• Proceedings of the Korean Geotechical Society Conference
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• 2009.03a
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• pp.987-992
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• 2009

Continuous Compaction Control is a new cutting edge technique in United States, Japan and European construction market that uses an instrumented compactor to measure soil stiffness in real time usually with vehicle tracking system such as Global Navigation Satellite System (GNSS). In this study, soil stiffness was evaluated by adapting Fourier transforming technique with acceleration data obtained from accelerometers used as a continuous compaction control instrument. The soil stiffness obtained by accelerometers gave analogous results with reference results such as dry density, elastic modulus obtained from Geogauge and Light falling deflectometer.

• Journal of the Korea Society of Computer and Information
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• v.11 no.5 s.43
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• pp.211-221
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• 2006

Manufacturing Execution System(MES) immediately enables users to share the information of systems industrywide, efficiently manages synthetic information with data collection through treating the data in a fast way, and helps their decision-making. MES for real-time information processing requires certain conditions such as data modeling of RFID, which has recently attracted attentions, and monitoring of each product unit from manufacture to sales. However, in the middle of processing the unit with a RFID tag, transponders(readers) can't often read the tag due to reader's malfunctions, intentional damages, loss and the circumstantial effects; for that reason, users are unable to confirm the location of the product unit. In this case, users cannot avoid tracing the path of units with uncertain clues. In this paper we suggest that the unique MES based on RFID and Bayesian Network can immediately track the product unit, and show how to evaluate it.

• Smart Structures and Systems
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• v.25 no.1
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• pp.111-122
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• 2020

Researchers up to date have introduced several Structural Health Monitoring (SHM) techniques with varying advantages and drawbacks for each. Satellite positioning systems (GPS, GLONASS and GALILEO) based techniques proved to be promising, especially for high natural period structures. Particularly, the GPS has proved sufficient performance and reasonable accuracy in tracking real time dynamic displacements of flexible structures independent of atmospheric conditions, temperature variations and visibility of the monitored object. Tall structures are particularly sensitive to oscillations produced by different sources of dynamic actions; such as typhoons. Wind forces induce in the structure both longitudinal and perpendicular displacements with respect to the wind direction, resulting in torsional effects, which are usually more complex to be detected. To efficiently track the horizontal rotations of the in-plane sections of such flexible structures, two main issues have to be considered: a suitable sensor topology (i.e., location, installation, and combination of sensors), and the methodology used to process the data recorded by sensors. This paper reports the contributions of the measurements recorded from dual frequency GPS receivers and uni-axial accelerometers in a full-scale experimental campaign. The Canton tower in Guangzhou-China is the case study of this research, which is instrumented with a long-term structural health monitoring system deploying both accelerometers and GPS receivers. The elaboration of combining the obtained rather long records provided by these two types of sensors in detecting the torsional behavior of the tower under ambient vibration condition and during strong wind events is discussed in this paper. Results confirmed the reliability of GPS receivers in obtaining the dynamic characteristics of the system, and its ability to capture the torsional response of the tower when used alone or when they are combined with accelerometers integrated data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.5
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• pp.2103-2123
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• 2018

Ships built today are larger in scale and feature more complex structures. The ever-evolving systems used on board a ship require vast amounts of data processing. In the future, with the advent of smart ships, unmanned ships and other next-generation ships, the volumes of data to be processed will continue to increase. Yet, to date, ship data has been processed using wired networks. Placed at fixed locations, the nodes on wired networks often fail to process data from mobile devices. Despite many attempts made to use Wi-Fi on ships just as on land to create wireless networks, Wi-Fi has hardly been available due to the complex metal structures of ships. Therefore, Wi-Fi on ships has been patchy as the ship-wide total Wi-Fi coverage has not properly implemented. A new ship-wide wireless network environment is part of the technology conducive to the shipbuilding industry. The wireless network environment should not only serve the purpose of communication but also be able to manage and control multiple features in real-time: fault diagnostics, tracking, accident prevention and safety management. To better understand the characteristics of wireless frequencies for ships, this paper tests the widely used TETRA, UHF and Wi-Fi and sheds light on the features, advantages and disadvantages of each technology in ship settings. The proposed deployment of a Super Wi-Fi network leveraging the legacy UHF system of TMS generates a ship-wide wireless network environment. The experimental findings corroborate the feasibility of the proposed ship-wide Super Wi-Fi network environment.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.709-718
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• 2021

In this paper, it can be seen that the number of abandoned dogs increases every year through the statistics on the occurrence of abandoned animals. With the goal of reducing the number of stray dogs, a smart pet dog shoulder strap is implemented based on a real-time location tracking system using the ESP32 module and GPS sensor. It is an ESP32 module with a built-in Bluetooth module. It is input to the MCU using various sensors, and finally outputs to a smart-phone application, and communicates through the built-in blue-tooth module. In addition, it uses Neopixels to compensate the weaknesses at night through LED light emission, and automatically sets the warning distance to design a music playback system through the LED flashing effect and MP3 module. In addition, a smart pet dog shoulder strap was designed to help pet dog health care by measuring the moving distance according to the amount of activity through the gyro sensor.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.87-95
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• 2020

