scholarly journals Spatio-temporal clustering of earthquakes based on distribution of magnitudes

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Yuki Yamagishi ◽  
Kazumi Saito ◽  
Kazuro Hirahara ◽  
Naonori Ueda
Keyword(s):  
Likelihood Function ◽  
Empirical Evaluation ◽  
Detection Algorithm ◽  
Change Point Detection ◽  
Weighted Sums ◽  
Time Interval ◽  
Earthquake Catalog ◽  
Single Link ◽  
Spatio Temporal ◽  
Later Phase

AbstractIt is expected that the pronounced decrease in b-value of the Gutenberg–Richter law for some region during some time interval can be a promising precursor in forecasting earthquakes with large magnitudes, and thus we address the problem of automatically identifying such spatio-temporal change points as several clusters consisting of earthquakes whose b-values are substantially smaller than the total one. For this purpose, we propose a new method consisting of two phases: tree construction and tree separation. In the former phase, we employ one of two different declustering algorithms called single-link and correlation-metric developed in the field of seismology, while in the later phase, we employ a variant of the change-point detection algorithm, developed in the field of data mining. In the later phase, we also employ one of two different types of objective functions, i.e., the average magnitude which is inversely proportional to the b-value, and the likelihood function based on the Gutenberg–Richter law. Here note that since the magnitudes of most earthquakes are relatively small, we formulate our problem so as to produce one relatively large cluster and the other small clusters having substantially larger average magnitudes or smaller b-values. In addition, in order to characterize some properties of our proposed methods, we present a method of analyzing magnitude correlation over an earthquake network. In our empirical evaluation using earthquake catalog data covering the whole of Japan, we show that our proposed method employing the single-link strategy can produce more desirable results for our purpose in terms of the improvement of weighted sums of variances, average logarithmic likelihoods, visualization results, and magnitude correlation analyses.

scholarly journals A Doubly Stochastic Change Point Detection Algorithm for Noisy Biological Signals

2018 ◽  
Vol 8 ◽  
Author(s):  
Nathan Gold ◽  
Martin G. Frasch ◽  
Christophe L. Herry ◽  
Bryan S. Richardson ◽  
Xiaogang Wang
Keyword(s):  
Change Point ◽  
Detection Algorithm ◽  
Change Point Detection ◽  
Doubly Stochastic ◽  
Biological Signals ◽  
Point Detection

Dim moving target detection algorithm based on spatio-temporal classification sparse representation

2014 ◽  
Vol 67 ◽  
pp. 273-282 ◽  
Author(s):  
Zhengzhou Li ◽  
Zhen Dai ◽  
Hongxia Fu ◽  
Qian Hou ◽  
Zhen Wang ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Target Detection ◽  
Detection Algorithm ◽  
Moving Target ◽  
Moving Target Detection ◽  
Spatio Temporal

Determination of tonal signal parameters based on zero crossing detection

2021 ◽  
Vol 263 (5) ◽  
pp. 1794-1803
Author(s):  
Michal Luczynski ◽  
Stefan Brachmanski ◽  
Andrzej Dobrucki
Keyword(s):  
High Efficiency ◽  
Detection Algorithm ◽  
Sine Wave ◽  
Time Interval ◽  
Zero Crossing ◽  
Tonal Signal ◽  
Zero Crossings ◽  
Tonal Component ◽  
Signal Parameters ◽  
The Moment

This paper presents a method for identifying tonal signal parameters using zero crossing detection. The signal parameters: frequency, amplitude and phase can change slowly in time. The described method allows to obtain accurate detection using possibly small number of signal samples. The detection algorithm consists of the following steps: frequency filtering, zero crossing detection and parameter reading. Filtering of the input signal is aimed at obtaining a signal consisting of a single tonal component. Zero crossing detection allows the elimination of multiple random zero crossings, which do not occur in a pure sine wave signal. The frequency is based on the frequency of transitions through zero, the amplitude is the largest value of the signal in the analysed time interval, and the initial phase is derived from the moment at which the transition through zero occurs. The obtained parameters were used to synthesise a compensation signal in an active tonal component reduction algorithm. The results of the algorithm confirmed the high efficiency of the method.

