• Smart Structures and Systems
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• v.26 no.3
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• pp.291-309
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• 2020

To study oceanic and meteorological problems related to climate change, Korea has been operating several ocean research stations (ORSs). In 2011, the Gageocho ORS was attacked by Typhoon Muifa, and its structural members and several observation devices were severely damaged. After this event, the Gageocho ORS was rehabilitated with 5 m height to account for 100-yr extreme wave height, and the vibration measurement system was equipped to monitor the structural vibrational characteristics including natural frequencies and modal damping ratios. In this study, a mass reallocation method is presented for structural model updating of the Gageocho ORS based on the experimentally identified natural frequencies. A preliminary finite element (FE) model was constructed based on design drawings, and several of the candidate baseline FE models were manually built, taking into account the different structural conditions such as corroded thickness. Among these candidate baseline FE models, the most reasonable baseline FE model was selected by comparing the differences between the identified and calculated natural frequencies; the most suitable baseline FE model was updated based on the identified modal properties, and by using the pattern search method, which is one of direct search optimization methods. The mass reallocation method is newly proposed as a means to determine the equivalent mass quantities along the height and in a floor. It was found that the natural frequencies calculated based on the updated FE model was very close to the identified natural frequencies. In conclusion, it is expected that these results, which were obtained by updating a baseline FE model, can be useful for establishing the reference database for jacket-type offshore structures, and assessing the structural integrity of the Gageocho ORS.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.2666-2671
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• 2011

In this paper, a theoretical approach was studied to make a baseline box type model satisfying the stiffness condition of a cut-out model. First, we compared the sum of the sectional theoretical deflections and the FEM result of the cut-out model under the static load test conditions, and we obtained good correlations from both the results. Second, To obtain the thickness of the baseline model, we used the mean value of geometric moment of intertia of the side wall and roof structure. Also, we compared the theoretical results and the FEM result of a baseline model, and we obtained good correlations. It is considered that the developed theoretical approach can be used for the weight reduction of train carbodies.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.218-226
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• 2020

An experimental study was conducted on baseline model updating and damage estimation techniques for the health monitoring of offshore wind turbine tripod substructures. First, a procedure for substructure health monitoring was proposed. An initial baseline model for a scaled model of a tripod substructure was established. A baseline model was updated based on the natural frequencies and the mode shapes measured in the healthy state. A training pattern was then generated using the updated baseline model, and the damage was estimated by inputting the modal parameters measured in the damaged state into the trained neural network. The baseline model could be updated reasonably using the effective fixity model. The damage tests were performed, and the damage locations could be estimated reasonably. In addition, the estimated damage severity also increased as the actual damage severity increased. On the other hand, when the damage severity was relatively small, the corresponding damage location was detected, but it was more difficult to identify than the other cases. Further studies on small damage estimation and stiffness reduction quantification will be needed before the presented method can be used effectively for the health monitoring of tripod substructures.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.103-114
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• 2023

This study collected various data of urban roadways to analyze the effect of travel speed change, and a GRU-based short-term travel speed prediction model was developed using such big data. The baseline model and the double exponential smoothing model were selected as comparison models, and prediction errors were evaluated using the RMSE index. The model evaluation results revealed that the average RMSE of the baseline model and the double exponential smoothing model were 7.46 and 5.94, respectively. The average RMSE predicted by the GRU model was 5.08. Although there are deviations for each of the 15 links, most cases showed minimal errors in the GRU model, and the additional scatter plot analysis presented the same result. These results indicate that the prediction error can be reduced, and the model application speed can be improved when applying the GRU-based model in the process of generating travel speed information on urban roadways.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.2
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• pp.157-165
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• 2016

To provide the baseline for pricing, this paper proposes pricing-by-rating (PBR) as pricing model at micro-foundations level that can work as the baseline for all pricing models as well as an assessment criterion of business model in all circumstances. It sets up firstly WTP (willingness to pay/purchase) model from explicit needs and develops PBR based on the ordinal scale of the difference between the WTP and the WTS (willingness to supply/sell) by comparing individually the corresponding element/component of a firm's actual marketing mix 4P with that of the best SPEC (solution, price indicator by WTP, encouragement, channel) as an ideal 4P a customer expects and also by comparing the interaction between the 4P and the best SPEC as a whole collectively. And through illustrations it shows its applicability to evaluating business model in practice and finally asserts that PBR works as the baseline for pricing policy and as a criterion of business model evaluation in any circumstances.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.6
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• pp.319-325
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• 2023

