• 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.10 no.4
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• pp.271-278
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• 2021

In this paper, a tropospheric delay error was calculated by using meteorological data collect from weather station and Saastamoinen model, and an empirical variogram of the tropospheric delay in the Korean peninsula was estimated. In order to estimate the empirical variogram of the tropospheric delay according to weather condition, sunny day, rainy day, and typhoon day were selected as analysis days. Analysis results show that a maximum correlation range of the empirical variogram on sunny day was about 560 km because there is overall trend of the tropospheric delay. On the other hand, the maximum correlation range of the empirical variogram on rainy was about 150 km because the regional variation was large. Although there is regional variation when the typhoon exists, there is a trend of the tropospheric delay due to a movement of the typhoon. Therefore, the maximum correlation range of the empirical variogram on typhoon day was about 280 km which is between sunny and rainy day.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.4
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• pp.343-348
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• 2023

The ionosphere acts as the largest error source in the Global Navigation Satellite System (GNSS) signal transmission. Ionospheric total electron content (TEC) is also easily affected by changes in the space environment, such as solar activity and geomagnetic storms. In this study, we analyze changes in the regional ionosphere using the Qusai-Zenith Satellite System (QZSS), a regional satellite navigation system. Observations from 9 GNSS stations in South Korea are used for estimating the QZSS TEC. In addition, the performance of QZSS TEC is analyzed with observations from day of year (DOY) 199 to 206, 2023. To verify the performance of our results, we compare the estimated QZSS TEC and CODE Global Ionosphere Map (GIM) at the same location. Our results are in good agreement with the GIM product provided by the CODE over this period, with an averaged difference of approximately 0.1 TECU and a root mean square (RMS) value of 2.89 TECU.

• Journal of Positioning, Navigation, and Timing
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• v.13 no.3
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• pp.215-220
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• 2024

In urban areas, signals can be blocked and reflected by buildings, reducing the reliability of global navigation satellite systems (GNSS). To address this, the zonotope shadow matching (ZSM) algorithm has been proposed to estimate the set-valued receiver position by calculating the GNSS shadow based on the zonotope. However, the existing study only analyzed the performance of ZSM in dense urban areas where GNSS shadows occur frequently, and the performance analysis in various urban environments was insufficient. Therefore, in this paper, we analyzed the performance of the ZSM algorithm in four urban environments with different characteristics. The results showed that the receiver position estimation performance of ZSM was relatively poor in environments where buildings were not densely populated, and the performance of ZSM was shown to be effective in urban environments with narrow roads and tall buildings.

• Journal of Positioning, Navigation, and Timing
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• v.9 no.3
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• pp.175-182
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• 2020

This paper introduces a study to estimate the user altitude in need of rescue in an emergency. The altitude is estimated by using the barometric pressure sensor embedded in the smartphone. Compared to GPS, which is degraded in urban or indoor environments, it has the advantage of not having spatial restrictions. With the endless development of smartphone hardware, it is possible to estimate the absolute altitude using the measured value if only the bias of the embedded barometric pressure sensor is applied. The altitude information of the person in need of rescue in an emergency is a great help in reducing rescue time. Since time is tight, we propose online calibration that provides the barometric pressure sensor bias used for altitude estimation through database. Furthermore, experiments were conducted to understand the characteristics of the barometric pressure sensor, which is greatly affected by wind. At the end, the altitude estimation performance was confirmed through an actual field tests in various floors in the building.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.1A
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• pp.65-76
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• 2004

In this paper, a novel preamble structure, including a synchronization preamble and a cell search preamble, is proposed for OFDM-based cellular systems. An efficient algorithm for downlink synchronization and cell searching using the preamble is also proposed. The synchronization process includes the initial symbol timing estimation using continuously, or at least, periodically transmitted downlink signal, frame synchronization, the fine symbol timing estimation, and the frequency offset estimation using the synchronization preamble, and the cell identification using the cell searching preamble. Performance of each synchronization and cell searching step is analyzed and the analytic results including the overall performance of the synchronization and cell searching are verified by computer simulation. It is shown that the proposed preamble with the corresponding synchronization and cell searching algorithm can provide very robust synchronization and cell searching capability even in bad cellular environments.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.2
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• pp.91-100
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• 2023

