• Journal of the Institute of Electronics Engineers of Korea CI
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• v.40 no.5
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• pp.259-265
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• 2003

It leads to develop the query system with artificial intelligent technologies to handle inaccurate query. To develop the query system, metadata is essential to control a uncertain data, providing information about uncertainty of the data, and the classification system of metadata are necessary. This paper shows a classification of metadata based on fuzzy theory and the implementation processing to process the fuzzy query in a relational database system.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.1
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• pp.95-113
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• 1997

This paper outlines a framework for a diagnosis of a complex system with uncertain information. Sensor validation ploys a vital role in the ability of the overall system to correctly determine the state of a system monitored by imperfect sensors. Here, emphases are put on the heuristic technology and post-processor for reasoning. Heuristic Sensor Validation (HSV) exploits deeper knowledge about parameter interaction within the plant to cull sensor faults from the data stream. Finally the modified probability distributions and validated data are used as input to the reasoning scheme which is the runtime version of the influence diagram. The output of the influence diagram is a diagnostic mapping from the symptoms or sensor readings to a determination of likely failure modes. Once likely failure modes are identified, a detailed diagnostic knowledge base suggests corrective actions to improve performance. This framework for a diagnostic expert system with sensor validation and reasoning under uncertainty applies in $HEATXPRT^{TM}$ a data-driven on-line expert system for diagnosing heat rate degradation problems in fossil power plants [1].

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.521-521
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• 2000

This paper investigates the problem of robust H$_{\infty}$ filter with FIR(Finite Impulse Response) structure for linear continuous time-varying systems with sampled-data measurements. It is assumed that the system is subject to real time-varying uncertainty which is represented by the state-space model having parameter uncertainty. The robust H$_{\infty}$ FIR filter is proposed for the continuous-time linear parameter uncertain systems. It is also derived from the equivalence relationship between the robust linear H$_{\infty}$ FIR filter and the robust linear H$_{\infty}$ filter with sampled-data measurements.

• Journal of Sensor Science and Technology
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• v.32 no.6
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• pp.475-480
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• 2023

When modeling a sensor system mathematically, we assume that the sensor noise is Gaussian and white to simplify the model. If this assumption fails, the performance of the sensor model-based controller or estimator degrades due to incorrect modeling. In practice, non-Gaussian or non-white noise sources often arise in many digital sensor systems. Additionally, the noise parameters of the sensor model are not known in advance without additional noise statistical information. Moreover, disturbances or high nonlinearities often cause unknown sensor modeling errors. To estimate the uncertain noise and model parameters of a sensor system, this paper proposes an iterative batch calibration method using data-driven machine learning. Our simulation results validate the calibration performance of the proposed approach.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.9
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• pp.539-546
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• 2005

This paper describes a method of building the probability grid map for an autonomous mobile robot using the ultrasonic DAF(data association filter). The DAF, which evaluates the association of each data with the rest and removes the data affected by the specular reflection effect, can improve the reliability of the data for the Probability grid map. This method is based on the evaluation of possibility that the acquired data are all from the same object. Namely, the data from specular reflection have very few possibilities of detecting the same object, so that they are excluded from the data cluster during the process of the DAF. Therefore, the uncertain data corrupted by the specular reflection and/or multi-path effect, are not used to update the probability map, and hence building a good quality of a grid map is possible even in a specular environment. In order to verify the effectiveness of the DAF, it was applied to the Bayesian model and the orientation probability model which are the typical ones of a grid map. We demonstrate the experimental results using a real mobile robot in the real world.

