scholarly journals Instrumented flow-following sensor particles with magnetic position detection and buoyancy control

2016 ◽  
Vol 5 (1) ◽  
pp. 213-220 ◽  
Author(s):  
Sebastian Felix Reinecke ◽  
Uwe Hampel
Keyword(s):  
Data Storage ◽  
Large Scale ◽  
Control Unit ◽  
Fixed Time ◽  
Vertical Position ◽  
Full Data ◽  
Full Extension ◽  
Neutral Buoyancy ◽  
Position Detection ◽  
Buoyancy Control

Abstract. A concept for buoyancy control and magnetic position detection has been developed for the improvement of instrumented flow-following sensor particles. The sensor particles are used for investigation of hydrodynamic and biochemical processes in large-scale vessels such as biogas fermenters, bioreactors and aerated sludge basins. Neutral buoyancy of the sensor particles is required for tracing of the fluid flows. Buoyancy control is performed by adjustment of the sensor particles' volume, which is altered by an integrated piston. A miniaturized linear actuator, namely a stepper motor with linear transmission, is operated by a microcontroller to drive the piston. The buoyancy control unit enables accurate automated taring of the sensor particles in the stagnant process fluid to achieve neutral buoyancy. Therefore, the measured vertical position of the sensor particle as a function of the hydrostatic pressure is used as feedback. It has an incremental density change of 0.0136 % as compared to water and a residual drift velocity of approximately 3.6  ×  10−3 m s−1. Furthermore, a minimum density of 926 kg m−3 can be set by full extension of the piston, which allows floating of the sensor particles after a defined event, namely critical charge of battery, full data storage or the end of a fixed time cycle. Thus, recovery of the sensor particles can proceed easily from the fluid level. The sensor particles with a buoyancy control unit are tested for depths up to 15 m. Also, detection of a local magnetic position marker by the sensor particles has been implemented to enhance movement tracking. It was tested in a lab-scale biogas digester and was used for estimation of the liquid circulation time distribution and Peclét number to describe the macro-flow.

scholarly journals The nanofluidic confinement apparatus: studying confinement-dependent nanoparticle behavior and diffusion

2018 ◽  
Vol 9 ◽  
pp. 301-310 ◽  
Author(s):  
Stefan Fringes ◽  
Felix Holzner ◽  
Armin W Knoll
Keyword(s):  
Polymer Surface ◽  
Diffusion Constant ◽  
Numerical Aperture ◽  
Lateral Diffusion ◽  
Vertical Position ◽  
Detection Accuracy ◽  
Illumination Time ◽  
Electroviscous Effect ◽  
Position Detection ◽  
Hydrodynamic Effects

The behavior of nanoparticles under nanofluidic confinement depends strongly on their distance to the confining walls; however, a measurement in which the gap distance is varied is challenging. Here, we present a versatile setup for investigating the behavior of nanoparticles as a function of the gap distance, which is controlled to the nanometer. The setup is designed as an open system that operates with a small amount of dispersion of ≈20 μL, permits the use of coated and patterned samples and allows high-numerical-aperture microscopy access. Using the tool, we measure the vertical position (termed height) and the lateral diffusion of 60 nm, charged, Au nanospheres as a function of confinement between a glass surface and a polymer surface. Interferometric scattering detection provides an effective particle illumination time of less than 30 μs, which results in lateral and vertical position detection accuracy ≈10 nm for diffusing particles. We found the height of the particles to be consistently above that of the gap center, corresponding to a higher charge on the polymer substrate. In terms of diffusion, we found a strong monotonic decay of the diffusion constant with decreasing gap distance. This result cannot be explained by hydrodynamic effects, including the asymmetric vertical position of the particles in the gap. Instead we attribute it to an electroviscous effect. For strong confinement of less than 120 nm gap distance, we detect the onset of subdiffusion, which can be correlated to the motion of the particles along high-gap-distance paths.

Research and Design of the Distributed Mass Small File Storage System Based on WCF

2013 ◽  
Vol 765-767 ◽  
pp. 1087-1091
Author(s):  
Hong Lin ◽  
Shou Gang Chen ◽  
Bao Hui Wang
Keyword(s):  
Fault Tolerance ◽  
Distributed Computing ◽  
Data Storage ◽  
Large Scale ◽  
Storage System ◽  
Hardware Platform ◽  
File Storage ◽  
Computing Framework ◽  
Research And Design ◽  
Small File

Recently, with the development of Internet and the coming of new application modes, data storage has some new characters and new requirements. In this paper, a Distributed Computing Framework Mass Small File storage System (For short:Dnet FS) based on Windows Communication Foundation in .Net platform is presented, which is lightweight, good-expansibility, running in cheap hardware platform, supporting Large-scale concurrent access, and having certain fault-tolerance. The framework of this system is analyzed and the performance of this system is tested and compared. All of these prove this system meet requirements.

