• 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.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.77-87
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• 2023

Wind turbines playing a critical role in renewable energy generation, accurately assessing their operational status is crucial for maximizing energy production and minimizing downtime. This study conducts a comparative analysis of different neural network models for wind turbine condition diagnosis, evaluating their effectiveness using a dataset containing sensor measurements and historical turbine data. The study utilized supervisory control and data acquisition data, collected from 2 MW doubly-fed induction generator-based wind turbine system (Model HQ2000), for the analysis. Various neural network models such as artificial neural network, long short-term memory, and recurrent neural network were built, considering factors like activation function and hidden layers. Symmetric mean absolute percentage error were used to evaluate the performance of the models. Based on the evaluation, conclusions were drawn regarding the relative effectiveness of the neural network models for wind turbine condition diagnosis. The research results guide model selection for wind turbine condition diagnosis, contributing to improved reliability and efficiency through advanced neural network-based techniques and identifying future research directions for further advancements.

• Journal of Advanced Navigation Technology
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• v.25 no.6
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• pp.441-449
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• 2021

With advances in autonomous vehicles, there is a growing demand for more accurate position estimation. Especially, this is a case for a moving robot for the indoor operation which necessitates the higher accuracy in position estimation when the robot is required to execute the task at a predestined location. Thus, a method for improving the position estimation which is applicable to both the fixed and the moving object is proposed. The proposed method exploits the initial position estimation from Bluetooth beacon signals as observation signals. Then, it estimates the gravitational acceleration applied to each axis in an inertial frame coordinate through computing roll and pitch angles and combining them with magnetometer measurements to compute yaw angle. Finally, it refines the control inputs for an object with motion dynamics by computing acceleration on each axis, which is used for improving the performance of Kalman filter. The experimental assessment of the proposed algorithm shows that it improves the position estimation accuracy in comparison to a conventional Kalman filter in terms of average error distance at both the fixed and moving states.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.540-546
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• 2023

The scope of IoT use has expanded due to the development of related technologies, and various sensors have been developed and distributed to meet the demand for implementing various services. Measuring alcohol concentration using a sensor can be used to prevent drunk driving, and to make this possible, accurate alcohol concentration must be measured and safe transmission from the smartphone to the server must be guaranteed. Additionally, a process of converting the measured alcohol concentration value into a standard value for determining the level of drinking is necessary. In this paper, we propose and implement a system. Security with remote servers applies SSL at the network layer to ensure data integrity and confidentiality, and the server encrypts the received information and stores it in the database to provide additional security. As a result of analyzing the accuracy of alcohol concentration measurement and communication efficiency, it was confirmed that the measurement and transmission were within the error tolerance.

• Journal of Korean Society on Water Environment
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• v.39 no.6
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• pp.451-464
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• 2023

Harmful cyanobacterial monitoring is time-consuming and requires skilled professionals. Recently, Phycocyanin, the accessory pigment unique to freshwater cyanobacteria, has been proposed as an indicator for the presence of cyanobacteria, with the advantage of rapid and simple measurement. The purpose of this research was to evaluate the correlation between the harmful cyanobacterial cell density and the concentration of phycocyanin and to consider how to use the real-time water quality monitoring system for algae bloom monitoring. In the downstream of the Nakdong River, Microcystis spp. showed maximum cell density (99 %) in harmful cyanobacteria (four target genera). A strong correlation between phycocyanin(measured in the laboratory) concentrations and harmful cyanobacterial cell density was observed (r = 0.90, p < 0.001), while a weaker relationship (r = 0.65, p < 0.001) resulted between chlorophyll a concentration and harmful cyanobacterial cell density. As a result of comparing the phycocyanin concentration (measured in submersible fluorescence sensor) and harmful cyanobacterial cell density, the error range increased as the number of cyanobacteria cells increased. Before opening the estuary bank, the diurnal variations of phycocyanin concentrations did not mix by depth, and in the case of the surface layer, a pattern of increase and decrease over time was shown. This study is the result of analysis when Microcystis spp. is dominant in downstream of Nakdong River in summer, therefore the correlation between the harmful cyanobacteria density and phycocyanin concentrations should be more generalized through spatio-temporal expansion.

