• Journal of Astronomy and Space Sciences
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• v.35 no.4
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• pp.253-261
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• 2018

Korea Astronomy and Space Science Institute (KASI) has been developing the space optical and laser tracking (SOLT) system for space geodesy, space situational awareness, and Korean space missions. The SOLT system comprises satellite laser ranging (SLR), adaptive optics (AO), and debris laser tracking (DLT) systems, which share numerous subsystems, such as an optical telescope and tracking mount. It is designed to be capable of laser ranging up to geosynchronous Earth orbit satellites with a laser retro-reflector array, space objects imaging brighter than magnitude 10, and laser tracking low Earth orbit space debris of uncooperative targets. For the realization of multiple functions in a novel configuration, the SOLT system employs a switching mirror that is installed inside the telescope pedestal and feeds the beam path to each system. The SLR and AO systems have already been established at the Geochang station, whereas the DLT system is currently under development and the AO system is being prepared for testing. In this study, the design and development of the SOLT system are addressed and the SLR data quality is evaluated compared to the International Laser Ranging Service (ILRS) tracking stations in terms of single-shot ranging precision. The analysis results indicate that the SLR system has a good ranging performance, to a few millimeters precision. Therefore, it is expected that the SLR system will not only play an important role as a member of the ILRS tracking network, but also contribute to future Korean space missions.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.3
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• pp.29-38
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• 2006

Recent advances in wireless communication technology promotes many researches related to sensor network and brings several proposals to fit into various types of sensor network communication. The research direction for sensor network is divided into the method to maximize an energy efficiency and security researches that has not been remarkable so far. To maximize an energy efficiency, the methods to support data aggregation and cluster-head selection algorithm are proposed. To strengthen the security, the methods to support encryption techniques and manage a secret key that is applicable to sensor network are proposed, In. However, the combined method to satisfy both energy efficiency and security is in the shell. This paper is devoted to design the protocol that combines an efficient clustering protocol with key management algorithm that is fit into various types of sensor network communication. This protocol may be applied to sensor network systems that deal with sensitive data.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.159-166
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• 2022

Artificial Intelligence has stimulated every aspect of today's life. Human thinking quality is trying to be involved through digital tools in all research areas of the modern era. The education industry is also leveraging artificial intelligence magical power. Uses of digital technologies in pedagogical paradigms are being observed from the last century. The widespread involvement of artificial intelligence starts reshaping the educational landscape. Adaptive learning is an emerging pedagogical technique that uses computer-based algorithms, tools, and technologies for the learning process. These intelligent practices help at each learning curve stage, from content development to student's exam evaluation. The quality of information technology students and professionals training has also improved drastically with the involvement of artificial intelligence systems. In this paper, we will investigate adopted digital methods in the education sector so far. We will focus on intelligent techniques adopted for information technology students and professionals. Our literature review works on our proposed framework that entails four categories. These categories are communication between teacher and student, improved content design for computing course, evaluation of student's performance and intelligent agent. Our research will present the role of artificial intelligence in reshaping the educational process.

• Journal of Astronomy and Space Sciences
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• v.39 no.3
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• pp.117-126
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• 2022

The Ionospheric Anomaly Monitoring by Magnetometer And Plasma-probe (IAMMAP) is one of the scientific instruments for the Compact Advanced Satellite 500-3 (CAS 500-3) which is planned to be launched by Korean Space Launch Vehicle in 2024. The main scientific objective of IAMMAP is to understand the complicated correlation between the equatorial electro-jet (EEJ) and the equatorial ionization anomaly (EIA) which play important roles in the dynamics of the ionospheric plasma in the dayside equator region. IAMMAP consists of an impedance probe (IP) for precise plasma measurement and magnetometers for EEJ current estimation. The designated sun-synchronous orbit along the quasi-meridional plane makes the instrument suitable for studying the EIA and EEJ. The newly-devised IP is expected to obtain the electron density of the ionosphere with unprecedented precision by measuring the upper-hybrid frequency (f_UHR) of the ionospheric plasma, which is not affected by the satellite geometry, the spacecraft potential, or contamination unlike conventional Langmuir probes. A set of temperature-tolerant precision fluxgate magnetometers, called Adaptive In-phase MAGnetometer, is employed also for studying the complicated current system in the ionosphere and magnetosphere, which is particularly related with the EEJ caused by the potential difference along the zonal direction.

