• Proceedings of the KIEE Conference
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• 2004.11a
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• pp.223-226
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• 2004

Many extensive researches in the area of sensor's technology for corrosion monitoring in concrete structures have increasingly been carried out in recent years. This paper gives a brief discussion on the principles and usage of the role of sensors involved in both corrosion initiation and propagation steps of reinforcement corrosion monitoring in concrete structures. Special attention was given to the review of various sensing devices, selection of reliable sensing devices for detecting reinforcement corrosion at the particular environment and at the efficiency of the devices used. Various sensing operations in new and existing concrete structures are also described.

• The Transactions of the Korean Institute of Power Electronics
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• v.19 no.4
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• pp.297-302
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• 2014

In this paper, a LED driver compatible with TRIAC-dimmer applying active bleeder is proposed. If TRIAC-dimmer is connected with LED driver, flicker phenomenon occurs by TRIAC malfunction. In order to prevent this problem, a current over holding current must flow into TRIAC. Therefore, additional circuit compatible with TRIAC-dimmer is required to provide enough current. Passive bleeder has power loss in whole operation period. The proposed circuit apply a valley-fill circuit for power-factor-correction and a novel active bleeder detecting malfunction point of TRIAC. Therefore, it prevent malfunction of TRIAC-dimmer and have advantage of higher efficiency than passive bleeder. To verify the validity of proposed circuit, 13W-lighting LED driver prototype has been proposed.

• Journal of Korea Multimedia Society
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• v.14 no.5
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• pp.622-631
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• 2011

Corners play an important role in describing object features for pattern recognition and identification. This paper proposed a fast and adaptive corner detector in both coarse and fine scale, followed by the framework of the curvature scale space (CSS). An adaptive curvature threshold and evaluating of angles of corner candidates are added to original CSS to remove round corners and false corners in the detecting process. The efficiency of proposed method is compared to other popular detectors in both accuracy criteria, stability and time consuming. Results illustrate that the proposed method performs extremely surpass in both areas.

• Journal of Korea Multimedia Society
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• v.19 no.9
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• pp.1690-1697
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• 2016

In a beacon-enabled Zigbee network, the slotted CSMA/CA mechanism based on the super frame structure fairly provides communication chance for each node and makes a reasonable usage of the available energy. In the case of wireless nano sensors that are implanted into the target human body area for detecting disease symptoms or virus, such a nano-network requires a similar type of channel sharing and transmission of short length event-driven data. In this paper, for nano-network's in-body applications, we aim to design conceptually a new super frame derived from the existing beacon-enabled Zigbee MAC protocol. And we analyze the efficiency of the proposed super frame in the aspect of practical deployment.

• International Journal of Contents
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• v.1 no.2
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• pp.5-9
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• 2005

Currently most Internet documents including data are made based on predefined templates, but templates are usually formed only for main data and are not helpful for information retrieval against indexes, advertisements, header data etc. Templates in such forms are not appropriate when Internet documents are used as data for information retrieval. In order to process Internet documents in various areas of information retrieval, it is necessary to detect additional information such as advertisements and page indexes. Thus this study proposes a method of detecting the layout of Web pages by identifying the characteristics and structure of block tags that affect the layout of Web pages and calculating distances between Web pages. This method is purposed to reduce the cost of Web document automatic processing and improve processing efficiency by providing information about the structure of Web pages using templates through applying the method to information retrieval such as data extraction.

• Annual Conference of KIPS
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• 2004.05a
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• pp.1259-1262
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• 2004

Detecting environmental hazards and monitoring remote terrain are among many sensor network applications. In case of fire detection, it is significantly valuable to monitor fire-spot's shape and trend in time. Mobile ad hoc sensor nodes right round are responsible for sensoring, processing and networking packets, or even launching extinguisher. In this paper, we proposed a ring-oriented Multicast architecture based on "Fisheye State Routing" (MFSR) to organize a group of mobile ad hoc sensor nodes in a multicast way. It is familiar with traditional mesh-based multicast protocol [1] in mobile ad hoc network, trying to concentrates on efficiency and robustness simultaneously. Certain applications-based solution for hazards is proposed, quantitative results including architecture and recovery algorithms of MFSR are also investigated in this paper.

• Journal of information and communication convergence engineering
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• v.16 no.4
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• pp.242-247
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• 2018

The data of K-Water waterworks is collected from various sensors and used as basic data for the operation and analysis of various devices. In this way, the importance of the sensor data is very high, but it contains misleading data due to the characteristics of the sensor in the external environment. However, the cleansing method for the missing data is concentrated on the prediction of the missing data, so the research on the detection and prediction method of the missing data is poor. This is a study to detect wrong data by converting collected data into quintiles and patterning them. It is confirmed that the accuracy of detecting false data intentionally generated from real data is higher than that of the conventional method in all cases. Future research we will prove the proposed system's efficiency and accuracy in various environments.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1029-1035
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• 2019

In recent years, artificial intelligence (AI) services have become one of the most essential parts to extend human capabilities in various fields such as face recognition for security, weather prediction, and so on. Various learning algorithms for existing AI services are utilized, such as classification, regression, and deep learning, to increase accuracy and efficiency for humans. Nonetheless, these services face many challenges such as fake news spread on social media, stock selection, and volatility delay in stock prediction systems and inaccurate movie-based recommendation systems. In this paper, various algorithms are presented to mitigate these issues in different systems and services. Convolutional neural network algorithms are used for detecting fake news in Korean language with a Word-Embedded model. It is based on k-clique and data mining and increased accuracy in personalized recommendation-based services stock selection and volatility delay in stock prediction. Other algorithms like multi-level fusion processing address problems of lack of real-time database.

• Journal of Information Processing Systems
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• v.19 no.2
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• pp.189-202
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• 2023

In the era marked by information inundation, social network analysis is the most important part of big data analysis, with clique detection being a key technology in social network mining. Also, detecting maximal balance clique in signed networks with positive and negative relationships is essential. In this paper, we present two algorithms. The first one is an algorithm, MCDA1, that detects the maximal balance clique using the improved three-way concept lattice algorithm and object-induced three-way concept lattice (OE-concept). The second one is an improved formal concept analysis algorithm, MCDA2, that improves the efficiency of memory. Additionally, we tested the execution time of our proposed method with four real-world datasets.

• Annual Conference of KIPS
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• 2023.11a
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• pp.634-636
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• 2023

Surveillance systems play a pivotal role in ensuring the safety and security of various environments, including public spaces, critical infrastructure, and private properties. However, detecting abnormal human behavior in lowlight conditions is a critical yet challenging task due to the inherent limitations of visual data acquisition in such scenarios. This paper introduces a spatiotemporal framework designed to address the unique challenges posed by low-light environments, enhancing the accuracy and efficiency of human abnormality detection in surveillance camera systems. We proposed the pre-processing using lightweight exposure correction, patched frames pose estimation, and optical flow to extract the human behavior flow through t-seconds of frames. After that, we train the estimated-action-flow into autoencoder for abnormal behavior classification to get normal loss as metrics decision for normal/abnormal behavior.