scholarly journals Sediment Bed Borehole Advection Method

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3380
Author(s):  
Scott Augustine ◽  
Jaehyun Cho ◽  
Harald Klammler ◽  
Kirk Hatfield ◽  
Michael D. Annable
Keyword(s):  
Time Series Data ◽  
Low Cost ◽  
Vertical Direction ◽  
Series Data ◽  
Time Analysis ◽  
Sediment Bed ◽  
Concentration Time Series ◽  
Flux Measurements ◽  
Darcy Flux ◽  
Sediment Interface

This paper introduces and tests the Sediment Bed Borehole Advection Method (SBBAM), a low cost, point-measurement technique which utilizes a push-point probe to quantify the vertical direction and magnitude of Darcy flux at the surface water—groundwater sediment interface. The Darcy flux measurements are derived from the residence-time analysis of tracer arrival calculated from measured tracer concentration time-series data. The technique was evaluated in the laboratory using a sediment bed simulator tank at eight flow rates (1–90 cm/day). Triplicate test runs for each flow rate returned average errors between 4–20 percent; r2 = 0.9977.

scholarly journals Deep Convolutional and LSTM Networks on Multi-Channel Time Series Data for Gait Phase Recognition

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 789
Author(s):  
David Kreuzer ◽  
Michael Munz
Keyword(s):  
Gait Analysis ◽  
Time Series Data ◽  
Low Cost ◽  
Analysis Data ◽  
Gait Disorders ◽  
Human Gait ◽  
Series Data ◽  
Ageing Society ◽  
Time Motion ◽  
Gait Phase

With an ageing society comes the increased prevalence of gait disorders. The restriction of mobility leads to a considerable reduction in the quality of life, because associated falls increase morbidity and mortality. Consideration of gait analysis data often alters surgical recommendations. For that reason, the early and systematic diagnostic treatment of gait disorders can spare a lot of suffering. As modern gait analysis systems are, in most cases, still very costly, many patients are not privileged enough to have access to comparable therapies. Low-cost systems such as inertial measurement units (IMUs) still pose major challenges, but offer possibilities for automatic real-time motion analysis. In this paper, we present a new approach to reliably detect human gait phases, using IMUs and machine learning methods. This approach should form the foundation of a new medical device to be used for gait analysis. A model is presented combining deep 2D-convolutional and LSTM networks to perform a classification task; it predicts the current gait phase with an accuracy of over 92% on an unseen subject, differentiating between five different phases. In the course of the paper, different approaches to optimize the performance of the model are presented and evaluated.

scholarly journals Low-cost solution to the grid reliability problem with 100% penetration of intermittent wind, water, and solar for all purposes

2015 ◽  
Vol 112 (49) ◽  
pp. 15060-15065 ◽  
Author(s):  
Mark Z. Jacobson ◽  
Mark A. Delucchi ◽  
Mary A. Cameron ◽  
Bethany A. Frew
Keyword(s):  
Nuclear Power ◽  
Large Scale ◽  
Fossil Fuels ◽  
Phase Change Materials ◽  
Time Series Data ◽  
Low Cost ◽  
Series Data ◽  
Integration Model ◽  
Weather Model ◽  
Scale Integration

This study addresses the greatest concern facing the large-scale integration of wind, water, and solar (WWS) into a power grid: the high cost of avoiding load loss caused by WWS variability and uncertainty. It uses a new grid integration model and finds low-cost, no-load-loss, nonunique solutions to this problem on electrification of all US energy sectors (electricity, transportation, heating/cooling, and industry) while accounting for wind and solar time series data from a 3D global weather model that simulates extreme events and competition among wind turbines for available kinetic energy. Solutions are obtained by prioritizing storage for heat (in soil and water); cold (in ice and water); and electricity (in phase-change materials, pumped hydro, hydropower, and hydrogen), and using demand response. No natural gas, biofuels, nuclear power, or stationary batteries are needed. The resulting 2050–2055 US electricity social cost for a full system is much less than for fossil fuels. These results hold for many conditions, suggesting that low-cost, reliable 100% WWS systems should work many places worldwide.

scholarly journals Frequency–time analysis, low-rank reconstruction and denoising of turbulent flows using SPOD

