• 천문학회지
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• 제55권2호
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• pp.23-36
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• 2022

We present a method to determine nitrogen abundance ratios with respect to iron ([N/Fe]) from molecular CN-band features observed in low-resolution (R ~ 2000) stellar spectra obtained by the Sloan Digital Sky Survey (SDSS) and the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). Various tests are carried out to check the systematic and random errors of our technique, and the impact of signal-to-noise (S/N) ratios of stellar spectra on the determined [N/Fe]. We find that the uncertainty of our derived [N/Fe] is less than 0.3 dex for S/N ratios larger than 10 in the ranges T_eff = [4000, 6000] K, log g = [0.0, 3.5], [Fe/H] = [-3.0, 0.0], [C/Fe] = [-1.0, +4.5], and [N/Fe] = [-1.0, +4.5], the parameter space that we are interested in to identify N-enhanced stars in the Galactic halo. A star-by-star comparison with a sample of stars with [N/Fe] estimates available from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) also suggests a similar level of uncertainty in our measured [N/Fe], after removing its systematic error. Based on these results, we conclude that our method is able to reproduce [N/Fe] from low-resolution spectroscopic data, with an uncertainty sufficiently small to discover N-rich stars that presumably originated from disrupted Galactic globular clusters.

• 한국항행학회논문지
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• 제26권5호
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• pp.319-324
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• 2022

항법 장치가 존재하지 않거나 전파 방해가 발생할 경우, 고속 이동체의 전파 항법은 불가능해진다. 그럼에도 불구하고, 다수의 지상국이 존재하며 고속 이동체와 지상국간의 정밀 거리 측정치를 확보할 수 있다면 이동체의 위치 추정이 가능하다. 본 논문에서는 텔레메트리 (TLM; telemetry) 신호를 사용하여 생성한 고정밀 TDOA (time difference of arrival) 측정치를 이용한 위치 추정 방식을 제안한다. 제안한 방식에서는 TDOA 측정치를 사용하여 이동체의 공통 오차를 제거하였다. 또한 SOQPSK (shaped offset quadrature phase shift keying) PN (pseudo random noise) 심볼을 포함하여 정밀 시각 동기 및 측정이 가능한 TLM 신호를 기반으로 한 측정치를 사용하였다. 따라서 시스템 내 정밀 시각 동기가 이뤄진 상태이므로 지상국간의 시각 동기 오차가 매우 작은 값을 가진다. 이는 측정치의 정밀도를 높여 위치 추정 성능을 향상시킨다. 제안한 방식은 소프트웨어 기반의 시뮬레이션을 통해 검증되었으며, 고속 이동체의 위치 추정 성능이 목표했던 성능을 만족함을 확인하였다.

• 한국컴퓨터정보학회논문지
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• 제26권12호
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• pp.19-27
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• 2021

본 논문에서는 지역 차분 프라이버시(Local Differential Privacy, LDP) 기법을 이용하여 프라이버시를 보호하면서 수집한 차량 위치 데이터와 딥러닝 기법을 이용하여 교통량을 예측하기 위한 기법을 제시한다. 제시한 기법은 데이터를 수집하는 과정과 수집한 데이터를 이용하여 교통량을 예측하는 과정으로 구성된다. 첫 번째 단계에서는 데이터 수집 과정 중에 발생할 수 있는 프라이버시 침해 문제를 해결하기 위해 LDP 기법을 적용하여 차량의 위치 데이터를 수집한다. LDP 기법은 데이터 수집 시 원본 데이터에 노이즈를 추가해 사용자의 민감한 데이터가 외부에 노출되는 것을 방지한다. 이를 통해 운전자의 프라이버시를 보존하면서 차량의 위치 데이터를 수집할 수 있다. 두 번째 단계에서는 첫 번째 단계에서 수집한 데이터에 딥러닝 기법을 적용하여, 교통량을 예측한다. 또한, 본 논문에서 제안한 기법의 우수성을 입증하기 위해, 실데이터를 이용한 성능 평가를 진행한다. 성능 평가 결과는 본 논문에서 제안한 기법이 사용자의 프라이버시를 보호하면서 수집된 데이터를 이용하여 효과적으로 교통량을 예측할 수 있음을 입증한다.

