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Anil Kumar Sao

Other affiliations: Indian Institute of Technology Madras
Bio: Anil Kumar Sao is an academic researcher from Indian Institute of Technology Mandi. The author has contributed to research in topic(s): Sparse approximation & Face (geometry). The author has an hindex of 15, co-authored 79 publication(s) receiving 696 citation(s). Previous affiliations of Anil Kumar Sao include Indian Institute of Technology Madras.
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Journal ArticleDOI
TL;DR: The review discloses the incredible view of using the neural networks in solar forecast and summarizes the major applications of eight well recognized and often used neural network models of which the last two are custom based.
Abstract: Neural networks with a good modeling capability have been used increasingly to predict and forecast solar radiation. Even diverse application of neural network has been reported in literatures such as robotics, pattern recognition, forecasting, power systems, optimization and social/psychological sciences etc. The models have categorized the review under three major performance schemes such as delay, number of neurons and activation function for establishment of neural network architecture. In each of these categories, we summarize the major applications of eight well recognized and often used neural network models of which the last two are custom based. The anticipated model are initiated and validated with 10 metrological parameters further in sub-categories. Evaluation of its accuracy associated with special flexibility of the model is demonstrated through the results based on parameter range. In summary, we conclude the best result showing that the delays, neuron, transfer function, model, parameters and RMSE errors are in range of 15 or 30, 10 or 20, tansig, Elman Back Propagation network, bulb point temperature or direct normal radiation, 9–10 and 25–35% training to the test cases. The review discloses the incredible view of using the neural networks in solar forecast. The work of other researchers in the field of renewable energy and other energy systems is also reported which can be used in the future in the works of this field.

83 citations


Proceedings ArticleDOI
01 Nov 2013-
TL;DR: A consortium effort on building text to speech (TTS) systems for 13 Indian languages using the same common framework and the TTS systems are evaluated using Mean Opinion Score (DMOS) and Word Error Rate (WER).
Abstract: In this paper, we discuss a consortium effort on building text to speech (TTS) systems for 13 Indian languages. There are about 1652 Indian languages. A unified framework is therefore attempted required for building TTSes for Indian languages. As Indian languages are syllable-timed, a syllable-based framework is developed. As quality of speech synthesis is of paramount interest, unit-selection synthesizers are built. Building TTS systems for low-resource languages requires that the data be carefully collected an annotated as the database has to be built from the scratch. Various criteria have to addressed while building the database, namely, speaker selection, pronunciation variation, optimal text selection, handling of out of vocabulary words and so on. The various characteristics of the voice that affect speech synthesis quality are first analysed. Next the design of the corpus of each of the Indian languages is tabulated. The collected data is labeled at the syllable level using a semiautomatic labeling tool. Text to speech synthesizers are built for all the 13 languages, namely, Hindi, Tamil, Marathi, Bengali, Malayalam, Telugu, Kannada, Gujarati, Rajasthani, Assamese, Manipuri, Odia and Bodo using the same common framework. The TTS systems are evaluated using degradation Mean Opinion Score (DMOS) and Word Error Rate (WER). An average DMOS score of ≈3.0 and an average WER of about 20 % is observed across all the languages.

38 citations


Journal ArticleDOI
01 Mar 2017-Signal Processing
TL;DR: A robust super-resolution algorithm which adapts itself based on the noise-level in the image, which demonstrates better efficacy for optical and range images under different types and strengths of noise.
Abstract: Super-resolution from a single image is a challenging task, more so, in presence of noise with unknown strength. We propose a robust super-resolution algorithm which adapts itself based on the noise-level in the image. We observe that dependency among the gradient values of relatively smoother patches diminishes with increasing strength of noise. Such a dependency is quantified using the ratio of first two singular values computed from local image gradients. The ratio is inversely proportional to the strength of noise. The number of patches with smaller ratio increases with increasing strength of noise. This behavior is used to formulate some parameters that are used in two ways in a sparse-representation based super-resolution approach: i) in computing an adaptive threshold, used in estimating the sparse coefficient vector via the iterative thresholding algorithm, ii) in choosing between the components representing image details and non-local means of similar patches. Furthermore, our approach constructs dictionaries by coarse-to-fine processing of the input image, and hence does not require any external training images. Additionally, an edge preserving constraint helps in better edge retention. As compared to state-of-the-art approaches, our method demonstrates better efficacy for optical and range images under different types and strengths of noise.

