• Journal of Korean Library and Information Science Society
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• v.38 no.3
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• pp.223-246
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• 2007

This study examines the retrieval efficiency of the FRBR-based bibliographic retrieval system. To do this, we built two experimental retrieval systems(a FRBR-based system constructed through FRBRizing algorithms and an OPAC-based retrieval system) using 387 music materials coded in a KORMARC format. Next, we set up six hypotheses and compared these two systems in terms of recall, precision, and retrieval time using 28 participants and a questionnaire with 12 queries. The results show that the average recall value of the FRBR-based system Is higher than that of the OPAC system regardless of query types and the average precision and retrieval time values of manifestation queries of the OPAC system is more efficient that those of the FRBR-based system. This study results can be used to customize digital library interfaces as well as to improve the retrieval efficiency of the bibliographic retrieval system.

• Journal of Aerospace System Engineering
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• v.18 no.4
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• pp.43-52
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• 2024

This paper presents an anomaly detection system that uses an LSTM-Autoencoder model to identify early-stage anomalies in the blade pitch system of floating wind turbines. The sensor data used in power plant monitoring systems is primarily composed of multivariate time-series data for each component. Comprising two unidirectional LSTM networks, the system skillfully uncovers long-term dependencies hidden within sequential time-series data. The autoencoder mechanism, learning solely from normal state data, effectively classifies abnormal states. Thus, by integrating these two networks, the system can proficiently detect anomalies. To confirm the effectiveness of the proposed framework, a real multivariate time-series dataset collected from a wind turbine model was employed. The LSTM-autoencoder model showed robust performance, achieving high classification accuracy.

• Resources Recycling
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• v.27 no.1
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• pp.31-37
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• 2018

To enhance the recycling rate of used metal resources, it is strongly desired to develop a metal sorting system that can automatically identify metal type from mixed metal scraps and sort them separately. Laser-induced breakdown spectroscopy(LIBS) is a technique that enables real time classification of different metals based on multi-elemental and in-air analysis. In this work, we report the results of LIBS elemental analysis of field scrap samples acquired from a recycling company. By applying multivariate analysis, it was found that the LIBS signals of five different metals could be perfectly classified if surface contamination was removed. The classification accuracy degraded for LIBS signals including contaminant emission, which however could be overcome by performing the multivariate analysis using properly selected emission lines of higher correlation only. The significant improvement in classification accuracy and process speed by the selection of proper emission lines demonstrated the feasibility of LIBS technique as an industrial tool for metal scrap sorting.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.31 no.6
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• pp.258-263
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• 2021

ZnO varistor is a semiconductor device which can serve to protect the circuit from surge voltage because its non-linear I-V characteristics by controlling the microstructure of grain and grain boundaries. In order to obtain desired electrical properties, it is important to control microstructure evolution during the sintering process. In this research, we defined a dataset composed of process conditions of sintering and relative permittivity of sintered body, and collected experimental dataset with DOE. Meta-models can predict permittivity were developed by learning the collected experimental dataset on various machine learning algorithms. By utilizing the meta-model, we can derive optimized sintering conditions that could show the maximum permittivity from the numerical-based HMA (Hybrid Metaheuristic Algorithm) optimization algorithm. It is possible to search the optimal process conditions with minimum number of experiments if meta-model-based optimization is applied to ceramic processing.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.391-393
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• 2022

In recent years, technologies forecasting demand based on deep learning and big data have accelerated the smartification of the field of e-commerce, logistics and distribution areas. In particular, ports, which are the center of global transportation networks and modern intelligent logistics, are rapidly responding to changes in the global economy and port environment caused by the 4th industrial revolution. Port traffic forecasting will have an important impact in various fields such as new port construction, port expansion, and terminal operation. Therefore, the purpose of this study is to compare the time series analysis and deep learning analysis, which are often used for port traffic prediction, and to derive a prediction model suitable for the future container prediction of Busan Port. In addition, external variables related to trade volume changes were selected as correlations and applied to the multivariate deep learning prediction model. As a result, it was found that the LSTM error was low in the single-variable prediction model using only Busan Port container freight volume, and the LSTM error was also low in the multivariate prediction model using external variables.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.4
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• pp.128-136
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• 2011

