• Smart Structures and Systems
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• v.9 no.2
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• pp.145-164
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• 2012

In this paper, solar-powered, multi-scale, vibration-impedance sensor node on Imote2 platform is presented for hybrid structural health monitoring (SHM) in cable-stayed bridge. In order to achieve the objective, the following approaches are proposed. Firstly, vibration- and impedance-based hybrid SHM methods are briefly described. Secondly, the multi-scale vibration and impedance sensor node on Imote2-platform is presented on the design of hardware components and embedded software for vibration- and impedance-based SHM. In this approach, a solar-powered energy harvesting is implemented for autonomous operation of the smart sensor nodes. Finally, the feasibility and practicality of the smart sensor-based SHM system is evaluated on a full-scale cable-stayed bridge, Hwamyung Bridge in Korea. Successful level of wireless communication and solar-power supply for smart sensor nodes are verified. Also, vibration and impedance responses measured from the target bridge which experiences various weather conditions are examined for the robust long-term monitoring capability of the smart sensor system.

• Smart Structures and Systems
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• v.24 no.5
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2013.11a
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• pp.28-29
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• 2013

We have been measuring $CH_4$ flux in a rice paddy in Gimje using the eddy covariance method since July 2011. In order to measure the fast fluctuations of $CH_4$ concentration, an innovative LI-7700 open-path laser spectrometer is used. This high-precision, low power, light weight, low maintenance sensor enables us to operate it on a continuous and long-term basis. One particular feature, among other things, is the self-cleaning lower mirror which decreases maintenance requirements while ensuring more robust, continuous, high-quality dataset. Its cleaning is initiated at user-specified time intervals or a signal strength threshold, and its status is recorded as a diagnostic index. We have noticed that the operation of LI-7700 at Gimje site is quite challenging particularly due to its frequent mirror cleaning requirement and the associated sensitivity of the instrument. In this presentation, we present some field observation data regarding the mirror cleaning and their analysis, thereby suggesting the pertinent operation options for high-quality, maximum data retrieval in the field.

• International Journal of Industrial Entomology and Biomaterials
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• v.22 no.2
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• pp.65-74
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• 2011

The Scarites aterrimus (Coleoptera: Carabidae), is one of the carabid beetles dwelling exclusively on coastal sandy dunes. Habitat deterioration and equivalent activity have greatly concerned population declines in several species dwelling on the coastal sandy dunes. As a first step to establish long-term conservation strategy, we investigated the nation-wide magnitude and nature of genetic diversity of the species. As a first step, we sequenced a portion of mitochondrial COI gene, corresponding to "DNA Barcode" region (658 bp) from a total of 24 S. aterrimus individuals collected over nine sandy dunes belonging to four Korean provinces. The sequence analysis evidenced moderate to low magnitude of sequence diversity compared with other insect species distributed in Korean peninsula (0.152% to 0.912%). The presence of closely related haplotypes and relatively high gene flow estimate collectively suggest that there had been no historical barriers that bolster genetic subdivision. Population decline was postulated on the basis of several missing haplotypes that are well found in the species with a large population size. This interpretation is consistent with field observation of small population size in the coastal sandy dune habitats. The highest genetic diversity estimates were found in the coastal sand dune population of Seogwipo, Jeju Island, justifying a prior attention to the population, in order to sustain overall genetic diversity of the species. Further scrutinized study might be required for further robust conclusion.

• Journal of Radiation Protection and Research
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• v.46 no.2
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• pp.66-79
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• 2021

Background: The Japan Atomic Energy Agency (JAEA) is specified in the Disaster Counter-measures Basic Act as a designated public corporation for dealing with nuclear disasters. Materials and Methods: The Nuclear Emergency Assistance and Training Center (NEAT) was established in 2002 as the activity base providing technical assistance to both national and local governments during nuclear emergencies. The NEAT has a robust structure and utilities and special installations, and it organizes training and exercises. Results and Discussion: Due to an offshore earthquake that caused a devastating tsunami in March 2011, a nuclear accident occurred at the Tokyo Electric Power Company's Fukushima Daiichi Nuclear Power Station. The NEAT responded by conducting off-site environmental radiation monitoring and contamination screening, dispatching special vehicles, offering telephone consultations, and calculating the dispersion of radioactive materials. An examination of the emergency response activities revealed that the organization was prepared for these types of disasters and was able to plan long-term response. Conclusion: As a designated public corporation, the JAEA technically supports the national government, the Fukushima prefectural government, and the Ibaraki prefectural government, all of which responded to the off-site emergencies resulting from the March 2011 Fukushima Daiichi accident

• Smart Media Journal
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• v.7 no.4
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• pp.17-23
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• 2018

We proposed a LSTM-based RNN model that can effectively perform the automatic spacing characteristics. For those long or noisy sentences which are known to be difficult to handle within Neural Network Learning, we defined a proper input data format and decoding data format, and added dropout, bidirectional multi-layer LSTM, layer normalization, and attention mechanism to improve the performance. Despite of the fact that Sejong corpus contains some spacing errors, a noise-robust learning model developed in this study with no overfitting through a dropout method helped training and returned meaningful results of Korean word spacing and its patterns. The experimental results showed that the performance of LSTM sequence-to-sequence model is 0.94 in F1-measure, which is better than the rule-based deep-learning method of GRU-CRF.

