scholarly journals Monitoring of Multimode Processes Based on Quality-Related Common Subspace Separation

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Yunpeng Fan ◽  
Shipeng Li ◽  
Yingwei Zhang
Keyword(s):  
Process Variables ◽  
Monitoring Method ◽  
Data Set ◽  
Whole Space ◽  
Simulation Results ◽  
Subspace Separation

A new monitoring approach for multimode processes based on quality-related common subspace separation is proposed. In the model, the data set forms a larger space when the correlation between process variables and quality variables is considered. And then the whole space is decomposed: quality-related common subspace, quality-related specific subspace, and the residual subspace. Monitoring method is performed in every subspace, respectively. The simulation results show the proposed method is effective.

scholarly journals Magnetopause energy transfer dependence on the interplanetary magnetic field and the Earth's magnetic dipole axis orientation

2012 ◽  
Vol 30 (3) ◽  
pp. 515-526 ◽  
Author(s):  
M. Palmroth ◽  
R. C. Fear ◽  
I. Honkonen
Keyword(s):  
Magnetic Field ◽  
Energy Transfer ◽  
Interplanetary Magnetic Field ◽  
Large Data ◽  
Seasonal Effect ◽  
Dipole Orientation ◽  
Data Set ◽  
Axis Orientation ◽  
Simulation Results ◽  
The Imf

Abstract. We examine the spatial variation of magnetospheric energy transfer using a global magnetohydrodynamic (MHD) simulation (GUMICS-4) and a large data set of flux transfer events (FTEs) observed by the Cluster spacecraft. Our main purpose is to investigate whether it is possible to validate previous results on the spatial energy transfer variation from the GUMICS-4 simulation using the statistical occurrence of FTEs, which are manifestations of magnetospheric energy transfer. Previous simulation results have suggested that the energy transfer pattern at the magnetopause rotates according to the interplanetary magnetic field (IMF) orientation, and here we investigate whether a similar rotation is seen in the locations at which FTE signatures are observed. We find that there is qualitative agreement between the simulation and observed statistics, as the peaks in both distributions rotate as a function of the IMF clock angle. However, it is necessary to take into account the modulation of the statistical distribution that is caused by a bias towards in situ FTE signatures being observed in the winter hemisphere (an effect that has previously been predicted and observed in this data set). Taking this seasonal effect into account, the FTE locations support the previous simulation results and confirm the earlier prediction that the energy transfers in the plane of the IMF. In addition, we investigate the effect of the dipole orientation (both the dipole tilt angle and its orientation in the plane perpendicular to the solar wind flow) on the energy transfer spatial distribution. We find that the energy transfer occurs mainly in the summer hemisphere, and that the dayside reconnection region is located asymmetrically about the subsolar position. Finally, we find that the energy transfer is 10% larger at equinox conditions than at solstice, contributing to the discussion concerning the semiannual variation of magnetospheric dynamics (known as "the Russell-McPherron effect").

scholarly journals Segmentation Method for Magnetic Resonance-Guided High-Intensity Focused Ultrasound Therapy Planning

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
A. Vargas-Olivares ◽  
O. Navarro-Hinojosa ◽  
M. Maqueo-Vicencio ◽  
L. Curiel ◽  
M. Alencastre-Miranda ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
High Intensity ◽  
Focused Ultrasound ◽  
High Intensity Focused Ultrasound ◽  
Segmentation Algorithm ◽  
Focal Region ◽  
Therapy Planning ◽  
Mr Images ◽  
Monitoring Method ◽  
Data Set

High-intensity focused ultrasound (HIFU) is a minimally invasive therapy modality in which ultrasound beams are concentrated at a focal region, producing a rise of temperature and selective ablation within the focal volume and leaving surrounding tissues intact. HIFU has been proposed for the safe ablation of both malignant and benign tissues and as an agent for drug delivery. Magnetic resonance imaging (MRI) has been proposed as guidance and monitoring method for the therapy. The identification of regions of interest is a crucial procedure in HIFU therapy planning. This procedure is performed in the MR images. The purpose of the present research work is to implement a time-efficient and functional segmentation scheme, based on the watershed segmentation algorithm, for the MR images used for the HIFU therapy planning. The achievement of a segmentation process with functional results is feasible, but preliminary image processing steps are required in order to define the markers for the segmentation algorithm. Moreover, the segmentation scheme is applied in parallel to an MR image data set through the use of a thread pool, achieving a near real-time execution and making a contribution to solve the time-consuming problem of the HIFU therapy planning.

