• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2005.03a
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• pp.68-73
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• 2005

In succession of the previous works, seismo-acoustic analysis was conducted to collect ground truth events and to discriminate surface explosions from natural earthquakes in the Korean Peninsula for 2004. In this period, total 510 seismo-acoustic events corresponding to 10.8 percent of total seismic events occurred in and near the Korean Peninsula were analyzed and discriminated as artificial surface explosions. Events distribution of the seismo-acoustic events in 2004 is similar to the previous results of 1999-2003. And newly determined seismo-acoustic events were added to the surface explosions database. To extend infrasound detection capability, Korea Institute of Geoscience and Mineral Resources (KIGAM) and Southern Methodist University (SMU) installed new seismo-acoustic array (BRDAR) in Baekryoung Island last November, 2004. The array configuration and design is nearly same to previous seismo-acoustic arrays CHNAR, KSGAR, a triangular 1 km aperture. BRDAR consists of 5 short period vertical seismometers (GS-13) in seismic vaults and 13 microbarometers (Chaparral Model 2). Preliminary analysis using data collected from BRDAR shows an extension of infrasound detection capability to western part of the Korean Peninsula. Also, multiple observations of infrasound at BRDAR and other arrays gave an opportunity to localize sound source regions.

• Microbiology and Biotechnology Letters
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• v.22 no.6
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• pp.685-691
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• 1994

The biodegradation of aromatic compounds by mixed and monoculture was investigated in an artificial wastewater containing 500 mg/l of benzene(B), phenol(P), and toluene(T) in various combinations. None of three strains utilized P-xylene(X) as a carbon source, but they grew well on p-xylene in mixtures with benzene and toluene. In the mixed culture on mixed substrate, the length of lag phase was different depending on the nature of mixture. Cell growths of Flavobac- terium sp. BEN2 and Acinetobacter sp. GEM63 were inhibited in the presence of a 500 mg/l of phenol. When the mixed culture of three strains was cultured in a bench-scale reactor containing artificial wastewater, each of benzene, phenol, and toluene was not detected at 30 hrs, 50 hrs, and 12 hrs after incubation in the treatment. The removal rates of COD$_{t}$(total COD) and COD$_{s}$,(soluble COD) of upper phase after centrifugation during early 50 hrs were ca. 80% and ca. 93.8%, respectively.

• Korean Journal of Plant Resources
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• v.28 no.6
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• pp.710-717
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• 2015

Humankind has been searching for medicinal materials from various plant sources in an attempt to treat disease. Mistletoe is one indubitable plant source for these materials due to its effectiveness in treating various diseases, but it has almost disappeared from the mountainous areas of Korea due to excessive harvesting. In this study, in order to select host tree species for Korean mistletoe [Viscum album var. coloratum (Kom.) Ohwi] by seed inoculation and to clarify the effect of host specificity among various tree species were conducted for the purpose of gaining basic information for the artificial cultivation of Korean mistletoe. Almost all the seeds of Korean mistletoe germinated in vitro at the temperature of 15℃. Among host trees used in this study, Prunus mume showed the highest parasitic affinity with inoculated Korean mistletoe, compared with any other host plants. However, treatment of hormones could not increase the low survival rate of Korean mistletoe on the host trees.

• Journal of Power Electronics
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• v.11 no.5
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• pp.719-725
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• 2011

This paper describes a novel Direct Torque Control (DTC) method for adjustable speed Doubly-Fed Induction Machine (DFIM) drives which is supplied by a two-level Space Vector Modulation (SVM) voltage source inverter (DTC-SVM) in the rotor circuit. The inverter reference voltage vector is obtained by using input-output feedback linearization control and a DFIM model in the stator a-b axes reference frame with stator currents and rotor fluxes as state variables. Moreover, to make this nonlinear controller stable and robust to most varying electrical parameter uncertainties, a two layer recurrent Artificial Neural Network (ANN) is used to estimate a certain function which shows the machine lumped uncertainty. The overall system stability is proved by the Lyapunov theorem. It is shown that the torque and flux tracking errors as well as the updated weights of the ANN are uniformly ultimately bounded. Finally, effectiveness of the proposed control approach is shown by computer simulation results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.1
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• pp.74-90
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• 2014

Routing in mobile wireless sensor networks (MWSNs) is an extremely challenging issue due to the features of MWSNs. In this paper, we present a novel bio-inspired trusted routing protocol (B-iTRP) based on artificial immune system (AIS), ant colony optimization (ACO) and Physarum optimization (PO). For trust mechanism, B-iTRP monitors neighbors' behavior in real time and then assesses neighbors' trusts based on AIS. For routing strategy, each node proactively finds routes to the Sink based on ACO. When a backward ant is on the way to return source, it senses the energy residual and trust value of each node on the discovered route, and calculates the link trust and link energy of the route. Moreover, B-iTRP also assesses the availability of route based on PO to maintain the route table. Simulation results show how B-iTRP can achieve the effective performance compared to existing state-of-the-art algorithms.