In this study, we proposed a method for hyperparameter optimization in the building and training of a deep learning model designed to process point cloud data collected by a millimeter-wave radar system. The primary aim of this study is to facilitate the deployment of a baseline model in resource-constrained IoT devices. We evaluated a RadHAR baseline deep learning model trained on a public dataset composed of point clouds representing five distinct human activities. Additionally, we introduced a coarse-to-fine hyperparameter optimization procedure, showing substantial potential to enhance model efficiency without compromising predictive performance. Experimental results show the feasibility of significantly reducing model size without adversely impacting performance. Specifically, the optimized model demonstrated a 3.3% improvement in classification accuracy despite a 16.8% reduction in number of parameters compared th the baseline model. In conclusion, this research offers valuable insights for the development of deep learning models for resource-constrained IoT devices, underscoring the potential of hyperparameter optimization and model size reduction strategies. This work contributes to enhancing the practicality and usability of deep learning models in real-world environments, where high levels of accuracy and efficiency in data processing and classification tasks are required.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1997.04a
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• pp.97-105
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• 1997

A methodology to identify a baseline modal model of a complicated 3-D structure using limited structural and modal information is experimentally examined. In the first part, a system's identification theory for the methodology to identify, baseline modal responses of the structure is outlined. Next, an algorithm is designed to build a generic finite element model of the baseline structure and to calibrate the model by using only a set of post-damage modal parameters. In the second part, the feasibility of the methodology is examined experimentally using a field-tested truss bridge far which only post-damaged modal responses were measured for a few vibration modes. For the complex 3-D bridge with many members, we analyzed to identify unknown stiffness parameters of the structure by using modal parameters of the initial two modes of vibration.

• Journal of Korean Society of Steel Construction
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• v.10 no.2 s.35
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• pp.279-290
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• 1998

An existing Damage Index Method is verified to demonstrate its feasibility for detecting structural damage in truss bridges (1) for which modal parameters are available for a few modes of vibration and (2) for which baseline modal information is not available from its as-built state. The theory of approach to detect locations of damage and to identify baseline modal model is summarized on the basis of system identification theory and modal sensitivity theory. The feasibility of the Damage Index Method is demonstrated using a numerical example of a truss bridge with 11 subsystems of 211 members and for which only two modes of vibration were recorded for post-damaged state.

• Journal of Radiation Protection and Research
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• v.46 no.3
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• pp.83-97
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• 2021

Background: The lifetime risk of lung cancer incidence due to radiation for nonsmokers is overestimated because of the use of the average cancer baseline risk among a mixed population, including smokers. In recent years, the generalized multiplicative (GM)-excess relative risk (ERR) model has been developed in the life span study of atomic bomb survivors to consider the joint effect of radiation and smoking. Based on this background, this paper discusses the issues of radiation risk assessment considering smoking in two parts. Materials and Methods: In Part 1, we proposed a simple method of estimating the baseline risk for nonsmokers using current smoking data. We performed sensitivity analysis on baseline risk estimation to discuss the birth cohort effects. In Part 2, we applied the GM-ERR model for Japanese smokers to calculate lifetime attributable risk (LAR). We also performed a sensitivity analysis using other ERR models (e.g., simple additive (SA)-ERR model). Results and Discussion: In Part 1, the lifetime baseline risk from mixed population including smokers to nonsmokers decreased by 54% (44%-60%) for males and 24% (18%-29%) for females. In Part 2, comparison of LAR between SA- and GM-ERR models showed that if the radiation dose was ≤200 mGy or less, the difference between these ERR models was within the standard deviation of LAR due to the uncertainty of smoking information. Conclusion: The use of mixed population for baseline risk assessment overestimates the risk for lung cancer due to low-dose radiation exposure in Japanese males.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.10
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• pp.1842-1848
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• 2009

This paper summarizes a data modeling for developing the baseline network analysis software of the Korean energy management system (EMS). The study is concentrated on the following aspects. First, the data for operating the each application software are extracted. Some of the EMS network application softwares are selected for basis model. Those are based on the logical functions of each software and are not considered the other softwares. Second, the common data are extracted for equipment model and topological structure of power system in Korea. We propose the application common model(ACM) that can be applied whole EMS network application softwares. The ACM model includes the hierarchy and non-hierarchy power system structure, and is connected each other using the direct and indirect link. Proposed database model is tested using the Korea Electric Power Corporation(KEPCO) system. The real time SCADA data are provided for the test. Through the test, we verified that the proposed database structure can be effectively used to accomplish the Korean EMS system.