• Title/Summary/Keyword: A6016

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Design of The Precise Synchronized Clock Generator using GPS (GPS를 이용한 정밀 동기 클록 발생기 설계)

  • Kim, Chan-Mo;Jo, Yong-Beom
    • Journal of the Institute of Electronics Engineers of Korea SD
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    • v.38 no.6
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    • pp.446-455
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    • 2001
  • In this paper, the precise synchronized clock generator using GPS receiver is presented. The GPS receiver provides a synchronized IPPS signal which guaranties a reliable standard time mark. This signal allows us to do time synchronization and correct the time step. We designed and implemented the precise synchronized clock generator based on DPLL in order to generate a high-resolution clock from a low-cost inaccurate oscillator with ALTERA FLEX EPM6016TC144-3. We also implemented a hardware unit and proved that the unit provides 1MHz clock output which had a high resolution and accuracy when it was combined with GPS receiver.

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Experimental Validation of Crack Growth Prognosis under Variable Amplitude Loads (변동진폭하중 하에서 균열성장 예측의 실험적 검증)

  • Leem, Sang-Hyuck;An, Dawn;Lim, Che-Kyu;Hwang, Woongki;Choi, Joo-Ho
    • Journal of the Computational Structural Engineering Institute of Korea
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    • v.25 no.3
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    • pp.267-275
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    • 2012
  • In this study, crack growth in a center-cracked plate is predicted under mode I variable amplitude loading, and the result is validated by experiment. Huang's model is employed to describe crack growth with acceleration and retardation due to the variable loading effect. Experiment is conducted with Al6016-T6 plate, in which the load is applied, and crack length is measured periodically. Particle Filter algorithm, which is based on the Bayesian approach, is used to estimate model parameters from the experimental data, and predict the crack growth of the future in the probabilistic way. The prediction is validated by the run-to-failure results, from which it is observed that the method predicts well the unique behavior of crack retardation and the more data are used, the closer prediction we get to the actual run-to-failure data.

Development of the Hybrid Cold-Hot Stamping Process for the 1-Piece Aluminum Door Inner Part (1-Piece 알루미늄 도어 인너 냉간-열간 복합 성형공정 개발)

  • Nam, S.W.;Bae, G.H.
    • Transactions of Materials Processing
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    • v.30 no.5
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    • pp.242-246
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    • 2021
  • Aluminum alloy sheet is being applied to automobiles continuously for the purpose of reducing car body weight. However, due to low formability, there's a limit to application of products with a deep forming depth such as door inner parts. Therefore, the difficult-to-form parts are mainly segmented formed then joined together, which is also disadvantageous as it increases the cost of manufacturing. This study proposes a hybrid cold-hot stamping method for the 1-piece door inner part to reduce cost. To design the stamping process, numerical simulation method is established by using the temperature-dependent mechanical properties of AA6016. The formability according to the hybrid cold-hot stamping method is evaluated using numerical analysis. The suitability of the proposed stamping method is then verified through the stamping tryout.

Extracting Flooded Areas in Southeast Asia Using SegNet and U-Net (SegNet과 U-Net을 활용한 동남아시아 지역 홍수탐지)

  • Kim, Junwoo;Jeon, Hyungyun;Kim, Duk-jin
    • Korean Journal of Remote Sensing
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    • v.36 no.5_3
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    • pp.1095-1107
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    • 2020
  • Flood monitoring using satellite data has been constrained by obtaining satellite images for flood peak and accurately extracting flooded areas from satellite data. Deep learning is a promising method for satellite image classification, yet the potential of deep learning-based flooded area extraction using SAR data remained uncertain, which has advantages in obtaining data, comparing to optical satellite data. This research explores the performance of SegNet and U-Net on image segmentation by extracting flooded areas in the Khorat basin, Mekong river basin, and Cagayan river basin in Thailand, Laos, and the Philippines from Sentinel-1 A/B satellite data. Results show that Global Accuracy, Mean IoU, and Mean BF Score of SegNet are 0.9847, 0.6016, and 0.6467 respectively, whereas those of U-Net are 0.9937, 0.7022, 0.7125. Visual interpretation shows that the classification accuracy of U-Net is higher than SegNet, but overall processing time of SegNet is around three times faster than that of U-Net. It is anticipated that the results of this research could be used when developing deep learning-based flood monitoring models and presenting fully automated flooded area extraction models.

