Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Gaussian Process Regression and its Application in Data-driven Modeling and Optimization

Availability of large amounts of industrial process data is allowing researchers to explore new data-based modelling methods. In this thesis, Gaussian process (GP) regression, a relatively new Bayesian approach to non-parametric data based modelling is investigated in detail. One of the primary concerns regarding the application of such methods is their sensitivity to the presence of outlying observations. Another concern is that their ability to predict beyond the range of observed data is often poor which can limit their applicability. Both of these issues are explored in this work. The problem of sensitivity to outliers is dealt with by using a robust GP regression model. The common approach in literature for identification of this model is to approximate the marginal likelihood and maximize it using conjugate gradient algorithm. In this work, an EM algorithm based approach is proposed in which an approximate lower bound on the marginal likelihood is iteratively maximized. Models identified using this method are compared against those identified using conjugate gradient method in terms of prediction performance on many synthetic and real benchmark datasets. It is observed that the two approaches are similar in prediction performance. However the advantages of EM approach are numerical stability, ease of implementation and theoretical guarantee of convergence. The application of proposed robust GP regression in chemical engineering is also explored. An optimization problem for an industrial water treatment and steam generation network is formulated. Process models are constructed using material balance equations and used for data reconciliation and steady state optimization of the cost of steam production. Since the overall network is under manual operation, a dynamic optimization framework is constructed to find a set point change strategy which operators can use for minimizing steam production cost. Dynamic models for process units and tanks are integrated into this framework. Some of these models are identi-
Stochastic and hybrid linear equation solvers and their applications in vlsi design automation

This thesis presents two new linear equation solvers, and investigates their applications in VLSI design automation. Both solvers are derived in the context of a special class of large-scale sparse left-hand-side matrices that are commonly encountered in engineering applications, and techniques are presented that can potentially extend the theory to more general cases. The first is a stochastic solver that performs the computation by establishing the equivalence between linear equations and random walks. It has a desirable locality feature: a single unknown variable can be evaluated without solving the entire system. For complete solutions, it is competitive in applications with moderate accuracy requirement. The second is a hybrid solver: it combines the random walk technique and traditional iterative approaches. Given a set of linear equations, if the left-hand-side matrix satisfies certain conditions, it is proven that an incomplete triangular factorization can be obtained from random walks, and these factors can be used as a preconditioner in a traditional iterative linear equation solver to accelerate its convergence. In other words, the proposed hybrid solver is a stochastically preconditioned iterative solver. It is argued that our factor matrices have better quality, i.e., better accuracy-size tradeoffs, than preconditioners produced by existing incomplete factorization methods. Therefore the hybrid solver requires less computation than traditional preconditioned iterative solvers to solve a set of linear equations with the same error tolerance, and the advantage increases for larger and denser sets of linear equations. The application of these solvers on several problems in VLSI design is illustrated in this thesis, namely, power grid analysis, chip-level electrostatic discharge simulation, and quadratic placement. We not only demonstrate the efficiency of direct usage of the solvers, but also devise a set of application-specific techniques that are often based on indirect usage of the stochastic solver, due to the fact that the localized computation process of random walks carries meaningful information in various scenarios.
Multispectral Invisible Coating: Laminated Visible-Thermal Physical Attack against Multispectral Object Detectors Using Transparent Low-E Films

Multispectral object detection plays a vital role in safety-critical vision systems that require an around-the-clock operation and encounter dynamic real-world situations(e.g., self-driving cars and autonomous surveillance systems). Despite its crucial competence in safety-related applications, its security against physical attacks is severely understudied. We investigate the vulnerability of multispectral detectors against physical attacks by proposing a new physical method: Multispectral Invisible Coating. Utilizing transparent Low-e films, we realize a laminated visible-thermal physical attack by attaching Low-e films over a visible attack printing. Moreover, we apply our physical method to manufacture a Multispectral Invisible Suit that hides persons from the multiple view angles of Multispectral detectors. To simulate our attack under various surveillance scenes, we constructed a large-scale multispectral pedestrian dataset which we will release in public. Extensive experiments show that our proposed method effectively attacks the state-of-the-art multispectral detector both in the digital space and the physical world.
Evaluation of fecal Activin-A as a novel biomarker for early diagnosis of ulcerative colitis using experimental murine animal model.

