Traditional recommendation approaches for the mobile Apps basically depend on the Apps related features. Now a days many users are in quench of Apps recommendation based on the version description. ...Earlier mobile Apps recommendation system do not handle the cold start problem and also lacks in time for recommending the related and latest version of Apps. To overcome this issues, a hybrid Apps recommendation framework which is considering the version of the mobile Apps is proposed. This novel framework named "Probabilistic Evolution based Version Recommendation Model (PEVRM)" integrates the principles of Probabilistic Matrix Factorization (PMF) with Version Evolution Progress Model (VEPM). With the help this novel recommendation algorithm, the mobile users easily identify the specific Apps for particular task based on its version progression. At same time, this framework helps in resolving cold start problems of new users. Evaluations of this framework utilize a benchmark dataset, i.e., Apple's iTunes App Store3, for revealing its promising performance.
We experimentally demonstrate 16-GBaud probabilistic shaped 256-ary quadrature amplitude modulation (PS-256QAM) signal transmission over 104-m wireless distance at 339 GHz in a photonics-aided ...terahertz (THz)-wave communication system. Thanks to the pair of poly tetra fluoroethylene (PTFE) lenses and PS technique, we successfully achieve a record single line rate of 124.8 Gbit/s and net spectral efficiency (SE) of 6.2 bit/s/Hz. To the best of our current knowledge, it is the first time to achieve >100 m and >100 Gbit/s single-carrier 256QAM THz-wave signal wireless delivery.
Smart electricity meters are currently deployed in millions of households to collect detailed individual electricity consumption data. Compared with traditional electricity data based on aggregated ...consumption, smart meter data are much more volatile and less predictable. There is a need within the energy industry for probabilistic forecasts of household electricity consumption to quantify the uncertainty of future electricity demand in order to undertake appropriate planning of generation and distribution. We propose to estimate an additive quantile regression model for a set of quantiles of the future distribution using a boosting procedure. By doing so, we can benefit from flexible and interpretable models, which include an automatic variable selection. We compare our approach with three benchmark methods on both aggregated and disaggregated scales using a smart meter data set collected from 3639 households in Ireland at 30-min intervals over a period of 1.5 years. The empirical results demonstrate that our approach based on quantile regression provides better forecast accuracy for disaggregated demand, while the traditional approach based on a normality assumption (possibly after an appropriate Box-Cox transformation) is a better approximation for aggregated demand. These results are particularly useful since more energy data will become available at the disaggregated level in the future.
In this paper, a robust lane detection method based on vanishing point estimation is proposed. Estimating a vanishing point can be helpful in detecting lanes, because parallel lines converge on the ...vanishing point in a projected 2-D image. However, it is not easy to estimate the vanishing point correctly in an image with a complex background. Thus, a robust vanishing point estimation method is proposed that uses a probabilistic voting procedure based on intersection points of line segments extracted from an input image. The proposed voting function is defined with line segment strength that represents relevance of the extracted line segments. Next, candidate line segments for lanes are selected by considering geometric constraints. Finally, the host lane is detected by using the proposed score function, which is designed to remove outliers in the candidate line segments. Also, the detected host lane is refined by using inter-frame similarity that considers location consistency of the detected host lane and the estimated vanishing point in consecutive frames. Furthermore, in order to reduce computational costs in the vanishing point estimation process, a method using a lookup table is proposed. Experimental results show that the proposed method efficiently estimates the vanishing point and detects lanes in various environments.
In this paper, we propose a practical adaptive coding modulation scheme to approach the capacity of free-space optical (FSO) channels with intensity modulation/direct detection based on probabilistic ...shaping. The encoder efficiently adapts the transmission rate to the signal-to-noise ratio, accounting for the fading induced by the atmospheric turbulence. The transponder can support an arbitrarily large number of transmission modes using a low complexity channel encoder with a small set of supported rates. Hence, it can provide a solution for FSO backhauling in terrestrial and satellite communication systems to achieve higher spectral efficiency. We propose two algorithms to determine the capacity and capacity-achieving distribution of the scheme with unipolar <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula>-ary pulse amplitude modulation (<inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula>-PAM) signaling. Then, the signal constellation is probabilistically shaped according to the optimal distribution, and the shaped signal is channel encoded by an efficient binary forward error correction scheme. Extensive numerical results and simulations are provided to evaluate the performance. The proposed scheme yields a rate close to the tightest lower bound on the capacity of FSO channels. For instance, the coded modulator operates within 0.2 dB from the <inline-formula> <tex-math notation="LaTeX">M </tex-math></inline-formula>-PAM capacity, and it outperforms uniform signaling with more than 1.7 dB, at a transmission rate of 3 bits per channel use.