• Research in Plant Disease
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• v.29 no.3
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• pp.220-233
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• 2023

The phenomena of global warming has led to an increase in the average air temperature in temperate climates. Springtime frost damage is becoming more common, and after a period of dormancy, damage to buds, blooms, and developing fruits is greater significant than damage from low winter temperatures. Peaches are a crucial crop among moderate fruits. Spring frost damage in peaches can have a negative effect on crop growth, yield, and quality. It is noteworthy that these plants have evolved defenses against spring frost damage while being exposed to a variety of low temperatures in the early spring. In this current review, recent research advancements on spring frost damage avoidance in peaches were deliberated. Additionally, adaptive mechanisms of peach, such as deacclimation and reacclimation, were emphasized. Moreover, the emerging advancements using various omics approaches revealed the peach physiology and molecular mechanisms comprehensively. Furthermore, the use of chemical products and understanding the spring frost mechanisms through the use of environmental chamber temperature stimulation and infrared thermography studies were also discussed. This review is essential groundwork and paves the way to derive and design future research for agronomists and horticulturalists to overcome the challenges of spring frost damage avoidance and crop management in these circumstances.

• Journal of the Earthquake Engineering Society of Korea
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• v.27 no.2
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• pp.77-82
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• 2023

The structural analysis module is an essential part of any integrated structural system. Diverse integrated systems today require, from the analysis module, efficient real-time responses to real-time input such as earthquake signals, extreme weather-related forces, and man-made accidents. An integrated system may also be for the entire life span of a civil structure conceived during the initial conception, developed throughout various design stages, effectively used in construction, and utilized during usage and maintenance. All these integrated systems' essential part is the structural analysis module, which must be automated and computationally efficient so that responses may be almost immediate. The finite element method is often used for structural analysis, and for automation, many effective finite element meshes must be automatically generated for a given analysis. A computationally efficient finite element mesh generation scheme based on the r-h method of mesh refinement using strain deviations from the values at the Gauss points as error estimates from the previous mesh is described. Shape factors are used to sort out overly distorted elements. A standard cantilever beam analyzed by four-node plane stress elements is used as an example to show the effectiveness of the automated algorithm for a time-domain dynamic analysis. Although recent developments in computer hardware and software have made many new applications in integrated structural systems possible, structural analysis still needs to be executed efficiently in real-time. The algorithm applies to diverse integrated systems, including nonlinear analyses and general dynamic problems in earthquake engineering.

• Earthquakes and Structures
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• v.25 no.2
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• pp.125-134
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• 2023

In the present work, a new global damage index is proposed for the seismic performance and failure analysis of concrete gravity dams. Unlike the existing indices of concrete structures, this index doesn't need scaling with an ultimate or an upper value. For this purpose, the Beni-Haroun dam in north-eastern Algeria, is considered as a case study, for which an average seismic capacity curve is first evaluated by performing several incremental dynamic analyses. The seismic performance point of the dam is then determined using the N2 method, considering multiple modes and taking into account the stiffness degradation. The seismic demand is obtained from the design spectrum of the Algerian seismic regulations. A series of recorded and artificial accelerograms are used as dynamic loads to evaluate the nonlinear responses of the dam. The nonlinear behaviour of the concrete mass is modelled by using continuum damage mechanics, where material damage is represented by a scalar field damage variable. This modelling, which is suitable for cyclic loading, uses only a single damage parameter to describe the stiffness degradation of the concrete. The hydrodynamic and the sediment pressures are included in the analyses. The obtained results show that the proposed damage index faithfully describes the successive brittle failures of the dam which increase with increasing applied ground accelerations. It is found that minor damage can occur for ground accelerations less than 0.3 g, and complete failure can be caused by accelerations greater than 0.45 g.

