14 research outputs found
Automated Deployment of a Splash Application to the Distributed System via Splash Build Unit
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Όλ¬Έ(μμ¬) -- μμΈλνκ΅λνμ : 곡과λν μ κΈ°Β·μ 보곡νλΆ, 2021.8. νμ±μ.Splashλ μ μ 볡μ‘ν΄μ§λ μμ¨κΈ°κΈ°(autonomous machine)λ₯Ό μν μμ© νλ‘κ·Έλ¨ κ°λ°μ ν¨κ³Όμ μΌλ‘ ν μ μλ νλ‘κ·Έλλ° νλ μμν¬μ΄λ€. Splashλ κ·Έλν½ μ¬μ©μ μΈν°νμ΄μ€λ₯Ό μ΄μ©ν νλ‘κ·Έλλ° μΆμνλ₯Ό μ 곡ν λΏλ§ μλλΌ μ€μκ° μ μ΄ μμ€ν
μ νμμ μΈ μ€μκ° μ€νΈλ¦Ό μ²λ¦¬, μΌμ ν¨μ , λͺ¨λ λ³κ²½, μμΈ μ²λ¦¬ λ±κ³Ό κ°μ κΈ°λ₯λ€μ μ 곡νλ€. Splashλ ROS 2μ μ΄λ―Έ μ‘΄μ¬νλ κΈ°λ₯μ μ΄μ μ μ΄μ©νκΈ° μν΄μ ROS 2 κΈ°λ°μΌλ‘ κ°λ°λμλ€. λ°λΌμ, Splashλ₯Ό μ¬μ©νμ¬ μμ±λ μμ©μ Splash λ°νμ λΌμ΄λΈλ¬λ¦¬μ ROS 2 λ°νμ λΌμ΄λΈλ¬λ¦¬κ° μ€μΉλ λ¨μΌ νΉμ λΆμ° μ»΄ν¨ν
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νκ²½μμ Splash μμ©μ μ¬μ©νκΈ° μν΄μ κ° λ¨Έμ μ νκ²½μ λ§κ² μμ©κ³Ό λ°νμμ λΉλ λ° μ€μΉν΄μΌ νκ³ , μ΄λ μννΈμ¨μ΄ μ
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λ¨Έμ μ νμ₯μ±(scalability), κ°μ©μ±(availability), κ΄λ¦¬μ±(manageability)μ κ³ λ €ν μννΈμ¨μ΄ λ°°ν¬(deployment)μ νμμ±μ μ κΈ°νλ€. λ³Έ λ
Όλ¬Έμμλ Splash μμ© νλ‘κ·Έλ¨μ μλν λ°°ν¬ λ° κ΄λ¦¬λ₯Ό μν λ컀(Docker) κΈ°λ° Splash λΉλ μ λμ ꡬννλ€. Splash λΉλ μ λμ ν΅ν΄μ κ°λ°μλ μννΈμ¨μ΄ λΉλ λ° λ°°ν¬ νλ‘μΈμ€λ₯Ό μλννκ³ νλ‘κ·Έλ¨μ λ³λ ¬μ±(parallelism) λ° λμμ±(concurrency)μ μ΅μ νν μ μλ€. Splash λΉλ μ λμ μ΄μ©ν μννΈμ¨μ΄ μλν λ°°ν¬μ λν μ μ©μ±μ κ²μ¦νκΈ° μν΄μ λΆμ° μμ€ν
μμ Splash λΉλ μ λμ μ΄μ©νμ¬ λ₯λ¬λ κΈ°λ° μμ¨ μ£Όν μμ© νλ‘κ·Έλ¨μ μμ λ₯Ό μ§ννμλ€.Splash is a programming framework that can effectively support the application development of increasingly complex autonomous machines. Splash not only offers user-friendly programming abstraction like a graphic user interface, but also provides essential programming semantics for a real-time control system such as real-time stream processing, sensor fusion, mode change, exception handling, etc. Splash was developed based on ROS 2 to take advantages of using already existing features and packages. Splash applications are eligible to run on a single or distributed computing systems where Splash runtime library, ROS 2 runtime library and any other software that are necessary for the Splash applications are installed. Therefore, in order to run Splash application on a distributed computing system, it is required to build and install the applications, additional external libraries and corresponding runtime according to the environment of each machine. This particular process comes with cost and time issue of software update and maintenance. This inconvenience and burden raise the need for automated software deployment considering scalability, availability, and manageability in distributed computing systems. This thesis will introduce a Docker-based Splash build unit for automated deployment and management of Splash applications. Splash build unit allows