Adaptive Sliding Mode Impedance Control of Single-Link Flexible Manipulators interacting with the Environment at an Unknown Intermediate Point

Robotica ◽  
2019 ◽  
Vol 38 (9) ◽  
pp. 1642-1664 ◽  
Author(s):  
Ali Fayazi ◽  
Naser Pariz ◽  
Ali Karimpour ◽  
V. Feliu-Batlle ◽  
S. Hassan HosseinNia
Keyword(s):  
Position Control ◽  
Sliding Mode ◽  
Impedance Control ◽  
Detection Algorithm ◽  
Reference Trajectory ◽  
Constrained Motion ◽  
Intermediate Point ◽  
Control Scheme ◽  
Single Link ◽  
Motion Mode

SUMMARYThis paper proposes an adaptive robust impedance control for a single-link flexible arm when it encounters an environment at an unknown intermediate point. First, the intermediate collision point is estimated using a collision detection algorithm. The controller, then, switches from free to constrained motion mode. In the unconstrained motion mode, the exerted force to environment is nearly zero. Thus, the reference trajectory is a prescribed desired trajectory in position control. In the constrained motion mode, the reference trajectory is determined by the desired target dynamic impedance. The simulation results demonstrate the efficiency of proposed control scheme.

K2: A Novel Data Analysis Framework to Understand US Emotions in Space and Time

2019 ◽  
Vol 13 (01) ◽  
pp. 111-133
Author(s):  
Romita Banerjee ◽  
Karima Elgarroussi ◽  
Sujing Wang ◽  
Akhil Talari ◽  
Yongli Zhang ◽  
...  
Keyword(s):  
Data Analysis ◽  
Negative Emotion ◽  
Time Interval ◽  
Temporal Data ◽  
Analysis Framework ◽  
Spatial Clusters ◽  
Change Analysis ◽  
Spatio Temporal ◽  
Temporal Data Analysis ◽  
Very High

Twitter is one of the most popular social media platforms used by millions of users daily to post their opinions and emotions. Consequently, Twitter tweets have become a valuable knowledge source for emotion analysis. In this paper, we present a new framework, K2, for tweet emotion mapping and emotion change analysis. It introduces a novel, generic spatio-temporal data analysis and storytelling framework that can be used to understand the emotional evolution of a specific section of population. The input for our framework is the location and time of where and when the tweets were posted and an emotion assessment score in the range [Formula: see text], with [Formula: see text] representing a very high positive emotion and [Formula: see text] representing a very high negative emotion. Our framework first segments the input dataset into a number of batches with each batch representing a specific time interval. This time interval can be a week, a month or a day. By generalizing existing kernel density estimation techniques in the next step, we transform each batch into a continuous function that takes positive and negative values. We have used contouring algorithms to find the contiguous regions with highly positive and highly negative emotions belonging to each member of the batch. Finally, we apply a generic, change analysis framework that monitors how positive and negative emotion regions evolve over time. In particular, using this framework, unary and binary change predicate are defined and matched against the identified spatial clusters, and change relationships will then be recorded, for those spatial clusters for which a match occurs. We also propose animation techniques to facilitate spatio-temporal data storytelling based on the obtained spatio-temporal data analysis results. We demo our approach using tweets collected in the state of New York in the month of June 2014.

scholarly journals An Adaptive and Time-Efficient ECG R-Peak Detection Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Qin Qin ◽  
Jianqing Li ◽  
Yinggao Yue ◽  
Chengyu Liu
Keyword(s):  
Detection Algorithm ◽  
Peak Detection ◽  
Time Interval ◽  
Detection Accuracy ◽  
Ecg Signal ◽  
Peak Detection Algorithm ◽  
Wavelet Multiresolution Analysis ◽  
The Mean ◽  
Electrocardiogram Ecg ◽  
Mean Time

R-peak detection is crucial in electrocardiogram (ECG) signal analysis. This study proposed an adaptive and time-efficient R-peak detection algorithm for ECG processing. First, wavelet multiresolution analysis was applied to enhance the ECG signal representation. Then, ECG was mirrored to convert large negative R-peaks to positive ones. After that, local maximums were calculated by the first-order forward differential approach and were truncated by the amplitude and time interval thresholds to locate the R-peaks. The algorithm performances, including detection accuracy and time consumption, were tested on the MIT-BIH arrhythmia database and the QT database. Experimental results showed that the proposed algorithm achieved mean sensitivity of 99.39%, positive predictivity of 99.49%, and accuracy of 98.89% on the MIT-BIH arrhythmia database and 99.83%, 99.90%, and 99.73%, respectively, on the QT database. By processing one ECG record, the mean time consumptions were 0.872 s and 0.763 s for the MIT-BIH arrhythmia database and QT database, respectively, yielding 30.6% and 32.9% of time reduction compared to the traditional Pan-Tompkins method.

