Symbolic Aggregate Approximation Algorithm Research Articles

Monitoring Persistent Coal Fire Using Landsat Time Series Data From 1986 to 2020

Coal fires pose great threats to valuable energy resources, the ecological environment, and human safety. They are one of the most persistent fires on the Earth, which can burn for an extremely long-term period from decades to hundreds or even thousands of years. Remote sensing detection of coal fires is of significance for mitigating coal fire hazards. Nevertheless, short-term or temporal discrete land surface temperature (LST) data have limited capability in characterizing the persistent coal fire. This study proposed a methodology to monitor persistent coal fires using long-term Landsat thermal images and further to analyze spatiotemporal dynamics of coal fires. A total of 1118 high-quality Landsat images (each image containing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$446\times446$ </tex-math></inline-formula> pixels) spanning 35 years from 1986 to 2020 in the Wuda coalfield area (China) were processed to retrieve the LST. LST time series of each pixel was decomposed into the seasonal, trend, and remainder components. Coal fire areas were demarcated by using the range of the trend components. To trace the trend and change point of the LST time series, the Mann–Kendall test was applied to the trend components, and the Pettitt test was employed to the original time series vectors of those pixels located in the coal fire areas, respectively. The random sample consensus algorithm was utilized to identify the background temperature (inliers) and high temperature (outliers) and, thus, judge the coal fire burning period, and the symbolic aggregate approximation algorithm was used to evaluate the robustness of the judgment. The calibration was conducted according to the filed surveys, obtaining spatiotemporal 3-D coal fire dynamics. The proposed methodology was testified by comparisons with fieldwork and regional anomaly extractor algorithm, demonstrating good performance in comprehensive monitoring of persistent coal fires.

Identifying Subway Passenger Flow under Large-Scale Events Using Symbolic Aggregate Approximation Algorithm

Various social events, such as holidays, important sporting events, and major celebrations, may result in sudden large-scale passenger flows in certain sections and stations of urban rail transit systems. The sudden inbound passenger flows caused by these events can easily lead to continuous congestion of the subway network, which has a profound impact on the safety, reliability, and stability of a subway system. Because of the large magnitude of swipe data and the high dimensionality of time series, it is difficult to identify the emergence of such large passenger flows. Additionally, the recognition accuracy of the existing identification methods cannot meet the operational monitoring requirements. To address the above-mentioned issues, this paper proposes an optimized symbolic aggregate approximation (SAX) algorithm to identify historical sudden passenger flows caused by large-scale events around subways. Specifically, pre-set cluster types and dynamic time warping (DTW) are proposed to enhance the matching rate. Compared with the K-means method, the proposed method exhibits an average increase of 30% in mining accuracy, and the calculation time is shortened to one-sixteenth of the original value.

Soil-Moisture-Sensor-Based Automated Soil Water Content Cycle Classification With a Hybrid Symbolic Aggregate Approximation Algorithm

This article proposes a hybrid symbolic aggregate approximation and vector space model (SAX-VSM) method for automatically classifying soil water content cycles. In the proposed method, a novel similarity measure, the distance weighted cosine (DWC) similarity measure, is introduced to improve the classification performance of the SAX-VSM. The DWC similarity measure incorporates both direction and distance information of feature vectors. Meanwhile, a mixed-integer optimization problem is formulated to determine hyperparameters. An extended Rao-1 algorithm, I-Rao-1 algorithm, is developed to solve such optimization problems. To verify the feasibility and effectiveness of the proposed method, three soil moisture data sets collected from the Florida research trials are employed. Compared with state-of-the-art methods, the proposed method has achieved the best performance based on all data sets in terms of the highest accuracy, precision, and recall values. Therefore, it is promising to apply the proposed method into real applications in the smart irrigation system.

Assessment of intracoronary stent location and extension in intravascular ultrasound sequences.

