scholarly journals A Novel Method with Stacking Learning of Data-Driven Soft Sensors for Mud Concentration in a Cutter Suction Dredger

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6075
Author(s):  
Bin Wang ◽  
Shi-dong Fan ◽  
Pan Jiang ◽  
Han-hua Zhu ◽  
Ting Xiong ◽  
...  
Keyword(s):  
Service Life ◽  
Goodness Of Fit ◽  
Soft Sensor ◽  
Data Driven ◽  
Good Prediction ◽  
Soft Sensors ◽  
Construction Operations ◽  
Emergency Situations ◽  
Novel Method ◽  
Marine Mud

The dredger construction environment is harsh, and the mud concentration meter can be damaged from time to time. To ensure that the dredger can continue construction operations when the mud concentration meter is damaged, the development of a dredger with advantages of low price and simple operation that can be used in emergency situations is essential. The characteristic spare mud concentration meter is particularly critical. In this study, a data-driven soft sensor method is proposed that can predict the mud concentration in real time and can mitigate current marine mud concentration meter malfunctions, which affects continuous construction. This sensor can also replace the mud concentration meter when the construction is stable, thereby extending its service life. The method is applied to two actual construction cases, and the results show that the stacking generalization (SG) model has a good prediction effect in the two cases, and its goodness of fit R2 values are as high as 0.9774 and 0.9919, indicating that this method can successfully detect the mud concentration.

scholarly journals Laundry Fabric Classification in Vertical Axis Washing Machines Using Data-Driven Soft Sensors

Energies ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 4080 ◽  
Author(s):  
Marco Maggipinto ◽  
Elena Pesavento ◽  
Fabio Altinier ◽  
Giuliano Zambonin ◽  
Alessandro Beghi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Positive Impact ◽  
General Setting ◽  
Vertical Axis ◽  
Soft Sensor ◽  
Data Driven ◽  
Soft Sensors ◽  
Physical Measurements ◽  
Washing Machines ◽  
Using Data

Embedding household appliances with smart capabilities is becoming common practice among major fabric-care producers that seek competitiveness on the market by providing more efficient and easy-to-use products. In Vertical Axis Washing Machines (VA-WM), knowing the laundry composition is fundamental to setting the washing cycle properly with positive impact both on energy/water consumption and on washing performance. An indication of the load typology composition (cotton, silk, etc.) is typically provided by the user through a physical selector that, unfortunately, is often placed by the user on the most general setting due to the discomfort of manually changing configurations. An automated mechanism to determine such key information would thus provide increased user experience, better washing performance, and reduced consumption; for this reason, we present here a data-driven soft sensor that exploits physical measurements already available on board a commercial VA-WM to provide an estimate of the load typology through a machine-learning-based statistical model of the process. The proposed method is able to work in a resource-constrained environment such as the firmware of a VA-WM.

scholarly journals Data-Driven Study on the Sustainable Log Movements: Impact of Rail Car Fleet Size on Freight Storage and Car Idling

2020 ◽  
Vol 12 (11) ◽  
pp. 4563
Author(s):  
Sangpil Ko ◽  
Pasi Lautala ◽  
Kuilin Zhang
Keyword(s):  
Service Life ◽  
Seasonal Dynamics ◽  
Lake Superior ◽  
Data Driven ◽  
Short Term ◽  
Fleet Size ◽  
Interesting Observation ◽  
The Us ◽  
Term Storage ◽  
Scale Data

