scholarly journals Laboratory flume experiments with the Swiss plate geophone bed load monitoring system: 2. Application to field sites with direct bed load samples

2016 ◽  
Vol 52 (10) ◽  
pp. 7760-7778 ◽  
Author(s):  
Carlos R. Wyss ◽  
Dieter Rickenmann ◽  
Bruno Fritschi ◽  
Jens M. Turowski ◽  
Volker Weitbrecht ◽  
...  
Keyword(s):  
Monitoring System ◽  
Bed Load ◽  
Flume Experiments ◽  
Laboratory Flume ◽  
Load Monitoring ◽  
System 2

scholarly journals Laboratory flume experiments with the Swiss plate geophone bed load monitoring system: 1. Impulse counts and particle size identification

2016 ◽  
Vol 52 (10) ◽  
pp. 7744-7759 ◽  
Author(s):  
Carlos R. Wyss ◽  
Dieter Rickenmann ◽  
Bruno Fritschi ◽  
Jens M. Turowski ◽  
Volker Weitbrecht ◽  
...  
Keyword(s):  
Particle Size ◽  
Monitoring System ◽  
Bed Load ◽  
Flume Experiments ◽  
Laboratory Flume ◽  
Load Monitoring ◽  
System 1

Debris flow impact on a flexible barrier: laboratory flume experiments and force-based mechanical model validation

2021 ◽  
Vol 106 (1) ◽  
pp. 735-756
Author(s):  
R. Brighenti ◽  
L. Spaggiari ◽  
A. Segalini ◽  
R. Savi ◽  
G. Capparelli
Keyword(s):  
Debris Flow ◽  
Model Validation ◽  
Mechanical Model ◽  
Flume Experiments ◽  
Flexible Barrier ◽  
Laboratory Flume

Semi-supervised Intrusive Appliance Load Monitoring in Smart Energy Monitoring System

2021 ◽  
Vol 17 (3) ◽  
pp. 1-20
Author(s):  
Vanh Khuyen Nguyen ◽  
Wei Emma Zhang ◽  
Adnan Mahmood
Keyword(s):  
Supervised Learning ◽  
Monitoring System ◽  
End Users ◽  
Training Data ◽  
Smart Device ◽  
Energy Monitoring ◽  
Load Monitoring ◽  
Benchmark Datasets ◽  
Smart Home Environment ◽  
Smart Appliance

Intrusive Load Monitoring (ILM) is a method to measure and collect the energy consumption data of individual appliances via smart plugs or smart sockets. A major challenge of ILM is automatic appliance identification, in which the system is able to determine automatically a label of the active appliance connected to the smart device. Existing ILM techniques depend on labels input by end-users and are usually under the supervised learning scheme. However, in reality, end-users labeling is laboriously rendering insufficient training data to fit the supervised learning models. In this work, we propose a semi-supervised learning (SSL) method that leverages rich signals from the unlabeled dataset and jointly learns the classification loss for the labeled dataset and the consistency training loss for unlabeled dataset. The samples fit into consistency learning are generated by a transformation that is built upon weighted versions of DTW Barycenter Averaging algorithm. The work is inspired by two recent advanced works in SSL in computer vision and combines the advantages of the two. We evaluate our method on the dataset collected from our developed Internet-of-Things based energy monitoring system in a smart home environment. We also examine the method’s performances on 10 benchmark datasets. As a result, the proposed method outperforms other methods on our smart appliance datasets and most of the benchmarks datasets, while it shows competitive results on the rest datasets.

scholarly journals Analysis of load monitoring system in hydraulic mobile cranes

2017 ◽  
Vol 263 ◽  
pp. 062045
Author(s):  
G Kalairassan ◽  
M Boopathi ◽  
Rijo Mathew Mohan
Keyword(s):  
Monitoring System ◽  
Load Monitoring

Feature Extraction for Non-intrusive Load Monitoring System

2021 ◽  
Author(s):  
Qiuzhan Zhou ◽  
Jiahui Wei ◽  
Mingyu Sun ◽  
Cong Wang ◽  
Jing Rong ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Monitoring System ◽  
Load Monitoring

scholarly journals Flume experiments on vegetated alternate bars

2018 ◽  
Vol 40 ◽  
pp. 02034 ◽  
Author(s):  
Giulio Calvani ◽  
Simona Francalanci ◽  
Luca Solari
Keyword(s):  
Hydrological Regime ◽  
Spatial Arrangement ◽  
Flume Experiments ◽  
Rigid Vegetation ◽  
Hydraulic Conditions ◽  
Laboratory Flume ◽  
Alternate Bars ◽  
River Reach ◽  
Bed Slope ◽  
And Migration

