• Title/Summary/Keyword: Data-Driven Method

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The Effects of Corpus Use on Learning L2 Collocations of Light Verbs and Nouns

  • Yoshiho Satake
    • Asia Pacific Journal of Corpus Research
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    • v.4 no.2
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    • pp.41-55
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    • 2023
  • In data-driven learning (DDL), learners explore a corpus to understand vocabulary and grammar. Although many studies have emphasized the role of DDL in second language (L2) acquisition, L2 light verbs have been largely under-explored. To bridge this gap, this study focused on the learning outcomes of L2 light verbs among 29 intermediate-level Japanese university students. The research zeroed in on six prevalent light verbs in English: "make," "do," "take," "have," "give," and "get." Over nine weeks, the participants engaged with verb-noun collocations using worksheets that juxtaposed Japanese translations of the target collocations with their English equivalents, with the verbs omitted. With the aid of Wordbanks Online, they filled in the blanks and constructed accurate sentences. Before this activity, a 20-minute tutorial was given to the participants on how to interpret the concordance lines. The effectiveness of the DDL method was evaluated using pre-tests, immediate post-tests, and delayed post-tests. The results showed that DDL significantly improved the participants' knowledge of the target collocations of light verbs and nouns; the post-test and delayed post-test scores were significantly higher than the pre-test scores. The results showed that, overall, DDL contributed to memorizing the collocations of light verbs and nouns; however, DDL had different effects on the memorization of collocations across different light verbs. The extent of work on the worksheet is not the only factor in its retention, and observing concordance lines may promote learners' memorization of light-verb collocations.

Development of Maneuvering Scenario for Data-Driven Modeling of Ship Dynamics (선박 동역학의 데이터 기반 모델링을 위한 조종 시나리오 개발)

  • Dong-Hwan Kim;Minchang Kim;Seungbeom Lee;Jeonghwa Seo
    • Journal of the Society of Naval Architects of Korea
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    • v.61 no.4
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    • pp.226-235
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    • 2024
  • A method for quantifying the adaptability of ship maneuver scenarios for data-driven modeling of ship dynamics is developed based on the principal component analysis. A random maneuver scenario is suggested as a reference for ship dynamics, which can obtain the converged principal components of ship dynamics features by the Monte Carlo simulation. Principal components of conventional maneuver scenarios defined by the International Maritime Organization (IMO) are compared to that of the random maneuver. A conventional ship dynamics model for a container carrier vessel for four degrees of freedom dynamics is introduced to simulate the random and IMO maneuver scenarios. It is confirmed that the IMO tests follow the tendency of random maneuver scenario in terms of execution time and adaptability.

Regularized model-free adaptive control of smart base-isolated buildings

  • Alvaro Javier Florez;Luis Felipe Giraldo;Mariantonieta Gutierrez Soto
    • Smart Structures and Systems
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    • v.34 no.2
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    • pp.73-85
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    • 2024
  • Smart base-isolated buildings rest on flexible pads known as base isolators that minimize the effect of external disturbances along with active/semi-active actuators. The strategies used to control these active components are typically based on system models that are known a priori. Although these models describe some of the most important dynamics of the elements involved in the system, the high degree of uncertainty in the behavior of a structure under external disturbances is very difficult to characterize using a fixed model. In this work, we propose a strategy that deals with this issue: the input that controls the actuator in the base isolation system results from the compound action of a controller that relies on a model of the system that is known a priori, and a control policy that is designed based on online data-driven inferences on the behavior of the system. In this way, the control design process incorporates both the prior information about the system and the unknowns of the system, such as non-modeled parameters and nonlinear behaviors in the building. We show through simulations the performance of the proposed method in an eight-story building subjected to seismic loading.

