Trend Extraction Research Articles

Developing a perspective on the use of renewable energy in Iran

Despite numerable resources in Iran, the issue of renewable energies and the formulation of a grand strategy for the future are very important. This study identifies the most important effective factors, the extent and how they affect, and finally the possible scenarios for the future status of renewable energy in Iran. Therefore, this study was an applied descriptive-analytical study that is using expert groups and the Delphi method to identify the influential factors. After extraction of assumptions and prominent trends in Iran's renewable energy future (using cross-impact analysis in the MICMAC software), the effect of these trends on key variables was qualitatively determined (using causal layered analysis). This study presents Iran's renewable energy future scenarios in three dimensions of "long-term technology acquisition programs", "policy stabilization" and "attraction of foreign capital". The results showed that the success of the planning and implementation of renewable energy policy relies on the selection of policies that are the most adaptive with national targets, technical abilities, and the country's economy. Therefore, it is necessary to acquire accurate identification of limitations, obstacles, available facilities and technologies, international sanctions, and foreign investment in detail to develop the comprehensive plan of renewable energy efficiently and implement it successfully.

Design and Data Processing of Galloping Online Monitoring System for Positive Feeder of High‐Speed Railway Catenary in Gale Area

To monitor the galloping state of the catenary positive feeder of the Lanzhou–Urumqi high‐speed railway in the gale area, on the basis of previous research results, an online multi‐point distributed galloping monitoring system based on wireless sensor network (WSN) was designed, and the author improves the design of each part of the catenary positive feeder galloping monitoring system to enhance its availability and reliability. By the system to collect accelerations of each galloping monitoring point, and the displacement is obtained by integrating the acceleration twice, but the integration result was seriously distorted because of the trends. A localization algorithm is proposed to correct the galloping signal waveform. Firstly, in accordance with a detailed analysis of ensemble empirical mode decomposition (EEMD) anti‐modal aliasing principle and the steps of the waveform correction method, the original signal was decomposed to obtain ten intrinsic modal functions (IMFs). Secondly, with the IMFs' characteristics as basis, a new method of trend extraction was introduced to extract and eliminate the trends in the original signal. Finally, through the analysis of the measured signal of wire galloping, the feasibility and effectiveness of the proposed method were verified by comparing its results with those of the moving average method, and the galloping trajectory of the wire is obtained by curve fitting. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

TREND EXTRACTION FROM ECONOMIC TIME SERIES WITH MISSING OBSERVATIONS BY GENERALIZED HODRICK–PRESCOTT FILTERS

The Hodrick–Prescott (HP) filter has been a popular method of trend extraction from economic time series. However, it is impractical without modification if some observations are not available. This paper improves the HP filter so that it can be applied in such situations. More precisely, this paper introduces two alternative generalized HP filters that are applicable for this purpose. We provide their properties and a way of specifying those smoothing parameters that are required for their application. In addition, we numerically examine their performance. Finally, based on our analysis, we recommend one of them for applied studies.

Application of qualitative trend analysis in fault diagnosis of entrained-flow coal-water slurry gasifier

Entrained-flow coal-water slurry (CWS) gasification is a clean and efficient energy utilization technology and it was difficult to avoid misoperation during gasifier operation process. Qualitative trend analysis (QTA) is a data-driven method based on process history, and has been widely used in process monitoring and fault diagnosis. In this study, a suitable on-line basic primitive sequence processing method combining QTA and the extended sliding window trend extraction (SWTE) method is developed for the operation of entrained-flow CWS gasifier. Based on the optimized method, trend extraction and primitive sequence processing on three sets of data, i.e., gasifier temperature, gasifier pressure, and slag discharge hole pressure difference, were performed in this study. The optimized method could be effectively applied to the real-time monitoring and fault diagnosis of entrained-flow gasifier since its output was simple and accurate.

