Recognition of autism spectrum disorder in children based on electroencephalogram network topology

Li, Fali; Zhang, Shu; Jiang, Lin; Duan, Keyi; Feng, Rui; Zhang, Yingli; Zhang, Gao; Zhang, Yangsong; Li, Peiyang; Yao, Dezhong; Xie, Jin; Xu, Wei; Xu, Peng

doi:10.1007/s11571-023-09962-y

Cited by 1 publication

(1 citation statement)

References 51 publications

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“…Conversely, with a longer time series length, the model error decreases more rapidly, reaching an optimal solution in as few as 50 iterations. Upon comparing the algorithm introduced in this article with the recent study conducted by Li et al (2023) , it is evident that both algorithms exhibit a comparable number of iterations. This similarity in iteration counts indicates that the algorithm presented in this article efficiently attains an optimal solution, aligning with the observations made in the study by Li et al (2023) .…”

Section: Case Analysismentioning

confidence: 89%

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: Case Analysismentioning

confidence: 89%