• Journal of Astronomy and Space Sciences
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• v.26 no.1
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• pp.59-74
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• 2009

Relative navigation system is presented using GPS measurements from a single-channel global positioning system (GPS) simulator. The objective of this study is to provide the real-time inter-satellite relative positions as well as absolute positions for two formation flying satellites in low earth orbit. To improve the navigation performance, the absolute states are estimated using ion-free GRAPHIC (group and phase ionospheric correction) pseudo-ranges and the relative states are determined using double differential carrier-phase data and singled-differential C/A code data based on the extended Kalman filter and the unscented Kalman filter. Furthermore, pseudo-relative dynamic model and modified relative measurement model are developed. This modified EKF method prevents non-linearity of the measurement model from degrading precision by applying linearization about absolute navigation solutions not about the priori estimates. The LAMBDA method also has been used to improve the relative navigation performance by fixing ambiguities to integers for precise relative navigation. The software-based simulation has been performed and the steady state accuracies of 1 m and 6 mm ($1{\sigma}$ of 3-dimensional difference errors) are achieved for the absolute and relative navigation using EKF for a short baseline leader/follower formation. In addition, the navigation performances are compared for the EKF and the UKF for 10 hours simulation, and relative position errors are mm-level for the two filters showing the similar trends.

• Earthquakes and Structures
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• v.13 no.5
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• pp.497-507
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• 2017

Flooding induced scour has been long recognized as a major hazard to river-crossing bridges. Many studies in recent years have attempted to evaluate the effects of scour on the seismic performance of bridges, and probabilistic frameworks are usually adopted. However, direct and straightforward insight about how foundation scour affects bridges as a type of soil-foundation-structure system is usually understated. In this paper, we provide a comprehensive review of applied methods centering around seismic assessment of scoured bridges considering soil-foundation-structure interaction. When introducing these applied analysis and modeling methods, a simple bridge model is provided to demonstrate the use of these methods as a case study. Particularly, we propose the use of nonlinear modal pushover analysis as a rapid technique to model scoured bridge systems, and numerical validation and application of this procedure are given using the simple bridge model. All methods reviewed in this paper can serve as baseline components for performing probabilistic vulnerability or risk assessment for any river-crossing bridge system subject to flood-induced scour and earthquakes.

• Journal of Information Processing Systems
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• v.17 no.5
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• pp.867-878
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• 2021

In context awareness and user intention tasks, dataset construction is expensive because specific domain data are required. Although pretraining with a large corpus can effectively resolve the issue of lack of data, it ignores domain knowledge. Herein, we concentrate on data domain knowledge while addressing data scarcity and accordingly propose a multi-channel long short-term memory (LSTM). Because multi-channel LSTM integrates pretrained vectors such as task and general knowledge, it effectively prevents catastrophic forgetting between vectors of task and general knowledge to represent the context as a set of features. To evaluate the proposed model with reference to the baseline model, which is a single-channel LSTM, we performed two tasks: voice phishing with context awareness and movie review sentiment classification. The results verified that multi-channel LSTM outperforms single-channel LSTM in both tasks. We further experimented on different multi-channel LSTMs depending on the domain and data size of general knowledge in the model and confirmed that the effect of multi-channel LSTM integrating the two types of knowledge from downstream task data and raw data to overcome the lack of data.

• Journal of Korean Society of Rural Planning
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• v.27 no.2
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• pp.77-89
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• 2021

To predict the level of residents' participation in rural tourism project, we used agent-based model. The decision-making mechanism which calculates the utility related to attitude, subjective norm, perceived behavioral control of planned behavior theory was applied to the residents' decision to participate. As a result of the simulation over a period of 20 years, in the baseline scenario set similar to the general process of promoting rural projects, the proportion of indigenous people decreased and the participation rate decreased. In the scenarios with different learning frequencies in perceived behavioral control, overall participation rate decreased. Learning every five years had the effect of increasing the participation rate slightly. Participation rates increased significantly in the scenario that consider economic aspects and reputation in attitude and did not decline in the scenario where population composition was maintained. The virtuous cycle effect of subjective norm according to changes in participation rate due to influence of attitude and perceived behavioral control shows the dynamic relationship.

• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1057-1070
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• 2021

Case-related news filtering is crucial in legal text mining and divides news into case-related and case-unrelated categories. Because case-related news originates from various fields and has different writing styles, it is difficult to establish complete filtering rules or keywords for data collection. In addition, the labeled corpus for case-related news is sparse; therefore, to train a high-performance classification model, it is necessary to annotate the corpus. To address this challenge, we propose topic-enhanced positive-unlabeled learning, which selects positive and negative samples guided by topics. Specifically, a topic model based on a variational autoencoder (VAE) is trained to extract topics from unlabeled samples. By using these topics in the iterative process of positive-unlabeled (PU) learning, the accuracy of identifying case-related news can be improved. From the experimental results, it can be observed that the F1 value of our method on the test set is 1.8% higher than that of the PU learning baseline model. In addition, our method is more robust with low initial samples and high iterations, and compared with advanced PU learning baselines such as nnPU and I-PU, we obtain a 1.1% higher F1 value, which indicates that our method can effectively identify case-related news.

