• Journal of KIISE:Computing Practices and Letters
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• v.15 no.11
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• pp.806-818
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• 2009

Since the invention of the integrated circuit (IC) in 1950s, semiconductor technology has undergone dramatic development up to these days. A complete semiconductor is manufactured through a diversity of processes. For better semiconductor productivity, fault detection and classification (FDC) has been rigorously studied for finding faults even before the processes are completed. For FDC, various kinds of sensors are attached in many semiconductor manufacturing devices, and sensor values are collected in a periodic manner. The collection of sensor values consists of sequences of real numbers, and hence is regarded as a kind of time-series data. In this paper, we propose an algorithm for detecting and clustering faults in semiconductor processes. The proposed algorithm is a modification of the existing anomaly detection algorithm dealing with symbolically-represented time-series. The contributions of this paper are: (1) showing that a modification of the existing anomaly detection algorithm dealing with general time-series could be used for semiconductor process data and (2) presenting experimental results for improving correctness of fault detection and clustering. As a result of our experiment, the proposed algorithm caused neither false positive nor false negative.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3797-3822
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• 2020

The inspection of cracks on the surface of tunnel linings is a common method of evaluate the condition of the tunnel. In particular, determining the thickness and shape of a crack is important because it indicates the external forces applied to the tunnel and the current condition of the concrete structure. Recently, several automatic crack detection methods have been proposed to identify cracks using captured tunnel lining images. These methods apply an image-segmentation mechanism with well-annotated datasets. However, generating the ground truths requires many resources, and the small proportion of cracks in the images cause a class-imbalance problem. A weakly annotated dataset is generated to reduce resource consumption and avoid the class-imbalance problem. However, the use of the dataset results in a large number of false positives and requires post-processing for accurate crack detection. To overcome these issues, we propose a crack detection method using a ternary classifier. The proposed method significantly reduces the false positive rate, and the performance (as measured by the F1 score) is improved by 0.33 compared to previous methods. These results demonstrate the effectiveness of the proposed method.

• Journal of KIISE:Software and Applications
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• v.36 no.11
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• pp.912-919
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• 2009

A novel algorithm using 2D tensor voting and edge-based approach is proposed for text detection in natural scene images. The tensor voting is used based on the fact that characters in a text line are usually close together on a smooth curve and therefore the tokens corresponding to centers of these characters have high curve saliency values. First, a suitable edge-based method is used to find all possible text regions. Since the false positive rate of text detection result generated from the edge-based method is high, 2D tensor voting is applied to remove false positives and find only text regions. The experimental results show that our method successfully detects text regions in many complex natural scene images.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.2
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• pp.159-169
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• 2009

In this paper, optimum design of distributed detection is considered for a parallel sensor network system consisting of a fusion center and two passive sonar nodes. AND rule and OR rule are employed as the fusion rules of the sensor network. For the fusion rules, it is shown that a threshold rule of each sensor node has uniformly most powerful properties. Optimum threshold for each sensor is investigated that maximizes the probability of detection under the constraint of a specified probability of false alarm. It is also investigated through numerical experiments how signal strength, false alarm probability, and the distance between two sensor nodes affect the system detection performances.

• Journal of the Korea Society of Computer and Information
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• v.17 no.5
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• pp.33-40
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• 2012

In this paper we propose an anomaly detection scheme to detect new attack paths or new attack methods without false positives by monitoring HTTP Outbound Traffic after efficient training. Our proposed scheme detects web-based attacks by comparing tags or javascripts of HTTP Outbound Traffic with normal behavioral models which apply HMM(Hidden Markov Model). Through the verification analysis under the real-attacked environment, we show that our scheme has superior detection capability of 0.0001% false positive and 96% detection rate.

• Journal of the Korea Society of Computer and Information
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• v.22 no.12
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• pp.79-85
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• 2017

In this paper, we propose a CNN-based swimmer detection algorithm. Every year, water safety accidents have been occurred frequently, and accordingly, intelligent video surveillance systems are being developed to prevent accidents. Intelligent video surveillance system is a real-time system that detects objects which users want to do. It classifies or detects objects in real-time using algorithms such as GMM (Gaussian Mixture Model), HOG (Histogram of Oriented Gradients), and SVM (Support Vector Machine). However, HOG has a problem that it cannot accurately detect the swimmer in a complex and dynamic environment such as a beach. In other words, there are many false positives that detect swimmers as waves and false negatives that detect waves as swimmers. To solve this problem, in this paper, we propose a swimmer detection algorithm using CNN (Convolutional Neural Network), specialized for small object sizes, in order to detect dynamic objects and swimmers more accurately and efficiently in complex environment. The proposed CNN sets the size of the input image and the size of the filter used in the convolution operation according to the size of objects. In addition, the aspect ratio of the input is adjusted according to the ratio of detected objects. As a result, experimental results show that the proposed CNN-based swimmer detection method performs better than conventional techniques.

• The Journal of the Acoustical Society of Korea
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• v.36 no.4
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• pp.229-237
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• 2017

In this paper we propose an auto tonal detection method which exploits short term stationary when targets located in a detection beam area and then additional methods are proposed in order to reduce the computational complexity of the proposed method. The proposed method is adaptive to input signals and robust against interference caused by multiple targets because it compares an expected value of input signals with a threshold value which are estimated from a single beam while signals are keep stationary. The performances of the proposed methods are evaluated using by simulated data and acquired data from real ocean. The proposed method has shown better performance than conventional CFAR (Constant False Alarm Rate) methods.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.2
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• pp.363-370
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• 2021

The perimeter intrusion detection system is very important in physical security. In this study, a micro smart device (module) using MEMS sensor was developed in IoT environment for external intrusion detection. The outer intrusion detection system applying the smart device developed in this study is installed in various installation environments, such as barbed wire of various materials and shapes, the city center, the beach, and the mountain, so that it can detect external intrusion and its location as well as false alarms. As a smart sensor that can minimize the false alarm rate and economical construction cost, it is expected that it can be used for the safe operation of major facilities and prevention of disasters and crimes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.347-370
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• 2019

In the computer-aided detection (CAD) system of pulmonary nodules, a high false positive rate is common because the density and the computed tomography (CT) values of the vessel and the nodule in the CT images are similar, which affects the detection accuracy of pulmonary nodules. In this paper, a method of automatic detection of pulmonary nodules based on multi-scale enhancement filters and 3D shape features is proposed. The method uses an iterative threshold and a region growing algorithm to segment lung parenchyma. Two types of multi-scale enhancement filters are constructed to enhance the images of nodules and blood vessels in 3D lung images, and most of the blood vessel images in the nodular images are removed to obtain a suspected nodule image. An 18 neighborhood region growing algorithm is then used to extract the lung nodules. A new pulmonary nodules feature descriptor is proposed, and the features of the suspected nodules are extracted. A support vector machine (SVM) classifier is used to classify the pulmonary nodules. The experimental results show that our method can effectively detect pulmonary nodules and reduce false positive rates, and the feature descriptor proposed in this paper is valid which can be used to distinguish between nodules and blood vessels.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.13 no.4
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• pp.151-159
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• 2003

Internet as being generalized, intrusion detection system is needed to protect computer system from intrusions synthetically. We propose an intrusion detection method to identify and control the contradiction on self-explanation that happen at profiling process of anomaly detection methodology. Because many patterns can be created on profiling process with association method, we present effective application plan through clustering for rules. Finally, we propose similarity function to decide whether anomaly action or not for user pattern using clustered pattern database.