• Journal of KIISE:Computing Practices and Letters
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• v.11 no.2
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• pp.149-156
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• 2005

The HLA is the simulation architecture standard that the civilian and military simulation communities are deeply interested in. We can find various successful practices applying HLA to constructive simulations such as war games in domestics and overseas. However, any case of real-time distributed simulations has not been reported. The reason is that a message transmission period via RTI in a network layer varies according to computing power, simulation nodes, transmission types, and packet size; further a message processing time in an application layer depends on its processing methods, thus too difficult to set up real-time constraints for the enhancement of a real-time resolution. Hence, in this paper we have studied the real-time constraints of RTI for the development of the M-SAM simulator. Thus we have developed a HLA based pilot simulator using 6 PC's in LAN and then measured and analysed the performance of the RTI. As the results of our work, we could obtain the quantitative values for message delay, RTI overhead and RTI packet transmission ratio by a real operation scenario and loads, which are not shown in the previous works. We also expect that the results can be used as a guideline to set up the number of targets, transmission frequency and message processing method in the development of the M-SAM simulator and similar applications.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.11
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• pp.1015-1026
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• 2019

The purpose of this study is to develop evaluation indexes for improving the quality of long-term care institutions (facility salary) evaluation in the sense that the applicability and effectiveness of previous studies related to the development of evaluation indexes for long-term care facilities for elderly are insufficient. There was this. To this end, an analytical review of the 2018 long-term care institution (accommodation benefit) evaluation index, an analysis of Japan's elderly long-term care home evaluation index, and the elderly long-term care facility workers in Korea and the special care home for the elderly in Japan FGI on evaluation indicators and evaluation system was conducted. Based on the results of the research, evaluation indicators were developed in terms of supporting users to receive high quality services. The characteristics of the elderly, that is, the characteristics of elderly diseases that are difficult to maintain and improve, the direction and transparency of institutional operation, and the need for terminal care were reflected. Forty-three evaluation indicators were presented, covering institutional operations, environment and safety, beneficiary rights protection, payroll process, and payroll results. In addition, we proposed a four-step multi-level evaluation system that can improve the efficiency of the evaluation process by improving the redundant and unnecessary evaluation process.

• Journal of Korea Water Resources Association
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• v.51 no.12
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• pp.1217-1227
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• 2018

This paper aims to evaluate the applicability of dam inflow prediction model using recurrent neural network theory. To achieve this goal, the Artificial Neural Network (ANN) model and the Elman Recurrent Neural Network(RNN) model were applied to hydro-meteorological data sets for the Soyanggang dam and the Chungju dam basin during dam operation period. For the model training, inflow, rainfall, temperature, sunshine duration, wind speed were used as input data and daily inflow of dam for 10 days were used for output data. The verification was carried out through dam inflow prediction between July, 2016 and June, 2018. The results showed that there was no significant difference in prediction performance between ANN model and the Elman RNN model in the Soyanggang dam basin but the prediction results of the Elman RNN model are comparatively superior to those of the ANN model in the Chungju dam basin. Consequently, the Elman RNN prediction performance is expected to be similar to or better than the ANN model. The prediction performance of Elman RNN was notable during the low dam inflow period. The performance of the multiple hidden layer structure of Elman RNN looks more effective in prediction than that of a single hidden layer structure.

• The Journal of Engineering Geology
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• v.30 no.4
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• pp.603-615
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• 2020

The design factors of artificial recharge are determined by considering the hydrogeological characteristics of the aquifer. The optimal design factors for artificial recharge were derived after performing the injection tests step by step for each injection type (vertical well, ditch and mixed type), which were built in the test site of the study area. It was analyzed that the difference in the injection effect according to the diameter of the injection well was not large, and the 100 mm well was evaluated as appropriate in consideration of the availability and economy of land use. Since the injection effect was well maintained even in the upper rock, the depth of the injection well was proposed for the alluvial layer and the upper rock layer. On the other hand, in four cases of filter media in the ditch, it was analyzed that the penetration efficiency and the hydraulic interference effect indicated excellent injection performance when a filter medium of 10 to 30 mm diameter was filled in the ditch. In addition, the proper spacing of the injection wells was analyzed as 9~12 m considering the interference efficiency. The interference efficiency attenuation coefficient per 1 m of hole spacing was calculated to be 1.75% in this area. In the future study, the artificial recharge design factors obtained in this stage are applied and verified on site construction and operation. Also it is expected to contribute to securing water in areas where there is always a lack of water.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.1291-1300
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• 2020

