• 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.

• Structural Engineering and Mechanics
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• v.88 no.5
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• pp.493-500
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• 2023

The multiparameter eigenvalue method can be used to solve the damped finite element model updating problems. This method transforms the original problems into multiparameter eigenvalue problems. Comparing with the numerical methods based on various optimization methods, a big advantage of this method is that it can provide all possible choices of physical parameters. However, when solving the transformed singular multiparameter eigenvalue problem, the proposed method based on the generalised inverse of a singular matrix has some computational challenges and may fail. In this paper, more details on the transformation from the dynamic model updating problem to the multiparameter eigenvalue problem are presented and the structure of the transformed problem is also exposed. Based on this structure, the rigorous mathematical deduction gives the upper bound of the number of possible choices of the physical parameters, which confirms the singularity of the transformed multiparameter eigenvalue problem. More importantly, we present a row and column compression method to overcome the defect of the proposed numerical method based on the generalised inverse of a singular matrix. Also, two numerical experiments are presented to validate the feasibility and effectiveness of our method.

• Membrane and Water Treatment
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• v.13 no.5
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• pp.219-225
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• 2022

5th Assessment Report of the Intergovernmental Panel on Climate Change Weather (AR5) predicts that recent severe hydrological events will affect the quality of water and increase water pollution. To analyze changes in water quality due to future climate change, input data (precipitation, average temperature, relative humidity, average wind speed, and solar radiation) were compiled into a representative concentration curve (RC), defined using 8.5. AR5 and future use are calculated based on land use. Semi-distributed emission model Calculate emissions for each target period. Meteorological factors affecting water quality (precipitation, temperature, and flow) were input into a multiple linear regression (MLR) model and an artificial neural network (ANN) to analyze the data. Extensive experimental studies of flow properties have been carried out. In addition, an Acoustic Doppler Velocity (ADV) device was used to monitor the flow of a large open channel connection in a wastewater treatment plant in Ho Chi Minh City. Observations were made along different streams at different locations and at different depths. Analysis of measurement data shows average speed profile, aspect ratio, vertical position Measure, and ratio the vertical to bottom distance for maximum speed and water depth. This result indicates that the transport effect of the compound was considered when preparing the hazard analysis.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.2
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• pp.95-106
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• 2016

Biometric data may appear different depending on the person and sasang Medicine has a close relationship with the Department. Biometric data not only mean a human heart rate, a blood pressure, a heart rate, and the past medical history, degree of aging, body mass index, but also is used as a reference measure for determining the state of health of the person. So biometric data should be reproduced for the application purposes, depending on their applications. In previous studies, because the biometric data is changed in real time and applies only to snap shut at the time of the continuity of the current time is excluded. Therefore, in this study in order to solve this problem, we propose a biometric data patton analysis model comprising a continuity of time in the big data environment consisting of biometric data. The proposed model can help determine the choice of needle position carefully when using the electronic acupuncture for care and health promotion.

• Journal of Korea Multimedia Society
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• v.25 no.5
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• pp.748-756
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• 2022

Thanks to the competition of AlphaGo and Sedol Lee, machine learning has received world-wide attention and huge investments. The performance improvement of computing devices greatly contributed to big data processing and the development of neural networks. Artificial intelligence not only imitates human beings in many fields, but also seems to be better than human capabilities. Although humans' creation is still considered to be better and higher, several artificial intelligences continue to challenge human creativity. The quality of some creative outcomes by AI is as good as the real ones produced by human beings. Sometimes they are not distinguishable, because the neural network has the competence to learn the common features contained in big data and copy them. In order to confirm whether artificial intelligence can express the inherent characteristics of different arts, this paper proposes a new neural network model called Humming. It is an experimental model that combines vgg16, which extracts image features, and DeepJ's architecture, which excels in creating various genres of music. A dataset produced by our experiment shows meaningful and valid results. Different results, however, are produced when the amount of data is increased. The neural network produced a similar pattern of music even though it was a different classification of images, which was not what we were aiming for. However, these new attempts may have explicit significance as a starting point for feature transfer that will be further studied.

