Language modeling approaches to information retrieval are attractive and promising because they connect the problem of retrieval with that of language model estimation, which has been studied ...extensively in other application areas such as speech recognition. The basic idea of these approaches is to estimate a language model for each document, and to then rank documents by the likelihood of the query according to the estimated language model. A central issue in language model estimation is smoothing , the problem of adjusting the maximum likelihood estimator to compensate for data sparseness. In this article, we study the problem of language model smoothing and its influence on retrieval performance. We examine the sensitivity of retrieval performance to the smoothing parameters and compare several popular smoothing methods on different test collections. Experimental results show that not only is the retrieval performance generally sensitive to the smoothing parameters, but also the sensitivity pattern is affected by the query type, with performance being more sensitive to smoothing for verbose queries than for keyword queries. Verbose queries also generally require more aggressive smoothing to achieve optimal performance. This suggests that smoothing plays two different role---to make the estimated document language model more accurate and to "explain" the noninformative words in the query. In order to decouple these two distinct roles of smoothing, we propose a two-stage smoothing strategy, which yields better sensitivity patterns and facilitates the setting of smoothing parameters automatically. We further propose methods for estimating the smoothing parameters automatically. Evaluation on five different databases and four types of queries indicates that the two-stage smoothing method with the proposed parameter estimation methods consistently gives retrieval performance that is close to---or better than---the best results achieved using a single smoothing method and exhaustive parameter search on the test data.
With the rapid increase in photovoltaic (PV) power generation in microgrids, PV power fluctuations can initiate negative impacts on microgrid operations. This study presents a simulation analysis of ...PV power smoothing method based on hull enhanced linear exponential smoothing (HELES) technique using an energy storage system (ESS). The proposed method is employed to mitigate PV power fluctuations in microgrid systems. The ESS is allowed to charge and discharge for smoothing the PV output power based on a smoothing power reference provided by the HELES technique. The proposed method is investigated to acquire the smoothing performance considering smoothness and smoothing accuracy. In simulation analysis, the proposed method is analysed on the microgrid under both grid-connected and islanding modes to obtain its dynamic performance. The simulation results demonstrate that the proposed method evidently offers superior smoothing performance compared with the existing methods. Accordingly, the smoothing accuracy is successfully improved, and the ESS capacity is significantly reduced by improving smoothing accuracy. Moreover, the smoothness is effective to mitigate PV power fluctuations by using the proposed method. Eventually, the dynamic performance can be satisfactorily improved, and state of charge variation is reduced and also conserved to be available for unexpected PV power fluctuations.
We study the problem of distributed Kalman filtering and smoothing, where a set of nodes is required to estimate the state of a linear dynamic system from in a collaborative manner. Our focus is on ...diffusion strategies, where nodes communicate with their direct neighbors only, and the information is diffused across the network through a sequence of Kalman iterations and data-aggregation. We study the problems of Kalman filtering, fixed-lag smoothing and fixed-point smoothing, and propose diffusion algorithms to solve each one of these problems. We analyze the mean and mean-square performance of the proposed algorithms, provide expressions for their steady-state mean-square performance, and analyze the convergence of the diffusion Kalman filter recursions. Finally, we apply the proposed algorithms to the problem of estimating and tracking the position of a projectile. We compare our simulation results with the theoretical expressions, and note that the proposed approach outperforms existing techniques.
Delving Deep Into Label Smoothing Zhang, Chang-Bin; Jiang, Peng-Tao; Hou, Qibin ...
