• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2003.05a
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• pp.141-148
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• 2003

This paper presents a rotating ann test for assessment of an underwater hybrid navigation system for a semi-autonomous underwater vehicle. The navigation system consists of an inertial measurement unit (IMU), an ultra-short baseline (USBL) acoustic navigation sensor and a doppler velocity log (DVL) accompanying a magnetic compass. The errors of inertial measurement units increase with time due to the bias errors of gyros and accelerometers. A navigational system model is derived to include the error model of the USBL acoustic navigation sensor and the scale effect and bias errors of the DVL, of which the state equation composed of the navigation states and sensor parameters is 25 in the order. The conventional extended Kalman filter was used to propagate the error covariance, update the measurement errors and correct the state equation when the measurements are available. The rotating ann tests are conducted in the Ocean Engineering Basin of KRISO, KORDI to generate circular motion in laboratory, where the USBL system was absent in the basin. The hybrid underwater navigation system shows good tracking performance against the circular planar motion. Additionally this paper checked the effects of the sampling ratio of the navigation system and the possibility of the dead reckoning with the DVL and the magnetic compass to estimate the position of the vehicle.

• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.127-133
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• 2020

As the usages of artificial intelligence is increased, the demand to algorithms for small portable devices increases. Also as the embedded system becomes high-performance, it is possible to implement algorithms for high-speed computation and machine learning as well as operating systems. As the machine learning algorithms process repetitive calculations, it depend on the cloud environment by network connection. For an stand alone system, low power consumption and fast execution by optimized algorithm are required. In this study, for the purpose of smart control, an energy measurement sensor is connected to an embedded system, and a real-time monitoring system is implemented to store measurement information as a database. Continuously measured and stored data is applied to a learning algorithm, which can be utilized for optimal power control, and a system interfacing various sensors required for energy measurement was constructed.

• Smart Structures and Systems
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• v.31 no.1
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• pp.1-11
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• 2023

Assessment of the compressive strength of concrete plays a major role during formwork removal and in the prestressing process. In concrete, temperature changes occur due to hydration which is an influencing factor that decides the compressive strength of concrete. Many methods are available to find the compressive strength of concrete, but the maturity method has the advantage of prognosticating strength without destruction. The temperature-time factor is found using a LM35 temperature sensor through the IoT technique. An experimental investigation was carried out with 56 concrete cubes, where 35 cubes were for obtaining the compressive strength of concrete using a universal testing machine while 21 concrete cubes monitored concrete's temperature by embedding a temperature sensor in each grade of M25, M30, M35, and M40 concrete. The mathematical prediction model equation was developed based on the temperature-time factor during the early age compressive strength on the 1^st, 2^nd, 3^rd and 7^th days in the M25, M30, M35, and M40 grades of concrete with their temperature. The 14^th, 21^st and 28^th day's compressive strength was predicted with the mathematical predicted equation and compared with conventional results which fall within a 2% difference. The compressive strength of concrete at any desired age (day) before reaching 28 days results in the discovery of the prediction coefficient. Comparative analysis of the results found by the predicted mathematical model show that, it was very close to the results of the conventional method.

• Structural Engineering and Mechanics
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• v.54 no.2
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• pp.257-289
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• 2015

Shear connectors are generally used to link the slab and girders together in slab-on-girder bridge structures. Damage of shear connectors in such structures will result in shear slippage between the slab and girders, which significantly reduces the load-carrying capacity of the bridge. Because shear connectors are buried inside the structure, routine visual inspection is not able to detect conditions of shear connectors. A few methods have been proposed in the literature to detect the condition of shear connectors based on vibration measurements. This paper proposes a different dynamic condition assessment approach to identify the damage of shear connectors in slab-on-girder bridge structures based on power spectral density transmissibility (PSDT). PSDT formulates the relationship between the auto-spectral densities of two responses in the frequency domain. It can be used to identify shear connector conditions with or without reference data of the undamaged structure (or the baseline). Measured impact force and acceleration responses from hammer tests are analyzed to obtain the frequency response functions at sensor locations by experimental modal analysis. PSDT from the slab response to the girder response is derived with the obtained frequency response functions. PSDT vectors in the undamaged and damaged states can be compared to identify the damage of shear connectors. When the baseline is not available, as in most practical cases, PSDT vectors from the measured response at a reference sensor to those of the slab and girder in the damaged state can be used to detect the damage of shear connectors. Numerical and experimental studies on a concrete slab supported by two steel girders are conducted to investigate the accuracy and efficiency of the proposed approach. Identification results demonstrate that damages of shear connectors are identified accurately and efficiently with and without the baseline. The proposed method is also used to evaluate the conditions of shear connectors in a real composite bridge with in-field testing data.