Generally, occupational safety and particularly construction safety is an intricate phenomenon. Industry professionals have devoted vital attention to enforcing Occupational Safety and Health (OHS) from the last three decades to enhance safety management in construction. Despite the efforts of the safety professionals and government agencies, current safety management still relies on manual inspections which are infrequent, time-consuming and prone to error. Extensive research has been carried out to deal with high fatality rates confronting by the construction industry. Sensor systems, visualization-based technologies, and tracking techniques have been deployed by researchers in the last decade. Recently in the construction industry, computer vision has attracted significant attention worldwide. However, the literature revealed the narrow scope of the computer vision technology for safety management, hence, broad scope research for safety monitoring is desired to attain a complete automatic job site monitoring. With this regard, the development of a broader scope computer vision-based risk recognition system for correlation detection between the construction entities is inevitable. For this purpose, a detailed analysis has been conducted and related rules which depict the correlations (positive and negative) between the construction entities were extracted. Deep learning supported Mask R-CNN algorithm is applied to train the model. As proof of concept, a prototype is developed based on real scenarios. The proposed approach is expected to enhance the effectiveness of safety inspection and reduce the encountered burden on safety managers. It is anticipated that this approach may enable a reduction in injuries and fatalities by implementing the exact relevant safety rules and will contribute to enhance the overall safety management and monitoring performance.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.5
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• pp.761-768
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• 2023

In the 4th Industrial Revolution, AI(Artificial Intelligence) and IoT(Internet of Things) technologies are being applied to across various fields, with particularly prominence in asset management, disaster management, and meteorological observation. In these fields, it is necessary to accurately determine the real-time and precise tracking of the object's location and status, and to collect various data even in situations that are difficult to detect with existing sensors. In order to address these demands, the use of GNSS(Global Navigation Satellite System) is essential, and this technology enables the efficient management of assets, disaster prevent and response, and accurate weather forecasting. In this paper, we provide the investigated results for the latest trends in the application of GNSS in the fields of asset management, disaster management, and weather observation, among various fields incorporating AI and IoT and analyze them.

• Journal of Korea Water Resources Association
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• v.56 no.12
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• pp.845-853
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• 2023

Recently, along with the increase in chemical accidents, the number of accidents-related disasters has been increasing continuously since 2012, and when looking at the hydrogen fluoride incident which is a representative example of domestic chemical incidents, there is insufficient technology applicable to the incident site. The result was that the damage spread. Therefore, in this paper, we will adapt the water pollution accident response system to a location-based approach, and introduce a measurement method for alternative index tracking using a GPS electronic floater of a location-based index measurement method for real-time response in the water environment when a chemical incident occurs. The research target area is Gumi City, which is the area where the hydrogen fluoride incident occurred, and Gamcheon is selected, and alternative tracking using GPS electronic floater is conducted in the corresponding target area through water quality and flow measurement. As a result, it is possible to measure water quality and flow at the same time in tracker experiments using GPS electronic floater based on the research results, it is believed that using GPS electronic floater will be of great help in disaster response systems for spill incidents in the river.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.131-145
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• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

• Progress in Medical Physics
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• v.26 no.3
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• pp.143-152
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• 2015

In this study, we aim to design the architecture of the kV imaging system for tumor tracking in the dual-head gantry system and analyze its accuracy by simulations. We established mathematical formulas and algorithms to track the tumor position with the two-pair kV imaging systems when they are in the non-orthogonal positions. The algorithms have been designed in the homogeneous coordinate framework and the position of the source and the detector coordinates are used to estimate the tumor position. 4D XCAT (4D extended cardiac-torso) software was used in the simulation to identify the influence of the angle between the two-pair kV imaging systems and the resolution of the detectors to the accuracy in the position estimation. A metal marker fiducial has been inserted in a numerical human phantom of XCAT and the kV projections were acquired at various angles and resolutions using CT projection software of the XCAT. As a result, a positional accuracy of less than about 1mm was achieved when the resolution of the detector is higher than 1.5 mm/pixel and the angle between the kV imaging systems is approximately between $90^{\circ}$ and $50^{\circ}$. When the resolution is lower than 1.5 mm/pixel, the positional errors were higher than 1mm and the error fluctuation by the angles was greater. The resolution of the detector was critical in the positional accuracy for the tumor tracking and determines the range for the acceptable angle range between the kV imaging systems. Also, we found that the positional accuracy analysis method using XCAT developed in this study is highly useful and will be a invaluable tool for further refined design of the kV imaging systems for tumor tracking systems.