• Journal of Biosystems Engineering
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• v.43 no.4
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• pp.352-361
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• 2018

Purpose: The objective of this study was to develop a multilayer perceptron (MLP) model to estimate solar radiation using a solar module. Methods: Data for the short-circuit current of a solar module and other environmental parameters were collected for a year. For MLP learning, 14,400 combinations of input variables, learning rates, activation functions, numbers of layers, and numbers of neurons were trained. The best MLP model employed the batch backpropagation algorithm with all input variables and two hidden layers. Results: The root-mean-squared error (RMSE) of each learning cycle and its average over three repetitions were calculated. The average RMSE of the best artificial neural network model was $48.13W{\cdot}m^{-2}$. This result was better than that obtained for the regression model, for which the RMSE was $66.67W{\cdot}m^{-2}$. Conclusions: It is possible to utilize a solar module as a power source and a sensor to measure solar radiation for an agricultural sensor node.

• Nuclear Engineering and Technology
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• v.51 no.2
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• pp.424-431
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• 2019

The aim of the present study was to develop model for detonation cell sizes prediction based on a deep artificial neural network of hydrogen, methane and propane mixtures with air and oxygen. The discussion about the currently available algorithms compared existing solutions and resulted in a conclusion that there is a need for a new model, free from uncertainty of the effective activation energy and the reaction length definitions. The model offers a better and more feasible alternative to the existing ones. Resulting predictions were validated against experimental data obtained during the investigation of detonation parameters, as well as with data collected from the literature. Additionally, separate models for individual mixtures were created and compared with the main model. The comparison showed no drawbacks caused by fitting one model to many mixtures. Moreover, it was demonstrated that the model may be easily extended by including more independent variables. As an example, dependency on pressure was examined. The preparation of experimental data for deep neural network training was described in detail to allow reproducing the results obtained and extending the model to different mixtures and initial conditions. The source code of ready to use models is also provided.

• Nuclear Engineering and Technology
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• v.50 no.7
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• pp.1154-1159
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• 2018

Precise prediction of the radiation interaction position in scintillators plays an important role in medical and industrial imaging systems. In this research, the incident position of the gamma rays was predicted precisely in a plastic rod scintillator by using attenuation technique and multilayer perceptron (MLP) neural network, for the first time. Also, this procedure was performed using nonlinear regression (NLR) method. The experimental setup is comprised of a plastic rod scintillator (BC400) coupled with two PMTs at two sides, a $^{60}Co$ gamma source and two counters that record count rates. Using two proposed techniques (ANN and NLR), the radiation interaction position was predicted in a plastic rod scintillator with a mean relative error percentage less than 4.6% and 14.6%, respectively. The mean absolute error was measured less than 2.5 and 5.5. The correlation coefficient was calculated 0.998 and 0.984, respectively. Also, the ANN technique was confirmed by leave-one-out (LOO) method with 1% error. These results presented the superiority of the ANN method in comparison with NLR and the other methods. The technique and set up used are simpler and faster than other the previous position sensitive detectors. Thus, the time, cost and shielding and electronics requirements are minimized and optimized.

• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.113-116
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• 2021

As artificial intelligence technologies, including deep learning, develop, these technologies are being introduced to code similarity analysis. In the traditional analysis method of calculating the graph edit distance (GED) after converting the source code into a control flow graph (CFG), there are studies that calculate the GED through a trained graph neural network (GNN) with the converted CFG, Methods for analyzing code similarity through CNN by imaging CFG are also being studied. In this paper, to determine which approach will be effective and efficient in researching code similarity analysis methods using artificial intelligence in the future, code similarity is measured through funcGNN, which measures code similarity using GNN, and Siamese Network, which is an image similarity analysis model. The accuracy was compared and analyzed. As a result of the analysis, the error rate (0.0458) of the Siamese network was bigger than that of the funcGNN (0.0362).

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.5
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• pp.513-521
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• 2022

This study deals with an AI architecture model for collecting battlefield data using the tactical C4I system. Based on this model, the artificial staff can be utilized in tactical echelon. In the current structure of the Army's tactical C4I system, Servers are operated by brigade level and above and divided into an active and a standby server. In this C4I system structure, the AI server must also be installed in each unit and must be switched when the C4I server is switched. The tactical C4I system operates a server(DB) for each unit, so data matching is partially delayed or some data is not matched in the inter-working process between servers. To solve these issues, this study presents an operation concept so that all of alternate server can be integrated based on virtualization technology, which is used as an source data for AI Meta DB. In doing so, this study can provide criteria for the AI architectural model of the ground tactical echelon.