Condition assessment of stay cables through enhanced time series classification using a deep learning approach

  • Zhang, Zhiming;Yan, Jin;Li, Liangding;Pan, Hong;Dong, Chuanzhi
    • Smart Structures and Systems
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    • v.29 no.1
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    • pp.105-116
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    • 2022
  • Stay cables play an essential role in cable-stayed bridges. Severe vibrations and/or harsh environment may result in cable failures. Therefore, an efficient structural health monitoring (SHM) solution for cable damage detection is necessary. This study proposes a data-driven method for immediately detecting cable damage from measured cable forces by recognizing pattern transition from the intact condition when damage occurs. In the proposed method, pattern recognition for cable damage detection is realized by time series classification (TSC) using a deep learning (DL) model, namely, the long short term memory fully convolutional network (LSTM-FCN). First, a TSC classifier is trained and validated using the cable forces (or cable force ratios) collected from intact stay cables, setting the segmented data series as input and the cable (or cable pair) ID as class labels. Subsequently, the classifier is tested using the data collected under possible damaged conditions. Finally, the cable or cable pair corresponding to the least classification accuracy is recommended as the most probable damaged cable or cable pair. A case study using measured cable forces from an in-service cable-stayed bridge shows that the cable with damage can be correctly identified using the proposed DL-TSC method. Compared with existing cable damage detection methods in the literature, the DL-TSC method requires minor data preprocessing and feature engineering and thus enables fast and convenient early detection in real applications.

Destructive Test of a BLDC Motor Controller Utilizing a Modified Classification Tree Method (변형된 Classification Tree Method를 이용한 BLDC 모터제어기 파괴 시험)

  • Shin, Jae Hyuk;Chung, Ki Hyun;Choi, Kyung Hee
    • KIPS Transactions on Software and Data Engineering
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    • v.3 no.6
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    • pp.201-214
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    • 2014
  • In this paper, we propose a test case generation method adequate to destructive test of the BLDC(Brush Less Direct Current) motor controller used for the MDPS(Motor Driven Power Steering) system embedded in an automobile. The proposed method is a modified CTM(Classification Tree Method). CTM generates test cases assuming that all inputs are equally important. Therefore, it is very hard to generate test cases for extreme situations. To overcome the drawback and generate test cases specialized for destructive test. a modified CTM that compensates the limitation of traditional CTM is proposed. The proposed method has an advantage that it can intensively generate the test scenarios adequate to extreme situations by combining the test cases generated by the transitional CTM the while keeping the merit of the traditional CTM. The test scenarios for destructive test for the MDPS system embedded in a commercial automobile are generated utilizing the proposed method. The effectiveness of the proposed algorithm is verified through the test.

Multi-Attribute Data Fusion for Energy Equilibrium Routing in Wireless Sensor Networks

  • Lin, Kai;Wang, Lei;Li, Keqiu;Shu, Lei
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.4 no.1
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    • pp.5-24
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    • 2010
  • Data fusion is an attractive technology because it allows various trade-offs related to performance metrics, e.g., energy, latency, accuracy, fault-tolerance and security in wireless sensor networks (WSNs). Under a complicated environment, each sensor node must be equipped with more than one type of sensor module to monitor multi-targets, so that the complexity for the fusion process is increased due to the existence of various physical attributes. In this paper, we first investigate the process and performance of multi-attribute fusion in data gathering of WSNs, and then propose a self-adaptive threshold method to balance the different change rates of each attributive data. Furthermore, we present a method to measure the energy-conservation efficiency of multi-attribute fusion. Based on our proposed methods, we design a novel energy equilibrium routing method for WSNs, viz., multi-attribute fusion tree (MAFT). Simulation results demonstrate that MAFT achieves very good performance in terms of the network lifetime.