Development of a Torsional Behavior Powertrain Model for Multiple Misfire Detection

2005 ◽  
Author(s):  
Fabrizio Ponti
Keyword(s):  
Torsional Vibration ◽  
Engine Speed ◽  
Experimental Tests ◽  
Detection Algorithm ◽  
Operating Conditions ◽  
False Alarms ◽  
Time Interval ◽  
Short Time Interval ◽  
Torsional Behavior ◽  
Misfire Detection

Many methodologies have been developed in the past for misfire detection purposes based on the analysis of the instantaneous engine speed. The missing combustion is usually detected thanks to the sudden engine speed decrease that takes place after a misfire event. Misfire detection and in particular cylinder isolation is anyhow still a challenging issue for engines with a high number of cylinders, for engine operating conditions at low load or high engine speed and for multiple misfire events. When a misfire event takes place in fact a torsional vibration is excited and shows up in the instantaneous engine speed waveform. If a multiple misfire occurs this torsional vibration is excited more than once in a very short time interval. The interaction among these successive vibrations can generate false alarms or misdetection, and an increased complexity when dealing with cylinder isolation. The paper presents the development of a powertrain torsional behavior model in order to identify the effects of a misfire event on the instantaneous engine speed signal. The identified waveform has then been used to filter out the torsional vibration effects in order to enlighten the missing combustions even in the case of multiple misfire events. The model response is also used to quicken the setup process for the detection algorithm employed, evaluating before running specific experimental tests on a test bench facility, the values for the threshold and the optimal setup of the procedure. The proposed algorithm is developed in this paper for an SI L4 engine; Its application to other engine configurations is possible, as it is also discussed in the paper.

scholarly journals Machine Learning Methods for Seismic Hazards Forecast

Geosciences ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 308 ◽  
Author(s):  
Valeri G. Gitis ◽  
Alexander B. Derendyaev
Keyword(s):  
Machine Learning ◽  
Training Sample ◽  
Time Interval ◽  
Minimum Area ◽  
Learning Methods ◽  
Web Based ◽  
Strong Earthquakes ◽  
Machine Learning Methods ◽  
Regression Approach ◽  
Spatio Temporal

In this paper, we suggest two machine learning methods for seismic hazard forecast. The first method is used for spatial forecasting of maximum possible earthquake magnitudes ( M m a x ), whereas the second is used for spatio-temporal forecasting of strong earthquakes. The first method, the method of approximation of interval expert estimates, is based on a regression approach in which values of M m a x at the points of the training sample are estimated by experts. The method allows one to formalize the knowledge of experts, to find the dependence of M m a x on the properties of the geological environment, and to construct a map of the spatial forecast. The second method, the method of minimum area of alarm, uses retrospective data to identify the alarm area in which the epicenters of strong (target) earthquakes are expected at a certain time interval. This method is the basis of an automatic web-based platform that systematically forecasts target earthquakes. The results of testing the approach to earthquake prediction in the Mediterranean and Californian regions are presented. For the tests, well known parameters of earthquake catalogs were used. The method showed a satisfactory forecast quality.

scholarly journals Detecting regime shifts in fish stock dynamics

2015 ◽  
Vol 72 (11) ◽  
pp. 1619-1628 ◽  
Author(s):  
Tommi Perälä ◽  
Anna Kuparinen
Keyword(s):  
Gadus Morhua ◽  
Atlantic Cod ◽  
Stock Assessment ◽  
Detection Algorithm ◽  
Regime Shifts ◽  
Change Point Detection ◽  
Fish Stock ◽  
Fish Stocks ◽  
Data Generating Process ◽  
Change In Mean

Environmental factors such as water temperature, salinity, and the abundance of zooplankton can have major effects on certain fish stocks’ ability to produce juveniles and, thus, stock renewal ability. This variability in stock productivity manifests itself as different productivity regimes. Here, we detect productivity regime shifts by analyzing recruit-per-spawner time series with Bayesian online change point detection algorithm. The algorithm infers the time since the last regime shift (change in mean or variance or both) as well as the parameters of the data-generating process for the current regime sequentially. We demonstrate the algorithm’s performance using simulated recruitment data from an individual-based model and further apply the algorithm to stock assessment estimates for four Atlantic cod (Gadus morhua) stocks obtained from RAM legacy database. Our analysis shows that the algorithm performs well when the variability between the regimes is high enough compared with the variability within the regimes. The algorithm found several productivity regimes for all four cod stocks, and the findings suggest that the stocks are currently in low productivity regimes, which have started during the 1990s and 2000s.

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