Multivariate Adaptive Regression Splines in Standard Cell Characterization for Nanometer Technology in Semiconductor

Multivariate adaptive regression splines (MARSP) is a nonparametric regression method. It is an adaptive procedure which does not have any predetermined regression model. With that said, the model structure of MARSP is constructed dynamically and adaptively according to the information derived from the data. Because of its ability to capture essential nonlinearities and interactions, MARSP is considered as a great fit for high-dimension problems. This chapter gives an application of MARSP in semiconductor field, more specifically, in standard cell characterization. The objective of standard cell characterization is to create a set of high-quality models of a standard cell library that accurately and efficiently capture cell behaviors. In this chapter, the MARSP method is employed to characterize the gate delay as a function of many parameters including process-voltage-temperature parameters. Due to its ability of capturing essential nonlinearities and interactions, MARSP method helps to achieve significant accuracy improvement

Investigation of Emission Characteristics during Low Temperature Combustion using Multivariate Adaptive Regression Splines

Exhaust emissions from diesel engines operating in a low temperature combustion (LTC) regime are significantly affected by fuel composition and injection strategy. The starting point of this study is a collection of data correlating injection system parameters, and fuel characteristics, to response parameters such as engine-out emissions (oxides of nitrogen (NOx), total particulate matter (TPM), carbon monoxide (CO), hydrocarbons (HC)) and brake thermal efficiency (BTE).;The purpose of this work is to develop a statistical analysis tool to assist the emission analyst in modeling problems in which a response of interest is influenced by several variables and the objective is to optimize this response. The experimental data produced during LTC operation have been analyzed using an approach commonly known as Response Surface Methodology (RSM). Since the system under study may be responding to hidden inputs that are neither measured nor controlled, regression analysis must be performed via a flexible procedure. The methodology that will be used in this sense is called Multivariate Adaptive Regression Splines (MARS), which allows to approximate functions of many input variables given the value of the function at a collection of point in the input space.;Data was collected at West Virginia University\u27s Engine and Emissions Research Laboratory for the project CRC AVFL-16. The test engine was a turbo-charged GM 1.9L operated in the LTC mode utilizing a split injection strategy. Main and pilot SOI timing and fuel split were varied per a 5 X 3 X 3 full factorial design. Advanced Vehicle Fuel Lubricants (AVFL) Committee of the Coordinating Research Council (CRC) defined a matrix of nine test Fuels for Advanced Combustion Engines (FACE) based on the variation of three properties: cetane number, aromatic content, and 90 percent distillation temperature. The experimental data was used has a platform for the code development, and for its validation.;Using multivariate data analysis is not only useful in visualizing correlations that otherwise would be hidden by the large amount of experimental data points, but it is also capable to predict the behavior of those points inside the domain where no data are available. As suggested by the name this is a regression methodology capable of adapting the shape of the regression splines to the data analyzed. Validation datasets which were independent of the `calibration\u27 datasets were used to check the accuracy of the model predictions

Extended Joint Models for Longitudinal & Time-to-event Data:with applications in Cardiothoracic Surgery

In this thesis, we developed extensions for the joint modeling framework for longitudinal and time-to-event data, motivated by various clinical research questions in cardiothoracic surgery. These extensions focus in the handling of intermediate events during follow-up, feature selection in multivariate settings such as multiple longitudinal outcomes and multi-state processes using Bayesian shrinkage priors and sensitivity analysis for missing data under the joint modeling framework.<br/

Towards synthesis for nitrogen fertilisation using a decision support system

Nitrogen (N) fertilisation in crops can be made more efficient by moving from uniform application to meeting variable crop requirements within fields. Within field variable rate N fertilisation of winter wheat (Triticum aestivum L.) is practically feasible using information from web-based decision support systems (DSS). Data from different source platforms, such as satellite, unmanned aerial vehicle (UAV) or weather stations can be used for fertilisation planning. System output offers information that can be used  to instruct variable rate fertilizer spreaders to increase or decrease fertilizer application rate on-the-go. In Sweden, satellite-based variable rate N fertilisation was available for winter wheat via a DSS, however, the existing module could be improved in different ways. In this thesis work, a new N-uptake model was estimated and opportunities using UAV-based modelling of grain quality were tested. Transferability of UAV-based models to a satellite data scale improved understanding of the complexity of data transfer from UAV-scale to a satellite scale for use in a DSS. Furthermore, it was possible to model crop phenology from historical data, which can improve accuracy of current implemented models, by taking timing of field operations in to account

Transient engine model for calibration using two-stage regression approach

Engine mapping is the process of empirically modelling engine behaviour as a function of adjustable engine parameters, predicting the output of the engine. The aim is to calibrate the electronic engine controller to meet decreasing emission requirements and increasing fuel economy demands. Modern engines have an increasing number of control parameters that are having a dramatic impact on time and e ort required to obtain optimal engine calibrations. These are further complicated due to transient engine operating mode. A new model-based transient calibration method has been built on the application of hierarchical statistical modelling methods, and analysis of repeated experiments for the application of engine mapping. The methodology is based on two-stage regression approach, which organise the engine data for the mapping process in sweeps. The introduction of time-dependent covariates in the hierarchy of the modelling led to the development of a new approach for the problem of transient engine calibration. This new approach for transient engine modelling is analysed using a small designed data set for a throttle body inferred air ow phenomenon. The data collection for the model was performed on a transient engine test bed as a part of this work, with sophisticated software and hardware installed on it. Models and their associated experimental design protocols have been identi ed that permits the models capable of accurately predicting the desired response features over the whole region of operability. Further, during the course of the work, the utility of multi-layer perceptron (MLP) neural network based model for the multi-covariate case has been demonstrated. The MLP neural network performs slightly better than the radial basis function (RBF) model. The basis of this comparison is made on assessing relevant model selection criteria, as well as internal and external validation ts. Finally, the general ability of the model was demonstrated through the implementation of this methodology for use in the calibration process, for populating the electronic engine control module lookup tables

