159 Decomposed Effects of Noise Factors on Scalar Statistics in a PLIF Measurement

2015 ◽  
Vol 2015.64 (0) ◽  
pp. _159-1_-_159-2_
Author(s):  
Hiroki SUZUKI ◽  
Kouji NAGATA ◽  
Yasuhiko SAKAI ◽  
Yutaka HASEGAWA ◽  
Tatsuo USHIJIMA
Keyword(s):  
Noise Factors ◽  
Plif Measurement

OH-PLIF MEASUREMENT OF EDGE-FLAME ESTABLISHED IN COUNTERFLOW FIELD

2011 ◽  
Vol 1 (3) ◽  
Author(s):  
W. Nishiwaki ◽  
H. Mochizuki ◽  
T. Kouchi ◽  
Kenichi Takita
Keyword(s):  
Plif Measurement

Appearance FMEA: A Method for Appearance Quality Evaluation of Early Design Concepts

2009 ◽  
Author(s):  
Karin Forslund ◽  
Timo Kero ◽  
Rikard So¨derberg
Keyword(s):  
Industrial Design ◽  
Failure Modes ◽  
Negative Impact ◽  
Consumer Products ◽  
Product Development Process ◽  
Early Design ◽  
Product Experience ◽  
Design Concepts ◽  
Quality Aspects ◽  
Noise Factors

For consumer products, early design stages are often concerned with the product’s industrial design, with primary focus on the consumer’s product experience. At this stage, aspects such as manufacturability and robustness are often not thoroughly taken into account. Industrial design concepts not properly suited for manufacture, assembly and process variability can result in final products in which the appearance intent is not satisfactorily realized. This can have a negative impact on the customer’s product quality perception. If such problems are discovered late in the product development process, late design changes and increased project costs may follow. The main difficulty in evaluating perceived quality aspects during industrial design is that the product is still under development. It is not mature enough to enable prediction of the prerequisites for achieving high manufacturing quality. In this paper, we suggest that concepts instead could be evaluated as far as the intrinsic tendency of the product appearance to support manufacturing variation and other noise factors. This is addressed through the concept of visual robustness: the ability of a product’s visual appearance to stimulate the same product experience despite variety in its visual design properties. Here, a method is suggested based on the Failure Modes and Effects Analysis (FMEA). The method follows a structured procedure for addressing appearance issues.

scholarly journals Microfluidic Chip-Based Cancer Diagnosis and Prediction of Relapse by Detecting Circulating Tumor Cells and Circulating Cancer Stem Cells

Cancers ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1385
Author(s):  
Hyeon-Yeol Cho ◽  
Jin-Ha Choi ◽  
Joungpyo Lim ◽  
Sang-Nam Lee ◽  
Jeong-Woo Choi
Keyword(s):  
Stem Cells ◽  
Cancer Stem Cells ◽  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Metastatic Cancer ◽  
Cancer Prognosis ◽  
Optical Biosensors ◽  
Diagnosis And Prognosis ◽  
Noise Factors ◽  
Prediction Of Relapse

Detecting circulating tumor cells (CTCs) has been considered one of the best biomarkers in liquid biopsy for early diagnosis and prognosis monitoring in cancer. A major challenge of using CTCs is detecting extremely low-concentrated targets in the presence of high noise factors such as serum and hematopoietic cells. This review provides a selective overview of the recent progress in the design of microfluidic devices with optical sensing tools and their application in the detection and analysis of CTCs and their small malignant subset, circulating cancer stem cells (CCSCs). Moreover, discussion of novel strategies to analyze the differentiation of circulating cancer stem cells will contribute to an understanding of metastatic cancer, which can help clinicians to make a better assessment. We believe that the topic discussed in this review can provide brief guideline for the development of microfluidic-based optical biosensors in cancer prognosis monitoring and clinical applications.

Formal Solution of N-Type Taguchi Parameter Design Problems With Stochastic Noise Factors

1991 ◽  
Author(s):  
Nestor F. Michelena ◽  
Alice M. Agogino
Keyword(s):  
Performance Characteristic ◽  
Degrees Of Freedom ◽  
Formal Solution ◽  
Parameter Design ◽  
Absolute Deviation ◽  
Design Problems ◽  
Problem Reduction ◽  
Stochastic Parameters ◽  
Noise Factors ◽  
Monotonicity Analysis

Abstract The Taguchi method of product design is a statistical experimental technique aimed at reducing the variance of a product performance characteristic due to uncontrollable factors. The goal of this paper is to provide a monotonicity analysis based methodology to facilitate the solution of N-type parameter design problems. The obtained design is robust, i.e., the least sensitive to variations on uncontrollable factors (noise). The performance characteristic is unbiased in the sense that its expected value equals a target or specification. The proposed loss function is based on the absolute deviation of the characteristic with respect to the target, instead of the common square error approach. Conditions, like those imposed by monotonicity analysis, on the monotonic characteristics of the performance function are proven, despite the objective function is not monotonic and contains stochastic parameters. These conditions allow the qualitative analysis of the problem to identify the activity of some constraints. Identification of active sets of constraints allows a problem reduction strategy to be employed, where the solution to the original problem is obtained by solving a set of problems with fewer degrees of freedom. Results for the case of one uncontrollable factor are independent of the probability measure on the factor. However, conclusions for the multi-parametric case must take into account the characteristics of the probability space on which the random parameters are defined.