The existing indoor localization method using Wi-Fi fingerprinting has a high collection cost and relatively low accuracy, thus requiring integrated correction of convergence with other technologies. This paper proposes a new method that significantly reduces collection costs compared to existing methods using Wi-Fi fingerprinting. Furthermore, it does not require labeling of data at collection and can estimate pedestrian travel paths even in large indoor spaces. The proposed pedestrian movement path estimation process is as follows. Data collection is accomplished by setting up a feature area near an indoor space intersection, moving through the set feature areas, and then collecting data without labels. The collected data are processed using Kernel Linear Discriminant Analysis (KLDA) and the valley point of the Euclidean distance value between two data is obtained within the feature space of the data. We build learning data by labeling data corresponding to valley points and some nearby data by feature area numbers, and labeling data between valley points and other valley points as path data between each corresponding feature area. Finally, for testing, data are collected randomly through indoor space, KLDA is applied as previous data to build test data, the K-Nearest Neighbor (K-NN) algorithm is applied, and the path of movement of test data is estimated by applying a correction algorithm to estimate only routes that can be reached from the most recently estimated location. The estimation results verified the accuracy by comparing the true paths in indoor space with those estimated by the proposed method and achieved approximately 90.8% and 81.4% accuracy in two experimental spaces, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2431-2449
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• 2024

As problems such as water pollution and fish species depletion have become serious, a land-based fish farming is receiving a great attention for ensuring stable productivity. In the fish farming, it is important to determine the timing of shipments, as one of key factors to increase net profit on the aquaculture. In this paper, we propose a system for predicting net profit to support decision of timing of shipment using fish farming-related statistical data. The prediction system consists of growth and farm-gate price prediction models, a cost statistics table, and a net profit estimation algorithm. The Gaussian process regression (GPR) model is exploited for weight prediction based on the analysis that represents the characteristics of the weight data of cultured fish under the assumption of Gaussian probability processes. Moreover, the long short-term memory (LSTM) model is applied considering the simple time series characteristics of the farm-gate price data. In the case of GPR model, it allows to cope with data missing problem of the weight data collected from the fish farm in the time and temperature domains. To solve the problem that the data acquired from the fish farm is aperiodic and small in amount, we generate the corresponding data by adopting a data augmentation method based on the Gaussian model. Finally, the estimation method for net profit is proposed by concatenating weight, price, and cost predictions. The performance of the proposed system is analyzed by applying the system to the Korean flounder data.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.59 no.2
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• pp.173-178
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• 2010

Frequency is an important operating parameter of a power system. Due to the sudden change in generation and loads or faults in power system, the frequency is supposed to deviate from its nominal value. It is essential that the frequency of a power system be maintained very close to its nominal frequency. And monitoring and an accurate estimation of the power frequency by timing synchronized signal provided by FDR is essential to optimum operation and prevention for wide area blackout. As most conventional frequency estimation schemes are based on DFT filter, it has been pointed out that the gain error by change in magnitude could cause the defects when the power frequency is deviated from nominal value. In this paper, an advanced frequency estimation scheme using gain compensation for fault disturbance recorders (FDR) is presented. The proposed scheme can reduce the gain error caused when the power frequency is deviated from nominal value. Various simulation using both the data from EMTP package and user's defined arbitrary signals are performed to demonstrate the effectiveness of the proposed scheme. The simulation results show that the proposed scheme can provide better accuracy and higher robustness to harmonics and noise under both steady state tests and dynamic conditions.

• Journal of Embryo Transfer
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• v.31 no.3
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• pp.261-265
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• 2016

The aim of the present study was to evaluate and estimate timing of artificial insemination (AI) in Hanwoo heifer (Korean native cattle) that is the most popular breed of beef cattle in Korea. To determine changes in body weight of heifers around AI, body weight were measured at different stages either before or after AI. We found that daily body weight gain was higher in the pregnant cows after AI. We also investigate correlation between body mass measured by shoulder height and body length, and conception rates, used (body length+ height)2 instead of height2 for body mass index (BMI), and found that relatively more BMI heifers (>55) showed higher conception rates. Finally, we estimated body weight by measuring should height (SH), heart girth (HG), and body length (BL); $BW=3.93372^*HG-2.90985^*SH-0.021^*BL$. In addition, we observed that HG is most closely correlated with BW; $y(BW)=1.77355^*x(HG)$, $R^2=0.98881$. In summary, we can determine the best timing of AI using body measurement and its application including BMI.