An application of adaptive fault-tolerant control to nano-spacecraft

Since nano-spacecraft are small, low cost and do not undergo the same rigor of testing as conventional spacecraft, they have a greater risk of failure. In this paper we address the problem of attitude control of a nano-spacecraft that experiences different types of faults. Based on the traditional quaternion feedback control method, an adaptive fault-tolerant control method is developed, which can ensure that the control system still operates when the actuator fault happens. This paper derives the fault-tolerant control logic under both actuator gain fault mode and actuator deviation fault mode. Taking the parameters of the UKube-1 in the simulation model, a comparison between a traditional spacecraft control method and the adaptive fault-tolerant control method in the presence of a fault is undertaken. It is shown that the proposed controller copes with faults and is able to complete an effective attitude control manoeuver in the presence of a fault

A computational study of photoisomerization in Al3O3- ­clusters

Ab initio calculations are employed to understand the photoisomerization process in small Al3O3- clusters. This process is the first example of a photoinduced isomerization observed in an anion cluster gas-phase system. Potential energy surfaces for the ground state and the excited state (S1 and T1) are explored by means of B3LYP, MP2, CI-singles, and CASSCF methods. We demonstrate that the isomerization process occurs between the global minimum singlet state Book structure (C2v,1A1) and the triplet state Ring structure (C2v,3B2). The calculated vertical excitation energy is 3.62 eV at the CASSCF level of approximation, in good agreement with the experimental value (3.49 eV). A nonplanar conical intersection, which hosts the intersystem crossing between the S1 and T1 surfaces is identified at the region of around R(1,6)=2.4 Å. Beyond the experimental results, we predict, that this isomerization is reversible upon absorption of a phonon with energy of 1.92 eV. Our results describe a unique system, whose structure depends on its spin multiplicity; it exists as the Book structure on singlet states and as the Ring structure on triplet states

W-jet Tagging: Optimizing the Identification of Boosted Hadronically-Decaying W Bosons

A method is proposed for distinguishing highly boosted hadronically decaying W's (W-jets) from QCD-jets using jet substructure. Previous methods, such as the filtering/mass-drop method, can give a factor of ~2 improvement in S/sqrt(B) for jet pT > 200 GeV. In contrast, a multivariate approach including new discriminants such as R-cores, which characterize the shape of the W-jet, subjet planar flow, and grooming-sensitivities is shown to provide a much larger factor of ~5 improvement in S/sqrt(B). For longitudinally polarized W's, such as those coming from many new physics models, the discrimination is even better. Comparing different Monte Carlo simulations, we observe a sensitivity of some variables to the underlying event; however, even with a conservative estimates, the multivariate approach is very powerful. Applications to semileptonic WW resonance searches and all-hadronic W+jet searches at the LHC are also discussed. Code implementing our W-jet tagging algorithm is publicly available at http://jets.physics.harvard.edu/wtagComment: Version to appear in PR

ANN application in maritime industry : Baltic Dry Index forecasting & optimization of the number of container cranes

This dissertation is a study of dry bulk freight index forecasting and port planning, both based on Artificial Neural network application. First the dry bulk market is reviewed, and the reason for the high fluctuation of freight rates through the demand-supply mechanism is examined. Due to the volatile BDI, the traditional linear regression forecasting method cannot guarantee the performance of forecasting, but ANN overcomes this difficulty and gives better performance especially in a short time. Besides, in order to improve the performance of ANN further, wavelet is introduced to pre-process the BDI data. But when the noise (high frequency parts) is stripped, the hidden useful data may also be eliminated. So the performance of different degrees of de-noising models is evaluated, and the best one (most suitable de-noising model) is chosen to forecast BDI, which avoids over de-noising and keeps a fair ability of forecasting. In the second case study, the collected container terminals and ranked, and the throughput of each combination (different crane number) is estimated by applying a trained BP network. The BP network with DEA output is combined, simulating the efficiency of each combination. And finally, the optimal container crane number is fixed due to the highest efficiency and practical reasons. The Conclusion and Recommendation chapter gives some further advice, and many recommendations are given

Power vs. Spectrum 2-D Sensing in Energy Harvesting Cognitive Radio Networks

Energy harvester based cognitive radio is a promising solution to address the shortage of both spectrum and energy. Since the spectrum access and power consumption patterns are interdependent, and the power value harvested from certain environmental sources are spatially correlated, the new power dimension could provide additional information to enhance the spectrum sensing accuracy. In this paper, the Markovian behavior of the primary users is considered, based on which we adopt a hidden input Markov model to specify the primary vs. secondary dynamics in the system. Accordingly, we propose a 2-D spectrum and power (harvested) sensing scheme to improve the primary user detection performance, which is also capable of estimating the primary transmit power level. Theoretical and simulated results demonstrate the effectiveness of the proposed scheme, in term of the performance gain achieved by considering the new power dimension. To the best of our knowledge, this is the first work to jointly consider the spectrum and power dimensions for the cognitive primary user detection problem