurement. Users can obtain real-time location information through this platform and further analyze and process it.

Application of Fuzzy algorithms in conjunction with 1 H-NMR spectroscopy for alcoholic beverages differentiation.

According to recent statistics elaborated at the European Union level, wine represents one of the most falsified commodities. In this context, the development of reliable classification models for alcoholic beverages differentiation requires, besides sensitive analytical tools, the use of the most suitable data processing methods like those based on advanced statistical tools or artificial intelligence. The present study aims to establish a new innovative approach for alcoholic beverages differentiation (wines and fruit distillates) able to increase the discrimination rate of the developed models. Thus, a data dimensionality reduction step, performed on the obtained 1 H-NMR profiles, was applied. This stage consisted in the application of Fuzzy Principal Component Analysis (FPCA) before themodel development through discriminant analysis. The enhancement of the classification potential given by the application of FPCA as compared to Principal Component Analysis (PCA) was discussed. The association between 1 H-NMR spectroscopy and an appropriate statistical approach proved to be a very effective tool for alcoholic beverages differentiation. In order to develop reliable metabolomic approaches for the differentiation of wines and fruit distillates, 1 H-NMR spectroscopic data were exploited in corroboration with fuzzy algorithms for data dimensionality reduction. The study proved the efficiency of using FPCA scores over those obtained through the widely applied PCA. Based on the proposed approach, it was proved that for wine classification a perfect separation was obtained according to the geographical origin, cultivar and vintage. A percentage of 100% correctly classified samples was also achieved for botanical and geographical differentiation of fruit distillates. This article is protected by copyright. All rights reserved.

Lifting Hospital Electronic Health Record Data Treasures: Challenges and Opportunities.

Electronic health records (EHRs) have been successfully used in data science and machine learning projects. However, most of these data are collected for clinical use rather than for retrospective analysis. This means that researchers typically face many different issues when attempting to access and prepare the data for secondary use. We aimed to investigate how raw EHRs can be accessed and prepared in retrospective data science projects in a disciplined, effective, and efficient way. We report our experience and findings from a large-scale data science project analyzing routinely acquired retrospective data from the Kepler University Hospital in Linz, Austria. The project involved data collection from more than 150,000 patients over a period of 10 years. It included diverse data modalities, such as static demographic data, irregularly acquired laboratory test results, regularly sampled vital signs, and high-frequency physiological waveform signals. Raw medical data can be corrupted in many unexpected ways that demand thorough manual inspection and highly individualized data cleaning solutions. We present a general data preparation workflow, which was shaped in the course of our project and consists of the following 7 steps: obtain a rough overview of the available EHR data, define clinically meaningful labels for supervised learning, extract relevant data from the hospital's data warehouses, match data extracted from different sources, deidentify them, detect errors and inconsistencies therein through a careful exploratory analysis, and implement a suitable data processing pipeline in actual code. Only few of the data preparation issues encountered in our project were addressed by generic medical data preprocessing tools that have been proposed recently. Instead, highly individualized solutions for the specific data used in one's own research seem inevitable. We believe that the proposed workflow can serve as a guidance for practitioners, helping them to identify and address potential problems early and avoid some common pitfalls.

Using cloud computing techniques to monitor long-term variations in ecohydrological dynamics of small seasonally-flooded wetlands in semi-arid South Africa