An intraluminal coronary stent is a metal scaffold deployed in a stenotic artery during percutaneous coronary intervention (PCI). In order to have an effective deployment, a stent should be optimally placed with regard to anatomical structures such as bifurcations and stenoses. Intravascular ultrasound (IVUS) is a catheter-based imaging technique generally used for PCI guiding and assessing the correct placement of the stent. A novel approach that automatically detects the boundaries and the position of the stent along the IVUS pullback is presented. Such a technique aims at optimizing the stent deployment. The method requires the identification of the stable frames of the sequence and the reliable detection of stent struts. Using these data, a measure of likelihood for a frame to contain a stent is computed. Then, a robust binary representation of the presence of the stent in the pullback is obtained applying an iterative and multiscale quantization of the signal to symbols using the Symbolic Aggregate approXimation algorithm. The technique was extensively validated on a set of 103 IVUS of sequences of invivo coronary arteries containing metallic and bioabsorbable stents acquired through an international multicentric collaboration across five clinical centers. The method was able to detect the stent position with an overall F-measure of 86.4%, a Jaccard index score of 75% and a mean distance of 2.5mm from manually annotated stent boundaries, and in bioabsorbable stents with an overall F-measure of 88.6%, a Jaccard score of 77.7 and a mean distance of 1.5mm from manually annotated stent boundaries. Additionally, a map indicating the distance between the lumen and the stent along the pullback is created in order to show the angular sectors of the sequence in which the malapposition is present. Results obtained comparing the automatic results vs the manual annotation of two observers shows that the method approaches the interobserver variability. Similar performances are obtained on both metallic and bioabsorbable stents, showing the flexibility and robustness of the method.

String Grammar Unsupervised Possibilistic Fuzzy C-Medians for Gait Pattern Classification in Patients with Neurodegenerative Diseases.

Neurodegenerative diseases that affect serious gait abnormalities include Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and Huntington disease (HD). These diseases lead to gait rhythm distortion that can be determined by stride time interval of footfall contact times. In this paper, we present a new method for gait classification of neurodegenerative diseases. In particular, we utilize a symbolic aggregate approximation algorithm to convert left-foot stride-stride interval into a sequence of symbols using a symbolic aggregate approximation. We then find string prototypes of each class using the newly proposed string grammar unsupervised possibilistic fuzzy C-medians. Then in the testing process the fuzzy k-nearest neighbor is used. We implement the system on three 2-class problems, i.e., the classification of ALS against healthy patients, that of HD against healthy patients , and that of PD against healthy patients. The system is also implemented on one 4-class problem (the classification of ALS, HD, PD, and healthy patients altogether) called NDDs versus healthy. We found that our system yields a very good detection result. The average correct classification for ALS versus healthy is 96.88%, and that for HD versus healthy is 97.22%, whereas that for PD versus healthy is 96.43%. When the system is implemented on 4-class problem, the average accuracy is approximately 98.44%. It can provide prototypes of gait signals that are more understandable to human.

Machine learning-based anomaly detection via integration of manufacturing, inspection and after-sales service data

PurposeQuality management of products is an important part of manufacturing process. One way to manage and assure product quality is to use machine learning algorithms based on relationship among various process steps. The purpose of this paper is to integrate manufacturing, inspection and after-sales service data to make full use of machine learning algorithms for estimating the products’ quality in a supervised fashion. Proposed frameworks and methods are applied to actual data associated with heavy machinery engines.Design/methodology/approachBy following Lenzerini’s formula, manufacturing, inspection and after-sales service data from various sources are integrated. The after-sales service data are used to label each engine as normal or abnormal. In this study, one-class classification algorithms are used due to class imbalance problem. To address multi-dimensionality of time series data, the symbolic aggregate approximation algorithm is used for data segmentation. Then, binary genetic algorithm-based wrapper approach is applied to segmented data to find the optimal feature subset.FindingsBy employing machine learning-based anomaly detection models, an anomaly score for each engine is calculated. Experimental results show that the proposed method can detect defective engines with a high probability before they are shipped.Originality/valueThrough data integration, the actual customer-perceived quality from after-sales service is linked to data from manufacturing and inspection process. In terms of business application, data integration and machine learning-based anomaly detection can help manufacturers establish quality management policies that reflect the actual customer-perceived quality by predicting defective engines.

