• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.56-64
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• 2018

In recent years, imbalanced data is one of the most important and frequent issue for quality control in industrial field. As an example, defect rate has been drastically reduced thanks to highly developed technology and quality management, so that only few defective data can be obtained from production process. Therefore, quality classification should be performed under the condition that one class (defective dataset) is even smaller than the other class (good dataset). However, traditional multi-class classification methods are not appropriate to deal with such an imbalanced dataset, since they classify data from the difference between one class and the others that can hardly be found in imbalanced datasets. Thus, one-class classification that thoroughly learns patterns of target class is more suitable for imbalanced dataset since it only focuses on data in a target class. So far, several one-class classification methods such as one-class support vector machine, neural network and decision tree there have been suggested. One-class support vector machine and neural network can guarantee good classification rate, and decision tree can provide a set of rules that can be clearly interpreted. However, the classifiers obtained from the former two methods consist of complex mathematical functions and cannot be easily understood by users. In case of decision tree, the criterion for rule generation is ambiguous. Therefore, as an alternative, a new one-class classifier using hyper-rectangles was proposed, which performs precise classification compared to other methods and generates rules clearly understood by users as well. In this paper, we suggest an approach for improving the limitations of those previous one-class classification algorithms. Specifically, the suggested approach produces more improved one-class classifier using hyper-rectangles generated by using Gaussian function. The performance of the suggested algorithm is verified by a numerical experiment, which uses several datasets in UCI machine learning repository.

• Proceedings of the IEEK Conference
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• 2002.06a
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• pp.195-198
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• 2002

One-dimensional radar signature, such as range profile, is highly dependent on the aspect angle. Therefore, radar target recognition over wide angular region is a very difficult task. In this paper, we propose the Bayes classifier with Gaussian mixture model for radar target recognition over wide-angular region and compare performances of proposed technique and radar target recognition with subclasses concept in the literature of probability of correct classification ratio.

• Journal of the Korea Society of Computer and Information
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• v.21 no.1
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• pp.115-123
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• 2016

In this study, we collect various side effect pairs which are appeared frequently at many drugs, and select side effect pairs that have higher severity. For every selected side effect pair, we extract common genetic networks which are shared by side effects' genes and drugs' target genes based on PPI(Protein-Protein Interaction) network. For this work, firstly, we gather drug related data, side effect data and PPI data. Secondly, for extracting common genetic network, we find shortest paths between drug target genes and side effect genes based on PPI network, and integrate these shortest paths. Thirdly, we develop a classification model which uses this common genetic network as a classifier. We calculate similarity score between the common genetic network and genetic network of a drug for classifying the drug. Lastly, we validate our classification model by means of AUC(Area Under the Curve) value.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.20-25
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• 2021

The target-object classification method was implemented using a deep-learning-based detection model in real-time images. The object detection model was a deep-learning-based detection model that allowed extensive data collection and machine learning processes to classify similar target-objects. The recognition model was implemented by changing the processing structure of the detection model and combining developed the vision-processing module. To classify the target-objects, the identity and similarity were defined and applied to the detection model. The use of the recognition model in industry was also considered by verifying the effectiveness of the recognition model using the real-time images of an actual soccer game. The detection model and the newly constructed recognition model were compared and verified using real-time images. Furthermore, research was conducted to optimize the recognition model in a real-time environment.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.9
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• pp.779-784
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• 2004

This paper describes a sonic polygonal multiple reflection range sensor (SPMRS), which uses multiple reflection properties usually ignored in ultrasonic sensors as disturbances or noises. Targets such as a plane, corner, edge, or cylinder in indoor environments can easily be detected by the multiple reflection patterns obtained with a SPMRS system. Target classification and feature data extraction, such as distance and azimuth to the target, are computed simultaneously by considering the geometrical relationships between the detected targets, and finally the environment model is generated by refining the detected targets. In addition, the narrow field of view of a sonar range sensor is increased and the scanning time is reduced by active motion of the SPMRS stepping servomechanism.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.830-835
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• 2003

This paper describes a sonic polygonal multiple reflection range sensor (SPMRS), which uses multiple reflection properties usually ignored in ultrasonic sensors as disturbances or noises. Targets such as a plane, corner, edge, or cylinder in indoor environments can easily be detected by the multiple reflection patterns obtained with a SPMRS system. Target classification and feature data extraction, such as distance and azimuth to the target, are computed simultaneously by considering the geometrical relationships between the detected targets, and finally the environment model is generated by refining the detected targets. In addition, the narrow field of view of a sonar range sensor is increased and the scanning time is reduced by active motion of the SPMRS stepping servomechanism.

