• Title/Summary/Keyword: Feature modeling

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The Test of Mechanism Operation for 3D Printer Using Polygon Mirror (폴리곤 미러를 이용한 3D 프린터 기구부 동작 테스트)

  • Kwon, Dong-hyun;Heo, Sung-uk;Lim, Ji-yong;Oh, Am-suk;Kim, Wan-sik
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2016.10a
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    • pp.735-737
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    • 2016
  • In this paper, we conducted a test of the 3D printer injection method and LSU (Laser Scanning Unit) feature a fusion of the polygon mirror scanning system that is the core mechanism operation for 3D printers for office laser printers SLA system. These tests ensure that the laser was operating and control well was confirmed that a certain point is output to the X-axis by means of a laser module and a polygon mirror. And confirmed after the F-theta lens is incident on the fixed laser power of the beam, and correction according to the correction beam on the mirror reflection was confirmed jineunji the focus according to the Z-axis upper plate.

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A New Iron Emission Template for Active Galactic Nuclei

  • Park, Daeseong;Barth, Aaron J.;Ho, Luis C.;Laor, Ari
    • The Bulletin of The Korean Astronomical Society
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    • v.44 no.2
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    • pp.36.2-36.2
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    • 2019
  • Fe II emission is a prominent and ubiquitous feature in the spectra of broad-line Active Galactic Nuclei (AGN) by producing a pseudo-continuum from UV to optical with complex and strong blends of the numerous emission lines themselves, other emission lines, and continuum. Since theoretical modeling of such intricate Fe II emission is very difficult and still far from able to reproduce observed data in detail, an empirical iron emission template, derived from observations of a narrow-line Seyfert 1 galaxy, is an essential and practical tool to obtain accurate measurements of all the emission lines and continuum in AGN spectra. However, the existing iron templates, based on the single prototypical strong Fe II emitter I Zw 1, are suffering from inadequate S/N and non-simultaneous, inconsistent data with limited wavelength coverage, which consequently limit the accuracy of all the spectral measurements. To overcome the limitations and construct an improved iron template with wide spectral coverage, high-quality UV and optical spectra for the new and better identified template galaxy, Mrk 493, were successfully obtained from our HST STIS program (GO-14744). We will show the preliminary results for multicomponent spectral decomposition of the data and template construction with application tests to various AGN spectra and comparison with previous templates.

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Spatial Data Modeling for Feature-based Efficient Updating and History Management (객체기반의 효율적인 갱신 및 이력 관리를 위한 공간 데이터 모델 설계)

  • Sang Yeob Kim;Hyeongsoo Kim;Sungbo Seo;Keun Ho Ryu
    • Annual Conference of KIPS
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    • 2008.11a
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    • pp.352-355
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    • 2008
  • 최근 센서와 모바일 기술의 발달에 따라 대용량 데이터 처리가 가능해지고, 유비쿼터스와 텔레매틱스 등의 도입으로 공간 데이터가 다양한 환경에 응용되거나 활용 분야가 점차 증가하고 있다. 기존의 수치지도 관리시스템은 공간 데이터를 도엽 단위로 관리하여 데이터의 구축이 용이하지만, 객체 단위의 데이터 구축, 관리, 제공 및 갱신을 효율적으로 지원하기 어렵다. 따라서 이 논문에서는 기존 도엽기반 시스템의 문제점을 해결하기위해 객체기반 UFID 부여방안, 연속성 표현, 객체 단위의 효율적인 갱신 및 이력관리를 위한 객체기반 공간 데이터 모델을 설계한다. 제안하는 객체기반의 공간 데이터 모델은 지형지물에 UFID를 부여하고 도엽 단위로 구축된 수치지도 데이터의 조인 연산을 통해 연속적인 표현이 가능하다. 아울러 갱신으로 인해 변경된 데이터를 이력 DB에 시간간격 단위로 저장, 관리하여 사용자에게 객체단위 이력 정보를 제공할 수 있다.

