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Early identification of process deviation based on convolutional neural network

Chinese Journal of Chemical Engineering 2023年第4期56卷 104-118页

作者： Fangyuan Ma Cheng Ji Jingde Wang Wei Sun College of Chemical Engineering Beijing University of Chemical TechnologyBeijing 100029China Center of Process Monitoring and Data Analysis Wuxi Research Institute of Applied TechnologiesTsinghua UniversityWuxi 214072China

A novel process monitoring method based on convolutional neural network(CNN)is proposed and applied to detect faults in industrial *** utilizing the CNN algorithm,cross-correlation and autocorrelation among variables are captured to establish a prediction model for each process variable to approximate the first-principle of physical/chemical relationships among different variables under normal operating *** the process is operated under pre-set operating conditions,prediction residuals can be assumed as noise if a proper model is *** process faults occur,the residuals will increase due to the changes of correlation among variables.A principal component analysis(PCA)model based on the residuals is established to realize process *** monitoring the changes in main feature of prediction residuals,the faults can be promptly *** studies on a numerical nonlinear example and data from two industrial processes are presented to validate the performance of process monitoring based on CNN.

关键词： process monitoring Residual Principal component analysis process systems Systems engineering

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Can the Compositional Nature of Compositional data Be Ignored by Using Deep Learning Approaches? 50th

Can the Compositional Nature of Compositional Data Be Igno...

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50th Scientific Meeting of the Italian Statistical Society, SIS 2021

作者： Templ, Matthias Institute of Data Analysis and Process Design Zurich University of Applied Sciences Rosenstrasse 3 Winterthur8404 Switzerland

ISBN: (纸本)9783031166082

The application of non-compositional methods to compositional data representing parts of a whole should be avoided from a theoretical point of view. For example, one can show that Pearson correlations applied to compositional data are biased to be negative. Moreover, almost all statistical methods lead to biased estimates when applied to compositional data. One way out is to analyze data after representing them in log-ratio coordinates. However, several implications arise, such as interpretation in log-ratios and dealing with zeros and non-detects where log-ratios are undefined. When focusing on settings where only the prediction and classification error is important rather than an interpretation of results, one might argue to rather use non-linear methods to avoid those implications. Generally, it is known that misclassification and prediction errors are lower with a log-ratio approach when using machine learning methods that model the linear relationship between variables. However, is this also true when training a neural network who may learn the inner relationships between parts of a whole also without representing the data in log-ratios? This paper gives an answer of this matter based on applications with multiple real data sets, leading to the recommendation to use a compositional treatment of compositional data in any case. Misclassification and prediction errors are lower when nonlinear methods, and in particular deep learning methods, are applied together with a compositional treatment of compositional data. The compositional treatment of compositional data therefore remains very important even in the context of focusing on prediction errors using deep artificial neural networks or other nonlinear methods. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Neural networks

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Is calibration a fairness requirement?: An argument from the point of view of moral philosophy and decision theory 22

Is calibration a fairness requirement?: An argument from the...

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5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022

作者： Loi, Michele Heitz, Christoph Department of Mathematics Politecnico di Milano Italy Institute of Data Analysis and Process Zurich University of Applied Sciences Switzerland

ISBN: (纸本)9781450393522

In this paper, we provide a moral analysis of two criteria of statistical fairness debated in the machine learning literature: 1) calibration between groups and 2) equality of false positive and false negative rates between groups. In our paper, we focus on moral arguments in support of either measure. The conflict between group calibration vs. false positive and false negative rate equality is one of the core issues in the debate about group fairness definitions among practitioners. For any thorough moral analysis, the meaning of the term "fairness"has to be made explicit and defined properly. For our paper, we equate fairness with (non-)discrimination, which is a legitimate understanding in the discussion about group fairness. More specifically, we equate it with "prima facie wrongful discrimination"in the sense this is used in Prof. Lippert-Rasmussen's treatment of this definition. In this paper, we argue that a violation of group calibration may be unfair in some cases, but not unfair in others. Our argument analyzes in great detail two specific hypothetical examples of usage of predictions in decision making. The most important practical implication is that between-group calibration is defensible as a bias standard in some cases but not others;we show this by referring to examples in which the violation of between-group calibration is discriminatory, and others in which it is not. This is in line with claims already advanced in the literature, that algorithmic fairness should be defined in a way that is sensitive to context. The most important practical implication is that arguments based on examples in which fairness requires between-group calibration, or equality in the false-positive/false-negative rates, do no generalize. For it may be that group calibration is a fairness requirement in one case, but not in another. © 2022 ACM.

关键词： Calibration

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What's Distributive Justice Got to Do with It? Rethinking Algorithmic Fairness from the Perspective of Approximate Justice 24

What's Distributive Justice Got to Do with It? Rethinking Al...