• Journal of IKEEE
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• v.24 no.1
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• pp.97-105
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• 2020

A thermal imaging sensor receives the radiating energy from the target and the background, so it has been widely used for detection, tracking, and classification of targets at night for military purpose. In recognizing the target automatically using thermal images, if the correct edges of object are used then it can generate the classification results with high accuracy. However since the thermal images have lower spatial resolution and more blurred edges than color images, the accuracy of the classification using thermal images can be decreased. In this paper, to overcome this problem, a new hierarchical classifier using both shape and local features based on the segmentation reliabilities, and the class/pose updating method for vehicle classification are proposed. The proposed classification method was validated using thermal video sequences of more than 20,000 images which include four types of military vehicles - main battle tank, armored personnel carrier, military truck, and estate car. The experiment results showed that the proposed method outperformed the state-of-the-arts methods in classification accuracy.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.550-553
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• 2005

To acquire radar target signature, various kinds of target are necessary. Measurement is one of the data acquiring method, but much time and high cost is required to get the target data from the real targets. Even if we can afford that, the targets we can access are very limited. To obtain target signatures avoiding these problems, we build the target CAD (Computer Aided Design) model for the calculation of target signatures. To speed up RCS calculation, we applied adaptive super-sampling and tested quite complex tank CAD model which is 1.4 hundred of thousands facet. We use calculated RCS data for ID range profile and 2D ISAR (Inverse Synthetic Aperture Radar) image formation. We adopted IFFT (Inverse Fast Fourier Transform) algorithm combined with polar formatting algorithm for the ISAR imaging. We could confirm the possibility of the construction of database from the images of CAD models for target classification applications.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.3
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• pp.203-216
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• 2003

In this paper a new feature extraction and target classification method is proposed for the recognition part of FLIR(Forwar Looking Infrared)-image-based ATR system. Proposed feature extraction method is "cluster(=set of classes)-based"version of previous fisherfaces method that is known by its robustness to illumination changes in face recognition. Expecially introduced class clustering and cluster-based projection method maximizes the performance of fisherfaces method. Proposed target image classification method is based on the mixture of experts model which consists of RBF-type experts and MLP-type gating networks. Mixture of experts model is well-suited with ATR system because it should recognizee various targets in complexed feature space by variously mixed conditions. In proposed classification method, one expert takes charge of one cluster and the separated structure with experts reduces the complexity of feature space and achieves more accurate local discrimination between classes. Proposed feature extraction and classification method showed distinguished performances in recognition test with customized. FLIR-vehicle-image database. Expecially robustness to pixelwise sensor noise and un-wanted intensity variations was verified by simulation.

• Journal of Korea Multimedia Society
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• v.21 no.10
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• pp.1195-1202
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• 2018

Artificial neural network is widely used for its excellent performance and implementability. However, traditional neural network needs to learn the system from scratch, with the addition of new input data, the variation of the observation environment, or the change in the form of input/output data. To resolve such a problem, the technique of transfer learning has been proposed. Transfer learning constructs a newly developed target system partially updating existing system and hence provides much more efficient learning process. Until now, transfer learning is mainly studied in the field of image processing and is not yet widely employed in acoustic data processing. In this paper, focusing on the scalability of transfer learning, we apply the concept of transfer learning to the problem of guitar chord classification and evaluate its performance. For this purpose, we build a target system of convolutional neutral network (CNN) based 48 guitar chords classification system by applying the concept of transfer learning to a source system of CNN based 24 guitar chords classification system. We show that the system with transfer learning has performance similar to that of conventional system, but it requires only half the learning time.