The planform morphology of a river reach is the result of the combined actions of sediment motion (erosion, transport and deposition), hydrological regime, development and growth of vegetation. However, the interactions among these processes are still poorly understood and rarely investigated in laboratory flume experiments. In these experiments and also in numerical modelling, vegetation is usually represented by rigid cylinders, although it is widely recognized that this schematization cannot reproduce the effects of root stabilization and binding on riverbed sediment. In this work, we focus on the effects of added vegetation on morphological dynamics of alternate bars in a straight channel by means of flume experiments. We performed laboratory experiments reproducing hydraulic conditions that are typical of gravel bed rivers, in terms of water depth, bed slope and bed load; these conditions led to the formation of freely migrating alternate bars. We then employed rigid vegetation that was deployed on the reproduced alternate bars according to field observations. Various vegetation scenarios, in terms of density and spatial arrangement, were deployed in the flume experiments such to mimic different maintenance strategies. Results show the effects of rigid vegetation on the alternate bar configuration on the overall topographic pattern, the main alternate bar characteristics (such as amplitude and wavelength) and migration rate.

The Design of Wireless Load Monitoring System of Auxiliary Shift Based on Labview

2012 ◽  
Vol 182-183 ◽  
pp. 753-757
Author(s):  
Xing Ming Xiao ◽  
Na Ma
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Software Design ◽  
Design Principle ◽  
Monitor System ◽  
Actual Load ◽  
Load Monitoring ◽  
Working Principle ◽  
Formation Design

According to the working principle of load monitored oil pressure, in order to real-time monitor the actual load of auxiliary shift, and make the execution of alarming on the malfunctions in the working state of the equipment concerned, we designed a monitor system of auxiliary shift based on Labview[1]. This system can provide guarantee of the safety lifting. So the formation, design principle, hardware and software design well be introduced in this article.

scholarly journals Evaluating a Laboratory Flume Microbiome as a Window Into Natural Riverbed Biogeochemistry

2021 ◽  
Vol 3 ◽  
Author(s):  
Matthew H. Kaufman ◽  
John G. Warden ◽  
M. Bayani Cardenas ◽  
James C. Stegen ◽  
Emily B. Graham ◽  
...  
Keyword(s):  
16S Rrna Gene Sequencing ◽  
Spatiotemporal Variability ◽  
Rrna Gene ◽  
Natural Ecosystems ◽  
Flume Experiments ◽  
Riverbed Sediments ◽  
Laboratory Flume ◽  
Curtis Dissimilarity ◽  
The Common ◽  
Rrna Gene Sequencing

Riverbeds are hotspots for microbially-mediated reactions that exhibit pronounced variability in space and time. It is challenging to resolve biogeochemical mechanisms in natural riverbeds, as uncontrolled settings complicate data collection and interpretation. To overcome these challenges, laboratory flumes are often used as proxies for natural riverbed systems. Flumes capture spatiotemporal variability and thus allow for controlled investigations of riverbed biogeochemistry. These investigations implicitly rely on the assumption that the flume microbiome is similar to the microbiome of natural riverbeds. However, this assumption has not been tested and it is unknown how the microbiome of a flume compares to natural aquatic settings, including riverbeds. To evaluate the fundamental assumption that a flume hosts a microbiome similar to natural riverbed systems, we used 16s rRNA gene sequencing and publicly available data to compare the sediment microbiome of a single large laboratory flume to a wide variety of natural ecosystems including lake and marine sediments, river, lake, hyporheic, soil, and marine water, and bank and wetland soils. Richness and Shannon diversity metrics, analyses of variance, Bray-Curtis dissimilarity, and analysis of the common microbiomes between flume and river sediment all indicated that the flume microbiome more closely resembled natural riverbed sediments than other ecosystems, supporting the use of flume experiments for investigating natural microbially-mediated biogeochemical processes in riverbeds.

Flume Experiments on the Erosive Energy of Bed Load Impacts on Rough and Planar Beds

2021 ◽  
Author(s):  
J. E. Larimer ◽  
E. M. Yager ◽  
B. J. Yanites ◽  
A. J. C. Witsil
Keyword(s):  
Bed Load ◽  
Flume Experiments
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