A Study on multi-translation system for e-business collaboration (e-비즈니스 협업에 적합한 다중변환 시스템 연구)

  • Ahn, Kyeong-Rim;Chung, Jin-Wook
    • Journal of Internet Computing and Services
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    • v.7 no.6
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    • pp.123-130
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    • 2006
  • The transaction was happened within a single business entity or a single marketplace at the stage of e-business. It becomes to grow to complex form. Expecially, the need for business collaboration between business entities or marketplaces has being on the rise as the core topic. The format translation between documents is very important factor according to various the exchanged document formats. In this paper, we define ebXML as the basic format of exchanged document according to object-oriented business transaction. Also we design the multi-format translation system to support the translation of various document formats. The proposed system in this paper, is designed with model-driven method and it is possible to construct with various structure as for system environment. The proposed translation system is designed to use the proposed system as adding the corresponding parsing module even though any format of document. Also, we increase the reusability of data as using the common data set. In this paper, we prove the superiority of the proposed system to compare the performance with the legacy system for various format translation.

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Changes in Statistical Knowledge and Experience of Data-driven Decision-making of Pre-service Teachers who Participated in Data Analysis Projects (데이터 분석 프로젝트 참여한 예비 교사의 통계적 지식에 대한 변화와 데이터 기반 의사 결정의 경험)

  • Suh, Heejoo;Han, Sunyoung
    • Communications of Mathematical Education
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    • v.35 no.2
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    • pp.153-172
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    • 2021
  • Various competencies such as critical thinking, systems thinking, problem solving competence, communication skill, and data literacy are likely to be required in the 4th industrial revolution. The competency regarding data literacy is one of those competencies. To nurture citizens who will live in the future, it is timely to consider research on teacher education for supporting teachers' development of statistical thinking as well as statistical knowledge. Therefore, in this study we developed and implemented a data analysis project for pre-service teachers to understand their changes in statistical knowledge in addition to their experiences of data-driven decision making process that required them utilizing their statistical thinking. We used a mixed method (i.e., sequential explanatory design) research to analyze the quantitative and qualitative data collected. The findings indicated that pre-service teachers have low knowledge level of their understanding on the relationship between population means and sample means, and estimation of the population mean and its interpretation. When it comes to the data-driven decision making process, we found that the pre-service teachers' experiences varied even when they worked as a small group for the project. We end this paper by presenting implications of the study for the fields of teacher education and statistics education.

Development of Message Broker-Based Real-Time Control Method for Road Traffic Safety Facilities Equipment and Devices Integrated Management System

  • JeongHo Kho;Eum Han
    • Journal of the Korea Society of Computer and Information
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    • v.29 no.1
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    • pp.195-209
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    • 2024
  • The current road traffic signal controller developed in the 1990s has limitations in flexibility and scalability due to power supply problems, various communication methods, and hierarchical black box structures for various equipment and devices installed to improve traffic safety for road users and autonomous cooperative driving. In this paper, we designed a road traffic safety facilities equipment and devices integrated management system that can cope with the rapidly changing future traffic environment by solving the using direct current(DC) and power supply problem through the power over ethernet(PoE) technology and centralized data-driven control through message broker technology. In addition, a data-driven real-time control method for road traffic safety facilities equipment and devices operating based on time series data was implemented and verified.

Consideration of Set-up Effect in Wave Equation Analysis of Pile Driving. (Set-up 효과를 반영한 타입말뚝의 파동이론해석)

  • 천병식;조천환
    • Journal of the Korean Geotechnical Society
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    • v.15 no.2
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    • pp.95-104
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    • 1999
  • The bearing capacity of piles driven in soils showing set-up tendency increases with time. Though WEAP is an excellent tool for evaluating the driveability of driven pile, it has some limitations to predict reliable bearing capacity of pile after driving. It is because the existing WEAP method cannot take into account time-dependent soil properties after driving. The set-up effect should be accounted for to obtain a reliable bearing capacity by the WEAP. Unfortunately, there are no sufficient methods to take the set-up effect into consideration in wave equation analysis. This paper suggests an alternative to consider time effect in wave equation analysis through statistical analysis of dynamic load test data both at the end of driving and in the beginning of restrike. It is shown that the suggested parameters(quake and damping) would be more reliable than the existing one for the wave equation analysis of driven piles.

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