• Geomechanics and Engineering
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• v.31 no.2
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• pp.129-147
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• 2022

The properties of soil are naturally highly variable and thus, to ensure proper safety and reliability, we need to test a large number of samples across the length and depth. In pile foundations, conducting field tests are highly expensive and the traditional empirical relations too have been proven to be poor in performance. The study proposes a state-of-art Particle Swarm Optimization (PSO) hybridized Artificial Neural Network (ANN), Extreme Learning Machine (ELM) and Adaptive Neuro Fuzzy Inference System (ANFIS); and comparative analysis of metaheuristic models (ANN-PSO, ELM-PSO, ANFIS-PSO) for prediction of bearing capacity of pile foundation trained and tested on dataset of nearly 300 dynamic pile tests from the literature. A novel ensemble model of three hybrid models is constructed to combine and enhance the predictions of the individual models effectively. The authenticity of the dataset is confirmed using descriptive statistics, correlation matrix and sensitivity analysis. Ram weight and diameter of pile are found to be most influential input parameter. The comparative analysis reveals that ANFIS-PSO is the best performing model in testing phase (R² = 0.85, RMSE = 0.01) while ELM-PSO performs best in training phase (R² = 0.88, RMSE = 0.08); while the ensemble provided overall best performance based on the rank score. The performance of ANN-PSO is least satisfactory compared to the other two models. The findings were confirmed using Taylor diagram, error matrix and uncertainty analysis. Based on the results ELM-PSO and ANFIS-PSO is proposed to be used for the prediction of bearing capacity of piles and ensemble learning method of joining the outputs of individual models should be encouraged. The study possesses the potential to assist geotechnical engineers in the design phase of civil engineering projects.

• Asia-Pacific Journal of Business
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• v.14 no.3
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• pp.115-147
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• 2023

Purpose - This study examines previous research on organizational silence for several decades since the concept of organizational silence was firstly suggested in 2000. In this study, based on previous studies on organizational silence published in domestic and international journals from 2010 to 2022, research trends were analyzed, issues were derived, and future research was suggested. Design/methodolgy/approach - The authors searched relevant keywords such as organizational silence, employee silence, employee voice and so on in the domestic as well as international academic database. 63 domestic papers were found, and based on these articles, we analyzed the research trends. Findings - Similar variables were proven with only different contextual samples without any originality in the theoretical perspective. Moreover, studies on the causal relationship between each type of organizational silence and the occurrence of organizational silence over time were also insufficient. In addition, because research on public organizations was limited to police officers and public officials, future research is needed to investigate more different organizational situations. Furthermore, other variables such as personal characteristics and leadership factors were also relatively unexplored. Based on these limitations, future research is needed to consider more diverse demographics, Korean cultural factors, organizational characteristics, and the patterns changes in time. Research implications or Originality - This study suggests limitations as well as future directions by summarizing the previous research on organizational silence which is an emerging issue in global societies and the organizational management filed.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3383-3397
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• 2023

Scene graphs serve as semantic abstractions of images and play a crucial role in enhancing visual comprehension and reasoning. However, the performance of Scene Graph Generation is often compromised when working with biased data in real-world situations. While many existing systems focus on a single stage of learning for both feature extraction and classification, some employ Class-Balancing strategies, such as Re-weighting, Data Resampling, and Transfer Learning from head to tail. In this paper, we propose a novel approach that decouples the feature extraction and classification phases of the scene graph generation process. For feature extraction, we leverage a transformer-based architecture and design an adaptive calibration function specifically for predicate classification. This function enables us to dynamically adjust the classification scores for each predicate category. Additionally, we introduce a Distribution Alignment technique that effectively balances the class distribution after the feature extraction phase reaches a stable state, thereby facilitating the retraining of the classification head. Importantly, our Distribution Alignment strategy is model-independent and does not require additional supervision, making it applicable to a wide range of SGG models. Using the scene graph diagnostic toolkit on Visual Genome and several popular models, we achieved significant improvements over the previous state-of-the-art methods with our model. Compared to the TDE model, our model improved mR@100 by 70.5% for PredCls, by 84.0% for SGCls, and by 97.6% for SGDet tasks.