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- Kolczewski, C., et al.
(författare)
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Detailed study of pyridine at the C1s and N1s ionization thresholds : The influence of the vibrational fine structure
- 2001
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Ingår i: Journal of Chemical Physics. - : AIP Publishing. - 0021-9606 .- 1089-7690. ; 115:14, s. 6426-6437
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Tidskriftsartikel (refereegranskat)abstract
- High resolution, vibrationally resolved, near-edge x-ray absorption fine structure (NEXAFS) spectra at the C 1s and N 1s ionization thresholds of pyridine and deuterated d(5)-pyridine in the gas phase have been recorded. The high resolution of 65 meV (150 meV) at the C s (N 1s) ionization thresholds reveals vibrational structures in the spectra. Detailed ab initio and density functional theory (DFT) calculations were performed to interpret the experimental spectra and to assign the observed peaks. In particular we focused on the previously unexplained intensity ratio for the two components of the C1s -->1 pi* transition. For this transition the vibrational structure is included through a linear coupling model in the DFT calculations and leads to the experimentally observed similar to2:3 intensity ratio between the two pi* components in the C1s spectrum rather than the similar to3:2 ratio obtained without vibrational effects. After inclusion of relaxation effects in the excited states, in addition to the vibrational effects, both theoretical methods yield almost perfect agreement with experiment.
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- Mashad Nemati, Hassan, 1982-, et al.
(författare)
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Stream Data Cleaning for Dynamic Line Rating Application
- 2018
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Ingår i: Energies. - Basel : MDPI. - 1996-1073. ; 11:8
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Tidskriftsartikel (refereegranskat)abstract
- The maximum current that an overhead transmission line can continuously carry depends on external weather conditions, most commonly obtained from real-time streaming weather sensors. The accuracy of the sensor data is very important in order to avoid problems such as overheating. Furthermore, faulty sensor readings may cause operators to limit or even stop the energy production from renewable sources in radial networks. This paper presents a method for detecting and replacing sequences of consecutive faulty data originating from streaming weather sensors. The method is based on a combination of (a) a set of constraints obtained from derivatives in consecutive data, and (b) association rules that are automatically generated from historical data. In smart grids, a large amount of historical data from different weather stations are available but rarely used. In this work, we show that mining and analyzing this historical data provides valuable information that can be used for detecting and replacing faulty sensor readings. We compare the result of the proposed method against the exponentially weighted moving average and vector autoregression models. Experiments on data sets with real and synthetic errors demonstrate the good performance of the proposed method for monitoring weather sensors.
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- Menezes, Maria Luiza Recena, 1983-, et al.
(författare)
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Towards emotion recognition for virtual environments : an evaluation of eeg features on benchmark dataset
- 2017
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Ingår i: Personal and Ubiquitous Computing. - London : Springer London. - 1617-4909 .- 1617-4917. ; 21:6, s. 1003-1013
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Tidskriftsartikel (refereegranskat)abstract
- One of the challenges in virtual environments is the difficulty users have in interacting with these increasingly complex systems. Ultimately, endowing machines with the ability to perceive users emotions will enable a more intuitive and reliable interaction. Consequently, using the electroencephalogram as a bio-signal sensor, the affective state of a user can be modelled and subsequently utilised in order to achieve a system that can recognise and react to the user’s emotions. This paper investigates features extracted from electroencephalogram signals for the purpose of affective state modelling based on Russell’s Circumplex Model. Investigations are presented that aim to provide the foundation for future work in modelling user affect to enhance interaction experience in virtual environments. The DEAP dataset was used within this work, along with a Support Vector Machine and Random Forest, which yielded reasonable classification accuracies for Valence and Arousal using feature vectors based on statistical measurements and band power from the and waves and High Order Crossing of the EEG signal. © 2017, The Author(s).
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