On the Effect of Adaptive and Nonadaptive Analysis of Time-Series Sensory Data

With the growing popularity of information and communications technologies and information sharing and integration, cities are evolving into large interconnected ecosystems by using smart objects and sensors that enable interaction with the physical world. However, it is often difficult to perform real-time analysis of large amount on heterogeneous data and sensory information that are provided by various resources. This paper describes a framework for real-time semantic annotation and aggregation of data streams to support dynamic integration into the Web using the advanced message queuing protocol. We provide a comprehensive analysis on the effect of adaptive and nonadaptive window size in segmentation of time series using SensorSAX and symbolic aggregate approximation (SAX) approaches for data streams with different variation and sampling rate in real-time processing. The framework is evaluated with three parameters, namely window size parameter of the SAX algorithm, sensitivity level, and minimum window size parameters of the SensorSAX algorithm based on the average data aggregation and annotation time, CPU consumption, data size, and data reconstruction rate. Based on a statistical analysis, a detailed comparison between various sensor points is made to investigate the memory and computational cost of the stream-processing framework. Our results suggests that regardless of utilized segmentation approach, due to the fact that each geographically different sensory environment has got a different dynamicity level, it is desirable to find the optimal data aggregation parameters in order to reduce the energy consumption and improve the data aggregation quality.

Hypotension Risk Prediction via Sequential Contrast Patterns of ICU Blood Pressure.

Acute hypotension is a significant risk factor for in-hospital mortality at intensive care units. Prolonged hypotension can cause tissue hypoperfusion, leading to cellular dysfunction and severe injuries to multiple organs. Prompt medical interventions are thus extremely important for dealing with acute hypotensive episodes (AHE). Population level prognostic scoring systems for risk stratification of patients are suboptimal in such scenarios. However, the design of an efficient risk prediction system can significantly help in the identification of critical care patients, who are at risk of developing an AHE within a future time span. Toward this objective, a pattern mining algorithm is employed to extract informative sequential contrast patterns from hemodynamic data, for the prediction of hypotensive episodes. The hypotensive and normotensive patient groups are extracted from the MIMIC-II critical care research database, following an appropriate clinical inclusion criteria. The proposed method consists of a data preprocessing step to convert the blood pressure time series into symbolic sequences, using a symbolic aggregate approximation algorithm. Then, distinguishing subsequences are identified using the sequential contrast mining algorithm. These subsequences are used to predict the occurrence of an AHE in a future time window separated by a user-defined gap interval. Results indicate that the method performs well in terms of the prediction performance as well as in the generation of sequential patterns of clinical significance. Hence, the novelty of sequential patterns is in their usefulness as potential physiological biomarkers for building optimal patient risk stratification systems and for further clinical investigation of interesting patterns in critical care patients.

Efficient temporal mining of micro-blog texts and its application to event discovery

In this paper we present a novel method for clustering words in micro-blogs, based on the similarity of the related temporal series. Our technique, named SAX*, uses the Symbolic Aggregate ApproXimation algorithm to discretize the temporal series of terms into a small set of levels, leading to a string for each. We then define a subset of "interesting" strings, i.e. those representing patterns of collective attention. Sliding temporal windows are used to detect co-occurring clusters of tokens with the same or similar string. To assess the performance of the method we first tune the model parameters on a 2-month 1 % Twitter stream, during which a number of world-wide events of differing type and duration (sports, politics, disasters, health, and celebrities) occurred. Then, we evaluate the quality of all discovered events in a 1-year stream, "googling" with the most frequent cluster n-grams and manually assessing how many clusters correspond to published news in the same temporal slot. Finally, we perform a complexity evaluation and we compare SAX* with three alternative methods for event discovery. Our evaluation shows that SAX* is at least one order of magnitude less complex than other temporal and non-temporal approaches to micro-blog clustering.

A Harmony Search Algorithm with Multi-pitch Adjustment Rate for Symbolic Time Series Data Representation

The representation task in time series data mining has been a critical issue because the direct manipulation of continuous, high-dimensional data is extremely difficult to complete efficiently. One time series representation approach is a symbolic representation called the Symbolic Aggregate Approximation (SAX). The main function of SAX is to find the appropriate numbers of alphabet symbols and word size that represent the time series. The aim is to achieve the largest alphabet size and maximum word length with the minimum error rate. The purpose of this study is to propose an integrated approach for a symbolic time series data representation that attempts to improve SAX by improving alphabet and word size. The Relative Frequency (RF) binning method is employed to obtain alphabet size and is integrated with the proposed Multipitch Harmony Search (HS) algorithm to calculate the optimum alphabet and word size. RF is used because of its ability to obtain a sufficient number of intervals with a low error rate compared to other related techniques. HS algorithm is an optimization algorithm that randomly generates solutions for alphabet and word sizes and selects the best solutions. HS algorithms are compatible with multi-pitch adjustment. The integration of the RF and HS algorithms is developed to maximize information rather than to improve the error rate. The algorithms are tested on 20 standard time series datasets and are compared with the meta-heuristic algorithms GENEBLA and the original SAX algorithm. The experimental results show that the proposed method generates larger alphabet and word sizes and achieves a lower error rate than the compared methods. With larger alphabet and word sizes, the proposed method is capable of preserving important information.

