• Journal of Korea Multimedia Society
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• v.24 no.11
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• pp.1526-1537
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• 2021

Due to the difference between the underwater communication environment and the terrestrial communication environment, the radio communication mainly used on the ground cannot be used in underwater. For this reason, in the underwater communication environment, various communication media such as acoustic waves, infrared rays, light and so on has been studied, but there exist several difficulties in operating them individually due to their physical limitations. The concept for overcoming these difficulties is the very underwater multi-media communication, a method to select a communication medium best suitable for the current underwater environment among underwater communication multimedia whenever there occurs underwater communication failure. In this paper, we present an underwater multi-media communication network based on star topology and a fragmentation and reassembly technique to solve the problems caused by the different MTU (Maximum Transmission Unit) sizes among different underwater communication media. We also present the estimations and analysis on processing times in each of fragmentation and reassembly and the total data amount for transmitting fragments in our proposed underwater multi-media communication network.

• Nuclear Engineering and Technology
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• v.52 no.9
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• pp.1998-2008
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• 2020

Solenoid operated valves (SOV) play important roles in industrial process to control the flow of fluids. Solenoid valves can be found in so many industries as well as the nuclear plant. The ability to be able to detect the presence of faults and predicting the remaining useful life (RUL) of the SOV is important in maintenance planning and also prevent unexpected interruptions in the flow of process fluids. This paper proposes a fault diagnosis method for the alternating current (AC) powered SOV. Previous research work have been focused on direct current (DC) powered SOV where the current waveform or vibrations are monitored. There are many features hidden in the AC waveform that require further signal analysis. The analysis of the AC powered SOV waveform was done in the time and frequency domain. A total of sixteen features were obtained and these were used to classify the different operating modes of the SOV by applying a machine learning technique for classification. Also, a deep neural network (DNN) was developed for the prediction of RUL based on the failure modes of the SOV. The results of this paper can be used to improve on the condition based monitoring of the SOV.

• Smart Structures and Systems
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• v.29 no.3
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• pp.513-522
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• 2022

Due to the importance of accurate analysis of bearing capacity in civil engineering projects, this paper studies the efficiency of two novel metaheuristic-based models for this objective. To this end, black hole algorithm (BHA) and multi-verse optimizer (MVO) are synthesized with an artificial neural network (ANN) to build the proposed hybrid models. Based on the settlement of a two-layered soil (and a shallow footing) system, the stability values (SV) of 0 and 1 (indicating the stability and failure, respectively) are set as the targets. Each model predicted the SV for 901 stages. The results indicated that the BHA and MVO can increase the accuracy (i.e., the area under the receiving operating characteristic curve) of the ANN from 94.0% to 96.3 and 97.2% in analyzing the SV pattern. Moreover, the prediction accuracy rose from 93.1% to 94.4 and 95.0%. Also, a comparison between the ANN's error decreased by the BHA and MVO (7.92% vs. 18.08% in the training phase and 6.28% vs. 13.62% in the testing phase) showed that the MVO is a more efficient optimizer. Hence, the suggested MVO-ANN can be used as a reliable approach for the practical estimation of bearing capacity.

• Geomechanics and Engineering
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• v.37 no.6
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• pp.629-641
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• 2024

The presence of excavations or cavities beneath the foundations of a building can have a significant impact on their stability and cause extensive damage. Traditional methods for calculating the bearing capacity and subsidence of foundations over cavities can be complex and time-consuming, particularly when dealing with conditions that vary. In such situations, machine learning (ML) and deep learning (DL) techniques provide effective alternatives. This study concentrates on constructing a prediction model based on the performance of ML and DL algorithms that can be applied in real-world settings. The efficacy of eight algorithms, including Regression Analysis, k-Nearest Neighbor, Decision Tree, Random Forest, Multivariate Regression Spline, Artificial Neural Network, and Deep Neural Network, was evaluated. Using a Python-assisted automation technique integrated with the PLAXIS 2D platform, a dataset containing 272 cases with eight input parameters and one target variable was generated. In general, the DL model performed better than the ML models, and all models, except the regression models, attained outstanding results with an R² greater than 0.90. These models can also be used as surrogate models in reliability analysis to evaluate failure risks and probabilities.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.12
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• pp.1170-1178
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• 2013

Thanks to various benefits, low-cost real-time communication networks so called fieldbus have been widely used in many industrial applications including military systems, such as aircrafts, submarines, and robots. This paper presents a reliability analysis of dual-channel CAN(Controller Area Network) fieldbus which is used for controlling various equipment of submarine combat system. A submarine combat system playing a critical role to the success of missions and survivability consists of various devices including sensors/actuators and computers. Since a communication network for submarine combat system must satisfy an extremely high level of reliability, a dual channel technique is commonly adopted. In this paper, a Petri Net based reliability model for dual-channel CAN is discussed. A reliability model called generalized stochastic Petri Nets (GSPN) is built by utilizing the information on physical faults with CAN. The effectiveness of the proposed model is analyzed in terms of unreliability with respect to failure rate and repair rate.