The present study was undertaken to evaluate fecal activin-A, in comparison with the currently used calprotectin, as a potential biomarker for early detection of ulcerative colitis (UC). Rats were divided into 4 groups (5 rats each). These included the acute UC group, where rats received 3% dextran sodium sulphate (DSS) for six consecutive days, and the chronic UC group, where rats received 3% DSS for three cycles (each cycle composed of 3 days of treatment and 12 days off). Control groups included animals that received tap water for six days and kept it as a negative control for short-term treatment and those received tap water for 45 days and served as a negative control for long-term treatment. Fecal samples were collected from all animal groups at the end of the study. Activin A and calprotectin levels were measured in collected fecal samples using a commercial ELISA kit for rat Calprotectin and rat Activin-A. The results revealed significant increases in fecal activin-A, and fecal calprotectin in chronic UC and a significant increase in fecal activin-A in acute colitis. It could be concluded that fecal activin-A is a useful biomarker for both acute and chronic UC. However, fecal calprotectin can be used as an indicator for chronic UC.
Performance evaluation of robust parametric control strategies applied on suppression of oscillations effects due to constant power loads in multi-converter buck-buck systems

Multi-converter electronic systems are becoming widely used in many industrial applications; therefore, the stability of the cascaded system is a big concern to real-world power supplies applications. Instability in cascaded systems may occur due to the constant power load (CPL), which is a behavior of the tightly regulated converters. CPLs exhibit incremental negative resistance behavior causing a high risk of instability in interconnected converters; therefore, the mitigation of this problem is an important issue in the multiple-stage switched mode power supply design. Thus, it is important to guarantee stability of the whole system. However, some difficulties remains besides the CPL, e.g., non-linearities due to the inductive element and uncertainties due to imprecision of mathematical models and/or variation of nominal values of the discrete elements that compose the DC/DC buck converter. Aiming to evaluate the performance of the proposed robust methodologies in this work to mitigate the instability problem caused by a CPL, several tests were developed by using an experimental plant and Matlab/Simulink, when the multi-converter buck-buck system is subjected a variation of power reference. The results show the improved performance of the proposed methodologies. Key-word: multi-converter system; robust control; robust parametric control; constant power load; parametric variation; buck converter; kharitonov stability theorem; chebyshev theorem.
Estonian Energy Roadmap to carbon neutrality

The aim of this study is to demonstrante Estonian Energy Roadmap 2040 modelling process, taking into account energy consumption in accordance with the expectation and improvement of the living standard. In the frame of this study the evaluation of the possibility for Estonian Energy sector to become carbon neutral by 2040 have been presented. The roadmap to climate neutrality models three sectors: electricity, heating / cooling and transport together, aiming at reducing overall energy consumption, sustainability, security of national supply and the implementation of balanced economic principles. Balanced economic principles in this case mean high resource efficiency and reduced ammount of subsidies. Results of modelling show dnergy demand, energy mix and carbon emissions for these sectors in 2030 and 2040 and are compared with these indicators in 2021.The study estimates the amount of investment required to achieve carbon neutrality of Estonian energy sector. In assessing the future economy, the total environmental impact of activities, the preservation of the well-being of the Estonian population and economic reasonableness are examined in parallel with traditional economic indicators.
Transductive clustering optimization learning for few-shot image classification

Abstract. Few-shot image classification aims to perform image classification on new categories with only a small amount of labeled training data. However, it is difficult to complete such a task under the existing conditions. Therefore, the current few-shot learning methods are built on the paradigm of transfer learning, where the core idea is to learn a priori that can solve unknown new tasks. The idea of meta-learning is fast learning, which is similar to the idea of few-shot learning. Therefore, conventional few-shot learning methods generally adopt a meta-training approach with episodic training to construct knowledge prior. However, research work indicates that an embedding model with powerful feature representations is simpler and more effective than the existing sophisticated few-shot learning methods. Inspired by this insight, we propose a few-shot image classification method based on the transductive clustering optimization learning. First, memory block is introduced to store the internal feature structure of each category, namely comprehensive representation of various features. Second, during the training process, sample features are compared with the memory content to further expand the differences between different categories and improve the performance of feature extractor. Finally, in the transductive few-shot setting, a clustering optimization module has been introduced to select appropriate samples for clustering to alleviate the fundamental problem of sample scarcity. A large number of experimental results show that the proposed few-shot image classification method based on the transductive clustering optimization learning effectively improves the classification accuracy under various training conditions.
Reduced models accounting for parallel magnetic perturbations: gyrofluid and finite Larmor radius–Landau fluid approaches

