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Biomedical subjects

C K Yoo

Publications and source records attributed to C K Yoo.

8 recordsLinked to original sources
PubMed2006

Sensor validation and reconciliation for a partial nitrification process.

Wastewater treatment plants (WWTP) are notorious for poor data quality and sensor reliability due to the hostile environment in which the measurement equipment has to function. In this paper, a structured residual approach with maximum sensitivity (SRAMS) based on the redundancy of the measurements is used to detect, identify and reconstruct single and multiple sensor faults in a single reactor for high activity ammonia removal over nitrite (SHARON) process. SRAMS is based on inferences, which are insensitive to the faults in the sensor of interest and sensitive to faults in the other sensors. It is used for four types of sensor failure detection: bias, drift, complete failure and precision degradation. The application of sensor validation shows that single and multiple sensor faults can be detected and that the fault magnitude and fault type can be estimated by the reconstruction scheme. This sensor validation method is not limited by the type or application of the considered sensors. The methodology can thus easily be applied for sensor surveillance of other continuously measuring sensors and analysers.

Bioreactors↗
PubMed2004

Dynamic monitoring system for full-scale wastewater treatment plants.

This paper proposes a new process monitoring method using dynamic independent component analysis (ICA), ICA is a recently developed technique to extract the hidden factors that underlie sets of measurements, whereas principal component analysis (PCA) is a dimensionality reduction technique in terms of capturing the variance of the data. Its goal is to find a linear representation of non-Gaussian data so that the components are statistically independent. PCA aims at finding PCs that are uncorrelated and are linear combinations of the observed variables, while ICA is designed to separate the ICs that are independent and constitute the observed variables. The dynamic ICA monitoring method is applying ICA to the augmenting matrix with time-lagged variables. The dynamic monitoring method was applied to detect and monitor disturbances in a full-scale biological wastewater treatment (WWTP), which is characterized by a variety of dynamic and non-Gaussian characteristics. The dynamic ICA method showed more powerful monitoring performance on a WWTP application than the dynamic PCA method since it can extract source signals which are independent of time and cross-correlation of variables.

Algorithms↗
PubMed2003

New monitoring technique with an ICA algorithm in the wastewater treatment process.

A new monitoring method using independent component analysis (ICA) is suggested for the wastewater treatment process (WWTP). ICA is an extension of PCA (Principal Component Analysis). While PCA can only impose independence up to the second order (mean and variance) with constraint on the direction vectors to be orthogonal, ICA imposes statistical independence up to more than second order on the individual component and has no orthogonal condition. When the variables have the Gaussian distribution, PCA itself provides a satisfactory result in monitoring performance. However, the measured variables are not often normally distributed. In this case, ICA can provide better monitoring results than PCA since ICA is based on the assumption that the latent variables are not normally distributed. In this paper, the ICA monitoring algorithm with kernel density estimation was applied to fault detection and diagnosis of the wastewater simulation benchmark. ICA with kernel density estimation gives better results than PCA in disturbance detection in spite of severe periodic features of the wastewater plant.

Algorithms↗
PubMed2002

Disturbance detection and isolation in the activated sludge process.

This paper proposes a new fault detection and isolation (FDI) method. This method monitors the distribution of process data and detects changes in this distribution, which reflect changes in the corresponding operating condition. A modified dissimilarity index and a FDI technique are defined to quantitatively evaluate the difference between data sets. This technique considers the importance of each transformed variable in the multivariate system. The FDI technique is applied to a benchmark simulation and to data from a real wastewater treatment plant. Simulation results show that it immediately detects disturbances and automatically distinguishes between serious and minor anomalies for various types of fault. The method not only detects the disturbances, but also isolates the scale of the disturbance, facilitating the interpretation of the disturbance source. The proposed monitoring technique is found to be appropriate for analyzing the biological wastewater treatment process, which is characterized by a variety of fault and disturbance sources and non-stationary characteristics.

Benchmarking↗
PubMed2001

Closed-loop identification and control application for dissolved oxygen concentration in a full-scale coke wastewater treatment plant.

The objective of this paper is to apply a closed-loop identification to actual dissolved oxygen control system in the coke wastewater treatment plant. It approximates the dissolved oxygen dynamics to a high order model using the integral transform method and reduces it to the first-order plus time delay (FOPTD) or second-order plus time delay (SOPTD) for the PID controller tuning. To experiment the process identification on the real plant, a simple set-point change of the speed of surface aerator under the closed-loop control without any mode change was used as an activation signal of the identification. The full-scale experimental results show a good identification performance and a good tracking ability for set-point change. As a result of improved control performance, the fluctuation of dissolved oxygen concentration variation has been decreased and the electric power saving has been accomplished.

Biomass↗
PubMed1999

A new rehabilitation training system for postural balance control using virtual reality technology.

A new rehabilitation training system, designated as a virtual cycling system, was developed to improve postural balance control by combining virtual reality (VR) technology with a bicycle. Several parameters including path deviation, path deviation velocity, cycling time, and head movement were extracted and evaluated to quantify the extent of control. The system was effective as a training device and, in addition, the technology might have a wider applicability to the rehabilitation field.

Adult↗