• Nuclear Engineering and Technology
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• v.44 no.4
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• pp.393-404
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• 2012

After the Fukushima nuclear accident in 2011, there has been increasing concern regarding severe accidents in nuclear facilities. Severe accident scenarios are difficult for operators to monitor and identify. Therefore, accurate prediction of a severe accident is important in order to manage it appropriately in the unfavorable conditions. In this study, artificial intelligence (AI) techniques, such as support vector classification (SVC), probabilistic neural network (PNN), group method of data handling (GMDH), and fuzzy neural network (FNN), were used to monitor the major transient scenarios of a severe accident caused by three different initiating events, the hot-leg loss of coolant accident (LOCA), the cold-leg LOCA, and the steam generator tube rupture in pressurized water reactors (PWRs). The SVC and PNN models were used for the event classification. The GMDH and FNN models were employed to accurately predict the important timing representing severe accident scenarios. In addition, in order to verify the proposed algorithm, data from a number of numerical simulations were required in order to train the AI techniques due to the shortage of real LOCA data. The data was acquired by performing simulations using the MAAP4 code. The prediction accuracy of the three types of initiating events was sufficiently high to predict severe accident scenarios. Therefore, the AI techniques can be applied successfully in the identification and monitoring of severe accident scenarios in real PWRs.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.233-234
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• 2021

The freezing of the water meter due to the cold wave in winter causes safety accidents caused by freezing and suspending the supply of tap water and various inconveniences. In this study, the water meter develops a test device similar to the environment in which the actual freezing occurs and tests repeatedly by changing the temperature, humidity, flow rate, pressure, valve improvement, pump operation status, etc. Based on the data obtained through this, it is planning to predict the timing of freezing by applying AI technology to correlation between freeze influencing factors.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.227-233
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• 2021

Predictive maintenance has been one of important applications of data science technology that creates a predictive model by collecting numerous data related to management targeted equipment. It does not predict equipment failure with just one or two signs, but quantifies and models numerous symptoms and historical data of actual failure. Statistical methods were used a lot in the past as this predictive maintenance method, but recently, many machine learning-based methods have been proposed. Such proposed machine learning-based methods are preferable in that they show more accurate prediction performance. However, with the exception of some learning models such as decision tree-based models, it is very difficult to explicitly know the structure of learning models (Black-Box Model) and to explain to what extent certain attributes (features or variables) of the learning model affected the prediction results. To overcome this problem, a recently proposed study is an explainable artificial intelligence (AI). It is a methodology that makes it easy for users to understand and trust the results of machine learning-based learning models. In this paper, we propose an explainable AI method to further enhance the explanatory power of the existing learning model by targeting the previously proposedpredictive model [5] that learned data from a core facility (Hyper Compressor) of a domestic chemical plant that produces polyethylene. The ensemble prediction model, which is a black box model, wasconverted to a white box model using the Explainable AI. The proposed methodology explains the direction of control for the major features in the failure prediction results through the Explainable AI. Through this methodology, it is possible to flexibly replace the timing of maintenance of the machine and supply and demand of parts, and to improve the efficiency of the facility operation through proper pre-control.

• Proceedings of the Korean Society of Computer Information Conference
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• 2019.01a
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• pp.377-380
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• 2019

To accommodate for the difficult task of satisfying application's system timing constraints, we are developing Splash, a real time stream processing language for embedded AI applications. Splash is a graphical programming language that designs applications through data flow graph which, later automatically generates into codes. The codes are compiled and executed on top of the Splash runtime system. The Splash runtime system supports two aspects of the application. First, it supports the basic stream processing functions required for an application to operate on multiple streams of data. Second, it supports the checking and handling of the user configurated timing constraints. In this paper we explain the implementation of the first aspect of the Splash runtime system which is being developed using a real time communication middleware called DDS.

• Journal of Positioning, Navigation, and Timing
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• v.10 no.1
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• pp.49-54
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• 2021

Indoor positioning system becomes of increasing interests due to the demands for accurate indoor location information where Global Navigation Satellite System signal does not approach. Wi-Fi access points (APs) built in many construction in advance helps developing a Wi-Fi Received Signal Strength Indicator (RSSI) based indoor localization. This localization method first collects pairs of position and RSSI measurement set, which is called fingerprint database, and then estimates a user's position when given a query measurement set by comparing the fingerprint database. The challenge arises from nonlinearity and noise on Wi-Fi RSSI measurements and complexity of handling a large amount of the fingerprint data. In this paper, machine learning techniques have been applied to implement Wi-Fi based localization. However, most of existing indoor localizations focus on single position estimation. The main contribution of this paper is to develop multi-target localization by using deep neural, which is beneficial when a massive crowd requests positioning service. This paper evaluates the proposed multilocalization based on deep learning from a multi-story building, and analyses its learning effect as increasing number of target positions.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.2
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• pp.91-98
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• 2022