Finite element analysis of spring back caused by frictional force in area of flange in press bending process (프레스 벤딩 공정에서 플랜지부의 마찰력이 스프링백에 미치는 영향에 대한 해석적 고찰)

  • Yun, Jae-Woong;Oh, Seung-Ho;Choi, Kye-Kwang;Lee, Chun-Kyu
    • Design & Manufacturing
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    • v.15 no.2
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    • pp.63-69
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    • 2021
  • Springback is an essential task to be solved in order to make high-precision products in sheet metal forming. In this study, materials with four different elastic regions were used. For the forming analysis, the change of springback caused by the frictional force generated in the flange part during hat shape forming was considered by using the AutoForm analysis program. Factors affecting frictional force were blank holder force, friction coefficient, bead R and bead height. As a result of the forming analysis, the springback increases as the material with a larger elastic region increases. In addition, as the frictional force of the flange part increased, the tensile force in the forming direction increased and the springback decreased. In particular, the blank holder force and friction coefficient had a great effect on springback in mild materials (DC04, Al6016), and the bead effectively affects all materials. Through this study, it was considered that the springback decreased as the material with a smaller elastic region and the tensile force in the forming direction increased.

New Stress-Strain Model for Identifying Plastic Deformation Behavior of Sheet Materials (판재의 소성변형 거동을 동정하기 위한 새로운 응력-변형률 모델)

  • Kim, Young Suk;Pham, Quoc Tuan;Kim, Chan Il
    • Journal of the Korean Society for Precision Engineering
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    • v.34 no.4
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    • pp.273-279
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    • 2017
  • In sheet metal forming numerical analysis, the strain hardening equation has a significant effect on calculation results, especially in the field of spring-back. This study introduces the Kim-Tuan strain hardening model. This model represents sheet material behavior over the entire strain hardening range. The proposed model is compared to other well known strain hardening models using a series of uniaxial tensile tests. These tests are performed to determine the stress-strain relationship for Al6016-T4, DP980, and CP Ti sheets. In addition, the Kim-Tuan model is used to integrate the CP Ti sheet strain hardening equation in ABAQUS analysis to predict spring-back amount in a bending test. These tests highlight the improved accuracy of the proposed equation in the numerical field. Bending tests to evaluate prediction accuracy are also performed and compared with numerical analysis results.

Advanced speed control of the seven-phase PM brush less DC motor using fuzzy logic controller (퍼지제어기를 이용한 영구자석형 7상 브러시리스 직류전동기의 속도제어 성능개선)

  • Park, Sang-Hoon;Yu, Dong-Hwan;Lee, Hee-Jun;Won, Chung-Yuen
    • Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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    • 2008.05a
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    • pp.440-444
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    • 2008
  • The 7-phase BLDC motor is possible for higher efficiency per the unit area, high power and high speed due to the increasing number of phase. Also, it can be looking forward to reduce the current ripple at a point of commutation by the increasing number of phase. Thus, a study for applications of servo system, medical and military instruments is progressing about the BLDC motor is manufactured with multi-phase, currently. This paper is used the fuzzy logic control method for speed control of 7-phase BLDC motor and this is compared with the conventional PI controller using by simulation and experimental results for verification validity of the fuzzy logic controller in this system. The 7-phase BLDC motor and controller are modeled by PSIM6.0 software of PowerSim co. in simulation and we are experimented by the test board that is composed with TMS320VC33-150 DSP controller of Texas Instruments co. and FLEX EPF6016TC144-3 of ALTERA co.

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A Logistic Model Including Risk Factors for Lymph Node Metastasis Can Improve the Accuracy of Magnetic Resonance Imaging Diagnosis of Rectal Cancer

  • Ogawa, Shimpei;Itabashi, Michio;Hirosawa, Tomoichiro;Hashimoto, Takuzo;Bamba, Yoshiko;Kameoka, Shingo
    • Asian Pacific Journal of Cancer Prevention
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    • v.16 no.2
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    • pp.707-712
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    • 2015
  • Background: To evaluate use of magnetic resonance imaging (MRI) and a logistic model including risk factors for lymph node metastasis for improved diagnosis. Materials and Methods: The subjects were 176 patients with rectal cancer who underwent preoperative MRI. The longest lymph node diameter was measured and a cut-off value for positive lymph node metastasis was established based on a receiver operating characteristic (ROC) curve. A logistic model was constructed based on MRI findings and risk factors for lymph node metastasis extracted from logistic-regression analysis. The diagnostic capabilities of MRI alone and those of the logistic model were compared using the area under the curve (AUC) of the ROC curve. Results: The cut-off value was a diameter of 5.47 mm. Diagnosis using MRI had an accuracy of 65.9%, sensitivity 73.5%, specificity 61.3%, positive predictive value (PPV) 62.9%, and negative predictive value (NPV) 72.2% [AUC: 0.6739 (95%CI: 0.6016-0.7388)]. Age (<59) (p=0.0163), pT (T3+T4) (p=0.0001), and BMI (<23.5) (p=0.0003) were extracted as independent risk factors for lymph node metastasis. Diagnosis using MRI with the logistic model had an accuracy of 75.0%, sensitivity 72.3%, specificity 77.4%, PPV 74.1%, and NPV 75.8% [AUC: 0.7853 (95%CI: 0.7098-0.8454)], showing a significantly improved diagnostic capacity using the logistic model (p=0.0002). Conclusions: A logistic model including risk factors for lymph node metastasis can improve the accuracy of MRI diagnosis of rectal cancer.