developers to automate the software build and deployment process to distributed computing machines by using Docker containerization. It also allows developers to focus on optimizing parallelism and concurrency of programs. In order to demonstrate the effectiveness and utility of software deployment using Splash build unit, the case study of a LFA(Lane Following Assist) application with DNN-based lane detection and DNN-model training program was conducted using Splash build unit in a distributed system.μ 1 μ₯ μ λ‘ 1
μ 2 μ₯ λ°°κ²½ μ§μ 3
μ 1 μ ROS 2 3
μ 2 μ Splash 7
μ 3 μ₯ λ컀 κΈ°λ° λΉλ μ λ 15
μ 1 μ λΉλ μ λ 15
μ 2 μ λ컀 κΈ°λ° λΉλ μ λ λ° κ΅¬ν 17
μ 3 μ λΉλ μ λ λ°°ν¬ 19
μ 4 μ₯ Case Study 21
μ 1 μ μμ νκ²½ 21
μ 2 μ μμ κ΅¬μ± 22
μ 3 μ μμ κ²°κ³Ό λ° νκ° 24
μ 5 μ₯ κ²° λ‘ 26
μ°Έκ³ λ¬Έν 27
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Thesis(doctoral)--μμΈλνκ΅ λνμ :μ 기곡νλΆ,2006.Thesis(doctoral)-
Surface Morphology, Molecular Orientation, and Liquid Crystal Alignment Properties of Polyimide Nanofilms
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Όλ¬Έ(μμ¬)--μμΈλνκ΅ λνμ :μ‘°μ ν΄μ곡νκ³Ό,2006.Maste
κ·Ήλ―ΈμΈ κ³΅μ κΈ°μ μ κ°λ°κ³Ό 30nm nMOSFETμμ μ μ©μ κ΄ν μ°κ΅¬
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Όλ¬Έ(μμ¬)--μμΈλνκ΅ λνμ :μ κΈ°Β·μ»΄ν¨ν°κ³΅νλΆ,2002.Maste
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WO3 Nano-Helixes Array Photoanode for Enhanced Solar-To-Hydrogen Efficiency of Photoelectrochemical Cells
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Prevalence of Gait Features in Healthy Adolescents and Adults
Forensic gait analysis is the identification of individuals by their gait style and walking characteristics. This study aimed to examine the prevalence of gait patterns in healthy adolescents and adults. Five hundred healthy participants (mean age, 36.9 years) were enrolled and divided into four age groups: 13-20, 21-35, 36-50, and β₯51 years. The gait of the participants was recorded in a gait analysis laboratory. Five specialists experienced in gait analysis selected several gait features. The prevalence of out-toe, in-toe, planovalgus, and turtleneck was 25.0%, 1.6%, 6.8%, and 4.2%, respectively. The prevalence of genu varum (10.4%) was higher than that of genu valgum (5.6%). Genu valgum and hindfoot valgus were more common in younger than in older subjects (P=0.018 and P=0.029, respectively). Genu varum was more prevalent in older subjects (P<0.001).
The prevalence of out-toe was higher in males (P<0.001), whereas the in-toe and genu valgum were more common in females than in males (P=0.027 and P=0.038, respectively). We have documented the prevalence of several gait features in healthy adolescents and adults. These gait features can be used to enhance evidentiary competence in forensic gait analysis and thereby help improve the arrest rate of offenders.N