scholarly journals Thermal-Feature System Identification for a Machine Tool Spindle

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1209
Author(s):  
Yuh-Chung Hu ◽  
Ping-Jung Chen ◽  
Pei-Zen Chang
Keyword(s):  
System Identification ◽  
Surface Temperature ◽  
State Space Model ◽  
Feature Model ◽  
Training Data ◽  
Internal Temperature ◽  
Mechanical Stiffness ◽  
Temperature Training ◽  
Time Invariant ◽  
Thermal Feature

The internal temperature is an important index for the prevention and maintenance of a spindle. However, the temperature inside the spindle is undetectable directly because there is no space to embed a temperature sensor, and drilling holes will reduce its mechanical stiffness. Therefore, it is worthwhile understanding the thermal-feature of a spindle. This article presents a methodology to identify the thermal-feature model of an externally driven spindle. The methodology contains self-made hardware of the temperature sensing and wireless transmission module (TSWTM) and software for the system identification (SID); the TSWTM acquires the temperature training data, while the SID identifies the parameters of the thermal-feature model of the spindle. Then the resulting thermal-feature model is written into the firmware of the TSWTM to give it the capability of accurately calculating the internal temperature of the spindle from its surface temperature during the operation, or predicting its temperature at various speeds. The thermal-feature of the externally driven spindle is modeled by a linearly time-invariant state-space model whose parameters are identified by the SID, which integrates the command “n4sid” provided by the System ID Toolbox of MATLAB and the k-fold cross-validation that is common in machine learning. The present SID can effectively strike a balance between the bias and variance of the model, such that both under-fitting and over-fitting can be avoided. The resulting thermal-feature model can not only predict the temperature of the spindle rotating at various speeds but can also calculate the internal temperature of the spindle from its surface temperature. Its validation accuracy is higher than 98.5%. This article illustrates the feasibility of accurately calculating the internal temperature (undetectable directly) of the spindle from its surface temperature (detectable directly).

Multivariable modeling and control of an activated sludge process

1998 ◽  
Vol 37 (12) ◽  
pp. 149-156 ◽  
Author(s):  
Carl-Fredrik Lindberg
Keyword(s):  
Activated Sludge ◽  
Nitrate Concentration ◽  
State Space Model ◽  
Activated Sludge Process ◽  
Invariant State ◽  
Linear Quadratic ◽  
Modeling And Control ◽  
Space Model ◽  
And Control ◽  
Time Invariant

This paper contains two contributions. First it is shown, in a simulation study using the IAWQ model, that a linear multivariable time-invariant state-space model can be used to predict the ammonium and nitrate concentration in the last aerated zone in a pre-denitrifying activated sludge process. Secondly, using the estimated linear model, a multivariable linear quadratic (LQ) controller is designed and used to control the ammonium and nitrate concentration.

scholarly journals Effects of Diurnal Variations on Tropical Equilibrium States: A Two-Dimensional Cloud-Resolving Modeling Study

2007 ◽  
Vol 64 (2) ◽  
pp. 656-664 ◽  
Author(s):  
Shouting Gao ◽  
Yushu Zhou ◽  
Xiaofan Li
Keyword(s):  
Equilibrium State ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Zenith Angle ◽  
Solar Zenith Angle ◽  
Diurnal Variations ◽  
Equilibrium States ◽  
Sea Surface ◽  
Two Dimensional ◽  
Time Invariant

Abstract Effects of diurnal variations on tropical heat and water vapor equilibrium states are investigated based on hourly data from two-dimensional cloud-resolving simulations. The model is integrated for 40 days and the simulations reach equilibrium states in all experiments. The simulation with a time-invariant solar zenith angle produces a colder and drier equilibrium state than does the simulation with a diurnally varied solar zenith angle. The simulation with a diurnally varied sea surface temperature generates a colder equilibrium state than does the simulation with a time-invariant sea surface temperature. Mass-weighted mean temperature and precipitable water budgets are analyzed to explain the thermodynamic differences. The simulation with the time-invariant solar zenith angle produces less solar heating, more condensation, and consumes more moisture than the simulation with the diurnally varied solar zenith angle. The simulation with the diurnally varied sea surface temperature produces a colder temperature through less latent heating and more IR cooling than the simulation with the time-invariant sea surface temperature.

