• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1406-1416
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• 2017

In complex and large-scale industries, properly designed fault detection and identification (FDI) systems considerably improve safety, reliability and availability of target processes. In thermal power plants (TPPs), generating units operate under very dangerous conditions; system failures can cause severe loss of life and property. In this paper, we propose a bagged auto-associative kernel regression (AAKR)-based FDI approach for steam boilers in TPPs. AAKR estimates new query vectors by online local modeling, and is suitable for TPPs operating under various load levels. By combining the bagging method, more stable and reliable estimations can be achieved, since the effects of random fluctuations decrease because of ensemble averaging. To validate performance, the proposed method and comparison methods (i.e., a clustering-based method and principal component analysis) are applied to failure data due to water wall tube leakage gathered from a 250 MW coal-fired TPP. Experimental results show that the proposed method fulfills reasonable false alarm rates and, at the same time, achieves better fault detection performance than the comparison methods. After performing fault detection, contribution analysis is carried out to identify fault variables; this helps operators to confirm the types of faults and efficiently take preventive actions.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.10
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• pp.958-964
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• 2015

In this paper, an object recognition method based on the depth information from the RGB-D camera, Xtion, is proposed for an indoor mobile robot. First, the RANdom SAmple Consensus (RANSAC) algorithm is applied to the point cloud obtained from the RGB-D camera to detect and remove the floor points. Next, the removed point cloud is classified by the k-means clustering method as each object's point cloud, and the normal vector of each point is obtained by using the k-d tree search. The obtained normal vectors are classified by the trained multi-layer perceptron as 18 classes and used as features for object recognition. To distinguish an object from another object, the similarity between them is measured by using Levenshtein distance. To verify the effectiveness and feasibility of the proposed object recognition method, the experiments are carried out with several similar boxes.

• Journal of Communications and Networks
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• v.17 no.3
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• pp.267-274
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• 2015

Due to the difficulty of coordination in the cellular uplink, it is a practical challenge how to achieve the optimal throughput scaling with distributed scheduling. In this paper, we propose a distributed and opportunistic user scheduling (DOUS) that achieves the optimal throughput scaling in a single-input multiple-output interfering multiple-access channel, i.e., a multi-cell uplink network, with M antennas at each base station (BS) and N users in a cell. In a distributed fashion, each BS adopts M random receive beamforming vectors and then selects M users such that both sufficiently large desired signal power and sufficiently small generating interference are guaranteed. As a main result, it is proved that full multiuser diversity gain can be achieved in each cell when a sufficiently large number of users exist. Numerical evaluation confirms that in a practical setting of the multi-cell network, the proposed DOUS outperforms the existing distributed user scheduling algorithms in terms of sum-rate.

• Communications for Statistical Applications and Methods
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• v.24 no.2
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• pp.143-154
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• 2017

A variable selection method based on probabilistic principal component analysis (PCA) using penalized likelihood method is proposed. The proposed method is a two-step variable reduction method. The first step is based on the probabilistic principal component idea to identify principle components. The penalty function is used to identify important variables in each component. We then build a model on the original data space instead of building on the rotated data space through latent variables (principal components) because the proposed method achieves the goal of dimension reduction through identifying important observed variables. Consequently, the proposed method is of more practical use. The proposed estimators perform as the oracle procedure and are root-n consistent with a proper choice of regularization parameters. The proposed method can be successfully applied to high-dimensional PCA problems with a relatively large portion of irrelevant variables included in the data set. It is straightforward to extend our likelihood method in handling problems with missing observations using EM algorithms. Further, it could be effectively applied in cases where some data vectors exhibit one or more missing values at random.

• ETRI Journal
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• v.37 no.3
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• pp.606-616
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• 2015

In this paper, we propose a novel method for road recognition using 3D point clouds based on a Markov random field (MRF) framework in unstructured and complex road environments. The proposed method is focused on finding a solution for an analysis of traversable regions in challenging environments without considering an assumption that has been applied in many past studies; that is, that the surface of a road is ideally flat. The main contributions of this research are as follows: (a) guidelines for the best selection of the gradient value, the average height, the normal vectors, and the intensity value and (b) how to mathematically transform a road recognition problem into a classification problem that is based on MRF modeling in spatial and visual contexts. In our experiments, we used numerous scans acquired by an HDL-64E sensor mounted on an experimental vehicle. The results show that the proposed method is more robust and reliable than a conventional approach based on a quantity evaluation with ground truth data for a variety of challenging environments.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.3
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• pp.8-13
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• 1989

