577 research outputs found
Concurrent Product and Supply Chain Architecture Design Considering Modularity and Sustainability
Since sustainability is a growing concern, businesses aim to integrate sustainability principles and practices into product and supply chain (SC) architecture (SCA) design. Modular product architecture (MPA) is essential for meeting sustainability demands, as it defines detachable modules by selecting appropriate components from various potential combinations. However, the prevailing practice of MPA emphasizes architectural aspects over interface complexity and design production processes for the structural dimension, potentially impending manufacturing, assembly/disassembly, and recovery efficiency. Most MPA has been developed assuming equal and/or fixed relations among modules rather than configuring for SC effectiveness. Therefore, such methods cannot offer guidance on modular granularity and its impact on product and SCA sustainability. Additionally, there is no comparative assessment of MPA to determine whether the components within the configured modules could share multiple facilities to achieve economic benefits and be effective for modular manufacture and upgrade. Therefore, existing modular configuration fails to link modularization drivers and metrics with SCA, hampering economic design, modular recycling, and efficient assembly/disassembly for enhancing sustainability.
This study focuses on the study of design fundamentals and implementation of sustainable modular drivers in coordination with SCA by developing a mathematical model. Here, the architectural and interface relations between components are quantified and captured in a decision structure matrix which acts as the foundation of modular clustering for MPA. Again, unlike previous design approaches focused only on cost, the proposed work considers facility sharing through a competitive analysis of commonality and cost. It also evaluates MPA's ease of disassembly and upgradeability by a comparative assessment of different MPA to enhance SCA sustainability. The primary focus is concurrently managing the interdependency between MPA and SCA by developing mathematical models. Consistent with the mathematical model, this thesis also proposes better solution approaches.
In summary, the proposed methods provide a foundation for modeling the link between product design and SC to 1) demonstrate how sustainable modular drivers affect the sustainability performance, 2) evaluate the contribution of modularity to the reduction of assembly/disassembly complexity and cost, 3) develop MPA in coordination with SC modularity by trading off modular granularity, commonality, and cost, and 4) identify a sustainable product family for combined modularity considering the similarity of operations, ease of disassembly and upgradability in SCA.
Using metaheuristic algorithms, case studies on refrigerators showed that MPA and its methodology profoundly impact SCA sustainability. It reveals that interactions between components with levels based on sustainable modular drivers should be linked with modular granularity for SCA sustainability. Another key takeaway is that instead of solely focusing on cost, facility sharing and ensuring ease of disassembly and upgradeability can help to reap sustainability benefits
Discovering Causal Relations and Equations from Data
Physics is a field of science that has traditionally used the scientific
method to answer questions about why natural phenomena occur and to make
testable models that explain the phenomena. Discovering equations, laws and
principles that are invariant, robust and causal explanations of the world has
been fundamental in physical sciences throughout the centuries. Discoveries
emerge from observing the world and, when possible, performing interventional
studies in the system under study. With the advent of big data and the use of
data-driven methods, causal and equation discovery fields have grown and made
progress in computer science, physics, statistics, philosophy, and many applied
fields. All these domains are intertwined and can be used to discover causal
relations, physical laws, and equations from observational data. This paper
reviews the concepts, methods, and relevant works on causal and equation
discovery in the broad field of Physics and outlines the most important
challenges and promising future lines of research. We also provide a taxonomy
for observational causal and equation discovery, point out connections, and
showcase a complete set of case studies in Earth and climate sciences, fluid
dynamics and mechanics, and the neurosciences. This review demonstrates that
discovering fundamental laws and causal relations by observing natural
phenomena is being revolutionised with the efficient exploitation of
observational data, modern machine learning algorithms and the interaction with
domain knowledge. Exciting times are ahead with many challenges and
opportunities to improve our understanding of complex systems.Comment: 137 page
Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey
Adversarial attacks and defenses in machine learning and deep neural network
have been gaining significant attention due to the rapidly growing applications
of deep learning in the Internet and relevant scenarios. This survey provides a
comprehensive overview of the recent advancements in the field of adversarial
attack and defense techniques, with a focus on deep neural network-based
classification models. Specifically, we conduct a comprehensive classification
of recent adversarial attack methods and state-of-the-art adversarial defense
techniques based on attack principles, and present them in visually appealing
tables and tree diagrams. This is based on a rigorous evaluation of the
existing works, including an analysis of their strengths and limitations. We
also categorize the methods into counter-attack detection and robustness
enhancement, with a specific focus on regularization-based methods for
enhancing robustness. New avenues of attack are also explored, including
search-based, decision-based, drop-based, and physical-world attacks, and a
hierarchical classification of the latest defense methods is provided,
highlighting the challenges of balancing training costs with performance,
maintaining clean accuracy, overcoming the effect of gradient masking, and
ensuring method transferability. At last, the lessons learned and open
challenges are summarized with future research opportunities recommended.Comment: 46 pages, 21 figure
Demand Response in Smart Grids
The Special Issue âDemand Response in Smart Gridsâ includes 11 papers on a variety of topics. The success of this Special Issue demonstrates the relevance of demand response programs and events in the operation of power and energy systems at both the distribution level and at the wide power system level. This reprint addresses the design, implementation, and operation of demand response programs, with focus on methods and techniques to achieve an optimized operation as well as on the electricity consumer
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
Statistical Modeling: Regression, Survival Analysis, and Time Series Analysis
Statistical Modeling provides an introduction to regression, survival analysis, and time series analysis for students who have completed calculus-based courses in probability and mathematical statistics. The book uses the R language to fit statistical models, conduct Monte Carlo simulation experiments and generate graphics. Over 300 exercises at the end of the chapters makes this an appropriate text for a class in statistical modeling.
Part 1: RegressionChapter 1: Simple Linear Regression Chapter 2: Inference in Simple Linear Regression Chapter 3: Topics in RegressionPart II: Survival Analysis Chapter 4: Probability Models in Survival AnalysisChapter 5: Statistical Methods in Survival Analysis Chapter 6: Topics in Survival Analysis Part III: Time Series Analysis Chapter 7: Basic Methods in Time Series AnalysisChapter 8: Modeling in Time Series Analysis Chapter 9: Topics in Time Series Analysi
Essays in Applied Economics
This dissertation consists of five independent chapters contributing to the field of applied economics. The first three chapters analyze workers' perceptions of the wage penalty associated with working part-time, further evaluating the labor supply implications of biased beliefs. Chapter 4 quantifies the effects of raising the normal retirement age on the career trajectories of middle-aged workers far from retirement. Chapter 5 examines the expectation management of the German government in the early phase of the COVID-19 pandemic
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Rare-Event Estimation and Calibration for Large-Scale Stochastic Simulation Models
Stochastic simulation has been widely applied in many domains. More recently, however, the rapid surge of sophisticated problems such as safety evaluation of intelligent systems has posed various challenges to conventional statistical methods. Motivated by these challenges, in this thesis, we develop novel methodologies with theoretical guarantees and numerical applications to tackle them from different perspectives.
In particular, our works can be categorized into two areas: (1) rare-event estimation (Chapters 2 to 5) where we develop approaches to estimating the probabilities of rare events via simulation; (2) model calibration (Chapters 6 and 7) where we aim at calibrating the simulation model so that it is close to reality.
In Chapter 2, we study rare-event simulation for a class of problems where the target hitting sets of interest are defined via modern machine learning tools such as neural networks and random forests. We investigate an importance sampling scheme that integrates the dominating point machinery in large deviations and sequential mixed integer programming to locate the underlying dominating points. We provide efficiency guarantees and numerical demonstration of our approach.
