Performance evaluation of Compass dual-mode receiver

Recently, navigation technology based on satellite navigation system has got more and more attention in international society because of its great values for both military and civil application. While navigation signal simulator which can generate satellite navigation signals and simulate different scenarios, not only satisfy cooperating test requirements of ground operation control system, test of the signal receivers, but also been playing a very important role in the development, test and evaluation of different navigation receiver, such as radio determination satellite system (RDSS) receiver, radio navigation satellite system (RNSS) receiver and dual-mode receivers. It is an indispensable tool for the design of the receivers．Compared to other satellite navigation systems, such as Global Positioning System (GPS) and GLobal Navigation Satellite System (GLONASS), BeiDou Navigation Satellite System (Compass/BDS) have some different unique characteristics, especially in RDSS determination. Firstly, the principle of comprehensive RDSS position and report is presented. Then function architecture of Compass signal simulator and requirements of the dynamic navigation signal simulation are analyzed. Finally, two key mathematical models are established to simulate the signals arriving at the receiver antenna for dual-mode receiver test, which are: the transmitting time iterative model and efficient satellite orbit approximation model. The results show that the Compass dual-mode simulator can satisfy dual-mode receiver’s test requirements

The impact of wind uncertainty on the strategic valuation of distributed electricity storage

The intermittent nature of wind energy generation has introduced a new degree of uncertainty to the tactical planning of energy systems. Short-term energy balancing decisions are no longer (fully) known, and it is this lack of knowledge that causes the need for strategic thinking. But despite this observation, strategic models are rarely set in an uncertain environment. And even if they are, the approach used is often inappropriate, based on some variant of scenario analysis—what-if analysis. In this paper we develop a deterministic strategic model for the valuation of electricity storage (a battery), and ask: “Though leaving out wind speed uncertainty clearly is a simplification, does it really matter for the valuation of storage?”. We answer this question by formulating a stochastic programming model, and compare its valuation to that of its deterministic counterpart. Both models capture the arbitrage value of storage, but only the stochastic model captures the battery value stemming from wind speed uncertainty. Is the difference important? The model is tested on a case from Lancaster University’s campus energy system where a wind turbine is installed. From our analysis, we conclude that considering wind speed uncertainty can increase the estimated value of storage with up to 50 % relative to a deterministic estimate. However, we also observe cases where wind speed uncertainty is insignificant for storage valuation

Synergy of smart grids and hybrid distributed generation on the value of energy storage

In smart grids, demand response and distributed energy systems aim to provide a higher degree of flexibility for load-shifting operations and the leverage to control intermittent wind supply. In this more dynamic energy system, deployment of energy storage at the site of consumption is envisioned to create synergies with the local distributed generation (DG) system. From a large end-user perspective, this paper contributes to the practical understanding of smart grids by modelling the impact of real-time pricing schemes (smart grids) on a hybrid DG system (mixed generation for heating and electricity loads) coupled with storage units. Specifically, we address: How does the portfolio of DG units affect the value of energy storage? and, what is the value of energy storage when assessing different designs of demand response for the end-user? To this end, we formulate a dynamic optimization model to represent a real-life urban community’s energy system composed of a co-generation unit, gas boilers, electrical heaters and a wind turbine. We discuss the techno-economic benefits of complementing this end-user’s energy system with storage units (thermal storage and battery devices). The paper analyses the storages policy strategies to simultaneously satisfy heat and electricity demand through the efficient use of DG units under demand response mechanisms. Results indicate that the storage units reduce energy costs by 7–10% in electricity and 3% in gas charges. In cases with a large DG capacity, the supply–demand mismatch increases, making storage more valuable

Dynamic Booking Control for Car Rental Revenue Management : A Decomposition Approach

This paper considers dynamic booking control for a single-station car rental revenue management problem. Different from conventional airline revenue management, car rental revenue management needs to take into account not only the existing bookings but also the lengths of the existing rentals and the capacity flexibility via fleet shuttling, which yields a high-dimensional system state space. In this paper, we formulate the dynamic booking control problem as a discrete-time stochastic dynamic program over an infinite horizon. Such a model is computationally intractable. We propose a decomposition approach and develop two heuristics. The first heuristic is an approximate dynamic program (ADP) which approximates the value function using the value functions of the decomposed problems. The second heuristic is constructed directly from the optimal booking limits computed from the decomposed problems, which is more scalable compared to the ADP heuristic. Our numerical study suggests that the performances of both heuristics are close to optimum and significantly outperform the commonly used probabilistic non-linear programming (PNLP) heuristic in most of the instances. The dominant performance of our second heuristic is evidenced in a case study using sample data from a major car rental company in the UK

Generalising Fine-Grained Sketch-Based Image Retrieval

Fine-grained sketch-based image retrieval (FG-SBIR) addresses matching specific photo instance using free-hand sketch as a query modality. Existing models aim to learn an embedding space in which sketch and photo can be directly compared. While successful, they require instance-level pairing within each coarse-grained category as annotated training data. Since the learned embedding space is domain-specific, these models do not generalise well across categories. This limits the practical applicability of FGSBIR. In this paper, we identify cross-category generalisation for FG-SBIR as a domain generalisation problem, and propose the first solution. Our key contribution is a novel unsupervised learning approach to model a universal manifold of prototypical visual sketch traits. This manifold can then be used to paramaterise the learning of a sketch/photo representation. Model adaptation to novel categories then becomes automatic via embedding the novel sketch in the manifold and updating the representation and retrieval function accordingly. Experiments on the two largest FG-SBIR datasets, Sketchy and QMUL-Shoe-V2, demonstrate the efficacy of our approach in enabling crosscategory generalisation of FG-SBIR

Etanercept Inhibits Pro-inflammatory Cytokines Expression in Titanium Particle-Stimulated Peritoneal Macrophages

Purpose: To investigate the inhibitory role of Etanercept in pro-inflammatory cytokines such as TNF-α, IL-1β and IL-6 production in titanium (Ti) particle stimulated macrophages.Methods: Peritoneal macrophages were stimulated with 1 × 109 Ti particles and treated simultaneously with or without 10, 100, or 1000 ng/mL Etanercept. The levels of TNF-α, IL-1β and IL-6 in the culture supernatants were measured using ELISA.Results: Titanium particles could stimulate TNF-α, IL-1β and IL-6 secretion in peritoneal macrophages. Etanercept inhibited Ti particle-induced TNF-α release by 29.7 % at 10 ng/ml (19.19 ± 4.72 pg/mL, p < 0.01), 49.3 % at 100 ng/mL (13.83 ± 3.72 pg/ml, p < 0.01) and 60.4 % at 1000 ng/mL (10.82 ± 3.87 pg/mL, p < 0.001), IL-1β release by 5.23 % at 10 ng/mL (34.79 ± 7.83 pg/mL, p > 0.05), 21.06 % at 100 ng/mL (28.98 ± 4.81 pg/mL, p < 0.01) and 29.83 % at 1000 ng/mL (25.76 ± 5.23 pg/ml, p < 0.001), and IL-6 release by 38.69 % at 10 ng/mL (256.8 ± 99.56 pg/mL, p < 0.01), by 42.13 % at 100 ng/mL (242.4 ± 33.26 pg/mL, p < 0.01) and 53.4 % at 1000 ng/ml (195.2 ± 48.82 pg/mL, p < 0.001).Conclusion: Etanercept has potent ability to prevent wear debris–induced osteolysis and may be valuable as a therapeutic agent for the treatment of prosthetic loosening in humans.Keywords: Etanercept; titanium particle; proinflammatory cytokines; peritoneal macrophage