Recently, a deep learning-based non-profiling side-channel analysis was proposed. The deep learning-based non-profiling analysis is a technique that trains a neural network model for all guessed keys and then finds the correct secret key through the difference in the training metrics. As the performance of non-profiling analysis varies greatly depending on the neural network training model design, a correct model design criterion is required. This paper describes the two types of loss functions and eight labeling methods used in the training model design. It predicts the analysis performance of each labeling method in terms of non-profiling analysis and power consumption model. Considering the characteristics of non-profiling analysis and the HW (Hamming Weight) power consumption model is assumed, we predict that the learning model applying the HW label without One-hot encoding and the Correlation Optimization (CO) loss will have the best analysis performance. And we performed actual analysis on three data sets that are Subbytes operation part of AES-128 1 round. We verified our prediction by non-profiling analyzing two data sets with a total 16 of MLP-based model, which we describe.

• Journal of Korea Water Resources Association
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• v.55 no.spc1
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• pp.1283-1293
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• 2022

The purpose of this study is to predict residual chlorine in order to maintain stable residual chlorine concentration in sedimentation basin by using artificial intelligence algorithms in water treatment process employing pre-chlorination. Available water quantity and quality data are collected and analyzed statistically to apply into mathematical multiple regression and artificial intelligence models including multi-layer perceptron neural network, random forest, long short term memory (LSTM) algorithms. Water temperature, turbidity, pH, conductivity, flow rate, alkalinity and pre-chlorination dosage data are used as the input parameters to develop prediction models. As results, it is presented that the random forest algorithm shows the most moderate prediction result among four cases, which are long short term memory, multi-layer perceptron, multiple regression including random forest. Especially, it is result that the multiple regression model can not represent the residual chlorine with the input parameters which varies independently with seasonal change, numerical scale and dimension difference between quantity and quality. For this reason, random forest model is more appropriate for predict water qualities than other algorithms, which is classified into decision tree type algorithm. Also, it is expected that real time prediction by artificial intelligence models can play role of the stable operation of residual chlorine in water treatment plant including pre-chlorination process.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.3
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• pp.945-953
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• 2018

In this paper, we use 3-D LIDAR for obstacle detection and avoidance maneuver for autonomous unmanned operation. It is aimed to avoid obstacle avoidance in unmanned water under marine condition using only single sensor. 3D lidar uses Quanergy's M8 sensor to collect surrounding obstacle data and includes layer information and intensity information in obstacle information. The collected data is converted into a three-dimensional Cartesian coordinate system, which is then mapped to a two-dimensional coordinate system. The data including the obstacle information converted into the two-dimensional coordinate system includes noise data on the water surface. So, basically, the noise data generated regularly is defined by defining a hypothetical region of interest based on the assumption of unmanned water. The noise data generated thereafter are set to a threshold value in the histogram data calculated by the Vector Field Histogram, And the noise data is removed in proportion to the amount of noise. Using the removed data, the relative object was searched according to the unmanned averaging motion, and the density map of the data was made while keeping one cell on the virtual grid map. A polar histogram was generated for the generated obstacle map, and the avoidance direction was selected using the boundary value.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.715-720
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• 2023