• KIEE International Transactions on Power Engineering
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• v.4A no.4
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• pp.254-261
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• 2004

Traditionally, electrical power systems had formed the vertically integrated industry structures based on the economics of scale. However, power systems have been recently reformed to increase their energy efficiency. According to these trends, the Korean power industry underwent partial reorganization and competition in the generation market was initiated in 2001. In competitive electric markets, accurate load data is one of the most important issues to maintaining flexibility in the electric markets as well as reliability in the power systems. In practice, the measuring load data can be uncertain because of mechanical trouble, communication jamming, and other issues. To obtain reliable load data, an efficient evaluation technique to adjust the missing load data is required. This paper analyzes the load pattern of historical real data and then the tuned ARIMA (Autoregressive Integrated Moving Average), PCHIP (Piecewise Cubic Interpolation) and Branch & Bound method are applied to seek the missing parameters. The proposed method is tested under a variety of conditions and also tested against historical measured data from the Korea Energy Management Corporation (KEMCO).

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.150-155
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• 2003

We discussed a quantitative structure-activity relationships (QSAR) technique on incomplete data set. We proposed a new solver that used 2 kinds of multi-layer neural networks. One is to compensate the defect data, and another is to evaluate the QSAR. The solver can predict the defects in model QSAR data. By using them, we get very high precision QSAR. It is 5-10 times higher than that of a traditional method. However, in case of anti-cancer Carboquone, the prediction is not so complete. It was about O(3) wrong than the model calculation. The predicted values would have rather large error. It is caused by noisy observations of Carboquone. However, if we used the uncertain predictions, new data are included in QSAR. If not, they were omitted. The effect would not be little. Therefore, we evaluated the QSAR. The results are contrary to the expectation, are not so wrong. We believe that the wrong effect is suppressed by including information of new data.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.143-143
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• 2022

Precipitation plays an essential role in water resources management and disaster prevention. Therefore, the understanding related to spatiotemporal characteristics of rainfall is necessary. Nowadays, highly accurate precipitation is mainly obtained from gauge observation systems. However, the density of gauge stations is a sparse and uneven distribution in mountainous areas. With the proliferation of technology, satellite-based precipitation sources are becoming increasingly common and can provide rainfall information in regions with complex topography. Nevertheless, satellite-based data is that it still remains uncertain. To overcome the above limitation, this study aims to take the strengthens of machine learning to generate a new reanalysis of precipitation data by fusion of multiple satellite precipitation products (SPPs) with gauge observation data. Several machine learning algorithms (i.e., Random Forest, Support Vector Regression, and Artificial Neural Network) have been adopted. To investigate the robustness of the new reanalysis product, observed data were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the machine learning model showed higher accuracy than original satellite rainfall products, and its spatiotemporal variability was better reflected than others. Thus, reanalysis of satellite precipitation product based on machine learning can be useful source input data for hydrological simulations in ungauged river basins.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.283-286
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• 1996

This study suggests a new methodology for the fault diagnosis based on the signed digraph in developing the fault diagnosis system of a boiler plant. The suggested methodology uses the new model, fault-effect tree. The SDG has the advantage, which is simple and graphical to represent the causal relationship between process variables, and therefore is easy to understand. However, it cannot handle the broken path cases arisen from data uncertainty as it assumes consistent path. The FET is based on the SDG to utilize the advantages of the SDG, and also covers the above problem. The proposed FET model is constructed by clustering of measured variables, decomposing knowledge base and searching the fault propagation path from the possible faults. The search is performed automatically. The fault diagnosis system for a boiler plant, ENDS was constructed using the expert system shell G2 and the advantages of the presented method were confirmed through case studies.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.39 no.4
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• pp.386-392
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• 1990

This paper discribes a sonar-based certainty grid, the probabilistic representation of the uncertain and incomplete sensor knowledge, for autonomous mobile robot navigation. We use sonar sensor range data to build a map of the robot's surroundings. This range data provides information about the location of the objects which may exist in front of the sensor. From this information, we can compute the probability of being occupied and that of being empty for each cell. In this paper, a new method using Bayesian formula is introduced, which enables us to overcome some difficulties of the Ad-Hoc formula that has been the only way of updating the grids. This new formula can be applied to other kinds of sensors as well as sonar sensor. The validity of this formula in the real world is verified through simulation and experiment. This paper also shows that a wide angle sensor such as sonar sensor can be used effectively to identify the empty area, and the simultaneous use of multiple sensors and fusion in a certainty grid can improve the quality of the map.