System Identification Based on Dynamical Training for Recurrent Interval Type-2 Fuzzy Neural Network

2013 ◽  
pp. 188-205
Author(s):  
Tsung-Chih Lin ◽  
Yi-Ming Chang ◽  
Tun-Yuan Lee
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
System Identification ◽  
Nonlinear System ◽  
Fuzzy Neural Networks ◽  
Training Data ◽  
Fuzzy Neural ◽  
Interval Type ◽  
Recurrent Interval

This paper proposes a novel fuzzy modeling approach for identification of dynamic systems. A fuzzy model, recurrent interval type-2 fuzzy neural network (RIT2FNN), is constructed by using a recurrent neural network which recurrent weights, mean and standard deviation of the membership functions are updated. The complete back propagation (BP) algorithm tuning equations used to tune the antecedent and consequent parameters for the interval type-2 fuzzy neural networks (IT2FNNs) are developed to handle the training data corrupted by noise or rule uncertainties for nonlinear system identification involving external disturbances. Only by using the current inputs and most recent outputs of the input layers, the system can be completely identified based on RIT2FNNs. In order to show that the interval IT2FNNs can handle the measurement uncertainties, training data are corrupted by white Gaussian noise with signal-to-noise ratio (SNR) 20 dB. Simulation results are obtained for the identification of nonlinear system, which yield more improved performance than those using recurrent type-1 fuzzy neural networks (RT1FNNs).

Exploiting Rules to Enhance Machine Learning in Extracting Information From Multi-Institutional Prostate Pathology Reports

2020 ◽  
pp. 865-874
Author(s):  
Enrico Santus ◽  
Tal Schuster ◽  
Amir M. Tahmasebi ◽  
Clara Li ◽  
Adam Yala ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hybrid Systems ◽  
High Performance ◽  
Feature Model ◽  
Training Data ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Set ◽  
Extreme Gradient Boosting ◽  
Pathology Reports

PURPOSE Literature on clinical note mining has highlighted the superiority of machine learning (ML) over hand-crafted rules. Nevertheless, most studies assume the availability of large training sets, which is rarely the case. For this reason, in the clinical setting, rules are still common. We suggest 2 methods to leverage the knowledge encoded in pre-existing rules to inform ML decisions and obtain high performance, even with scarce annotations. METHODS We collected 501 prostate pathology reports from 6 American hospitals. Reports were split into 2,711 core segments, annotated with 20 attributes describing the histology, grade, extension, and location of tumors. The data set was split by institutions to generate a cross-institutional evaluation setting. We assessed 4 systems, namely a rule-based approach, an ML model, and 2 hybrid systems integrating the previous methods: a Rule as Feature model and a Classifier Confidence model. Several ML algorithms were tested, including logistic regression (LR), support vector machine (SVM), and eXtreme gradient boosting (XGB). RESULTS When training on data from a single institution, LR lags behind the rules by 3.5% (F1 score: 92.2% v 95.7%). Hybrid models, instead, obtain competitive results, with Classifier Confidence outperforming the rules by +0.5% (96.2%). When a larger amount of data from multiple institutions is used, LR improves by +1.5% over the rules (97.2%), whereas hybrid systems obtain +2.2% for Rule as Feature (97.7%) and +2.6% for Classifier Confidence (98.3%). Replacing LR with SVM or XGB yielded similar performance gains. CONCLUSION We developed methods to use pre-existing handcrafted rules to inform ML algorithms. These hybrid systems obtain better performance than either rules or ML models alone, even when training data are limited.

