• Smart Structures and Systems
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• v.16 no.2
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• pp.295-328
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• 2015

In recent years the monitoring of structural behavior through acquisition of vibrational data has become common practice. In addition, recent advances in sensor development have made the collection of diverse dynamic information feasible. Other than the commonly collected acceleration information, Global Position System (GPS) receivers and non-contact, optical techniques have also allowed for the synchronous collection of highly accurate displacement data. The fusion of this heterogeneous information is crucial for the successful monitoring and control of structural systems especially when aiming at real-time estimation. This task is not a straightforward one as measurements are inevitably corrupted with some percentage of noise, often leading to imprecise estimation. Quite commonly, the presence of noise in acceleration signals results in drifting estimates of displacement states, as a result of numerical integration. In this study, a new approach based on a time domain identification method, namely the Unscented Kalman Filter (UKF), is proposed for correcting the "drift effect" in displacement or rotation estimates in an online manner, i.e., on the fly as data is attained. The method relies on the introduction of artificial white noise (WN) observations into the filter equations, which is shown to achieve an online correction of the drift issue, thus yielding highly accurate motion data. The proposed approach is demonstrated for two cases; firstly, the illustrative example of a single degree of freedom linear oscillator is examined, where availability of acceleration measurements is exclusively assumed. Secondly, a field inspired implementation is presented for the torsional identification of a tall tower structure, where acceleration measurements are obtained at a high sampling rate and non-collocated GPS displacement measurements are assumed available at a lower sampling rate. A multi-rate Kalman Filter is incorporated into the analysis in order to successfully fuse data sampled at different rates.

• Journal of Advanced Navigation Technology
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• v.21 no.5
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• pp.479-484
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• 2017

A Neustrelitz TEC model (NTCM) developed by Deutsches Zentrum $f{\ddot{u}}r$ Luft- und Raumfahrt (DLR) provides a better accuracy than the global positioning system (GPS) Klobuchar model for predicting ionospheric delay. The NTCM model accuracy is comparable to Galileo NeQuick model, and it has less computation time. The NTCM model uses F10.7 values as a parameter of solar activity function, while a NTCM-Broadcast (NTCM-BC) uses TEC values from a Klobuchar model. For this reason, a NTCM-BC model can be used for real-time ionosphere correction. In this paper, vertical ionospheric delay and GPS positioning errors in Korea by using a NTCM-BC ionosphere model from 2009 to 2014 are analyzed and compared with those of a Klobuchar model. In the 6-year statistics, the vertical ionospheric delay is reduced by 17.7 %, and horizontal and vertical positioning accuracies by the NTCM-BC model are improved by 25.6 % and 6.7 %, respectively, over the Klobuchar model.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.27 no.4
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• pp.361-370
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• 2017

Objectives: This study evaluated the diesel engine exhaust (DEE) exposure levels of door-to-door deliverers in Daegu from July to September. Methods: We measured exposure levels of DEE surrogates for the same door-to-door deliverers who joined the particulate matter 2.5 exposure study previously published in this journal. Black carbon(BC) concentrations were measured using real-time BC monitoring devices with 1 minute interval. $NO_2$ concentrations were monitored using passive badges. DEE exposure data were analyzed using the same characteristics and GPS information as the first study. Results: A total of 40 measurements of BC concentrations and $NO_2$ concentrations were collected during delivery of parcels. The average exposure levels to BC, and $NO_2$ were $2.23{\mu}g/m^3$ ($0.001-350.85{\mu}g/m^3$) and 21.26 ppb(3.3-61.37 ppb), respectively. Exposure levels to BC according to the day of a week and coverage areas were not significantly different(p>0.05). Delivery trucks manufactured before 2006 caused significantly higher exposure to BC than the trucks manufactured after 2006(p<0.05). Exposure levels of BC integrated for each time in residential area and roadsides were $1.96{\mu}g/m^3$ and $3.46{\mu}g/m^3$, respectively, and the difference was statistically significant(p<0.001). The Pearson correlation coefficients between the ambient $PM_{2.5}$ and BC was significant, r=0.26(p<0.01); however, the correlations between $PM_{2.5}$ and ambient $PM_{2.5}$, and between BC of DEE and $PM_{2.5}$ of DEE did not show a significant correlation Conclusions: BC and $NO_2$ exposure levels were significantly lower when door-to-door deliverers drove newer trucks. BC exposure levels of deliverers were higher in roadsides than in residential area. DEE from nearby vehicles through open windows might be the main source of BC exposure.

• Journal of Digital Convergence
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• v.13 no.5
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• pp.159-167
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• 2015

The nature of port logistics requires global visibility and traceability. However, the traditional RFID technology still applied cannot meet these demands. IP-USN and M2M in port logistics have faced challenges of the prerequisite of network composition and immense communications at the base where a grand number of containers are installed. To resolve the issue, this study suggests an IP-RFID-based smart port logistics service platform. The IP-RFID-based port logistics service system resolved the communication problem by separating the RFID tag and AP, which allows internet connection, from the tag. The tags connected with thermo-sensors, humidity sensors, pressure sensors, GPS, etc. are attached to the containers insuring global visibility and traceability, key factors in logistics, by obtaining desired real-time information regardless of time and location.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.4
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• pp.183-189
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• 2021