• Semi-arid Southern African wetlands are lost due to unsustainable use and poor management. • Ramsar Convention on wetlands encourages development of frameworks for wetlands conservation. • The implementation of these frameworks is hindered by a dearth of high-resolution data. • Google Earth Engine (GEE) offers opportunities to address data challenges. • Findings underscores the relevance of GEE in evaluating wetland ecohydrological dynamics. Wetlands in drylands have high inter- and intra-annual ecohydrological variations that are driven to a great extent by climate variability and anthropogenic influences. The Ramsar Convention on Wetlands encourages the development of frameworks for national action and international cooperation for ensuring conservation and wise use of wetlands and their resources at local, national and regional scales. However, the implementation of these frameworks remains a challenge. This is mainly due to limited availability of high-resolution data and suitable big data processing techniques for assessing and monitoring wetland ecohydrological dynamics at large spatial scales, particularly in the sub-Saharan African region. The availability of cloud computing platforms such as Google Earth Engine (GEE) offers unique big data handling and processing opportunities to address some of these challenges. In this study, we applied the GEE cloud computing platform to monitor the long-term ecohydrological dynamics of a seasonally flooded part of the Nylsvley floodplain wetland complex in north-eastern South Africa over a 20-year period (2000–2020). The specific objectives of the study were 1) to evaluate wetland ecohydrological dynamics using the 20-year multi-date Landsat composite data coupled with the Random Forest machine learning algorithm, and 2) to establish the major drivers of wetland ecohydrological changes, using selected spectral indices (i.e. Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI) and Normalised Difference Phenology Index (NDPI)) coupled with climate data. The ecohydrology of the wetland changed over time, with some classes increasing twice when compared to the previous measurement, while others decreasing significantly during the study period. Notably, the bare surface class increased at rates of 230% and 350% between 2006–2010 and 2016–2020, respectively. Moreover, the indices showed similar trends throughout the 20-year period, with NDWI having minimum values less than zero in all cases. This implied no surface inundation, although the presence of some wetland vegetation indicated seasonal to semi-permanent soil saturation conditions. A comparative analysis of climate data and remotely sensed indices showed that annual changes of precipitation and evapotranspiration were the main drivers of wetland ecohydrological variations. The findings of the study underscore the relevance of cloud computing artificial intelligence techniques, and particularly the GEE platform, in evaluating wetland ecohydrological dynamics for semi-arid southern African systems which are deteriorating due to the unsustainable use and poor management resulting from limited knowledge about their changes over time.

Modified approximate entropy analysis for data processing of electrochemical noise with high time resolution toward corrosion monitoring

Electrochemical noise measurement (ENM) is a promising technique for online corrosion monitoring. However, developing suitable data processing methods of ENM recordings for describing corrosion systems with high time resolution remains a challenge. In this work, we introduced the modified approximate entropy (m-ApEn) algorithm to ENM data processing and successfully employed it in the analysis of magnesium corrosion in NaCl solution. This work not only provides a descriptor of m-ApEn to quantify the corrosion activity of the system, but also sheds light on novel data analysis methods of ENM with high time resolution toward corrosion monitoring.

On the Estimation of Resonance Widths of Field Line Resonances Using Ground Magnetometer Data

This paper presents a comparison of three methods to estimate the latitudinal resonance width of field line resonant, ultra-low frequency waves detected at the ground. These are a spatial domain, full-width half-maximum method and frequency domain amplitude-phase and amplitude-division methods. These methods were used to estimate the resonance width of several field line resonant intervals occurring on 26 November 2001, 1 October 2012, and 19 June 2015. The 19 June 2015 interval used data from one low, two mid, and one high latitudes. It was found that the resonance width estimates were different for each method and with how the data were processed. The most suitable methods and data processing were determined from a damped driven harmonic oscillator model. The amplitude-division methods yielded the most accurate results when the ground magnetic field data were processed with a boxcar window function or a frequency domain, exponential smoothing taper. The amplitude-phase method tended to underestimate the resonance width. The full-width half-maximum method gave accurate results for a high spatial resolution linear piece-wise curve fitted to the spectral amplitude with latitude profile. An accurate estimate of the latitudinal resonance width requires a correct choice of data processing, estimate method, resonance profile with latitude, and resonance model.

Identification and Classification of Depressed Mental State for End-User over Social Media.

