• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.10
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• pp.2696-2707
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• 2009

Although, a number of DBMS vendors are scrambling to extend their products to handle XML, there is a need for a lightweight, DBMS and platform-independent XML repository as well. In this paper, we describe such an XML integrated repository system, that solves the following functions : generating relational schema from XML DTDs for storage of XML documents, importing data from XML documents into relational tables, creating XML documents according to a XMLQL(XML Query Language) from data extracted from a database and synchronizing the replicated XML documents. In the XML repository systems, the efficient change detection techniques for XML documents is required to maintain the consistency of replicated XML data because the same data in the repository can be replicated between so many different XML documents. In this paper, we propose a message digest based change detection technique to maintain the consistency of replicated data between client XML documents and a XML data in XML repository systems.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.8
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• pp.293-298
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• 2014

Detecting and understanding the changes between RDF data is crucial in the evolutionary process, synchronization system, and versioning system on the web of data. However, current researches on detecting changes still remain unsatisfactory in that they did neither consider the large scale of RDF data nor accurately produce the RDF deltas. In this paper, we propose a scalable and effective change detection using a MapReduce framework which has been used in many fields to process and analyze large volumes of data. In particular, we focus on the structure-based change detection that adopts a strategy for the comparison of blank nodes in RDF data. To achieve this, we employ a method which is composed of two MapReduce jobs. First job partitions the triples with blank nodes by grouping each triple with the same blank node ID and then computes the incoming path to the blank node. Second job partitions the triples with the same path and matchs blank nodes with the Hungarian method. In experiments, we show that our approach is more accurate and effective than the previous approach.

• Journal of the Korea institute for structural maintenance and inspection
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• v.6 no.3
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• pp.193-199
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• 2002

Structral crack of RC structure generally occurs when the tension stress by applied load is larger than tension resistance of concrete, and it means deterioration of structure and the decrease of load resistance. Because structural crack of structure can occur critical damage to structure occasionally, the research on crack detection algorithm of RC structure is needed for assurance of structural safety and effective maintenance of structure. In this paper, we executed the laboratory test on measuring strain of RC beam's tension and compression zone, using strain gauge which is widely used on strain measurement of civil structure. By using measured strain, we analyzed strain change, elastic modulus change, and neutral axis change to detect crack of RC beam. As a result, we proposed the simple and effective crack detection algorithm using trends of neutral axis position change.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.179-188
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• 2023

Recently, fake news disguises the form of news content and appears whenever important events occur, causing social confusion. Accordingly, artificial intelligence technology is used as a research to detect fake news. Fake news detection approaches such as automatically recognizing and blocking fake news through natural language processing or detecting social media influencer accounts that spread false information by combining with network causal inference could be implemented through deep learning. However, fake news detection is classified as a difficult problem to solve among many natural language processing fields. Due to the variety of forms and expressions of fake news, the difficulty of feature extraction is high, and there are various limitations, such as that one feature may have different meanings depending on the category to which the news belongs. In this paper, emotional change patterns are presented as an additional identification criterion for detecting fake news. We propose a model with improved performance by applying a convolutional neural network to a fake news data set to perform analysis based on content characteristics and additionally analyze emotional change patterns. Sentimental polarity is calculated for the sentences constituting the news and the result value dependent on the sentence order can be obtained by applying long-term and short-term memory. This is defined as a pattern of emotional change and combined with the content characteristics of news to be used as an independent variable in the proposed model for fake news detection. We train the proposed model and comparison model by deep learning and conduct an experiment using a fake news data set to confirm that emotion change patterns can improve fake news detection performance.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1083-1094
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• 2021

In order to monitor nuclear activity in inaccessible areas, it is necessary to establish a methodology to analyze changesin nuclear activity-related objects using high-resolution satellite images. However, traditional object detection and change detection techniques using satellite images have difficulties in applying detection results to various fields because effects of seasons and weather at the time of image acquisition. Therefore, in this paper, an object of interest was detected in a satellite image using a deep learning model, and object changes in the satellite image were analyzed based on object detection results. An initial training of the deep learning model was performed using an open dataset for object detection, and additional training dataset for the region of interest were generated and applied to transfer learning. After detecting objects by multitemporal and multisensory satellite images, we tried to detect changes in objects in the images by using them. In the experiments, it was confirmed that the object detection results of various satellite images can be directly used for change detection for nuclear activity-related monitoring in inaccessible areas.