Google Trends Extraction Tool for Google Trends Extended for Health data

<h2>Abstract</h2> Researchers who apply for an API key to the Google Extended Trends for Health API gain access to higher quality Google Trends data than from the Google Trends website but must access the data using Python code customized for each extraction. Researchers can also only access one time-series sample per individual query per day. The Google Trends Extraction Tool provides full access to all the API methods in an Excel-based GUI, requiring only a unique API key from Google. It employs a novel multiple sampling method to circumvent the API limitation, records an audit trail, and automatically plots results.

Multi-Sensor Data Fusion Algorithm for Indoor Fire Early Warning Based on BP Neural Network

Fire early warning is an important way to deal with the faster burning rate of modern home fires and ensure the safety of the residents’ lives and property. To improve real-time fire alarm performance, this paper proposes an indoor fire early warning algorithm based on a back propagation neural network. The early warning algorithm fuses the data of temperature, smoke concentration and carbon monoxide, which are collected by sensors, and outputs the probability of fire occurrence. In this study, non-uniform sampling and trend extraction were used to enhance the ability to distinguish fire signals and environmental interference. Data from six sets of standard test fire scenarios and six sets of no-fire scenarios were used to test the algorithm proposed in this paper. The test results show that the proposed algorithm can correctly alarm six standard test fires from these 12 scenarios, and the fire detection time is shortened by 32%.

Bonding, structure, and mechanical stability of 2D materials: the predictive power of the periodic table.

This tutorial review describes the ongoing effort to convert main-group elements of the periodic table and their combinations into stable 2D materials, which is sometimes called modern 'alchemy'. Theory is successfully approaching this goal, whereas experimental verification is lagging far behind in the synergistic interplay between theory and experiment. The data collected here gives a clear picture of the bonding, structure, and mechanical performance of the main-group elements and their binary compounds. This ranges from group II elements, with two valence electrons, to group VI elements with six valence electrons, which form not only 1D structures but also, owing to their variable oxidation states, low-symmetry 2D networks. Outside of these main groups reviewed here, predominantly ionic bonding may be observed, for example in group II-VII compounds. Besides high-symmetry graphene with its shortest and strongest bonds and outstanding mechanical properties, low-symmetry 2D structures such as various borophene and tellurene phases with intriguing properties are receiving increasing attention. The comprehensive discussion of data also includes bonding and structure of few-layer assemblies, because the electronic properties, e.g., the band gap, of these heterostructures vary with interlayer layer separation and interaction energy. The available data allows the identification of general relationships between bonding, structure, and mechanical stability. This enables the extraction of periodic trends and fundamental rules governing the 2D world, which help to clear up deviating results and to estimate unknown properties. For example, the observed change of the bond length by a factor of two alters the cohesive energy by a factor of four and the extremely sensitive Young's modulus and ultimate strength by more than a factor of 60. Since the stiffness and strength decrease with increasing atom size on going down the columns of the periodic table, it is important to look for suitable allotropes of elements and binaries in the upper rows of the periodic table when mechanical stability and robustness are issues. On the other hand, the heavy compounds are of particular interest because of their low-symmetry structures with exotic electronic properties.

Robust enhanced trend filtering with unknown noise

One important step in time series analysis is the extraction of an underlying trend. However, the true trend is often submerged by complex background noise, especially non-Gaussian noise or outliers. Accurate trend extraction against outliers from a raw signal is a challenging task. To address this challenge, this paper extends l1 trend filtering to a robust enhanced trend filtering called RobustETF by combining mix of Gaussian (MoG) and non-convex sparsity-inducing functions. We first model the noise as a MoG distribution to allow RobustETF to be robust in the presence of any type of non-Gaussian noise or outliers. After that, to handle the biased estimation of the l1 norm, we use the Gibbs distribution embedding smoothed and non-convex sparsity-inducing functions to faithfully preserve the amplitude of the trend. Furthermore, we design an extended EM algorithm to solve the resulting non-convex optimization problem. Finally, we show the results of experiments on both real-world and synthetic data to compare the performance of the proposed algorithm against other state-of-the-art methods. Finally, the corresponding Matlab codes are available at https://github.com/ZhaoZhibin/RobustETF.

