VTT Technical Research Centre of Finland
Recent publications
In the proposed system setup, a communication link is established between base station (BS) and end-user (UE) via a relay node mounted on the high-speed train (HST). The information is conveyed over backhaul RF links between BS and relay and over VLC links between relay and UE inside the train. It is assumed that RF links are encountered with dual shadowing due to slow-moving vehicles and pedestrians. Moreover, the relay node is not able to estimate the channel information perfectly due to HST mobility. Firstly, the statistical characteristics, such as the probability density function (PDF) and cumulative distribution function of the BS-relay link, are derived under imperfect channel information. Then, the system performance is examined by deriving the two key metrics, outage probability, and average bit error rate. Furthermore, we investigate the secrecy performance of the proposed system when the RF eavesdropper overhears the link between BSandrelay andthe VLC eavesdropper captures the information via the relay-UE link. To this end, the secure outage probability is derived into a closed form. Our results reveal that the proposed system setup can be adopted as a network architecture for existing as well as for future HST networks.
The direct epoxidation of mixed ethene and propene feedstocks using hydrogen peroxide over a titanium silicalite (TS-1) catalyst was investigated within a continuous trickle bed reactor operating in laboratory scale. Methanol was employed as the reaction solvent. This study aimed to streamline the epoxidation process by obviating the need for prior separation of alkenes, thereby enhancing process efficiency. An extensive array of operational parameters was explored in a trickle bed reactor, encompassing experimental parameters such as temperature, total pressure, hydrogen peroxide concentration, liquid flow rate, and gas composition. In contrast to prior investigations involving separate ethene and propene epoxidation, this study revealed a reduction in epoxide selectivity. The principal by-products observed were methoxy species, formed through the interaction between the epoxide and methanol, resulting in a ring-opening reaction. The influence of water on this ring-opening process was negligible. Notably, the tunability of the system was demonstrated, highlighting low temperature and elevated partial ethene pressure as pivotal factors for augmentingthe epoxide selectivity. The findings suggest that binary olefin mixtures exhibit diminished selectivity but improved stability. This behavior is potentially linked to the olefin solubility in methanol, or alterations in the surface species concentrations, typically associated with catalyst activity variations. These insights offer a valuable foundation for understanding and optimizing the direct epoxidation of mixed ethene and propene feedstock. Graphical Abstract
This chapter proposes a Model Predictive Control (MPC) scheme for robot trajectory tracking, which can optimize tracking error, velocity norm and acceleration norm. The research background of this work is discussed in Sect. 2.1, Sect. 2.2 gives the problem description, Sect. 2.3 provides the theoretical verifications, the simulation results are presented in Sect. 2.4. Lastly, the conclusions are summarized in Sect. 2.5.
This chapter provides the conclusions of this book and some future research work. Section 8.1 presents the conclusion for this book, Section 8.2 gives future work for robot control and calibration.
This chapter discusses an improved covariance matrix adaptation evolution strategy and a quadratic interpolated beetle antennae search algorithm for robot calibration. Firstly, Section 6.1 introduces the research motivation for robot calibration. In Sect. 6.2, we present the learning rule of an extended Kalman filter, an improved covariance matrix adaptive evolution strategy and a quadratic interpolated beetle antennae search algorithm. In addition, Section 6.3 provides the experiments for the developed novel evolutionary computing algorithms. And finally, Section 6.4. concludes this chapter.
This chapter develops an efficient calibrator based on a novel unscented Kalman filter and a variable step-size Levenberg-Marquardt algorithm (UKF-VSLM). Sect. 7.1 first discusses the research background for robot calibration, then Sect. 7.2 introduces the basic principles for an unscented Kalman filter and a variable step-size Levenberg-Marquardt algorithm. Furthermore, Sect. 7.3 presents extensive experiments on an ABB IRB120 industrial robot. Lastly, the conclusions are drawn in Sect. 7.4.
Recently, zeroing neural networks plays a vital role in robot control and trajectory tracking. This chapter designs a projected zeroing neural network for redundant robot control, which achieves high superiority and efficiency. The research background about robot control by zeroing neural networks is presented in Sect. 4.1. In Sect. 4.2, we study the feedback-considered scheme of the robot. Neural network design is briefly discussed in Sect. 4.3. The experiments are given in Sect. 4.4. Lastly, the conclusions and future work are concluded in Sect. 4.5.
This chapter investigates six regularization schemes, such as L1, L2, dropout, elastic, log, and swish. Then, an efficient ensemble incorporates six regularizations to achieve high calibration accuracy. Firstly, Sect. 5.1 discuss the research background of robot calibration. In Sect. 5.2, we introduce six regularized robot calibration schemes and the principle of an ensemble. Then, Sect. 5.3 presents experiments for the proposed ensemble. Lastly, conclusions and future work are summarized in Sect. 5.4.
