• Journal of Ocean Engineering and Technology
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• v.34 no.3
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• pp.227-236
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• 2020

Underwater acoustics, which is the domain that addresses phenomena related to the generation, propagation, and reception of sound waves in water, has been applied mainly in the research on the use of sound navigation and ranging (SONAR) systems for underwater communication, target detection, investigation of marine resources and environment mapping, and measurement and analysis of sound sources in water. The main objective of remote sensing based on underwater acoustics is to indirectly acquire information on underwater targets of interest using acoustic data. Meanwhile, highly advanced data-driven machine-learning techniques are being used in various ways in the processes of acquiring information from acoustic data. The related theoretical background is introduced in the first part of this paper (Yang et al., 2020). This paper reviews machine-learning applications in passive SONAR signal-processing tasks including target detection/identification and localization.

• Journal of Information Technology Services
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• v.22 no.1
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• pp.1-14
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• 2023

This paper aims to share the course, performance and implications of Project-Based Learning (PBL) education in healthcare data science (HDS). The HDS team of the business group of Soonchunhyang University, which was selected for the health care field of 'University Innovation Project', considered that the health care IT-based education of the current university differs greatly from the rapidly changing health care 3.0 environment of the fourth industry, and emphasized the PBL practice-oriented specialization program as a learning model. The PBL focused on self-directed learning experiences, real analysis problems, and team-oriented classes. In other words, it was implemented with three specialized strategies: 'Field Inside Education', 'Fusion-type Track Education', and 'Training to strengthen resilience and change response'. This collaborative, practical learning experience, etc. resulted in significant results. The results were recognized as being rated A by the Korea Research Foundation and the comprehensive evaluation, and the results were significantly elevated through the analysis of the student survey and the results index.

• Computers and Concrete
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• v.32 no.6
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• pp.625-637
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• 2023

The application of ML approaches in determining the resisting capacity of fire damaged RC columns is introduced in this paper, on the basis of analysis data driven ML modeling. Considering the characteristics of the structural behavior of fire damaged RC columns, the representative five approaches of Kernel SVM, ANN, RF, XGB and LGBM are adopted and applied. Additional partial monotonic constraints are adopted in modelling, to ensure the monotone decrease of resisting capacity in RC column with fire exposure time. Furthermore, additional suggestions are also added to mitigate the heterogeneous composition of the training data. Since the use of ML approaches will significantly reduce the computation time in determining the resisting capacity of fire damaged RC columns, which requires many complex solution procedures from the heat transfer analysis to the rigorous nonlinear analyses and their repetition with time, the introduced ML approach can more effectively be used in large complex structures with many RC members. Because of the very small amount of experimental data, the training data are analytically determined from a heat transfer analysis and a subsequent nonlinear finite element (FE) analysis, and their accuracy was previously verified through a correlation study between the numerical results and experimental data. The results obtained from the application of ML approaches show that the resisting capacity of fire damaged RC columns can effectively be predicted by ML approaches.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.115-115
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• 2019

하천수 흐름예측에 대한 연구는 대부분 WRF-Hydro와 같은 과정기반 모델링 시스템을 이용한다. 과정기반 모델링 시스템은 물리적 현상을 일반화한 수식으로 구성되어있다. 일반화된 수식은 불확실성을 내포하고 있으며 지역적 특성도 반영하지 못한다. 특히 수식에 사용되는 입력자료는 측정값으로 오차가 존재한다. 따라서 과정기반 모델링 시스템 예측결과는 계통오차와 우연오차가 존재한다. 현재 매개변수 보정을 통해 예측결과를 개선하는 방법을 사용하고 있으나 한계가 있다. 본 연구는 이러한 한계를 극복하기 위해 상호보완적인 Data-driven 모델을 구축하여 과정기반 모델링 시스템 결과를 개선하고자 하였다. Data-driven 모델 구축을 위해 머신러닝 기법인 instance-based weighting(IBW)과 support vector regression(SVR)을 사용하였다. 구축된 Data-driven 모델은 한반도 지역 주요 저수지 및 호수의 하천수 흐름예측을 통해 검증하였다. 검증을 위해 과정기반 모델링 시스템으로 WRF-Hydro를 구동하였다. 입력자료는 기상청의 국지수치예측모델자료(LDAPS), HydroSHEDS의 수치표고모델자료(DEM), 국가지리정보원의 저수지 및 호수 연속수치지형도를 사용하였다. 본 연구를 통해 구축된 Data-driven모델은 기존 과정기반 모델링 시스템의 오류수정 한계를 머신러닝을 이용하여 개선할 수 있는 가능성을 제시하였다.

