• Structural Engineering and Mechanics
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• v.71 no.2
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• pp.175-183
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• 2019

This paper deals with damage detection in a girder bridge using transmissibility functions as input data to Artificial Neural Networks (ANNs). The original contribution in this work is that these two novel methods are combined to detect damage in a bridge. The damage was simulated in a real bridge in Vietnam, i.e. Ca-Non Bridge. Finite Element Method (FEM) of this bridge was used to show the reliability of the proposed technique. The vibration responses at some points of the bridge under a moving truck are simulated and used to calculate the transmissibility functions. These functions are then used as input data to train the ANNs, in which the target is the location and the severity of the damage in the bridge. After training successfully, the network can be used to assess the damage. Although simulated responses data are used in this paper, the practical application of the technique to real bridge data is potentially high.

• Journal of the Korean Society of Safety
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• v.37 no.4
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• pp.1-10
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• 2022

The purposes of this study were to suggest 1) a future direction for Korea's machine tool industry and 2) how to secure the safety and reliability of emerging intelligent or automated machine tooling. The study concludes that, overseas, the machine tool industry is growing again while promoting innovation by converging with ICT. Accordingly, Korea also promotes ICT innovation to advance the machine tool industry, which is at the core of the national economy. As a result, unlike in the past, the frequency of serious injuries like entrapment accidents has recently decreased, while the proportion of collision accidents has increased. In addition, a new type of accident has become possible. Since ICT is network-based, the distinction between work and rest can become ambiguous; there is a risk of hacking, working hours and places are flexible and there are risk factors for diseases like chronic fatigue due to overload of specific personnel. As robots and automation are introduced, there is also a high probability of problems caused by physical and psychological burdens on system operators and resulting fatigue.

• Steel and Composite Structures
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• v.44 no.1
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• pp.119-139
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• 2022

This paper aims to use the artificial intelligence approach to develop a new model for predicting the ultimate axial strength of the circular concrete-filled steel tubular (CFST) stub columns. For this, the results of 314 experimentally tested circular CFST stub columns were employed in the generation of the design model. Since the influence of the column diameter, steel tube thickness, concrete compressive strength, steel tube yield strength, and column length on the ultimate axial strengths of columns were investigated in these experimental studies, here, in the development of the design model, these variables were taken into account as input parameters. The model was developed using the backpropagation algorithm named Bayesian Regularization. The accuracy, reliability, and consistency of the developed model were evaluated statistically, and also the design formulae given in the codes (EC4, ACI, AS, AIJ, and AISC) and the previous empirical formulations proposed by other researchers were used for the validation and comparison purposes. Based on this evaluation, it can be expressed that the developed design model has a strong and reliable prediction performance with a considerably high coefficient of determination (R-squared) value of 0.9994 and a low average percent error of 4.61. Besides, the sensitivity of the developed model was also monitored in terms of dimensional properties of columns and mechanical characteristics of materials. As a consequence, it can be stated that for the design of the ultimate axial capacity of the circular CFST stub columns, a novel artificial intelligence-based design model with a good and robust prediction performance was proposed herein.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.1-7
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• 2022

Machine learning (ML) algorithms have been intended to seamlessly collaborate for enabling intelligent networking in terms of massive service differentiation, prediction, and provides high-accuracy recommendation systems. Mobile edge computing (MEC) servers are located close to the edge networks to overcome the responsibility for massive requests from user devices and perform local service offloading. Moreover, there are required lightweight methods for handling real-time Internet of Things (IoT) communication perspectives, especially for ultra-reliable low-latency communication (URLLC) and optimal resource utilization. To overcome the abovementioned issues, this paper proposed an intelligent scheme for traffic steering based on the integration of MEC and lightweight ML, namely support vector machine (SVM) for effectively routing for lightweight and resource constraint networks. The scheme provides dynamic resource handling for the real-time IoT user systems based on the awareness of obvious network statues. The system evaluations were conducted by utillizing computer software simulations, and the proposed approach is remarkably outperformed the conventional schemes in terms of significant QoS metrics, including communication latency, reliability, and communication throughput.

• Progress in Superconductivity and Cryogenics
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• v.11 no.3
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• pp.26-30
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• 2009

Korea Superconducting Tokamak Advanced Research(KSTAR) device is composed of 30 superconducting magnets, magnet structure, vacuum vessel, cryostat, current feeder system, and etc. KSTAR device is operated in the cryogenic temperature and high magnetic field. We install about 800 sensors - temperature sensors, stain gages, displacement gages, hall sensors - to monitor the thermal, mechanical, electrical status of KSTAR during operation. As a tremendous numbers of sensors should be installed for monitoring the KSTAR device, the method of effective installation was developed. The sensor test was successfully carried out to check its reliability and its reproduction in the cryogenic temperature. The sensor signal is processed by PXI-based DAQ system and communicated with central control system via machine network and is shown by Operator Interface(OPI) display in the main control room. In order to safely operate the device, any violations of mechanical & superconductive characteristic of the device components were informed to its operation system & operator. If the monitored values exceed the pre-set values, the protective action should be taken against the possible damage. In this paper, the system composition, operation criteria, operation result were presented.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.166-166
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• 2020

