• Journal of Advanced Navigation Technology
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• v.27 no.5
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• pp.658-665
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• 2023

Take-off weight is a key factor for improving accuracy when estimating an aircraft's carbon emissions and fuel consumption. However, the takeoff weight contains sensitive payload information that can infer the airline's management strategy, making it impossible to leak it outside. Although several models for estimating takeoff weight have been presented in previous studies, the researcher points out that there are limitations of the study caused by variables at the pilot's discretion. In this paper, several variables related to takeoff weight are identified to suggest a way to control these limits. Among them, variables that can improve the accuracy of takeoff weight are selected and an estimation equation is presented by applying them to ADS-B information. The proposed estimation does not estimate the average takeoff weight but has the advantage of being able to estimate all ranges of the takeoff weight.

• Journal of Korea Water Resources Association
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• v.57 no.9
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• pp.627-631
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• 2024

We attempted to check the observation failure and error status of rain detectors for weather observation introduced and used in the 1980s and improve the collection and calculation algorithm of 1-minute rain detector data to enhance observation efficiency. Error status analysis revealed that among weather observation devices, rain detectors undergo manual quality control (MQC) the most frequently. It was determined that the precipitation recognition rate could be improved by refining the precipitation calculation algorithm. We examined and selected domestic and international rainfall detection algorithms and compared their precipitation recognition rates using random data. The algorithm that determined 'rainfall' when precipitation was measured at least once every 10 seconds showed the highest precipitation recognition rate. Although the algorithm tends to oversimulate precipitation, this can be improved through quality control of raw data. Based on the results of this study, it is believed that it can contribute to reducing the error rate and improving the accuracy of rain detectors.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.5
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• pp.85-89
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• 2017

In this papers, we apply high performance hardware and machine learning algorithm to build an advanced VAD algorithm for speech enhancement. Since speech is made of series of phoneme, using recurrent neural network (RNN) which consider previous data is proper method to build a speech model. It is impossible to study every noise in real world. So our algorithm is builded by phoneme based study. we detect voice present frames in noisy speech signal and make enhancement of the speech signal. Phoneme based RNN model shows advanced performance in speech signal which has high correlation among each frames. To verify the performance of proposed algorithm, we compare VAD result with label data and speech enhancement result in various noise environments with previous speech enhancement algorithm.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.2
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• pp.41-45
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• 2019

In this paper, we propose the interference cancellation scheme of end-to-end method algorithm for power line communication (PLC) systems in smart grid. The proposed scheme estimates the channel noise information of receiver by applying a deep learning model at the receiver. Then, the estimated channel noise is updated in database. In the modulator, the channel noise which reduces the power line communication performance is effectively removed through interference cancellation technique. As an impulsive noise model, Middleton Class A interference model was employed. The performance is evaluated in terms of bit error rate (BER). From the simulation results, it is confirmed that the proposed scheme has better BER performance compared to the theoretical model based on additive white Gaussian noise. As a result, the proposed interference cancellation with deep learning improves the signal quality of PLC systems by effectively removing the channel noise. The results of the paper can be applied to PLC for smart grid and general communication systems.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.4
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• pp.434-439
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• 2019

This paper proposed a control algorithm to compensate the delay time to improve the signal to noise, and the proposed control algorithm estimate the target information to apply the continuous wave radar equation. The proposed control algorithm improves the output signal of each array element bv multiplying the weight of the receive signal to the signal to noise ratio. Radar radiate a signal in spatial and the target information is estimated by the echoed signal from the target. But the signal in the wireless communication environment occurs the delay time due to the multipath which appear human and natural structures. It is difficult to accurately estimate the desired information because of the degradation for the system performance due to the interference signal and the signal distortion. The target information can be improved by compensating the delay signal to apply the weight to the received signal by using the uniform linear array antenna. As a simulation result, we show that the performance of the proposed control algorithm and the non-compensated delay time are compared. The proposed control algorithm proved that the target distance estimation information is improved.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.4
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• pp.29-33
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• 2022

In this paper, we propose the deep learning based pre interference cancellation scheme algorithm for power line communication (PLC) systems in smart grid. The proposed scheme estimates the channel noise information by applying a deep learning model at the transmitter. Then, the estimated channel noise is updated in database. In the modulator, the channel noise which reduces the power line communication performance is effectively removed through interference cancellation technique. As an impulsive noise model, Middleton Class A interference model was employed. The performance is evaluated in terms of bit error rate (BER). From the simulation results, it is confirmed that the proposed scheme has better BER performance compared to the theoretical model based on additive white Gaussian noise. As a result, the proposed interference cancellation with deep learning improves the signal quality of PLC systems by effectively removing the channel noise. The results of the paper can be applied to PLC for smart grid and general communication systems.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.5
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• pp.1373-1382
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• 2014

This paper presents a hydraulic performance graph model in which the flow carrying capacity of a channel system was determined by accounting the interacting backwater effect among channel reaches and incoming lateral flow. The method utilizes hydraulic performance graphs (HPGs), and the method is applied to a natural channel Nakdong River to examine its applicability. This research shows that estimation results using HPG are close to records from the stage station and the results from a widely-accepted model, HEC-RAS. Assuming that a water level gage site is ungaged, water level estimations by HPGs compared with observation show that with a flood event, the HPGs underestimate in the water level ascension phase, but in the recession phase they overestimate results. The accuracy of estimation with HPGs was greatly improved by considering the time difference of flooding between the observation and estimation locations.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1185-1199
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• 2016

Two phase sampling (double sampling) is often used when there is inadequate population information for proper stratification. Many recent papers have been devoted to the estimation method to improve the precision of the estimator using first phase information. In this study we suggested outlier weight adjustment methods to improve estimation precision based on the weight of the generalized ratio-cum-product estimator. Small simulation studies are conducted to compare the suggested methods and the usual method. Real data analysis is also performed.

• Journal of Korea Society of Industrial Information Systems
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• v.9 no.3
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• pp.38-45
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• 2004

The goal of this research is to develop a method of identifying a topic of a given text by looking at relationship of keywords defined in an ontology hierarchy. The keywords which are extracted from important sentences of the given text are mapped onto their correspond concepts which exist in the hierarchy. After all the words are mapped, the correspond concepts will be generalized into one single concept. The single concept will most likely be the topic of text. Our research have an approach that promotes both satisfaction in term of robustness and accuracy using ontologies and word frequency. So, this attempts are done in what they call as a hybrid approach. We try to take the challenge by using knowledge-statistical base approach. Experimental results show that proposed method outperforms the existing method using knowledge-base only.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.7 no.2
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• pp.111-116
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• 2014

In this paper, we estimates the direction of arrival of desired a target using linear array antenna in wireless communication. Direction of arrival estimation is to estimate for desired target position among incident signals on receiver array antennas. This paper improved estimation of direction of arrival for target using optimum weight, high resolution adaptive beamforming algorithm, and sparse matrix for driection of arrival estimation. Through simulation, we showed that we are performance the analysis to compare general algorithm with proposed algorithm. We show that propose algorithm more improve for direction of estimation than general beamforming algorithm.