Extraction of Nonlinear Trends in Time Series of Rainfall Using Singular Spectrum Analysis

AbstractCharacterization of nonlinear trends in time series of hydroclimatic variables exhibiting nonstationarity is necessary for more realistic projections of climate change and for optimal desig...

Connection between 222Rn emission and geophysical-geochemical parameters recorded during the volcanic unrest at Campi Flegrei caldera (2011–2017)

Studies concerning 222Rn emission from soils are growing interest in the Earth Sciences, due to the gas potential as a tracer of natural phenomena. This paper presents a study of radon monitoring in two sites of Campi Flegrei caldera (Neaples, Italy) during the period July 1, 2011–December 31, 2017. This area was characterized by several phases of volcanic unrest. A hybrid method based on Multiple Linear Regression + Remote Radon Estimation method + Singular Spectrum Analysis (MLR + RRE + SSA) is developed for the trend extraction and the identification of anomalies in the time series of 222Rn. The results are compared with several routinely used geo-indicators of the caldera unrest. The comparisons show strong correlation among the signals. The present study proves the 222Rn is a potential indicator of the evolution of a volcanic crisis.

Information Granules-Based BP Neural Network for Long-Term Prediction of Time Series

Long-term time series prediction is a challenging and essential task both in theory and practice. Recently, information granulation is shown to be an appropriate tool for the long-term forecast. Though some models for the long-term prediction problem have been proposed using information granulation recently, there is still a growing need to develop new prediction approaches for time series data based on information granule, which can capture the dynamic trend change with high accuracy. In this article, a long-term prediction approach, based on back-propagation neural network and information granule, is proposed. First, the individual numerical intervals for the time series are obtained by using the principle of justifiable granularity in information granule. Then, an automatic linear trend extraction method is developed to extract the trend change, which is inherited in granules. Finally, a hierarchy of neural network is constructed to carry out prediction by using information granule as input. Experiments using publicly available time series datasets demonstrate that the proposed approach can achieve better performance than the existing models for long-term prediction.

A working condition recognition method based on multivariable trend analysis for gold–antimony rougher flotation

Rougher flotation is a complex process, making it difficult for a single variable to comprehensively and accurately reflects real-time working conditions. This paper proposes a new multivariable recognition method for rougher flotation conditions using pulp flow and froth size as parameters. It utilizes qualitative trend analysis to recognise rougher flotation condition for the first time, where an improved trend extraction method is used to improve extraction accuracy, and fuzzy logic is adopted to calculate the matching degree of trends. Considering the change in knowledge base caused by the increase in variables, this paper presents a scheme of building knowledge base for multivariable rougher flotation conditions. Experimental results of gold–antimony rougher flotation show that the proposed method can accurately recognise rougher flotation conditions.

Topic extraction to provide an overview of research activities: The case of the high-temperature superconductor and simulation and modelling

For those who are not experts in a particular scientific field, it is difficult to understand scientific research trends. Although studies on the extraction of research trends have been conducted, most focus on extracting global trends from large-scale data, and the methods are often complicated. The purpose of this study is to develop a method of obtaining overviews of a scientific field for non-experts by capturing research trends simply and then to verify the method. To extract research topics which should express research trends, text analysis was performed using abstracts over 12 years of articles on high-temperature superconductors. We characterised three topics for the extracted word groups that frequently occurred. For these topics, we studied their appropriateness using a method that has been little used: examining research articles, review literature and co-citations among research articles used to extract the words, comparisons with controlled index terms assigned to the articles and confirming that there were no contradictions. Based on the established method, we have also applied this method to another research field: ‘simulation and modelling’. Although the method used in this article is simple, important topics were extracted, and the relations with the original articles are clear, which can lead to further investigation of the extracted topics.

