Evidence of power-law behavior in cognitive IoT applications

Bebortta, Sujit; Senapati, Dilip; Rajput, Nikhil Kumar; Singh, Amit Kumar; Rathi, Vipin Kumar; Pandey, Hari Mohan; Jaiswal, Amit Kumar; Qian, Jia; Tiwari, Prayag

doi:10.1007/s00521-020-04705-0

Cited by 31 publications

(9 citation statements)

References 40 publications

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“…The animal information is also helpful for the study of the welfare of the animals, and the concept of animal rights (Andersen and Winter, 2017). The technologies for the Internet of Things (Gochhayat et al, 2019;Kumar et al, 2019;Bebortta et al, 2020;Qian et al, 2020) are also widely used in different domains for understanding the functionality and structure of the brain and address some problems in human daily life. De Albuquerque et al (2017) investigate the applications of brain computer interface systems.…”

Section: Related Workmentioning

confidence: 99%

Species Classification for Neuroscience Literature Based on Span of Interest Using Sequence-to-Sequence Learning Model

Zhu

Zeng

Wang

et al. 2020

Front. Hum. Neurosci.

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Large-scale neuroscience literature call for effective methods to mine the knowledge from species perspective to link the brain and neuroscience communities, neurorobotics, computing devices, and AI research communities. Structured knowledge can motivate researchers to better understand the functionality and structure of the brain and link the related resources and components. However, the abstracts of massive scientific works do not explicitly mention the species. Therefore, in addition to dictionary-based methods, we need to mine species using cognitive computing models that are more like the human reading process, and these methods can take advantage of the rich information in the literature. We also enable the model to automatically distinguish whether the mentioned species is the main research subject. Distinguishing the two situations can generate value at different levels of knowledge management. We propose SpecExplorer project which is used to explore the knowledge associations of different species for brain and neuroscience. This project frees humans from the tedious task of classifying neuroscience literature by species. Species classification task belongs to the multi-label classification which is more complex than the single-label classification due to the correlation between labels. To resolve this problem, we present the sequence-to-sequence classification framework to adaptively assign multiple species to the literature. To model the structure information of documents, we propose the hierarchical attentive decoding (HAD) to extract span of interest (SOI) for predicting each species. We create three datasets from PubMed and PMC corpora. We present two versions of annotation criteria (mention-based annotation and semantic-based annotation) for species research. Experiments demonstrate that our approach achieves improvements in the final results. Finally, we perform species-based analysis of brain diseases, brain cognitive functions, and proteins related to the hippocampus and provide potential research directions for certain species.

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Section: Related Workmentioning

confidence: 99%

Species Classification for Neuroscience Literature Based on Span of Interest Using Sequence-to-Sequence Learning Model

Zhu

Zeng

Wang

et al. 2020

Front. Hum. Neurosci.

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“…A single evaluation index has a single connotation and does not reflect the operation of the whole system; therefore, a comprehensive assessment needs to be carried out [16][17][18]. The comprehensive assessment of power quality reflects the overall level of quality of electrical energy provided by the grid and essentially represents the level of operation of the entire grid structure as well as the power supply capacity of the power system [19][20][21]. Determining the market price of electric energy according to the quality of electric energy is an important trend in the current power supply market, so the quality of electric energy will be an important factor affecting the feed-in tariff [22][23].…”

Section: Introductionmentioning

confidence: 99%

Research on power quality assessment based on ubiquitous power IoT

Zhang¹,

Zhi²,

Zhao³

et al. 2023

Applied Mathematics and Nonlinear Sciences

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With the rapid development of the Internet in this era, the traditional power grid is no longer able to meet the growing demand of the people for electricity. In order to realize a new generation of power system with comprehensive sensing, reliable transmission, intelligent processing and interconnection, the State Grid proposes the strategic goal of developing ubiquitous power IoT. Firstly, the concept and basic architecture of ubiquitous power IoT are elaborated, and then the main key technologies of the architecture of power energy consumption monitoring and management system are explained. The power quality parameters are measured and calculated, and the G1 method, which is computationally small and does not require consistency testing, is used to determine the subjective weights in the power quality evaluation process, which can truly reflect the power quality level of power grids in different regions. The information entropy method is used to determine the objective weights in the process of power quality evaluation to solve the error of artificially determined weights, and the combination of subjective weights and objective weights can more truly reflect the influence of each index on the comprehensive level of power quality. The results of the study show that PV is rated between 2 and 3 good when grid-connected and off-grid, and wind turbines are rated between 3 and 4 medium when grid-connected and off-grid. The results of the integrated assessment levels are in line with the actual situation and can meet the growing demand for electricity.

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“…Multimodal medical image fusion (MMIF) has recently become a popular topic in the scientific community [ 1 , 2 ]. Multimodal medical image fusion refers to the fusing of two or more images derived from distinct imaging modalities.…”

Section: Introductionmentioning

confidence: 99%