IEEE transactions on image processing,
2021, Volume:
30
Journal Article
Peer reviewed
Open access
Label smoothing is an effective regularization tool for deep neural networks (DNNs), which generates soft labels by applying a weighted average between the uniform distribution and the hard label. It ...is often used to reduce the overfitting problem of training DNNs and further improve classification performance. In this paper, we aim to investigate how to generate more reliable soft labels. We present an Online Label Smoothing (OLS) strategy, which generates soft labels based on the statistics of the model prediction for the target category. The proposed OLS constructs a more reasonable probability distribution between the target categories and non-target categories to supervise DNNs. Experiments demonstrate that based on the same classification models, the proposed approach can effectively improve the classification performance on CIFAR-100, ImageNet, and fine-grained datasets. Additionally, the proposed method can significantly improve the robustness of DNN models to noisy labels compared to current label smoothing approaches. The source code is available at our project page: https://mmcheng.net/ols/
As theincrease of wind power penetration, the fluctuations of wind power results in serious power system frequency turbulences. This article proposes a low-pass virtual filter (VF) for wind energy ...conversion systems (WECSs) to smoothen output power. First, a linearized model is established for a WECS. Then, a low-pass VF is derived for the active power control loop in WECS to modulate the kinetic energy stored in rotating mass for output smoothing. The principle is to modify the traditional wind power controller to emulate a physical energy storage system controlled as a low-pass filter to smooth the wind power output. The proposed low-pass VF does not need the physical deployment of energy storage system but still can filter the high-frequency fluctuations in the output wind power. Furthermore, in order to ensure operation stability of the wind turbine (WT), a stability-constrained coefficient is determined according to the proposed stability criteria to ensure the stability of WTs. Simulation and experiment results verify the effectiveness of the proposed VF in WECS for output wind power fluctuation reduction and the stability-constrained coefficient for stability ensuring.
It is known that local filtering-based edge preserving smoothing techniques suffer from halo artifacts. In this paper, a weighted guided image filter (WGIF) is introduced by incorporating an ...edge-aware weighting into an existing guided image filter (GIF) to address the problem. The WGIF inherits advantages of both global and local smoothing filters in the sense that: 1) the complexity of the WGIF is O(N) for an image with N pixels, which is same as the GIF and 2) the WGIF can avoid halo artifacts like the existing global smoothing filters. The WGIF is applied for single image detail enhancement, single image haze removal, and fusion of differently exposed images. Experimental results show that the resultant algorithms produce images with better visual quality and at the same time halo artifacts can be reduced/avoided from appearing in the final images with negligible increment on running times.
The integrated particle filter (IPF) is an algorithm for single-target tracking in clutter, which incorporates the probability of target existence (PTE) into the traditional particle filter as a ...track quality measure for false track discrimination (FTD). This study investigates two IPF-based fixed-interval smoothing algorithms: the IP smoothing (IPS) algorithm and the IP-Rauch–Tung–Striebel backward smoothing (IP-RTSBS) algorithm, both of which are capable of trajectory estimation and FTD. The IPS algorithm fuses the propagations for each pair of forward IPF and backward IPF particles to obtain the smoothing propagation that is used to update the track state by applying all available measurements in the current scan. The IP-RTSBS algorithm employs the forward filtering backward smoothing approach to smooth the trajectory state, which is then applied to the RTS smoothing methodology to obtain the smoothing propagation used to update the PTE. As a result, both FTD and trajectory estimation are improved. The smoothing benefits of the two algorithms are validated in the simulations, where a sliding batch mode with overlapping measurements is utilised to limit the smoothing lag.
Temporal Parallelization of Bayesian Smoothers Sarkka, Simo; Garcia-Fernandez, Angel F.
IEEE transactions on automatic control,
2021-Jan., 2021-1-00, 20210101, Volume:
66, Issue:
1
Journal Article
Peer reviewed
Open access
This article presents algorithms for temporal parallelization of Bayesian smoothers. We define the elements and the operators to pose these problems as the solutions to all-prefix-sums operations for ...which efficient parallel scan-algorithms are available. We present the temporal parallelization of the general Bayesian filtering and smoothing equations, and specialize them to linear/Gaussian models. The advantage of the proposed algorithms is that they reduce the linear complexity of standard smoothing algorithms with respect to time to logarithmic.
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