• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권11호
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• pp.4084-4104
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• 2021

Visual place recognition (VPR) is a fundamental yet challenging task of mobile robot navigation and localization. The existing VPR methods are usually based on some pairwise similarity of image descriptors, so they are sensitive to visual appearance change and also computationally expensive. This paper proposes a simple yet effective four-step method that achieves adaptive convolutional neural network (CNN) landmark matching for VPR. First, based on the features extracted from existing CNN models, the regions with higher significance scores are selected as landmarks. Then, according to the coordinate positions of potential landmarks, landmark matching is improved by removing mismatched landmark pairs. Finally, considering the significance scores obtained in the first step, robust image retrieval is performed based on adaptive landmark matching, and it gives more weight to the landmark matching pairs with higher significance scores. To verify the efficiency and robustness of the proposed method, evaluations are conducted on standard benchmark datasets. The experimental results indicate that the proposed method reduces the feature representation space of place images by more than 75% with negligible loss in recognition precision. Also, it achieves a fast matching speed in similarity calculation, satisfying the real-time requirement.

• 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.

• Smart Structures and Systems
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• 제32권5호
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• pp.319-338
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• 2023

The study presents a new hybrid data-driven method by combining radial basis functions neural networks (RBF-NN) with the Jaya algorithm (JA) to provide effective structural health monitoring of arch dams. The novelty of this approach lies in that only one user-defined parameter is required and thus can increase its effectiveness and efficiency, as compared to other machine learning techniques that often require processing a large amount of training and testing model parameters and hyper-parameters, with high time-consuming. This approach seeks rapid damage detection in arch dams under dynamic conditions, to prevent potential disasters, by utilizing the RBF-NNN to seamlessly integrate the dynamic elastic modulus (DEM) and modal parameters (such as natural frequency and mode shape) as damage indicators. To determine the dynamic characteristics of the arch dam, the JA sequentially optimizes an objective function rooted in vibration-based data sets. Two case studies of hyperbolic concrete arch dams were carefully designed using finite element simulation to demonstrate the effectiveness of the RBF-NN model, in conjunction with the Jaya algorithm. The testing results demonstrated that the proposed methods could exhibit significant computational time-savings, while effectively detecting damage in arch dam structures with complex nonlinearities. Furthermore, despite training data contaminated with a high level of noise, the RBF-NN and JA fusion remained the robustness, with high accuracy.

• Wind and Structures
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• 제37권6호
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• pp.445-460
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• 2023

Accurate estimation of modal parameters (i.e., natural frequency, damping ratio) of tall buildings is of great importance to their structural design, structural health monitoring, vibration control, and state assessment. Based on the combination of variational mode decomposition, smoothed discrete energy separation algorithm-1, and Half-cycle energy operator (VMD-SH), this paper presents a method for structural modal parameter estimation. The variational mode decomposition is proved to be effective and reliable for decomposing the mixed-signal with low frequencies and damping ratios, and the validity of both smoothed discrete energy separation algorithm-1 and Half-cycle energy operator in the modal identification of a single modal system is verified. By incorporating these techniques, the VMD-SH method is able to accurately identify and extract the various modes present in a signal, providing improved insights into its underlying structure and behavior. Subsequently, a numerical study of a four-story frame structure is conducted using the Newmark-β method, and it is found that the relative errors of natural frequency and damping ratio estimated by the presented method are much smaller than those by traditional methods, validating the effectiveness and accuracy of the combined method for the modal identification of the multi-modal system. Furthermore, the presented method is employed to estimate modal parameters of a full-scale tall building utilizing acceleration responses. The identified results verify the applicability and accuracy of the presented VMD-SH method in field measurements. The study demonstrates the effectiveness and robustness of the proposed VMD-SH method in accurately estimating modal parameters of tall buildings from acceleration response data.

• Advances in concrete construction
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• 제16권1호
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• pp.69-78
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• 2023

This investigation delves into the adverse repercussions stemming from the impact of arsenic on steel pipes concealed within soil designated for rice cultivation. Simultaneously, the study aims to ascertain effective techniques for detecting arsenic in the soil and to provide strategies for mitigating the corrosion of steel pipes. The realm of nanotechnology presents promising avenues for addressing the intricate intersection of renewable energy, oil, and environmental pollution from a novel perspective. Nanostructured materials, characterized by distinct chemical and physical attributes, unveil novel pathways for pioneering materials that exert a substantial impact across diverse realms of food production, storage, packaging, and quality control. Within the scope of the food industry, the scope of nanotechnology encompasses processes, storage methodologies, packaging paradigms, and safeguards to ensure the safety of consumables. Of particular note, silver nanoparticles, in addition to their commendable antibacterial efficacy, boast anti-fungal and anti-inflammatory prowess, environmental compatibility, minimal irritability and allergenicity, resilience to microbial antagonism, thermal stability, and robustness. Confronting the pressing issue of arsenic contamination within both environmental settings and the food supply is of paramount importance to preserve public health and ecological equilibrium. In response, this study introduces detection kits predicated upon silver nanoparticles, providing an expeditious and economically feasible avenue for identifying arsenic concentrations ranging from 0.5 to 3 ppm within rice. Subsequent quantification employs Hydride Atomic Absorption Spectroscopy (HG-AAS), which features a detection threshold of 0.05 ㎍/l. A salient advantage inherent in the HG-AAS methodology lies in its capacity to segregate analytes from the sample matrix, thereby significantly reducing instances of spectral interference. Importantly, the presence of arsenic in the soil beneath rice cultivation establishes a causative link to steel pipe corrosion, with potential consequences extending to food contamination-an intricate facet embedded within the broader tapestry of renewable energy, oil, and environmental pollution.

