scholarly journals Methods for preprocessing time and distance series data from personal monitoring devices

MethodsX ◽  
2020 ◽  
Vol 7 ◽  
pp. 100959 ◽  
Author(s):  
Tomasz Wiktorski ◽  
Magnus Bjørkavoll-Bergseth ◽  
Stein Ørn
Keyword(s):  
Series Data ◽  
Personal Monitoring ◽  
Monitoring Devices

scholarly journals Technical Breakdown of a Time-Series Data Federation System

10.29007/vwdh ◽  
2018 ◽  
Author(s):  
Ashley Sommer ◽  
Matthew Stenson ◽  
Ross Searle
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Service Providers ◽  
Series Data ◽  
Loosely Coupled ◽  
Farm Scale ◽  
Disparate Data ◽  
On Farm ◽  
Monitoring Devices ◽  
Scale Data

A very large volume of climatic and agricultural data is captured and recorded by on-farm monitoring devices that is uploaded to various different data service providers. It is consistently difficult for land managers to discover, access, understand and use the data due to its disparate nature, limited access to it and multiple proprietary formats used. The Soil Sensing project is developing tools and technologies to help improve the ability to discover, access, understand, and use time-series farm-scale data across disparate data providers. This is achieved by the development and deployment of loosely-coupled web services in the form of a Data Streams Integrator system (DSI), which implements a combined brokered and federated data supply chain pattern. The DSI is composed of the Data Brokering Layer, the Observations and Measurements translation layer, the Sensor Observation Service interface and a metadata registry and repository.

scholarly journals Improved Interpolation and Anomaly Detection for Personal PM2.5 Measurement

2020 ◽  
Vol 10 (2) ◽  
pp. 543 ◽  
Author(s):  
JinSoo Park ◽  
Sungroul Kim
Keyword(s):  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Interpolation Method ◽  
Training Data ◽  
Series Data ◽  
Mean Square ◽  
Fine Dust ◽  
Transmission Errors ◽  
Monitoring Devices

With the development of technology, especially technologies related to artificial intelligence (AI), the fine-dust data acquired by various personal monitoring devices is of great value as training data for predicting future fine-dust concentrations and innovatively alerting people of potential danger. However, most of the fine-dust data obtained from those devices include either missing or abnormal data caused by various factors such as sensor malfunction, transmission errors, or storage errors. This paper presents methods to interpolate the missing data and detect anomalies in PM2.5 time-series data. We validated the performance of our method by comparing ours to well-known existing methods using our personal PM2.5 monitoring data. Our results showed that the proposed interpolation method achieves more than 25% improved results in root mean square error (RMSE) than do most existing methods, and the proposed anomaly detection method achieves fairly accurate results even for the case of the highly capricious fine-dust data. These proposed methods are expected to contribute greatly to improving the reliability of data.

scholarly journals Shrinking personal monitoring devices

2008 ◽  
Vol 42 (3) ◽  
pp. 654-654
Author(s):  
Naomi Lubick
Keyword(s):  
Personal Monitoring ◽  
Monitoring Devices

Quantitative Measurements on the Ion Etching of TMV

1973 ◽  
Vol 31 ◽  
pp. 336-337
Author(s):  
Irwin Bendet ◽  
Nabil Rizk
Keyword(s):  
Electron Microscope ◽  
Tobacco Mosaic Virus ◽  
Mosaic Virus ◽  
Ion Current ◽  
Beam Diameter ◽  
Ion Etching ◽  
Preliminary Results ◽  
Quantitative Measurements ◽  
Monitoring Devices

Preliminary results reported last year on the ion etching of tobacco mosaic virus indicated that the diameter of the virus decreased more rapidly at 10KV than at 5KV, perhaps reaching a constant value before disappearing completely.In order to follow the effects of ion etching on TMV more quantitatively we have designed and built a second apparatus (Fig. 1), which incorporates monitoring devices for measuring ion current and vacuum as well as accelerating voltage. In addition, the beam diameter has been increased to approximately 1 cm., so that ten electron microscope grids can be exposed to the beam simultaneously.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

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