scholarly journals 3D DNA structural barcode copying and random access

2020 ◽  
Author(s):  
Filip Bošković ◽  
Alexander Ohmann ◽  
Ulrich F. Keyser ◽  
Kaikai Chen
Keyword(s):  
Data Storage ◽  
Single Molecule ◽  
Self Assembly ◽  
Large Scale ◽  
Three Dimensional ◽  
Random Access ◽  
Scale Production ◽  
Digital Information ◽  
Dna Nanostructures ◽  
Large Scale Production

AbstractThree-dimensional (3D) DNA nanostructures built via DNA self-assembly have established recent applications in multiplexed biosensing and storing digital information. However, a key challenge is that 3D DNA structures are not easily copied which is of vital importance for their large-scale production and for access to desired molecules by target-specific amplification. Here, we build 3D DNA structural barcodes and demonstrate the copying and random access of the barcodes from a library of molecules using a modified polymerase chain reaction (PCR). The 3D barcodes were assembled by annealing a single-stranded DNA scaffold with complementary short oligonucleotides containing 3D protrusions at defined locations. DNA nicks in these structures are ligated to facilitate barcode copying using PCR. To randomly access a target from a library of barcodes, we employ a non-complementary end in the DNA construct that serves as a barcode-specific primer template. Readout of the 3D DNA structural barcodes was performed with nanopore measurements. Our study provides a roadmap for convenient production of large quantities of self-assembled 3D DNA nanostructures. In addition, this strategy offers access to specific targets, a crucial capability for multiplexed single-molecule sensing and for DNA data storage.

scholarly journals Hallucinating Optical Flow Features for Video Classification

2019 ◽  
Author(s):  
Yongyi Tang ◽  
Lin Ma ◽  
Lianqiang Zhou
Keyword(s):  
Optical Flow ◽  
Data Storage ◽  
Large Scale ◽  
State Of The Art ◽  
Video Content ◽  
Video Classification ◽  
Temporal Relationships ◽  
Optical Flow Computation ◽  
Flow Features ◽  
Optical Flow Features

Appearance and motion are two key components to depict and characterize the video content. Currently, the two-stream models have achieved state-of-the-art performances on video classification. However, extracting motion information, specifically in the form of optical flow features, is extremely computationally expensive, especially for large-scale video classification. In this paper, we propose a motion hallucination network, namely MoNet, to imagine the optical flow features from the appearance features, with no reliance on the optical flow computation. Specifically, MoNet models the temporal relationships of the appearance features and exploits the contextual relationships of the optical flow features with concurrent connections. Extensive experimental results demonstrate that the proposed MoNet can effectively and efficiently hallucinate the optical flow features, which together with the appearance features consistently improve the video classification performances. Moreover, MoNet can help cutting down almost a half of computational and data-storage burdens for the two-stream video classification. Our code is available at: https://github.com/YongyiTang92/MoNet-Features

scholarly journals Short-Term Master-Slave Forecast Method for Distributed Photovoltaic Plants Based on the Spatial Correlation

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jia Ning ◽  
Guanghao Lu ◽  
Sipeng Hao ◽  
Aidong Zeng ◽  
Hualei Wang
Keyword(s):  
Power Systems ◽  
Spatial Correlation ◽  
Data Storage ◽  
Power Plants ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Storage Condition ◽  
Support Vector ◽  
Forecast Method ◽  
Short Term

With the large-scale integration of distributed photovoltaic (DPV) power plants, the uncertainty of photovoltaic generation is intensively influencing the secure operation of power systems. Improving the forecast capability of DPV plants has become an urgent problem to solve. However, most of the DPV plants are not able to make generation forecast on their own due to the constraints of the investment cost, data storage condition, and the influence of microscope environment. Therefore, this paper proposes a master-slave forecast method to predict the power of target plants without forecast ability based on the power of DPV plants with comprehensive forecast system and the spatial correlation between these two kinds of plants. First, a characteristics pattern library of DPV plants is established with K-means clustering algorithm considering the time difference. Next, the pattern most spatially correlated to the target plant is determined through online matching. The corresponding spatial correlation mapping relationship is obtained by numerical fitting using least squares support vector machine (LS-SVM), and the short-term generation forecast for target plants is achieved with the forecast of reference plants and mapping relationship. Simulation results demonstrate that the proposed method could improve the overall forecast accuracy by more than 52% for univariate prediction and by more than 22% for multivariate prediction and obtain short-term generation forecast for DPV or newly built DPV plants with low investment.