In researching social network data and depression, it is often necessary to manually label depressed and non-depressed users, which is time-consuming and labor-intensive. The aim of this study is that it explores the relationship between social network data and depression. It can also contribute to detecting and identifying depression. Through collecting and analyzing college students' microblog social data, a preliminary screening algorithm for college students' suspected depression microblogs based on depression keywords, and semantic expansion is researched; a comprehensive lexical grammar was proposed. This research provided has a preliminary screening method based on depression keywords and semantic expansion for college students' suspected depression microblogs, with a screening accuracy. This method forms a depression keyword table by constructing the basic keyword table and the semantic expansion based on the word embedding learning model Word2Vec. Finally, the word table is used to calculate the semantic similarity of the tested microblogs and then identify whether it is a suspected depression microblog. The experimental results on the microblog dataset of college students show that the comprehensive lexical method is better than the SDS questionnaire segmentation method and the expert lexical method in terms of screening accuracy; the comprehensive lexical approach can quickly and automatically screen out a tiny proportion of suspected doubts from a large number of college students' microblogs. Depression Weibo can reduce the workload of experts' annotation, improve annotation efficiency, and provide a suitable data processing basis for the subsequent accurate identification (classification problem) of patients with depression.

In-situ boundary layer transition detection on multi-segmental (a)synchronous morphing wings

This paper presents an experimental method to detect in-situ the location of transition on a multi-segmental trailing edge camber morphing wing during synchronous and asynchronous morphing. The wing consists of six independently morphing segments with two of the segments instrumented with eight embedded piezoelectric sensors distributed uniformly along the chord. Using suitable data processing, each of the sensors gives a signal that can be used to determine the state of the boundary layer (laminar, transitional, turbulent) at the location of that sensor. The results showed that synchronous morphing can substantially shift the location of transition, up to 20% of the chord length for angles of attack below 9°. Differences in the location of transition up to 5% are found between the near-root and near-tip segment. Using a dedicated data processing approach, the location of transition could be reconstructed in case of complex asynchronous morphing involving one to five segments. The results show a shift in the location of transition when morphing neighboring segments, but also show that non-neighboring segments have a minimal effect. This sensing method holds significant promise for online advanced morphing control to delay transition and thereby reducing skin friction drag.

Knowledge Integration in Smart Factories

Knowledge integration is well explained by the human–organization–technology (HOT) approach known from knowledge management. This approach contains the horizontal and vertical interaction and communication between employees, human-to-machine, but also machine-to-machine. Different organizational structures and processes are supported with the help of appropriate technologies and suitable data processing and integration techniques. In a Smart Factory, manufacturing systems act largely autonomously on the basis of continuously collected data. The technical design concerns the networking of machines, their connectivity and the interaction between human and machine as well as machine-to-machine. Within a Smart Factory, machines can be considered as intelligent manufacturing systems. Such manufacturing systems can autonomously adapt to events through the ability to intelligently analyze data and act as adaptive manufacturing systems that consider changes in production, the supply chain and customer requirements. Inter-connected physical devices, sensors, actuators, and controllers form the building block of the Smart Factory, which is called the Internet of Things (IoT). IoT uses different data processing solutions, such as cloud computing, fog computing, or edge computing, to fuse and process data. This is accomplished in an integrated and cross-device manner.

Application of Pulsed Thermography and Post-processing Techniques for CFRP Industrial Components

Several studies demonstrate the effectiveness of pulsed thermography for detection and visualization of sub-superficial flaws in composites. Continuous improvement of thermal data manipulation makes active thermography an attractive and powerful inspection method for industrial process control and maintenance aims. Therefore, temperature image-processing is the major ongoing challenge in the thermographic research field. However, the particular interest for thermographic inspections is to be more addressed to its simple and relatively fast industrial application; an appropriate image processing tool should be implemented and verified on industrial components, containing manufacturing and in-service defects. In the proposed research, well-established and previously proposed methods were analysed and compared for different defect typology inside three CFRP components. The main goal is not solely focused on establishing the suitable data processing approach, providing detection limits of processed data in terms of damage type, size and distribution. The aim of proposed work is to present detailed examples of thermal imaging methods applied on similar critical defects, evaluating different results among methods in terms of defects mapping capabilities and Tanimoto evaluation criterion, coupled also with the signal-to-noise ratio as assessment of defect detectability.

Simulation-based Evaluation of Hydrographic Data Analysis for Dune Tracking on the River Rhine

Knowledge of the static and morphodynamic components of the river bed is important for the maintenance of waterways. Under the action of a current, parts of the river bed sediments can move in the form of dunes. Recordings of the river bed by multibeam echosounding are used as input data within a morphological analysis in order to compute the bedload transport rate using detected dune shape and migration. Before the morphological analysis, a suitable processing of the measurement data is essential to minimize inherent uncertainties. This paper presents a simulation-based evaluation of suitable data processing concepts for vertical sections of bed forms based on a case study at the river Rhine. For the presented spatial approaches, suitable parameter sets are found, which allow the reproduction of nominal dune parameters in the range of a few centimetres. However, if parameter sets are chosen inadequately, the subsequently derived dune parameters can deviate by several decimetres from the simulated truth. A simulation-based workflow is presented, to find the optimal hydrographic data processing strategy for a given dune geometry.

Recent advances in earthquake monitoring I: Ongoing revolution of seismic instrumentation

Seismic networks have significantly improved in the last decade in terms of coverage density, data quality, and instrumental diversity. Moreover, revolutionary advances in ultra-dense seismic instruments, such as nodes and fiber-optic sensing technologies, have recently provided unprecedented high-resolution data for regional and local earthquake monitoring. Nodal arrays have characteristics such as easy installation and flexible apertures, but are limited in power efficiency and data storage and thus most suitable as temporary networks. Fiber-optic sensing techniques, including distributed acoustic sensing, can be operated in real time with an in-house power supply and connected data storage, thereby exhibiting the potential of becoming next-generation permanent networks. Fiber-optic sensing techniques offer a powerful way of filling the observation gap particularly in submarine environments. Despite these technological advancements, various challenges remain. First, the data characteristics of fiber-optic sensing are still unclear. Second, it is challenging to construct software infrastructures to store, transfer, visualize, and process large amount of seismic data. Finally, innovative detection methods are required to exploit the potential of numerous channels. With improved knowledge about data characteristics, enhanced software infrastructures, and suitable data processing techniques, these innovations in seismic instrumentation could profoundly impact observational seismology.

Motor Classification with Machine Learning Methods for Predictive Maintenance

Abstract Reducing costs is an important part in todays buisness. Therefore manufacturers try to reduce unnecessary work processes and storage costs. Machine maintenance is a big, complex, regular process. In addition, the spare parts required for this must be kept in stock until a machine fails. In order to avoid a production breakdown in the event of an unexpected failure, more and more manufacturers rely on predictive maintenance for their machines. This enables more precise planning of necessary maintenance and repair work, as well as a precise ordering of the spare parts required for this. A large amount of past as well as current information is required to create such a predictive forecast about machines. With the classification of motors based on vibration, this paper deals with the implementation of predictive maintenance for thermal systems. There is an overview of suitable sensors and data processing methods, as well as various classification algorithms. In the end, the best sensor-algorithm combinations are shown.

Quantum-Enhanced Barcode Decoding and Pattern Recognition

Quantum hypothesis testing is one of the most fundamental problems in quantum information theory, with crucial implications in areas like quantum sensing, where it has been used to prove quantum advantage in a series of binary photonic protocols, e.g., for target detection or memory cell readout. In this work, we generalize this theoretical model to the multi-partite setting of barcode decoding and pattern recognition. We start by defining a digital image as an array or grid of pixels, each pixel corresponding to an ensemble of quantum channels. Specializing each pixel to a black and white alphabet, we naturally define an optical model of barcode. In this scenario, we show that the use of quantum entangled sources, combined with suitable measurements and data processing, greatly outperforms classical coherent-state strategies for the tasks of barcode data decoding and classification of black and white patterns. Moreover, introducing relevant bounds, we show that the problem of pattern recognition is significantly simpler than barcode decoding, as long as the minimum Hamming distance between images from different classes is large enough. Finally, we theoretically demonstrate the advantage of using quantum sensors for pattern recognition with the nearest neighbor classifier, a supervised learning algorithm, and numerically verify this prediction for handwritten digit classification.