2021 ◽  
Vol 926 ◽  
Author(s):  
Akhil Nekkanti ◽  
Oliver T. Schmidt
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Large Scale ◽  
Time Series Data ◽  
High Energy ◽  
Turbulent Jet ◽  
Low Rank ◽  
Series Data ◽  
Time Analysis ◽  
Frequency Domain Approach

Four different applications of spectral proper orthogonal decomposition (SPOD) are demonstrated on large-eddy simulation data of a turbulent jet. These are: low-rank reconstruction, denoising, frequency–time analysis and prewhitening. We demonstrate SPOD-based flow-field reconstruction using direct inversion of the SPOD algorithm (frequency-domain approach) and propose an alternative approach based on projection of the time series data onto the modes (time-domain approach). We further present a SPOD-based denoising strategy that is based on hard thresholding of the SPOD eigenvalues. The proposed strategy achieves significant noise reduction while facilitating drastic data compression. In contrast to standard methods of frequency–time analysis such as wavelet transform, a proposed SPOD-based approach yields a spectrogram that characterises the temporal evolution of spatially coherent flow structures. A convolution-based strategy is proposed to compute the time-continuous expansion coefficients. When applied to the turbulent jet data, SPOD-based frequency–time analysis reveals that the intermittent occurrence of large-scale coherent structures is directly associated with high-energy events. This work suggests that the time-domain approach is preferable for low-rank reconstruction of individual snapshots, and the frequency-domain approach for denoising and frequency–time analysis.

Estimation of Elasticities of Substitution for CES and VES Production Functions using Firm-level Data for Food-processing Industries in Pakistan

1988 ◽  
Vol 27 (1) ◽  
pp. 59-71 ◽  
Author(s):  
George E. Barrese ◽  
Sohail J. Malik
Keyword(s):  
Food Processing ◽  
Agricultural Development ◽  
Agricultural Sector ◽  
Time Series Data ◽  
Low Cost ◽  
Series Data ◽  
Firm Level ◽  
Acre Yield ◽  
Level Data ◽  
The Government

This study, based on the time-series data covering the period from 1956 to 1986, estimates production function in the agricultural sector of Pakistan. The strategy for agricultural development in the country has been based on greater utilization of "high pay-off' low-cost technology. The government advanced loans through financial institutions to make it possible for the farmers to acquire this technology. Despite the infusion of seed-fertilizer technology, per acre yield of major crops like wheat, rice, cereal and sugar-cane in Pakistan is lower than in most LDCs in the region. Therefore, it is concluded that the use of present technology has reached a plateau and it is time to look for additional inputs for improvement in productivity.

TSCache

2021 ◽  
Vol 14 (13) ◽  
pp. 3253-3266
Author(s):  
Jian Liu ◽  
Kefei Wang ◽  
Feng Chen
Keyword(s):  
Time Series ◽  
High Speed ◽  
Time Series Data ◽  
Low Cost ◽  
Series Data ◽  
Efficient System ◽  
Indexing Structure ◽  
System Solution ◽  
Challenges And Opportunities ◽  
Cache System

Time-series databases are becoming an indispensable component in today's data centers. In order to manage the rapidly growing time-series data, we need an effective and efficient system solution to handle the huge traffic of time-series data queries. A promising solution is to deploy a high-speed, large-capacity cache system to relieve the burden on the backend time-series databases and accelerate query processing. However, time-series data is drastically different from other traditional data workloads, bringing both challenges and opportunities. In this paper, we present a flash-based cache system design for time-series data, called TSCache . By exploiting the unique properties of time-series data, we have developed a set of optimization schemes, such as a slab-based data management, a two-layered data indexing structure, an adaptive time-aware caching policy, and a low-cost compaction process. We have implemented a prototype based on Twitter's Fatcache. Our experimental results show that TSCache can significantly improve client query performance, effectively increasing the bandwidth by a factor of up to 6.7 and reducing the latency by up to 84.2%.

scholarly journals FIKWater: A Water Consumption Dataset from Three Restaurant Kitchens in Portugal

Data ◽  
2021 ◽  
Vol 6 (3) ◽  
pp. 26
Author(s):  
Lucas Pereira ◽  
Vitor Aguiar ◽  
Fábio Vasconcelos
Keyword(s):  
Water Demand ◽  
Time Series Data ◽  
Cold Water ◽  
Low Cost ◽  
Sampling Frequency ◽  
Research Community ◽  
Series Data ◽  
Flow Meters ◽  
The Internet Of Things ◽  
Size Data

