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- Abadei, S., et al.
(författare)
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Microwave properties of tunable capacitors basee on magnetron sputtered ferroelectric Na0.5K0.5NbO3 film on low and high resistivity silicon substrates
- 2001
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Ingår i: Integrated Ferroelectrics. - : Informa UK Limited. - 1058-4587 .- 1607-8489. ; 39:1-4, s. 359-366
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- In this work, small signal DC voltage dependent dielectric permittivity, loss tangent, and tuneability of magnetron sputtered epitaxial Na0.5K0.5NO3 films are studied experimentally. (100)-oriented Na0.5K0.5NbO3 films are deposited onto SiO2-buffered CMOS grade low resistivity (p = 10-20 cm) and high resistivity (p = 15-45 kcm) silicon substrates. Planar capacitors with 2 or 4 m gaps between electrodes have been fabricated on top of ferroelectric films. These devices have been characterized in the frequency range 1.0 MHz to 50 GHz at temperatures 30 - 300K. Na0.5K0.5NbO3/SiO2/Si structures on high resistivity silicon substrate exhibit C-V performances typical for Metal-Insulator- Semiconductor (MIS) capacitors. At low frequencies, f 1.0 GHz, the large tuneability and large losses are associated with the MIS structure, while at higher microwave frequencies the tuneability is mainly associated with the ferroelectric, film. At 1.0 MHz and room temperature, the tuneability of Na0.5K0.5NbO3/SiO2/Si structures more than 90%, reducing to 10-15 % at 50 GHz. The losses decrease with increasing the DC bias and frequency. A Q-factor more than 15 at 50 GHz is observed. The dielectric permittivity of the Na0.5K0.5NbO3 film is in the range 50-150 at frequencies 0.045-50 GHz. On low resistivity substrate the performance of Na0.5K0.5NbO3 films is completely screened by the high losses in silicon, and the tuneability is negligible. © 2001 Taylor and Francis.
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| 5. |
- Borngrund, Carl, 1992-
(författare)
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Towards Deep-learning-based Autonomous Navigation in the Short-loading Cycle
- 2023
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Earth-moving machines, such as wheel loaders, are a type of heavy-duty machinery used within the construction industry to perform vital tasks, such as digging, transporting, and mining applications. One of these tasks is the short-loading cycle, where an operator manoeuvres the wheel loader to move material from a pile to the tipping body of a dump truck, through navigation, scooping, and dumping. The short-loading cycle is a repetitive task performed in high quantities, often as part of a larger refinement process, making it interesting for automation.The main objective of this thesis work is to investigate challenges facing the automation of the short-loading cycle, focusing in particular on subtasks that can be efficiently addressed with deep learning methods. A secondary objective is to examine how alternative development paths, such as scale models, or simulations, can be used to enable data-driven automation of the short-loading cycle, as directly experimenting on real vehicles has a high associated cost when large numbers of timesteps are needed to gather enough data.To investigate the two objectives, the literature is systematically reviewed to identify research gaps, challenges, and the usage of deep learning techniques. Secondly, a set of deep learning techniques is investigated to address perception and actuation problems identified as challenging and important for the automation of the short-loading cycle.The investigation of deep learning techniques involves training and validating a realtime object detector neural network to identify key components (wheels, tipping body, and cab) on a scale model dump truck while testing on a real vehicle. This resulted in a localisation and classification degradation of only 14% between the scale model and the real dump truck, with no additional training. In addition, an examination to minimize the annotation workload of humans found that it is possible to decrease the workload by 95% while still retaining similar detection performance by leveraging linear interpolation.Lastly, this thesis presents an investigation regarding the usage of reinforcement learning for navigation during the short-loading cycle. The results indicate that training the agent in simulation is currently required as the agent obtains the maximum reward after timesteps in the order of millions before being capable of performing the task. The results suggest that the trained agent is capable of bridging the gap between simulation and reality to complete a simplified version of the navigation task during the short-loading cycle.The experiments presented in this thesis provide proof of concept that indicates deep learning techniques can aid in the realisation of an autonomous solution. Moreover, the results show that development paths allowing for experiments providing large numbers of timesteps can facilitate the practical use of such techniques.