30 citations


Journal ArticleDOI
TL;DR: A template-matching approach for face verification, which neither synthesizes the face image nor builds a model of theFace verification approach is proposed based on autoassociative neural network models to verify the identity of a person.
Abstract: Human faces are similar in structure with minor differences from person to person. These minor differences may average out while trying to synthesize the face image of a given person, or while building a model of face image in automatic face recognition. In this paper, we propose a template-matching approach for face verification, which neither synthesizes the face image nor builds a model of the face image. Template matching is performed using an edginess-based representation of the face image. The edginess-based representation of face images is computed using 1-D processing of images. An approach is proposed based on autoassociative neural network models to verify the identity of a person. The issues of pose and illumination in face verification are addressed.

28 citations


Journal ArticleDOI
TL;DR: This paper proposes to use a multilevel decomposition (having multiple layers), also known as the deep sparse representation (DSR), to derive a feature representation for speech recognition, and reveals that the representations obtained at different sparse layers of the proposed DSR model have complimentary information.
Abstract: Features derived using sparse representation (SR)-based approaches have been shown to yield promising results for speech recognition tasks. In most of the approaches, the SR corresponding to speech signal is estimated using a dictionary, which could be either exemplar based or learned. However, a single-level decomposition may not be suitable for the speech signal, as it contains complex hierarchical information about various hidden attributes. In this paper, we propose to use a multilevel decomposition (having multiple layers), also known as the deep sparse representation (DSR), to derive a feature representation for speech recognition. Instead of having a series of sparse layers, the proposed framework employs a dense layer between two sparse layers, which helps in efficient implementation. Our studies reveal that the representations obtained at different sparse layers of the proposed DSR model have complimentary information. Thus, the final feature representation is derived after concatenating the representations obtained at the sparse layers. This results in a more discriminative representation, and improves the speech recognition performance. Since the concatenation results in a high-dimensional feature, principal component analysis is used to reduce the dimension of the obtained feature. Experimental studies demonstrate that the proposed feature outperforms existing features for various speech recognition tasks.

27 citations


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Journal ArticleDOI
Abstract: Solar photovoltaic plants are widely integrated into most countries worldwide. Due to the ever-growing utilization of solar photovoltaic plants, either via grid-connection or stand-alone networks, dramatic changes can be anticipated in both power system planning and operating stages. Solar photovoltaic integration requires the capability of handling the uncertainty and fluctuations of power output. In this case, solar photovoltaic power forecasting is a crucial aspect to ensure optimum planning and modelling of the solar photovoltaic plants. Accurate forecasting provides the grid operators and power system designers with significant information to design an optimal solar photovoltaic plant as well as managing the power of demand and supply. This paper presents an extensive review on recent advancements in the field of solar photovoltaic power forecasting. This paper aims to analyze and compare various methods of solar photovoltaic power forecasting in terms of characteristics and performance. This work classifies solar photovoltaic power forecasting methods into three major categories i.e., time-series statistical methods, physical methods, and ensemble methods. To date, Artificial Intelligence approaches are widely used due to their capability in solving the non-linear and complex structure of data. The performance analysis shows that these methods outperform the traditional methods. Recently, the ensemble methods were also developed by researchers to extract the unique features of single models to enhance the forecast model performances. This combination produces accurate results compared to individual models. This paper also elaborates on the metrics assessment which was implemented to evaluate the forecast model performances. This work provides information which is beneficial for researchers and engineers who are involved in the modelling and planning of the solar photovoltaic plant.

