• Journal of the Korea Society of Computer and Information
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• v.20 no.12
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• pp.29-36
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• 2015

In this paper, we propose the horizontal line detection using the Haar-like features and linear regression in infrared images. In the marine environment horizon image is very useful information on a variety of systems. In the proposed method Haar-like features it was noted that the standard deviation be calculated in real time on a static area. Based on the pixel position, calculating the standard deviation of the around area in real time and, if the reaction is to filter out the largest pixel can get the energy map of the area containing the straight horizontal line. In order to select a horizontal line of pixels from the energy map, we applied the linear regression, calculating a linear fit to the transverse horizontal line across the image to select the candidate optimal horizontal. The proposed method was carried out in a horizontal line detecting real infrared image experiment for day and night, it was confirmed the excellent detection results than the legacy methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.3
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• pp.713-718
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• 2014

This paper proposed a real-time monitoring system that can realize effective operation of snowplows each of the local autonomous entities secures to cope with disasters in Korea like a wintertime heavy snowfall and also can promptly cope with the spot facing a heavy snowfall disaster by doing real-time monitoring on the information of the snow-removal site and the mobility of the vehicles. Also, the study has designed a relay terminal analysis tool so that the proposed system can analyze all kinds of controlling information and diagnose the relay terminal effectively. The proposed system can realize effective and emergent coping with the situations of a heavy snowfall disaster through real-time routing trace as well as effective work progress within a short time by doing real-time monitoring on the information about the status of snow-removal work and vehicle controlling for snow-removal work as well as the location information of snow-removal vehicles in the situations of a heavy snowfall.

• Journal of Wellbeing Management and Applied Psychology
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• v.5 no.4
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• pp.19-31
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• 2022

Purpose: This study was conducted to identify indoor air quality in various living spaces using sensors that can measure noise, vibration, fine dust, and odor in real time and to propose optimal indoor air quality maintenance management using Internet of Things(IoT). Research design, data and methodology: Using real-time sensors to monitor physical factors and environmental air pollutants that affect the comfort of the residential environment, Noise, Vibration, Atmospheric Pressure, Blue Light, Formaldehyde, Hydrogen Sulfide, Illumination, Temperature, Ozone, PM10, Aldehyde, Amine, LVOCs and TVOCs were measured. It were measured every 1 seconds from 4 offices and 4 stores on a small scale from November 2018 to January 2019. Results: The difference between illuminance and blue light for each measuring point was found to depend on lighting time, and the ratio of blue light in total illumination was 0.358 ~ 0.393. Formaldehyde and hydrogen sulphide were found to be higher than those that temporarily attract people in an indoor office space that is constantly active, requiring office air ventilation. The noise was found to be 50dB higher than the office WHO recommendation noise level of 35 ~ 40dB. The most important factors for indoor environmental quality were temperature> humidity> illumination> blue light in turn. Conclusions: Various factors that determine the comfort of indoor living space can be measured with real-time sensors. Further, it is judged that the use of IoT can help maintain indoor air quality comfortably.

• Quantitative Bio-Science
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• v.37 no.2
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• pp.73-79
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• 2018

As high frequency (HF, for short) time series is now prevalent in the presence of real time big data, volatility computations based on traditional ARCH/GARCH models need to be further developed to suit the high frequency characteristics. This article reviews realized volatilities (RV) and multivariate GARCH (MGARCH) to deal with high frequency volatility computations. As a (functional) infinite dimensional models, the fARCH and fGARCH are introduced to accommodate ultra high frequency (UHF) volatilities. The fARCH and fGARCH models are developed in the recent literature by Hormann et al. [1] and Aue et al. [2], respectively, and our discussions are mainly based on these two key articles. Real data applications to domestic UHF financial time series are illustrated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2148-2161
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• 2019

In this paper, we present a real-time cattle action recognition algorithm to detect the estrus phase of cattle from a live video stream. In order to classify cattle movement, specifically, to detect the mounting action, the most observable sign of the estrus phase, a simple yet effective feature description exploiting motion history images (MHI) is designed. By learning the proposed features using the support vector machine framework, various representative cattle actions, such as mounting, walking, tail wagging, and foot stamping, can be recognized robustly in complex scenes. Thanks to low complexity of the proposed action recognition algorithm, multiple cattle in three enclosures can be monitored simultaneously using a single fisheye camera. Through extensive experiments with real video streams, we confirmed that the proposed algorithm outperforms a conventional human action recognition algorithm by 18% in terms of recognition accuracy even with much smaller dimensional feature description.

