LDAVPM: A Latch Design and Algorithm-Based Verification Protected Against Multiple-Node-Upsets in Harsh Radiation Environments

Yan, Aibin; Li, Zhixing; Cui, Jie; Huang, Zhengfeng; Ni, Tianming; Girard, Patrick; Wen, Xiaoqing

doi:10.1109/tcad.2022.3213212

Cited by 26 publications

(6 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This research introduces a multi-hop network model utilising the Hotspot Mitigated Prairie with a Genetic Algorithm (HM-PGA), which includes Sensor Nodes (SNs) with an energy harvesting feature and a single Base Station (BS) that provides an endless energy supply. This model supports efficient data routing and energy usage, with SNs functioning as data collectors and routers [51,52]. The data is routed using BS and sampled using SN, where these SNs act as routers.…”

Section: Network Modelmentioning

confidence: 99%

An effective hotspot mitigation system for Wireless Sensor Networks using hybridized prairie dog with Genetic Algorithm

Aalsalem

2024

PLoS ONE

View full text Add to dashboard Cite

Wireless Sensor Networks (WSNs) consist of small, multifunctional nodes distributed across various locations to monitor and record parameters. These nodes store data and transmit signals for further processing, forming a crucial topic of study. Monitoring the network’s status in WSN applications using clustering systems is essential. Collaboration among sensors from various domains enhances the precision of localised information reporting. However, nodes closer to the data sink consume more energy, leading to hotspot challenges. To address these challenges, this research employs clustering and optimised routing techniques. The aggregation of information involves creating clusters, further divided into sub-clusters. Each cluster includes a Cluster Head (CH) or Sensor Nodes (SN) without a CH. Clustering inherently optimises CHs’ capabilities, enhances network activity, and establishes a systematic network topology. This model accommodates both multi-hop and single-hop systems. This research focuses on selecting CHs using a Genetic Algorithm (GA), considering various factors. While GA possesses strong exploration capabilities, it requires effective management. This research uses Prairie Dog Optimization (PDO) to overcome this challenge. The proposed Hotspot Mitigated Prairie with Genetic Algorithm (HM-PGA) significantly improves WSN performance, particularly in hotspot avoidance. With HM-PGA, it achieves a network lifetime of 20913 milliseconds and 310 joules of remaining energy. Comparative analysis with existing techniques demonstrates the superiority of the proposed approach.

show abstract

Section: Network Modelmentioning

confidence: 99%

An effective hotspot mitigation system for Wireless Sensor Networks using hybridized prairie dog with Genetic Algorithm

Aalsalem

2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Machine learning and deep learning have been used in different domains for optimization, scheduling, etc. [32][33][34][35]. Even though several defect prognosticators exist in the literature, few comprehensive benchmarking studies exist.…”

Section: Related Workmentioning

confidence: 99%

Software Defect Prediction Using Dagging Meta-Learner-Based Classifiers

Babatunde,

Ogundokun,

Adeoye

et al. 2023

Mathematics

View full text Add to dashboard Cite

To guarantee that software does not fail, software quality assurance (SQA) teams play a critical part in the software development procedure. As a result, prioritizing SQA activities is a crucial stage in SQA. Software defect prediction (SDP) is a procedure for recognizing high-risk software components and determining the influence of software measurements on the likelihood of software modules failure. There is a continuous need for sophisticated and better SDP models. Therefore, this study proposed the use of dagging-based and baseline classifiers to predict software defects. The efficacy of the dagging-based SDP model for forecasting software defects was examined in this study. The models employed were naïve Bayes (NB), decision tree (DT), and k-nearest neighbor (kNN), and these models were used on nine NASA datasets. Findings from the experimental results indicated the superiority of SDP models based on dagging meta-learner. Dagging-based models significantly outperformed experimented baseline classifiers built on accuracy, the area under the curve (AUC), F-measure, and precision-recall curve (PRC) values. Specifically, dagging-based NB, DT, and kNN models had +6.62%, +3.26%, and +4.14% increments in average accuracy value over baseline NB, DT, and kNN models. Therefore, it can be concluded that the dagging meta-learner can advance the recognition performances of SDP methods and should be considered for SDP processes.

show abstract

“…Given the error caused by the SOC estimation algorithm and battery capacity that was regarded as constant, a modified fusion method with capacity correction is proposed, in which the initial SOC value of the battery is determined by open-circuit voltage and then corrected by correction factors that are related to the charge/discharge current, the Coulombic efficiency, and temperature, and obtained by the charge and discharge tests of the battery pack [22,23]. To accurately estimate the SOC during charging and discharging, the capacity correction of the fusion method should be defined as follows:…”

Section: Soc Capacity Correction Based On the Fusion Algorithmmentioning

confidence: 99%

ARWLS-AFEKE: SOC Estimation and Capacity Correction of Lithium Batteries Based on a Fusion Algorithm

Chen¹,

Zhang

Xin

et al. 2023

Processes

View full text Add to dashboard Cite

Accuracy of battery charge status (SOC) estimation plays a significant role in the management of electric vehicle power batteries. However, recently, abrupt changes from SOC data often occurs in the actual operation of electric vehicles and some errors appear in the establishment of battery models and noise models, which give rise to the poorly adaptive and robust performance of traditional algorithms in the process of SOC estimation. The fusion algorithm proposed in this paper can effectively improve the accuracy of models and SOC estimation of lithium-ion batteries. Based on the second-order R-C network model, this method optimizes the accuracy of parameter identification by adopting the adaptive recursive weighted least square algorithm (ARWLS). In addition, the adaptive fading extended Kalman filter algorithm (AFEKF) is applied to estimate the SOC of lithium-ion batteries. Additionally, via introducing a fading factor, the optimal Kalman gain is updated in real-time, which can reduce the impact of data mutation on battery modeling. Compared with the offline AEKF algorithm and the EKF algorithm, the adaptive recursive weighted least square-adaptive fading extended Kalman filter (ARWLS-AFEKF) fusion algorithm had higher accuracy and adaptability, which can be adapted to the variable noise environment.

show abstract

LDAVPM: A Latch Design and Algorithm-Based Verification Protected Against Multiple-Node-Upsets in Harsh Radiation Environments

Cited by 26 publications

References 20 publications

An effective hotspot mitigation system for Wireless Sensor Networks using hybridized prairie dog with Genetic Algorithm

An effective hotspot mitigation system for Wireless Sensor Networks using hybridized prairie dog with Genetic Algorithm

Software Defect Prediction Using Dagging Meta-Learner-Based Classifiers

ARWLS-AFEKE: SOC Estimation and Capacity Correction of Lithium Batteries Based on a Fusion Algorithm

Contact Info

Product

Resources

About