Sequential Monte Carlo Methods for System Identification

One of the key challenges in identifying nonlinear and possibly non-Gaussian state space models (SSMs) is the intractability of estimating the system state. Sequential Monte Carlo (SMC) methods, such as the particle filter (introduced more than two decades ago), provide numerical solutions to the nonlinear state estimation problems arising in SSMs. When combined with additional identification techniques, these algorithms provide solid solutions to the nonlinear system identification problem. We describe two general strategies for creating such combinations and discuss why SMC is a natural tool for implementing these strategies.Comment: In proceedings of the 17th IFAC Symposium on System Identification (SYSID). Added cover pag

Rapidly Prepared Nanocellulose Hybrids as Gas Barrier, Flame Retardant, and Energy Storage Materials

Cellulose nanofibril (CNF) hybrid materials show great promise as sustainable alternatives to oil-based plastics owing to their abundance and renewability. Nonetheless, despite the enormous success achieved in preparing CNF hybrids at the laboratory scale, feasible implementation of these materials remains a major challenge due to the time-consuming and energy-intensive extraction and processing of CNFs. Here, we describe a scalable materials processing platform for rapid preparation (<10 min) of homogeneously distributed functional CNF−gibbsite and CNF−graphite hybrids through a pH-responsive self-assembly mechanism, followed by their application in gas barrier, flame retardancy, and energy storage materials. Incorporation of 5 wt % gibbsite results in strong, transparent, and oxygen barrier CNF−gibbsite hybrid films in 9 min. Increasing the gibbsite content to 20 wt % affords them self-extinguishing properties, while further lowering their dewatering time to 5 min. The strategy described herein also allows for the preparation of freestanding CNF−graphite hybrids (90 wt % graphite) that match the energy storage performance (330 mA h/g at low cycling rates) and processing speed (3 min dewatering) of commercial graphite anodes. Furthermore, these ecofriendly electrodes can be fully recycled, reformed, and reused while maintaining their initial performance. Overall, this versatile concept combines a green outlook with high processing speed and material performance, paving the way toward scalable processing of advanced ecofriendly hybrid material

Patient’s life changes after stoma operation

Bakalaura darbs ,,Pacienta dzīves izmaiņas pēc stomas izvades operācijas” izstrādāts ar mērķi – noskaidrot kādi fiziskie, psiholoģiskie un sociālie faktori ietekmē pacienta dzīvi pēc stomas izvades operācijas. Tas balstīts uz Betija Ņūmenas māszinību teoriju. Teorētiskajā daļā, izmantojot pieejamo literatūru apskatīti stomas pacientu klīniskie, psiholoģiskie un sociālie aspekti. Pētījums veikts izmantojot kvantitatīvo pētniecības metodi – anketēšanu. Aptaujā piedalījās 100 pacienti ar kolostomu vai ileostomu. Izvirzītā hipotēze, ka spējot pieņemt dzīves veida un paštēla izmaiņas, pacients spēj fiziski un psiholoģiski adaptēties pēc stomas izvades operācijas, pētījuma laikā apstiprinās. Darbā izmantoti 36 literatūras avoti (latviešu, angļu un krievu valodā). Pētījuma rezultāti tika vizualizēti 10 diagrammās. Darbs sastāv no 5. nodaļām, 10. apakšnodaļām un 2. pielikumiem. Atslēgvārdi:kolostoma, ileostoma, stomas aprūpe, adaptācija, rehabilitācija, stomas izvadīšanas operācija.The aim of my graduation work, Patient’s life changes after stoma operation, is to explore which of the physical, psychological and social factors influence a client’s (patient’s) life after a colostomy operation. The work is based on Betty Neuman’s nursing theory. With the use of references, clinical, psychological and social aspects of stoma patients are considered in a theoretical part of my work. Practical research of the work was spent with use of a quantitative method. Questioning 100 colostomy patients (clients) participated in a questioning. The research has confirmed a hypothesis, that a patient after a stoma operation is able to accept physical changes of the body and the changes of his or her life style. 36 literary sources in Latvian, English and Russian are used in my work “Patient’s life changes after stoma operation”. The rresults of my research are visualized in 10 diagrams. The work consists of 5 chapters, 10 sub-chapters and 2 appendixes. Key-words:colostomy, ileostomy, stoma care, adaptation, rehabilitation, stoma operation

Kontinuerlig kartering med Gaussprocesser

The topic of this thesis is occupancy mapping for mobile robots, with an emphasis on a novel method for continuous occupancy mapping using Gaussian processes. In the new method, spatial correlation is accounted for in a natural way, and an a priori discretization of the area to be mapped is not necessary as within most other common methods. The main contribution of this thesis is the construction of a Gaussian process library for C++, and the use of this library to implement the continuous occupancy mapping algorithm. The continuous occupancy mapping is evaluated using both simulated and real world experimental data. The main result is that the method, in its current form, is not fit for online operations due to its computational complexity. By using approximations and ad hoc solutions, the method can be run in real time on a mobile robot, though not without losing many of its benefits