• Journal of Internet Computing and Services
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• v.18 no.3
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• pp.1-9
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• 2017

Smart Cities intelligently manage numerous infrastructures, including Smart-City IoT devices, and provide a variety of smart-city applications to citizen. In order to provide various information needed for smart-city applications, Smart Cities require a function to intelligently process large-scale streamed big data that are constantly generated from a large number of IoT devices. To provide smart services in Smart-City, the Smart-City Consortium uses stream reasoning. Our stream reasoning requires real-time processing of big data. However, there are limitations associated with real-time processing of large-scale streamed big data in Smart Cities. In this paper, we introduce one of our researches on cloud computing based real-time distributed-parallel-processing to be used in stream-reasoning of IoT big data in Smart Cities. The Smart-City Consortium introduced its previously developed smart-city middleware. In the research for this paper, we made cloud computing based real-time distributed-parallel-processing available in the cloud computing platform of the smart-city middleware developed in the previous research, so that we can perform real-time distributed-parallel-processing with them. This paper introduces a real-time distributed-parallel-processing method and system for stream reasoning with IoT big data transmitted from various sensors of Smart Cities and evaluate the performance of real-time distributed-parallel-processing of the system where the method is implemented.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.11
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• pp.39-49
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• 2011

Smart Grid has two main objectives on both supply and demand aspects which are to distribute the renewable energy sources on supply side and to develop realtime price responses on demand side. Renewable energy does not consume fossil fuels, therefore it improves the eco-friendliness and saves the cost of power system operation at the same time. Demand response increases the flexibility of the power system by mitigating the fluctuation from renewable energies, and reduces the capacity investment cost by shedding the peak load to off-peak periods. Currently Smart Grid technologies mainly focus on energy monitoring and display services but it has been proved that enabling technologies can induce the higher demand responses through many pilot projects in USA. On this context, this paper provides a price responsive algorithm for HEMS (home energy management system) on the real time pricing environment. This paper identifies the demand response as a core function of HEMS and classifies the demand into 3 categories of fixed, transferable, and realtime responsive loads which are coordinated and operated for the utility maximization or cost minimization with the optimal usage combination of three kinds of demand.

• Transactions of the Korean Society of Automotive Engineers
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• v.14 no.4
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• pp.199-206
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• 2006

Real time vehicle dynamics models have been developed with the subsystem synthesis method for intelligent vehicle HILS system. Three different models for solving subsystem equations are compared in order to find out the best suitable model for HILS applications. The first model is based on the generalized coordinate partitioning technique, and the second one is on the approximate function approach, and the last one is on the constraint stabilization method. To investigate the theoretical efficiency of three proposed methods, arithmetic operators used in the formulations of three models are counted. Bump run simulations with half-sine bump have also carried out with three different models to measure the actual CPU time to validate theoretical investigation.

• International Journal of Contents
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• v.10 no.3
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• pp.9-16
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• 2014

Depth-image-based rendering is generally used in real-time 2D-to-3D conversion for 3DTV. However, inaccurate depth maps cause flickering issues between image frames in a video sequence, resulting in eye fatigue while viewing 3DTV. To resolve this flickering issue, we propose a new 2D-to-3D conversion scheme based on fast and robust depth-map generation from a 2D video sequence. The proposed depth-map generation algorithm divides an input video sequence into several cuts using a color histogram. The initial depth of each cut is assigned based on a hypothesized depth-gradient model. The initial depth map of the current frame is refined using color and motion information. Thereafter, the depth map of the next frame is updated using the difference image to reduce depth flickering. The experimental results confirm that the proposed scheme performs real-time 2D-to-3D conversions effectively and reduces human eye fatigue.

• Journal of Microbiology and Biotechnology
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• v.18 no.11
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• pp.1841-1847
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• 2008

Vibrio vulnificus is a causative agent of serious diseases in humans, resulting from the contact of wound with seawater or consumption of raw seafood. Several studies aimed at detecting V. vulnificus have targeted vvh as a representative virulence toxin gene belonging to the bacterium. In this study, we targeted the rpoS gene, a general stress regulator, to detect V. vulnificus. PCR specificity was identified by amplification of 8 V. vulnificus templates and by the loss of a PCR product with 36 non-V. vulnificus strains. The PCR assay had the 273-bp fragment and the sensitivity of 10 pg DNA from V. vulnificus. SYBR Green I-based real-time PCR assay targeting the rpoS gene showed a melting temperature of approximately $84^{\circ}C$ for the V. vulnificus strains. The minimum level of detection by real-time PCR was 2 pg of purified genomic DNA, or $10^3$ V. vulnificus cells from pure cultured broth and $10^3$ cells in 1 g of oyster tissue homogenates. These data indicate that real-time PCR is a sensitive, species-specific, and rapid method for detecting this bacterium, using the rpoS gene in pure cultures and in infected oyster tissues.