• Journal of Environmental Impact Assessment
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• v.24 no.4
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• pp.367-374
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• 2015

To examine the effect of thermal discharge from nuclear power plants, Sea Surface Temperature (SST) is one of the most important variables measured by satellite remote sensing. However, the study was not much comparison of field data and satellite SST from operational Landsat 8 Thermal Infrared Sensor(TIRS) and Landsat 7 ETM+. The Landsat 8 TIRS have 2 spilt Thermal Infrared channels but ETM+ uses one channel for extracting of SST. In spite of that this research carried out that Landsat 7 ETM+ have more profitable for correction of SST than Landsat 8 TIRS. The used 15 Landsat 7 and 8 Thermal Infrared data of path/row 114-36 were processed by SST algorithm of ENVI and IDL. The in-situ SST data from KHOA(Korea Hydrographic and Oceanographic Administration) compared with satellite SST and the accuracy of extracted SST were assessed by each field sites in-situ point data with time series satellite SST.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.2
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• pp.119-129
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• 2010

Many algorithms for processing LIDAR data have been developed for diverse applications not limited to patch segmentation, bare-earth filtering and building extraction. However, since we cannot exactly know the true locations of individual LIDAR points, it is difficult to assess the performance of a LIDAR data processing algorithm. In this paper, we thus attempted the performance assessment of the segmentation algorithm developed by Lee (2006) using the LIDAR data generated through simulation based on sensor modelling. Consequently, based on simulation, we can perform the performance assessment of a LIDAR processing algorithm more objectively and quantitatively with an automatic procedure.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.6
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• pp.735-744
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• 2019

One of the key factors for recognition performance in the automatic target recognition for synthetic aperture radar imagery(SAR-ATR) system is reliability of the SAR target database. To achieve optimal performance, the database should be constructed using the images obtained under the same operating condition as the SAR sensor. However, it is impractical to have the extensive set of real-world SAR images, and thus those from the electro magnetic prediction tool with 3-D CAD models are suggested as an alternative where their reliability can be always questionable. In this paper, a method for similarity assessment between target SAR images is presented inspired by the fact that a target SAR image is mainly characterized by the features of scattering centers. The method is demonstrated using a variety of examples and quantitatively measures the similarity related to reliability. Its assessment performance is further compared with that of the existing metric, structural similarity(SSIM).

• Smart Structures and Systems
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• v.24 no.3
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• pp.319-332
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• 2019

Structural health monitoring (SHM) is of great importance to super high-rise buildings. The Shanghai Tower is currently the tallest building in China, and a complete SHM system was simultaneously constructed at the beginning of the construction of the tower. Due to the variety of sensor types and the large number of measurement points in the SHM system, an online automatic structural health assessment method with few computations and no manual intervention is needed. This paper introduces a structural health assessment method for the Shanghai Tower that uses the coefficients of an autoregressive (AR) time series model as structural state indicators. An analysis of collected data indicates that the coefficients of the AR model are affected by environmental factors, and the principal component analysis method is used to remove the influence of environmental factors. Finally, the control chart method is used to track the changes in structural state indicators, and a plan for online automatic structure health state evaluation is proposed. This method is applied to long-term acceleration and inclination data from the Shanghai Tower and successfully identifies the changes in the structural state. Overall, the structural state indicators of the Shanghai Tower are stable, and the structure is in a healthy state.

• Journal of Environmental Health Sciences
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• v.48 no.3
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• pp.131-137
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• 2022

Public interest has been increasing the focus on the management of exposure to pollutants and the related health effects. This study reviewed exposure assessment methodologies and addressed future directions. Exposure can be assessed by direct (exposure monitoring) or indirect approaches (exposure modelling). Exposure modelling is a cost-effective tool to assess exposure among individuals, but direct personal monitoring provides more accurate exposure data. There are several population exposure models: stochastic human exposure and dose simulation (SHEDS), air pollutants exposure (APEX), and air pollution exposure distributions within adult urban population in Europe (EXPOLIS). A South Korean population exposure model is needed since the resolution of ambient concentrations and time-activity patterns are country specific. Population exposure models could be useful to find the association between exposure to pollutants and adverse health effects in epidemiologic studies. With the advancement of sensor technology and the internet of things (IoT), exposure assessment could be applied in a real-time surveillance system. In the future, environmental health services will be useful to protect and promote human health from exposure to pollutants.

• Atmosphere
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• v.19 no.2
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• pp.213-226
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• 2009

In order to investigate the characteristics of Changma over the Korean peninsula, KEOP-2007 IOP (Intensive Observing Period) was conducted from 15 June 2007 to 15 July 2007. KEOP-2007 IOP is high spatial and temporal radiosonde observations (RAOB) which consisted of three special stations (Munsan, Haenam, and Ieodo) from National Institute of Meteorological Research, five operational stations (Sokcho, Baengnyeongdo, Pohang, Heuksando, and Gosan) from Korea Meteorological Administration (KMA), and two operational stations (Osan and Gwangju) from Korean Air Force (KAF) using four different types of radiosonde sensors. The error statistics of the sensor of radiosonde were investigated using quality control check. The minimum and maximum error frequency appears at the sensor of RS92-SGP and RS1524L respectively. The error frequency of DFM-06 tends to increase below 200 hPa but RS80-15L and RS1524L show vice versa. Especially, the error frequency of RS1524L tends to increase rapidly over 200 hPa. Systematic biases of radiosonde show warm biases in case of temperature and dry biases in case of relative humidity compared with ECMWF (European Center for Medium-Range Weather Forecast) analysis data and precipitable water vapor from GPS. The maximum and minimum values of systematic bias appear at the sensor of DFM-06 and RS92-SGP in case of temperature and RS80-15L and DFM-06 in case of relative humidity. The systematic warm and dry biases at all sensors tend to increase during daytime than nighttime because air temperature around sensor increases from the solar heating during daytime. Systematic biases of radiosonde are affected by the sensor type and the height of the sun but random errors are more correlated with the moisture conditions at each observation station.