Modeling acute respiratory illness during the 2007 San Diego wildland fires using a coupled emissions-transport system and general additive modeling

Background A study of the impacts on respiratory health of the 2007 wildland fires in and around San Diego County, California is presented. This study helps to address the impact of fire emissions on human health by modeling the exposure potential of proximate populations to atmospheric particulate matter (PM) from vegetation fires. Currently, there is no standard methodology to model and forecast the potential respiratory health effects of PM plumes from wildland fires, and in part this is due to a lack of methodology for rigorously relating the two. The contribution in this research specifically targets that absence by modeling explicitly the emission, transmission, and distribution of PM following a wildland fire in both space and time. Methods Coupled empirical and deterministic models describing particulate matter (PM) emissions and atmospheric dispersion were linked to spatially explicit syndromic surveillance health data records collected through the San Diego Aberration Detection and Incident Characterization (SDADIC) system using a Generalized Additive Modeling (GAM) statistical approach. Two levels of geographic aggregation were modeled, a county-wide regional level and division of the county into six sub regions. Selected health syndromes within SDADIC from 16 emergency departments within San Diego County relevant for respiratory health were identified for inclusion in the model. Results The model captured the variability in emergency department visits due to several factors by including nine ancillary variables in addition to wildfire PM concentration. The model coefficients and nonlinear function plots indicate that at peak fire PM concentrations the odds of a person seeking emergency care is increased by approximately 50% compared to non-fire conditions (40% for the regional case, 70% for a geographically specific case). The sub-regional analyses show that demographic variables also influence respiratory health outcomes from smoke. Conclusions The model developed in this study allows a quantitative assessment and prediction of respiratory health outcomes as it relates to the location and timing of wildland fire emissions relevant for application to future wildfire scenarios. An important aspect of the resulting model is its generality thus allowing its ready use for geospatial assessments of respiratory health impacts under possible future wildfire conditions in the San Diego region. The coupled statistical and process-based modeling demonstrates an end-to-end methodology for generating reasonable estimates of wildland fire PM concentrations and health effects at resolutions compatible with syndromic surveillance data

Book of Abstracts XVIII Congreso de Biometría CEBMADRID

Abstracts of the XVIII Congreso de Biometría CEBMADRID held from 25 to 27 May in MadridInteractive modelling and prediction of patient evolution via multistate models / Leire Garmendia Bergés, Jordi Cortés Martínez and Guadalupe Gómez Melis : This research was funded by the Ministerio de Ciencia e Innovación (Spain) [PID2019104830RBI00]; and the Generalitat de Catalunya (Spain) [2017SGR622 and 2020PANDE00148].Operating characteristics of a model-based approach to incorporate non-concurrent controls in platform trials / Pavla Krotka, Martin Posch, Marta Bofill Roig : EU-PEARL (EU Patient-cEntric clinicAl tRial pLatforms) project has received funding from the Innovative Medicines Initiative (IMI) 2 Joint Undertaking (JU) under grant agreement No 853966. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA and Children’s Tumor Foundation, Global Alliance for TB Drug Development non-profit organisation, Spring works Therapeutics Inc.Modeling COPD hospitalizations using variable domain functional regression / Pavel Hernández Amaro, María Durbán Reguera, María del Carmen Aguilera Morillo, Cristobal Esteban Gonzalez, Inma Arostegui : This work is supported by the grant ID2019-104901RB-I00 from the Spanish Ministry of Science, Innovation and Universities MCIN/AEI/10.13039/501100011033.Spatio-temporal quantile autoregression for detecting changes in daily temperature in northeastern Spain / Jorge Castillo-Mateo, Alan E. Gelfand, Jesús Asín, Ana C. Cebrián / Spatio-temporal quantile autoregression for detecting changes in daily temperature in northeastern Spain : This work was partially supported by the Ministerio de Ciencia e Innovación under Grant PID2020-116873GB-I00; Gobierno de Aragón under Research Group E46_20R: Modelos Estocásticos; and JC-M was supported by Gobierno de Aragón under Doctoral Scholarship ORDEN CUS/581/2020.Estimation of the area under the ROC curve with complex survey data / Amaia Iparragirre, Irantzu Barrio, Inmaculada Arostegui : This work was financially supported in part by IT1294-19, PID2020-115882RB-I00, KK-2020/00049. The work of AI was supported by PIF18/213.INLAMSM: Adjusting multivariate lattice models with R and INLA / Francisco Palmí Perales, Virgilio Gómez Rubio and Miguel Ángel Martínez Beneito : This work has been supported by grants PPIC-2014-001-P and SBPLY/17/180501/000491, funded by Consejería de Educación, Cultura y Deportes (Junta de Comunidades de Castilla-La Mancha, Spain) and FEDER, grant MTM2016-77501-P, funded by Ministerio de Economía y Competitividad (Spain), grant PID2019-106341GB-I00 from Ministerio de Ciencia e Innovación (Spain) and a grant to support research groups by the University of Castilla-La Mancha (Spain). F. Palmí-Perales has been supported by a Ph.D. scholarship awarded by the University of Castilla-La Mancha (Spain)

AI/ML Algorithms and Applications in VLSI Design and Technology

An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations