Smart manufacturing is a revolutionary domain integrating advanced sensing technology, machine learning methods, and the industrial internet of things (IIoT). The development of sensing technology provides large amounts and various types of data (e.g., profile, image, point cloud, etc.) to describe each stage of a manufacturing process. The machine learning methods have the advantages of efficiently and effectively processing and fusing large-scale datasets and demonstrating outstanding performance in different tasks (e.g., diagnosis, monitoring, etc.). Despite the advantages of incorporating machine learning methods into smart manufacturing, there are some widely existing concerns in practice: (1) Most of the edge devices in the manufacturing system only have limited memory space and computational capacity; (2) Both the performance and interpretability of the data analytics method are desired; (3) The connection to the internet exposes the manufacturing system to cyberattacks, which decays the trustiness of data, models, and results. To address these limitations, this dissertation proposed systematic engineering-driven machine learning methods to improve the system intelligence for smart manufacturing. The contributions of this dissertation can be summarized in three aspects. First, tensor decomposition is incorporated to approximately compress the convolutional (Conv) layer in Deep Neural Network (DNN), and a novel layer is proposed accordingly. Compared with the Conv layer, the proposed layer significantly reduces the number of parameters and computational costs without decaying the performance. Second, a physics-informed stochastic surrogate model is proposed by incorporating the idea of building and solving differential equations into designing the stochastic process. The proposed method outperforms pure data-driven stochastic surrogates in recovering system patterns from noised data points and exploiting limited training samples to make accurate predictions and conduct uncertainty quantification. Third, a Wasserstein-based out-of-distribution detection (WOOD) framework is proposed to strengthen the DNN-based classifier with the ability to detect adversarial samples. The properties of the proposed framework have been thoroughly discussed. The statistical learning bound of the proposed loss function is theoretically investigated. The proposed framework is generally applicable to DNN-based classifiers and outperforms state-of-the-art benchmarks in identifying out-of-distribution samples. / Doctor of Philosophy / The global industries are experiencing the fourth industrial revolution, which is characterized by the use of advanced sensing technology, big data analytics, and the industrial internet of things (IIoT) to build a smart manufacturing system. The massive amount of data collected in the engineering process provides rich information to describe the complex physical phenomena in the manufacturing system. The big data analytics methods (e.g., machine learning, deep learning, etc.) are developed to exploit the collected data to complete specific tasks, such as checking the quality of the product, diagnosing the root cause of defects, etc. Given the outstanding performances of the big data analytics methods in these tasks, there are some concerns arising from the engineering practice, such as the limited available computational resources, the model's lack of interpretability, and the threat of hacking attacks. In this dissertation, we propose systematic engineering-driven machine learning methods to address or mitigate these widely existing concerns. First, the model compression technique is developed to reduce the number of parameters and computational complexity of the deep learning model to fit the limited available computational resources. Second, physics principles are incorporated into designing the regression method to improve its interpretability and enable it better explore the properties of the data collected from the manufacturing system. Third, the cyberattack detection method is developed to strengthen the smart manufacturing system with the ability to detect potential hacking threats.

Challenges within the field of pathology leads to a high workload for pathologists. Machine learning has the ability to assist pathologists in their daily work and has shown good performance in a research setting. Anomaly detection is useful for preventing machine learning models used for classification and segmentation to be applied on data outside of the training distribution of the model. The purpose of this work was to create an optimal anomaly detection pipeline for digital pathology data using a latent diffusion model and various image similarity metrics. An anomaly detection pipeline was created which used a partial diffusion process, a combined similarity metric containing the result of multiple other similarity metrics and a contrast matching strategy for better anomaly detection performance. The anomaly detection pipeline had a good performance in an out-of-distribution detection task with an ROC-AUC score of 0.90. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

Changes to the signal environment of a radar warning receiver (RWR) over time through dataset drift can negatively affect a machine learning (ML) model, deployed for radar emitter classification (REC). The training data comes from a simulator at Saab AB, in the form of pulsed radar in a time-series. In order to investigate this phenomenon on a neural network (NN), this study first implements an underlying classifier (UC) in the form of a deep ensemble (DE), where each ensemble member consists of an NN with two independently trained bidirectional LSTM channels for each of the signal features pulse repetition interval (PRI), pulse width (PW) and carrier frequency (CF). From tests, the UC performs best for REC when using all three features. Because dataset drift can be treated as detecting out-of-distribution (OOD) samples over time, the aim is to reduce NN overconfidence on data from unseen radar emitters in order to enable OOD detection. The method estimates uncertainty with predictive entropy and classifies samples reaching an entropy larger than a threshold as OOD. In the first set of tests, OOD is defined from holding out one feature modulation from the training dataset, and choosing this as the only modulation in the OOD dataset used during testing. With this definition, Stagger and Jitter are most difficult to detect as OOD. Moreover, using DEs with 6 ensemble members and implementing LogitNorm to the architecture improves the OOD detection performance. Furthermore, the OOD detection method performs well for up to 300 emitter classes and predictive entropy outperforms the baseline for almost all tests. Finally, the model performs worse when OOD is simply defined as signals from unseen emitters, because of a precision decrease. In conclusion, the implemented changes managed to reduce the overconfidence for this particular NN, and improve OOD detection for REC.