Interfacial Polymerization of Cellulose Nanocrystal Polyamide Janus Nanocomposites with Controlled Architectures

The widespread use of renewable nanomaterials has been limited due to poor integration with conventional polymer matrices. Often, chemical and physical surface modifications are implemented to improve compatibility, however, this comes with environmental and economic cost. This work demonstrates that renewable nanomaterials, specifically cellulose nanocrystals (CNCs), can be utilized in their unmodified state and presents a simple and versatile, one-step method to produce polyamide/CNC nanocomposites with unique Janus-like properties. Nanocomposites in the form of films, fibers, and capsules are prepared by dispersing as-prepared CNCs in the aqueous phase prior to the interfacial polymerization of aromatic diamines and acyl chlorides. The diamines in the aqueous phase not only serve as a monomer for polymerization, but additionally, adsorb to and promote the incorporation of CNCs into the nanocomposite. Regardless of the architecture, CNCs are only present along the surface facing the aqueous phase, resulting in materials with unique, Janus-like wetting behavior and potential applications in filtration, separations, drug delivery, and advanced fibers.QC 20191119</p

Bayesian nonparametric identification of piecewise affine ARX systems

We introduce a Bayesian nonparametric approach to identification of piecewise affine ARX systems. The clustering properties of the Dirichlet process are used to construct a prior over the partition of the regressor space as well as the parameters of each local model. This enables us to probabilistically reason about and to identify the number of modes, the partition of the regressor space, and the linear dynamics of each local model from data. By appropriate choices of base measure and likelihood function, we give explicit expressions for how to perform both inference and prediction. Simulations and experiments on real data from a pick and place machine are used to illustrate the capabilities of the new approach

Incorporation of cellulose nanocrystals into polyamide nanocomposites with controlled architecture via interfacial polymerization

The widespread use of renewable nanomaterials has been limited due to poor integration with conventional polymer matrices. Often, chemical and physical surface modificationsare implemented to improve compatibility, however this comes with environmental and economic cost. This work demonstrates that renewable nanomaterials, specifically cellulose nanocrystals (CNCs), can be utilized in their unmodified state and presents a simple and versatile, one-step method to produce polyamide/CNC nanocomposites with unique Janus-like properties. Nanocomposites in the form of films, fibres and capsules areprepared by dispersing as prepared CNCs in the aqueous phase prior to the interfacial polymerization of aromatic diamines and acyl chlorides. The diamines in the aqueous phase not only serve as a monomer for polymerization but, additionally adsorb to, and promote the incorporation of CNCs into the nanocomposite. Regardless of the architecture CNCs are only present along the surfacefacing the aqueous phaseresulting in materials with unique, Janus-likewetting behaviour and potential applications in filtration, separations, drug delivery and advanced fibres. QC 20190918</p

Incorporation of cellulose nanocrystals into polyamide nanocomposites with controlled architecture via interfacial polymerization

The widespread use of renewable nanomaterials has been limited due to poor integration with conventional polymer matrices. Often, chemical and physical surface modificationsare implemented to improve compatibility, however this comes with environmental and economic cost. This work demonstrates that renewable nanomaterials, specifically cellulose nanocrystals (CNCs), can be utilized in their unmodified state and presents a simple and versatile, one-step method to produce polyamide/CNC nanocomposites with unique Janus-like properties. Nanocomposites in the form of films, fibres and capsules areprepared by dispersing as prepared CNCs in the aqueous phase prior to the interfacial polymerization of aromatic diamines and acyl chlorides. The diamines in the aqueous phase not only serve as a monomer for polymerization but, additionally adsorb to, and promote the incorporation of CNCs into the nanocomposite. Regardless of the architecture CNCs are only present along the surfacefacing the aqueous phaseresulting in materials with unique, Janus-likewetting behaviour and potential applications in filtration, separations, drug delivery and advanced fibres. QC 20190918</p

Prediction Performance After Learning in Gaussian Process Regression

This paper considers the quantification of the prediction performance in Gaussian process regression. The standard approach is to base the prediction error bars on the theoretical predictive variance, which is a lower bound on the mean square-error (MSE). This approach, however, does not take into account that the statistical model is learned from the data. We show that this omission leads to a systematic underestimation of the prediction errors. Starting from a generalization of the Cramér-Rao bound, we derive a more accurate MSE bound which provides a measure of uncertainty for prediction of Gaussian processes. The improved bound is easily computed and we illustrate it using synthetic and real data examples