• Smart Structures and Systems
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• v.24 no.1
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• pp.53-65
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• 2019

Misaligned wind-wave and seismic loading render offshore wind turbines suffering from excessive bi-directional vibration. However, most of existing research in this field focused on unidirectional vibration mitigation, which is insufficient for research and real application. Based on the authors' previous work (Sun and Jahangiri 2018), the present study uses a three dimensional pendulum tuned mass damper (3d-PTMD) to mitigate the nacelle structural response in the fore-aft and side-side directions under wind, wave and near-fault ground motions. An analytical model of the offshore wind turbine coupled with the 3d-PTMD is established wherein the interaction between the blades and the tower is modelled. Aerodynamic loading is computed using the Blade Element Momentum (BEM) method where the Prandtl's tip loss factor and the Glauert correction are considered. Wave loading is computed using Morison equation in collaboration with the strip theory. Performance of the 3d-PTMD is examined on a National Renewable Energy Lab (NREL) monopile 5 MW baseline wind turbine under misaligned wind-wave and near-fault ground motions. The robustness of the mitigation performance of the 3d-PTMD under system variations is studied. Dual linear TMDs are used for comparison. Research results show that the 3d-PTMD responds more rapidly and provides better mitigation of the bi-directional response caused by misaligned wind, wave and near-fault ground motions. Under system variations, the 3d-PTMD is found to be more robust than the dual linear TMDs to overcome the detuning effect. Moreover, the 3d-PTMD with a mass ratio of 2% can mitigate the short-term fatigue damage of the offshore wind turbine tower by up to 90%.

• The Journal of shipping and logistics
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• v.34 no.4
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• pp.695-710
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• 2018

Following the collapse of Hanjin Shipping, building a strong maritime cluster is one of the policy measures for the re-construction of the Korean shipping and shipbuilding industries. Thus, the purpose of this study is to develop a policy alternative for building a maritime cluster. Using Porter's diamond models, we analyzed the Imabari maritime cluster of Japan, which is characterized by cooperation between key industries, such as shipping, shipbuilding, shipbuilding equipment, and finance. The Imabari Maritime Cluster is equipped with complete domestic demand conditions and related supporting industry conditions. Although the strengths of the production conditions include excellent family-based management and strong support from regional administration and banks which develops independency among cluster members, the weak points include the absense of robust port services and difficulty in recruiting young talent in small and medium-sized cities. We can confirm that the company's strategy is focused on stable management, rather than a short-term view.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.99-109
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• 2019

We compared the confidence intervals of estimators using various bootstrap methods for a Random Coefficient Autoregressive(RCA) model. We consider a Quasi score estimator and M-Quasi score estimator using Huber, Tukey, Andrew and Hempel functions as bounded functions, that do not have required assumption of distribution. A standard bootstrap method, percentile bootstrap method, studentized bootstrap method and hybrid bootstrap method were proposed for the estimations, respectively. In a simulation study, we compared the asymptotic confidence intervals of the Quasi score and M-Quasi score estimator with the bootstrap confidence intervals using the four bootstrap methods when the underlying distribution of the error term of the RCA model follows the normal distribution, the contaminated normal distribution and the double exponential distribution, respectively.

• Journal of Korean Society for Quality Management
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• v.50 no.3
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• pp.473-490
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• 2022

Purpose: The purpose of this study is to propose a new useful suggestion to monitor the stability of process by developing a stability ratio or index related to investigating how well the process is controlled or operated to the specified target. Methods: The proposed method to monitor the stability of process is building up a new measure index which is making up for the weakness of the existing index in terms of short or long term period of production. This new index is a combined one considering both stability and capability of process to the specification limits. We suppose that both process mean and process variation(or deviation) are changing on time period. Results: The results of this study are as follows: regarding the stability of process as well as capability of process, it was shown that two indices, called SI(stability index) and PI(performance index), can be expressed in two-dimensional X-Y graph simultaneously. This graph is categorized as 4 separated partitions, which are characterized by its numerical value intervals of SI and PI which are evaluated by test statistics. Conclusion: The new revised index is more robust than the existing one in investigating the stability of process in terms of short and long period of production, even in case both process mean and variation are changing.