scholarly journals Tsunami arrival time detection system applicable to discontinuous time series data with outliers

2016 ◽  
Vol 16 (12) ◽  
pp. 2603-2622
Author(s):  
Jun-Whan Lee ◽  
Sun-Cheon Park ◽  
Duk Kee Lee ◽  
Jong Ho Lee
Keyword(s):  
Time Series ◽  
Arrival Time ◽  
Time Series Data ◽  
Detection System ◽  
Detection Algorithm ◽  
Series Data ◽  
False Alarms ◽  
Detection Algorithms ◽  
Tsunami Detection ◽  
Tsunami Signal

Abstract. Timely detection of tsunamis with water level records is a critical but logistically challenging task because of outliers and gaps. Since tsunami detection algorithms require several hours of past data, outliers could cause false alarms, and gaps can stop the tsunami detection algorithm even after the recording is restarted. In order to avoid such false alarms and time delays, we propose the Tsunami Arrival time Detection System (TADS), which can be applied to discontinuous time series data with outliers. TADS consists of three algorithms, outlier removal, gap filling, and tsunami detection, which are designed to update whenever new data are acquired. After calibrating the thresholds and parameters for the Ulleung-do surge gauge located in the East Sea (Sea of Japan), Korea, the performance of TADS was discussed based on a 1-year dataset with historical tsunamis and synthetic tsunamis. The results show that the overall performance of TADS is effective in detecting a tsunami signal superimposed on both outliers and gaps.

scholarly journals Tsunami arrival time detection system applicable to discontinuous time-series data with outliers

2016 ◽  
Author(s):  
Jun-Whan Lee ◽  
Sun-Cheon Park ◽  
Duk Kee Lee ◽  
Jong Ho Lee
Keyword(s):  
Time Series ◽  
Arrival Time ◽  
Time Series Data ◽  
Detection System ◽  
Series Data ◽  
Gap Filling ◽  
2011 Tohoku Tsunami ◽  
Overall Performance ◽  
Tsunami Signal ◽  
Tohoku Tsunami

Abstract. Timely detection of tsunamis with water-level records is a critical but logistically challenging task because of outliers and gaps. We propose a tsunami arrival time detection system (TADS) that can be applied to discontinuous time-series data with outliers. TADS consists of three major algorithms that are designed to update at every new data acquisition: outlier detection, gap-filling, and tsunami detection. To detect a tsunami from a record containing outliers and gaps, we propose the concept of the event period. In this study, we applied this concept in our test of the TADS at the Ulleung-do surge gauge located in the East Sea. We calibrated the thresholds to identify tsunami arrivals based on the 2011 Tohoku tsunami, and the results show that the overall performance of TADS is effective at detecting a small tsunami signal superimposed on both an outlier and gap.

Excretion Detection System with Gas Sensor – Proposal and Verification of Algorithm Based on Time-Series Clustering –

2017 ◽  
Vol 29 (2) ◽  
pp. 353-363 ◽  
Author(s):  
Yoshimi Ui ◽  
◽  
Yutaka Akiba ◽  
Shohei Sugano ◽  
Ryosuke Imai ◽  
...  
Keyword(s):  
Time Series ◽  
Gas Sensor ◽  
Gas Sensors ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Detection System ◽  
Detection Algorithm ◽  
Sensor Data ◽  
Series Data ◽  
Processing Unit

[abstFig src='/00290002/09.jpg' width='300' text='Standard Lifilm configuration' ] In this study, we propose an excretion detection system, Lifi, which does not require sensors inside diapers, and we verify its capabilities. It consists of a sheet with strategically placed air intakes, a set of gas sensors, and a processing unit with a newly developed excretion detection algorithm. The gas sensor detects chemicals with odor in the excrement, such as hydrogen sulfide and urea. The time-series data from the gas sensor was used for the detection of not only excretion, but also of the presence/absence of the cared person on the bed. We examined two algorithms, one with a simple threshold and another based on the clustering of sensor data, obtained using the<span class=”bold”>k</span>-means method. The results from both algorithms were satisfactory and similar, once the algorithms were customized for each cared person. However, we adopted the clustering algorithm because it possesses a higher level of flexibility that can be explored and exploited. Lifi was conceived from an overwhelming and serious desire of caretakers to discover the excretion of bed-ridden cared persons, without opening their diapers. We believe that Lifi, along with the clustering algorithm, can help caretakers in this regard.

scholarly journals TimeTrial: An Interactive Application for Optimizing the Design and Analysis of Transcriptomic Time-Series Data in Circadian Biology Research

2020 ◽  
Vol 35 (5) ◽  
pp. 439-451 ◽  
Author(s):  
Elan Ness-Cohn ◽  
Marta Iwanaszko ◽  
William L. Kath ◽  
Ravi Allada ◽  
Rosemary Braun
Keyword(s):  
Time Series ◽  
Experimental Design ◽  
Time Series Data ◽  
Synthetic Data ◽  
Optimal Choice ◽  
Series Data ◽  
Detection Accuracy ◽  
Periodic Patterns ◽  
Detection Algorithms ◽  
The Impact

The circadian rhythm drives the oscillatory expression of thousands of genes across all tissues, coordinating physiological processes. The effect of this rhythm on health has generated increasing interest in discovering genes under circadian control by searching for periodic patterns in transcriptomic time-series experiments. While algorithms for detecting cycling transcripts have advanced, there remains little guidance quantifying the effect of experimental design and analysis choices on cycling detection accuracy. We present TimeTrial, a user-friendly benchmarking framework using both real and synthetic data to investigate cycle detection algorithms’ performance and improve circadian experimental design. Results show that the optimal choice of analysis method depends on the sampling scheme, noise level, and shape of the waveform of interest and provides guidance on the impact of sampling frequency and duration on cycling detection accuracy. The TimeTrial software is freely available for download and may also be accessed through a web interface. By supplying a tool to vary and optimize experimental design considerations, TimeTrial will enhance circadian transcriptomics studies.

scholarly journals Locality-Based Visual Outlier Detection Algorithm for Time Series

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Zhihua Li ◽  
Ziyuan Li ◽  
Ning Yu ◽  
Steven Wen
Keyword(s):  
Time Series ◽  
Outlier Detection ◽  
Extraction Method ◽  
Time Series Data ◽  
Data Extraction ◽  
Detection Algorithm ◽  
Series Data ◽  
Practical Application ◽  
Detection Model ◽  
Efficient Data

Physiological theories indicate that the deepest impression for time series data with respect to the human visual system is its extreme value. Based on this principle, by researching the strategies of extreme-point-based hierarchy segmentation, the hierarchy-segmentation-based data extraction method for time series, and the ideas of locality outlier, a novel outlier detection model and method for time series are proposed. The presented algorithm intuitively labels an outlier factor to each subsequence in time series such that the visual outlier detection gets relatively direct. The experimental results demonstrate the average advantage of the developed method over the compared methods and the efficient data reduction capability for time series, which indicates the promising performance of the proposed method and its practical application value.

scholarly journals bin3C : Exploiting Hi-C sequencing data to accurately resolve metagenome-assembled genomes (MAGs)

2018 ◽  
Author(s):  
Matthew Z. DeMaere ◽  
Aaron E. Darling
Keyword(s):  
Time Series ◽  
High Throughput Sequencing ◽  
Time Series Data ◽  
Detection Algorithm ◽  
Divide And Conquer ◽  
Alternative Methods ◽  
Series Data ◽  
Sequencing Data ◽  
Promising Alternative

AbstractMost microbes inhabiting the planet cannot be easily grown in the lab. Metagenomic techniques provide a means to study these organisms, and recent advances in the field have enabled the resolution of individual genomes from metagenomes, so-called Metagenome Assembled Genomes (MAGs). In addition to expanding the catalog of known microbial diversity, the systematic retrieval of MAGs stands as a tenable divide and conquer reduction of metagenome analysis to the simpler problem of single genome analysis. Many leading approaches to MAG retrieval depend upon time-series or transect data, whose effectiveness is a function of community complexity, target abundance and depth of sequencing. Without the need for time-series data, promising alternative methods are based upon the high-throughput sequencing technique called Hi-C.The Hi-C technique produces read-pairs which capture in-vivo DNA-DNA proximity interactions (contacts). The physical structure of the community modulates the signal derived from these interactions and a hierarchy of interaction rates exists (īntra-chromosomal > Inter-chromosomal > Inter-cellular).We describe an unsupervised method that exploits the hierarchical nature of Hi-C interaction rates to resolve MAGs from a single time-point. As a quantitative demonstration, next, we validate the method against the ground truth of a simulated human faecal microbiome. Lastly, we directly compare our method against a recently announced proprietary service ProxiMeta, which also performs MAG retrieval using Hi-C data.bin3C has been implemented as a simple open-source pipeline and makes use of the unsupervised community detection algorithm Infomap (https://github.com/cerebis/bin3C).

scholarly journals Analysis and Prediction of Alerts in Perimeter Intrusion Detection System

2020 ◽  
Vol 70 (6) ◽  
pp. 619-625
Author(s):  
Rizul Aggarwal ◽  
Anjali Goswami ◽  
Jitender Kumar ◽  
Gwyneth Abdiel Chullai
Keyword(s):  
Time Series ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Time Series Data ◽  
Detection System ◽  
Series Data ◽  
Support Vector ◽  
Short Term ◽  
Term Prediction

Perimeter surveillance systems play an important role in the safety and security of the armed forces. These systems tend to generate alerts in advent of anomalous situations, which require human intervention. The challenge is the generation of false alerts or alert flooding which makes these systems inefficient. In this paper, we focus on short-term as well as long-term prediction of alerts in the perimeter intrusion detection system. We have explored the dependent and independent aspects of the alert data generated over a period of time. Short-term prediction is realized by exploiting the independent aspect of data by narrowing it down to a time-series problem. Time-series analysis is performed by extracting the statistical information from the historical alert data. A dual-stage approach is employed for analyzing the time-series data and support vector regression is used as the regression technique. It is helpful to predict the number of alerts for the nth hour. Additionally, to understand the dependent aspect, we have investigated that the deployment environment has an impact on the alerts generated. Long-term predictions are made by extracting the features based on the deployment environment and training the dataset using different regression models. Also, we have compared the predicted and expected alerts to recognize anomalous behaviour. This will help in realizing the situations of alert flooding over the potential threat.

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