Recently, As the frequency of localized heavy rains increases, the use of high-resolution radar data is increasing. The produced radar rainfall has still gaps of spatial and temporal compared to gauge observation rainfall, and in many studies, various statistical techniques are performed for correct rainfall. In this study, the precipitation correction of the S-band Dual Polarization radar in use in the flood forecast was performed using the ConvAE algorithm, one of the Convolutional Neural Network. The ConvAE model was trained based on radar data sets having a 10-min temporal resolution: radar rainfall data, gauge rainfall data for 790minutes(July 2017 in Cheongju flood event). As a result of the validation of corrected radar rainfall were reduced gaps compared to gauge rainfall and the spatial correction was also performed. Therefore, it is judged that the corrected radar rainfall using ConvAE will increase the reliability of the gridded rainfall data used in various physically-based distributed hydrodynamic models.

• Journal of the Korean Society of Industry Convergence
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• v.26 no.5
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• pp.867-874
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• 2023

In this paper, a wireless communication-based sensor data monitoring device with an explosion-proof (Exd IIC) case was implemented to enable installation at explosion-risk industrial sites such as plants. In existing industrial plant sites, most of the temperature sensors and vibration and impact sensors are wired up to several kilometers, which takes a lot of time and money to bury long pipes and cables. In addition, there are not many cases where some wireless devices have been applied to actual plant industry sites due to communication quality problems. Therefore, in order to solve this problem, zigbee mesh wireless communication was applied to provide high reliability wireless communication quality to industrial plant sites, and the time and cost incurred in new or additional installation of sensors could be greatly reduced. In particular, in the event of loss or error of some wireless communication devices, the communication network is automatically bypassed or recovered to enable real-time data monitoring.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.494-510
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• 2024

Internet users are exposed to sophisticated cyberattacks that intrusion detection systems have difficulty detecting. Therefore, research is increasing on intrusion detection methods that use artificial intelligence technology for detecting novel cyberattacks. Unsupervised learning-based methods are being researched that learn only from normal data and detect abnormal behaviors by finding patterns. This study developed an anomaly-detection method based on unsupervised machines and deep learning for a network intrusion detection system (NIDS). We present a hybrid anomaly detection approach based on unsupervised learning techniques using the autoencoder (AE), Isolation Forest (IF), and Local Outlier Factor (LOF) algorithms. An oversampling approach that increased the detection rate was also examined. A hybrid approach that combined deep learning algorithms and traditional machine learning algorithms was highly effective in setting the thresholds for anomalies without subjective human judgment. It achieved precision and recall rates respectively of 88.2% and 92.8% when combining two AEs, IF, and LOF while using an oversampling approach to learn more unknown normal data improved the detection accuracy. This approach achieved precision and recall rates respectively of 88.2% and 94.6%, further improving the detection accuracy compared with the hybrid method. Therefore, in NIDS the proposed approach provides high reliability for detecting cyberattacks.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.41 no.9
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• pp.73-80
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• 2004

In this paper, we present the cell resequencing buffer to solve the cell sequence integrity problem of the Cyclic banyan network that is a high-performance fault-tolerant cell switch. By offering multiple paths between input ports and output ports, using the deflection self-routing, the Cyclic banyan switch offer high reliability, and it also solves congestion problem for the internal links of the switch. By the way, these multiple paths can be different lengths for each other. Therefore, the cells departing from an identical source port and arriving at an identical destination port can reach to the output port as the order that is different from the order arriving at input port. The proposed cell resequencing buffer is a hardware sliding window mechanism. to solve such cell sequence integrity problem. To calculate the size of sliding window that cause the prime cost of the presented device, we analyzed the distribution of the cell delay through the simulation analyses under traffic load that have a nonuniform address distribution that express tile Property of traffic of the Internet. Through these analyses, we found out that we can make a cell resequencing buffer by which the cell sequence integrity is to be secured, by using a, few of ordinary memory and control logic. The cell resequencing buffer presented in this paper can be used for other multiple paths switching networks.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.7A
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• pp.844-851
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• 2004

Various types of error are caused due to many factors of various environment in air interface channel of wireless communications. In this case, the reliability of the channel is much lower than that of wired case. IEEE 802.15.3 high-rate WPAN, which operates in an ad hoc networking environment, is more susceptible to such errors. The problem has been investigated for wireless LANs, for example, as follows. If the queue size of a certain node is longer than that of other nodes, the node estimates that its channel state is bad and the resource of the node is decreased. However this method has a disadvantage that a central controller must always monitor the status. To avoid this disadvantage, in this paper, a new MAC protocol that the throughput of overall piconet is increased by LDS (Link-status Dependent Scheduling) is proposed.