On a multisensor knowledge fusion heuristic for the Internet of Things

Martins, Gabriel; Souza, Sergio Guedes de; Santos, Igor Leão dos; Pìrmez, Luci; Farias, Claudio M. de

doi:10.1016/j.comcom.2021.04.025

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…Figure 3 comprises four pivotal modules: perception and identification technology, network and communication technology ( Yang & Wang, 2021 ; Martins et al, 2021 ), computing and service technology, and management and support technology ( Chen et al, 2021 ; Paudel & Neupane, 2021 ). These modules assume critical roles in the system’s functionality and operational dynamics.…”

Section: Substation Topology Identification and Piot Technologymentioning

confidence: 99%

Identifying topology of distribution substation in power Internet of Things using dynamic voltage load fluctuation flow analysis

Xu,

Lv,

Wang

et al. 2024

PeerJ Computer Science

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At present, the reconfiguration, maintenance, and review of power lines play a pivotal role in maintaining the stability of electrical grid operations and ensuring the accuracy of electrical energy measurements. These essential tasks not only guarantee the uninterrupted functioning of the power system, thereby improving the reliability of the electricity supply but also facilitate precise electricity consumption measurement. In view of these considerations, this article endeavors to address the challenges posed by power line restructuring, maintenance, and inspections on the stability of power grid operations and the accuracy of energy metering. To accomplish this goal, this article introduces an enhanced methodology based on the hidden Markov model (HMM) for identifying the topology of distribution substations. This approach involves a thorough analysis of the characteristic topology structures found in low-voltage distribution network (LVDN) substations. A topology identification model is also developed for LVDN substations by leveraging time series data of electricity consumption measurements and adhering to the principles of energy conservation. The HMM is employed to streamline the dimensionality of the electricity consumption data matrix, thereby transforming the topology identification challenge of LVDN substations into a solvable convex optimization problem. Experimental results substantiate the effectiveness of the proposed model, with convergence to minimal error achieved after a mere 50 iterations for long time series data. Notably, the method attains an impressive discriminative accuracy of 0.9 while incurring only a modest increase in computational time, requiring a mere 35.1 milliseconds. By comparison, the full-day data analysis method exhibits the shortest computational time at 16.1 milliseconds but falls short of achieving the desired accuracy level of 0.9. Meanwhile, the sliding time window analysis method achieves the highest accuracy of 0.95 but at the cost of a 50-fold increase in computational time compared to the proposed method. Furthermore, the algorithm reported here excels in terms of energy efficiency (0.89) and load balancing (0.85). In summary, the proposed methodology outperforms alternative approaches across a spectrum of performance metrics. This article delivers valuable insights to the industry by fortifying the stability of power grid operations and elevating the precision of energy metering. The proposed approach serves as an effective solution to the challenges entailed by power line restructuring, maintenance, and inspections.

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Section: Substation Topology Identification and Piot Technologymentioning

confidence: 99%

Identifying topology of distribution substation in power Internet of Things using dynamic voltage load fluctuation flow analysis

Xu,

Lv,

Wang

et al. 2024

PeerJ Computer Science

View full text Add to dashboard Cite

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“…It is emergent to find a method for generating new datasets for tackling this challenge. Multi-sensor information fusion [41][42][43][44][45][46][47][48][49][50] analyzes and processes the multi-source information collected by sensors and combines them. The combination of multi-source information can be automatically or semi-automatically carried out [51][52][53][54].…”

Section: Introductionmentioning

confidence: 99%

Broad Learning System for Tackling Emerging Challenges in Face Recognition

Zhang¹,

Wang²

2023

Computer Modeling in Engineering &Amp; Sciences

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Face recognition has been rapidly developed and widely used. However, there is still considerable uncertainty in the computational intelligence based on human-centric visual understanding. Emerging challenges for face recognition are resulted from information loss. This study aims to tackle these challenges with a broad learning system (BLS). We integrated two models, IR 3 C with BLS and IR 3 C with a triplet loss, to control the learning process. In our experiments, we used different strategies to generate more challenging datasets and analyzed the competitiveness, sensitivity, and practicability of the proposed two models. In the model of IR 3 C with BLS, the recognition rates for the four challenging strategies are all 100%. In the model of IR 3 C with a triplet loss, the recognition rates are 94.61%, 94.61%, 96.95%, 96.23%, respectively. The experiment results indicate that the proposed two models can achieve a good performance in tackling the considered information loss challenges from face recognition.

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