In the next generation wireless communication system, the beamforming technique based on a massive antenna is one of core technologies for transmitting and receiving huge amounts of data, efficiently and accurately. For highly performed and highly reliable beamforming, it is required to accurately estimate the Angle of Arrival (AOA) for the desired signal incident to an antenna. Employing the massive antenna with a large number of elements, although the accuracy of the AOA estimation is enhanced, its computational complexity is dramatically increased so much that real-time communication is difficult. In order to improve this problem, AOA estimation algorithms based on the massive antenna with the low computational complexity have been actively studied. In this paper, we compute and analyze the computational complexity of the cascade AOA estimation algorithm based on the Flexible Massive Concentric Circular Array (FMCCA). In addition, its computational complexity is compared to conventional AOA estimation techniques such as the Multiple Signal Classification (MUSIC) algorithm with the high resolution and the Only Beamspace MUSIC (OBM) algorithm.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.1
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• pp.37-42
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• 2023

Wi-Fi Received Signal Strength Indicator (RSSI) is considered one of the most important sensor data types for indoor localization. However, collecting a RSSI fingerprint, which consists of pairs of a RSSI measurement set and a corresponding location, is costly and time-consuming. In this paper, we propose a Wi-Fi RSSI learning technique without true location data to overcome the limitations of static database construction. Instead of the true reference positions, inertial measurement unit (IMU) data are used to generate pseudo locations, which enable a trainer to move during data collection. This improves the efficiency of data collection dramatically. From an experiment it is seen that the proposed algorithm successfully learns the unsupervised Wi-Fi RSSI positioning model, resulting in 2 m accuracy when the cumulative distribution function (CDF) is 0.8.

• Convergence Security Journal
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• v.23 no.1
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• pp.147-159
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• 2023

Recently, Chat GPT artificial intelligence (AI) is of keen interest to all governments, companies, and military sectors around the world. In the existing era of literacy AI, it has entered an era in which communication with humans is possible with generative AI that creates words, writings, and pictures. Due to the complexity of the current laws and ordinances issued during the recent national crisis in Korea and the ambiguity of the timing of application of laws and ordinances, the golden time of situational measures was often missed. For these reasons, it was not able to respond properly to every major disaster and military conflict with North Korea. Therefore, the purpose of this study was to revise the National Crisis Management Basic Act, which can act as a national tower in the event of a national crisis, and to promote artificial intelligence governance by linking artificial intelligence technology with the civil, government, military, and police.

• Journal of Animal Science and Technology
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• v.63 no.1
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• pp.36-45
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• 2021

Presently, there is an increased demand for livestock products all over the world which has led to more devotion on improving livestock population. Although goats have been bred for a long time in Korea, but there is not much research conducted on traditional Korean black goat (Capra hircus coreanae) compared to other livestock populations. Mutton consumption has been dramatically changing from medicinal use to edible meat and this trend directs the black goat populations declining and also mutton import quantities are increasing consistently. The present study introduced a new estrus synchronizing technique with subsequent artificial insemination (AI) for Korean black goats to enable crossbreeding with non-native breeds for the small or subsistent farmers. Our data highlighted that, the percentage of motile sperm from the electro-ejaculated samples declined significantly after freezing and melting. In addition, the sperm motility significantly declined with regard to sperm incubation period (0, 5, 60, and 120 min at 37℃) and was negatively correlated (64.2 ± 7.9%, 63.3 ± 5.8%, 49.9 ± 6.3%, and 35.9 ± 7.6%, respectively) in frozen-thawed sperm samples. Moreover, the E2 levels were unchanged even 24 h after controlled internal drug releas (CIDR) withdrawal. But, 48 h and 72 h after CIDR removal, E2 levels increased significantly. These data helps us to consider the two time points for AI; CIDR removal after 24 h, at which E2 decreases, and after 48 h, as the time at which progesterone increases. Additionally, the AI after 48 h of CIDR removal group exhibited significantly higher pregnancy and parturition rates (42.9%) compared to AI after 24 h after CIDR removal 28.6% group. In conclusion, these studies will propose an optimal estrus synchronisation process with subsequent timing of AI and also will promote the Korean black goat breeding industry.

• Communications for Statistical Applications and Methods
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• v.15 no.1
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• pp.117-124
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• 2008

The liquidity risk is defined as an additional risk in the market due to the timing and size of a trade. A recent work by Cetin et ai. (2003) proposes a rigorous mathematical model incorporating this liquidity risk into the arbitrage pricing theory. A practical problem arising in a real market application is an estimation problem of a liquidity cost. In this paper, we propose to estimate the liquidity cost function in the context of Cetin et al. (2003) using the constrained least square (LS) method, and illustrate it by analyzing the Kellogg company data.