• 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.6
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• pp.1555-1560
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• 2012

Recently, it has been increasing attention on health care that can provide remote medical service to an aging population, people with disabilities and people having a medical checkup periodically due to increasing people's average life span. Home health care system should provide reasonable cost, on-line basic health status monitoring, embedded basic medical helper function and intuitive interface. In this paper, we developed a prototype of 3G networked pulse measurement system that can detect pulse signal information from subject's fingertip using the optical sensor. The prototype had been analyzed in terms of abnormalities, feeling, timing and pulse counting accuracy. Finally we evaluated its suitability.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.3
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• pp.565-573
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• 2017

This paper presents a multistage in-flight alignment (MIFA) method for a strapdown inertial navigation system (SDINS) suitable for moving vehicles with no initial attitude references. A SDINS mounted on a moving vehicle frequently loses attitude information for many reasons, and it makes solving navigation equations impossible because the true motion is coupled with an undefined vehicle attitude. To determine the attitude in such a situation, MIFA consists of three stages: a coarse horizontal attitude, coarse heading, and fine attitude with adaptive Kalman navigation filter (AKNF) in order. In the coarse horizontal alignment, the pitch and roll are coarsely estimated from the second order damping loop with an input of acceleration differences between the SDINS and GPS. To enhance estimation accuracy, the acceleration is smoothed by a scalar filter to reflect the true dynamics of a vehicle, and the effects of the scalar filter gains are analyzed. Then the coarse heading is determined from the GPS tracking angle and yaw increment of the SDINS. The attitude from these two stages is fed back to the initial values of the AKNF. To reduce the estimated bias errors of inertial sensors, special emphasis is given to the timing synchronization effects for the measurement of AKNF. With various real flight tests using an UH60 helicopter, it is proved that MIFA provides a dramatic position error improvement compared to the conventional gyro compass alignment.

• Journal of Korea Multimedia Society
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• v.24 no.5
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• pp.676-683
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• 2021

Wi-Fi Round-trip timing (RTT) based location estimation technology estimates the distance between the user and the AP based on the transmission and reception time of the signal. This is because reception instability and signal distortion are greater than that of a Received Signal Strength Indicator (RSSI) based fingerprint in an indoor NLOS environment, resulting in a large position error due to multipath fading. To solve this problem, in this paper, we propose an end-to-end based WiFi Trilateration Net (WTN) that combines neural network-based RTT correction and trilateral positioning network, respectively. The proposed WTN is composed of an RNN-based correction network to improve the RTT distance accuracy and a neural network-based trilateral positioning network for real-time positioning implemented in an end-to-end structure. The proposed network improves learning efficiency by changing the trilateral positioning algorithm, which cannot be learned through differentiation due to mathematical operations, to a neural network. In addition, in order to increase the stability of the TOA based RTT, a correction network is applied in the scanning step to collect reliable distance estimation values from each RTT AP.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.1
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• pp.19-24
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• 2023

In traditional fish farming way, the workers have to observe all of the pools every time and every day to feed at the right timing. This method causes tremendous stress on workers and wastes time. To solve this problem, we implemented an automatic detection system for feeding time using deep neural network. The detection system consists of two steps: classification of the presence or absence of feed and checking DO (Dissolved Oxygen) of the pool. For the classification, the pretrained ResNet18 model and transfer learning with custom dataset are used. DO is obtained from the DO sensor in the pool through HTTP in real time. For better accuracy, the next step, checking DO proceeds when the result of the classification is absence of feed several times in a row. DO is checked if it is higher than a DO reference value that is set by the workers. These actions are performed automatically in the UI programs developed with LabVIEW.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.427-435
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• 2023