Rail car availability and the challenges associated with the seasonal dynamics of log movements have received growing attentions in the Lake Superior region of the US, as a portion of rail car fleet is close to reaching the end of its service life. This paper proposes a data-driven study on the rail car peaking issue to explore the fleet of rail cars dedicated to being used for log movements in the region, and to evaluate how the number of cars affects both the storage need at the sidings and the time the cars are idled. This study is based on the actual log scale data collected from a group of forest companies in cooperation with the Lake State Shippers Association (LSSA). The results of our analysis revealed that moving the current log volumes in the region would require approximately 400–600 dedicated and shared log cars in ideal conditions, depending on the specific month. While the higher fleet size could move the logs as they arrive to the siding, the lower end would nearly eliminate the idling of rail cars and enable stable volumes throughout the year. However, this would require short-term storage and additional handling of logs at the siding, both elements that increase the costs for shippers. Another interesting observation was the fact that the reduction of a single day in the loading/unloading process (2.5 to 1.5 days) would eliminate almost 100 cars (20%) of the fleet without reduction in throughput.

scholarly journals BR-Sensor: An On-line Data-driven Soft Sensor of Downhole Pressure∗∗Final support from Petrobas S.A. is acknowledged.

2015 ◽  
Vol 48 (6) ◽  
pp. 311-316 ◽  
Author(s):  
Edson F.A. Rezende ◽  
Alex F. Teixeira ◽  
Eduardo M.A.M. Mendes
Keyword(s):  
Soft Sensor ◽  
Data Driven ◽  
On Line

scholarly journals Estimation of Rheological Properties of Ice Cream Unfrozen Liquid Phase by FT-NIR Spectroscopy

1970 ◽  
Vol 66 (2) ◽  
Author(s):  
Florina A. SILAGHI ◽  
Alessandro GIUNCHI ◽  
Angelo FABBRI ◽  
Luigi RAGNI
Keyword(s):  
Liquid Phase ◽  
Rheological Properties ◽  
Powder Mixture ◽  
Goodness Of Fit ◽  
Flow Behavior ◽  
Nir Spectroscopy ◽  
Ice Cream ◽  
Rheological Parameters ◽  
Good Prediction ◽  
Flow Behavior Index

The control of ice cream powder mixture production is carried out evaluating the ice cream liquid phase. The present study was conduced on ice cream and unfrozen liquid phase in order to indirectly evaluate the rheological properties by measuring the powder mixture. The calibration set was formed by samples with different percentage of thickeners, maintaining constant the concentration of the other remaining compounds. After the NIR acquisitions the powders were mixed with warm milk, blended and than settled in order to obtain the unfrozen liquid phase needed for the rheological measurements. For each recipe three batches were prepared. The flow curves were evaluated by using the Ostwald de Waele’s equation and the goodness of fit was evaluated by the R2, which was above 0.95. Predictive models of rheological parameters were set up by means of PLS regressions in order to predict the consistency coefficient (K) and the flow behavior index (n) from spectral acquisitions. High correlation of calibration was found for both parameters and NIR spectra obtaining R2 of 0.884 for K and 0.874 for n. The good prediction of the models encourages applying them to reduce significantly the time of the powder mixing control during production.

Data-driven soft sensor approach for online quality prediction using state dependent parameter models

2017 ◽  
Vol 162 ◽  
pp. 130-141 ◽  
Author(s):  
Bahareh Bidar ◽  
Jafar Sadeghi ◽  
Farhad Shahraki ◽  
Mir Mohammad Khalilipour
Keyword(s):  
Soft Sensor ◽  
Data Driven ◽  
Quality Prediction ◽  
State Dependent ◽  
Dependent Parameter

Data-Driven Soft Sensor Model Based on Deep Learning for Quality Prediction of Industrial Processes

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Xianglin Zhu ◽  
Khalil Ur Rehman ◽  
Wang Bo ◽  
Muhammad Shahzad ◽  
Ahmad Hassan
Keyword(s):  
Deep Learning ◽  
Soft Sensor ◽  
Data Driven ◽  
Industrial Processes ◽  
Quality Prediction ◽  
Model Based ◽  
Sensor Model

Heterogeneous sensing in a multifunctional soft sensor for human-robot interfaces