Deep learning-based dose prediction is a promising approach to automated radiotherapy planning but carries with it the risk of failing silently when the inputs are highly abnormal compared to the training data. One way to address this issue is to develop a dedicated outlier detector capable of detecting anomalous patient geometries. I examine the potential of so-called generative models to handle this task. These models are promising due to being able to model the distribution of the input data regardless of the downstream task, but they have also been shown to suffer from serious biases when applied to outlier detection. No consensus has been reached regarding the root cause of these biases, or how to address them. I investigate this by attempting to design a variational autoencoder-based outlier detector trained to detect anomalous samples of shapes represented in a binary voxel format. I find the standard procedure application to suffer from severe bias when encountering cropped shapes, leading to systematic misclassification of some outlier patient cases. I overcome this by adopting a segmentation metric as an out-of-distribution metric and show that this outperforms recently proposed general-purpose solutions to the likelihood bias issue. I then benchmark my proposed method on clinical samples and conclude that this approach achieves performance comparable to a one-class support vector machine model that uses handcrafted domain-specific features. / Djupinlärningsbaserad dosprediktion är en mycket lovande metod för att automatiskt generera behandlingsplaner för strålterapi. Djupinlärningsmodeller kan dock endast förväntas fungera på data som är tillräckligt lik träningsdatan, vilket skapar en säkerhetsrisk i kliniska miljöer. Ett möjlig lösning på detta problem är att använda en särskild detektor som klarar av att identifiera avvikande data. I denna uppsats undersöker jag om en generativa djupinlärningsmodell kan användas som en sådan detektor. Generativa modeller är särskilt intressanta för detta ändamål då de är både kraftfulla och flexibla. Dessvärre har generativa modeller visats kunna vilseledas av vissa typer av data. Orsakerna och de underliggande faktorerna till detta har ännu inte identifierats. Jag undersöker denna problematik genom att designa en detektor baserad på en variationell autokodare. Jag upptäcker att den en naiv applikation av denna modell inte är tillräcklig för den kliniska datan, då modellen systematiskt felvärderar beskärda former. Jag löser detta problem genom att nyttja ett modifierat segmenteringsmått som detektionsmått, och visar att denna metod fungerar bättre än mer allmänna lösningar på vilseledningsproblemet. Jag evaluerar metoderna på klinisk data och finner att min metod fungerar lika bra som en en-klass stödvektormaskin som använder sig av handgjorda domänspecifika features.

Out-of-distribution detection considers methods used to detect data that deviates from the underlying data distribution used to train some machine learning model. This is an important topic, as artificial neural networks have previously been shown to be capable of producing arbitrarily confident predictions, even for anomalous samples that deviate from the training distribution. Previous work has developed many reportedly effective methods for out-of-distribution detection, but these are often evaluated on data that is semantically different from the training data, and therefore does not necessarily reflect the true performance that these methods would show in more challenging conditions. In this work, six unsupervised out-of- distribution detection methods are evaluated and compared under more challenging conditions, in the context of classification of semantically similar image data using deep neural networks. It is found that the performance of all methods vary significantly across the tested datasets, and that no one method is consistently superior. Encouraging results are found for a method using ensembles of deep neural networks, but overall, the observed performance for all methods is considerably lower than in many related works, where easier tasks are used to evaluate the performance of these methods. / Begreppet “out-of-distribution detection” (OOD-detektion) avser metoder vilka används för att upptäcka data som avviker från den underliggande datafördelningen som använts för att träna en maskininlärningsmodell. Detta är ett viktigt ämne, då artificiella neuronnät tidigare har visat sig benägna att generera godtyckligt säkra förutsägelser, även på data som avviker från den underliggande träningsfördelningen. Tidigare arbeten har producerat många välpresterande OOD-detektionsmetoder, men dessa har ofta utvärderats på data som är semantiskt olikt träningsdata, och reflekterar därför inte nödvändigtvis metodernas förmåga under mer utmanande förutsättningar. I detta arbete utvärderas och jämförs sex oövervakade OOD-detektionsmetoder under utmanande förhållanden, i form av klassificering av semantiskt liknande bilddata med hjälp av djupa neuronnät. Arbetet visar att resultaten för samtliga metoder varierar markant mellan olika data och att ingen enskild modell är konsekvent överlägsen de andra. Arbetet finner lovande resultat för en metod som utnyttjar djupa neuronnätsensembler, men överlag så presterar samtliga modeller sämre än vad tidigare arbeten rapporterat, där mindre utmanande data har nyttjats för att utvärdera metoderna.