Char Oxidation Model Validation in Detailed Coal Simulations

1999 ◽  
Author(s):  
Stefan P. Domino ◽  
Philip J. Smith
Keyword(s):  
Coal Combustion ◽  
Unburned Carbon ◽  
Pulverized Coal Combustion ◽  
Active Oxidation ◽  
Data Set ◽  
Carbon Burnout ◽  
Oxidation Model ◽  
Simulation Results ◽  
Char Oxidation ◽  
Residual Weight

Abstract The controlling mechanisms governing the amount of unburned carbon in fly ash from practical pulverized coal combustion systems have been studied by incorporating elements of the carbon burnout kinetic model (CBK) proposed by Hurt et al. in a computational fluid dynamics based combustion, cfd, simulator. An experimental data set from The International Flame Research Foundation are compared to evaluate mechanistic variations in the carbon burnout processes. Simulation results comparing total exit LOI (defined as the residual weight of carbon in the ash) and centerline carbon burnout indicates that use of a char oxidation submodel, including the effects of a statistical reactivity distribution, thermal annealing of active oxidation sites and a developing ash film resistance layer, is necessary to predict experimentally measured exit LOI and centerline burnout profiles.

Improved Extrudability of High Strength Alloys Using an Optimization Method Based on a Combination of Experiments and FEM Software

2013 ◽  
Vol 585 ◽  
pp. 165-171 ◽  
Author(s):  
Stanka Tomovic-Petrovic ◽  
Rune Østhus ◽  
Ola Jensrud
Keyword(s):  
Material Flow ◽  
Silicon Content ◽  
High Strength ◽  
Optimization Method ◽  
Extrusion Process ◽  
Model Alloy ◽  
Process Variables ◽  
Die Geometry ◽  
Simulation Results ◽  
6Xxx Series

Numerical analysis of the material flow during the extrusion process for high alloyed variants of the AA 6xxx series is presented in this paper. The analysis was performed by using the commercial FE code Forge2011®. Another issue considered in the paper was an interrelation between the die geometry and the critical extrusion process variables. For optimization of the die exit geometry, the model was produced with the use of linked equation in SolidWorks® combined with Mode FRONTIER. Several extrusion trials were performed to provide a basis for the verification of simulation results as extrusion temperature, speed and force. For the purpose, rods of a model alloy designated as AlMgSi4, based on an industrial AA6082 aluminium alloy with significantly higher silicon content, were extruded. A good correlation between measured and calculated results was obtained. This approach may enable simplifying when dealing with design of a new alloy.

scholarly journals Pattern Recognition in Multivariate Time Series: Towards an Automated Event Detection Method for Smart Manufacturing Systems

2020 ◽  
Vol 4 (3) ◽  
pp. 88 ◽  
Author(s):  
Vadim Kapp ◽  
Marvin Carl May ◽  
Gisela Lanza ◽  
Thorsten Wuest
Keyword(s):  
Time Series ◽  
Real World ◽  
Manufacturing Systems ◽  
Multivariate Time Series ◽  
Series Data ◽  
Smart Manufacturing ◽  
Monitoring Method ◽  
Data Set ◽  
Failure Patterns ◽  
Smart Manufacturing Systems

This paper presents a framework to utilize multivariate time series data to automatically identify reoccurring events, e.g., resembling failure patterns in real-world manufacturing data by combining selected data mining techniques. The use case revolves around the auxiliary polymer manufacturing process of drying and feeding plastic granulate to extrusion or injection molding machines. The overall framework presented in this paper includes a comparison of two different approaches towards the identification of unique patterns in the real-world industrial data set. The first approach uses a subsequent heuristic segmentation and clustering approach, the second branch features a collaborative method with a built-in time dependency structure at its core (TICC). Both alternatives have been facilitated by a standard principle component analysis PCA (feature fusion) and a hyperparameter optimization (TPE) approach. The performance of the corresponding approaches was evaluated through established and commonly accepted metrics in the field of (unsupervised) machine learning. The results suggest the existence of several common failure sources (patterns) for the machine. Insights such as these automatically detected events can be harnessed to develop an advanced monitoring method to predict upcoming failures, ultimately reducing unplanned machine downtime in the future.