• Dementia and Neurocognitive Disorders
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• v.22 no.4
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• pp.121-129
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• 2023

Background and Purpose: As it becomes an aging society, interest in senile diseases is increasing. Alzheimer's dementia (AD) and osteoporosis are representative senile diseases. Various studies have reported that AD and osteoporosis share many risk factors that affect each other's incidence. This aimed to determine if active medication treatment of AD could affect the development of osteoporosis. Methods: The Health Insurance Review and Assessment Service provided data consisting of diagnosis, demographics, prescription drug, procedures, medical materials, and healthcare resources. In this study, data of all AD patients in South Korea who were registered under the national health insurance system were obtained. The cohort underwent conversion to an Observational Medical Outcomes Partnership-Common Data Model version 5 format. Results: This study included 11,355 individuals in the good persistent group and an equal number of 11,355 individuals in the poor persistent group from the National Health Claims database for AD drug treatment. In primary analysis, the risk of osteoporosis was significantly higher in the poor persistence group than in the good persistence group (hazard ratio, 1.20 [95% confidence interval, 1.09-1.32]; p<0.001). Conclusions: We found that the good persistence group treated with anti-dementia drugs for AD was associated with a significant lower risk of osteoporosis in this nationwide study. Further studies are needed to clarify the pathophysiological link in patients with two chronic diseases.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.817-826
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• 2022

AI technology is being successfully introduced in many fields, and models deployed as a service are deployed with black box environment that does not expose the model's information to protect intellectual property rights and data. In a black box environment, attackers try to steal data or parameters used during training by using model output. This paper proposes a method of inferring the type of model to directly find out the composition of layer of the target model, based on the fact that there is no attack to infer the information about the type of model from the deep learning model. With ResNet, VGGNet, AlexNet, and simple convolutional neural network models trained with MNIST datasets, we show that the types of models can be inferred using the output values in the gray box and black box environments of the each model. In addition, we inferred the type of model with approximately 83% accuracy in the black box environment if we train the big and small relationship feature that proposed in this paper together, the results show that the model type can be infrerred even in situations where only partial information is given to attackers, not raw probability vectors.

• The Journal of Bigdata
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• v.5 no.2
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• pp.17-27
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• 2020

District heating system supplies heat from low-cost high-efficiency heat production facilities to heat demand areas through a heat pipe network. For efficient heat supply system operation, it is important to accurately predict the heat demand within the region and optimize the heat production plan accordingly. In this study, a heat demand forecasting model is proposed considering real-time calorimeter information from local heat demands. Previous models considered ambient temperature and heat demand history data to predict future heat demands. To improve forecast accuracy, the proposed heat demand forecast model added big data from real-time calorimeters installed in the heat demands within the target region. By employing calorimeter information directly in the model, it is expected that the proposed forecast model is to reflect heat use pattern of each demand. Computational experiemtns based on the actual heat demand data shows that the forecast accuracy of the proposed model improved when the calorimeter big data is reflected.

• International Commerce and Information Review
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• v.13 no.3
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• pp.377-401
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• 2011

Under the WTO system, direct export support system that provides financial and tax related support is altogether prohibited. This presented an obstacle in strengthening competitiveness of Korean export business and in increasing exports continuously. One of the methods used to solve this problem was to actively leverage export insurance. In Korea, export insurance services have been conducted by the Korea Trade Insurance Corporation (k-sure) to promote export. Korea has been among the world's active users of the export insurance system. Given this situation, this paper examines the effectiveness of the Korea export insurance system in the promotion of export. In particular, this study analyzed about discriminating effects of the export insurance on the export of big and small-medium business. In order to analyze, We introduce a Export Supply Function model. In this paper, We construct two model. The one is about big business, the other is small-medium business. For empirical analysis, unit-root test was conducted to understand the safety of time series. The results show that all variables are not I(0) time series. Instead, they are I(1) time series. To this, cointegration verification was conducted based on the use of Johansen verification method to define the existence (or non-existence) of long-term balance relationship among variables. The results come out as follows. The export insurance of big business has a stronger effect on export than that of small-medium business. The cause of these results is due to the distinct structure of Korea industries. In view of the fact that the insurance can make the risk decreased. We can say that the export insurance affects the export of a high-risk country.

• 한국신재생에너지학회:학술대회논문집
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• 2011.11a
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• pp.33.2-33.2
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• 2011

The 'NREL Phase VI' model with a 10.06m diameter was tested in the NASA Ames tunnel to make a reference data of the computational models. The test was conducted at the one rotational speed, blade tip speed 38m/s and the Reynolds number of the sectional airfoils in that test was around 1E6. The 1/5 scale down model of the 'NREL Phase VI' model was used in this paper to study the power characteristics in low Reynolds number region, 0.1E6 ~ 0.4E6 which is achievable range for the conventional wind tunnel facilities. The torque generated by the blade was directly measured by using the torque sensor installed in the rotating axis for a given wind speed and rotational speed. The power characteristics below the stall condition, lambda > 4, was presented in this paper. The power coefficient is very low in the condition below the Re. 0.2E6 and rapidly increases as the Re. increases. And it still increases but the variation is not so big in the condition above the Re. 0.3E6. This results shows that to study the performance of the wind turbine blade by using the scaled down model, the Re. should be larger than the 0.3E6.