FI-NPI: Exploring Optimal Control in Parallel Platform Systems

Wang, Ruiyang; Gu, Qiuxiang; Lu, Siyu; Tian, Jiawei; Yin, Zhengtong; Yin, Lirong; Zheng, Wenfeng

doi:10.3390/electronics13071168

Cited by 29 publications

(2 citation statements)

References 33 publications

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“…This dataset includes all-inclusive real-world and re-identification data to reduce the problem complexity. The UFPR-ALPR [65,66] dataset includes 45,000 images that are completely annotated in real-world scenarios, with pixel sizes of 1920 × 1080, taken from go pro hero 4 silver, Huawei P9 Lite, and iPhone 7 plus equipment. The labels of the portrayed attributes include the manufacturer, models and years of the cars and motorcycles, as well as the position, identification, and characters of the license plates (LP) of the vehicles.…”

Section: Hardware Experimentationmentioning

confidence: 99%

An Efficient Real-Time Vehicle Classification from a Complex Image Dataset Using eXtreme Gradient Boosting and the Multi-Objective Genetic Algorithm

Mani,

Rakkiya Goundar Komarasamy,

Rajamanickam

et al. 2024

Processes

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Recent advancements in image processing and machine-learning technologies have significantly improved vehicle monitoring and identification in road transportation systems. Vehicle classification (VC) is essential for effective monitoring and identification within large datasets. Detecting and classifying vehicles from surveillance videos into various categories is a complex challenge in current information acquisition and self-processing technology. In this paper, we implement a dual-phase procedure for vehicle selection by merging eXtreme Gradient Boosting (XGBoost) and the Multi-Objective Optimization Genetic Algorithm (Mob-GA) for VC in vehicle image datasets. In the initial phase, vehicle images are aligned using XGBoost to effectively eliminate insignificant images. In the final phase, the hybrid form of XGBoost and Mob-GA provides optimal vehicle classification with a pioneering attribute-selection technique applied by a prominent classifier on 10 publicly accessible vehicle datasets. Extensive experiments on publicly available large vehicle datasets have been conducted to demonstrate and compare the proposed approach. The experimental analysis was carried out using a myRIO FPGA board and HUSKY Lens for real-time measurements, achieving a faster execution time of 0.16 ns. The investigation results show that this hybrid algorithm offers improved evaluation measures compared to using XGBoost and Mob-GA individually for vehicle classification.

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Section: Hardware Experimentationmentioning

confidence: 99%

An Efficient Real-Time Vehicle Classification from a Complex Image Dataset Using eXtreme Gradient Boosting and the Multi-Objective Genetic Algorithm

Mani,

Rakkiya Goundar Komarasamy,

Rajamanickam

et al. 2024

Processes

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“…One of the major issues in PFC circuits is the development of controller even more robust to load disturbance, and output voltage variations 13 . Literature regarding active power factor correction has introduced many modern control theories focused on achieving high performance against various uncertainties and parameters variations.…”

Section: Introductionmentioning

confidence: 99%

A dSPACE-based implementation of ANFIS and predictive current control for a single phase boost power factor corrector

Babes,

Latrèche,

Bouafassa

et al. 2024

Sci Rep

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This paper presents an innovative control scheme designed to significantly enhance the power factor of AC/DC boost rectifiers by integrating an adaptive neuro-fuzzy inference system (ANFIS) with predictive current control. The innovative control strategy addresses key challenges in power quality and energy efficiency, demonstrating exceptional performance under diverse operating conditions. Through rigorous simulation, the proposed system achieves precise input current shaping, resulting in a remarkably low total harmonic distortion (THD) of 3.5%, which is well below the IEEE-519 standard threshold of 5%. Moreover, the power factor reaches an outstanding 0.990, indicating highly efficient energy utilization and near-unity power factor operation. To validate the theoretical findings, a 500 W laboratory prototype was implemented using the dSPACE ds1104 digital controller. Steady-state analysis reveals sinusoidal input currents with minimal THD and a power factor approaching unity, thereby enhancing grid stability and energy efficiency. Transient response tests further demonstrate the system’s robustness against load and voltage fluctuations, maintaining output voltage stability within an 18 V overshoot and a 20 V undershoot during load changes, and achieving rapid response times as low as 0.2 s. Comparative evaluations against conventional methods underscore the superiority of the proposed control strategy in terms of both performance and implementation simplicity. By harnessing the strengths of ANFIS-based voltage regulation and predictive current control, this scheme offers a robust solution to power quality issues in AC/DC boost rectifiers, promising substantial energy savings and improved grid stability. The results affirm the potential of the proposed system to set new benchmarks in power factor correction technology.

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Energy‐efficient mechanisms for sustainable operations in vehicular fog networks: Challenges, state‐of‐the‐art, and future directions

Jiang

2024

Int J Communication

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SummaryVehicular fog networks (VFNs) improve vehicular communications by incorporating fog computing into the existing vehicular infrastructure. The integration of fog computing and vehicular communication results in the development of a diverse range of novel applications and services, including intelligent transportation systems and real‐time data analytics. This paradigm has evolved as an innovative strategy that deals with the load on cloudlet nodes, improves service response time during high‐demand periods, and saves energy in battery‐powered devices. VFN is realized by offloading user workloads to vehicular devices, taking advantage of the underutilized computational power of nearby vehicles. While VFN holds great promise, its widespread adoption faces many challenges, including ineffective energy‐latency tradeoff mechanisms and optimal resource allocation strategies. This study provides a thorough analysis that explores the challenges, examines current approaches, and suggests ways to improve the tradeoffs between energy and latency, thereby optimizing network resources. The study offers constructive recommendations for enhancing the development and establishing more robust VFNs.

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FI-NPI: Exploring Optimal Control in Parallel Platform Systems

Cited by 29 publications

References 33 publications

An Efficient Real-Time Vehicle Classification from a Complex Image Dataset Using eXtreme Gradient Boosting and the Multi-Objective Genetic Algorithm

An Efficient Real-Time Vehicle Classification from a Complex Image Dataset Using eXtreme Gradient Boosting and the Multi-Objective Genetic Algorithm

A dSPACE-based implementation of ANFIS and predictive current control for a single phase boost power factor corrector

Energy‐efficient mechanisms for sustainable operations in vehicular fog networks: Challenges, state‐of‐the‐art, and future directions

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