• Journal of Auto-vehicle Safety Association
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• v.14 no.4
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• pp.27-34
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• 2022

In the development of automated driving, interest in the interior parts of vehicle is to become more significant in terms of the occupant safety and comfort. This study proposed an optimal design of front seat according to the design requirements for frame stiffness and seat comfort. For the seat comfort, the appropriate foam thickness was obtained using the structural analysis under reclined occupant loadings. While the strength and stiffness analyses were performed to evaluate the seat frame structure. Topology optimization was carried out based on the simulation results and the derived optimal model and baseline seat design was updated. The conceptual seat design for the autonomous vehicle in this study showed that the model development process is appropriate for the design parameters in both frame stiffness and seat comfort.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2606-2626
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• 2022

In this paper, a unmanned aerial vehicle (UAV) access deployment algorithm is proposed, which is based on an improved virtual force model to solve the poor coverage quality of UAVs caused by limited number of UAVs and random mobility of users in the deployment process of UAV base station. First, the UAV-adapted Harris Hawks optimization (U-AHHO) algorithm is proposed to maximize the coverage of users in a given hotspot. Then, a virtual force improvement model based on user perception (UP-VFIM) is constructed to sense the mobile trend of mobile users. Finally, a UAV motion algorithm based on multi-virtual force sharing (U-MVFS) is proposed to improve the ability of UAVs to perceive the moving trend of user equipments (UEs). The UAV independently controls its movement and provides follow-up services for mobile UEs in the hotspot by computing the virtual force it receives over a specific period. Simulation results show that compared with the greedy-grid algorithm with different spacing, the average service rate of UEs of the U-AHHO algorithm is increased by 2.6% to 35.3% on average. Compared with the baseline scheme, using UP-VFIM and U-MVFS algorithms at the same time increases the average of 34.5% to 67.9% and 9.82% to 43.62% under different UE numbers and moving speeds, respectively.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.904-911
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• 2022

Occlusion is one of the most challenging problems for computer vision-based construction monitoring. Due to the intrinsic dynamics of construction scenes, vision-based technologies inevitably suffer from occlusions. Previous researchers have proposed the occlusion handling methods by leveraging the prior information from the sequential images. However, these methods cannot be employed for construction object detection in non-sequential images. As an alternative occlusion handling method, this study proposes a data augmentation-based framework that can enhance the detection performance under occlusions. The proposed approach is specially designed for rebar occlusions, the distinctive type of occlusions frequently happen during construction worker detection. In the proposed method, the artificial rebars are synthetically generated to emulate possible rebar occlusions in construction sites. In this regard, the proposed method enables the model to train a variety of occluded images, thereby improving the detection performance without requiring sequential information. The effectiveness of the proposed method is validated by showing that the proposed method outperforms the baseline model without augmentation. The outcomes demonstrate the great potential of the data augmentation techniques for occlusion handling that can be readily applied to typical object detectors without changing their model architecture.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.3
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• pp.8-17
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• 2024

There is a demand for introducing a challenging and innovative R&D system to develop new technologies to generate weapon system requirements. Despite the increasing trend in annual core technology development tasks, the infrastructure expansion, including personnel in research management institutions, is relatively insufficient. This situation continuously exposes difficulties in task planning, selection, execution, and management. Therefore, there is a pressing need for strategies to initiate timely research and development and enhance budget execution efficiency through the streamlining of task agreement schedules. In this study, we propose a strategic model utilizing a flexible workforce model, considering constraints and optimizing workload distribution through resource allocation to minimize bottlenecks for efficient task agreement schedules. Comparative analysis with the existing operational environment confirms that the proposed model can handle an average of 67 more core technology development tasks within the agreement period compared to the baseline. In addition, the risk management analysis, which considered the probabilistic uncertainty of the fluctuating number of core technology research and development projects, confirmed that up to 115 core technology development can be contracted within the year under risk avoidance.

• Korean Journal of Organic Agriculture
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• v.32 no.2
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• pp.137-159
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• 2024

Major crop prices have been raised significantly in recent years by COVID-19, the war in Ukraine, and weather-induced reductions in South American soybean production by unfavoured weather in 2022. Rising international crop prices are likely to destabilize food security in South Korea, which is highly dependent on foreign crops. This study analyzed the impact of soybean import price changes on the domestic soybean market and soybean food self-sufficiency rate from 2024 to 2029 using a dynamic partial equilibrium model. According to the scenario analysis results, if the import prices rise by 10% compared to the baseline, the soybean food self-sufficiency rate would increase by 1.33% in 2024, but it is expected to decrease to -0.58% in 2029 due to the continuous decrease in production. The results of this study are expected to be used as valuable information for policy authorities in establishing policies related to improving food self-sufficiency.