Barrett's Esophagus and β-carotene Therapy: Symptomatic Improvement in GERD and Enhanced HSP70 Expression in Esophageal Mucosa

  • Dutta, Sudhir K.;Agrawal, Kireet;Girotra, Mohit;Fleisher, A. Steven;Motevalli, Mahnaz;Mah'moud, Mitchell A.;Nair, Padmanabhan P.
    • Asian Pacific Journal of Cancer Prevention
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    • v.13 no.12
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    • pp.6011-6016
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    • 2012
  • Introduction: Epidemiological studies suggest a protective role for ${\beta}$-carotene with several malignancies. Esophageal adenocarcinoma frequently arises from Barrett's esophagus (BE). We postulated that ${\beta}$-carotene therapy maybe protective in BE. Materials and Method: We conducted a prospective study in which 25 mg of ${\beta}$-carotene was administered daily for six-months to six patients. Each patient underwent upper endoscopy before and after therapy and multiple mucosal biopsies were obtained. Additionally, patients completed a gastroesophageal reflux disease (GERD) symptoms questionnaire before and after therapy and severity score was calculated. To study the effect of ${\beta}$-carotene at molecular level, tissue extracts of the esophageal mucosal biopsy were subjected to assessment of heat-shock protein 70 (HSP70). Results: A significant (p<0.05) reduction in mean GERD symptoms severity score from $7.0{\pm}2.4$ to $2.7{\pm}1.7$ following ${\beta}$-carotene therapy was noted. Measurement of Barrett's segment also revealed a significant reduction in mean length after therapy. In fact, two patients had complete disappearance of intestinal metaplasia. Furthermore, marked enhancement of HSP70 expression was demonstrated in biopsy specimens from Barrett's epithelium in four cases that were tested. Conclusions: Long-term ${\beta}$-carotene therapy realizes amelioration of GERD symptoms along with restitution of the histological and molecular changes in esophageal mucosa of patients with BE, associated with concurrent increase in mucosal HSP70 expression.

Application of Machine Learning on Voice Signals to Classify Body Mass Index - Based on Korean Adults in the Korean Medicine Data Center (머신러닝 기반 음성분석을 통한 체질량지수 분류 예측 - 한국 성인을 중심으로)

  • Kim, Junho;Park, Ki-Hyun;Kim, Ho-Seok;Lee, Siwoo;Kim, Sang-Hyuk
    • Journal of Sasang Constitutional Medicine
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    • v.33 no.4
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    • pp.1-9
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    • 2021
  • Objectives The purpose of this study was to check whether the classification of the individual's Body Mass Index (BMI) could be predicted by analyzing the voice data constructed at the Korean medicine data center (KDC) using machine learning. Methods In this study, we proposed a convolutional neural network (CNN)-based BMI classification model. The subjects of this study were Korean adults who had completed voice recording and BMI measurement in 2006-2015 among the data established at the Korean Medicine Data Center. Among them, 2,825 data were used for training to build the model, and 566 data were used to assess the performance of the model. As an input feature of CNN, Mel-frequency cepstral coefficient (MFCC) extracted from vowel utterances was used. A model was constructed to predict a total of four groups according to gender and BMI criteria: overweight male, normal male, overweight female, and normal female. Results & Conclusions Performance evaluation was conducted using F1-score and Accuracy. As a result of the prediction for four groups, The average accuracy was 0.6016, and the average F1-score was 0.5922. Although it showed good performance in gender discrimination, it is judged that performance improvement through follow-up studies is necessary for distinguishing BMI within gender. As research on deep learning is active, performance improvement is expected through future research.