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A synthetic air data system (SADS) is an analytical redundancy technique that is crucial for unmanned aerial vehicles (UAVs) and is used as a backup system during air data sensor failures. Unfortunately, the existing state-of-the-art approaches for SADS require GPS signals or high-fidelity dynamic UAV models. To address this problem, a novel synthetic airspeed estimation method that leverages deep learning and an unscented Kalman filter (UKF) for analytical redundancy is proposed. Our novel fusion-based method only requires an inertial measurement unit (IMU), elevator control input, and airflow angles while GPS, lift/drag coefficients, and complex aircraft dynamic models are not required. Additionally, we demonstrate that our proposed temporal convolutional network (TCN) is a more efficient model for airspeed estimation than the renowned models, such as ResNet or bidirectional long short-term memory (LSTM). Our deep learning-aided UKF was experimentally verified on long-duration real flight data and has promising performance compared with the state-of-the-art methods. In particular, it is confirmed that our proposed method robustly estimates the airspeed even under dynamic flight conditions where the performance of conventional methods is degraded.

This letter proposes a novel method for model-aided synthetic airspeed estimation of UAVs. The major contribution of the proposed algorithm is that the synthetic airspeed measurement is newly formulated for analytical redundancy. This filter only requires inertial measurement unit (IMU), airflow angles, and elevator control input along with a simple aircraft model containing only three lift coefficient parameters; no GPS or complex aircraft dynamic model are required. Particularly, two novel filters (unscented Kalman filter and complementary filter) are proposed and evaluated without direct airspeed and GPS measurements. Flight test results of a UAV demonstrated that the proposed algorithm yields accurate estimated airspeed, demonstrating its effectiveness for analytical redundancy.

We introduce a novel, robust, cost-effective, and scalable approach for the preparation of a large-area force-assembled triboelectric nanogenerator (FTENG), which allows a stable and high electric output under a wide range of humidity conditions through its dual-sized morphology (i.e., microstructures and nanostructures). In this study, hexagonally packed colloidal arrays prepared by a force assembly approach rather than by conventional self-assembly were used as a mold for a triboelectric poly(dimethylsiloxane) (PDMS) replica with desired pattern shapes (intaglio and embossed structures) and sizes. The morphological size of the PDMS films was determined by the diameter of the force-assembled colloids. The electrical output performance of FTENGs composed of electrodes and a PDMS film increased substantially as the size of the micropores (for intaglio-structured PDMS) or embossed features (for embossed-structured PDMS) decreased. Furthermore, the triboelectric PDMS film with micro-/nanosized features (i.e., dual-embossed PDMS) displayed a remarkable electrical output of 207 V (open-circuit voltage under a compressive force of 90 N in relative humidity (RH) of 20%) and high hydrophobicity compared to that of PDMS films with flat, intaglio or embossed structures. This device maintained a high electric output even in a high-humidity environment (i.e., open-circuit output voltage ~175 V in RH 80%). Our approach using force-assembly and hierarchical surface morphology will provide a novel and effective framework for developing strong power sources in various self-powered electronics.

Lists all possible combinations of GO relations where relation reasoning can be applied. Figure S1-S2. shows ROC and PRC along with their area under the curves obtained from cross-validation, and independent test results. (PDF 799Â kb)

In this paper, a vision-aided terrain referenced navigation (VATRN) algorithm constructed by point-mass filter (PMF) is accelerated by graphic processing unit (GPU). The terrain referenced navigation algorithm estimates the vehicle's position by blending INS data with measured terrain height, and matching that data with the stored digital terrain elevation database (DTED). On the other hands, the VATRN algorithm obtains odometry data from visual sensors instead of inertial sensors. The odometry data is estimated by the homography relationship of two successive ground images of a monocular camera. Point-mass filter is one of the TRN algorithm based on the Bayesian estimation theory, and it contains convolutional integral of each points for the time update process. The convolution is the computational burden and can be accelerated by parallel computing to improve the estimation performance of PMF with sufficient grid points. GPU is employed to accelerate the PMF and numerical simulations are performed to analyze and evaluate the performance of the proposed method. The results show that the precise autonomous navigation of unmanned aircraft is achieved by the accelerated vision-based TRN algorithm.

Magnetocaloric effect (MCE) has drawn much attention because its magnetic cooling property enables refrigeration without producing noxious gas or using rapidly depleting resources. However, applications for everyday life are yet distant. In addition, we need to understand more about the practical aspect of the MCE. Here, we introduce a phenomenological model to explain the quasi-adiabatic MCE. Correction factors to the equilibrium thermodynamic feature implied by the entropy landscape are devised in analytic forms. To demonstrate the validity of the model, the MCE from two different materials is investigated. The recently discovered metallic paramagnet, YbPt 2 Sn, shows a linear and reversible MCE which is typical of a paramagnetic system and suitable for cryogenics without 3He. On the other hand, a complex-phase material, Ce 0.5 La 0.5 B 6 , exhibits a pronounced irreversible MCE especially across a magnetic phase boundary. A term that describes the field induced heating near a phase transition turns out to be essential in resolving the irreversible, non-equilibrium MCE.

Background Interactions between biological entities such as genes, proteins and metabolites, so called pathways, are key features to understand molecular mechanisms of life. As pathway information is being accumulated rapidly through various knowledge resources, there are growing interests in maintaining the integrity of the heterogeneous databases. Methods Here, we defined conflict as a status where two contradictory pieces of evidence (i.e. 'A increases B' and 'A decreases B') coexist in a same pathway. This conflict damages unity so that inference of simulation on the integrated pathway network might be unreliable. We defined rule and rule group. A rule consists of interaction of two entities, meta-relation (increase or decrease), and contexts terms about tissue specificity or environmental conditions. The rules, which have the same interaction, are grouped into a rule group. If the rules don't have a unanimous meta-relation, the rule group and the rules are judged as being conflicting. Results This analysis revealed that almost 20% of known interactions suffer from conflicting information and conflicting information occurred much more frequently in the literature than the public database. With consideration for dual functions depending on context, we thought it might resolve conflict to consider context. We grouped rules, which have the same context terms as well as interaction. It's revealed that up to 86% of the conflicts could be resolved by considering context. Subsequent analysis also showed that those contradictory records generally compete each other closely, but some information might be suspicious when their evidence levels are seriously imbalanced. Conclusions By identifying and resolving the conflicts, we expect that pathway databases can be cleaned and used for better secondary analyses such as gene/protein annotation, network dynamics and qualitative/quantitative simulation.