• Journal of Korean Society of Rural Planning
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• v.20 no.4
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• pp.25-33
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• 2014

This study has intended to build a rural landscape evaluation model based on an integrated landscape assessment paradigm of rural region using an additive integration index method and applied the model to the Seondong Region of Gochang-gun, Jeollabuk-do, Korea. To reach this goal, this study developed a model to calculate Integrated Landscape Assessment Index. The model has employed the Objective Landscape Index, the Subjective Landscape Index, and the weighted values, and was applied to the Seondong region. This study has found the following results: 1) forests and water spaces were assessed with relatively better visual preferences and better landscape ecosystem; 2) the historic cultural area and scenic agriculture as well as general farm land were assessed with moderate ratings; and, 3) the villages included in development plan, their adjacent arable farming land, and the village watercourses were forming relatively poorer landscape.

• Journal of Internet Computing and Services
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• v.24 no.5
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• pp.17-27
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• 2023

Today, as AI (Artificial Intelligence) technology develops and its practicality increases, it is widely used in various application fields in real life. At this time, the AI model is basically learned based on various statistical properties of the learning data and then distributed to the system, but unexpected changes in the data in a rapidly changing data situation cause a decrease in the model's performance. In particular, as it becomes important to find drift signals of deployed models in order to respond to new and unknown attacks that are constantly created in the security field, the need for lifecycle management of the entire model is gradually emerging. In general, it can be detected through performance changes in the model's accuracy and error rate (loss), but there are limitations in the usage environment in that an actual label for the model prediction result is required, and the detection of the point where the actual drift occurs is uncertain. there is. This is because the model's error rate is greatly influenced by various external environmental factors, model selection and parameter settings, and new input data, so it is necessary to precisely determine when actual drift in the data occurs based only on the corresponding value. There are limits to this. Therefore, this paper proposes a method to detect when actual drift occurs through an Anomaly analysis technique based on XAI (eXplainable Artificial Intelligence). As a result of testing a classification model that detects DGA (Domain Generation Algorithm), anomaly scores were extracted through the SHAP(Shapley Additive exPlanations) Value of the data after distribution, and as a result, it was confirmed that efficient drift point detection was possible.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.3
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• pp.288-295
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• 2013

In this paper, we compare the existing channel estimation technique for 4S (Ship to Ship, Ship to Shore) maritime communications under AWGN channel model, Rician fading channel model, and Rayleigh fading channel model respectively. In general, the received signal is corrupted by multipath and ISI (Inter Symbol Interference). The estimation of a time-varying multipath fading channel is a difficult task for the receiver. Its performance can be improved if an appropriate channel estimation filter is used. The simulation is performed in MATLAB. In this simulation, we use the popular estimation algorithms, LMS (Least Mean Square) and RLS (Recursive Least-Squares) are compared with respect to AWGN, Rician and Rayleigh channels.

• Bulletin of the Korean Chemical Society
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• v.27 no.11
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• pp.1819-1824
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• 2006

The general objective of the present study was to investigate the potential application of the UV spectroscopic method for determination of the polymeric additives present in papermaking fibrous stock solutions. The study also intended to establish the surface-chemical retention model associated with the adsorption kinetics of additives on fiber surfaces. Polyamide epichlorohydrin (PAE) wet strength resin and imidazolinium quaternary (IZQ) softening agents were selected to evaluate the analytical method. Concentrations of PAE and IZQ in solution were proportional to the UV absorption at 314 and 400 nm, respectively. The time-dependent behavior of polymeric additives obeyed a mono-molecular layer adsorption as characterized in Langmuir-type expression. The kinetic modeling for polymeric adsorption on fiber surfaces was based on a concept that polymeric adsorption on fiber surfaces has two distinguishable stages including initial dynamic adsorption phase and the final near-equilibrium state. The simulation model predicted not only the real-time additive adsorption behavior for polymeric additives at high accuracy once the kinetic parameters were determined, but showed a good agreement with the experimental data. The spectroscopic method examined on the PAE and IZQ adsorption study could potentially be considered as an effective tool for the wet-end retention control as applied to the paper industry.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.2C
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• pp.41-48
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• 2005

In this paper, we present a low complexity modified Gaussian model based pre-processing filter to improve the performance of H.264 compressed video. Video sequence captured by general imaging system represents the degraded version due to the additive noise which decreases coding efficiency and results in unpleasant coding artifacts due to higher frequency components. By incorporating local statistics and quantization parameter into filtering process, the spurious noise is significantly attenuated and coding efficiency is improved for given quantization step size. In addition, in order to reduce the complexity of the pre-processing filter, the simplified local statistics and quantization parameter are introduced. The simulation results show the capability of the proposed algorithm.