309 citations


Journal ArticleDOI
01 Jul 2018-Solar Energy
TL;DR: This paper presents a preliminary study on how to review solar irradiance and photovoltaic power forecasting using text mining, which serves as the first part of a forthcoming series of text mining applications in solar forecasting.
Abstract: Text mining is an emerging topic that advances the review of academic literature. This paper presents a preliminary study on how to review solar irradiance and photovoltaic (PV) power forecasting (both topics combined as “solar forecasting” for short) using text mining, which serves as the first part of a forthcoming series of text mining applications in solar forecasting. This study contains three main contributions: (1) establishing the technological infrastructure (authors, journals & conferences, publications, and organizations) of solar forecasting via the top 1000 papers returned by a Google Scholar search; (2) consolidating the frequently-used abbreviations in solar forecasting by mining the full texts of 249 ScienceDirect publications; and (3) identifying key innovations in recent advances in solar forecasting (e.g., shadow camera, forecast reconciliation). As most of the steps involved in the above analysis are automated via an application programming interface, the presented method can be transferred to other solar engineering topics, or any other scientific domain, by means of changing the search word. The authors acknowledge that text mining, at its present stage, serves as a complement to, but not a replacement of, conventional review papers.

235 citations


26


Journal ArticleDOI
17 Jan 2018-IEEE Access
TL;DR: To bridge the gap between theory and practicality of CS, different CS acquisition strategies and reconstruction approaches are elaborated systematically in this paper.
Abstract: Compressive Sensing (CS) is a new sensing modality, which compresses the signal being acquired at the time of sensing. Signals can have sparse or compressible representation either in original domain or in some transform domain. Relying on the sparsity of the signals, CS allows us to sample the signal at a rate much below the Nyquist sampling rate. Also, the varied reconstruction algorithms of CS can faithfully reconstruct the original signal back from fewer compressive measurements. This fact has stimulated research interest toward the use of CS in several fields, such as magnetic resonance imaging, high-speed video acquisition, and ultrawideband communication. This paper reviews the basic theoretical concepts underlying CS. To bridge the gap between theory and practicality of CS, different CS acquisition strategies and reconstruction approaches are elaborated systematically in this paper. The major application areas where CS is currently being used are reviewed here. This paper also highlights some of the challenges and research directions in this field.

217 citations


Journal ArticleDOI
01 Mar 2019-Solar Energy
TL;DR: An attempt has been made to scrutinize the applications of artificial neural network (ANN) as an intelligent system-based method for optimizing and the prediction of different solar energy devices’ performance.
Abstract: The development of different solar energy (SE) systems becomes one of the most important solutions to the problem of the rapid increase in energy demand. This may be achieved by optimizing the performance of solar-based devices under some operating conditions. Intelligent system-based techniques are used to optimize the performance of such systems. In present review, an attempt has been made to scrutinize the applications of artificial neural network (ANN) as an intelligent system-based method for optimizing and the prediction of different SE devices’ performance, like solar collectors, solar assisted heat pumps, solar air and water heaters, photovoltaic/thermal (PV/T) systems, solar stills, solar cookers, and solar dryers. The commonly used artificial neural network types and architectures in literature, such as multilayer perceptron neural network, a neural network using wavelet transform, Elman neural network, and radial basis function, are also briefly discussed. Different statistical criteria that used to assess the performance of artificial neural network in modeling SE systems have been introduced. Previous studies have reported that artificial neural network is a useful technique to predict and optimize the performance of different solar energy devices. Important conclusions and suggestions for future research are also presented.

186 citations


Journal ArticleDOI
TL;DR: Overall, this review provides preliminary guidelines, research gaps and recommendations for developing a better and more user-friendly DG energy planning optimisation tool.
Abstract: An overview of numerical and mathematical modelling-based distributed generation (DG) system optimisation techniques is presented in this review paper. The objective is to compare different aspects of these two broad classes of DG optimisation techniques, explore their applications, and identify potential research directions from reviewed studies. Introductory descriptions of general electrical power system and DG system are first provided, followed by reviews on renewable resource assessment, load demand analysis, model formulation, and optimisation techniques. In renewable resource assessment model review, uncertain solar and wind energy resources are emphasised whereas applications of forecasting models have been highlighted based on their prediction horizons, computational power requirement, and training data intensity. For DG optimisation framework, (solar, wind and tidal) power generator, energy storage and energy balance models are discussed; in optimisation technique section, both numerical and mathematical modelling optimisation methods are reviewed, analysed and criticised with recommendations for their improvements. In overall, this review provides preliminary guidelines, research gaps and recommendations for developing a better and more user-friendly DG energy planning optimisation tool.

176 citations


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Author's H-index: 15

No. of papers from the Author in previous years
YearPapers
20213
20204
20195
201814
20178
201611

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