• Journal of Korea Multimedia Society
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• v.15 no.12
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• pp.1492-1498
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• 2012

One of the major issues in current internet architecture is that it was not designed to support the user mobility. In order to overcome this restriction, in this paper, we propose an identifier/locator separation architecture which contain the overlay mapping system to store the identifier-to-locator mapping record. In addition, we design the mobility management scheme base on Identifier/Locator separation above for Furture Internet architecture. We then devise the analysis model to evaluate the signaling cost of our scheme. By conducting the simulation. we show that our scheme can operate with lower signaling cost than other schemes.

• Journal of the Semiconductor & Display Technology
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• v.9 no.4
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• pp.51-56
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• 2010

As the price of image acquisition component becomes low enough, the compact and easily accessible handheld devices are generally equipped with image acquisition functionality. This trend speeds up various applications in diverse areas such as image related services and software. Therefore users strongly need to identify their images effectively and efficiently so that the duplicated images are perceived as one physical entity. In order to handle this environment, we propose a number of methods that generate image identifiers utilizing fundamental image features. In this paper, we analyze the identifier generation methods in terms of various size patterns, especially for tiny size cases, since the small images does not contain abundant pixels for feature extraction. In this paper, experimental evaluation over identifier generation methods' behavior according to different sizes is demonstrated.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.6
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• pp.1162-1169
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• 2007

In this paper, we proposed a container identifier recognition method for containers used in harbors. After converting a real container image to a gray image, edges are detected from the gray image applying Prewitt mask and candidate identifier area is extracted using morphological features of individual identifier for identifying containers. Because noises are included in the extracted candidate identifier area, noises are eliminated and each identifier is separated using 4-directional edge tracking algorithm and Grassfire algorithm. Each identifier in the noise-free candidate identifier area is recognized using FCM-based row RBF network for discriminating containers. We used 300 real container images for experiment to evaluate the performance of the proposed method, and we could verify the proposed method is better than a conventional method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.1
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• pp.98-107
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• 2017

In these days, the increase of applications that highly use network resources has revealed the limitations of the current research phase from the traffic classification for network management. Various researches have been conducted to solutions for such limitations. The representative study is automatic finding of the common pattern of traffic. However, since the study of automatic signature generation is a semi-automatic system, users should collect the traffic. Therefore, these limitations cause problems in the traffic collection step leading to untrusted accuracy of the signature verification process because it does not contain any of the generated signature. In this paper, we propose an automated traffic collection, signature management, signature generation and signature verification process to overcome the limitations of the automatic signature update system. By applying the proposed method in the campus network, actual traffic signatures maintained the completeness with no false-positive.

• Journal of the Korea Society of Computer and Information
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• v.28 no.2
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• pp.89-97
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• 2023

In this paper, we propose factors that make log analysis difficult and design technique for detecting various objects embedded in the logs which helps in the subsequent analysis. In today's IT systems, logs have become a critical source data for many advanced AI analysis techniques. Although logs contain wealth of useful information, it is difficult to directly apply techniques since logs are semi-structured by nature. The factors that interfere with log analysis are various objects such as file path, identifiers, JSON documents, etc. We have designed a BERT-based object pattern recognition algorithm for these objects and performed object identification. Object pattern recognition algorithms are based on object definition, GROK pattern, and regular expression. We find that simple pattern matchings based on known patterns and regular expressions are ineffective. The results show significantly better accuracy than using only the patterns and regular expressions. In addition, in the case of the BERT model, the accuracy of classifying objects reached as high as 99%.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.47-60
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• 2012

Video data comes in the form of the unstructured and the complex structure. As the importance of efficient management and retrieval for video data increases, studies on the video parsing based on the visual features contained in the video contents are researched to reconstruct video data as the meaningful structure. The early studies on video parsing are focused on splitting video data into shots, but detecting the shot boundary defined with the physical boundary does not cosider the semantic association of video data. Recently, studies on structuralizing video shots having the semantic association to the video scene defined with the semantic boundary by utilizing clustering methods are actively progressed. Previous studies on detecting the video scene try to detect video scenes by utilizing clustering algorithms based on the similarity measure between video shots mainly depended on color features. However, the correct identification of a video shot or scene and the detection of the gradual transitions such as dissolve, fade and wipe are difficult because color features of video data contain a noise and are abruptly changed due to the intervention of an unexpected object. In this paper, to solve these problems, we propose the Scene Detector by using Color histogram, corner Edge and Object color histogram (SDCEO) that clusters similar shots organizing same event based on visual features including the color histogram, the corner edge and the object color histogram to detect video scenes. The SDCEO is worthy of notice in a sense that it uses the edge feature with the color feature, and as a result, it effectively detects the gradual transitions as well as the abrupt transitions. The SDCEO consists of the Shot Bound Identifier and the Video Scene Detector. The Shot Bound Identifier is comprised of the Color Histogram Analysis step and the Corner Edge Analysis step. In the Color Histogram Analysis step, SDCEO uses the color histogram feature to organizing shot boundaries. The color histogram, recording the percentage of each quantized color among all pixels in a frame, are chosen for their good performance, as also reported in other work of content-based image and video analysis. To organize shot boundaries, SDCEO joins associated sequential frames into shot boundaries by measuring the similarity of the color histogram between frames. In the Corner Edge Analysis step, SDCEO identifies the final shot boundaries by using the corner edge feature. SDCEO detect associated shot boundaries comparing the corner edge feature between the last frame of previous shot boundary and the first frame of next shot boundary. In the Key-frame Extraction step, SDCEO compares each frame with all frames and measures the similarity by using histogram euclidean distance, and then select the frame the most similar with all frames contained in same shot boundary as the key-frame. Video Scene Detector clusters associated shots organizing same event by utilizing the hierarchical agglomerative clustering method based on the visual features including the color histogram and the object color histogram. After detecting video scenes, SDCEO organizes final video scene by repetitive clustering until the simiarity distance between shot boundaries less than the threshold h. In this paper, we construct the prototype of SDCEO and experiments are carried out with the baseline data that are manually constructed, and the experimental results that the precision of shot boundary detection is 93.3% and the precision of video scene detection is 83.3% are satisfactory.