• Journal of KIISE
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• v.44 no.11
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• pp.1231-1235
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• 2017

Given the network structure in online social network, it is important to determine a way to distinguish spam accounts from the network features. In online social network, the service provider attempts to detect social spamming to maintain their service quality. However the spammer group changes their strategies to avoid being detected. Even though the spammer attempts to act as legitimate users, certain distinguishable structural features are not easily changed. In this paper, we investigate a way to generate meaningful network structure features, and suggest spammer detection method using recursive structural features. From a result of real-world dataset experiment, we found that the proposed algorithm could improve the classification performance by about 8%.

• Journal of Broadcast Engineering
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• v.13 no.5
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• pp.662-676
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• 2008

This paper presents a video copy detection algorithm that blocks online transfer of illegally copied DTV broadcast programs. Particularly, the proposed algorithm establishes a set of keyframes by detecting abrupt changes of luminance, and then exploits the spatio-temporal features of keyframes. Comparing with the preregistered features stored in the database of DTV broadcast programs, the proposed scheme performs a function of video filtering in order to distinguish whether an uploaded video is illegally copied or not. Note that we analyze only a set of keyframes instead of an entire video frame. Thus, it is highly efficient to identify illegal copied video when we deal with a vast size of broadcast programs. Also, we confirm that the proposed technique is robust to a variety of video edit-effects that are often applied by online video redistribution, such as apsect-ratio change, logo insertion, caption insertion, visual quality degradation, and resolution change (downscaling). In addition, we perform a benchmark test in which the proposed scheme outperforms previous techniques.

• Journal of the Korea Society of Computer and Information
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• v.24 no.8
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• pp.19-28
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• 2019

In this paper, an online video summarization algorithm based on multiple object detection is proposed. As crime has been on the rise due to the recent rapid urbanization, the people's appetite for safety has been growing and the installation of surveillance cameras such as a closed-circuit television(CCTV) has been increasing in many cities. However, it takes a lot of time and labor to retrieve and analyze a huge amount of video data from numerous CCTVs. As a result, there is an increasing demand for intelligent video recognition systems that can automatically detect and summarize various events occurring on CCTVs. Video summarization is a method of generating synopsis video of a long time original video so that users can watch it in a short time. The proposed video summarization method can be divided into two stages. The object extraction step detects a specific object in the video and extracts a specific object desired by the user. The video summary step creates a final synopsis video based on the objects extracted in the previous object extraction step. While the existed methods do not consider the interaction between objects from the original video when generating the synopsis video, in the proposed method, new object clustering algorithm can effectively maintain interaction between objects in original video in synopsis video. This paper also proposed an online optimization method that can efficiently summarize the large number of objects appearing in long-time videos. Finally, Experimental results show that the performance of the proposed method is superior to that of the existing video synopsis algorithm.

• International journal of advanced smart convergence
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• v.4 no.2
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• pp.20-28
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• 2015

In this paper, we propose an effective tracking algorithm with an appearance model based on features extracted from a video frame with posture variation and camera view point adaptation by employing the non-adaptive random projections that preserve the structure of the image feature space of objects. The existing online tracking algorithms update models with features from recent video frames and the numerous issues remain to be addressed despite on the improvement in tracking. The data-dependent adaptive appearance models often encounter the drift problems because the online algorithms does not get the required amount of data for online learning. So, we propose an effective tracking algorithm with an appearance model based on features extracted from a video frame.

• Smart Structures and Systems
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• v.21 no.5
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• pp.687-694
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• 2018

The problem of wheel tread defects has become a major challenge for the health management of high-speed rail as a wheel defect with small radius deviation may suffice to give rise to severe damage on both the train bogie components and the track structure when a train runs at high speeds. It is thus highly desirable to detect the defects soon after their occurrences and then conduct wheel turning for the defective wheelsets. Online wheel condition monitoring using wheel impact load detector (WILD) can be an effective solution, since it can assess the wheel condition and detect potential defects during train passage. This study aims to develop an FBG-based track-side wheel condition monitoring method for the detection of wheel tread defects. The track-side sensing system uses two FBG strain gauge arrays mounted on the rail foot, measuring the dynamic strains of the paired rails excited by passing wheelsets. Each FBG array has a length of about 3 m, slightly longer than the wheel circumference to ensure a full coverage for the detection of any potential defect on the tread. A defect detection algorithm is developed for using the online-monitored rail responses to identify the potential wheel tread defects. This algorithm consists of three steps: 1) strain data pre-processing by using a data smoothing technique to remove the trends; 2) diagnosis of novel responses by outlier analysis for the normalized data; and 3) local defect identification by a refined analysis on the novel responses extracted in Step 2. To verify the proposed method, a field test was conducted using a test train incorporating defective wheels. The train ran at different speeds on an instrumented track with the purpose of wheel condition monitoring. By using the proposed method to process the monitoring data, all the defects were identified and the results agreed well with those from the static inspection of the wheelsets in the depot. A comparison is also drawn for the detection accuracy under different running speeds of the test train, and the results show that the proposed method can achieve a satisfactory accuracy in wheel defect detection when the train runs at a speed higher than 30 kph. Some minor defects with a depth of 0.05 mm~0.06 mm are also successfully detected.