• Geomechanics and Engineering
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• 제33권1호
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• pp.11-18
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• 2023

This study explores a new energy partitioning approach to determine the fracture toughness of 3-D printed pristine/recycled high density polyethylene (HDPE) blends employing the essential work of fracture (EWF) concept. The traditional EWF approach conducts a uniaxial tensile test with double-edge notched tensile (DENT) specimens and measures the total energy defined by the area under a load-displacement curve until failure. The approach assumes that the entire total energy contributes to the fracture process only. This assumption is generally true for extruded polymers that fracture occurs in a material body. In contrast to the traditional extrusion manufacturing process, the current 3-D printing technique employs fused deposition modeling (FDM) that produces layer-by-layer structured specimens. This type of specimen tends to include separation energy even after the complete failure of specimens when the fracture test is conducted. The separation is not relevant to the fracture process, and the raw experimental data are likely to possess random variation or noise during fracture testing. Therefore, the current EWF approach may not be suitable for the fracture characterization of 3-D printed specimens. This paper proposed a new energy partitioning approach to exclude the irrelevant energy of the specimens caused by their intrinsic structural issues. The approach determined the energy partitioning location based on experimental data and observations. Results prove that the new approach provided more consistent results with a higher coefficient of correlation.

• Current Optics and Photonics
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• 제7권3호
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• pp.237-247
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• 2023

In middle- and long-distance imaging systems, due to the atmospheric turbulence caused by temperature, wind speed, humidity, and so on, light waves propagating in the air are distorted, resulting in image-quality degradation such as geometric deformation and fuzziness. In remote sensing, astronomical observation, and traffic monitoring, image information loss due to degradation causes huge losses, so effective restoration of degraded images is very important. To restore images degraded by atmospheric turbulence, an image-restoration method based on improved compound multibranch feature fusion (CMFNetPro) was proposed. Based on the CMFNet network, an efficient channel-attention mechanism was used to replace the channel-attention mechanism to improve image quality and network efficiency. In the experiment, two-dimensional random distortion vector fields were used to construct two turbulent datasets with different degrees of distortion, based on the Google Landmarks Dataset v2 dataset. The experimental results showed that compared to the CMFNet, DeblurGAN-v2, and MIMO-UNet models, the proposed CMFNetPro network achieves better performance in both quality and training cost of turbulent-image restoration. In the mixed training, CMFNetPro was 1.2391 dB (weak turbulence), 0.8602 dB (strong turbulence) respectively higher in terms of peak signal-to-noise ratio and 0.0015 (weak turbulence), 0.0136 (strong turbulence) respectively higher in terms of structure similarity compared to CMFNet. CMFNetPro was 14.4 hours faster compared to the CMFNet. This provides a feasible scheme for turbulent-image restoration based on deep learning.

• 한국지진공학회논문집
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• 제27권1호
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• pp.37-47
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• 2023

A new clamped mechanical splice system was proposed to develop structural performance and constructability for precast concrete connections. The proposed mechanical splice resists external loading immediately after the engagement. The mechanical splices applicable for both large-scale rebars for plants and small-scale rebars for buildings were developed with the same design concept. Quasi-static lateral cyclic loading tests were conducted with reinforced and precast concrete members to verify the seismic performance. Also, shaking table tests with three types of seismic wave excitation, 1) random wave with white noise, 2) the 2016 Gyeongju earthquake, and 3) the 1999 Chi-Chi earthquake, were conducted to confirm the dynamic performance. All tests were performed with real-scale concrete specimens. Sensors measured the lateral load, acceleration, displacement, crack pattern, and secant system stiffness, and energy dissipation was determined by lateral load-displacement relation. As a result, the precast specimen provided the emulative performance with RC. In the shaking table tests, PC frames' maximum acceleration and displacement response were amplified 1.57 - 2.85 and 2.20 - 2.92 times compared to the ground motions. The precast specimens utilizing clamped mechanical splice showed ductile behavior with energy dissipation capacity against strong motion earthquakes.