• The Journal of the Acoustical Society of Korea
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• v.26 no.8
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• pp.415-425
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• 2007

This paper proposes a method of underwater source localization using the wideband interference patterns matching. By matching two interference patterns in the spectrogram, it is estimated a ratio of the range from source to sensor5, and then this ratio is applied to the Apollonius circle. The Apollonius circle is defined as the locus of all points whose distances from two fixed points are in a constant value so that it is possible to represent the locus of potential source location. The Apollonius circle alone, however still keeps the ambiguity against the correct source location. Therefore another equation is necessary to estimate the unique locus of the source location. By estimating time differences of signal arrivals between source and sensors, the hyperbola equation is used to get the cross point of the two equations, where the point being assumed to be the source position. Simulations are performed to get performances of the proposed algorithm. Also, comparisons with real sea experiment data are made to prove applicability of the algorithm in real environment. The results show that the proposed algorithm successfully estimates the source position within an error bound of 10%.

• The Journal of the Acoustical Society of Korea
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• v.25 no.4
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• pp.184-190
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• 2006

Recently, Lee et al. have proposed an algorithm utilizing the signals from different paths by using bottom mounted simple linear array to estimate 3-D location of oceanic target. But this algorithm assumes that sound velocity is constant along depth of sea. Consequently, serious performance loss is appeared in real oceanic environment that sound speed is changed variously. In this paper, we present a 3-D near field localization algorithm for inhomogeneous sound speed. The proposed algorithm adopt localization function that utilize ray propagation model for multipath environment with linear sound speed profile(SSP), after that, the proposed algorithm searches for the instantaneous azimuth angle, range and depth from the localization cost function. Several simulations using linear SSP and non linear SSP similar to that of real oceans are used to demonstrate the performance of the proposed algorithm. The estimation error in range and depth is decreased by 100m and 50m respectively.

• The Journal of the Acoustical Society of Korea
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• v.26 no.4
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• pp.166-172
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• 2007

In this paper, we address the target positioning performance of Multi-Static sonar with respect to target positioning method and measurement error. Based on the analysis on two candidate solution approaches, namely, Least Square (LS) using range and angular information simultaneously and Maximum Likelihood (ML) using only range information as the existing information fusion methods for possible application to Multi-Static sonar, we propose to employ ML using range and angular information. Assuming that each sensor can receive range and angular information, we conduct representative comparison experiments over the existing and proposed methods under various measurement noise scenarios. We also investigate the target positioning performance according to number of sensors, distance between transmitter and receiver. According to the experimental results, RMSE of the proposed ML with distance and direction information is found to be more superior to ML using distance alone and to LS in case distance between transmitter and receiver is longer and number of receiver is smaller.

• The Journal of the Acoustical Society of Korea
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• v.43 no.1
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• pp.89-94
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• 2024

Since Ultra-Short BaseLine (USBL) uses an array with narrow sensor spacing, precise synchronization is required to improve source localization performances. However, in the underwater environment, synchronization errors occur due to relatively strong noise and underwater acoustic channels such as multipath and Doppler, which deteriorates the source localization performances. This paper proposes a covariance-based synchronization compensation method to improve the source localization performances of the underwater USBL systems. The proposed method arranges the received signals through cross-correlation and calculates the covariance of the arranged signals. The synchronization error is related to the phase difference in the covariance. Thus, the phase difference is estimated as the covariance and compensated. Computer simulations demonstrate that the proposed method has better source localization performances than the conventional cross-correlation method.

• Smart Structures and Systems
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• v.33 no.3
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• pp.189-199
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• 2024

To properly extract the strain components under varying operational conditions is very important in bridge health monitoring. The abnormal sensor readings can be correctly identified and the expected operational performance of the bridge can be better understood if each strain components can be accurately quantified. In this study, strain components under varying load conditions, i.e., temperature variation and live-load variation are evaluated based on field strain measurements collected from a real concrete box-girder bridge. Temperature-induced strain is mainly regarded as the trend variation along with the ambient temperature, thus a smoothing technique based on the wavelet packet decomposition method is proposed to estimate the temperature-induced strain. However, how to effectively extract the vehicle-induced strain is always troublesome because conventional threshold setting-based methods cease to function: if the threshold is set too large, the minor response will be ignored, and if too small, noise will be introduced. Therefore, an autoencoder framework is proposed to evaluate the vehicle-induced strain. After the elimination of temperature and vehicle-induced strain, the left of which, defined as the model error, is used to assess the operational performance of the bridge. As empirical techniques fail to detect the degraded state of the structure, a clustering technique based on Gaussian Mixture Model is employed to identify the damage occurrence and the validity is verified in a simulation study.