• Smart Structures and Systems
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• v.29 no.1
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• pp.17-28
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• 2022

The cable component of cable-stayed bridges is gradually impacted by weather conditions, vehicle loads, and material corrosion. The stayed cable is a critical load-carrying part that closely affects the operational stability of a cable-stayed bridge. Damaged cables might lead to the bridge collapse due to their tension capacity reduction. Thus, it is necessary to develop structural health monitoring (SHM) techniques that accurately identify damaged cables. In this work, a combinational identification method of three efficient techniques, including statistical analysis, clustering, and neural network models, is proposed to detect the damaged cable in a cable-stayed bridge. The measured dataset from the bridge was initially preprocessed to remove the outlier channels. Then, the theory and application of each technique for damage detection were introduced. In general, the statistical approach extracts the parameters representing the damage within time series, and the clustering approach identifies the outliers from the data signals as damaged members, while the deep learning approach uses the nonlinear data dependencies in SHM for the training model. The performance of these approaches in classifying the damaged cable was assessed, and the combinational identification method was obtained using the voting ensemble. Finally, the combination method was compared with an existing outlier detection algorithm, support vector machines (SVM). The results demonstrate that the proposed method is robust and provides higher accuracy for the damaged cable detection in the cable-stayed bridge.

• KEPCO Journal on Electric Power and Energy
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• v.8 no.1
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• pp.15-19
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• 2022

The radial distribution systems (RDS) commonly used around the world has the following disadvantages. First, when the DL is operated on a radial system, the line utilization rate is usually kept low. Second, if a fault occurs in the radial DL, a power outage of 3 to 5 minutes is occurring depending on the operator's proficiency and fault situation until the fault section is separated and the normal section is replaced. To solve this problem, Various methods have been proposed at domestic and foreign to solve this problem, and in Korea, research is underway on the advanced system of operating multiple linked DL always. A system that is electrically linked always, and that is built to enable high-speed communication during the protection coordination is named networked distribution system (NDS). Because the load shares the DL, the line utilization rate can be improved, and even if the line faults, the normal section does not need to be cut off, so the normal section does not experience a power outage. However, since it is impossible to predict in which direction the fault current will flow when a failure occurs in the NDS, a communication-based protection coordination is used, but there is no backup protection coordination method in case of communication failure. Therefore, in this paper, we propose a protective cooperation method to apply as a backup method when communication fails in NDS. The new method is to change TCC by location of CB using voltage drop in case of fault.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.23-28
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• 2023

In a wireless smart utility network communication system, an asynchronous low power MAC is standardized and used according to IEEE 802.15.4e. An asynchronous MAC called RIT (Receiver Initiated Transmission) has a characteristic in which delay time and power consumption are greatly affected by a check-in interval (RIT period). By waking up from sleep every check-in interval and checking whether there is data to be received, power consumption in the receiving end can be drastically reduced, but power consumption in the transmitting end occurs due to an excessive wakeup sequence. If an excessive wake-up sequence is reduced by shortening the check interval, power consumption of the receiving end increases due to too frequent wake-up. In the RIT asynchronous MAC technique, power consumption performance according to traffic load and operation of check-in interval is analyzed and applied to Wi-SUN construction.

• Composites Research
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• v.22 no.6
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• pp.1-6
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• 2009

This paper presents an experimental study on the piezoresistive behavior of nanocomposite strain sensors subjected to various loading modes and their capability to detect structural deformations and damages. The electrically conductive nanocomposites were fabricated in the form of a film using various types of thermoplastic polymers and multi-walled carbon nanotubes (MWNTs) at various loadings. In this study, the nanocomposite strain sensors were bonded to a substrate and subjected to tension, flexure, or compression. In tension and flexure, the resistivity change showed dependence on measurement direction, indicating that the sensors can be used for multi-directional strain sensing. In addition, the sensors exhibited a decreasing behavior in resistivity as the compressive load was applied, suggesting that they can be used for pressure sensing. This study demonstrates that the nanocomposite strain sensors can provide a pathway to affordable, effective, and versatile structural health monitoring.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.108-110
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• 2021

Since 2015, attacks using the IoT protocol have been continuously reported. Among various IoT protocols, attackers attempt DDoS attacks using SSDP(Simple Service Discovery Protocol), and as statistics of cyber shelters, Korea has about 1 million open SSDP servers. Vulnerable SSDP servers connected to the Internet can generate more than 50Gb of traffic and the risk of attack increases gradually. Until recently, distributed denial of service attacks and distributed reflective denial of service attacks have been a security issue. Accordingly, the purpose of this study is to analyze the request packet of the existing SSDP protocol to identify an amplification attack and to avoid a response when an amplification attack is suspected, thereby preventing network load due to the occurrence of a large number of response packets due to the role of traffic reflection amplification.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1697-1705
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• 2022

The safety of marine construction workers and marine pollution problems are occurring due to large-scale offshore construction. In particular, underwater construction work in the sea has a higher risk than other work, so it is necessary to apply an unmanned alternative system that considers the safety of the workers. In this paper, the ROV system for offshore construction has been developed for underwater unmanned work. A monitoring system was developed for position control through the control of underwater propellants, pneumatic gripper, and monitoring of underwater work. As a result of the performance evaluation, the underwater movement speed of the ROV was evaluated to be 0.89 m/s, and it was confirmed that the maximum load of the pneumatic gripper was 80 kg. In addition, the network bandwidth required for underwater ROV control and underwater video streaming was evaluated to be more than 300Mbps, wired communication at 92.7 ~ 95.0Mbit/s at 205m, and wireless communication at 78.3 ~ 84.8Mbit/s.