With the advent of the Internet of Things (IoT) and low-cost sensing technologies, the availability of data has reached levels never imagined before by the research community. However, independently of their size, data are only as valuable as the ability to have access to them. This paper presents the FIKWater dataset, which contains time series data for hot and cold water demand collected from three restaurant kitchens in Portugal for consecutive periods between two and four weeks. The measurements were taken using ultrasonic flow meters, at a sampling frequency of 0.2 Hz. Additionally, some details of the monitored spaces are also provided.

scholarly journals Foreign influence on Pakistan's exports and imports Evidence from Pakistan

2016 ◽  
Vol 2 (2) ◽  
pp. 117-128
Author(s):  
Irfan Hussain Khan ◽  
Shumaila Hashim ◽  
Muhammad Rizwan Yaseen
Keyword(s):  
Trading Volume ◽  
Time Series Data ◽  
Low Cost ◽  
Basic Research ◽  
Developed Countries ◽  
Theoretical Background ◽  
Series Data ◽  
Annual Data ◽  
Study Results ◽  
The Impact

The purpose of this study is to investigate the impact of the Pakistani currency phase action on exports and imports. Two time series data base year and quarterly basic research use. Starting from the 1970 annual data for about 40 years, beginning with the beginning of 2000 to 2012 quarterly data. Johnson estimates quarterly observations using common integration techniques. In the current study results show that Pakistan first began trading volume for the US and developed countries, the UK and Europe. As a combination of export and import time Pakistan has improved. Production and manufacture of semi-finished goods and primary product alternatives, while the import of consumer goods, capital goods and petroleum products expanded. Due to low-cost elasticity of the export and import activity of the exchange of theoretical background reaction support. On the other hand, if the value of the rupees fell against the dollar, the import costs rose more than the export bills. In support of this study, Pakistan should focus on a small number of countries to reduce trade and expand trade. Similarly, on the basis of the goods may add some other goods.

Real-time analysis and management of big time-series data

2013 ◽  
Vol 57 (3/4) ◽  
pp. 8:1-8:12 ◽  
Author(s):  
A. Biem ◽  
H. Feng ◽  
A. V. Riabov ◽  
D. S. Turaga
Keyword(s):  
Time Series ◽  
Real Time ◽  
Time Series Data ◽  
Series Data ◽  
Time Analysis ◽  
Real Time Analysis

Analysis of Privacy Preservation Techniques in IoT

2019 ◽  
Vol 10 (3) ◽  
pp. 27-33
Author(s):  
Ravindra Sadashivrao Apare ◽  
Satish Narayanrao Gujar
Keyword(s):  
Artificial Intelligence ◽  
Time Series ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Time Series Data ◽  
Low Cost ◽  
Series Data ◽  
Automation System ◽  
Sensing Technology ◽  
Iot Devices

IoT (Internet of Things) is a sophisticated analytics and automation system that utilizes networking, big data, artificial intelligence, and sensing technology to distribute absolute systems for a service or product. The major challenges in IoT relies in security restrictions related with generating low cost devices, and the increasing number of devices that generates further opportunities for attacks. Hence, this article intends to develop a promising methodology associated with data privacy preservation for handling the IoT network. It is obvious that the IoT devices often generate time series data, where the range of respective time series data can be extremely large.

Analysis of Privacy Preservation Techniques in IoT

2021 ◽  
pp. 1343-1351
Author(s):  
Ravindra Sadashivrao Apare ◽  
Satish Narayanrao Gujar
Keyword(s):  
Artificial Intelligence ◽  
Time Series ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Time Series Data ◽  
Low Cost ◽  
Series Data ◽  
Automation System ◽  
Sensing Technology ◽  
Iot Devices

IoT (Internet of Things) is a sophisticated analytics and automation system that utilizes networking, big data, artificial intelligence, and sensing technology to distribute absolute systems for a service or product. The major challenges in IoT relies in security restrictions related with generating low cost devices, and the increasing number of devices that generates further opportunities for attacks. Hence, this article intends to develop a promising methodology associated with data privacy preservation for handling the IoT network. It is obvious that the IoT devices often generate time series data, where the range of respective time series data can be extremely large.

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