Incremental Granular Model Improvement Using Particle Swarm Optimization

Yeom, Chan-Uk; Kwak, Keun-Chang

doi:10.3390/sym11030390

Cited by 6 publications

(6 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [ 143 ], the authors recommend an ILP-based demand management system to raise the load for end-users in smart grids to account for the electrically moved equipment and the displacement over time. To put more pressure on smart grids for users to move the electrically delivered equipment and the equipment over time, the authors propose a demand management program employing ILP [ 144 ]. Smart meters, appliance interfaces and home appliances are the network’s three main building blocks.…”

Section: Blockchain Application In Sg 20mentioning

confidence: 99%

“…Smart meters are crucial to the proposed system because they allow interface-based client data collection from devices and usage plans. The smart meter was created utilizing the data optimization method, according to the authors’ statement in [ 144 ]. The three main elements of the proposed network are smart meters, appliance interfaces and home appliances.…”

Section: Blockchain Application In Sg 20mentioning

confidence: 99%

See 1 more Smart Citation

Energy Internet Opportunities in Distributed Peer-to-Peer Energy Trading Reveal by Blockchain for Future Smart Grid 2.0

Zafar

Sami

2022

Sensors

View full text Add to dashboard Cite

The Energy Internet (EI) and Smart Grid 2.0 (SG 2.0) concepts are potential challenges in industry and research. The purpose of SG 2.0 and EI is to automate innovative power grid operations. To move from Distribution Network Operators (DSO) to consumer-centric distributed power grid management, the blockchain and smart contracts are applicable. Blockchain technology and integrated SGs will present challenges, limiting the deployment of Distributed Energy Resources (DERs). This review looks at the decentralization of the Smart Grid 2.0 using blockchain technology. Energy trading has increased due to access to distributed energy sources and electricity producers who can financially export surplus fuels. The energy trading system successfully combines energy from multiple sources to ensure consistent and optimal use of available resources and better facilities for energy users. Peer-to-peer (P2P) energy trading is a common field of study that presents some administrative and technical difficulties. This article provides a general overview of P2P energy exchange. It discusses how blockchain can improve transparency and overall performance, including the degree of decentralization, scalability, and device reliability. The research is extended to examine unresolved issues and potential directions for P2P blockchain-based energy sharing in the future. In fact, this paper also demonstrates the importance of blockchain in future smart grid activities and its blockchain-based applications. The study also briefly examines the issues associated with blockchain integration, ensuring the decentralized, secure and scalable operation of autonomous electric grids in the future.

show abstract

Section: Blockchain Application In Sg 20mentioning

confidence: 99%

Section: Blockchain Application In Sg 20mentioning

confidence: 99%

Energy Internet Opportunities in Distributed Peer-to-Peer Energy Trading Reveal by Blockchain for Future Smart Grid 2.0

Zafar

Sami

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Particle swarm optimization algorithm (PSO) has a fast convergence speed and is less affected by the change of dimension. Therefore, we use PSO to iteratively update our position by tracking the optimal position of fitness in the position experienced by the individual and the optimal position of fitness searched by all the particles in the population, that is the individual extremums P k z,i,d and the group extremums P k g,i,d [23]. Among them, the velocity of the ith particle in the d dimensional space is updated iteratively as follows:…”

Section: B Optimal Selection Of Parameters By Psomentioning

confidence: 99%

Research on Detection Method of Small Size Weld Bead Defects Based on Reluctance Measurement

Wang

2019

IEEE Access

View full text Add to dashboard Cite

To achieve non-destructive testing for small size weld beads of metal workpieces, we developed a new testing method of closed magnetic circuit reluctance measurement. According to the shape and volume of the small size weld defects of the metal workpiece, a reluctance detection model was established in the detection environment of alternating magnetic field. The relationship between different weld defects and closed magnetic circuit reluctance was quantitatively analyzed to achieve the identification of weld defects. A particle swarm optimization algorithm (PSO) was applied to optimize the cost-sensitive support vector machine (CS-SVM), which effectively reduced the coupling errors caused by the limitation of the workpiece coupling size. This new method was used to verify the weld bead detection of representative carbide saw blades. Compared with the basic support vector machine, the improved cost-sensitive support vector machine has better performance in the classification of unbalanced samples. The experimental results showed this new method can detect the weld bead of carbide saw blade with the correct rate to 98.2%. It reduced the interference of coupling error effectively. The improved cost-sensitive support vector machine not only improved the detection accuracy, but also avoided the possibility that the defective weld workpiece samples are misclassified into qualified workpieces. This study provides a guarantee for safe production and has great significance in engineering applications. The new method provides an effective solution for the application of reluctance testing technology in small size weld bead detection. INDEX TERMS Closed magnetic circuit, cost-sensitive support vector machine, coupling error, particle swarm optimization, reluctance measurement, small size weld bead.

show abstract

“…PSO uses groups of individuals (called particles) to perform searches as with evolutionary algorithms, and particles can be updated from each iteration to the other [26][27][28][29][30]. In order to find the optimal solution, each particle changes its search direction based on two factors: its best previous location (p best ) and all other members' best locations (g best ) [31][32][33][34]. Shi et al called p best the cognitive part and g best the social part [35].…”

Section: Introductionmentioning

confidence: 99%

Incorporating Particle Swarm Optimization into Improved Bacterial Foraging Optimization Algorithm Applied to Classify Imbalanced Data

Lee

et al. 2020

Symmetry

View full text Add to dashboard Cite

In this paper, particle swarm optimization is incorporated into an improved bacterial foraging optimization algorithm, which is applied to classifying imbalanced data to solve the problem of how original bacterial foraging optimization easily falls into local optimization. In this study, the borderline synthetic minority oversampling technique (Borderline-SMOTE) and Tomek link are used to pre-process imbalanced data. Then, the proposed algorithm is used to classify the imbalanced data. In the proposed algorithm, firstly, the chemotaxis process is improved. The particle swarm optimization (PSO) algorithm is used to search first and then treat the result as bacteria, improving the global searching ability of bacterial foraging optimization (BFO). Secondly, the reproduction operation is improved and the selection standard of survival of the cost is improved. Finally, we improve elimination and dispersal operation, and the population evolution factor is introduced to prevent the population from stagnating and falling into a local optimum. In this paper, three data sets are used to test the performance of the proposed algorithm. The simulation results show that the classification accuracy of the proposed algorithm is better than the existing approaches.

show abstract

Incremental Granular Model Improvement Using Particle Swarm Optimization

Cited by 6 publications

References 48 publications

Energy Internet Opportunities in Distributed Peer-to-Peer Energy Trading Reveal by Blockchain for Future Smart Grid 2.0

Energy Internet Opportunities in Distributed Peer-to-Peer Energy Trading Reveal by Blockchain for Future Smart Grid 2.0

Research on Detection Method of Small Size Weld Bead Defects Based on Reluctance Measurement

Incorporating Particle Swarm Optimization into Improved Bacterial Foraging Optimization Algorithm Applied to Classify Imbalanced Data

Contact Info

Product

Resources

About