Using Artificial Intelligence to Predict the Discharge Performance of Cathode Materials for Lithium-ion Batteries Applications

A comprehensive understanding of the composition-structure-property relationships for doped cathode materials used in lithium-ion batteries remains lacking which delays the progress of developing new cathode materials. This thesis proposes that machine learning (ML) techniques can be used to predict the discharge capacities of the cathode materials whilst revealing these underlying relationships. To achieve this, the data for three different doped cathodes are curated from the publications, namely, the doped spinel cathode, LiMxMn2−xO4, the M-doped nickel- cobalt-manganese layered cathode, LiNixCoyMnzM1−x−y−zO2, and the carbon -coated and doped olivine cathode, C/LiM1M2PO4 (M1, M2 denote different metal ions). Several linear and non-linear ML models are trained with the data and compared for the power of predicting initial and higher cycle discharge capacity. Gradient boosting models have shown the best prediction power for predicting the initial and 20th cycle end discharge capacity of 102 doped spinel cathode and the initial and 50th cycle discharge capacity of 168 doped nickel-cobalt-manganese layered cathodes. For the doped spinel cathode, higher discharge capacities at both cycles can be achieved through increasing the material formula mass, reducing the crystal lattice constant and using dopants with smaller electronegativity. For the doped layered cathodes, it is revealed that the higher lithium content, lower formula molar mass, small doping content and doped with low electronegativity dopant are more likely to possess greater capacities at both cycles. Bayesian ridge regression and gradient boosting model are shown to have the highest prediction power over the initial and the 20th cycle discharge capacity of carbon-coated and doped olivine cathode. In addition, the olivine systems with lower dopant content, higher base-metal content and smaller unit cells are shown to be more likely to possess higher capacities at both cycles. Finally, future research directions are presented including the suggestion of involving other new input variables and using principal component analysis and feature selection algorithms to use to improve the model performance

A Framework for Meta-heuristic Parameter Performance Prediction Using Fitness Landscape Analysis and Machine Learning

The behaviour of an optimization algorithm when attempting to solve a problem depends on the values assigned to its control parameters. For an algorithm to obtain desirable performance, its control parameter values must be chosen based on the current problem. Despite being necessary for optimal performance, selecting appropriate control parameter values is time-consuming, computationally expensive, and challenging. As the quantity of control parameters increases, so does the time complexity associated with searching for practical values, which often overshadows addressing the problem at hand, limiting the efficiency of an algorithm. As primarily recognized by the no free lunch theorem, there is no one-size-fits-all to problem-solving; hence from understanding a problem, a tailored approach can substantially help solve it. To predict the performance of control parameter configurations in unseen environments, this thesis crafts an intelligent generalizable framework leveraging machine learning classification and quantitative characteristics about the problem in question. The proposed parameter performance classifier (PPC) framework is extensively explored by training 84 high-accuracy classifiers comprised of multiple sampling methods, fitness types, and binning strategies. Furthermore, the novel framework is utilized in constructing a new parameter-free particle swarm optimization (PSO) variant called PPC-PSO that effectively eliminates the computational cost of parameter tuning, yields competitive performance amongst other leading methodologies across 99 benchmark functions, and is highly accessible to researchers and practitioners. The success of PPC-PSO shows excellent promise for the applicability of the PPC framework in making many more robust parameter-free meta-heuristic algorithms in the future with incredible generalization capabilities

Spatializing Partisan Gerrymandering Forensics: Local Measures and Spatial Specifications

abstract: Gerrymandering is a central problem for many representative democracies. Formally, gerrymandering is the manipulation of spatial boundaries to provide political advantage to a particular group (Warf, 2006). The term often refers to political district design, where the boundaries of political districts are “unnaturally” manipulated by redistricting officials to generate durable advantages for one group or party. Since free and fair elections are possibly the critical part of representative democracy, it is important for this cresting tide to have scientifically validated tools. This dissertation supports a current wave of reform by developing a general inferential technique to “localize” inferential bias measures, generating a new type of district-level score. The new method relies on the statistical intuition behind jackknife methods to construct relative local indicators. I find that existing statewide indicators of partisan bias can be localized using this technique, providing an estimate of how strongly a district impacts statewide partisan bias over an entire decade. When compared to measures of shape compactness (a common gerrymandering detection statistic), I find that weirdly-shaped districts have no consistent relationship with impact in many states during the 2000 and 2010 redistricting plan. To ensure that this work is valid, I examine existing seats-votes modeling strategies and develop a novel method for constructing seats-votes curves. I find that, while the empirical structure of electoral swing shows significant spatial dependence (even in the face of spatial heterogeneity), existing seats-votes specifications are more robust than anticipated to spatial dependence. Centrally, this dissertation contributes to the much larger social aim to resist electoral manipulation: that individuals & organizations suffer no undue burden on political access from partisan gerrymandering.Dissertation/ThesisDoctoral Dissertation Geography 201

Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus

This is an open access book. It gathers the first volume of the proceedings of the 31st edition of the International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2022, held on June 19 – 23, 2022, in Detroit, Michigan, USA. Covering four thematic areas including Manufacturing Processes, Machine Tools, Manufacturing Systems, and Enabling Technologies, it reports on advanced manufacturing processes, and innovative materials for 3D printing, applications of machine learning, artificial intelligence and mixed reality in various production sectors, as well as important issues in human-robot collaboration, including methods for improving safety. Contributions also cover strategies to improve quality control, supply chain management and training in the manufacturing industry, and methods supporting circular supply chain and sustainable manufacturing. All in all, this book provides academicians, engineers and professionals with extensive information on both scientific and industrial advances in the converging fields of manufacturing, production, and automation

Market Segmentation Analysis: Understanding It, Doing It, and Making It Useful

This open access book offers something for everyone working with market segmentation: practical guidance for users of market segmentation solutions; organisational guidance on implementation issues; guidance for market researchers in charge of collecting suitable data; and guidance for data analysts with respect to the technical and statistical aspects of market segmentation analysis. Even market segmentation experts will find something new, including a vast array of useful visualisation techniques that make interpretation of market segments and selection of target segments easier. All calculations are accompanied not only with a detailed explanation, but also with R code that allows readers to replicate any aspect of what is being covered in the book using R, the open-source environment for statistical computing and graphics.Dieses Open Access Buch offeriert allen etwas, die mit Marktsegmentierung zu tun haben: praktische Anleitungen für Anwender von Marktsegmentierungslösungen, organisatorische Hilfe zur Umsetzung und Datensammlung, sowie Hilfe zur technischen und statistischen Umsetzung von Marktsegmentierungsanalysen. Auch Experten der Marktsegmentierung finden neue Werkzeuge, inbesonders eine umfangreiche Sammlung von Visualisierungsmethoden zur einfacheren Interpretation und Selektion von Marktsegmenten. Alle Berechnungen werden nicht nur detailliert erklärt, sondern von R Code begleitet, welcher es dem Leser erlaubt, alle Analysen im Buch mit Hilfe der Open Source Statistiksoftware R zu replizieren

Market Segmentation Analysis

Business; Management science; Market research; Statistics