Analyzing psychotherapy process time series using neural maps

1999 ◽  
Author(s):  
T. Villmann
Keyword(s):  
Time Series ◽  
Psychotherapy Process ◽  
Process Time ◽  
Neural Maps

scholarly journals WDSchain: A Toolbox for Enhancing the Security Using Blockchain Technology in Water Distribution System

Water ◽  
2021 ◽  
Vol 13 (14) ◽  
pp. 1944
Author(s):  
Haitham H. Mahmoud ◽  
Wenyan Wu ◽  
Yonghao Wang
Keyword(s):  
Time Series ◽  
Distribution Systems ◽  
Water Distribution ◽  
Time Series Data ◽  
Series Data ◽  
Process Time ◽  
Time Interval ◽  
Security Level ◽  
Data Validation ◽  
Simulated Time

This work develops a toolbox called WDSchain on MATLAB that can simulate blockchain on water distribution systems (WDS). WDSchain can import data from Excel and EPANET water modelling software. It extends the EPANET to enable simulation blockchain of the hydraulic data at any intended nodes. Using WDSchain will strengthen network automation and the security in WDS. WDSchain can process time-series data with two simulation modes: (1) static blockchain, which takes a snapshot of one-time interval data of all nodes in WDS as input and output into chained blocks at a time, and (2) dynamic blockchain, which takes all simulated time-series data of all the nodes as input and establishes chained blocks at the simulated time. Five consensus mechanisms are developed in WDSchain to provide data at different security levels using PoW, PoT, PoV, PoA, and PoAuth. Five different sizes of WDS are simulated in WDSchain for performance evaluation. The results show that a trade-off is needed between the system complexity and security level for data validation. The WDSchain provides a methodology to further explore the data validation using Blockchain to WDS. The limitations of WDSchain do not consider selection of blockchain nodes and broadcasting delay compared to commercial blockchain platforms.

scholarly journals STATE ANALYSIS INTERNET OF THINGS DEVICES BASED ON BAGGING

T-Comm ◽  
2020 ◽  
Vol 14 (12) ◽  
pp. 45-50
Author(s):  
Mikhail E. Sukhoparov ◽  
◽  
Ilya S. Lebedev ◽  
Keyword(s):  
Time Series ◽  
Internet Of Things ◽  
The State ◽  
Point Of View ◽  
Operating Conditions ◽  
Process Time ◽  
K Nearest Neighbors ◽  
Computational Point ◽  
Iot Devices ◽  
The Internet Of Things

The development of IoT concept makes it necessary to search and improve models and methods for analyzing the state of remote autonomous devices. Due to the fact that some devices are located outside the controlled area, it becomes necessary to develop universal models and methods for identifying the state of low-power devices from a computational point of view, using complex approaches to analyzing data coming from various information channels. The article discusses an approach to identifying IoT devices state, based on parallel functioning classifiers that process time series received from elements in various states and modes of operation. The aim of the work is to develop an approach for identifying the state of IoT devices based on time series recorded during the execution of various processes. The proposed solution is based on methods of parallel classification and statistical analysis, requires an initial labeled sample. The use of several classifiers that give an answer "independently" from each other makes it possible to average the error by "collective" voting. The developed approach is tested on a sequence of classifying algorithms, to the input of which the time series obtained experimentally under various operating conditions were fed. Results are presented for a naive Bayesian classifier, decision trees, discriminant analysis, and the k nearest neighbors method. The use of a sequence of classification algorithms operating in parallel allows scaling by adding new classifiers without losing processing speed. The method makes it possible to identify the state of the Internet of Things device with relatively small requirements for computing resources, ease of implementation, and scalability by adding new classifying algorithms.

scholarly journals From the Streets to the Mountains: The Dynamics of Transition from a Protest Wave to an Insurgency in Kashmir

2014 ◽  
Vol 19 (3) ◽  
pp. 309-327 ◽  
Author(s):  
Tijen Demirel-Pegg
Keyword(s):  
Time Series ◽  
Civil Wars ◽  
Transition Process ◽  
External Support ◽  
Process Time ◽  
Key Factors ◽  
Comprehensive Understanding ◽  
State Repression ◽  
Major Shift ◽  
External Actors

This study investigates the dynamics of transition from a peaceful protest wave to a violent insurgency. It examines the causal path leading to a major shift in the intensity of a protest wave and argues that the transition is the product of the interactions between the dissidents, the state, and external actors. By studying the protest wave in Kashmir (1979-88), it identifies state repression and external support as the key factors driving the transition process. Time series analysis is used to analyze the original empirical evidence collected through content analysis. By providing a comprehensive understanding of the origins of the insurgency in Kashmir, this study shows that protest waves and civil wars are intimately linked.

scholarly journals Using RGISTools to Estimate Water Levels in Reservoirs and Lakes

2020 ◽  
Vol 12 (12) ◽  
pp. 1934 ◽  
Author(s):  
Ana F. Militino ◽  
Manuel Montesino-SanMartin ◽  
Unai Pérez-Goya ◽  
M. Dolores Ugarte
Keyword(s):  
Time Series ◽  
Mean Squared Error ◽  
Water Levels ◽  
Process Time ◽  
Landsat 8 ◽  
Northern Spain ◽  
Software Environment ◽  
Efficient Manner ◽  
Radar Images

The combination of freely accessible satellite imagery from multiple programs improves the spatio-temporal coverage of remote sensing data, but it exhibits barriers regarding the variety of web services, file formats, and data standards. Ris an open-source software environment with state-of-the-art statistical packages for the analysis of optical imagery. However, it lacks the tools for providing unified access to multi-program archives to customize and process the time series of images. This manuscript introduces RGISTools, a new software that solves these issues, and provides a working example on water mapping, which is a socially and environmentally relevant research field. The case study uses a digital elevation model and a rarely assessed combination of Landsat-8 and Sentinel-2 imagery to determine the water level of a reservoir in Northern Spain. The case study demonstrates how to acquire and process time series of surface reflectance data in an efficient manner. Our method achieves reasonably accurate results, with a root mean squared error of 0.90 m. Future improvements of the package involve the expansion of the workflow to cover the processing of radar images. This should counteract the limitation of the cloud coverage with multi-spectral images.

Gaussian Process Time Series Model for Life Prognosis of Metallic Structures

2009 ◽  
Vol 20 (8) ◽  
pp. 887-896 ◽  
Author(s):  
Subhasish Mohanty ◽  
Santanu Das ◽  
Aditi Chattopadhyay ◽  
Pedro Peralta
Keyword(s):  
Time Series ◽  
Gaussian Process ◽  
Time Series Model ◽  
Process Time ◽  
Metallic Structures ◽  
Life Prognosis

scholarly journals Gaussian Process Time-Series Models for Structures under Operational Variability

2017 ◽  
Vol 3 ◽  
Author(s):  
Luis David Avendaño-Valencia ◽  
Eleni N. Chatzi ◽  
Ki Young Koo ◽  
James M. W. Brownjohn
Keyword(s):  
Time Series ◽  
Gaussian Process ◽  
Process Time ◽  
Time Series Models

scholarly journals DSeg: A Dynamic Image Segmentation Program to Extract Backbone Patterns for Filamentous Bacteria and Hyphae Structures

2019 ◽  
Vol 25 (3) ◽  
pp. 711-719 ◽  
Author(s):  
Hanqing Zhang ◽  
Niklas Söderholm ◽  
Linda Sandblad ◽  
Krister Wiklund ◽  
Magnus Andersson
Keyword(s):  
Time Series ◽  
Automatic Segmentation ◽  
Persistence Length ◽  
Synthetic Data ◽  
Image Data ◽  
Growth Direction ◽  
Process Time ◽  
Transmission Electron ◽  
Size Constraints ◽  
Microscopy Images

AbstractAnalysis of numerous filamentous structures in an image is often limited by the ability of algorithms to accurately segment complex structures or structures within a dense population. It is even more problematic if these structures continuously grow when recording a time-series of images. To overcome these issues we present DSeg; an image analysis program designed to process time-series image data, as well as single images, to segment filamentous structures. The program includes a robust binary level-set algorithm modified to use size constraints, edge intensity, and past information. We verify our algorithms using synthetic data, differential interference contrast images of filamentous prokaryotes, and transmission electron microscopy images of bacterial adhesion fimbriae. DSeg includes automatic segmentation, tools for analysis, and drift correction, and outputs statistical data such as persistence length, growth rate, and growth direction. The program is available at Sourceforge.

Modeling psychotherapy process by time-series panel analysis (TSPA)

2009 ◽  
Vol 19 (4-5) ◽  
pp. 469-481 ◽  
Author(s):  
Wolfgang Tschacher ◽  
Fabian Ramseyer
Keyword(s):  
Time Series ◽  
Psychotherapy Process ◽  
Panel Analysis

scholarly journals A Reservation Aggregation Framework Design for Demand Estimation

10.14311/860 ◽  
2006 ◽  
Vol 46 (4) ◽  
Author(s):  
T. Vítek ◽  
D. Pachner
Keyword(s):  
Time Series ◽  
Management Practices ◽  
Linear Models ◽  
Process Time ◽  
Hotel Management ◽  
Control Engineering ◽  
Demand Prediction ◽  
Pricing Decisions ◽  
Framework Design ◽  
Market Search

Effective management practices in the tourism and hotel area have seldom been more important than at the present time. Pricing decisions cannot be taken without serious thought. IT has provided the opportunity for a customer to make a quick market search and it offers decision support systems that can be used in the hotel management. The heart of yield management system consists of the predicting machine, which estimates the number of incoming reservations. Incoming reservations arrive randomly in time. The time series calculi as well as the estimators known from control engineering require properly defined time rows (with a constant period). This requirement is usually not fulfilled, so the input data are not exploited properly. This paper outlines a procedure that aggregates the reservations into a time series that is useful for demand prediction. The algorithm prepares the data systematically for further processing. Any method that process time rows can be used for subsequent prediction: time series, linear models or time extrapolation. 

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