In recent years, active research has been devoted toward developing a monitoring system using ambient vibration data in order to quantitatively determine the deterioration occurring in a structure over a long period of time. This study developed a low-cost edge computing system that detects the abnormalities in structures by utilizing the dynamic characteristics acquired from the structure over the long term for ensemble learning. The system hardware consists of the Raspberry Pi, an accelerometer, an inclinometer, a GPS RTK module, and a LoRa communication module. The structural abnormality detection afforded by the ensemble learning using dynamic characteristics is verified using a laboratory-scale structure model vibration experiment. A real-time distributed processing algorithm with dynamic feature extraction based on the experiment is installed on the Raspberry Pi. Based on the stable operation of installed systems at the Community Service Center, Pohang-si, Korea, the validity of the developed system was verified on-site.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.4
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• pp.45-54
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• 2004

In this paper, a view point tracking system has been realized for outdoor augmented reality including broad area monitoring. Since the surroundings of the moving view point are changing, it is necessary to track the position and observation moment of the view point system for consistency between real and virtual images. For this reason, the GPS(Global Positioning System) is applied to the realized system for tracking the information on position and direction of the moving system. In addition, an optical position tracking system that is able to track view point in a limited area is used, because the local tracking system has to trace the image variation, seen to the observer in a moving vehicle, at a particular position and time. It was found that the realized outdoor augmented reality system, which combined the virtual information tracked in real time with the real image, can be very practical in various application area.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.7
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• pp.297-306
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• 2017

Citizens want more accurate forecast information using Bus Information System. However, most bus information systems that use an average based short-term prediction algorithm include many errors because they do not consider the effects of the traffic flow, signal period, and halting time. In this paper, we try to improve the precision of forecast information by analyzing the influencing factors of the error, thereby making the convenience of the citizens. We analyzed the influence factors of the error using BIS data. It is shown in the analyzed data that the effects of the time characteristics and geographical conditions are mixed, and that effects on halting time and passes speed is different. Therefore, the halt time is constructed using Generalized Additive Model with explanatory variable such as hour, GPS coordinate and number of routes, and we used Hidden Markov Model to construct a pattern considering the influence of traffic flow on the unit section. As a result of the pattern construction, accurate real-time forecasting and long-term prediction of route travel time were possible. Finally, it is shown that this model is suitable for travel time prediction through statistical test between observed data and predicted data. As a result of this paper, we can provide more precise forecast information to the citizens, and we think that long-term forecasting can play an important role in decision making such as route scheduling.

• Aerospace Engineering and Technology
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• v.12 no.1
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• pp.128-136
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• 2013

Recently, the localization recognition system research has been studied using various sensors according to increased interest in autonomous navigation flight. In case of indoor environment which cannot support GPS information, we have to look for another way to recognize current position. The Image-based localization recognition system has been interested although there are lots of way to know current pose. In this paper, we explain the localization recognition system based on mark and implementation of autonomous navigation flight. In order to apply to real environment which cannot support marks, localization based on real-time 3D map building is discussed.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.340-342
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• 2003

Location based services based on positions of moving objects are expanding the business area gradually. The location is included all estimate position of the future as well as the position of the present and the past. Location based routing service is active business application in which the position information of moving objects is applied efficiently. This service includes the trajectory of past positions, the real-time tracing of present position of special moving objects, and the shortest and optimized paths combined with map information. In this paper, we describes the location based routing services is extend in distributed web GIS environment. Web GIS service systems provide the various GIS services of analyzing and displaying the spatial data with friendly user - interface. That is, we propose the efficient architecture and technologies for servicing the location based routing services in distributed web GIS environment. The position of moving objects is acquired by GPS (Global Positioning System) and converted the coordinate of real world by map matching with geometric information. We suppose the swapping method between main memory and storages to access the quite a number of moving objects. And, the result of location based routing services is wrapped the web-styled data format. We design the schema based on the GML. We design these services as components were developed in object-oriented computing environment, and provide the interoperability, language-independent, easy developing environment as well as re - usability.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.145-152
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• 2021

In the Recent times, various technological enhancements in the field of artificial intelligence and big data has been noticed. This advancement coupled with the evolution of the 5G communication and Internet of Things technologies, has helped in the development in the domain of smart mine construction. The development of unmanned vehicles with enhanced and smart scheduling system for open-pit mine transportation is one such much needed application. Traditional open-pit mining systems, which often cause vehicle delays and congestion, are controlled by human authority. The number of sensors has been used to operate unmanned cars in an open-pit mine. The sensors haves been used to prove the real-time data in large quantity. Using this data, we analyses and create an improved transportation scheduling mechanism so as to optimize the paths for the vehicles. Considering the huge amount the data received and aggregated through various sensors or sources like, the GPS data of the unmanned vehicle, the equipment information, an intelligent, and multi-target, open-pit mine unmanned vehicle schedules model was developed. It is also matched with real open-pit mine product to reduce transport costs, overall unmanned vehicle wait times and fluctuation in ore quality. To resolve the issue of scheduling the transportation, we prefer to use algorithms based on artificial intelligence. To improve the convergence, distribution, and diversity of the classic, rapidly non-dominated genetic trial algorithm, to solve limited high-dimensional multi-objective problems, we propose a decomposition-based restricted genetic algorithm for dominance (DBCDP-NSGA-II).