• The Journal of Information Systems
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• v.15 no.4
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• pp.211-224
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• 2006

This study investigates the application of data mining techniques such as artificial neural networks, rough sets, and induction teaming to the intrusion detection systems. To maximize the effectiveness of data mining for intrusion detection systems, we introduced the asymmetric costs with false positive errors and false negative errors. And we present a method for intrusion detection systems to utilize the asymmetric costs of errors in data mining. The results of our empirical experiment show our intrusion detection model provides high accuracy in intrusion detection. In addition the approach using the asymmetric costs of errors in rough sets and neural networks is effective according to the change of threshold value. We found the threshold has most important role of intrusion detection model for decreasing the costs, which result from false negative errors.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.4
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• pp.359-364
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• 2022

The change detection in remote sensing results in errors due to the camera's optical factors, seasonal factors, and land cover characteristics. The inclination of the building in the image was simulated according to the camera angle using the Cycle Generative Adversarial Network method, and the simulated image was used to contribute to the improvement of change detection accuracy. Based on CycleGAN, the inclination of the building was similarly simulated to the building in the other image based on the image of one of the two periods, and the error of the original image and the inclination of the building was compared and analyzed. The experimental data were taken at different times at different angles, and Kompsat-3A high-resolution satellite images including urban areas with dense buildings were used. As a result of the experiment, the number of incorrect detection pixels per building in the two images for the building area in the image was shown to be reduced by approximately 7 times from 12,632 in the original image and 1,730 in the CycleGAN-based simulation image. Therefore, it was confirmed that the proposed method can reduce detection errors due to the inclination of the building.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.10
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• pp.1345-1352
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• 2021

Most of weather radar systems are used to monitor the whole weather situation for the very wide and medium-to-long range area. However, as the likelihood of occurrence of the local weather hazards is increased in recent days, it is very important to detect these wether phenomena with a local weather radar. For this purpose, it is necessary to detect the fast varying low altitude weather conditions and the effect of the ground surface clutter is more evident. Therefore, in this paper, the newly suggested method is explained and analyzed for detection of weather hazards such as the gust and wind shear using the fluctuation of wind velocities and the gradient of wind velocities among range cells. It is shown that the suggested method can be used efficiently in the future for faster detection of weather change through the simple algorithm implementation and also the effect of the ground clutter can be minimized in the detection procedure.

• Journal of IKEEE
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• v.25 no.4
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• pp.689-696
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• 2021

In this paper, in order to apply Prognostics and Health Management(PHM) to an electronic system or circuit, a circuit capable of detecting and predicting defect characteristics inside the system or circuit is implemented, and the results are described. In the previous study, we demonstrated that the frequency of the amplitude of S-parameter changed as the circuit defect progressed. These characteristics were measured by network analyser. but in this study, even if the same defect detection method is used, a circuit is proposed to check the progress of the defect, the remaining time, and the occurrence of the defect without large measurement devices. The circuit is designed to detect the change in impedance that generates changes of S-parameter, and it is verified through simulation using the measurement results of Bond-wires.

• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.75-83
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• 2018

Recently, autonomous vehicles have been actively studied. Pedestrian detection and recognition technology is important in autonomous vehicles. Pedestrian detection using CNN(Convolutional Neural Netwrok), which is mainly used recently, generally shows good performance, but there is a performance degradation depending on the environment of the image. In this paper, we propose a pedestrian detection system applying long-term memory structure of hippocampal neural network based on CNN network with LGP-FL (Local Gradient Pattern-Feature Layer) added. First, change the input image to a size of $227{\times}227$. Then, the feature is extracted through a total of 5 layers of convolution layer. In the process, LGP-FL adds the LGP feature pattern and stores the high-frequency pattern in the long-term memory. In the detection process, it is possible to detect the pedestrian more accurately by detecting using the LGP feature pattern information robust to brightness and color change. A comparison of the existing methods and the proposed method confirmed the increase of detection rate of about 1~4%.