• 한국빅데이터학회지
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• 제6권2호
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• pp.39-49
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• 2021

최근 국가 관측망, 기업 공기 측정기 등을 통해 많고 다양한 기상 데이터가 수집되고 있다. 기계학습 기법을 통해 기상 예측하려는 노력이 곳곳에서 이루어지고 있으며, 국내 미세먼지는 농도가 증가해오고 사람들의 관심이 높아 가장 관심있는 예측 대상 중 하나이다. 본 연구에서는 서울시 전역에 설치된 840여 개실외공기측정기 데이터를 사용하여 PM10·PM2.5 예측 모형을 비교하고자 한다. 5분 뒤 미세먼지 농도 예측을 통해 실시간으로 정보를 제공할 수 있으며, 이는 10분·30분·1시간 뒤 예측 모형 개발에 기반이 될 수 있다. 잡음 제거, 결측치 대체 등의 데이터 전처리를 진행하였고, 시·공간 변수를 고려할 수 있는 파생 변수를 생성하였다. 모형의 매개변수는 반응 표면 방법을 통해 선택하였다. XGBoost, 랜덤포레스트, 딥러닝(Multilayer Perceptron)을 예측 모형으로 사용하여, 미세먼지 농도와 예측값의 차이를 확인하고, 모형 간 성능을 비교하고자 한다.

• 산업경영시스템학회지
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• 제46권3호
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• pp.139-147
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• 2023

The metal bush assembling process is a process of inserting and compressing a metal bush that serves to reduce the occurrence of noise and stable compression in the rotating section. In the metal bush assembly process, the head diameter defect and placement defect of the metal bush occur due to metal bush omission, non-pressing, and poor press-fitting. Among these causes of defects, it is intended to prevent defects due to omission of the metal bush by using signals from sensors attached to the facility. In particular, a metal bush omission is predicted through various data mining techniques using left load cell value, right load cell value, current, and voltage as independent variables. In the case of metal bush omission defect, it is difficult to get defect data, resulting in data imbalance. Data imbalance refers to a case where there is a large difference in the number of data belonging to each class, which can be a problem when performing classification prediction. In order to solve the problem caused by data imbalance, oversampling and composite sampling techniques were applied in this study. In addition, simulated annealing was applied for optimization of parameters related to sampling and hyper-parameters of data mining techniques used for bush omission prediction. In this study, the metal bush omission was predicted using the actual data of M manufacturing company, and the classification performance was examined. All applied techniques showed excellent results, and in particular, the proposed methods, the method of mixing Random Forest and SA, and the method of mixing MLP and SA, showed better results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권1호
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• pp.192-210
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• 2024

Most frequency-domain remote sensing image watermarking algorithms embed watermarks at random locations, which have negative impact on the watermark invisibility. In this study, we propose an adaptive watermarking scheme for remote sensing images that considers the information complexity to select where to embed watermarks to improve watermark invisibility without affecting algorithm robustness. The scheme converts remote sensing images from RGB to YCbCr color space, performs two-level DWT on luminance Y, and selects the high frequency coefficient of the low frequency component (HH^Y₂) as the watermark embedding domain. To achieve adaptive embedding, HH^Y₂ is divided into several 8*8 blocks, the entropy of each sub-block is calculated, and the block with the maximum entropy is chosen as the watermark embedding location. During embedding phase, the watermark image is also decomposed by two-level DWT, and the resulting high frequency coefficient (HH^W₂) is then embedded into the block with maximum entropy using α- blending. The experimental results show that the watermarked remote sensing images have high fidelity, indicating good invisibility. Under varying degrees of geometric, cropping, filtering, and noise attacks, the proposed watermarking can always extract high identifiable watermark images. Moreover, it is extremely stable and impervious to attack intensity interference.

• 산업경영시스템학회지
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• 제47권2호
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• pp.1-9
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• 2024

New motor development requires high-speed load testing using dynamo equipment to calculate the efficiency of the motor. Abnormal noise and vibration may occur in the test equipment rotating at high speed due to misalignment of the connecting shaft or looseness of the fixation, which may lead to safety accidents. In this study, three single-axis vibration sensors for X, Y, and Z axes were attached on the surface of the test motor to measure the vibration value of vibration. Analog data collected from these sensors was used in classification models for anomaly detection. Since the classification accuracy was around only 93%, commonly used hyperparameter optimization techniques such as Grid search, Random search, and Bayesian Optimization were applied to increase accuracy. In addition, Response Surface Method based on Design of Experiment was also used for hyperparameter optimization. However, it was found that there were limits to improving accuracy with these methods. The reason is that the sampling data from an analog signal does not reflect the patterns hidden in the signal. Therefore, in order to find pattern information of the sampling data, we obtained descriptive statistics such as mean, variance, skewness, kurtosis, and percentiles of the analog data, and applied them to the classification models. Classification models using descriptive statistics showed excellent performance improvement. The developed model can be used as a monitoring system that detects abnormal conditions of the motor test.