Floquet Experimental Modal Analysis for System Identification of Linear Time-Periodic Systems

2007 ◽  
Author(s):  
Matthew S. Allen
Keyword(s):  
System Identification ◽  
Linear Time ◽  
Time Varying ◽  
Response Model ◽  
State Matrix ◽  
Linear Time Invariant ◽  
Response Data ◽  
Time Varying Systems ◽  
Time Invariant ◽  
Time Periodic

A variety of systems can be faithfully modeled as linear with coefficients that vary periodically with time or Linear Time-Periodic (LTP). Examples include anisotropic rotorbearing systems, wind turbines, satellite systems, etc… A number of powerful techniques have been presented in the past few decades, so that one might expect to model or control an LTP system with relative ease compared to time varying systems in general. However, few, if any, methods exist for experimentally characterizing LTP systems. This work seeks to produce a set of tools that can be used to characterize LTP systems completely through experiment. While such an approach is commonplace for LTI systems, all current methods for time varying systems require either that the system parameters vary slowly with time or else simply identify a few parameters of a pre-defined model to response data. A previous work presented two methods by which system identification techniques for linear time invariant (LTI) systems could be used to identify a response model for an LTP system from free response data. One of these allows the system’s model order to be determined exactly as if the system were linear time-invariant. This work presents a means whereby the response model identified in the previous work can be used to generate the full state transition matrix and the underlying time varying state matrix from an identified LTP response model and illustrates the entire system-identification process using simulated response data for a Jeffcott rotor in anisotropic bearings.

scholarly journals Influence of surface topography on brightness temperature of the regions along the road

2020 ◽  
Vol 145 ◽  
pp. 02025
Author(s):  
Gang Xu ◽  
Ying Yang ◽  
Zhiguo Meng
Keyword(s):  
Surface Temperature ◽  
Brightness Temperature ◽  
Surface Topography ◽  
Microwave Radiometer ◽  
Physical Parameters ◽  
Surface Slope ◽  
Internal Temperature ◽  
Low Latitude ◽  
The Road ◽  
Macro Scale

Internal temperature of the road is one of the important indicators to evaluate the safety of the road, and the microwave radiometer data is only efficient way to acquire the internal temperatures. This study is to evaluate the influence of the surface topography on the brightness temperature (TB) measured the microwave radiometer data. The results are as follows. (1) The surface slope (θ) and its direction play the important roles on the TB. (2) The influence of θ on TB is weaker compared to that of the surface temperature. (3) At least in low latitude regions, the influence of topography on the TB can be neglected in macro scale. The conclusions are essential to better understand the internal physical parameters of the road with the microwave radiometer data.

scholarly journals The LAILAPS Search Engine: Relevance Ranking in Life Science Databases

2010 ◽  
Vol 7 (2) ◽  
pp. 1-11 ◽  
Author(s):  
Matthias Lange ◽  
Karl Spies ◽  
Joachim Bargsten ◽  
Gregor Haberhauer ◽  
Matthias Klapperstück ◽  
...  
Keyword(s):  
Neural Network ◽  
Search Engine ◽  
Life Science ◽  
Expert Knowledge ◽  
Feature Model ◽  
Training Data ◽  
Relevance Ranking ◽  
User Interactions ◽  
Daily Work ◽  
Machine Learning Approach

SummarySearch engines and retrieval systems are popular tools at a life science desktop. The manual inspection of hundreds of database entries, that reflect a life science concept or fact, is a time intensive daily work. Hereby, not the number of query results matters, but the relevance does. In this paper, we present the LAILAPS search engine for life science databases. The concept is to combine a novel feature model for relevance ranking, a machine learning approach to model user relevance profiles, ranking improvement by user feedback tracking and an intuitive and slim web user interface, that estimates relevance rank by tracking user interactions. Queries are formulated as simple keyword lists and will be expanded by synonyms. Supporting a flexible text index and a simple data import format, LAILAPS can easily be used both as search engine for comprehensive integrated life science databases and for small in-house project databases.With a set of features, extracted from each database hit in combination with user relevance preferences, a neural network predicts user specific relevance scores. Using expert knowledge as training data for a predefined neural network or using users own relevance training sets, a reliable relevance ranking of database hits has been implemented.In this paper, we present the LAILAPS system, the concepts, benchmarks and use cases. LAILAPS is public available for SWISSPROT data at http://lailaps.ipk-gatersleben.de