• Asian-Australasian Journal of Animal Sciences
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• v.19 no.11
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• pp.1536-1540
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• 2006

Crossbreeding effects for wool quality traits viz. greasy fleece weight (kg), staple length (cm), average fibre diameter (${\mu}$) and medulation percentage were estimated using the Dickerson's and Kinghorn's models. The data analyzed involved 15 genetic groups including Nali purebred, $F_1$'s of two and three breeds, $F_2$'s and reciprocal crossbred obtained from the crossing of Nali (N), Merino (M) and Corriedale (C) breeds during 1980-96. Nali and Corriedale breeds had non-significant negative additive genetic effects (Dickerson's model) on greasy fleece weight, while effects of Corriedale were negative for staple length only from both models. In general additive genetic effects of all three breeds were non-significant for all the wool traits except medulation percentage. Non significant heterotic and recombination effects (epistatic loss) were estimated from both models. However, the estimates of crossbreeding effects varied between the models both in magnitude as well as in direction barring few exceptions. Undesirable positive heterosis was found on medulation percentage for all types of combinations involving three breeds. Comparison of least squares means of various genetic groups revealed that both two breed and three breed crosses were superior to the Nali breed for all wool quality traits. Fibre diameter of MN crossbreds was significantly less than CN crossbreds. Results also indicated that as the inheritance of Nali breed in a cross is decreased, the medulation percentage decreases which is desirable. Inter se mating of crossbreds (two breed, three breed) has not resulted in a decline in the wool quality traits. These results indicate that the synthetic population derived from three breeds can be stabilized easily for wool traits as there may not be epistatic loss on subsequent inter se mating of crossbreds.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.5
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• pp.229-236
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• 2023

Since 3D printed medical implant parts usually have surface defects, it is necessary to inspect the surface after manufacturing. In order to automate the surface inspection, it is effective to 3D scan the implant and reconstruct it as a scan model such as a point cloud. When constructing a scan model, the characteristics of the shape and material of the implant must be considered because it has characteristics different from those of general 3D printed parts. In this paper, we present a method to reconstruct the 3D scan model of a 3D printed metal bone-plate that is one kind of medical implant parts. Multiple partial scan data are produced by multi-view 3D scan, and then, we reconstruct a scan model by alignment and merging of partial data. We also present the process of the scan model reconstruction through experiments.

• 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 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.

• Journal of Environmental Health Sciences
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• v.34 no.5
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• pp.361-368
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• 2008

Objective to assess whether exposure to air pollutants is associated with postneonatal infant death, using a timeseries methodology, between 1999 and 2003 in Seoul, Korea.. Methods We investigated the short-term effects of air pollution for 548,725 live births during the study period. The daily count of postneonatal infant deaths from all causes and from SIDS (sudden infant death syndrome) by birth order was analyzed by a Generalized Additive Poisson model, with controlling for the effects of seasonal trends, air temperature, relative humidity, barometric pressure, and day of the week as covariates. Results During the study period, we observed 699 deaths from all causes and 47 deaths from SIDS. We did not find any significant associations between daily mortality and ambient levels of air pollutants except for CO and $NO_2$. The estimated relative risk of postneonatal infant death from all causes was 1.17 (95% CI=1.04-1.32) and 1.16 (95% CI=1.03-1.29) by IQR (interquartile range) for CO and $NO_2$ respectively. Also, we observed no clear trend of the mortality effects of air pollution by birth orders. Conclusion In conclusion, our findings suggest that air pollution, in general, influenced adversely postneonatal infant death from all-cause and SIDS although it was not statistically significant. This study may support that the rationale.