• Korean Journal of Construction Engineering and Management
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• v.17 no.6
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• pp.13-23
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• 2016

Construction automation based on information and communication technology(ICT) has been studied for improving productivity in the construction industry. This study investigates domestic research trends in ICT-based construction automation using text mining techniques. The results show that 'Technology to collect and analyze project progress(26%)' and 'Technology to analyze and apply the automation element of construction machinery(28%)' are the major research area. The word of 'construction information' is showed as important keywords in the area of 'Technology to collect and analyze project progress', and researches focusing on resource management, site management, information management, and real-time information monitoring have been mainly conducted. The word of 'ubiquitous' is shown as important keywords in the area of 'Technology to analyze and apply the automation element of construction machinery', and researches focusing on ubiquitous information management, ubiquitous site management, and measurement system have been mainly conducted.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.11
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• pp.1507-1514
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• 2016

Full-image guided filter reflects all pixels of image in filtering by using weight propagation and two-pass model, whereas the existing guide filter is processed based on the kernel window. Therefore the computational complexity can be improved while maintaining characteristics of guide filter, such as edge-preserving, smoothing, and so on. In this paper, we propose an efficient VLSI architecture for the full-image guided filter by analyzing the data dependency, the data frequency and the PSNR analysis of the image in order to achieve enough speed for various applications such as stereo vision, real-time systems, etc. In addition, the proposed efficient scheduling enables the realtime process by minimizing the idle period in weight computation. The proposed VLSI architecture shows 214MHz of maximum operating frequency (image size: 384*288, 965 fps) and 76K of gates (internal memory excluded).

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.12
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• pp.8896-8900
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• 2015

Recently, various career paths draw young workers from twenty to forty to the metro city in Korea. The korea's agriculture sector has decrease in population and productivity which result a threat for it to become an aging society. Also, our country has a difficulty in a tough competition with other countries through agricultural market-opening such as WTO and FTA. In this paper, we introduce a technology using open-source project including Raspberry that easily accessible and applicable to an agricultural industry. In other words, as we build a device monitoring the production environment, everyone can use agricultural sector through an IoT technology, solve the problem with a labor shortage through production process automation, check the condition of the agricultural environment in real time, enhance the quality of the agricultural product by corresponding a certain condition, and improve the competitiveness through a competitive price comparing to the worldwide farm product. Also, we find a way to use data to the other business through data collection and analysis in a process of using the IoT.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2007.11a
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• pp.313-318
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• 2007

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes maximum torque control of IPMSM drive using adaptive fuzzy neural network controller and artificial neural network(ANN). This control method is applicable over the entire speed range which considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using Adaptive-FNN controller and ANN controller. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper reposes speed control of IPMSM using Adaptive-FNN and estimation of speed using ANN controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is a lied to IPMSM drive system controlled Adaptive-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the Adaptive-FNN and ANN controller.

• Journal of Korean Society of Transportation
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• v.25 no.6
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• pp.111-120
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• 2007

More accurate vehicle trajectory data are now readily available through Differential Global Positioning Systems (DGPS). A variety of research opportunities emerge with utilization of such high resolution traffic data. A novel approach of this study is to explore drivers' responsive behavior to variable message signs (VMS) by using individual vehicle trajectories extracted from in-vehicle DGPS data. Responsive characteristics of drivers traveling on urban freeways, which can be represented by speeds and acceleration rates, under the provision of real-time traffic information through VMS are statistically investigated. In addition to conducting an ANOVA test, probability density functions of acceleration rates were estimated. The findings of this study can be used to understand the impact of drivers' workload when providing VMS messages on traffic flow patterns. Furthermore, results can be important fundamentals to assist in conducting more realistic traffic simulations.