Changes in flowering time due to weather fluctuations impact plant growth and ecosystem dynamics. Accurate prediction of flowering timing is crucial for effective forest ecosystem management. This study uses a process-based model to predict flowering timing in 2023 for five major tree species in Korean forests. Models are developed based on nine years (2009-2017) of flowering data for Abeliophyllum distichum, Robinia pseudoacacia, Rhododendron schlippenbachii, Rhododendron yedoense f. poukhanense, and Sorbus commixta, distributed across 28 regions in the country, including mountains. Weather data from the Automatic Mountain Meteorology Observation System (AMOS) and the Korea Meteorological Administration (KMA) are utilized as inputs for the models. The Single Triangle Degree Days (STDD) and Growing Degree Days (GDD) models, known for their superior performance, are employed to predict flowering dates. Daily temperature readings at a 1 km spatial resolution are obtained by merging AMOS and KMA data. To improve prediction accuracy nationwide, random forest machine learning is used to generate region-specific correction coefficients. Applying these coefficients results in minimal prediction errors, particularly for Abeliophyllum distichum, Robinia pseudoacacia, and Rhododendron schlippenbachii, with root mean square errors (RMSEs) of 1.2, 0.6, and 1.2 days, respectively. Model performance is evaluated using ten random sampling tests per species, selecting the model with the highest R². The models with applied correction coefficients achieve R² values ranging from 0.07 to 0.7, except for Sorbus commixta, and exhibit a final explanatory power of 0.75-0.9. This study provides valuable insights into seasonal changes in plant phenology, aiding in identifying honey harvesting seasons affected by abnormal weather conditions, such as those of Robinia pseudoacacia. Detailed information on flowering timing for various plant species and regions enhances understanding of the climate-plant phenology relationship.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.9
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• pp.182-189
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• 2014

A tapped delay line time-to-digital converter (TDC) can be easily implemented using internal carry chains in a field-programmable gate array, and hence, its use is widespread. However, the tapped delay line TDC suffers from performance degradation because of differences in the delay times of dedicated carry chains. In this paper, a dual edge measurement method is proposed instead of a typical step signal to the delay cell to compensate for the performance degradation caused by wide-delay cells in carry chains. By applying a pulse of a fixed width as an input to the carry chains and using the time information between the up and down edges of the signal pulse, the timing accuracy can be increased. Two dedicated carry chain sites are required for the dual edge measurements. By adopting the proposed dual edge measurement method, the average delay widths of the two carry chains were improved by more than 35%, from 17.3 ps and 16.7 ps to 11.2 ps and 10.1 ps, respectively. In addition, the maximum delay times were improved from 41.4 ps and 42.1 ps to 20.1 ps and 20.8 ps, respectively.

• Journal of the Korea Society of Computer and Information
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• v.29 no.7
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• pp.33-40
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• 2024

The live weight of livestock is important information for managing their health and housing conditions, and it can be used to determine the optimal amount of feed and the timing of shipment. In general, it takes a lot of human resources and time to weigh livestock using a scale, and it is not easy to measure each stage of growth, which prevents effective breeding methods such as feeding amount control from being applied. In this paper, we aims to improve the accuracy of weight measurement of piglets, weaned pigs, nursery pigs, and fattening pigs by collecting, analyzing, learning, and predicting video and image data in animal husbandry and pig farming. For this purpose, we trained using Pytorch, YOLO(you only look once) 5 model, and Scikit Learn library and found that the actual and prediction graphs showed a similar flow with a of RMSE(root mean square error) 0.4%. and MAPE(mean absolute percentage error) 0.2%. It can be utilized in the mammalian pig, weaning pig, nursery pig, and fattening pig sections. The accuracy is expected to be continuously improved based on variously trained image and video data and actual measured weight data. It is expected that efficient breeding management will be possible by predicting the production of pigs by part through video reading in the future.

