Arctangent entropy: a new fast threshold segmentation entropy for light colored character image on semiconductor chip surface

Liu, Jianxun; Shi, Jinfei; Hao, Fei; Dai, Min; Zhang, Zhisheng

doi:10.1007/s10044-022-01079-y

Cited by 2 publications

(1 citation statement)

References 25 publications

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“…However, the traditional arctangent function has a large steady-state error and weak anti-interference ability [33]. Most importantly, it ignores the amplification of small signals.…”

Section: Incremental Parameter Depth-value Calculationmentioning

confidence: 99%

A Federated Learning Framework Based on Incremental Weighting and Diversity Selection for Internet of Vehicles

et al. 2022

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With the rapid increase of data, centralized machine learning can no longer meet the application requirements of the Internet of Vehicles (IoV). On the one hand, both car owners and regulators pay more attention to data privacy and are unwilling to share data, which forms the isolated data island challenge. On the other hand, the incremental data generated in IoV are massive and diverse. All these issues have brought challenges of data increment and data diversity. The current common federated learning or incremental learning frameworks cannot effectively integrate incremental data with existing machine learning (ML) models. Therefore, this paper proposes a Federated Learning Framework Based on Incremental Weighting and Diversity Selection for IoV (Fed-IW&DS). In Fed-IW&DS, a vehicle diversity selection algorithm was proposed, which uses a variety of performance indicators to calculate diversity scores, effectively reducing homogeneous computing. Also, it proposes a vehicle federated incremental algorithm that uses an improved arctangent curve as the decay function, to realize the rapid fusion of incremental data with existing ML models. Moreover, we have carried out several sets of experiments to test the validity of the proposed Fed-IW&DS framework’s performance. The experimental results show that, under the same global communication round and similar computing time, the Fed-IW&DS framework has significantly improved performance in all aspects compared to the frameworks FED-AVG, FED-SGD, FED-prox & the decay functions linear, square curve and arc tangent. Specifically, the Fed-IW&DS framework improves the Acc (accuracy), loss (loss), and Matthews correlation coefficient (MCC) by approximately 32%, 83%, and 66%, respectively. This result shows that Fed-IW&DS is a more reliable solution than the common frameworks of federated learning, and it can effectively deal with the dynamic incremental data in the IoV scenario. Our findings should make a significant contribution to the field of federated learning.

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“…However, the traditional arctangent function has a large steady-state error and weak anti-interference ability [33]. Most importantly, it ignores the amplification of small signals.…”

Section: Incremental Parameter Depth-value Calculationmentioning

confidence: 99%