2020 ◽  
Vol 5 (49) ◽  
pp. eabc6878 ◽  
Author(s):  
Taekyoung Kim ◽  
Sudong Lee ◽  
Taehwa Hong ◽  
Gyowook Shin ◽  
Taehwan Kim ◽  
...  
Keyword(s):  
Form Factor ◽  
Form Factors ◽  
Soft Sensor ◽  
Compact Form ◽  
Original Form ◽  
Entire Body ◽  
Soft Sensors ◽  
Input Stimulus ◽  
Human Robot Interfaces ◽  
Deformation Modes

Soft sensors have been playing a crucial role in detecting different types of physical stimuli to part or the entire body of a robot, analogous to mechanoreceptors or proprioceptors in biology. Most of the currently available soft sensors with compact form factors can detect only a single deformation mode at a time due to the limitation in combining multiple sensing mechanisms in a limited space. However, realizing multiple modalities in a soft sensor without increasing its original form factor is beneficial, because even a single input stimulus to a robot may induce a combination of multiple modes of deformation. Here, we report a multifunctional soft sensor capable of decoupling combined deformation modes of stretching, bending, and compression, as well as detecting individual deformation modes, in a compact form factor. The key enabling design feature of the proposed sensor is a combination of heterogeneous sensing mechanisms: optical, microfluidic, and piezoresistive sensing. We characterize the performance on both detection and decoupling of deformation modes, by implementing both a simple algorithm of threshold evaluation and a machine learning technique based on an artificial neural network. The proposed soft sensor is able to estimate eight different deformation modes with accuracies higher than 95%. We lastly demonstrate the potential of the proposed sensor as a method of human-robot interfaces with several application examples highlighting its multifunctionality.

“Select and retrieve via direct upsampling” network (SARDU-Net): a data-driven, model-free, deep learning approach for quantitative MRI protocol design

2020 ◽  
Author(s):  
Francesco Grussu ◽  
Stefano B. Blumberg ◽  
Marco Battiston ◽  
Lebina S. Kakkar ◽  
Hongxiang Lin ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Diffusion Tensor ◽  
Protocol Design ◽  
Quantitative Mri ◽  
Data Driven ◽  
Acquisition Time ◽  
Model Free ◽  
Spherical Mean ◽  
Mri Protocol

AbstractPurposeWe introduce “Select and retrieve via direct upsampling” network (SARDU-Net), a data-driven framework for model-free quantitative MRI (qMRI) protocol design, and demonstrate it on in vivo brain and prostate diffusion-relaxation imaging (DRI).MethodsSARDU-Net selects subsets of informative measurements within lengthy pilot scans, without the requirement to identify tissue parameters for which to optimise for. The algorithm consists of a selector, identifying measurement subsets, and a predictor, estimating fully-sampled signals from the subsets. We implement both using deep neural networks, which are trained jointly end-to-end. We demonstrate the algorithm on brain (32 diffusion-/T1-weightings) and prostate (16 diffusion-/T2-weightings) DRI scans acquired on 3 healthy volunteers on two separate 3T Philips systems each. We used SARDU-Net to identify sub-protocols of fixed size, assessing the reproducibility of the procedure and testing sub-protocols for their potential to inform multi-contrast analyses via T1-weighted spherical mean diffusion tensor (T1-SMDT, brain) and hybrid multi-dimensional MRI (HM-MRI, prostate) modelling.ResultsIn both brain and prostate, SARDU-Net identifies sub-protocols that maximise information content in a reproducible manner across training instantiations. The sub-protocols enable multi-contrast modelling for which they were not optimised explicitly, providing robust T1-SMDT and HM-MRI maps and goodness-of-fit in the top 5% against extensive sub-protocol comparisons.ConclusionsSARDU-Net gives new opportunities to identify economical but informative qMRI protocols from a subset of the pilot scans that can be used for acquisition-time-sensitive applications. The simple architecture makes the algorithm easy to train when exhaustive searches are intractable, and applicable to a variety of anatomical contexts.

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