• Journal of the Korean Geotechnical Society
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• v.38 no.8
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• pp.39-52
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• 2022

Multilayer perceptron neural network was trained to determine the factor of safety and slip surface of the slope. Slope geometry is a simple slope based on Korean design standards, and the case of dry and existing groundwater levels are both considered, and the properties of the soil composing the slope are considered to be sandy soil including fine particles. When curating the data required for model training, slope stability analysis was performed in 42,000 cases using the limit equilibrium method. Steady-state seepage analysis of groundwater was also performed, and the results generated were applied to slope stability analysis. Results show that the multilayer perceptron model can predict the factor of safety and failure arc with high performance when the slope's physical properties data are input. A method for quantitative validation of the model performance is presented.

• The Journal of the Korea Contents Association
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• v.12 no.3
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• pp.44-54
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• 2012

In wireless sensor networks, a moving object tracking scheme is one of core technologies for real applications such as environment monitering and enemy moving tracking in military areas. However, no works have been carried out on processing the failure of object tracking in sparse sensor networks with holes. Therefore, the energy consumption in the existing schemes significantly increases due to plenty of failures of moving object tracking. To overcome this problem, we propose a novel moving object tracking scheme based on polynomial regression prediction in sparse sensor networks. The proposed scheme activates the minimum sensor nodes by predicting the trajectory of an object based on polynomial regression analysis. Moreover, in the case of the failure of moving object tracking, it just activates only the boundary nodes of a hole for failure recovery. By doing so, the proposed scheme reduces the energy consumption and ensures the high accuracy for object tracking in the sensor network with holes. To show the superiority of our proposed scheme, we compare it with the existing scheme. Our experimental results show that our proposed scheme reduces about 47% energy consumption for object tracking over the existing scheme and achieves about 91% accuracy of object tracking even in sensor networks with holes.

• Journal of the Korean Geosynthetics Society
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• v.17 no.4
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• pp.199-212
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• 2018

This research concerns the prediction method for ground movement and wall member force due to determination structural stability check and failure check during deep excavation construction. First, research related with excavation influence parameters is conducted. Then, numerical analysis for various excavation conditions were conducted using Finite Element Method and Beam-column elasto-plasticity method. Excavation analysis database was then constructed. Using this database, development of ANN (artificial neural network) was performed for each ground movements and using structural member forces. By comparing the numerical analysis results with ANN's prediction, it is validated that development of ANN can be used efficient for prediction of ground movement and structural member forces in deep excavation site.

• Journal of Ginseng Research
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• v.46 no.4
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• pp.609-619
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• 2022

Obesity is a primary factor provoking various chronic disorders, including cardiovascular disease, diabetes, and cancer, and causes the death of 2.8 million individuals each year. Diet, physical activity, medications, and surgery are the main therapies for overweightness and obesity. During weight loss therapy, a decrease in energy stores activates appetite signaling pathways under the regulation of neuropeptides, including anorexigenic [corticotropin-releasing hormone, proopiomelanocortin (POMC), cholecystokinin (CCK), and cocaine- and amphetamine-regulated transcript] and orexigenic [agoutirelated protein (AgRP), neuropeptide Y (NPY), and melanin-concentrating hormone] neuropeptides, which increase food intake and lead to failure in attaining weight loss goals. Ginseng and ginsenosides reverse these signaling pathways by suppressing orexigenic neuropeptides (NPY and AgRP) and provoking anorexigenic neuropeptides (CCK and POMC), which prevent the increase in food intake. Moreover, the results of network pharmacology analysis have revealed that constituents of ginseng radix, including campesterol, beta-elemene, ginsenoside Rb1, biotin, and pantothenic acid, are highly correlated with neuropeptide genes that regulate energy balance and food intake, including ADIPOQ, NAMPT, UBL5, NUCB2, LEP, CCK, GAST, IGF1, RLN1, PENK, PDYN, and POMC. Based on previous studies and network pharmacology analysis data, ginseng and its compounds may be a potent source for obesity treatment by regulating neuropeptides associated with appetite.

• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.6
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• pp.15-20
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• 2008

Hydraulic analysis of water distribution system can be divided into demand-driven analysis and pressure-driven analysis. Demanddriven analysis can give unrealistic results to simulate hydraulic conditions under abnormal operating conditions such as sudden demand increase and pipe failure. In Korea, demand-driven analysis has been used to establish emergency water supply plan in many water projects, but it is necessary to use pressure-driven analysis for establishment of emergency water supply plan. In this study, WaterGEMS model that was developed for pressure-driven analysis is used to evaluation of emergency water supply plan of J city. As the results, it was able to draw up more efficient plan for water supply in small block, and established emergency water supply plan of J city was determined to be appropriate.