Reduced models are derived for a strongly magnetized collisionless plasma at scales which are large relative to the electron thermal gyroradius and in two asymptotic regimes. One corresponds to cold ions and the other to far sub-ion scales. By including the electron pressure dynamics, these models improve the Hall reduced magnetohydrodynamics (MHD) and the kinetic Alfvén wave model of Boldyrev et al. (2013 Astrophys. J., vol. 777, 2013, p. 41), respectively. We show that the two models can be obtained either within the gyrofluid formalism of Brizard (Phys. Fluids, vol. 4, 1992, pp. 1213–1228) or as suitable weakly nonlinear limits of the finite Larmor radius (FLR)–Landau fluid model of Sulem and Passot (J. Plasma Phys., vol 81, 2015, 325810103) which extends anisotropic Hall MHD by retaining low-frequency kinetic effects. It is noticeable that, at the far sub-ion scales, the simplifications originating from the gyroaveraging operators in the gyrofluid formalism and leading to subdominant ion velocity and temperature fluctuations, correspond, at the level of the FLR–Landau fluid, to cancellation between hydrodynamic contributions and ion finite Larmor radius corrections. Energy conservation properties of the models are discussed and an explicit example of a closure relation leading to a model with a Hamiltonian structure is provided.
Scale-independent object detection with an Implicit Shape Model

In this paper we propose an improvement to the implicit shape model (ISM) based robust object detection system proposed by Leibe et al. Object detection with ISM allows to approach the classification and tracking in a probabilistic way with multiple hypotheses. Unlike the original approach, our method is independent from object scale in the training sets, and this allows to work with a much smaller training sets and also to avoid to supply information about scale to the trainer. This is done while maintaining the robustness of the original approach. Leibe et al. mentioned a potential solution to overcome the scale problem in the training set, i.e., the usage of the scale produced by the local descriptor. Our proposal is different: since we believe that the scale measure generated by local descriptors is subject to noise, we try to walk around this noise by estimating the scale measure from the only evidence collected in the image. (6 pages)
Robust H/sub infinity / control for class of uncertain linear time-invariant systems

The problem of robust state feedback H∞ control design for a class of linear time-invariant systems with parameter uncertainty in the state space model is investigated. Uncertain systems with time-invariant norm-bounded parameter uncertainty in the state matrix are considered. A state feedback control design which robustly stabilises the plant and guarantees a robust H∞ performance for the closed-loop system in the face of parameter uncertainty is presented. It is shown that a suitable stabilising feedback law can be constructed in terms of a positive definite solution to a certain parameter-dependent algebraic Riccati equation.
Target recognition in synthetic aperture radar images using binary morphological operations

Abstract. Feature extraction and matching are two important steps in synthetic aperture radar automatic target recognition. This paper uses the binary target region as the feature and proposes a matching scheme for the target regions using binary morphological operations. The residuals between the testing target region and its corresponding template target regions are processed by the morphological opening operation. Then, a similarity measure is defined based on the residual remains to evaluate the similarities between different targets. Afterward, a Bayesian decision fusion is employed to fuse the similarities gained by different structuring elements to further enhance the recognition performance. The nonlinearity of the opening operation as well as the Bayesian decision fusion makes the proposed method robust to the nonlinear deformations of the target region. Experimental results on the moving and stationary target acquisition and recognition dataset demonstrate the validity of the proposed method.
Electrospun Nanofibrous Mesh Based on PVA, Chitosan, and Usnic Acid for Applications in Wound Healing

Injuries and diseases of the skin require accurate treatment using nontoxic and noninvasive biomaterials, which aim to mimic the natural structures of the body. There is a strong need to develop biodevices capable of accommodating nutrients and bioactive molecules and generating the process of vascularization. Electrospinning is a robust technique, as it can form fibrous structures for tissue engineering and wound dressings. The best way of forming such meshes for wound healing is to choose two polymers that complement each other regarding their properties. On the one hand, PVA is a water-soluble synthetic polymer widely used for the preparation of hydrogels in the field of biomedicine owing to its biocompatibility, water solubility, nontoxicity, and considerable mechanical properties. PVA is easy to subject to electrospinning and can offer strong mechanical stability of the mesh, but it is necessary to improve its biological properties. On the other hand, CS has good biological properties, including biodegradability, nontoxicity, biocompatibility, and antimicrobial properties. Still, it is harder to electrospin and does not possess as good mechanical properties as PVA. As these structures also allow the incorporation of bioactive agents due to their high surface-area-to-volume ratio, the interesting point was to incorporate usnic acid into the structure as it is a natural and suitable alternative agent for burn wounds treatment which avoids an improper or overuse of antibiotics and other invasive biomolecules. Thus, we report the fabrication of an electrospun nanofibrous mesh based on PVA, chitosan, and usnic acid with applications in wound healing. The obtained nanofibers mesh was physicochemically characterized by Fourier transform infrared spectroscopy (FT-IR) and scanning electron microscopy (SEM). In vitro biological assays were performed to evaluate the antimicrobial properties of the samples using the MIC (minimum inhibitory concentration) assay and evaluating the influence of fabricated meshes on the Staphylococcus aureus biofilm development, as well as their biocompatibility (demonstrated by fluorescence microscopy results, an XTT assay, and a glutathione (GSH) assay).
Singularities and mixing in fluid mechanics