• Journal of Positioning, Navigation, and Timing
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• v.5 no.4
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• pp.181-191
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• 2016

A Satellite Based Augmentation System (SBAS) provides differential correction and integrity information through geostationary satellite to users in order to reduce Global Navigation Satellite System (GNSS)-related errors such as ionospheric delay and tropospheric delay, and satellite orbit and clock errors and calculate a protection level of the calculated location. A SBAS is a system, which has been set as an international standard by the International Civilian Aviation Organization (ICAO) to be utilized for safe operation of aircrafts. Currently, the Wide Area Augmentation System (WAAS) in the USA, the European Geostationary Navigation Overlay Service (EGNOS) in Europe, MTSAT Satellite Augmentation System (MSAS) in Japan, and GPS-Aided Geo Augmented Navigation (GAGAN) are operated. The System for Differential Correction and Monitoring (SDCM) in Russia is now under construction and testing. All SBASs that are currently under operation including the WAAS in the USA provide correction and integrity information about the Global Positioning System (GPS) whereas the SDCM in Russia that started SBAS-related test services in Russia in recent years provides correction and integrity information about not only the GPS but also the GLONASS. Currently, LUCH-5A(PRN 140), LUCH-5B(PRN 125), and LUCH-5V(PRN 141) are assigned and used as geostationary satellites for the SDCM. Among them, PRN 140 satellite is now broadcasting SBAS test messages for SDCM test services. In particular, since messages broadcast by PRN 140 satellite are received in Korea as well, performance analysis on GPS/GLONASS Multi-Constellation SBAS using the SDCM can be possible. The present paper generated correction and integrity information about GPS and GLONASS using SDCM messages broadcast by the PRN 140 satellite, and performed analysis on GPS/GLONASS Multi-Constellation SBAS performance and APV-I availability by applying GPS and GLONASS observation data received from multiple reference stations, which were operated in the National Geographic Information Institute (NGII) for performance analysis on GPS/GLONASS Multi-Constellation SBAS according to user locations inside South Korea utilizing the above-calculated information.

• Journal of Positioning, Navigation, and Timing
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• v.4 no.2
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• pp.43-55
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• 2015

In the case of satellite navigation positioning, the shielding of satellite signals is determined by the environment of the region at which a user is located, and the navigation performance is determined accordingly. The accuracy of user position determination varies depending on the dilution of precision (DOP) which is a measuring index for the geometric characteristics of visible satellites; and if the minimum visible satellites are not secured, position determination is impossible. Currently, the GLObal NAvigation Satellite system (GLONASS) of Russia is used to supplement the navigation performance of the Global Positioning System (GPS) in regions where GPS cannot be used. In addition, the European Satellite Navigation System (Galileo) of the European Union, the Chinese Satellite Navigation System (BeiDou) of China, the Quasi-Zenith Satellite System (QZSS) of Japan, and the Indian Regional Navigation Satellite System (IRNSS) of India are aimed to achieve the full operational capability (FOC) operation of the navigation system. Thus, the number of satellites available for navigation would rapidly increase, particularly in the Asian region; and when integrated navigation is performed, the improvement of navigation performance is expected to be much larger than that in other regions. To secure a stable and prompt position solution, GPS-GLONASS integrated navigation is generally performed at present. However, as available satellite navigation systems have been diversified, finding the minimum satellite constellation combination to obtain the best navigation performance has recently become an issue. For this purpose, it is necessary to examine and predict the navigation performance that could be obtained by the addition of the third satellite navigation system in addition to GPS-GLONASS. In this study, the current status of the integrated navigation performance for various satellite constellation combinations was analyzed based on 2014, and the navigation performance in 2020 was predicted based on the FOC plan of the satellite navigation system for each country. For this prediction, the orbital elements and nominal almanac data of satellite navigation systems that can be observed in the Korean Peninsula were organized, and the minimum elevation angle expecting signal shielding was established based on Matlab and the performance was predicted in terms of DOP. In the case of integrated navigation, a time offset determination algorithm needs to be considered in order to estimate the clock error between navigation systems, and it was analyzed using two kinds of methods: a satellite navigation message based estimation method and a receiver based method where a user directly performs estimation. This simulation is expected to be used as an index for the establishment of the minimum satellite constellation for obtaining the best navigation performance.