The military is driving innovative changes such as AI, cloud computing, and drone operation through the Fourth Industrial Revolution. It is expected that such changes will lead to a rapid increase in the demand for information exchange requirements, reaching all lower-ranking soldiers, as networking based on IoT occurs. The flow of such information must ensure efficient information distribution through various infrastructures such as ground networks, stationary satellites, and low-earth orbit small communication satellites, and the demand for information exchange that is distributed through them must be appropriately dispersed. In this study, we redefined the DSCP, which is closely related to QoS (Quality of Service) in information dissemination, into 11 categories and performed research to map each cluster group identified by cluster analysis to the defense "information exchange requirement list" on a one-to-one basis. The purpose of the research is to ensure efficient information dissemination within a multi-layer integrated network (ground network, stationary satellite network, low-earth orbit small communication satellite network) with limited bandwidth by re-establishing QoS policies that prioritize important information exchange requirements so that they are routed in priority. In this paper, we evaluated how well the information exchange requirement lists classified by cluster analysis were assigned to DSCP through M&S, and confirmed that reclassifying DSCP can lead to more efficient information distribution in a network environment with limited bandwidth.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.5
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• pp.1907-1914
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• 2013

Textile-type heat exchangers installed on the tunnel walls for facilitating ground source heat pump systems, so called "energy textile", was installed in an abandoned railroad tunnel around Seocheon, South Korea. To evaluate thermal performance of the energy textile, a series of long-term monitoring was performed by artificially applying daily intermittent cooling and heating loads on the energy textile. In the course of the experimental measurement, the inlet and outlet fluid temperatures of the energy textile, pumping rate, temperature distribution in the ground, and air temperature inside the tunnel were continuously measured. From the long-term monitoring, the heat exchange rate was recorded as in the range of 57.6~143.5 W per one unit of the energy textile during heating operation and 362.3~558.4 W per one unit during cooling operation. In addition, the heat exchange rate of energy textile was highly sensitive to a change in air temperature inside the tunnel. The field measurements were verified by a 3D computational fluid dynamics analysis (FLUENT) with the consideration of air temperature variation inside the tunnel. The verified numerical model was used to evaluate parametrically the effect of drainage layer in the energy textile.

• Archives of Plastic Surgery
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• v.38 no.6
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• pp.894-898
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• 2011

Purpose: Coverage of full-thickness large flank defect is a challenging procedure for plastic surgeons. Some authors have reported external oblique turnover muscle flap with skin grafting, inferiorly based rectus abdominis musculocutaneous flap, and two independent pedicled perforator flaps for flank reconstruction. But these flaps can cover only certain portions of the flank and may not be helpful for larger or more lateral defects. We report a case of large flank defect after resection of extraskeletal Ewing's sarcoma which is successfully reconstructed with reverse latissimus dorsi myocutaneous flap. Methods: A 24-year-old male patient had $13.0{\times}7.0{\times}14.0$ cm sized Ewing's sarcoma on his right flank area. Department of chest surgery and general surgery operation team resected the mass with 5.0 cm safety margin. Tenth, eleventh and twelfth ribs, latissimus dorsi muscle, internal and external oblique muscles and peritoneum were partially resected. The peritoneal defect was repaired with double layer of Prolene mesh by general surgeons. $24{\times}25$ cm sized soft tissue defect was noted and the authors designed reverse latissimus dorsi myocutaneous flap with $21{\times}10$ cm sized skin island on right back area. To achieve sufficient arc of rotation, the cephalic border of the origin of latissimus dorsi muscle was divided, and during this procedure, ninth intercostal vessels were also divided. The thoracodorsal vessels were ligated for 15 minutes before divided to validate sufficient vascular supply of the flap by intercostal arteries. Results: Mild congestion was found on distal portion of the skin island on the next day of operation but improved in two days with conservative management. Stitches were removed in postoperative 3 weeks. The flap was totally viable. Conclusion: The authors reconstructed large soft tissue defect on right flank area successfully with reverse latissimus dorsi myocutaneous flap even though ninth intercostal vessel that partially nourishes the flap was divided. The reverse latissimus dorsi myocutaneous flap can be used for coverage of large soft tissue defects on flank area as well as lower back area.