To date, neural networks with high learning ability have been widely used in natural language processing, process control and other fields. In this chapter, a new recurrent neural network (RNN) is proposed to deal with time-varying underdetermined linear systems with disturbances, thereby achieving better control results. The related background of the underdetermined linear system is described in Sect. 3.1. In Sect. 3.2, we introduce the problem description. The theoretical analysis is discussed in Sect. 3.3. The experimental results are presented in Sect. 3.4. Finally, the conclusions and future research work are given in Sect. 3.5.
When developing the construction industry, also the work at the construction site needs to be understood. Moreover, the construction work needs to be developed as part of the developing of the construction industry; the work at the site does not take place in isolation. The worker level, investigated from the worker's perspective, is mainly missing in the studies about the construction industry. In this study, the demands of the drywall installers are analysed according to Core-Task Analysis. The demands are classified between dynamics, complexity, and uncertainty related demands. Further analysis shows that the party primarily responsible for managing the demands can be also some other party than the worker him/herself. Moreover, some of the demands are out of proportion. We argue that the experience of the workers is vital for the successful construction work. The superiors often lack experience needed at the site and the workers then need to use their experience in tackling daily challenges. Especially, the experienced worker is able to compensate the deficiencies in the floorplans and construction drawings. The managing of some of the demands can be supported by providing the workers visual information. Visual information represents a means to deliver information also when the workers do not share a common language.
Bifuran motifs can be accessed with nickel‐bipyridine electrocatalyzed homocouplings of bromine‐substituted methyl furancarboxylates, which, in turn, can be prepared from hemicellulose‐derived furfural. The described protocol uses sustainable carbon‐based graphite electrodes in the simplest setup – an undivided cell with constant current electrolysis. The reported method avoids using a sacrificial anode by employing triethanolamine as an electron donor.
Hydrophobins are a family of small-sized proteins featuring a distinct hydrophobic patch on the protein’s surface, rendering them amphiphilic. This particularity allows hydrophobins to self-assemble into monolayers at any hydrophilic/hydrophobic interface. Moreover, stable pure protein bilayers can be created from two interfacial hydrophobin monolayers by contacting either their hydrophobic or their hydrophilic sides. In this study, this is achieved via a microfluidic approach, in which also the bilayers’ adhesion energy can be determined. This enables us to study the origin of the adhesion of hydrophobic and hydrophilic core bilayers made from the class II hydrophobins HFBI and HFBII. Using different fluid media in this setup and introducing genetically modified variants of the HFBI molecule, the different force contributions to the adhesion of the bilayer sheets are studied. It was found that in the hydrophilic contact situation, the adhesive interaction was higher than that in the hydrophobic contact situation and could be even enhanced by reducing the contributions of electrostatic interactions. This effect indicates that the van der Waals interaction is the dominant contribution that explains the stability of the observed bilayers.
This paper introduces a novel distributed algorithm designed to optimize the deployment of access points within Mobile Ad Hoc Networks (MANETs) for better service quality in infrastructure-less environments. The algorithm operates based on local, independent execution by each network node, thus ensuring a high degree of scalability and adaptability to changing network conditions. The primary focus is to match the spatial distribution of access points with the distribution of client devices while maintaining strong connectivity to the network root. Using autonomous decision-making and choreographed path-planning, this algorithm bridges the gap between demand-responsive network service provision and the maintenance of crucial network connectivity links. The assessment of the performance of this approach is motivated by using numerical results generated by simulations.
Lightweight construction materials have received extensive attention due to their better thermal insulation, which helps improve energy efficiency in buildings. This study aimed at developing lightweight, thermally resistive and fire-protective green plastering mortar by adopting the low-carbon LC3 binder. A green and economical ternary blended binder has been prepared by substituting 60 wt% of the ordinary Portland cement with a blend of limestone powder and metakaolin at a ratio of limestone to metakaolin of 1:2 (wt%). The binder has been blended with expanded polystyrene (EPS) beads as fine aggregate with different aggregate contents of 25, 50 and 75 vol. %. Polypropylene microfibers were added at a constant ratio of 0.5% by weight of binder. The compressive and flexural strengths, capillary water absorption, bulk density, thermal conductivity, fire resistance and microstructure of the developed mortars were studied after 28 days of hydration. The newly developed LC3 fiber-reinforced mortar complies with the standard recommended criteria of lightweight and thermal insulation characteristics, with a density as low as 654 kg/m3, a thermal conductivity as low as 0.18 W/mK and an improved compressive strength of 11.89 MPa. The integration of EPS into the fiber-reinforced LC3 binder has distinctly provided an enhanced fire resistance rating; the greatest enhancement of about 125% was attained for the LC3 lightweight mortar with 50 vol. % EPS.
