• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.12A
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• pp.1182-1188
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• 2006

Adaptive OFDM(Orthogonal Frequency Division Multiplexing) improves data capacity and system performance over multipath fading by adaptively changing modulation schemes according to channel state information(CSI). To achieve a good performance in adaptive OFDM systems, CSI should be transmitted from receiver to transmitter in real time through feedback channel. However, practically, the CSI feedback delay d which is the sum of the data processing delay and the propagation delay is not negligible and damages to the reliability of CSI such that the performance of adaptive OFDM is degraded. This paper presents an adaptive OFDM system with a multistep predictor on the frequency axis to effectively compensate the multiple feedback delays $d(\geq2)$. Via computer simulation we compare the proposed scheme and existing adaptive OFDM schemes with respect to data capacity and system performance.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.6
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• pp.28-34
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• 1993

An algorithm for MMADF(modified multiplication-free adaptive filter) which need not to multiplication arithmatic operation is proposed to improve the performance of FSE (fractionally spaced equalizer) which reduce the ISI(intersymbol interference) in signal transfer channel. The input signals are quantized using DPCM and the reference signals is processed using a first-order linear prediction filter. The convergence properties of Sign. MADF and M-MADF algorithm for updating of the coefficients of a FIR digital filter of the fractionally spaced equalizer (FSE) are investigated and compared with one another. The convergence properties are characterized by the steady state error and the convergence speed. It is shown that the convergence speed of M-MADF is almost same as Sign algorithm and is faster than MADF in the condition of same steady state error. Especially it is very useful for high correlated signals.

• Membrane and Water Treatment
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• v.3 no.2
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• pp.77-98
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• 2012

A two-dimensional (2D) steady state numerical model of concentration polarisation (CP) phenomena in a membrane channel has been developed using the commercially available computational fluid dynamics (CFD) package CFX (Ansys, Inc., USA). The model incorporates the transmembrane pressure (TMP), axially variable permeate flux, variable diffusivity and viscosity, and osmotic pressure effects. The model has been verified against several benchmark analytical and empirical solutions from the membrane literature. Additionally, the model is able to predict the rejection of an arbitrary solute by the membrane using a pore model, given some basic knowledge of the geometry of the solute molecule or particle, and the membrane pore geometry. This allows for predictive design of membrane systems without experimental determination of the membrane rejection for the specified operating conditions. A demonstration of the model is presented against experimental results for two uncharged test compounds (sucrose and PEG1000) from the literature. The model will be extended to incorporate charge effects, transient simulations, three-dimensional (3D) geometry and turbulent effects in future work.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.3A
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• pp.205-214
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• 2011

In 3rd generation project partnership long term evolution, wireless multicast techniques which send the same data to multiple users under single frequency networks have attracted much attention. In the multicast system, the transmission mode needs to be selected for efficient data transfer while satisfying the multicast coverage requirement. To achieve this, users' channel state information (CSI) should be available at the transmitter. However, it requires too much uplink feedback resource if all the users are allowed to transmit their CSI at all the time. To solve this problem, in this paper, the multicast coverage prediction is suggested. In the proposed algorithm, each user measures its transition probabilities between the success and the fail state of the decoding. Then, it periodically transmits its CSI to the basestation. Using these feedbacks, the basestation can predict the multicast coverage. From the simulation results, we demonstrate that the proposed scheme can predict the multicast system coverage.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.463-481
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• 2020

Cutter suction dredgers (CSDs) are widely used in various dredging constructions such as channel excavation, wharf construction, and reef construction. During a CSD construction, the main operation is to control the swing speed of cutter to keep the slurry concentration in a proper range. However, the slurry concentration cannot be monitored in real-time, i.e., there is a "time-lag effect" in the log of slurry concentration, making it difficult for operators to make the optimal decision on controlling. Concerning this issue, a solution scheme that using real-time monitored indicators to predict current slurry concentration is proposed in this research. The characteristics of the CSD monitoring data are first studied, and a set of preprocessing methods are presented. Then we put forward the concept of "index class" to select the important indices. Finally, an ensemble learning algorithm is set up to fit the relationship between the slurry concentration and the indices of the index classes. In the experiment, log data over seven days of a practical dredging construction is collected. For comparison, the Deep Neural Network (DNN), Long Short Time Memory (LSTM), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and the Bayesian Ridge algorithm are tried. The results show that our method has the best performance with an R² of 0.886 and a mean square error (MSE) of 5.538. This research provides an effective way for real-time predicting the slurry concentration of CSDs and can help to improve the stationarity and production efficiency of dredging construction.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.4
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• pp.83-91
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• 2018

Land reclamation, coastal construction, coastline extension and port construction, all of which involve dredging, are increasingly required to meet the growing economic and societal demands in the coastal zone. During the land reclamation, a portion of landfills are lost from the desired location due to a variety of causes, and therefore prediction of sediment transport is very important for economical and efficient land reclamation management. In this study, laboratory disposal tests were performed using an open channel, and suspended sediment transport was analyzed according to flow velocity and grain size. The relationships between the average and standard deviation of the deposition distance and the flow velocity were almost linear, and the relationships between the average and standard deviation of deposition distance and the grain size were found to have high non-linearity in the form of power law. The deposition distribution of sediments was demonstrated to have log-normal distributions regardless of the flow velocity. Based on the experimental results, modeling of suspended sediment transport was performed using deep neural network, one of deep learning techniques, and the deposition distribution was reproduced through log-normal distribution.