In Chapter 3, we propose a new efficiency criterion for importance sampling, which we call probabilistic efficiency. Conventionally, an estimator is regarded as efficient if its relative error is sufficiently controlled. It is widely known that when a rare-event set contains multiple "important regions" encoded by the dominating points, importance sampling needs to account for all of them via mixing to achieve efficiency. We argue that the traditional analysis recipe could suffer from intrinsic looseness by using relative error as an efficiency criterion. Thus, we propose the new efficiency notion to tighten this gap. In particular, we show that under the standard Gartner-Ellis large deviations regime, an importance sampling that uses only the most significant dominating points is sufficient to attain this efficiency notion.
In Chapter 4, we consider the estimation of rare-event probabilities using sample proportions output by crude Monte Carlo. Due to the recent surge of sophisticated rare-event problems, efficiency-guaranteed variance reduction may face implementation challenges, which motivate one to look at naive estimators. In this chapter we construct confidence intervals for the target probability using this naive estimator from various techniques, and then analyze their validity as well as tightness respectively quantified by the coverage probability and relative half-width.
In Chapter 5, we propose the use of extreme value analysis, in particular the peak-over-threshold method which is popularly employed for extremal estimation of real datasets, in the simulation setting. More specifically, we view crude Monte Carlo samples as data to fit on a generalized Pareto distribution. We test this idea on several numerical examples. The results show that in the absence of efficient variance reduction schemes, it appears to offer potential benefits to enhance crude Monte Carlo estimates.
In Chapter 6, we investigate a framework to develop calibration schemes in parametric settings, which satisfies rigorous frequentist statistical guarantees via a basic notion that we call eligibility set designed to bypass non-identifiability via a set-based estimation. We investigate a feature extraction-then-aggregation approach to construct these sets that target at multivariate outputs. We demonstrate our methodology on several numerical examples, including an application to calibration of a limit order book market simulator.
In Chapter 7, we study a methodology to tackle the NASA Langley Uncertainty Quantification Challenge, a model calibration problem under both aleatory and epistemic uncertainties. Our methodology is based on an integration of distributionally robust optimization and importance sampling. The main computation machinery in this integrated methodology amounts to solving sampled linear programs. We present theoretical statistical guarantees of our approach via connections to nonparametric hypothesis testing, and numerical performances including parameter calibration and downstream decision and risk evaluation tasks
Path-based splitting methods for SDEs and machine learning for battery lifetime prognostics
In the first half of this Thesis, we present the numerical analysis of splitting methods for
stochastic differential equations (SDEs) using a novel path-based approach. The application
of splitting methods to SDEs can be viewed as replacing the driving Brownian-time path
with a piecewise linear path, producing a âcontrolled-differential-equationâ (CDE). By Taylor
expansion of the SDE and resulting CDE, we show that the global strong and weak errors of
splitting schemes can be obtained by comparison of the iterated integrals in each. Matching
all integrals up to order p+1 in expectation will produce a weak order p+0.5 scheme, and in
addition matching the integrals up to order p+0.5 strongly will produce a strong order p
scheme. In addition, we present new splitting methods utilising the âspace-timeâ L´evy area
of Brownian motion which obtain global strong Oph1.5q and Oph2q weak errors for a class
of SDEs satisfying a commutativity condition. We then present several numerical examples
including Multilevel Monte Carlo.
In the second half of this Thesis, we present a series of papers focusing on lifetime prognostics
for lithium-ion batteries. Lithium-ion batteries are fuelling the advancing renewable-energy
based world. At the core of transformational developments in battery design, modelling and
management is data. We start with a comprehensive review of publicly available datasets.
This is followed by a study which explores the evolution of internal resistance (IR) in cells,
introducing the original concept of âelbowsâ for IR. The IR of cells increases as a cell degrades
and this often happens in a non-linear fashion: where early degradation is linear until an
inflection point (the elbow) is reached followed by increased rapid degradation. As a follow up
to the exploration of IR, we present a model able to predict the full IR and capacity evolution
of a cell from one charge/discharge cycle. At the time of publication, this represented a
significant reduction (100x) in the number of cycles required for prediction. The published
paper was the first to show that such results were possible.
In the final paper, we consider
experimental design for battery testing. Where we focus on the important question of how
many cells are required to accurately capture statistical variation
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