• 융합보안논문지
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• 제24권2호
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• pp.9-17
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• 2024

본 논문은 기업 내 생성형 AI(Generative Artificial Intelligence) 시스템의 보안 위협과 대응 방안을 제시한다. AI 시스템이 방대한 데이터를 다루면서 기업의 핵심 경쟁력을 확보하는 한편, AI 시스템을 표적으로 하는 보안 위협에 대비해야 한다. AI 보안 위협은 기존 사람을 타겟으로 하는 사이버 보안 위협과 차별화된 특징을 가지므로, AI에 특화된 대응 체계 구축이 시급하다. 본 연구는 AI 시스템 보안의 중요성과 주요 위협 요인을 분석하고, 기술적/관리적 대응 방안을 제시한다. 먼저 AI 시스템이 구동되는 IT 인프라 보안을 강화하고, AI 모델 자체의 견고성을 높이기 위해 적대적 학습 (adversarial learning), 모델 경량화(model quantization) 등 방어 기술을 활용할 것을 제안한다. 아울러 내부자 위협을 감지하기 위해, AI 질의응답 과정에서 발생하는 이상 징후를 탐지할 수 있는 AI 보안 체계 설계 방안을 제시한다. 또한 사이버 킬 체인 개념을 도입하여 AI 모델 유출을 방지하기 위한 변경 통제와 감사 체계 확립을 강조한다. AI 기술이 빠르게 발전하는 만큼 AI 모델 및 데이터 보안, 내부 위협 탐지, 전문 인력 육성 등에 역량을 집중함으로써 기업은 안전하고 신뢰할 수 있는 AI 활용을 통해 디지털 경쟁력을 제고할 수 있을 것이다.

• 대한원격탐사학회지
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• 제40권3호
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• pp.257-268
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• 2024

Drone-mounted hyperspectral sensors (DHSs) have revolutionized remote sensing in agriculture by offering a cost-effective and flexible platform for high-resolution spectral data acquisition. Their ability to capture data at low altitudes minimizes atmospheric interference, enhancing their utility in agricultural monitoring and management. This study focused on addressing the challenges of radiometric and geometric distortions in preprocessing drone-acquired hyperspectral data. Radiometric correction, using the empirical line method (ELM) and spectral reference panels, effectively removed sensor noise and variations in solar irradiance, resulting in accurate surface reflectance values. Notably, the ELM correction improved reflectance for measured reference panels by 5-55%, resulting in a more uniform spectral profile across wavelengths, further validated by high correlations (0.97-0.99), despite minor deviations observed at specific wavelengths for some reflectors. Geometric correction, utilizing a rubber sheet transformation with ground control points, successfully rectified distortions caused by sensor orientation and flight path variations, ensuring accurate spatial representation within the image. The effectiveness of geometric correction was assessed using root mean square error(RMSE) analysis, revealing minimal errors in both east-west(0.00 to 0.081 m) and north-south directions(0.00 to 0.076 m).The overall position RMSE of 0.031 meters across 100 points demonstrates high geometric accuracy, exceeding industry standards. Additionally, image mosaicking was performed to create a comprehensive representation of the study area. These results demonstrate the effectiveness of the applied preprocessing techniques and highlight the potential of DHSs for precise crop health monitoring and management in smart agriculture. However, further research is needed to address challenges related to data dimensionality, sensor calibration, and reference data availability, as well as exploring alternative correction methods and evaluating their performance in diverse environmental conditions to enhance the robustness and applicability of hyperspectral data processing in agriculture.

• 아태비즈니스연구
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• 제14권4호
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• pp.233-254
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• 2023

Purpose - This study aims to verify whether the effect of tax avoidance on corporate value is non-linear in the Korean financial markets. Design/methodology/approach - This study believes that the cause of the inconsistent empirical analysis results of previous studies that verified the relationship between tax avoidance and firm value may be an error in assuming linearity, and verifies whether a nonlinear relationship exists. The sample company in this study is a December settlement corporation listed on the Korean stock market, and the analysis period is from 2000 to 2021. In the empirical analysis model, Tobin's Q is used as a proxy for corporate value, tax avoidance is used as the main independent variable, and a regression model is designed with corporate size, growth rate, and debt ratio set as control variables. Findings - As a result of the empirical analysis, it can be confirmed that there is an inverted U-shaped nonlinear relationship between tax avoidance and corporate value. In the additional analysis using Ohlson (1995) firm valuation model for the robustness of the results of the empirical analysis, the same nonlinear value relationship between tax avoidance can be confirmed. Research implications or Originality - This study is considered to be meaningful in that it verifies the non-linear relationship of tax avoidance, which has not been attempted in previous studies. The meaning of the inverted U-shaped nonlinear relationship presented in this study is that corporate tax avoidance acts as a factor that increases corporate value up to a certain level, but rather becomes a factor that decreases corporate value when it exceeds a critical point. These results are expected to provide new perspectives and perspectives on tax avoidance to companies belonging to the Korean capital market.

• Journal of Pharmaceutical Investigation
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• 제35권3호
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• pp.137-142
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• 2005

A rapid, selective and sensitive reversed-phase HPLC method for the determination of glipizide in human serum was validated and applied to the pharmacokinetic study of glipizide. Glipizide and internal standard, tolbutamide, were extracted from human serum by liquid-liquid extraction with benzene and analyzed on a Nova Pak $C_{18}\;60{\AA}$ column with the mobile phase of acetonitrile-potassium dihydrogen phosphate (10 mM, pH 3.5) (4:6, v/v). Detection wavelength of 275 nm and flow rate of 0.7 ml/min were fixed for the study. The assay robustness for the changes of mobile phase pH, organic solvent content, and flow rate was confirmed by $3^3$ factorial design using a fixed glipizide concentration (500 ng/ ml) with respect to its peak area and retention time. And also, the ruggedness of this method was investigated at three different laboratories using same quality control (QC) samples. This method showed linear response over the concentration range of 10-1000 ng/ml with correlation coefficient greater than 0.999. The lower limit of quantitation using 0.5 ml of serum was 10.0 ng/ml, which was sensitive enough for pharmacokinetic studies. The overall accuracy of the quality control samples ranged from 82.6 to 105.0% for glipizide with overall precision (% C.V.) being 1.13-13.20%. The percent recovery for human serum was in the range of 85.2 93.5%. Stability studies showed that glipizide was stable during storage, or during the assay procedure in human serum. The peak area and retention time of glipizide were not significantly affected by the changes of mobile phase pH, organic solvent content, and flow rate under the conditions studied. This method showed good ruggedness (within 15% C.V.) and was successfully used for the analysis of glipizide in human serum samples for the pharmacokinetic studies at three different laboratories, demonstrating the suitability of the method.

• Journal of Pharmaceutical Investigation
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• 제35권6호
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• pp.437-443
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• 2005

A rapid, selective and sensitive reversed-phase HPLC method for the determination of a major metabolite of terfenadine, fexofenadine, in human serum was developed, validated, and applied to the pharmacokinetic study of terfenadine. Fexofenadine and internal standard, haloperidol were extracted from human serum by liquid-liquid extraction with acetonitrile and analyzed on a $Symmetry^{TM}$ C8 column with the mobile phase of 1% triethylamine phosphate (pH 3.7)-acetonitrile (67:33, v/v, adjusted to pH 5.6 with triethylamine). Detection wavelength of 230 nm for excitation, 280 nm for emission and flow rate of 1.0 mL/min were fixed for the study. The assay robustness for the changes of mobile phase pH, organic solvent content, and flow rate was confirmed by $3^{3}$ factorial design using a fixed fexofenadine concentration (50 ng/mL) with respect to its peak area and retention time. In addition, the ruggedness of this method was investigated at three different laboratories using same quality control (QC) samples. This method showed linear response over the concentration range of 10-500 ng/mL with correlation coefficients greater than 0.999. The lower limit of quantification using 0.5 mL of serum was 10 ng/mL, which was sensitive enough for the pharmacokinetic studies of terfenadine. The overall accuracy of the quality control samples ranged from 95.70 to 114.58% for fexofenadine with overall precision (% C.V.) being 3.53-14.39%. The relative mean recovery of fexofenadine for human serum was 90.17%. Stability studies (freeze-thaw, short-term, extracted serum sample and stock solution) showed that fexofenadine was stable during storage, or during the assay procedure in human serum. However, the storage at $-70^{\circ}C$ for 4 weeks showed that fexofenadine was not stable. The peak area and retention time of fexofenadine were not significantly affected by the changes of mobile phase pH, organic solvent content, and flow rate under the conditions studied. This method showed good ruggedness (within 15% C.V.) and was successfully used for the analysis of fexofenadine in human serum samples for the pharmacokinetic studies of orally administered Tafedine tablet (60 mg as terfenadine) at three different laboratories, demonstrating the suitability of the method.