System identification models for waves in the inner magnetosphere

2020 ◽  
Author(s):  
Richard Boynton ◽  
Homayon Aryan ◽  
Walker Simon ◽  
Michael Balikhin
Keyword(s):  
Machine Learning ◽  
Solar Wind ◽  
System Identification ◽  
Moving Average ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Time Lags ◽  
Inner Magnetosphere ◽  
Geomagnetic Indices ◽  
The Waves

<p>This research develops forecast models of the spatiotemporal evolution of emissions throughout the inner magnetosphere between L=2-6 and at all MLT. The system identification, or machine learning, technique based on Nonlinear AutoRegressive Moving Average eXogenous (NARMAX) models is employed to deduce the forecasting models of the lower band chorus, Hiss, and magnetosonic waves using solar wind and geomagnetic indices as inputs. It is difficult to develop machine leaning based spatiotemporal models of the waves in the inner magnetosphere as the data is sparse and machine learning techniques require large amounts of data to deduce a model. To solve this problem, the spatial co-ordinates at the time of the measurements are included as inputs to the model along with time lags of the solar wind and geomagnetic indices, while the measurement of the waves by the Van Allen Probes are used as the output to train the models. The estimates of the resultant models are compared with separate data to the training data to assess the performance of the models. The models are then used to reconstruct the spatiotemporal waves over the inner magnetosphere, as the waves respond to changes in the solar wind and geomagnetic indices.  </p>

scholarly journals Explaining Savings for Visuomotor Adaptation: Linear Time-Invariant State-Space Models Are Not Sufficient

2008 ◽  
Vol 100 (5) ◽  
pp. 2537-2548 ◽  
Author(s):  
Eric Zarahn ◽  
Gregory D. Weston ◽  
Johnny Liang ◽  
Pietro Mazzoni ◽  
John W. Krakauer
Keyword(s):  
State Space ◽  
Linear Time ◽  
State Space Model ◽  
Visuomotor Adaptation ◽  
Invariant State ◽  
Visuomotor Rotation ◽  
Body Memory ◽  
Linear Time Invariant ◽  
And Coping ◽  
Time Invariant

Adaptation of the motor system to sensorimotor perturbations is a type of learning relevant for tool use and coping with an ever-changing body. Memory for motor adaptation can take the form of savings: an increase in the apparent rate constant of readaptation compared with that of initial adaptation. The assessment of savings is simplified if the sensory errors a subject experiences at the beginning of initial adaptation and the beginning of readaptation are the same. This can be accomplished by introducing either 1) a sufficiently small number of counterperturbation trials (counterperturbation paradigm [ CP]) or 2) a sufficiently large number of zero-perturbation trials (washout paradigm [ WO]) between initial adaptation and readaptation. A two-rate, linear time-invariant state-space model (SSMLTI,2) was recently shown to theoretically produce savings for CP. However, we reasoned from superposition that this model would be unable to explain savings for WO. Using the same task (planar reaching) and type of perturbation (visuomotor rotation), we found comparable savings for both CP and WO paradigms. Although SSMLTI,2 explained some degree of savings for CP it failed completely for WO. We conclude that for visuomotor rotation, savings in general is not simply a consequence of LTI dynamics. Instead savings for visuomotor rotation involves metalearning, which we show can be modeled as changes in system parameters across the phases of an adaptation experiment.

Sign in / Sign up

Export Citation Format

Share Document