HE-Co-HOG and k-SVM classifier for finger knuckle and palm print-based multimodal biometric recognition

Sensor Review ◽  
2020 ◽  
Vol 40 (2) ◽  
pp. 203-216
Author(s):  
S. Veluchamy ◽  
L.R. Karlmarx
Keyword(s):  
Research Field ◽  
Biometric Identification ◽  
Svm Classifier ◽  
Identification System ◽  
Biometric Recognition ◽  
Data Set ◽  
Content Type ◽  
Proposed Model ◽  
Simulation Results ◽  
Multimodal Biometric Recognition

Purpose Biometric identification system has become emerging research field because of its wide applications in the fields of security. This study (multimodal system) aims to find more applications than the unimodal system because of their high user acceptance value, better recognition accuracy and low-cost sensors. The biometric identification using the finger knuckle and the palmprint finds more application than other features because of its unique features. Design/methodology/approach The proposed model performs the user authentication through the extracted features from both the palmprint and the finger knuckle images. The two major processes in the proposed system are feature extraction and classification. The proposed model extracts the features from the palmprint and the finger knuckle with the proposed HE-Co-HOG model after the pre-processing. The proposed HE-Co-HOG model finds the Palmprint HE-Co-HOG vector and the finger knuckle HE-Co-HOG vector. These features from both the palmprint and the finger knuckle are combined with the optimal weight score from the fractional firefly (FFF) algorithm. The layered k-SVM classifier classifies each person's identity from the fused vector. Findings Two standard data sets with the palmprint and the finger knuckle images were used for the simulation. The simulation results were analyzed in two ways. In the first method, the bin sizes of the HE-Co-HOG vector were varied for the various training of the data set. In the second method, the performance of the proposed model was compared with the existing models for the different training size of the data set. From the simulation results, the proposed model has achieved a maximum accuracy of 0.95 and the lowest false acceptance rate and false rejection rate with a value of 0.1. Originality/value In this paper, the multimodal biometric recognition system based on the proposed HE-Co-HOG with the k-SVM and the FFF is developed. The proposed model uses the palmprint and the finger knuckle images as the biometrics. The development of the proposed HE-Co-HOG vector is done by modifying the Co-HOG with the holoentropy weights.

Impact of different and time-varying land cover data sets in a regional climate model on regional and local climate over Europe

2020 ◽  
Author(s):  
Mingyue Zhang ◽  
Jürgen Helmert ◽  
Merja Tölle
Keyword(s):  
Land Cover ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Data Sets ◽  
Time Varying ◽  
Data Set ◽  
Local Climate ◽  
Land Cover Data ◽  
Simulation Results

<p>According to IPCC, Land use and Land Cover (LC) changes have a key role to adapt and mitigate future climate change aiming to stabilize temperature rise up to 2°C. Land surface change at regional scale is associated to global climate change, such as global warming. It influences the earth’s water and energy cycles via influences on the heat, moisture and momentum transfer, and on the chemical composition of the atmosphere. These effects show variations due to different LC types, and due to their spatial and temporal resolutions.  Thus, we incorporate a new time-varying land cover data set based on ESACCI into the regional climate model COSMO-CLM(v5.0). Further, the impact on the regional and local climate is compared to the standard operational LC data of GLC2000 and GlobCover 2009. Convection-permitting simulations with the three land cover data sets are performed at 0.0275° horizontal resolution over Europe for the time period from 1992 to 2015.</p><p>Overall, the simulation results show comparable agreement to observations. However, the simulation results based on GLC2000 and GlobCover 2009 (with 23 LC types) LC data sets show a fluctuation of 0.5K in temperature and 5% of precipitation. Even though the LC is classified into the same types, the difference in LC distribution and fraction leads to variations in climate simulation results. Using all of the 37 LC types of the ESACCI-LC data set show noticeable differences in distribution of temperature and precipitation compared to the simulations with GLC2000 and GlobCover 2009. Especially in forest areas, slight differences of the plant cover type (e.g. Evergreen or Deciduous) could result in up to 10% differences (increase or decrease) in temperature and precipitation over the simulation domain. Our results demonstrate how LC changes as well as different land cover type effect regional climate. There is need for proper and time-varying land cover data sets for regional climate model studies. The approach of including ESACCI-LC data set into regional climate model simulations also improved the external data generation system.</p><p>We anticipate this research to be a starting point for involving time-varying LC data sets into regional climate models. Furthermore, it will give us a possibility to quantify the effect of time-varying LC data on regional climate accurately.</p><p><strong>Acknowledgement</strong>:</p><p>1: Computational resources were made available by the German Climate Computing Center (DKRZ) through support from the Federal Ministry of Education and Research in Germany (BMBF). We acknowledge the funding of the German Research Foundation (DFG) through grant NR. 401857120.</p><p>2: Appreciation for the support of Jürg Luterbacher and Eva Nowatzki.</p><p> </p>