To monitor and predict the change of coastal environment according to the construction of Saemangeum sea dyke and the development of land reclamation, we have done real-time and periodic ocean observation and numerical simulation since 2002. Saemangeum coastal environmental data can be largely classified to marine meteorology, ocean physics and circulation, water quality, marine geology and marine ecosystem and each part of data has been generated continuously and accumulated over about 10 years. The collected coastal environmental data are huge amounts of heterogeneous dataset and have some characteristics of multi-dimension, multivariate and spatio-temporal distribution. Thus the implementation of information system possible to data collection, processing, management and service is necessary. In this study, through the implementation of Saemangeum coastal environmental information system using geographic information system, it enables the integral data collection and management and the data querying and analysis of enormous and high-complexity data through the design of intuitive and effective web user interface and scientific data visualization using statistical graphs and thematic cartography. Furthermore, through the quantitative analysis of trend changed over long-term by the geo-spatial analysis with geo- processing, it's being used as a tool for provide a scientific basis for sustainable development and decision support in Saemangeum coast. Moreover, for the effective web-based information service, multi-level map cache, multi-layer architecture and geospatial database were implemented together.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.53-59
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• 2008

Recently, vision-based hand gesture recognition techniques have been developed for assisting elderly and disabled people to control home appliances. Frequently occurred problems which lower the hand gesture recognition rate are due to the inter-person variation and intra-person variation. The recognition difficulty caused by inter-person variation can be handled by using user dependent model and model selection technique. And the recognition difficulty caused by intra-person variation can be handled by using fuzzy logic. In this paper, we propose multivariate fuzzy decision tree learning and classification method for a hand motion recognition system for multiple users. When a user starts to use the system, the most appropriate recognition model is selected and used for the user.

• Journal of Internet Computing and Services
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• v.25 no.4
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• pp.121-130
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• 2024

As internet and communication technology (ICT) is improved exponentially, types and amount of available data also increase. Even though data analysis including statistics is significant to utilize this large amount of data, there are inevitable limits to process various and complex data in general way. Meanwhile, there are many attempts to apply machine learning (ML) in various fields to solve the problems according to the enhancement in computational performance and increase in demands for autonomous systems. Especially, data processing for the model input and designing the model to solve the objective function are critical to achieve the model performance. Data processing methods according to the type and property have been presented through many studies and the performance of ML highly varies depending on the methods. Nevertheless, there are difficulties in deciding which data processing method for data analysis since the types and characteristics of data have become more diverse. Specifically, multi-variate data processing is essential for solving non-linear problem based on ML. In this paper, we present a multi-variate tabular data processing scheme for ML-aided data analysis by using Titanic dataset from Kaggle including various kinds of data. We present the methods like input variable filtering applying statistical analysis and normalization according to the data property. In addition, we analyze the data structure using visualization. Lastly, we design an ML model and train the model by applying the proposed multi-variate data process. After that, we analyze the passenger's survival prediction performance of the trained model. We expect that the proposed multi-variate data processing and visualization can be extended to various environments for ML based analysis.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.407-409
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• 1998

In order to improve the tracking performance of $2{\times}2$ multivariable control systems, a fuzzy control algorithm with feedforward compensator is represented. The method consists in two steps. First, neglecting interconnections. one designs a fuzzy controller to each individual loop. In the second stage, low-order transfer functions of outputs to reference inputs are estimated. We propose a design method of the feed forward compensator based on the transfer functions. An illustrative example are shown.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.610-614
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• 2010

지구온난화에 따른 이상기후 현상으로 불확실성에 대한 고려가 더욱 중요해진 지금 설계빈도의 무조건적인 상향조정에 의존하기보다는 추계학적 방법을 도입한 수문량의 확충 및 매개변수의 불확실성을 고려하기 위한 연구가 활발히 진행중이다. 본 연구에서는 강우발생의 불확실성을 반영하여 제내지에서의 침수 범위를 GIS상에서 검토하기 위해 log-ratio 방법, Johnson 시스템, 직교변환을 활용한 다변량 Monte Carlo 기법으로 추계학적 시간에 따른 강우변동을 생성하였다. 생성된 강우변동 결과를 토대로 수문분석, 홍수위 분석 등을 실시하고 FLUMEN 모형을 적용하여 해당유역에 대한 홍수범람시 침수범위를 산정하였다. 본 연구결과는 실제 강우의 불확실성을 반영하고 있어 시 공간적 강우특성이 반영된 유역별 주민대피지도, 홍수위험지도 등을 제작하는데 활용될 수 있을 것으로 기대된다.