PREDICTING CORPORATE FINANCIAL CRISIS USING SOM-BASED NEUROFUZZY MODEL

  • Jieh-Haur Chen;Shang-I Lin;Jacob Chen;Pei-Fen Huang
    • International conference on construction engineering and project management
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    • 2011.02a
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    • pp.382-388
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    • 2011
  • Being aware of the risk in advance necessitates intricate processes but is feasible. Although previous studies have demonstrated high accuracy, their performance still leaves room for improvement. A self-organizing feature map (SOM) based neurofuzzy model is developed in this study to provide another alternative for forecasting corporate financial distress. The model is designed to yield high prediction accuracy, as well as reference rules for evaluating corporate financial status. As a database, the study collects all financial reports from listed construction companies during the latest decade, resulting in over 1000 effective samples. The proportion of "failed" and "non-failed" companies is approximately 1:2. Each financial report is comprised of 25 ratios which are set as the input variable s. The proposed model integrates the concepts of pattern classification, fuzzy modeling and SOM-based optimization to predict corporate financial distress. The results exhibit a high accuracy rate at 85.1%. This model outperforms previous tools. A total of 97 rules are extracted from the proposed model which can be also used as reference for construction practitioners. Users may easily identify their corporate financial status by using these rules.

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Genetic Algorithm based hyperparameter tuned CNN for identifying IoT intrusions

  • Alexander. R;Pradeep Mohan Kumar. K
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.18 no.3
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    • pp.755-778
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    • 2024
  • In recent years, the number of devices being connected to the internet has grown enormously, as has the intrusive behavior in the network. Thus, it is important for intrusion detection systems to report all intrusive behavior. Using deep learning and machine learning algorithms, intrusion detection systems are able to perform well in identifying attacks. However, the concern with these deep learning algorithms is their inability to identify a suitable network based on traffic volume, which requires manual changing of hyperparameters, which consumes a lot of time and effort. So, to address this, this paper offers a solution using the extended compact genetic algorithm for the automatic tuning of the hyperparameters. The novelty in this work comes in the form of modeling the problem of identifying attacks as a multi-objective optimization problem and the usage of linkage learning for solving the optimization problem. The solution is obtained using the feature map-based Convolutional Neural Network that gets encoded into genes, and using the extended compact genetic algorithm the model is optimized for the detection accuracy and latency. The CIC-IDS-2017 and 2018 datasets are used to verify the hypothesis, and the most recent analysis yielded a substantial F1 score of 99.23%. Response time, CPU, and memory consumption evaluations are done to demonstrate the suitability of this model in a fog environment.

Sentiment Analysis on 'HelloTalk' App Reviews Using NRC Emotion Lexicon and GoEmotions Dataset

  • Simay Akar;Yang Sok Kim;Mi Jin Noh
    • Smart Media Journal
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    • v.13 no.6
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    • pp.35-43
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    • 2024
  • During the post-pandemic period, the interest in foreign language learning surged, leading to increased usage of language-learning apps. With the rising demand for these apps, analyzing app reviews becomes essential, as they provide valuable insights into user experiences and suggestions for improvement. This research focuses on extracting insights into users' opinions, sentiments, and overall satisfaction from reviews of HelloTalk, one of the most renowned language-learning apps. We employed topic modeling and emotion analysis approaches to analyze reviews collected from the Google Play Store. Several experiments were conducted to evaluate the performance of sentiment classification models with different settings. In addition, we identified dominant emotions and topics within the app reviews using feature importance analysis. The experimental results show that the Random Forest model with topics and emotions outperforms other approaches in accuracy, recall, and F1 score. The findings reveal that topics emphasizing language learning and community interactions, as well as the use of language learning tools and the learning experience, are prominent. Moreover, the emotions of 'admiration' and 'annoyance' emerge as significant factors across all models. This research highlights that incorporating emotion scores into the model and utilizing a broader range of emotion labels enhances model performance.

The Analysis of Regional Scale Topographic Effect Using MM5-A2C Coupling Modeling (국지규모 지형영향을 고려하기 위한 MM5-A2C 결합 모델링 특성 분석)

  • Choi, Hyun-Jeong;Lee, Soon-Hwan;Kim, Hak-Sung
    • Journal of the Korean earth science society
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    • v.36 no.3
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    • pp.210-221
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    • 2015
  • The terrain features and surface characteristics are the most important elements not only in meteorological modeling but also in air quality modeling. The diurnal evolution of local climate over complex terrain may be significantly controlled by the ground irregularities. Such topographic features can affect a thermally driven flow, either directly by causing changes in the wind direction or indirectly, by inducing significant variations in the ground temperature. Over a complex terrain, these variations are due to the nonuniform distribution of solar radiation, which is highly determined by the ground geometrical characteristics, i.e. slope and orientation. Therefore, the accuracy of prediction of regional scale circulation is strong associated with the accuracy of land-use and topographic information in meso-scale circulation assessment. The objective of this work is a numerical simulation using MM5-A2C model with the detailed topography and land-use information as the surface boundary conditions of the air flow field in mountain regions. Meteorological conditions estimated by MM5-A2C command a great influence on the dispersion of mountain areas with the reasonable feature of topography where there is an important difference in orographic forcing.