The parameters of bipolar random square-wave signal process, amplitude and phase with unknown probability distribution are shown to be simultaneously estimated by using Gauss-Markov estimator so that transmitted digital data can be recovered under the additive Gaussinan noise environment. However, we see that the preprocessing stage using the correlator composed of the multiplier and the running integrator is needed to convert the received process into the sampled sequences and to obtain the observed data vectors, which can be used for Gauss-Markov estimation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.1
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• pp.235-244
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• 1999

This Paper proposes a new compaction algorithm for IDDQ testing in CMOS Circuits. A primary test pattern is generated by the primitive fault pattern which is able to detect GOS(gate-oxide short) and the bridging faults in an internal primitive gate. The new algorithm can reduce the number of the test vectors by decreasing the don't care(X) in the primary test pattern. The controllability of random number is used on processing of the backtrace together four ones of heuristics. The simulation results for the ISCAS-85 benchmark circuits show that the test vector reduction is more than 45% for the large circuits on the average compared to static compaction algorithms.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.2
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• pp.192-197
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• 2012

With the advent of DNA microarray technologies, researches for disease diagnosis has been actively in progress. In typical experiments using microarray data, problems such as the large number of genes and the relatively small number of samples, the inherent measurement noise and the heterogeneity across different samples are the cause of the performance decrease. To overcome these problems, a new method using functional modules (e.g. signaling pathways) used as markers was proposed. They use the method using an activity of pathway summarizing values of a member gene's expression values. It showed better classification performance than the existing methods based on individual genes. The activity calculation, however, used in the method has some drawbacks such as a correlation between individual genes and each phenotype is ignored and characteristics of individual genes are removed. In this paper, we propose a method based on the ensemble classifier. It makes weak classifiers based on feature vectors using subsets of genes in selected pathways, and then infers the final classification result by combining the results of each weak classifier. In this process, we improved the performance by minimize the search space through a filtering process using gene-gene interaction information and the mRMR filter. We applied the proposed method to a classifying the lung cancer, it showed competitive classification performance compared to existing methods.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.35-45
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• 2022

Even in recent years, treatment of first-aid patients is still often delayed due to a shortage of medical resources in marginalized areas. Research on automating the analysis of medical data to solve the problems of inaccessibility for medical services and shortage of medical personnel is ongoing. Computer vision-based medical inspection automation requires a lot of cost in data collection and labeling for training purposes. These problems stand out in the works of classifying lesion that are rare, or pathological features and pathogenesis that are difficult to clearly define visually. Anomaly detection is attracting as a method that can significantly reduce the cost of data collection by adopting an unsupervised learning strategy. In this paper, we propose methods for detecting abnormal images on chest X-RAY images as follows based on existing anomaly detection techniques. (1) Normalize the brightness range of medical images resampled as optimal resolution. (2) Some feature vectors with high representative power are selected in set of patch features extracted as intermediate-level from lesion-free images. (3) Measure the difference from the feature vectors of lesion-free data selected based on the nearest neighbor search algorithm. The proposed system can simultaneously perform anomaly classification and localization for each image. In this paper, the anomaly detection performance of the proposed system for chest X-RAY images of PA projection is measured and presented by detailed conditions. We demonstrate effect of anomaly detection for medical images by showing 0.705 classification AUROC for random subset extracted from the PadChest dataset. The proposed system can be usefully used to improve the clinical diagnosis workflow of medical institutions, and can effectively support early diagnosis in medically poor area.

• The Journal of Korean Society for Radiation Therapy
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• v.28 no.1
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• pp.27-34
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• 2016

Purpose : Retrospective evaluation of setup changes using the corrected position during helical tomotherapy Materials and Methods : Head and neck cancer patients were randomly sampled and summarized into 3 groups: Group 1(32) Brain, Group 2 2(28)Maxillar, Nasal cavity, Group 3 (35) Nasopharynx(NPX), Tongue, Tonsil, and Oropharynx(OPX). In 3 groups, the statistical tests based on repeated measurements among 30 times of the duration of treatment by applying X, Y, Z axis errors, roll, weight changes, and vectors as variables. Results : The statistical test results showed that there was no difference between x-axis (p = 0.458) and y-axis (p=0.986) and in roll (p = 0.037), weight change (p <0.001), and the vector (p <0.001). In addition, the pattern between the three groups based on the fraction revealed no difference in x-axis (p = 0.430) and roll (p = 0.299) but a difference in y-axis (.023), weight change (p = 0.001), and vector (p = 0.028). Conclusion : The results of the retrospective evaluation found the change in the group 3 with respect Y, Z, weight, and vector and a larger random error during the treatment including low neck.