• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.2
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• pp.235-240
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• 2006

Ionospheric scintillation induces a rapid change in the amplitude and phase of radio wave signals. This is due to irregularities of electron density in the F-region of the ionosphere. It reduces the accuracy of both pseudorange and carrier phase measurements in GPS/satellite based Augmentation system (SBAS) receivers, and can cause loss of lock on the satellite signal. Scintillation is not as strong at mid-latitude regions such that positioning is not affected as much. Severe effects of scintillation occur mainly in a band approximately 20 degrees on either side of the magnetic equator and sometimes in the polar and auroral regions. Most scintillation occurs for a few hours after sunset during the peak years of the solar cycle. This paper focuses on estimation of the effects of ionospheric scintillation on GPS and SBAS signals using a software receiver. Software receivers have the advantage of flexibility over conventional receivers in examining performance. PC based receivers are especially effective in studying errors such as multipath and ionospheric scintillation. This is because it is possible to analyze IF signal data stored in host PC by the various processing algorithms. A L1 C/A software GPS receiver was developed consisting of a RF front-end module and a signal processing program on the PC. The RF front-end module consists of a down converter and a general purpose device for acquiring data. The signal processing program written in MATLAB implements signal acquisition, tracking, and pseudorange measurements. The receiver achieves standalone positioning with accuracy between 5 and 10 meters in 2drms. Typical phase locked loop (PLL) designs of GPS/SBAS receivers enable them to handle moderate amounts of scintillation. So the effects of ionospheric scintillation was estimated on the performance of GPS L1 C/A and SBAS receivers in terms of degradation of PLL accuracy considering the effect of various noise sources such as thermal noise jitter, ionospheric phase jitter and dynamic stress error.

• 한국정보통신학회논문지
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• 제13권12호
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• pp.2586-2594
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• 2009

디지털 워터마크 기법은 지적 창작물(음악, 영상, 동영상)에 대한 저작권과 재산권의 보호 및 인증, 데이터 손실 여부 판단, 복사 방지 및 추적 등을 목적으로 한 사후 재산권의 보호 기술로 제안되었다. 본 연구에서는 워터마크의 기하학적인 왜곡에 대한 공격에 견고하게 하도록 영상의 전처리 과정을 거친 후 선택된 기하학적인 불변점을 골라 그 점을 중심으로 워터마크를 삽입하여 추출한다. 특히 의료 영상의 병변 관찰을 위해 발생된 RST(Rotation Scale, Translation) 변환 및 여러 가지 영상처리에 강인하며 일정 기간 압축 저장되는 영상에서도 워터마킹이 유지되도록 워터쉐드(watershed) 분할 방법을 통한 견고한 워터 마킹 알고리즘을 제안 하였다. 이를 통해 본 논문에서 제안한 워터마킹 알고리즘은 의료 영상에 대한 JPEG압축공격, RST공격, 그리고 필터링 공격보다 강인함을 확인하였다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2019년도 학술발표회
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• pp.154-154
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• 2019

Characterization of reliable field-scale root-zone soil moisture (RZSM) variability contribute to effective hydro-meterological monitoring. Although a promising cosmic-ray neutron probe (CRNP) holds the pontential for field-scale RZSM measurement, it is often restricted at deeper depths due to the non-unique sensitivity of CRNP-measured fast neutron signal to other hydrogen pools. In this study, a merging framework relied on coupling cosmic-ray soil moisture with a representative additional RZSM, was introduced to scale shallower CRNP effective depth to represent root-zone layer. We tested our proposed framework over a densely vegetated region in South Korea covering a network of one CRNP and nine in-situ point measurements. In particular, cosmic-ray soil moisture and ancillary RZSM retrieved from the most time stable location were considered as input datasets; whereas the remaining point locations were used to generate a reference RZSM product. The errors between these two input datasets and the reference were forecasted by a linear autoregressive model. A linear combination of forecasts was then employed to compute a suitable weight for merging two input products from the predicted errors. The performance of merging framework was evaluated against reference RZSM in comparison to the two original products and a commonly used exponential filter technique. The results of this study showed that merging framework outperformed other products, demonstrating its robustness in improving field-scale RZSM. Moreover, a strong relationship between the quality of input data and the performance merging framework in light of CRNP effective depth variation has been also underlined via the merging framework.

• Structural Engineering and Mechanics
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• 제71권4호
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• pp.391-405
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• 2019

Compared to the ambient vibration test mainly identifying the structural modal parameters, such as frequency, damping and mode shapes, the impact testing, which benefits from measuring both impacting forces and structural responses, has the merit to identify not only the structural modal parameters but also more detailed structural parameters, in particular flexibility. However, in traditional impact tests, an impacting hammer or artificial excitation device is employed, which restricts the efficiency of tests on various bridge structures. To resolve this problem, we propose a new method whereby a moving vehicle is taken as a continuous exciter and develop a corresponding flexibility identification theory, in which the continuous wheel forces induced by the moving vehicle is considered as structural input and the acceleration response of the bridge as the output, thus a structural flexibility matrix can be identified and then structural deflections of the bridge under arbitrary static loads can be predicted. The proposed method is more convenient, time-saving and cost-effective compared with traditional impact tests. However, because the proposed test produces a spatially continuous force while classical impact forces are spatially discrete, a new flexibility identification theory is required, and a novel structural identification method involving with equivalent load distribution, the enhanced Frequency Response Function (eFRFs) construction and modal scaling factor identification is proposed to make use of the continuous excitation force to identify the basic modal parameters as well as the structural flexibility. Laboratory and numerical examples are given, which validate the effectiveness of the proposed method. Furthermore, parametric analysis including road roughness, vehicle speed, vehicle weight, vehicle's stiffness and damping are conducted and the results obtained demonstrate that the developed method has strong robustness except that the relative error increases with the increase of measurement noise.

• Current Optics and Photonics
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• 제5권3호
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• pp.270-277
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• 2021

The adaptive sparse representation (ASR) can effectively combine the structure information of a sample dictionary and the sparsity of coding coefficients. This algorithm can effectively consider the correlation between training samples and convert between sparse representation-based classifier (SRC) and collaborative representation classification (CRC) under different training samples. Unlike SRC and CRC which use fixed norm constraints, ASR can adaptively adjust the constraints based on the correlation between different training samples, seeking a balance between l₁ and l₂ norm, greatly strengthening the robustness and adaptability of the classification algorithm. The correlation coefficients (CC) can better identify the pixels with strong correlation. Therefore, this article proposes a hyperspectral image classification method called correlation coefficients and adaptive sparse representation (CCASR), based on ASR and CC. This method is divided into three steps. In the first step, we determine the pixel to be measured and calculate the CC value between the pixel to be tested and various training samples. Then we represent the pixel using ASR and calculate the reconstruction error corresponding to each category. Finally, the target pixels are classified according to the reconstruction error and the CC value. In this article, a new hyperspectral image classification method is proposed by fusing CC and ASR. The method in this paper is verified through two sets of experimental data. In the hyperspectral image (Indian Pines), the overall accuracy of CCASR has reached 0.9596. In the hyperspectral images taken by HIS-300, the classification results show that the classification accuracy of the proposed method achieves 0.9354, which is better than other commonly used methods.

• Current Optics and Photonics
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• 제6권5호
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• pp.497-505
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• 2022

Topological photonic nanocavities are considered to possess outstanding optical performance, and provide new platforms for realizing strong interaction between light and matter, due to their robustness to impurities and defects. Here hybrid plasmonic topological photonic nanocavities are proposed, by embedding various plasmon nanoantennas such as gold nanospheres, cylinders, and rectangles in a topological photonic crystal corner-state nanocavity. The maximum quality factor Q and minimum effective mode volume V_eff of these hybrid nanocavities can reach the order of 10⁴ and 10^-4 (𝜆/n)³ respectively, and the high figures of merit Q/V_eff for all of these hybrid nanocavites are stable and on the order of 10⁵ (𝜆/n)^-3. The relative positions of the plasmon nanoantennas will influence the coupling strength between the plasmon structures and the topological nanocavity. The hybrid nanocavity with gold nanospheres possesses much higher Q, but relatively large V_eff. The presence of a gold rectangular structure can confine more electromagnetic energy within a smaller space, since its V_eff is smallest, although Q is lowest among these structures. This work provides an outstanding platform for cavity quantum electrodynamics and has a wide range of applications in topological quantum light sources, such as single-photon sources and nanolasers.

• 실천공학교육논문지
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• 제14권2호
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• pp.333-339
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• 2022

본 연구는 공과대학 학생들이 온라인 학습을 진행하였을 때 노트 필기를 병행하며 학습을 하는 경우에 대해 학습 효과 및 만족도, 집중력에 미치는 영향에 대해 연구하였다. 온라인 학습에서 학습 도구로 사용하기 위한 양식으로 코넬노트를 사용하였다. 설문 조사 결과, 학생들은 온라인 학습에서의 노트 필기가 수업 참여의 성실성, 적극성, 집중력에 도움이 되는 것으로 파악되었다. 통계 분석 결과, 노트 필기 제출 횟수와 학업성취도와의 양의 상관관계를 확인하였으며 단일/다중 회귀분석을 통해서 노트 필기 제출 횟수와 학업 성취도가 통계적으로 유의미함을 확인하였다. 다중 회귀 분석 결과, 평균적으로 학생들의 노트 필기 제출 횟수가 1회 증가할 경우, 이는 중간고사 점수 0.253점, 기말고사 점수 0.287점 상승에 통계적으로 유의미한 것을 확인하였다. 부트스트래핑 회귀분석을 실시한 결과에서도 필기노트 제출 횟수가 성적과도 유의미한 결과를 얻어 단일/다중회귀 분석의 결과가 적정함을 확인하였다. 온라인 상에서 강의를 수강하며 노트를 필기하고 온라인 제출함으로써 온라인 수업에서 학습의 질을 높일 수 있는 수업 전략이 될 수 있음을 확인하였다.

