• Journal of the Earthquake Engineering Society of Korea
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• v.5 no.3
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• pp.9-17
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• 2001

This paper focuses on the earthquake hazard delineation and physical loss estimation for lifelines and utilities. Emphasis is given to geographic information systems(GIS) and their application to pipeline networks in evaluating the spatial characteristics of earthquake effects. The paper examines the GIS databases for water supply performance obtained for the 1994 northridge. Relationships among buried lifeline damage and various seismic parameters are examined, and the parameters that are statistically most significant are identified. Using GIS data from the Northridge earthquake, the relationships among pipeline repair rate, type of pipe, diameter, and various seismic parameters are assessed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.2
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• pp.283-290
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• 2006

The cellular neural networks have the structure that consists of an array of the same cell which is a simple processing element, and each of the cells has local connectivity and space invariant template properties. So, it has a very suitable structure for the hardware implementation. But, it is impossible to have a one-to-one mapping between the CNN hardware processors and the pixels of the practical large image. In this paper, a $5{\times}5$ CNN hardware processor with pipeline input and output that can be applied to the time-multiplexing processing scheme, which processes the large image with a small CNN cell block, is designed. the operation of the implemented $5{\times}5$ CNN hardware processor is verified from the edge detection and the shadow detection experimentations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1086-1103
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• 2020

The automatic identification and classification of image-based weld defects is a difficult task due to the complex texture of the X-ray images of the weld defect. Several depth learning methods for automatically identifying welds were proposed and tested. In this work, four different depth convolutional neural networks were evaluated and compared on the 1631 image set. The concavity, undercut, bar defects, circular defects, unfused defects and incomplete penetration in the weld image 6 different types of defects are classified. Another contribution of this paper is to train a CNN model "RayNet" for the dataset from scratch. In the experiment part, the parameters of convolution operation are compared and analyzed, in which the experimental part performs a comparative analysis of various parameters in the convolution operation, compares the size of the input image, gives the classification results for each defect, and finally shows the partial feature map during feature extraction with the classification accuracy reaching 96.5%, which is 6.6% higher than the classification accuracy of other existing fine-tuned models, and even improves the classification accuracy compared with the traditional image processing methods, and also proves that the model trained from scratch also has a good performance on small-scale data sets. Our proposed method can assist the evaluators in classifying pipeline welding defects.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.3
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• pp.125-131
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• 2011

Water leak detection system is a system based on wireless sensor networks(WSNs) which detect a leak on water supply, localize the leak position and finally inform a water management center. A traditional leak detection method is to use experienced personnel who walk along a pipeline listening to the sound that is generated by the leaks and their effectiveness depend on the experience of the user. Also making more successful detection, it should be processed at middle of the night when people do not use water, as the result users have to operate the leak detection system at midnight. In this paper, we propose a new method for the water leak detection system based on the WSNs and describe it in detail. Leak detection devices which detect a leakage of water transmit and receive the result of water leak detection with each other by configuring WSNs to improve reliability of the detection result. Also, we analyzed the sound from water flowed in pipeline, proposed the pre-signal processing to separate a leakage sound from noisy sound. And lastly, It is especially important to make a time synchronization with water leak detection devices that are installed on the pipeline, we used 1PPS(1 Pulse Per Second) signal generated by GPS, therefore we could get a precise time synchronization. The proposed system set up in Namyangju and performances were evaluated.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.1B
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• pp.21-30
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• 2012

Sensor networks are composed of a great number of sensor nodes with constrained battery. Disjoint multipath scheme based flooding method has a merit that efficiently construct multipath in irregular networks, but causes lots of energy consumption in networks. Flooding method is not a suitable technology in wireless sensor networks with constrained battery. We introduce energy-efficient geographic routing scheme considered as an efficient, simple, and scalable routing protocol for wireless sensor networks. The geographic routing scheme on multipath generates a problem with a congestion. So we introduce the concept of multipath pipeline as a congestion avoidance strategy. But multipath pipelines have a big problem on the boundary of holes under irregular networks. We propose a novel disjoint multipath scheme as combined method with geographic routing scheme and hole detouring algorithm on multipath. A novel disjoint multipath scheme constructs disjoint multipath pipelines efficiently for reliability without a collision in irregular wireless sensor networks. Simulation results are provided to validate the claims.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.1
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• pp.93-114
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• 2023