scholarly journals Digitalization of Business Processes of Enterprises of the Ecosystem of Industry 4.0: Virtual-Real Aspect of Economic Growth Reserves

2021 ◽  
Vol 18 ◽  
pp. 569-580
Author(s):  
Kateryna Kraus ◽  
Nataliia Kraus ◽  
Oleksandr Manzhura
Keyword(s):  
Economic Growth ◽  
Virtual Reality ◽  
Data Storage ◽  
Industry 4.0 ◽  
Large Scale ◽  
Business Processes ◽  
Digital Transformation ◽  
Cloud Services ◽  
Scale Production ◽  
Cloud Platform

The purpose of the research is to present the features of digitization of business processes in enterprises as a foundation on which the gradual formation of Industry 4.0 and the search for economic growth in new virtual reality, which has every chance to be a decisive step in implementing digital strategy for Ukraine and development of the innovation ecosystem. Key problems that arise during the digitalization of business processes in enterprises are presented, among which are: the historical orientation of production to mass, “running” sizes and large batches; large-scale production load; the complexity of cooperation and logic between production sites. It is determined that high-quality and effective tools of innovation-digital transformation in the conditions of virtual reality should include: a single system of on-line order management for all enterprises (application registration – technical expertise – planning – performance control – shipment); Smart Factory, Predictive Maintenance, IIoT, CRM, SCM. Features of digital transformation in the part of formation of enterprises of the ecosystem of Industry 4.0 are revealed. The capabilities and benefits of using Azure cloud platform in enterprises, which includes more than 200 products and cloud services, are analyzed. Azure is said to support open source technologies, so businesses have the ability to use tools and technologies they prefer and are more useful. After conducting a thorough analysis of the acceleration of deep digitalization of business processes by enterprises, authors proposed to put into practice Aruba solution for tracking contacts in the fight against COVID-19. Aruba technology helps locate, allowing you to implement flexible solutions based on Aruba Partner Ecosystem using a USB interface. It is proposed to use SYNTEGRA – a data integration service that provides interactive analytics and provides data models and dashboards in order to accelerate the modernization of data storage and management, optimize reporting in the company and obtain real-time analytics. The possibilities of using Azure cloud platform during the digitization of business processes of enterprises of the ecosystem of Industry 4.0 in the conditions of virtual reality are determined.

scholarly journals Upscaled diurnal cycles of land-atmosphere fluxes: a new global half-hourly data product

2018 ◽  
Author(s):  
Paul Bodesheim ◽  
Martin Jung ◽  
Fabian Gans ◽  
Miguel D. Mahecha ◽  
Markus Reichstein
Keyword(s):  
Data Storage ◽  
Large Scale ◽  
Gross Primary Production ◽  
Net Ecosystem Exchange ◽  
Predictor Variables ◽  
Energy Fluxes ◽  
Diurnal Cycles ◽  
Monthly Average ◽  
Hourly Data ◽  
Data Products

Abstract. Interactions between the biosphere and the atmosphere can be well characterized by fluxes between the two. In particular, carbon and energy fluxes play a major role for understanding biogeochemical processes on ecosystem level or global scale. However, the fluxes can only be measured at individual sites by eddy covariance towers and an upscaling of these local observations is required to analyze global patterns. Previous work focused on upscaling monthly, eight-day, or daily average values and global maps for each flux have been provided accordingly. In this paper, we raise the upscaling of carbon and energy fluxes between land and atmosphere to the next level by increasing the temporal resolution to subdaily scales. We provide continuous half-hourly fluxes for the period from 2001 to 2014 at 0.5◦ spatial resolution, which allows for analyzing diurnal cycles globally. The dataset contains four fluxes: gross primary production (GPP), net ecosystem exchange (NEE), latent heat (LE), and sensible heat (H). We propose two prediction approaches for the diurnal cycles based on large-scale regression models and compare them in extensive cross-validation experiments using different sets of predictor variables. We analyze the results for a set of FLUXNET tower sites showing the suitability of our approaches for this upscaling task. Finally, we have selected one approach to calculate the global half- hourly data products based on predictor variables from remote sensing and meteorology at daily resolution as well as half-hourly potential radiation. In addition, we provide a derived product that only contains monthly average diurnal cycles, which is a lightweight version in terms of data storage that still enables to study the important characteristics of diurnal courses globally. We recommend to primarily use these monthly average diurnal cycles, because they are less affected by the impacts of day-to-day variation, observation noise, and short- term fluctuations on subdaily scales compared to the plain half-hourly flux products. The global half-hourly data products are available at https://doi.org/10.17871/BACI.224.

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