Thapar University

About: Thapar University is a education organization based out in Patiāla, Punjab, India. It is known for research contribution in the topics: Cloud computing & Fuzzy logic. The organization has 2944 authors who have published 8558 publications receiving 130392 citations.

Frequency excursion mitigation strategy using a novel COA optimised fuzzy controller in wind integrated power systems

Abstract: The uncertain demeanour from wind generators and loads adversely affect the grid operational stability. Various control approaches have been explored to remedy the system uncertainties while maintaining generation and load demand balance. This study proposes a fuzzy-based proportional–fractional integral–derivative with filter controller to sustain frequency stability in wind integrated power systems having different configurations. The controller parameters have been tuned using a recently developed coyote optimisation algorithm (COA). The proposed control approach is executed and validated on three distinct configurations of two-area power systems. All test models are integrated with a doubly fed induction generator (DFIG) type wind turbine units (WTUs). Different case scenarios have been considered to analyse the efficacy of the proposed control strategy in the presence of WTU. Furthermore, the impact of inertial support delivered by the DFIG-WTU and higher penetration of wind energy in the power system has been studied. The analysis reveals that the control scheme in coordination with WTU support reduces the stress on a wind turbine during the inertial control scheme and maintains the grid frequency stability under unexpected load disturbances. Stability and robustness analysis are also conducted to verify the validity of the introduced control approach.

Fake News Detection Using a Blend of Neural Networks: An Application of Deep Learning

Abstract: Fake news and its consequences carry the potential of impacting different aspects of different entities, ranging from a citizen’s lifestyle to a country’s global relations, there are many related works for collecting and determining fake news, but no reliable system is commercially available. This study aims to propose a deep learning model which predicts the nature of an article when given as an input. It solely uses text processing and is insensitive to history and credibility of the author or the source. In this paper, authors have discussed and experimented using word embedding (GloVe) for text pre-processing in order to construct a vector space of words and establish a lingual relationship. The proposed model which is the blend of convolutional neural network and recurrent neural networks architecture has achieved benchmark results in fake news prediction, with the utility of word embeddings complementing the model altogether. Further, to ensure the quality of prediction, various model parameters have been tuned and recorded for the best results possible. Among other variations, addition of dropout layer reduces overfitting in the model, hence generating significantly higher accuracy values. It can be a better solution than already existing ones, viz: gated recurrent units, recurrent neural networks or feed-forward networks for the given problem, which generates better precision values of 97.21% while considering more input features.

A Novel Energy Efficient Stable Clustering Approach for Wireless Sensor Networks

Abstract: Wireless sensor network (WSN) consists of densely distributed nodes that are deployed to observe and react to events within the sensor field. In WSNs, energy management and network lifetime optimization are major issues in the designing of routing protocols. Clustering is an efficient data gathering technique that effectively reduces the energy consumption by organizing nodes into groups. However, in clustering protocols, cluster heads (CHs) bear additional load for coordinating various activities within the cluster. Improper selection of CHs causes increased energy consumption and also degrades the performance of WSN. Therefore, proper CH selection and their load balancing using efficient routing protocol is a critical aspect for the long run operation of WSN. Clustering a network with proper load balancing is an NP-hard problem. To solve such problems having vast search area, optimization algorithm is the preeminent possible solution. In this paper, differential evolution based clustering algorithm for WSNs named threshold-sensitive energy-efficient delay-aware routing protocol (TEDRP), is proposed to prolong network lifetime. Dual-hop communication between CHs and BS is utilized to achieve load balancing of distant CHs and energy minimization. The paper also considers stability-aware model of TEDRP named stable TEDRP (STEDRP) with an intend to extend the stability period of the network. In STEDRP, energy aware heuristics is applied for CH selection in order to improve the stability period. The results demonstrate that the proposed protocols significantly outperform existing protocols in terms of energy consumption, system lifetime and stability period.

Single image dehazing using gradient channel prior

Abstract: The dehazing techniques designed so far are not so-effective at preserving texture details, especially in case of a complex background and large haze gradient image. Therefore, the exploration of new alternatives for designing an effective prior is desirable. Thus, in this research work, Gradient profile prior (GPP) is designed to evaluate depth map from hazy images. The transmission map is also improved by utilizing Guided anisotropic diffusion and iterative learning based image filter (GADILF). The restoration model is also improved to reduce the effect of pixels saturation and color distortion from restored images. Performance analysis demonstrates that GPP can naturally restore the hazy image especially at the edges of sudden changes in the obtained depth map. Through extensive analysis, it has been found that GPP based dehazing can effectively suppress visual artefacts for hazy images and yield high-quality results as compared to the competitive dehazing techniques both quantitatively and qualitatively. Moreover, the relatively high computational speed of the proposed technique will facilitate it in real-time applications.