• Geomechanics and Engineering
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• v.31 no.2
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• pp.129-147
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• 2022

The properties of soil are naturally highly variable and thus, to ensure proper safety and reliability, we need to test a large number of samples across the length and depth. In pile foundations, conducting field tests are highly expensive and the traditional empirical relations too have been proven to be poor in performance. The study proposes a state-of-art Particle Swarm Optimization (PSO) hybridized Artificial Neural Network (ANN), Extreme Learning Machine (ELM) and Adaptive Neuro Fuzzy Inference System (ANFIS); and comparative analysis of metaheuristic models (ANN-PSO, ELM-PSO, ANFIS-PSO) for prediction of bearing capacity of pile foundation trained and tested on dataset of nearly 300 dynamic pile tests from the literature. A novel ensemble model of three hybrid models is constructed to combine and enhance the predictions of the individual models effectively. The authenticity of the dataset is confirmed using descriptive statistics, correlation matrix and sensitivity analysis. Ram weight and diameter of pile are found to be most influential input parameter. The comparative analysis reveals that ANFIS-PSO is the best performing model in testing phase (R² = 0.85, RMSE = 0.01) while ELM-PSO performs best in training phase (R² = 0.88, RMSE = 0.08); while the ensemble provided overall best performance based on the rank score. The performance of ANN-PSO is least satisfactory compared to the other two models. The findings were confirmed using Taylor diagram, error matrix and uncertainty analysis. Based on the results ELM-PSO and ANFIS-PSO is proposed to be used for the prediction of bearing capacity of piles and ensemble learning method of joining the outputs of individual models should be encouraged. The study possesses the potential to assist geotechnical engineers in the design phase of civil engineering projects.

• Journal of the Korea institute for structural maintenance and inspection
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• v.27 no.5
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• pp.67-74
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• 2023

A large number of concrete structures are built and used around the world. To ensure their safe and continuous use, these structures require constant inspection and maintenance. While man-powered inspection and maintenance techniques are efficient, they can only provide intermittent status checks at the time of on-site inspection. Therefore, there is a growing need for a system that can continuously monitor the condition of the structure. A study was conducted to detect cracks and damage by installing a conductive coating on the surface of a concrete structure. A parallel resistance pattern that can monitor the occurrence and progression of cracks was developed by reflecting the structural characteristics of concrete structure. An empirical study was conducted to veryfy the application of the proposed method. The crack detection pattern was installed on the reinforced concrete beams, and the crack monitoring method was verified through applying a load on the beams.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.1
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• pp.189-196
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• 2024

Frequent false positives alarm from the Intelligent Selective Control System have raised significant concerns. These persistent issues have led to declines in operational efficiency and market credibility among agents. Developing a new model or replacing the existing one to mitigate false positives alarm entails substantial opportunity costs; hence, improving the quality of the training dataset is pragmatic. However, smaller organizations face challenges with inadequate capabilities in dataset collection and refinement. This paper proposes an automatic human pose data collection system centered around a human pose estimation model, utilizing camera-based sensor fusion techniques and edge devices. The system facilitates the direct collection and real-time processing of field data at the network periphery, distributing the computational load that typically centralizes. Additionally, by directly labeling field data, it aids in constructing new training datasets.

• Steel and Composite Structures
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• v.51 no.4
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• pp.441-456
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• 2024

This paper aims to develop Machine Learning (ML) algorithms to predict the buckling resistance of cold-formed steel (CFS) channels with restrained flanges, widely used in typical CFS sheathed wall panels, and provide practical design tools for engineers. The effects of cross-sectional restraints were first evaluated on the elastic buckling behaviour of CFS channels subjected to pure axial compressive load or bending moment. Feedforward multi-layer Artificial Neural Networks (ANNs) were then trained on different datasets comprising CFS channels with various dimensions and properties, plate thicknesses, and restraining conditions on one or two flanges, while the elastic distortional buckling resistance of the elements were determined according to the Finite Strip Method (FSM). To develop less biased networks and ensure that every observation from the original dataset has the chance of appearing in the training and test set, a K-fold cross-validation technique was implemented. In addition, the hyperparameters of the ANNs were tuned using a grid search technique to provide ANNs with optimum performances. The results demonstrated that the trained ANNs were able to predict the elastic distortional buckling resistance of CFS flange-restrained elements with an average accuracy of 99% in terms of coefficient of determination. The developed models were then used to propose a simple ANN-based design formula for the prediction of the elastic distortional buckling stress of CFS flange-restrained elements. Finally, the proposed formula was further evaluated on a separate set of unseen data to ensure its accuracy for practical applications.