Using singular spectrum analysis for inference on seasonal time series with seasonal unit roots

The problem of optimal linear filtering, smoothing and trend extraction for m-period differences of processes with a unit root is studied. Such processes arise naturally in economics and finance, in the form of rates of change (price inflation, economic growth, financial returns) and finding an appropriate smoother is thus of immediate practical interest. The filter and resulting smoother are based on the methodology of singular spectrum analysis (SSA). An explicit representation for the asymptotic decomposition of the covariance matrix is obtained. The structure of the impulse and frequency response functions indicates that the optimal filter has a 'permanent' and a 'transitory component', with the corresponding smoother being the sum of two such components. Moreover, a particular form for the extrapolation coefficients that can be used in out-of-sample prediction is proposed. In addition, an explicit representation for the filtering weights in the context of SSA for an arbitrary covariance matrix is derived. This result allows one to examine the specific effects of smoothing in any situation. The theoretical results are illustrated using different datasets, namely US inflation and real GDP growth.

Using singular spectrum analysis for inference on seasonal time series with seasonal unit roots

The problem of optimal linear filtering, smoothing and trend extraction for m-period differences of processes with a unit root is studied. Such processes arise naturally in economics and finance, in the form of rates of change (price inflation, economic growth, financial returns) and finding an appropriate smoother is thus of immediate practical interest. The filter and resulting smoother are based on the methodology of singular spectrum analysis (SSA). An explicit representation for the asymptotic decomposition of the covariance matrix is obtained. The structure of the impulse and frequency response functions indicates that the optimal filter has a 'permanent' and a 'transitory component', with the corresponding smoother being the sum of two such components. Moreover, a particular form for the extrapolation coefficients that can be used in out-of-sample prediction is proposed. In addition, an explicit representation for the filtering weights in the context of SSA for an arbitrary covariance matrix is derived. This result allows one to examine the specific effects of smoothing in any situation. The theoretical results are illustrated using different datasets, namely US inflation and real GDP growth.

MINING SOCIAL MEDIA CROWD TRENDS FROM THAI TEXT POSTS AND COMMENTS

Text mining from social media stream has attracted wide interests from both businesses and academics. Very large numbers of self-posts from crowd sources contains hidden trends, which can be valuable to a business enterprise. Crowd trend mining methods, together with an easily understood visual presentation, are thus in great demand. We present an approach to mining crowd trends from Thai text posts. A Thai language preprocessing module was necessary to transform continuous text into series of words. Our method could then mine general unforeseen crowd trends by using an automatic context extraction technique, tf-idf score and an aggregated opinion score calculated from automatically classified sentiments for each post or comment. The best sentiment classifier was chosen based on extensive experiments on the same data source. These scores were combined into one unified term popularity which was visualized as a word cloud on a web application. A case study used a popular Thai discussion website - Pantip.com - and achieved three interwoven desired goals: (1) extraction of general and unforeseen crowd trends from a Thai discussion website, (2) assigning unified popularity scores to each candidate term and (3) presenting those terms to end users in an easily comprehended form.

Unsupervised extraction of patterns and trends within highway systems condition attributes data

Highway agencies combine expert opinions and basic regression modeling techniques to process vast amounts of time series condition attributes data to define highway network health. The health rating exhibit high variability and lack adequate detail for executive-level maintenance planning and resource allocation. This paper presents a new methodology for data abstraction, analysis, and clustering for pattern recognition of highway network health. The methodology describes mathematical and statistical data abstraction algorithms for data preprocessing (smoothening (unweighted moving average), scaling (normalization), and weights derivation (entropy) to compute a composite health index (CHI)), and salient features extraction. Data analysis involved cluster analysis to identify patterns in asset current health and future outlook. The outcome is a characterization of highway network health for executive-level decision making. The algorithms included in this methodology have been successfully applied in the fields of biology, finance, econometrics, bioinformatics, marketing, and social science for pattern recognition. The accuracy of the new methodology is illustrated with an experiment using 463 in-service pavement assets and internal/external metrics (including the degree to which methodology performance classification outcomes conform to national expert opinion). The results from the experiment confirm an accurate and computationally inexpensive methodology, which provides outcomes that compare to real-world pavement condition rating metrics.