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Proceedings of the 2024 AAAI/ACM Conference on AI, Ethics, and Society

作者： Corinna Hertweck Christoph Heitz Michele Loi Institute for Data Analysis and Process Design Zurich University of Applied Sciences Winterthur Switzerland and Department of Informatics University of Zurich Zurich Switzerland Institute for Data Analysis and Process Design Zurich University of Applied Sciences Winterthur Switzerland AlgorithmWatch Berlin Germany

In the field of algorithmic fairness, many fairness criteria have been proposed. Oftentimes, their proposal is only accompanied by a loose link to ideas from moral philosophy - which makes it difficult to understand when the proposed criteria should be used to evaluate the fairness of a decision-making system. More recently, researchers have thus retroactively tried to tie existing fairness criteria to philosophical concepts. Group fairness criteria have typically been linked to egalitarianism, a theory of distributive justice. This makes it tempting to believe that fairness criteria mathematically represent ideals of distributive justice and this is indeed how they are typically portrayed. In this paper, we will discuss why the current approach of linking algorithmic fairness and distributive justice is too simplistic and, hence, insufficient. We argue that in the context of imperfect decision-making systems - which is what we deal with in algorithmic fairness - we should not only care about what the ideal distribution of benefits/harms among individuals would look like but also about how deviations from said ideal are distributed. Our claim is that algorithmic fairness is concerned with unfairness in these deviations. This requires us to rethink the way in which we, as algorithmic fairness researchers, view distributive justice and use fairness criteria.

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Regime-dependent drivers of the EUR/CHF exchange rate

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Swiss Journal of Economics and Statistics 2023年第1期159卷 3页

作者： Kotlarz, Piotr Hanke, Michael Stöckl, Sebastian Liechtenstein Business School University of Liechtenstein Fürst-Franz-Josef-Strasse Vaduz Liechtenstein Institute of Data Analysis and Process Design School of Engineering Zurich University of Applied Sciences Technikumstrasse 71 Winterthur Switzerland

We analyze drivers of the EUR/CHF exchange rate in different regimes between 2000 and 2020. Structural breaks between these subperiods are estimated in an integrated way together with the drivers that are relevant during these subperiods. Overall, the main drivers of the exchange rate include European equity and volatility indices, interest rate and term structure slope differentials, as well as monetary policy interventions. For the “peg period” September 2011–January 2015, in addition to the observed exchange rate we also analyze the drivers of the latent exchange rate that could have been observed in the absence of the peg. Interestingly, the SNB’s foreign currency investments became a significant driver of the EUR/CHF exchange rate only after the end of the peg period when there was no longer an officially communicated target rate. © 2023, The Author(s).

关键词： EUR/CHF exchange rate Exchange rate drivers Foreign currency interventions Latent exchange rate Monetary policy

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Assessing Keyness using Permutation Tests

arXiv

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arXiv 2023年

作者： Mildenberger, Thoralf Institute of Data Analysis and Process Design Switzerland ZHAW Zurich University of Applied Sciences Switzerland

We propose a resampling-based approach for assessing keyness in corpus linguistics based on suggestions by Gries (2006, 2022). Traditional approaches based on hypothesis tests (e.g. Likelihood Ratio) model the copora as independent identically distributed samples of tokens. This model does not account for the often observed uneven distribution of occurences of a word across a corpus. When occurences of a word are concentrated in few documents, large values of LLR and similar scores are in fact much more likely than accounted for by the token-by-token sampling model, leading to false positives. We replace the token-by-token sampling model by a model where corpora are samples of documents rather than tokens, which is much closer to the way corpora are actually assembled. We then use a permutation approach to approximate the distribution of a given keyness score under the null hypothesis of equal frequencies and obtain p-values for assessing significance. We do not need any assumption on how the tokens are organized within or across documents, and the approach works with basically any keyness score. Hence, appart from obtaining more accurate p-values for scores like LLR, we can also assess significance for e.g. the logratio which has been proposed as a measure of effect size. An efficient implementation of the proposed approach is provided in the R package keyperm available from github. Copyright © 2023, The Authors. All rights reserved.

关键词： Lunar surface analysis

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High-Frequency Causality in the VIX Index and its derivatives: Empirical Evidence

arXiv

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arXiv 2022年

作者： Farokhnia, Kia Osterrieder, Joerg Institute of Data Analysis and Process Design School of Engineering Zurich University of Applied Sciences Switzerland

In February 2018, the VIX index has seen its largest ever increase and has lead to significant losses for some major volatility-related products. Despite many efforts, the precise underlying reasons are yet to be discovered. We study the role of linear causality in the VIX index and its derivatives during January and February 2018. Due to the shortcomings of statistical inferences for stochastic volatility models, the dynamics of the volatility expectation index VIX remain controversial. Leveraging intraday data, we discover novel empirical results describing their interaction. We find bidirectional causality between the VIX spot and the implied volatility of Standard & Poor's 500 options, suggesting a volatility feedback effect. The spot index tends to be lagging its own futures, while the vector autoregression's error correction mechanism reveals a significant mean-reverting equilibrium relationship. The evidence is consistent with recent theories indicating that implied volatility has stronger feedback than realized volatility. The paper reveals a retroactive information flow and highlights novel insights for the market microstructure of VIX derivatives and their related S&P 500 options. © 2022, CC0.

关键词： Error correction

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Greening urban logistics: Introducing a method to evaluate optimised decoupling hub locations

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Transportation Research Procedia 2023年 72卷 572-579页

作者： Maike Scherrer Albert Steiner Viola Rühlin Zurich University of Applied Sciences School of Engineering Institute of Sustainable Development Technikumstrasse 9 8401 Winterthur Switzerland Zurich University of Applied Sciences School of Engineering Institute of Data Analysis and Process Design Rosenstrasse 3 8401 Winterthur Switzerland

Sustainable urban logistics has become challenging over the course of the last decades due to logistics sprawl and increased demands of goods in urban areas. To overcome this problem, a fine-grained network of decoupling hubs, separating suppliers and customers in a spacial and timely manner, are needed. The paper at hand introduces a model of how to calculate optimal decoupling hub locations within urban areas with the goal that inhabitants walk or cycle to the next decoupling hub and do not take a motorised vehicle. The study at hand uses the northern district of Zurich as unit of analysis and shows that by using the developed model, 80% of the inhabitants need to walk less than 250 metres to their next decoupling hub, which is within the radius of 100 metres to the next public transport stop. This supports the integration of picking up or dropping off deliveries while commuting.

关键词： decoupling hubs sustainable urban logistics facility location probem

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Fault Detection for Industrial Chemical Production Using Siamese Autoencoder

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IFAC-PapersOnLine 2024年第14期58卷 823-828页

作者： Cheng Ji Fangyuan Ma Jingde Wang Wei Sun College of Chemical Engineering Beijing University of Chemical Technology 100029 North Third Ring Road 15 Chaoyang District Beijing China Center of process monitoring and data analysis Wuxi Research Institute of Applied Technologies Tsinghua University 214072 Wuxi China

Nowadays, deep learning has emerged as a transformative technology in various domains, including process monitoring. Massive advanced deep learning algorithms, such as autoencoder, recurrent neural network, and convolutional neural network, have been explored in the application of chemical processes to enhance the overall monitoring performance. Nevertheless, deep learning models often imply complex structures and a huge number of parameters, leading to limited generalization ability when they are employed in industrial chemical processes. In this work, the above limitation is addressed by monitoring the representative and discriminative features extracted in the latent space by Siamese autoencoder. The reconstruction ability and discriminative information between a pair of inputs are considered in the extraction of latent features, by which better process monitoring performance can be achieved with much fewer model parameters. Case study on an industrial chemical process is investigated to demonstrate the effectiveness of the proposed method.

关键词： process monitoring deep learning Siamese neural network catalytic reforming heat exchanger

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Resource Allocation with Karma Mechanisms

arXiv

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arXiv 2024年

作者： Riehl, Kevin Kouvelas, Anastasios Makridis, Michail Traffic Engineering Group Institute for Transport Planning and Systems ETH Zurich Stefano-Franscini-Platz 5 Zurich8093 Switzerland Transport and Traffic Engineering Group Institute of Data Analysis and Process Design Zurich University of Applied Sciences Technikumstrasse 81 Winterthur8400 Switzerland

Monetary markets serve as established resource allocation mechanisms, typically achieving efficient solutions with limited information. However, they are susceptible to market failures, particularly under the presence of public goods, externalities, or inequality of economic power. Moreover, in many resource allocating contexts, money faces social, ethical, and legal constraints. Consequently, research increasingly explores artificial currencies and non-monetary markets, with Karma emerging as a notable concept. Karma, a non-tradeable, resource-inherent currency for prosumer resources, operates on the principles of contribution and consumption of specific resources. It embodies fairness, near incentive compatibility, Pareto-efficiency, robustness to population heterogeneity, and can incentivize a reduction in resource scarcity. The literature on Karma is scattered across disciplines, varies in scope, and lacks of conceptual clarity and coherence. Thus, this study undertakes a comprehensive review of the Karma mechanism, systematically comparing its resource allocation applications and elucidating overlooked mechanism design elements. Through a systematic mapping study, this review situates Karma within its literature context, offers a structured design parameter framework, and develops a road-map for future research directions. © 2024, CC BY-NC-ND.

关键词： Resource allocation

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