• Steel and Composite Structures
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• 제47권2호
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• pp.269-287
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• 2023

The WGJ420 fire-resistant weathering (FRW) steel is developed and manufactured with standard yield strength of 420 MPa at room temperature, which is expected to significantly enhance the performance of steel structures with excellent fire and corrosion resistances, strong seismic capacity, high strength and ductility, good resilience and robustness. In this paper, the mechanical properties of FRW steel plates and buckling behavior of columns are investigated through tests at elevated temperatures. The stress-strain curves, mechanical properties of FRW steel such as modulus of elasticity, proof strength, tensile strength, as well as corresponding reduction factors are obtained and discussed. The recommended constitutive model based on the Ramberg-Osgood relationship, as well as the relevant formulas for mechanical properties are proposed, which provide fundamental mechanical parameters and references. A total of 12 FRW steel welded I-section columns with different slenderness ratios and buckling load ratios are tested under standard fire to understand the global buckling behavior in-depth. The influences of boundary conditions on the buckling failure modes as well as the critical temperatures are also investigated. In addition, the temperature distributions at different sections/locations of the columns are obtained. It is found that the buckling deformation curve can be divided into four stages: initial expansion stage, stable stage, compression stage and failure stage. The fire test results concluded that the residual buckling capacities of FRW steel columns are substantially higher than the conventional steel columns at elevated temperatures. Furthermore, the numerical results show good agreement with the fire test results in terms of the critical temperature and maximum axial elongation. Finally, the critical temperatures between the numerical results and various code/standard curves (GB 51249, Eurocode 3, AS 4100, BS 5950 and AISC) are compared and verified both in the buckling resistance domain and in the temperature domain. It is demonstrated that the FRW steel columns have sufficient safety redundancy for fire resistance when they are designed according to current codes or standards.

• Journal of Animal Science and Technology
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• 제65권2호
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• pp.365-376
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• 2023

Pig breeding management directly contributes to the profitability of pig farms, and pregnancy diagnosis is an important factor in breeding management. Therefore, the need to diagnose pregnancy in sows is emphasized, and various studies have been conducted in this area. We propose a computer-aided diagnosis system to assist livestock farmers to diagnose sow pregnancy through ultrasound. Methods for diagnosing pregnancy in sows through ultrasound include the Doppler method, which measures the heart rate and pulse status, and the echo method, which diagnoses by amplitude depth technique. We propose a method that uses deep learning algorithms on ultrasonography, which is part of the echo method. As deep learning-based classification algorithms, Inception-v4, Xception, and EfficientNetV2 were used and compared to find the optimal algorithm for pregnancy diagnosis in sows. Gaussian and speckle noises were added to the ultrasound images according to the characteristics of the ultrasonography, which is easily affected by noise from the surrounding environments. Both the original and noise added ultrasound images of sows were tested together to determine the suitability of the proposed method on farms. The pregnancy diagnosis performance on the original ultrasound images achieved 0.99 in accuracy in the highest case and on the ultrasound images with noises, the performance achieved 0.98 in accuracy. The diagnosis performance achieved 0.96 in accuracy even when the intensity of noise was strong, proving its robustness against noise.

• Korean Journal of Radiology
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• 제24권12호
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• pp.1179-1189
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• 2023

Objective: We aimed to evaluate the reporting quality of research articles that applied deep learning to medical imaging. Using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines and a journal with prominence in Asia as a sample, we intended to provide an insight into reporting quality in the Asian region and establish a journal-specific audit. Materials and Methods: A total of 38 articles published in the Korean Journal of Radiology between June 2018 and January 2023 were analyzed. The analysis included calculating the percentage of studies that adhered to each CLAIM item and identifying items that were met by ≤ 50% of the studies. The article review was initially conducted independently by two reviewers, and the consensus results were used for the final analysis. We also compared adherence rates to CLAIM before and after December 2020. Results: Of the 42 items in the CLAIM guidelines, 12 items (29%) were satisfied by ≤ 50% of the included articles. None of the studies reported handling missing data (item #13). Only one study respectively presented the use of de-identification methods (#12), intended sample size (#19), robustness or sensitivity analysis (#30), and full study protocol (#41). Of the studies, 35% reported the selection of data subsets (#10), 40% reported registration information (#40), and 50% measured inter and intrarater variability (#18). No significant changes were observed in the rates of adherence to these 12 items before and after December 2020. Conclusion: The reporting quality of artificial intelligence studies according to CLAIM guidelines, in our study sample, showed room for improvement. We recommend that the authors and reviewers have a solid understanding of the relevant reporting guidelines and ensure that the essential elements are adequately reported when writing and reviewing the manuscripts for publication.