Physical Activity Trend eXtraction: A Framework for Extracting Moderate-Vigorous Physical Activity Trends From Wearable Fitness Tracker Data.

BackgroundModerate-vigorous physical activity (MVPA) offers extensive health benefits but is neglected by many. As a result, a wide body of research investigating physical activity behavior change has been conducted. As many of these studies transition from paper-based methods of MVPA data collection to fitness trackers, a series of challenges arise in extracting insights from these new data.ObjectiveThe objective of this research was to develop a framework for preprocessing and extracting MVPA trends from wearable fitness tracker data to support MVPA behavior change studies.MethodsUsing heart rate data collected from fitness trackers, we propose Physical Activity Trend eXtraction (PATX), a framework that imputes missing data, recalculates personalized target heart zones, and extracts MVPA trends. We tested our framework on a dataset of 123 college study participants observed across 2 academic years (18 months) using Fitbit Charge HRs. To demonstrate the value of our frameworks’ output in supporting MVPA behavior change studies, we applied it to 2 case studies.ResultsAmong the 123 participants analyzed, PATX labeled 41 participants as experiencing a significant increase in MVPA and 44 participants who experienced a significant decrease in MVPA, with significance defined as P<.05. Our first case study was consistent with previous works investigating the associations between MVPA and mental health. Whereas the second, exploring how individuals perceive their own levels of MVPA relative to their friends, led to a novel observation that individuals were less likely to notice changes in their own MVPA when close ties in their social network mimicked their changes.ConclusionsBy providing meaningful and flexible outputs, PATX alleviates data concerns common with fitness trackers to support MVPA behavior change studies as they shift to more objective assessments of MVPA.

Underlying Trend Extraction via Joint Ensemble Intrinsic Timescale Decomposition Algorithm and Matching Pursuit Approach

Time series usually consist of an underlying trend and the irregularities. Therefore, the underlying trend extraction plays an important role in the analysis of the time series. This paper proposes a method which combines the ensemble intrinsic timescale decomposition (EITD) algorithm and the matching pursuit (MP) approach for performing the underlying trend extraction. In order to extract the underlying trend, the EITD algorithm is applied to obtain a set of components. Then, the first component which contains most of the noise is removed. Next, some appropriate components are selected by the MP approach. In particular, the total number of components that composites of the underlying trend is minimized. To guarantee that the underlying trend tracks the signal, it is required to impose a constraint on the maximum absolute error between the underlying trend and the denoised signal. As the selection of the EITD components is binary, this component selection problem is formulated as an $$ L_{0} $$ -norm binary programming problem subject to the specification on the maximum absolute error between the denoised signal and the underlying trend. This optimization problem is further solved by the MP approach. Finally, the underlying trend is constructed. Compared with the conventional empirical mode decomposition algorithm and the ensemble empirical mode decomposition algorithm, our proposed approach could extract a better underlying trend for some random time series.

Feature Trend Extraction and Adaptive Density Peaks Search for Intelligent Fault Diagnosis of Machines

Traditional machine fault diagnosis techniques are labor-intensive and hard for nonexperts to use. In this paper, a novel three-stage intelligent fault diagnosis approach is proposed for practical industrial process monitoring. A new feature processing technique is developed to enhance the identification accuracy and reduce the computation burden, which incorporates variational mode decomposition-based trend detection and self-weight algorithm. Furthermore, an adaptive density peaks search (ADPS) algorithm has been primarily proposed for adaptive clustering, whose effectiveness is verified in comparison with the original DPS, affinity propagation clustering, and K-medoids. The three-stage intelligent fault diagnosis approach is subsequently applied to three specific industrial cases. Results of bearing and gear fault diagnosis have well demonstrated that the proposed method is able to reliably and accurately identify different faults with less prior knowledge and diagnostic expertise. Moreover, the proposed technique can be adopted to adaptively monitor different conditions using unlabeled bearing run-to-failure testing data, which also shows it is well suitable for industrial online applications.