Conductivity of two‐component systems

Geophysics ◽  
1996 ◽  
Vol 61 (1) ◽  
pp. 162-168 ◽  
Author(s):  
A. de Kuijper ◽  
R. K. J. Sandor ◽  
J. P. Hofman ◽  
J. A. de Waal
Keyword(s):  
Computer Simulation ◽  
Simulation Data ◽  
Data Set ◽  
Axis Ratio ◽  
Anisotropic Conductivity ◽  
Component Systems ◽  
Two Component ◽  
Two Component Systems ◽  
Simulation Results ◽  
Prolate Ellipsoidal

We present measurements and computer simulation results on the electrical conductivity of nonconducting grains embedded in a conductive brine host. The shapes of the grains ranged from prolate‐ellipsoidal (with an axis ratio of 5:1) through spherical to oblate‐ellipsoidal (with an axis ratio of 1:5). The conductivity was studied as a function of porosity and packing, and Archie’s cementation exponent was found to depend on porosity. We used spatially regular and random configurations with aligned and nonaligned packings. The experimental results agree well with the computer simulation data. This data set will enable extensive tests of models for calculating the anisotropic conductivity of two‐component systems.

Determination of relative angles and anisotropic resistivity using multicomponent induction logging data

Geophysics ◽  
2004 ◽  
Vol 69 (4) ◽  
pp. 898-908 ◽  
Author(s):  
Zhiyi Zhang ◽  
Liming Yu ◽  
Berthold Kriegshäuser ◽  
Lev Tabarovsky
Keyword(s):  
Sensitivity Analysis ◽  
Azimuth Angle ◽  
Inversion Method ◽  
Inversion Algorithm ◽  
Data Set ◽  
Simultaneous Inversion ◽  
Induction Logging ◽  
Em Induction ◽  
Whole Space ◽  
Dip Angle

We have developed a new algorithm that retrieves information about relative dip angle, relative azimuth angle, vertical resistivity, and horizontal resistivity from multicomponent EM induction logging data. To investigate how relative dip and azimuth angles affect multicomponent induction logging data, we performed a sensitivity analysis using an anisotropic whole space model. Based upon the sensitivity analysis, we designed a two‐step procedure to recover relative dip, relative azimuth, horizontal resistivity, and vertical resistivity. In the first step, the observed data are transformed into a new data set independent of the azimuth angle; a simultaneous inversion method recovers relative dip angle, vertical resistivity, and horizontal resistivity. In the second step, a 1D line search is performed to decide relative azimuth angle. Synthetic and field data tests indicate that the new inversion algorithm can extract information about relative dip and azimuth angles as well as the anisotropic resistivity structure from multicomponent induction loggingdata.

scholarly journals Condition Monitoring Method for the Detection of Fault Graduality in Outer Race Bearing Based on Vibration-Current Fusion, Statistical Features and Neural Network

2021 ◽  
Vol 11 (17) ◽  
pp. 8033
Author(s):  
Juan-Jose Saucedo-Dorantes ◽  
Israel Zamudio-Ramirez ◽  
Jonathan Cureno-Osornio ◽  
Roque Alfredo Osornio-Rios ◽  
Jose Alfonso Antonino-Daviu
Keyword(s):  
Neural Network ◽  
Condition Monitoring ◽  
High Performance ◽  
Induction Motors ◽  
Statistical Features ◽  
Monitoring Method ◽  
Data Set ◽  
Linear Discriminant ◽  
Outer Race ◽  
Bearing Fault

Bearings are the elements that allow the rotatory movement in induction motors, and the fault occurrence in these elements is due to excessive working conditions. In induction motors, electrical erosion remains the most common phenomenon that damages bearings, leading to incipient faults that gradually increase to irreparable damages. Thus, condition monitoring strategies capable of assessing bearing fault severities are mandatory to overcome this critical issue. The contribution of this work lies in the proposal of a condition monitoring strategy that is focused on the analysis and identification of different fault severities of the outer race bearing fault in an induction motor. The proposed approach is supported by fusion information of different physical magnitudes and the use of Machine Learning and Artificial Intelligence. An important aspect of this proposal is the calculation of a hybrid-set of statistical features that are obtained to characterize vibration and stator current signals by its processing through domain analysis, i.e., time-domain and frequency-domain; also, the fusion of information of both signals by means of the Linear Discriminant Analysis is important due to the most discriminative and meaningful information is retained resulting in a high-performance condition characterization. Besides, a Neural Network-based classifier allows validating the effectiveness of fusion information from different physical magnitudes to face the diagnosis of multiple fault severities that appear in the bearing outer race. The method is validated under an experimental data set that includes information related to a healthy condition and five different severities that appear in the outer race of bearings.

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