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| 7. |
- Kugler, Veronika Mozhdeh, et al.
(författare)
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Low temperature growth and characterization of (Na,K)NbOx thin films
- 2003
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Ingår i: Journal of Crystal Growth. - 0022-0248 .- 1873-5002. ; 254:3-4, s. 400-404
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Tidskriftsartikel (refereegranskat)abstract
- Thin (Na,K)NbOx perovskite films (NKN) have been deposited on SiO2/Si(0 0 1) substrates at low temperatures, from 350°C to 550°C, by RF magnetron sputtering. The effects of substrate temperature on microstructure, electrical-, and mechanical properties of the NKN films have been studied. X-ray diffraction analysis revealed that films deposited at temperatures in the range of 450-550°C were crystalline, growing as a single phase, with a preferred orientation of (0 0 1). Films deposited at 350°C, were shown to be amorphous. The growth temperature had a strong influence on the electrical properties of the NKN films and the relative dielectric constants of the obtained films were in between 38 and 78. Variations of the mechanical properties of the NKN films were observed for different substrate temperatures: The elastic moduli and the hardness values ranged from 205±26 to 93±29 GPa, and from 12±2 to around 2 GPa, for films deposited at 550°C and 450°C, respectively.
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| 8. |
- Kugler, Veronika Mozhdeh, et al.
(författare)
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Microstructure/dielectric property relationship of low temperature synthesised (Na,K)NbOx thin films
- 2004
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Ingår i: Journal of Crystal Growth. - : Elsevier BV. - 0022-0248 .- 1873-5002. ; 262:1-4, s. 322-326
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Tidskriftsartikel (refereegranskat)abstract
- Thin films of (Na,K)NbOx (NKN) were grown by reactive RF magnetron sputtering on polycrystalline Pt80Ir20 substrates, at relatively low growth temperatures between 300°C and 450°C. The results show that the electrical performance and the microstructure of the films are a strong function of the substrate temperature. X-ray diffraction of films grown up to 400°C revealed the formation of only one crystalline NKN-phase with a preferred (0 0 2)-orientation. However, a mixed orientation together with a secondary, paraelectric potassium niobate phase, were observed for NKN films deposited at 450°C. The differences in the microstructure explains the variations in the dielectric constants and losses: The single phase NKN films displayed a dielectric constant and a dielectric loss of 506 and 0.011, respectively, while the films with mixed phases exhibited values of 475 and 0.022, respectively. The possibility of fabricating NKN films with relatively high dielectric properties at low growth temperatures, as demonstrated here, is of high technological importance.
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- Kurtser, Polina, 1990-, et al.
(författare)
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Robotic Platform for Precise Mechanical Stress Induction in Greenhouses Cultivation
- 2020
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Ingår i: 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE). - : IEEE. - 9781728169057 - 9781728169040 ; , s. 1558-1565
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Konferensbidrag (refereegranskat)abstract
- This paper presents an autonomous robotic platform for research of mechanically induced stress in plants growing in controlled greenhouse conditions. The platform provides a range of possibilities for mechanical stimuli including motion type, frequency, speed, and torque. The motions can be tailored for a single pot, making study of mechanical plant stress versatile, rapid and precise. We evaluate the performance of the platform for a use-case of basil plant cultivation. An eight week experiment was performed in greenhouse conditions on 220 basil plants. We show that the induction of mechanical stress by the platform significantly affects plant morphology, such as shortening stem length by 30 % -40 % and inter-node length by 50 % -80 %, while preserving leaf weight which is the main part of the basil plant used for culinary purposes. Results also show that variations in types of mechanical stimuli motions provides significant differences in the effect on plant morphology. Finally we show that decreasing the mechanical stimuli frequency to rates feasible to be performed manually significantly reduces the effect, stressing the need for autonomous systems capable of providing continuous stimuli during day and night. These results validate previously published findings in research of mechanical stress induction, and therefore implies the platform can be used for research of this phenomena.
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