Information extraction can facilitate the intensive analysis of documents by providing semantic triples which consist of named entities and their relations recognized in the texts. However, most of the research so far has been carried out separately for named entity recognition and relation extraction as individual studies, and as a result, the effective performance evaluation of the entire information extraction systems was not performed properly. This paper introduces two models of end-to-end information extraction that can extract various entity names in clinical records and their relationships in the form of semantic triples, namely pipeline and joint models and compares their performances in depth. The pipeline model consists of an entity recognition sub-system based on bidirectional GRU-CRFs and a relation extraction module using multiple encoding scheme, whereas the joint model was implemented with a single bidirectional GRU-CRFs equipped with multi-head labeling method. In the experiments using i2b2/VA 2010, the performance of the pipeline model was 5.5% (F-measure) higher. In addition, through a comparative experiment with existing state-of-the-art systems using large-scale neural language models and manually constructed features, the objective performance level of the end-to-end models implemented in this paper could be identified properly.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.2
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• pp.107.1-107.1
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• 2014

We apply a machine learning algorithm, artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts. The multi-dimensional samples consisting of data corresponding to the statistical and physical quantities from the coherent search pipeline are fed into the artificial neural network to distinguish simulated gravitational-wave signals from background noise artifacts. Our result shows that the data classification efficiency at a fixed false alarm probability is improved by the artificial neural network in comparison to the conventional detection statistic. Therefore, this algorithm increases the distance at which a gravitational-wave signal could be observed in coincidence with a gamma-ray burst. We also evaluate the gravitational-wave data within a few seconds of the selected short gamma-ray bursts' event times using the trained networks and obtain the false alarm probability. We suggest that artificial neural network can be a complementary method to the conventional detection statistic for identifying gravitational-wave signals related to the short gamma-ray bursts.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.12A
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• pp.1160-1170
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• 2010

Existing multipath routing mechanism has much overhead to maintain the state of nodes on the multipath route and does not guarantees completely disjoint multipath construction from source to destination. In this paper, we propose an Explicit Disjoint Multipath (EDM) routing algorithm to enhance energy efficiency through removing the flooding mechanism for route discovery process, minimizing the number of nodes participating in route update and balancing the traffic load for entire network. EDM constructs logical pipelines which can create disjoint multipaths in logical way. Then it physically performs anchor node based geographic routing along the logical pipeline in order to build multipath to the destination. EDM can provide the distribution effect of traffic load over the network, help to balance the energy consumption and therefore extend the network lifetime.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1246-1262
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• 2021

Recent studies have demonstrated the strong ability of deep convolutional neural networks (CNNs) to significantly boost the performance in single image super-resolution (SISR). The key concern is how to efficiently recover and utilize diverse information frequencies across multiple network layers, which is crucial to satisfying super-resolution image reconstructions. Hence, previous work made great efforts to potently incorporate hierarchical frequencies through various sophisticated architectures. Nevertheless, economical SISR also requires a capable structure design to balance between restoration accuracy and computational complexity, which is still a challenge for existing techniques. In this paper, we tackle this problem by proposing a competent architecture called Enhanced U-Net Network (EUN), which can yield ready-to-use features in miscellaneous frequencies and combine them comprehensively. In particular, the proposed building block for EUN is enhanced from U-Net, which can extract abundant information via multiple skip concatenations. The network configuration allows the pipeline to propagate information from lower layers to higher ones. Meanwhile, the block itself is committed to growing quite deep in layers, which empowers different types of information to spring from a single block. Furthermore, due to its strong advantage in distilling effective information, promising results are guaranteed with comparatively fewer filters. Comprehensive experiments manifest our model can achieve favorable performance over that of state-of-the-art methods, especially in terms of computational efficiency.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.113-121
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• 2023

Network pharmacology in traditional Korean and Chinese medicine studies the molecular and biological aspects of herbal medicine using computational methods. Despite variations in databases, techniques, and criteria, most studies follow similar steps: constructing herb-compound networks, compound-target networks, and target interpretation. To ensure efficient and consistent analysis in herbal medicine network pharmacology, we designed and implemented a common analysis pipeline. We showed its reliability with existing databases. The proposed system has a potential to facilitate network pharmacology analysis in traditional medicine, ensuring consistent analysis of various herbal medicines.