• Journal of Korea Multimedia Society
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• v.23 no.12
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• pp.1464-1475
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• 2020

Due to the spread of COVID-19, the online lecture has become more prevalent. However, it was found that a lot of students and professors are experiencing lack of communication. This study is therefore designed to improve interactive communication between professors and students in real-time online lectures. To do so, we explore deep learning approaches for automatic recognition of students' facial expressions and classification of their understanding into 3 classes (Understand / Neutral / Not Understand). We use 'BlazeFace' model for face detection and 'ResNet-GRU' model for facial expression recognition (FER). We name this entire process 'Degree of Understanding (DoU)' algorithm. DoU algorithm can analyze a multitude of students collectively and present the result in visualized statistics. To our knowledge, this study has great significance in that this is the first study offers the statistics of understanding in lectures using FER. As a result, the algorithm achieved rapid speed of 0.098sec/frame with high accuracy of 94.3% in CPU environment, demonstrating the potential to be applied to real-time online lectures. DoU Algorithm can be extended to various fields where facial expressions play important roles in communications such as interactions with hearing impaired people.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1231-1238
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• 2021

Recently, large online shopping malls are providing image search services as well as text or category searches. However, in the case of an image search service, there is a problem in that the search service cannot be used in the absence of an image. This paper describes the development of a system that allows users to find the clothes they want through hand-drawn images of the style of clothes when they search for clothes in an online clothing shopping mall. The hand-drawing data drawn by the user increases the accuracy of matching through neural network learning, and enables matching of clothes using various object detection algorithms. This is expected to increase customer satisfaction with online shopping by allowing users to quickly search for clothing they are looking for.

• Journal of Korea Multimedia Society
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• v.12 no.10
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• pp.1427-1435
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• 2009

E-Learning is different from the traditional classroom learning and examinees can take a class individually anywhere by online. And it is difficult to proctor an examination because they also take a test by online. However the results of the online test are included in their examination scores. Therefore, it is very important to authenticate the examinees. In this paper, we propose improvement of fairness system for online test using camera. Students can take a picture after every online classes and it has been saved. And during the test, ELTS(e-Learning Test System) takes images, detects the faces, and protects from getting another person to sit for cheating. After examination, the images have been transferred with the answer sheets to the cyber school management system. And a report card will be printed out with the user's images. Moreover, it will authenticate oneself and protect the online test from cheating.

• Structural Engineering and Mechanics
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• v.40 no.6
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• pp.745-762
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• 2011

Damage detection methodologies based on the direct examination of the nonlinear-nonstationary characteristics of the structure dynamic response may play an important role in online structural health monitoring applications. Different signal processing based damage detection methodologies have been proposed based on the uncovering of spikes in the high frequency component of the structural response obtained via Discrete Wavelet transforms, Hilbert-Huang transforms or high pass filtering. The performance of these approaches in systems subjected to different types of excitation is evaluated in this paper. It is found that in the case of random excitations, like earthquake accelerations, the effectiveness of such methodologies is limited. An alternative damage detection approach using the Continuous Wavelet Transform (CWT) is also evaluated to overcome this limitation. Using the CWT has the advantage that the central frequencies at which it operates can be defined by the user while the frequency bands of the detail functions obtained via DWT are predetermined by the sampling period of the signal.

• ETRI Journal
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• v.45 no.4
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• pp.713-723
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• 2023

Game bots are illegal programs that facilitate account growth and goods acquisition through continuous and automatic play. Early detection is required to minimize the damage caused by evolving game bots. In this study, we propose a game bot detection method based on action time intervals (ATIs). We observe the actions of the bots in a game and identify the most frequently occurring actions. We extract the frequency, ATI average, and ATI standard deviation for each identified action, which is to used as machine learning features. Furthermore, we measure the performance using actual logs of the Aion game to verify the validity of the proposed method. The accuracy and precision of the proposed method are 97% and 100%, respectively. Results show that the game bots can be detected early because the proposed method performs well using only data from a single day, which shows similar performance with those proposed in a previous study using the same dataset. The detection performance of the model is maintained even after 2 months of training without any revision process.