• Journal of Korean Society of Transportation
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• v.25 no.6
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• pp.7-18
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• 2007

There are few studies on air pollution due to vehicle emissions in spite of the importance of this field. Therefore, this study describes trends and suggests implications through analysis relating to existing emissions research. This study has been divided into three areas. The first part is about estimating vehicle emissions. In this part, the authors analyze limits in ways of calculating emissions in the existing macroscopic view and then suggest the development of a model for calculating emissions considering velocity and acceleration. These variables are a function of traffic and individual driving behavior in the microscopic view. The second part is about management techniques for reducing vehicle emissions. The traffic management techniques for reducing vehicle emissions should conform to regional characteristics. The final part is about traffic operation for reducing vehicle emissions. The authors suggest the development of a micro-simulator and then the development of strategies for traffic operation. It is necessary to design better models estimating emissions and then, using real time data, to make a monitoring system simulating emission rates. This study serves as a literature review to make a foundation for further research about emissions research for transportation engineering.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.5
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• pp.32-43
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• 2007

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes maximum torque control of IPMSM drive using adaptive learning fuzzy neural network and artificial neural network. This control method is applicable over the entire speed range which considered the limits of the inverter's current and voltage rated value. This paper proposes speed control of IPMSM using adaptive learning fuzzy neural network and estimation of speed using artificial neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled adaptive learning fuzzy neural network and artificial neural network, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the adaptive learning fuzzy neural network and artificial neural network.

• Journal of Environmental Health Sciences
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• v.44 no.5
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• pp.421-432
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• 2018

Objectives: In this study, we started to develop an indoor garden integrated IoT solution based on IAQ (indoor air quality) and interconnection with an environmental database for smart management of indoor gardens. The purpose of this study was to develop and apply an integrated solution for customized air purification from an indoor garden through big data analysis using IoT technology. Methods: An IoT-based IAQ monitoring system was established in three households within a new apartment building. Based on real-time and long-term data collected, $PM_{2.5}$, $CO_2$, temperature, and humidity changes were compared to those of indoor garden applications and the analyzed results were indexed. Results As a result of the installation, all three households had no results exceeding the standard for indoor air pollution on average $PM_{2.5}$ and $CO_2$ indices. In the case of indoor garden installation, the IAQ index increased to the "Good" section after the installation, and readings in the "Bad" section shown before the installation disappeared. The comfort index also did not dip into the "Uncomfortable" section, where it had been preinstallation, and significantly lowered the average score from "Uncomfortable for sensitive groups" to "Good". Overall, the IAQ composite index for the generation of installations decreased the "Good" interval, but "Bad" did not appear. Conclusions In this study on developing an integrated solution for IAQ based on IoT indoor gardens, big data was analyzed to determine IAQ and comfort indexes and an IAQ composite index. Through this process, it became understood that it is necessary to monitor IAQ based on IoT.

• Journal of KIISE
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• v.44 no.1
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• pp.71-77
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• 2017

There has been a recent spate in the usage of mobile device equipped GPS sensors, such as smart phones. This trend enables the posting of geo-tagged messages (i.e., multimedia messages with GPS locations) on social media such as Twitter and Facebook, and the volume of such spatial data is rapidly growing. However, the relationships between the location and content of messages are not always explicitly shown in such geo-tagged messages. Thus, the need arises to reorganize search results to find the relationship between keywords and the spatial distribution of messages. We find the smallest minimum bounding rectangle (MBR) that embedding k or more points in order to find the most dense rectangle of data, and it can be usefully used in the location search system. In this paper, we suggest efficient algorithms to discover a group of 2-Dimensional spatial data with a close distance, such as MBR. The efficiency of our proposed algorithms with synthetic and real data sets is confirmed experimentally.

• KIISE Transactions on Computing Practices
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• v.22 no.3
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• pp.145-150
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• 2016

Finding the exact location of faults in a program requires enormous time and effort. Several fault localization methods based on control flows of a program have been studied for decades. Unfortunately, these methods are not applicable to programs based on data-flow languages. A recently proposed mutation-based fault localization method is applicable to data-flow languages, as well as control-flow languages. However, there are no studies on the effectiveness of the mutation-based fault localization method for data-flow based programs. In this paper, we provided an experimental case study to evaluate the effectiveness of mutation-based fault localization on programs implemented in Function Block Diagram (FBD), a widely used data-flow based language in safety-critical systems implementation. We analyzed several real faults in the implementation of FBD programs of a nuclear reactor protection system, and evaluated the mutation-based fault localization effectiveness for each fault.