• Title/Summary/Keyword: 스크린라인 검증

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Validation and Correction of Expanded O/D with Link Observed Traffic Volumes at Screenlines (스크린라인 관측교통량을 이용한 전수화 O/D 자료의 검증과 수정)

  • Kim, Ik-Gi;Yun, Ji-Yeong;Chu, Sang-Ho
    • Journal of Korean Society of Transportation
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    • v.25 no.4
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    • pp.21-32
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    • 2007
  • The households to be surveyed are usually huge number at the level of a city or metropolitan survey, not to mention a nationwide travel survey. Therefore, household travel surveys to figure out true origin-destination (O/D) trip patterns (population O/D) are conducted through a sampling method rather than by surveying all of the population in the system. Therefore, the population O/D pattern can only be estimated by expanding the sampled O/D patterns to the population. It is very difficult to avoid the errors involved in the process of sampling, surveying and expanding O/D data. In order to minimize such errors while estimating the true O/D patterns of the population, the validation and adjustment process should employed by doing a comparison between the expanded sample O/D data and observed link traffic volumes. This study suggests a method of validation and adjustment of the expanded sample O/D data by comparing observed link volumes at several screenlines. The study also suggests a practical technique to modify O/D pairs which are excluded in the screenline validation process by comparing observed traffic volume with the results of traffic assignment analysis. An empirical study was also conducted as an example applying the suggested methods of validation and adjustment with Korea's nationwide O/D data and highway network.

Predicting Movie Success based on Machine Learning Using Twitter (트위터를 이용한 기계학습 기반의 영화흥행 예측)

  • Yim, Junyeob;Hwang, Byung-Yeon
    • KIPS Transactions on Software and Data Engineering
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    • v.3 no.7
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    • pp.263-270
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    • 2014
  • This paper suggests a method for predicting a box-office success of the film. Lately, as the growth of the film industry, a variety of studies for the prediction of market demand is being performed. The product life cycle of film is relatively short cultural goods. Therefore, in order to produce stable profits, marketing costs before opening as well as the number of screen after opening need a plan. To fulfill this plan, the demand for the product and the calculation of economic profit scale should be preceded. The cases of existing researches, as a variable for predicting, primarily use the factors of competition of the market or the properties of the film. However, the proportion of the potential audiences who purchase the goods is relatively insufficient. Therefore, in this paper, in order to consider people's perception of a movie, Twitter was utilized as one of the survey samples. The existing variables and the information extracted from Twitter are defined as off-line and on-line element, and applied those two elements in machine learning by combining. Through the experiment, the proposed predictive techniques are validated, and the results of the experiment predicted the chance of successful film with about 95% of accuracy.

A Study on the Development Method of Android App GUI Test Automation Tool (안드로이드 앱 GUI 테스트 자동화 툴 개발 방법에 관한 연구)

  • Park, Se-jun;Kim, Kyu-jung
    • The Journal of the Korea Contents Association
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    • v.21 no.8
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    • pp.403-412
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    • 2021
  • As the number of mobile apps increases exponentially, automation of tests performed in the app development process is becoming more important. Until the app is released, iterative verification is performed through various types of tests, and this study was conducted focusing on the GUI test among various types of tests. This study is meaningful in that it can contribute to the stable app distribution of the developer by suggesting the development direction of the GUI test. To develop Android's GUI test tool, I collected basic data before presenting the development method by researching Android's UI controls and Material design guideline. After that, for the existing GUI test automation tool, two tools based on screen capture test and four tools based on source code analysis test were studied. Through this, it was found that existing GUI test tools don't consider visual design, usability, and component arrangement. In order to supplement the shortcomings of existing tools, a new GUI test automation tool development method was presented based on the basic data previously studied.