Numerical Analysis of Horizontal Collector Well in Riverbank Filtration (수평 방사형 집수정 활용 강변여과 취수 수치 분석)

  • Kim, Hyoung-Soo;Jeong, Jae-Hoon
    • Journal of Soil and Groundwater Environment
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    • v.14 no.1
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    • pp.1-10
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    • 2009
  • Groundwater flow due to intake of horizontal collector well in riverbank filtration site was analyzed by use of numerical groundwater modeling program (FEFLOW 5.1). Drawdowns of groundwater table nearby collector well were evaluated according to variations of several conditions; pumping rate, thickness of aquifer, offset distance from well to shore line of stream, conductance of streambed. It is observed that the drawdowns of groundwater table are clearly changed according to the variations of these conditions. The results of sensitive analysis shows that the thickness of alluvial aquifer and the offset distance are more sensitive than the conductance of streambed in evaluation of drawdown. This result implies that hydrogeological conditions, as like thickness of aquifer and its distribution in the site are important factors in site selection and evaluating the availability of riverbank filtration intake using horizontal collector well system. It is also revealed that numerical modeling using FEFLOW with 1-D discrete element feature can give efficient quantitative evaluation of horizontal collector well and estimation of availability of riverbank filtration site.

Nonlinear Flexural Modeling of Prestressed Concrete Beams with Composite Materials (복합소재 프리스트레스트 콘크리트보의 비선형 휨 모델링)

  • ;;Naaman, Antoine
    • Magazine of the Korea Concrete Institute
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    • v.10 no.6
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    • pp.269-280
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    • 1998
  • Recently, application of composite materials such as fiber reinforced concretes(FRCs) and fiber reinforced plastics(FRPs) in conjunction with conventional structural components has become one of the main research areas. A proper use of advanced composite materials requires understanding their resistance mechanism and failure mode when they are applied to structures or their components. Particular considerations are given in this research to develop an analytical model which can predict the nonlinear flexural responses of bonded and unbonded prestressed concrete beams possibly having layers of different cementitious composite matrices in a section and/or FRP tendons. The block concept is used, which can be regarded as an intermediate modeling method between the couple method with one block and the layered method with multiply sliced layers in a section. In order to find a particular deflection point of a beam under load, solutions to the 2N-variables are found numerically by using approximate N-force equilibrium equations and N-moment equilibirum equations. The model is shown to successfully predict the flexual behavior of variously reinforced bonded and unbonded prestressed concrete beams. The model is also successful in simulating a gradually increasing load after sudden drop inload resistance due to fracture of one or more FRP tendons. This feature is useful in tracing the overall load-deflection response of a beam prestressed with brittle FRP tendons.

Method of Biological Information Analysis Based-on Object Contextual (대상객체 맥락 기반 생체정보 분석방법)

  • Kim, Kyung-jun;Kim, Ju-yeon
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2022.05a
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    • pp.41-43
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    • 2022
  • In order to prevent and block infectious diseases caused by the recent COVID-19 pandemic, non-contact biometric information acquisition and analysis technology is attracting attention. The invasive and attached biometric information acquisition method accurately has the advantage of measuring biometric information, but has a risk of increasing contagious diseases due to the close contact. To solve these problems, the non-contact method of extracting biometric information such as human fingerprints, faces, iris, veins, voice, and signatures with automated devices is increasing in various industries as data processing speed increases and recognition accuracy increases. However, although the accuracy of the non-contact biometric data acquisition technology is improved, the non-contact method is greatly influenced by the surrounding environment of the object to be measured, which is resulting in distortion of measurement information and poor accuracy. In this paper, we propose a context-based bio-signal modeling technique for the interpretation of personalized information (image, signal, etc.) for bio-information analysis. Context-based biometric information modeling techniques present a model that considers contextual and user information in biometric information measurement in order to improve performance. The proposed model analyzes signal information based on the feature probability distribution through context-based signal analysis that can maximize the predicted value probability.

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