Among the most important and most difficult open problems in the field of analysis are questions about the behavior of solutions to differential equations modeling the dynamics of fluids. The main issues that one must overcome in addressing them are frequently the nonlinearity and nonlocality of these equations. In this thesis we study these and related models, focusing on the possibility of singularity formation for their solutions as well as on ways such singular behavior can be suppressed. In the first chapter of this thesis, we discuss the small scale creation and possible singularity formation in PDEs of fluid mechanics, especially the Euler equations and the related models. Recently, Tom Hou and Guo Luo proposed a new scenario, so called the hyperbolic flow scenario, for the development of a finite time singularity in solutions to 3D incompressible Euler equation. We first give a clear and understandable picture of hyperbolic flow restricted in 1D. Then, based on the recent work by Alexander Kiselev and Vladimir Šverák, we look into the hyperbolic geometry in 2D. Finally, we go back to 3D problem, and analyze a simplified 1D model for the potential singularity of the 3D Euler equation by Tom Hou and Guo Luo. In the second chapter of this thesis, we investigate the problem about how to suppress the blowup. At the end of the second chapter, we demonstrate that incompressible mixing flow can indeed arrest the finite time blow up phenomenon. We first concentrate on understanding the mechanisms involved in mixing, studying mixing properties of the flows with different structure, and finding most efficient mixing flows. We resolve the problem of finding the optimal lower bound of the “mixing norm” under an enstrophy
Analysis of Diffusion Coefficients of Iron Monoboride and Diiron Boride Coating Formed on the Surface of AISI 420 Steel by Two Different Models: Experiments and Modelling

In the present work, two mathematical diffusion models have been used to estimate the growth of the iron monoboride and diiron boride coating formed on AISI 420 steel. The boronizing of the steel was carried out with the solid diffusion packing method at a boronizing temperature of 1123 K–1273 K. Experimental results show the two-coating system consists of an outer monoboride and an inner diiron boride coating with a predominantly planar structure at the propagation front. The depth of the boride coating increases according to temperature and treatment time. A parabolic curve characterizes the propagation of the boride coatings. The two proposed mathematical models of mass transfer diffusion are founded on the solution corresponding to Fick’s second fundamental law. The first is based on a linear boron concentration–penetration profile without time dependence, and the second model with time dependence (exact solution). For both models, the theoretical law of parabolic propagation and the average flux of boron atoms (Fick’s first fundamental law) at the growth interfaces (monoboride/diiron boride and diiron boride/substrate) are considered to estimate the propagation of the boride coatings (monoboride and diiron boride). To validate the mathematical models, a programming code is written in the MATLAB program (adaptation 7.5) designed to simulate the growth of the boride coatings (monoboride and diiron boride). The following parameters are used as input data for this computer code: (the layer thicknesses of the FeB and Fe2B phases, the operating temperature, the boronizing time, initial formation time of the boride coating, the surface boron concentration limits, FeB/Fe2B and Fe2B/Fe growth interfaces, and the mass transfer diffusion coefficient of boron in the iron monoboride and diiron boride phases). The outputs of the computer code are the constants εFeB and εFe2B. The assessment of activation energies of AISI 420 steel for the two mathematical models of mass transfer is coincident (QFeB=221.9 kJ∙mol−1 and QFe2B=209.1 kJ∙mol−1). A numerical analysis was performed using a standard Taylor series for clarification of the proximity between the two models. SEM micrographs exhibited a strong propensity toward a flat-fronted composition at expansion interfaces of the iron monoboride and diiron boride coating, confirmed by XRD analysis. Tribological characterizations included the Vickers hardness test method, pin-on-disc, and Daimler–Benz Rockwell-C indentation adhesion tests. After thorough analysis, the energies were compared to the existing literature to validate our experiment. We found that our models and experimental results agreed. The diffusion models we utilized were crucial in gaining a deeper understanding of the boronizing behavior of AISI 420 steel, and they also allowed us to predict the thicknesses of the iron monoboride and diiron boride coating. These models provide helpful approaches for predicting the behavior of these steels.