• Journal of English Language & Literature
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• v.56 no.6
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• pp.1047-1065
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• 2010

English inversion constructions are not only hard for non-native speakers to learn but also difficult to teach mainly because of their intriguing grammatical and discourse properties. This paper addresses grammatical issues in learning or teaching the so-called 'predicate inversion (PI)' construction (e.g., Equally important in terms of forest depletion is the continuous logging of the forests). In particular, we chart the grammatical (distributional, syntactic, semantic, pragmatic) properties of the PI construction, and argue for adata-driven teaching for English grammar. To depart from the arm-chaired style of grammar teaching (relying on author-made simple sentences), our teaching method introduces a datadriven teaching. With total 25 university students in a grammar-related class, students together have analyzed the British Component of the International Corpus of English (ICE-GB), containing about one million words distributed across a variety of textual categories. We have identified total 290 PI sentences (206 from spoken and 87 from written texts). The preposed syntactic categories of the PI involve five main types: AdvP, PP, VP(ed/ing), NP, AP, and so, all of which function as the complement of the copula. In terms of discourse, we have observed, supporting Birner and Ward's (1998) observation that these preposed phrases represent more familiar information than the postposed subject. The corpus examples gave us the three possible types: The preposed element is discourse-old whereas the postposed one is discourse-new as in Putting wire mesh over a few bricks is a good idea. Both preposed and postposed elements can also be discourse new as in But a fly in the ointment is inflation. These two elements can also be discourse old as in Racing with him on the near-side is Rinus. The dominant occurrence of the PI in the spoken texts also supports the view that the balance (or scene-setting) in information structure is the main trigger for the use of the PI construction. After being exposed to the real data and in-depth syntactic as well as informationstructure analysis of the PI construction, it is proved that the class students have had a farmore clear understanding of the construction in question and have realized that grammar does not mean to live on by itself but tightly interacts with other important grammatical components such as information structure. The study directs us toward both a datadriven and interactive grammar teaching.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.35 no.2
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• pp.45-52
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• 2019

BEMS(Building Energy Management Systems) have been applied to office buildings and collect relevant building energy data, e.g. temperatures, mass flow rates and energy consumptions of building mechanical systems and indoor spaces. The aforementioned measured data can be beneficially utilized for developing data-driven machine learning models which can be then used as part of MPC(Model Predictive Control) and/or optimal control strategies. In this study, the authors developed ANN(Artificial Neural Network) models of an AHU (Air Handling Unit) and a chiller for a real-life office building using BEMS data. Based on the ANN models, the authors developed optimal control strategies, e.g. daily operation schedule with regard to optimal start and stop of the AHU and the chiller (500 RT). It was found that due to the optimal start and stop of the AHU and the chiller, 4.5% and 16.4% of operation hours of the AHU and the chiller could be saved, compared to an existing operation.

• Journal of Sensor Science and Technology
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• v.30 no.1
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• pp.1-9
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• 2021

This mini review summarizes some of the recent advances in machine-learning (ML)-driven chemical and biological sensors. Specific focus is on field-effect-transistor (FET)-based sensors with a description of their structures and detection mechanisms. Key ML techniques are briefly reviewed for an audience not familiar with the basic principles. We mainly discuss two aspects: (1) data analysis based on ML and (2) ML applied to sensor design. In conclusion, the challenges and opportunities for the advancement of ML-based sensors are briefly considered.

• Journal of Ocean Engineering and Technology
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• v.34 no.2
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• pp.147-154
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• 2020

Underwater acoustics that is the study of the phenomenon of underwater wave propagation and its interaction with boundaries, has mainly been applied to the fields of underwater communication, target detection, marine resources, marine environment, and underwater sound sources. Based on the scientific and engineering understanding of acoustic signals/data, recent studies combining traditional and data-driven machine learning methods have shown continuous progress. Machine learning, represented by deep learning, has shown unprecedented success in a variety of fields, owing to big data, graphical processor unit computing, and advances in algorithms. Although machine learning has not yet been implemented in every single field of underwater acoustics, it will be used more actively in the future in line with the ongoing development and overwhelming achievements of this method. To understand the research trends of machine learning applications in underwater acoustics, the general theoretical background of several related machine learning techniques is introduced in this paper.

• Nuclear Engineering and Technology
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• v.52 no.12
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• pp.2687-2698
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• 2020

Most of the machine learning-based intrusion detection tools developed for Industrial Control Systems (ICS) are trained on network packet captures, and they rely on monitoring network layer traffic alone for intrusion detection. This approach produces weak intrusion detection systems, as ICS cyber-attacks have a real and significant impact on the process variables. A limited number of researchers consider integrating process measurements. However, in complex systems, process variable changes could result from different combinations of abnormal occurrences. This paper examines recent advances in intrusion detection algorithms, their limitations, challenges and the status of their application in critical infrastructures. We also introduce the discussion on the similarities and conflicts observed in the development of machine learning tools and techniques for fault diagnosis and cybersecurity in the protection of complex systems and the need to establish a clear difference between them. As a case study, we discuss special characteristics in nuclear power control systems and the factors that constraint the direct integration of security algorithms. Moreover, we discuss data reliability issues and present references and direct URL to recent open-source data repositories to aid researchers in developing data-driven ICS intrusion detection systems.

• Journal of KIBIM
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• v.6 no.4
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• pp.35-41
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• 2016

Learning effect is an observation that the more times a task is performed, the less time is required to produce the same amount of outcomes. The construction industry heavily relies on repeated tasks where the learning effect is an important measure to be used. However, most construction durations are calculated and applied in real projects without considering the learning effects in each of the repeated activities. This paper applied the learning effect to the repeated activities in a small sized apartment construction project. The result showed that there was about 10 percent of difference in duration (one approach of the total duration with learning effects in 41 days while the other without learning effect in 36.5 days). To make the comparison between the two approaches, a large number of BIM based computer simulations were generated and useful patterns were recognized using machine learning algorithm named Decision Tree (See5). Machine learning is a data-driven approach for pattern recognition based on observational evidence.