CONTINUOUS IMPROVEMENT THROUGH INTEGRATION OF QUALITY TOOLS

The relationship between major quality tools such as quality function development （QFD）, failure mode and effects analysis （FMEA）, design of experiments （DOE） and statistical process control （SPC） is analyzed through an extensive review of the literature and the concurrent quality engineering philosophy, and a basic structure for the integration of quality tools is presented. An integrated quality management system （IQMS） is developed using C＋＋ Builder, running in the Windows 2000 Server environment with the basic internet connections, and SQL Server 2000 as the platform for developing the database, An illustrative example applying IQMS to the continuous quality improvement for a crane equipment manufacturing is reported. The result shows that the application of IQMS can optimize the process of design and manufacturing, shorten the cycle time of product, reduce the cost, and realize quality improvement continuously, The proposed integrated framework with IOMS is believed to be applicable to continuous quality improvement in many manufacturing companies.

Deep Learning Applied to Computational Mechanics:A Comprehensive Review,State of the Art,and the Classics

Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.

Stabilization strategy of a car-following model with multiple time delays of the drivers

An extended car-following model with multiple delays is constructed to describe driver's driving behavior.Through stability analysis,the stability condition of this uncontrolled model is given.To dampen the negative impact of the driver's multiple delays(i.e.,stability condition is not satisfied),a novel control strategy is proposed to assist the driver in adjusting vehicle operation.The control strategy consists of two parts:the design of control term as well as the design of the parameters in the term.Bifurcation analysis is performed to illustrate the necessity of the design of parameters in control terms.In the course of the design of parameters in the control term,we improve the definite integral stability method to reduce the iterations by incorporating the characteristics of bifurcation,which can determine the appropriate parameters in the control terms more quickly.Finally,in the case study,we validate the control strategy by utilizing measured data and configuring scenario,which is closer to the actual traffic.The results of validation show that the control strategy can effectively stabilize the unstable traffic flow caused by driver's delays.

Improving Clinical Practice Guideline Development in Integration of Traditional Chinese Medicine and Western Medicine

Integration of traditional Chinese medicine （TCM） and Western medicine （WM）, or called as integrative medicine （IM） in China, came into being in 1950s as a new form of medicine. IM emphasizes the combination of both conventional WM and TCM alternative approaches to address all aspects of health and illness.（1） The major practice in IM includes the integration of disease diagnosis in WM and pattern classification （or syndrome differentiation） in TCM, and the integration of WM interventions and TCM interventions.

Multi-category diagnostic accuracy based on logistic regression

We provide a detailed review for the statistical analysis of diagnostic accuracy in a multi-category classification task.For qualitative response variables with more than two categories,many traditional accuracy measures such as sensitivity,specificity and area under the ROC curve are no longer applicable.In recent literature,new diagnostic accuracy measures are introduced in medical research studies.In this paper,important statistical concepts for multi-category classification accuracy are reviewed and their utilities are demonstrated with real medical examples.We offer problem-based R code to illustrate how to perform these statistical computations step by step.We expect such analysis tools will become more familiar to practitioners and receive broader applications in biostatistics.Our program can be adapted to many classifiers among which logistic regression may be the most popular approach.We thus base our discussion and illustration completely on the logistic regression in this paper.