INTEGRATING DATA MINING TO A PROCESS DESIGN USING THE ROBUST BAYESIAN APPROACH

2008 ◽  
Vol 15 (05) ◽  
pp. 441-464 ◽  
Author(s):  
HEAJIN JEONG ◽  
SUHILL SONG ◽  
SANGMUN SHIN ◽  
BYUNG RAE CHO
Keyword(s):  
Data Mining ◽  
Process Design ◽  
Research Work ◽  
Design Procedure ◽  
Design Tool ◽  
Raw Data ◽  
Correlation Based Feature Selection ◽  
Vital Component ◽  
Process Database ◽  
Noise Factors

Although process design optimization issues have received considerable attention from researchers for more than several decades, and a number of methodologies for modeling and optimizing the process have been developed, there is still ample room for improvement. Most research work has rarely considered the use of raw data from a manufacturing process database into the process design. However, the use of cumulative raw data can be a vital component in optimizing processes. To address this, we propose a new process design procedure called robust-Bayesian data mining (RBDM). First, we show how data mining techniques and a correlation-based feature selection (CBFS) method can be applied effectively to the selection of significant factors. Second, we then show how RBDM can be incorporated into robust design. Third, we present how the proposed RBDM estimates process parameters by considering the concept of robustness of the estimated parameters while incorporating the concept of noise factors. Finally, we present numerical examples to illustrate the efficiency of the proposed RBDM as a design tool for optimizing manufacturing processes.

3C PIV and PLIF measurement in turbulent mixing

2007 ◽  
Vol 10 (1) ◽  
pp. 99-110 ◽  
Author(s):  
D. Reungoat ◽  
N. Rivière ◽  
J. P. Fauré
Keyword(s):  
Turbulent Mixing ◽  
Plif Measurement

The Optimum Cage Position and Orientation on the ALIF with Facet Screw Fixation: A Finite Element Analysis and the Taguchi Method

2011 ◽  
Vol 27 (3) ◽  
pp. 309-320 ◽  
Author(s):  
C.-Y. Fan ◽  
C.-K. Chao ◽  
C.-C. Hsu ◽  
K.-H. Chao
Keyword(s):  
Finite Element ◽  
Taguchi Method ◽  
Screw Fixation ◽  
Three Dimensional ◽  
Element Model ◽  
Lateral Position ◽  
Element Analysis ◽  
Control Factors ◽  
Position And Orientation ◽  
Noise Factors

ABSTRACTAnterior Lumbar Interbody Fusion (ALIF) has been widely used to treat internal disc degeneration. However, different cage positions and their orientations may affect the initial stability leading to different fusion results. The purpose of the present study is to investigate the optimum cage position and orientation for aiding an ALIF having a transfacet pedicle screw fixation (TFPS). A three-dimensional finite element model (ALIF with TFPS) has been developed to simulate the stability of the L4/L5 fusion segment under five different loading conditions. The Taguchi method was used to evaluate the optimized placement of the cages. Three control factors and two noise factors were included in the parameter design. The control factors included the anterior-posterior position, the medio-lateral position, and the convergent-divergent angle between the two cages. The compressive preload and the strengths of the cancellous bone were set as noise factors. From the results of the FEA and the Taguchi method, we suggest that the optimal cage positioning has a wide anterior placement, and a diverging angle between the two cages. The results show that the optimum cage position simultaneously contributes to a stronger support of the anterior column and lowers the risk of TFPS loosening.

Classification of Weld Defects Based on Convolution Neural Network

2021 ◽  
pp. 23-36
Author(s):  
A.I. Gavrilov ◽  
M.Tr. Do
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Weld Seam ◽  
Control Process ◽  
Welding Process ◽  
Optimal Solution ◽  
Automatic Welding ◽  
Welding Seam ◽  
Noise Factors

Automatic welding technology has been widely applied in many industrial fields. It is a complex process with many nonlinear parameters and noise factors affecting weld quality. Therefore, it is necessary to inspect and evaluate the quality of the weld seam during welding process. However, in practice there are many types of welding seam defects, causes and the method of corrections are also different. Therefore, welding seam defects need to be classified to determine the optimal solution for the control process with the best quality. Previously, the welder used his experience to classify visually, or some studies proposed visual classification with image processing algorithms and machine learning. However, it requires a lot of time and accuracy is not high. The paper proposes a convolutional neural network structure to classify images of welding seam defects from automatic welding machines on pipes. Based on comparison with the classification results of some deep machine learning networks such as VGG16, Alexnet, Resnet-50, it shows that the classification accuracy is 99.46 %. Experimental results show that the structure of convolutional neural network is proposed to classify images of weld seam defects have availability and applicability

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