Analysis of Nonstationary Power-Quality Waveforms Using Iterative Hilbert Huang Transform and SAX Algorithm

The nonstationary nature of power-quality (PQ) waveforms requires a tool that can accurately analyze and visually identify the instants of transitions. One of the recently reported tools available to analyze nonstationary complex waveforms with a very good time resolution is the Hilbert Huang Transform (HHT). However, HHT has difficulty in resolving waveforms containing components with close frequencies with a ratio of less than two, and similar to other waveform classification techniques, it has difficulty in resolving the instants of sudden changes in the waveform. To overcome the problem with components containing close frequencies, a novel Iterative Hilbert Huang transform (IHHT) is proposed in this paper. To overcome the problem in identifying instants of sudden changes in the waveform, a new method using the Symbolic Aggregate ApproXimation (SAX) method is proposed. SAX converts the signal into symbols that can be utilized by a pattern detector algorithm to identify the boundaries of the stationary signals within a nonstationary signal. Results from IHHT and SAX method to analyze and visually identify simulated and measured nonstationary PQ waveforms will be provided and discussed. The proposed method is particularly useful when investigating the behavior of the harmonic components of a particular PQ waveform of interest in a given interval of time, following a data-mining search of a large database of PQ events. It can be used to both identify, and later isolate unique signatures within a provider's distribution system.

Composite metric method for time series similarity measurement based on symbolic aggregate approximation

Key point-based Symbolic Aggregate approximation(SAX) improving algorithm(KP_SAX) uses key points to measure point distance of time series based on SAX,which can measure the similarity of time series more effectively.However,it is too short of information about the patterns of time series to measure the similarity of time series reasonably.To overcome the defects,a composite metric method of time series similarity measurement based on SAX was proposed.The method synthesized both point distance measurement and pattern distance measurement.First,key points were used to further subdivide the Piecewise Aggregate Approximation(PAA) segments into several sub-segments,and then a triple including the information about the two kinds of distance measurement was used to represent each sub-segment.Finally a composite metric formula was used to measure the similarity between two time series.The calculation results can reflect the difference between two time series more effectively.The experimental results show that the proposed method is only 0.96% lower than KP_SAX algorithm in time efficiency.However,it is superior to the KP_SAX algorithm and the traditional SAX algorithm in differentiating between two time series.

Application of improved SAX algorithm to QAR data

As the Quick Access Recorder(QAR) data is a kind of time series data which is of very complex structure and large amount of data,it cannot avoid some of the shortcomings appeared in aspects such as time series amplitude flex and timeline drift,directly using the traditional Symbolic Aggregate Approximation(SAX) method to describe,store,retrieve QAR data.The paper put forward an improved SAX algorithm.As stated in the paper,the QAR data was divided into three steps as take-off,cruise and landing.With the improved algorithm,the cruise stage was filled as well,and the different lengths of fault model sequence were carried out effective similarity search.Through experiments and application in the aircraft fault diagnosis project,its feasibility and validity has been proven,the improved algorithm greatly improve the efficiency of troubleshooting.

The Symbolic Algorithm for Time Series Data Based on Statistic Feature

A new symbolic algorithm for Time Series Data Based on Statistic Feature is put forward in order to surmount the bugs with which SAX(Symbolic Aggregate Approximation) Algorithm can not describe time series information fully.This algorithm,differing from the SAX,considered the symbolic as vector,and Mean and variance from each subsequence were regarded as components by which its mean value and radiation degree are described respectively.Since it could provide more information described time series than SAX do,more accuracy result could be get when it is applied to time series data-mining.Its excellent behave.have been proved by a lot of experiments.