• Korean Journal of Remote Sensing
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• v.35 no.1
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• pp.151-162
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• 2019

Surface temperature has been derived from the MODIS/ASTER airborne simulator (MASTER) mid-wave infrared single channel data using the MODerate resolution atmospheric TRANsmission (MODTRAN) radiative transfer model with input data including the University of Wisconsin (UW) emissivity, the National Centers for Environmental Prediction (NCEP) atmospheric profiles, and solar and line-of-sight geometry. We have selected the study area that covers some surface types such as water, sand, agricultural (vegetated) land, and clouds. Results of the current study show the reasonable geographical distribution of surface temperature over land and water similar to the pattern of the MASTER L2 surface temperature. The thorough quantitative validation of surface temperature retrieved from this study is somehow limited due to the lack of in-situ measurements. One point comparison at the Salton Sea buoy shows that the present estimate is 1.8 K higher than the field data. Further comparison with the MASTER L2 surface temperature over the study area reveals statistically good agreement with mean differences of 4.6 K between two estimates. We further analyze the surface temperature differences between two estimates and find primary factors to be emissivity and atmospheric correction.

• Atmosphere
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• v.23 no.4
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• pp.453-470
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• 2013

As a part of the KIAPS Observation Processing System (KOPS), we have developed the modules of satellite radiance data pre-processing and quality control, which include observation operators to interpolate model state variables into radiances in observation space. AMSU-A (Advanced Microwave Sounding Unit-A) level-1d radiance data have been extracted using the BUFR (Binary Universal Form for the Representation of meteorological data) decoder and a first guess has been calculated with RTTOV (Radiative Transfer for TIROS Operational Vertical Sounder) version 10.2. For initial quality checks, the pixels contaminated by large amounts of cloud liquid water, heavy precipitation, and sea ice have been removed. Channels for assimilation, rejection, or monitoring have been respectively selected for different surface types since the errors from the skin temperature are caused by inaccurate surface emissivity. Correcting the bias caused by errors in the instruments and radiative transfer model is crucial in radiance data pre-processing. We have developed bias correction modules in two steps based on 30-day innovation statistics (observed radiance minus background; O-B). The scan bias correction has been calculated individually for each channel, satellite, and scan position. Then a multiple linear regression of the scan-bias-corrected innovations with several predictors has been employed to correct the airmass bias.

• Journal of Digital Contents Society
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• v.7 no.1
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• pp.47-52
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• 2006

This paper proposes an algorithm for human sensibility evaluation using 4-channel EEG signals of the prefrontal and the parietal lobes. The algorithm uses an artificial neural network and the multiple templates. The linear prediction coefficients are used as the feature parameters of human sensibility. Comfortableness and temperature/humidity are evaluated. Many conventional researches have emphasized that a wave of left prefrontal lobe is activated in case of positive sensibility and that of right prefrontal lobe is activated in case of negative sensibility. So the power ratio of n wave is obtained from for computation and the results are compared. The results of the comfortableness evaluation for temperature and humidity showed that the outputs of the proposed algorithm coincided with corresponding sensibilities depending on the task of the temperature and the humidity. The conventional method using a wave is hardly related with comfortableness. And it is also observed that the uncomfortable state due to the high temperature and humidity can be easily changed to the comfortable state by small drop of the temperature and the humidity.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.1057-1060
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• 2006

본 논문에서는 OFDMA-TDD 환경에서 서비스 사용자들에게 QoS 를 보장해줄 수 있도록 예측 알고리즘을 이용하여 한정된 무선 자원을 효율적이고 공정하게 스케줄링 해주는 알고리즘을 연구하였다. 예측 알고리즘은 각 사용자의 이동 정보와 단말들의 변화해온 채널상태의 history data 를 이용하여 앞으로의 채널상태를 예측하고, 예측된 결과는 사용자의 이동 정보와 함께 무선 자원 스케줄링에 적용한다. 또한 이동단말과 고정단말이 공존하는 환경에서는 QoS 보장에 있어 공정하지 않음을 밝히고, 이와 같은 문제를 해결하는 방안을 제안하였으며, 실험결과를 PF 알고리즘과 비교하였다.