Spatial Data Analysis for Deformation Monitoring of Bridge Structures

Weather conditions and different operational loads often cause changes in essential parts of engineering structures, and this affects the static and dynamic behavior and reliability of these structures. Therefore, geodetic monitoring is an integral part of the diagnosis of engineering structures and provides essential information about the current state (condition) of the structure. The development of measuring instruments enables deformation analyses of engineering structures using non-conventional surveying methods. Nowadays, one of the most effective techniques for spatial data collection is terrestrial laser scanning (TLS). TLS is frequently used for data acquisition in cases where three-dimensional (3D) data with high resolution is needed. Using suitable data processing, TLS can be used for static deformation analysis of the structure being monitored. For dynamic deformation measurements (structural health monitoring) of bridge structures, ground-based radar interferometry and accelerometers are often used for vibration mode determination using spectral analysis of frequencies. This paper describes experimental deformation monitoring of structures performed using TLS and ground-based radar interferometry. The procedure of measurement, the analysis of the acquired spatial data, and the results of deformation monitoring are explained and described.

Analysis of Chlorophyll Concentration in Potato Crop by Coupling Continuous Wavelet Transform and Spectral Variable Optimization

The analysis of chlorophyll concentration based on spectroscopy has great importance for monitoring the growth state and guiding the precision nitrogen management of potato crops in the field. A suitable data processing and modeling method could improve the stability and accuracy of chlorophyll analysis. To develop such a method, we collected the modelling data by conducting field experiments at the tillering, tuber-formation, tuber-bulking, and tuber-maturity stages in 2018. A chlorophyll analysis model was established using the partial least-square (PLS) algorithm based on original reflectance, standard normal variate reflectance, and wavelet features (WFs) under different decomposition scales (21–210, Scales 1–10), which were optimized by the competitive adaptive reweighted sampling (CARS) algorithm. The performances of various models were compared. The WFs under Scale 3 had the strongest correlation with chlorophyll concentration with a correlation coefficient of −0.82. In the model calibration process, the optimal model was the Scale3-CARS-PLS, which was established based on the sensitive WFs under Scale 3 selected by CARS, with the largest coefficient of determination of calibration set (Rc2) of 0.93 and the smallest Rc2−Rcv2 value of 0.14. In the model validation process, the Scale3-CARS-PLS model had the largest coefficient of determination of validation set (Rv2) of 0.85 and the smallest root–mean–square error of cross-validation (RMSEV) value of 2.77 mg/L, demonstrating good prediction capability of chlorophyll concentration. Finally, the analysis performance of the Scale3-CARS-PLS model was measured using the testing data collected in 2020; the R2 and RMSE values were 0.69 and 3.36 mg/L, showing excellent applicability. Therefore, the Scale3-CARS-PLS model could be used to analyze chlorophyll concentration. This study indicated the best decomposition scale of continuous wavelet transform and provided an important support method for chlorophyll analysis in the potato crops.

Optical Stepped Thermography of Defects in Photovoltaic Panels

Defects in photovoltaic panels can reduce the effective working area, and decrease the performance of the photovoltaic panels. Therefore, it is significant to identify such defects and to feed the fault information up to the production chain. Active infrared thermographic non-destructive testing has the advantages of non-contact, fast speed and large imaging area, and has been widely used in the detection of defects in various materials. In this article, a stepped thermography of the defects, which result in degradation of energy conversion efficiency of cells in photovoltaic panels, was proposed. The front surface of the photovoltaic panel was optically stimulated by halogen lamps in step heating way, while an infrared camera was employed to monitor the temperature evolution of the front surface and to capture infrared image sequences. The thermal image sequences were processed using data processing algorithms, focusing on enhancing the defect signatures. The techniques of background subtraction, discrete Fourier transform, polynomial fitting, first-order derivative, cross correlation coefficient and histogram equalization were mainly used. Compared to the raw thermal images, the reconstructed images using suitable algorithms are more helpful to evaluate the defects. The results show that the optical stepped thermography combined with suitable post data processing is a fast and effective technique to identify the defects in photovoltaic panels.