5G network slicing is promising in prioritizing time‐critical protection communication in wireless networks of smart grids. However, network slicing offered by telecommunication providers encompasses all smart grid applications, lacking granularity. Smart grid automation standards provide recommendations on prioritization of protection communication to improve reliability but only for wired connections. Therefore, this paper investigates hierarchical token bucket (HTB) traffic shaping and uplink (UL) bitrate adaptation of a live video stream for prioritizing protection communication within a 5G slice. An experimental setup combines controller‐hardware‐in‐the‐loop (CHIL) with a quality of service (QoS) measurement system for validation. The system under test consists of commercial 5G networks, intelligent electronic devices (IEDs), and merging units for validation with three smart grid applications: fault location, line differential, and intertrip protection. HTB traffic shaping improves protected faults by 47.57% in congested and 1.16% in non‐congested scenarios. UL bitrate of the video stream adaptation by 2 Mbps increases protected faults by 3.69%. HTB traffic shaping even improves prioritization with a wired connection without introducing additional delays. HTB traffic shaping must be deployed in routers to maintain good QoS for critical applications. Collaboration between utilities and telecommunication providers is essential to effectively deploy 5G network slicing on smart grids.
Zero-emission trucks for regional and long-haul missions are an option for fossil-free freight. The viability of such powertrains and system solutions was studied conceptually in project ESCALATE for trucks with GVW of 40 tonnes and beyond through various battery electric and fuel cell prime mover combinations. The study covers battery and fuel cell power sources with different degrees of battery electric as well as H2 and fuel cell operation. As a design basis, two different missions with a single-charge/H2 refill were analysed. The first mission was the VECTO long-haul profile repeated up to 750 km, whereas the second was a real 520 km on-road mission in Finland. Based on the simulated energy consumption on the driving cycle, on-board energy demand was estimated, and the initial single-charge and H2 refill operational scenarios were produced with five different power source topologies and on-board storage capacities. The traction motors of the tractor were dimensioned so that a secondary mission of GVW up to 76 tonnes on a shorter route or a longer route with more frequent battery recharge and/or H2 refill can be operated. Based on the powertrain and vehicle model, various infrastructure options for charging and H2 refuelling strategies as well as various operative scenarios with indicative total cost of ownership (TCO) were analysed.
Portfolio analysis is a crucial subject within modern finance. However, the classical Markowitz model, which was awarded the Nobel Prize in Economics in 1991, faces new challenges in contemporary financial environments. Specifically, it fails to consider transaction costs and cardinality constraints, which have become increasingly critical factors, particularly in the era of high-frequency trading. To address these limitations, this research is motivated by the successful application of machine learning tools in various engineering disciplines. In this work, three novel dynamic neural networks are proposed to tackle nonconvex portfolio optimization under the presence of transaction costs and cardinality constraints. The neural dynamics are intentionally designed to exploit the structural characteristics of the problem, and the proposed models are rigorously proven to achieve global convergence. To validate their effectiveness, experimental analysis is conducted using real stock market data of companies listed in the Dow Jones Index (DJI), covering the period from November 8, 2021 to November 8, 2022, encompassing an entire year. The results demonstrate the efficacy of the proposed methods. Notably, the proposed model achieves a substantial reduction in costs (which combines investment risk and reward) by as much as $56.71\%$ compared with portfolios that are averagely selected.
We demonstrate the construction of water-stable, biocompatible and self-standing hydrogels as scaffolds for the photosynthetic production of ethylene using a bioinspired all-polysaccharidic design combining TEMPO-oxidised cellulose nanofibers (TCNF) and a cereal plant hemicellulose called mixed-linkage glucan (MLG). We compared three different molecular weight MLGs from barley to increase the wet strength of TCNF hydrogels, and to reveal the mechanisms defining the favourable interactions between the scaffold components. The interactions between MLGs and TCNF were revealed via adsorption studies and interfacial rheology investigations using quartz crystal microbalance with dissipation monitoring (QCM-D). Our results show that both the MLG solution stability and adsorption behaviour did not exactly follow the well-known polymer adsorption and solubility theories especially in the presence of co-solute ions, in this case nitrates. We prepared hydrogel scaffolds for microalgal immobilisation, and high wet strength hydrogels were achieved with very low dosages of MLG (0.05 wt%) to the TCNF matrix. The all-polysaccharic biocatalytic architectures remained stable and produced ethylene for 120 h with yields comparable to the state-of-the-art scaffolds. Due to its natural origin and biodegradability, MLG offers a clear advantage in comparison to synthetic scaffold components, allowing the mechanical properties and water interactions to be tailored.
Institution pages aggregate content on ResearchGate related to an institution. The members listed on this page have self-identified as being affiliated with this institution. Publications listed on this page were identified by our algorithms as relating to this institution. This page was not created or approved by the institution. If you represent an institution and have questions about these pages or wish to report inaccurate content, you can contact us here.
1,495 members
P.O. Box 1000, FI-02044 VTT, Espoo, Finland
Head of institution
Antti Vasara, President & CEO
+358 20 722 111