This paper is concerned with the problem of reliable mixed H ∞ and passivity-based control for a class of stochastic Takagi-Sugeno (TS) fuzzy systems with Markovian switching and probabilistic time ...varying delays. Different from the existing works, the H∞ and passivity control problem with probabilistic occurrence of time-varying delays and actuator failures is considered in a unified framework, which is more general in some practical situations. The main aim of this paper is to design a reliable mixed H∞ and passivity-based controller such that the stochastic TS fuzzy system with Markovian switching is stochastically stable with a prescribed mixed H∞ and passivity performance level γ > 0 . Based on the Lyapunov-Krasovskii functional (LKF) involving lower and upper bound of probabilistic time delay and convex combination technique, a new set of delay-dependent sufficient condition in terms of linear matrix inequalities (LMIs) is established for obtaining the required result. Finally, a numerical example based on the modified truck-trailer model is given to demonstrate the effectiveness and applicability of the proposed design techniques.
•Field blast tests and axial compression tests reveal a significant decrease in the axial bearing capacity of the shallow-buried RC tunnel subjected to blast loads.•Numerical simulation algorithms ...and material models are verified by field blast test and post-blast axial compression test results.•The shallow-buried RC tunnel exhibits different damage modes under different blast scenarios.•The damage degree of a shallow-buried tunnel is sensitive to the explosive weight and detonation point.•The blast vulnerability of a shallow-buried tunnel under blast load is investigated.
The purpose of this investigation is to analyze the failure probability of a blast in a shallow RC tunnel. The field blast tests are carried out, and a refined finite element model (FEM) is established based on LS-DYNA. To account for blast uncertainty, the Monte Carlo method is used to determine 150 blast conditions. A failure evaluation method based on a mechanical performance index is proposed. The probabilistic blast demand model (PBDM) and the probabilistic blast capacity model (PBCM) are established. Finally, a probability assessment framework based on the structural performance index and the vulnerability curve for shallow tunnels are established. According to the findings, when the mass of TNT in the tunnel exceeds 300 kg, the tunnel is in danger of collapsing. At this moment, it is suggested that people enter the tunnel cautiously. The vulnerability curve predicts the failure probability of a tunnel under various blast loads. At the same time, it could assess its structural performance after the disaster.
As a matter of course, the unprecedented ascending penetration of distributed energy resources, mainly harvesting renewable energies, is a direct consequence of environmental concerns. This type of ...energy resource brings about more uncertainties in power system operation and planning; consequently, it necessitates probabilistic analyses of the system performance. This paper develops a new approach for probabilistic load flow (PLF) evaluation using the unscented transformation (UT) method. The UT method is recognized as a powerful approach in assessing stochastic problems with/without correlated uncertain variables. The capability of the UT method in modeling correlated uncertain variables is very appealing in the power system context, in which noticeable inherent correlation exists. The salient features of the UT method in probabilistic applications have been well proven in other engineering aspects. Following adaptation of the UT method for the PLF problem, three dimensionally different case studies are examined in order to inspect the performance of the proposed methodology. The obtained results are then compared with those of the Monte Carlo simulation as well as two-point estimation method with regards to both accuracy and execution time criteria.
On the Latent Variable Interpretation in Sum-Product Networks Peharz, Robert; Gens, Robert; Pernkopf, Franz ...
IEEE transactions on pattern analysis and machine intelligence,
2017-Oct.-1, 2017-10-00, 2017-10-1, 20171001, Letnik:
39, Številka:
10
Journal Article
Recenzirano
Odprti dostop
One of the central themes in Sum-Product networks (SPNs) is the interpretation of sum nodes as marginalized latent variables (LVs). This interpretation yields an increased syntactic or semantic ...structure, allows the application of the EM algorithm and to efficiently perform MPE inference. In literature, the LV interpretation was justified by explicitly introducing the indicator variables corresponding to the LVs' states. However, as pointed out in this paper, this approach is in conflict with the completeness condition in SPNs and does not fully specify the probabilistic model. We propose a remedy for this problem by modifying the original approach for introducing the LVs, which we call SPN augmentation. We discuss conditional independencies in augmented SPNs, formally establish the probabilistic interpretation of the sum-weights and give an interpretation of augmented SPNs as Bayesian networks. Based on these results, we find a sound derivation of the EM algorithm for SPNs. Furthermore, the Viterbi-style algorithm for MPE proposed in literature was never proven to be correct. We show that this is indeed a correct algorithm, when applied to selective SPNs, and in particular when applied to augmented SPNs. Our theoretical results are confirmed in experiments on synthetic data and 103 real-world datasets.
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