• Journal of Industrial Convergence
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• v.20 no.5
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• pp.61-68
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• 2022

In recent years, malicious codes are being produced using the developing information and communication technology, and it is insufficient to detect them with the existing detection system. In order to accurately and efficiently detect and respond to such intelligent malicious code, an intelligent detection model is required, and in order to maximize detection performance, it is important to train with the main characteristic information set of the malicious code. In this paper, we proposed a technique for designing an intelligent detection model and generating the data required for model training as a set of key feature information through transformation, dimensionality reduction, and feature selection steps. And based on this, the main characteristic information was classified by malicious code. In addition, based on the classified characteristic information, we derived common characteristic information that can be used to analyze and detect modified or newly emerging malicious codes. Since the proposed detection model detects malicious codes by learning with a limited number of characteristic information, the detection time and response are fast, so damage can be greatly reduced and Although the performance evaluation result value is slightly different depending on the learning algorithm, it was found through evaluation that most malicious codes can be detected.

• Journal of Korea Multimedia Society
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• v.22 no.1
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• pp.44-54
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• 2019

Recently, image-based object detection has made great progress with the introduction of Convolutional Neural Network (CNN). Many trials such as Region-based CNN, Fast R-CNN, and Faster R-CNN, have been proposed for achieving better performance in object detection. YOLO has showed the best performance under consideration of both accuracy and computational complexity. However, these data-driven detection methods including YOLO have the fundamental problem is that they can not guarantee the good performance without a large number of training database. In this paper, we propose a data sampling method using CycleGAN to solve this problem, which can convert styles while retaining the characteristics of a given input image. We will generate the insufficient data samples for training more robust object detection without efforts of collecting more database. We make extensive experimental results using the day-time and night-time road images and we validate the proposed method can improve the object detection accuracy of the night-time without training night-time object databases, because we converts the day-time training images into the synthesized night-time images and we train the detection model with the real day-time images and the synthesized night-time images.

• Smart Structures and Systems
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• v.29 no.6
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• pp.757-766
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• 2022

To train deep learning algorithms, a sufficient number of data are required. However, in most engineering systems, the acquisition of fault data is difficult or sometimes not feasible, while normal data are secured. The dearth of data is one of the major challenges to developing deep learning models, and fault diagnosis in particular cannot be made in the absence of fault data. With this context, this paper proposes an anomaly detection methodology for rotating machines using only normal data with self-labeling. Since only normal data are used for anomaly detection, a self-labeling method is used to generate a new labeled dataset. The overall procedure includes the following three steps: (1) transformation of normal data to self-labeled data based on a pretext task, (2) training the convolutional neural networks (CNN), and (3) anomaly detection using defined anomaly score based on the softmax output of the trained CNN. The softmax value of the abnormal sample shows different behavior from the normal softmax values. To verify the proposed method, four case studies were conducted, on the Case Western Reserve University (CWRU) bearing dataset, IEEE PHM 2012 data challenge dataset, PHMAP 2021 data challenge dataset, and laboratory bearing testbed; and the results were compared to those of existing machine learning and deep learning methods. The results showed that the proposed algorithm could detect faults in the bearing testbed and compressor with over 99.7% accuracy. In particular, it was possible to detect not only bearing faults but also structural faults such as unbalance and belt looseness with very high accuracy. Compared with the existing GAN, the autoencoder-based anomaly detection algorithm, the proposed method showed high anomaly detection performance.

• Proceedings of the KIEE Conference
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• 1996.07a
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• pp.638-640
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• 1996

This paper presents microprocessor-based Digital Position Detection Unit using the inductive radio line and the antennas in train signaling system. This comprises the Fail-Safe system due to the dual controlled system to drive with safe-side even in events of faults, and designs the FSC(failure safe comparator). And the method of Markov Modeling is described for the reliability evaluation of system.

• Proceedings of the KSR Conference
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• 2004.06a
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• pp.1374-1378
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• 2004

Railway conflict detection and resolution problem (RCDRP) involves complicated field constraints that should be considered for practical service. In this paper, we address those constraints in brief. Particularly, following situations are addressed; (1) temporal change of network topology, (2) consideration of diverse conditions of track and train, for example, single/double tracks and passenger/freight service, (3) siding capacity limitation, (4) bidirectional sides used by both inbound and outbound trains, (5) regulation for passenger transfer service, (6) consideration of siding length, (7) Restriction on stopping before the track segment with steep slope.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.971-972
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• 2008

In this paper, we present a novel method for classifying pornography images using adaptive skin detection. From an input image, we detect initial skin regions and construct an adaptive skin probability density model using color information for the detected skin regions. From the skin probability density model, we extract feature vectors and train the images using Support Vector Machine to classify pornography images.

• MALSORI
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• no.67
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• pp.149-166
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• 2008

In spoken document retrievals (SDR), subword (typically phonemes) indexing term is used to avoid the out-of-vocabulary (OOV) problem. It makes the indexing and retrieval process independent from any vocabulary. It also requires a small corpus to train the acoustic model. However, subword indexing term approach has a major drawback. It shows higher word error rates than the large vocabulary continuous speech recognition (LVCSR) system. In this paper, we propose an probabilistic slot detection and n-gram based string matching method for phone based spoken document retrievals to overcome high error rates of phone recognizer. Experimental results have shown 9.25% relative improvement in the mean average precision (mAP) with 1.7 times speed up in comparison with the baseline system.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.205-206
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• 1998

In this paper, we present the detection algorithm of interictal epileptic waveform using LVQ network and wavelet transform. First wavelet coefficients is used to represent the characteristics of a single channel EEG wave, and make a number of neural network input node smaller. Then, three-layer neural network employing LVQ network is trained and tested using parameters obtained from the first stage. This study showed that preprocessed EEG data can be successfully used to train ANNs to detect epileptogenic discharges with a high success.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.431-433
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• 1996

In MAGLEV system, the train detection can be achieved by using cross inductive radio lines and antennas, because it is impossible to obtain the short circuit between rail and iron-wheel. In this paper, the experimental results of speed profile which is held on MAGLEV at KIMM are presented. We could obtain the successful experimental results for the speed pulses by the inductive radio lines.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.905-908
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• 2010

Our purpose is to develope the system which detects splogs automatically among blogs on Web environment. After removing HTML of blogs, they are tagged by part of speech(POS) tagger. Words and their POS tags information is used as a feature type. Among features, we select useful features with $x^2$ statistics and train the SVM with the selected features. Our system acquired 90.5% of F1 measure with SPLOG data set.