Large scale database online modeling for blast furnace

Ito, Masahiro; Matsuzaki, Shinya; Odate, N.; Uchida, Kenko; Ogai, Harutoshi; Akizuki, Kageo

doi:10.1109/cca.2004.1387484

Cited by 12 publications

(11 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The practical method based on JIT modeling calls up past, similar operations of data that have been searched, and the prospective operation data is estimated. A schematic diagram of LOM [1][2][3][4][5] is presented in Fig. 4.…”

Section: Jit Modeling and Lommentioning

confidence: 99%

“…In addition, the multidimensional space is quantized. An objects process is a nonlinear dynamic system, and characteristics of the system are given by a regression model expressed in the following equation (1).…”

Section: Jit Modeling and Lommentioning

confidence: 99%

“…Then, the local model is discarded and neighboring data sets in measured data sets in an updated database are selected for the next estimation. The LOM [1][2][3][4][5] makes the retrieval of neighboring data more efficient by using the stepwise method and quantization. The stepwise method decreases the dimension of multidimensional space of the actual process.…”

Section: Jit Modeling and Lommentioning

confidence: 99%

“…In this study, we used "LOM" [1][2][3][4][5], one of the local modeling methods based on databases, to reproduce the characteristics of fatigue based on information derived from observation of biomedical signs.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore we decided it would be effective to use JIT modeling. We used LOM [1,2], one of the JIT modeling.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Estimation of Degree of Human Fatigue Score by Defined Data Distance in Large-Scale Data-Based Online Modeling

Hachiya

Ogawa

Kawanari

et al. 2015

Journal of Signal Processing

View full text Add to dashboard Cite

It is important to quantify and prevent mental fatigue in order to forestall absence from work and death from overwork. In this study, we used "Large-scale Data-based Online Modeling (LOM)", one of the local modeling methods based on databases, to reproduce the characteristics of fatigue based on information derived from observation of biomedical signs. The subjects in our study were 10 male university students. After assuming a relaxed seated position for 10 minutes, they performed two repeated sessions of a mental arithmetic task for 30 minutes each session with a 30-minute break. Subjective symptoms of feelings of fatigue score were evaluated on a 10-point scale. We measured several biomedical signs and subjective symptoms of feelings of fatigue score. We propose that it is possible to estimate the degree of acute mental fatigue from biomedical signs using the LOM method. We propose a method for determining the reliability of estimating "degree of fatigue" by distance of neighboring data.

show abstract

Section: Jit Modeling and Lommentioning

confidence: 99%

Section: Jit Modeling and Lommentioning

confidence: 99%

Section: Jit Modeling and Lommentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Therefore we decided it would be effective to use JIT modeling. We used LOM [1,2], one of the JIT modeling.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Estimation of Degree of Human Fatigue Score by Defined Data Distance in Large-Scale Data-Based Online Modeling

Hachiya

Ogawa

Kawanari

et al. 2015

Journal of Signal Processing

View full text Add to dashboard Cite

show abstract

Optimal Distance Function for Locally Weighted Average Prediction of Just‐in‐Time Methods

Fujimoto

Maruta

Sugie

2017

Asian Journal of Control

View full text Add to dashboard Cite

This paper discusses the optimal distance function for Just-in-Time prediction. We focus on the standard Locally Weighted Average (LWA) prediction with Mahalanobis distance, and find the optimal distance which minimizes the prediction error. The key idea of this work is to introduce an integral which works as a model of the LWA prediction. This integral approximates the LWA prediction, and it becomes easier to discuss analytically. The main result of this paper is to show that the optimal distance function for such integral is constructed through a convex optimization. A numerical example and an experiment with a motor are shown to demonstrate the validity of the proposed distance function.

show abstract

Data synchronization techniques and their impact on the prediction performance of automated recalibrated soft sensors in bioprocesses

Siegl,

Geier,

Andreeßen

et al. 2024

Biotechnol Bioproc E

View full text Add to dashboard Cite

Innovative soft sensor concepts can recalibrate automatically when the prediction performance decreases due to variations in raw materials, biological variability, and changes in process strategies. For automatic recalibration, data sets are selected from a data pool based on distance-based similarity criteria and then used for calibration. Nevertheless, the most appropriate data sets often are not reliably selected due to variances in the location of landmarks and process length of the bioprocesses. This can be overcome by synchronization methods that align the historical data sets with the current process and increase the accuracy of automatic selection and recalibration. This study investigated two different synchronization methods (dynamic time warping and curve registration) as preprocessing for the automatic selection of data sets using a distance-based similarity criterion for soft sensor recalibration. The prediction performance of the two soft sensors without synchronization was compared to the variants with synchronization and evaluated by comparing the normalized root mean squared errors. Curve registration improved the prediction performance on average by 24% (Pichia pastoris) and 9% (Bacillus subtilis). Using dynamic time warping, no substantial improvement in prediction performance could be achieved. A major factor behind this was the loss of information due to singularities caused by the changing process characteristics. The evaluation was performed on two target variables of real bioprocesses: biomass concentration prediction in P. pastoris and product concentration prediction in B. subtilis.

show abstract

Large scale database online modeling for blast furnace

Cited by 12 publications

References 5 publications

Estimation of Degree of Human Fatigue Score by Defined Data Distance in Large-Scale Data-Based Online Modeling

Estimation of Degree of Human Fatigue Score by Defined Data Distance in Large-Scale Data-Based Online Modeling

Optimal Distance Function for Locally Weighted Average Prediction of Just‐in‐Time Methods

Data synchronization techniques and their impact on the prediction performance of automated recalibrated soft sensors in bioprocesses

Contact Info

Product

Resources

About