• Title/Summary/Keyword: SAE

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A Study on Fatigue Durability through the Structural Analysis of Strut Bar (스트럿 바의 구조 해석을 통한 피로 내구성에 관한 연구)

  • Han, Moonsik;Cho, Jaeung
    • Transactions of the Korean Society of Automotive Engineers
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    • v.24 no.5
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    • pp.504-511
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    • 2016
  • This study investigates the durability of strut bar at car through structural and fatigue analyses. In this study, there are model 1 and model 2 as the analysis subjects. Model 1 is the existed one and model 2 is the improved one added with the reinforced part. Model 1 has the maximum equivalent stress of 165.11 MPa shown intensively at the welding part between the bracket and the bar. This stress is distributed over at the part of model 2 reinforced with this part. In case of fatigue analysis, there are three kinds of fatigue load as SAE bracket history, SAE transmission and sample history. The maximum fatigue life at SAE bracket history among three kinds of fatigue loads has the least value of $3.3693{\times}10^5$ cycles. The maximum fatigue life of model 2 becomes longer than that of model 1. As model 2 has the fatigue damage less than model 1, model 2 has the safety than model 1. As the fatigue durability about the configuration of strut bar is analyzed, it is thought to apply this study result into the real part effectively.

Properties of Adhesion in Flexure and Tension of Polymer Cement Mortar Using SAE Emulsion with Blast-Furnace and Fly Ash as a Repair Material (보수재료로서 고로슬래그 미분말 및 플라이애쉬를 혼입한 SAE 에멀젼 기반 폴리머 시멘트 모르타르의 휨접착 및 인장접착 특성)

  • Jo, Young-Kug
    • Journal of the Korea Institute of Building Construction
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    • v.19 no.6
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    • pp.485-494
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    • 2019
  • This study is to evaluate the effect of admixtures such as blast-furnace slag and fly ash on adhesion in flexure and tension of polymer cement mortar(PCM) using SAE emulsion. The test specimens are prepared with five polymer-cement ratios and five admixture contents, and tested for flexural strength, adhesion in flexure, tensile strength and adhesion in tension. Based on the test results, no improvement of flexural strength and adhesion in flexure caused by admixtures in PCM can be indicated, but the tensile strength and adhesion in tension is improved due to mixing of the admixtures. In particular, the maximum of adhesion in tension of PCM with P/C 20% and BF content of 10% is 3.35MPa which is about 2.36 times higher than that of ordinary cement mortar, and 1.32 times that of PCM that does not contain any admixture. The average ratio of adhesion in tension to tensile strength of PCM was 48.7%. It is apparent that admixture contents of 5% or 10% could be proposed for improvement of tensile strength and adhesion in tension of PCM.

Deep Learning-based UWB Distance Measurement for Wireless Power Transfer of Autonomous Vehicles in Indoor Environment (실내환경에서의 자율주행차 무선 전력 전송을 위한 딥러닝 기반 UWB 거리 측정)

  • Hye-Jung Kim;Yong-ju Park;Seung-Jae Han
    • KIPS Transactions on Computer and Communication Systems
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    • v.13 no.1
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    • pp.21-30
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    • 2024
  • As the self-driving car market continues to grow, the need for charging infrastructure is growing. However, in the case of a wireless charging system, stability issues are being raised because it requires a large amount of power compared with conventional wired charging. SAE J2954 is a standard for building autonomous vehicle wireless charging infrastructure, and the standard defines a communication method between a vehicle and a power transmission system. SAE J2954 recommends using physical media such as Wi-Fi, Bluetooth, and UWB as a wireless charging communication method for autonomous vehicles to enable communication between the vehicle and the charging pad. In particular, UWB is a suitable solution for indoor and outdoor charging environments because it exhibits robust communication capabilities in indoor environments and is not sensitive to interference. In this standard, the process for building a wireless power transmission system is divided into several stages from the start to the completion of charging. In this study, UWB technology is used as a means of fine alignment, a process in the wireless power transmission system. To determine the applicability to an actual autonomous vehicle wireless power transmission system, experiments were conducted based on distance, and the distance information was collected from UWB. To improve the accuracy of the distance data obtained from UWB, we propose a Single Model and Multi Model that apply machine learning and deep learning techniques to the collected data through a three-step preprocessing process.