Analysis of the Cost Intensity of Transportation in the Polish Rescue Services System

Suchanek, Michał; Mytlewski, Adam

doi:10.1007/978-3-319-51427-7_11

Cited by 2 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The fitness measure is estimated for obtaining the best solution for the routing process, and it is computed using Equation (9).…”

Section: Fitness Functionmentioning

confidence: 99%

“…Thus, V2G efficiently obtains peak shaving, load flattening, as well as frequency regulation in the entire day 8 . The electric vehicle is utilized for transporting 9 energy from remote renewable sources to loads during the urgent requirement of power supply 10 …”

Section: Introductionmentioning

confidence: 99%

“…Thus, V2G efficiently obtains peak shaving, load flattening, as well as frequency regulation in the entire day. 8 The electric vehicle is utilized for transporting 9 energy from remote renewable sources to loads during the urgent requirement of power supply. 10 The charging station is mainly specified as a significant aspect for providing charging resources in the electric vehicle.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Hybrid optimization enabled multi‐aggregator‐based charge scheduling of electric vehicle in internet of electric vehicles

Suresh

Shobana²,

Ramya

et al. 2023

Concurrency and Computation

View full text Add to dashboard Cite

Summary In modern days, electric vehicles are quickly industrialized as well as their penetration is also increased highly, which brings more challenges for the power system. The electric vehicle charge scheduling process is vital to encourage the daily usage of the electric vehicle. However, irregular charging methods for electric vehicles may disturb voltage security areas because of their stochastic characteristics. Moreover, an electric vehicle requires recurrent charging owing to its constrained battery capacity, but it is a time‐consuming process. In this article, an effective charge scheduling model is devised using the fractional social sea lion optimization (Fr‐SSLO) algorithm. At first, IoEV network is simulated along with charge station and electric vehicle location. Furthermore, multi aggregator‐based charge scheduling is done for increasing the profit and amount of scheduled electric vehicles. Then, routing is performed based on developed Fr‐SSLO algorithm. Moreover, several fitness measures, including distance, energy and variable energy purchase are included. Here, the devised Fr‐SSLO model is designed by integrating fractional calculus (FC) and sea lion optimization (SLnO) technique along with SOA. After the completion of routing process, charge scheduling is performed based on developed Fr‐SSLO approach. Moreover, various fitness functions are also considered for computing better performance.

show abstract

“…The fitness measure is estimated for obtaining the best solution for the routing process, and it is computed using Equation (9).…”

Section: Fitness Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Hybrid optimization enabled multi‐aggregator‐based charge scheduling of electric vehicle in internet of electric vehicles

Suresh

Shobana²,

Ramya

et al. 2023

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…The major intention of the demosaicing model is for interpolating missing blue, green, and red pixels from present pixels, thus the reconstructed image is similar to the actual image as probable. Furthermore, the computational complexity of demosaicing requirements should be less for cost effectual in real‐world applications 1,3,4 . Demosaicing model is naturally underspecified, since there are no total RGB triples in an image to study the image details.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the computational complexity of demosaicing requirements should be less for cost effectual in real-world applications. 1,3,4 Demosaicing model is naturally underspecified, since there are no total RGB triples in an image to study the image details. The original image can be reconstructed based on the local coherence among channels.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of video demosaicing techniques using optimization algorithm

Alphonse

Sylam

2023

Concurrency and Computation

View full text Add to dashboard Cite

Demosaicing and denoising are the most significant steps in the imaging process for both images and videos. The correlation noise corrupts the image quality, such that an accurate interpretation of the image is avoided. Thus, demosaicing is utilized to restructure the full-color image from imperfect color samples. In this article, cat shuffled shepherd optimization-based local polynomial approximation and intersection of confidence intervals (Cat SSO-based LPA-ICI) approach is developed for video demosaicing. The LPA-ICI model is employed for frame demosaicing in which the second-order polynomial interpolation filter coefficients are produced using the devised optimization algorithm, named cat SSO technique. Similarly, generative adversarial network classifier is also applied for frame demosaicing. In addition, initialization and flow estimation is performed for the demosaicing process. Besides, spatio temporal demosaicing model is utilized in order to get a noise-free sequence. However, the developed cat SSO technique is newly devised by the incorporation of the shuffled shepherd optimization algorithm and cat swarm optimization technique. The created video demosaicing model outperformed with peak signal-to-noise ratio, root mean square error, and second derivative-like measure of enhancement values of 56.85 dB, 0.46, and 60.62, respectively.

show abstract

Analysis of the Cost Intensity of Transportation in the Polish Rescue Services System

Cited by 2 publications

References 0 publications

Hybrid optimization enabled multi‐aggregator‐based charge scheduling of electric vehicle in internet of electric vehicles

Hybrid optimization enabled multi‐aggregator‐based charge scheduling of electric vehicle in internet of electric vehicles

Enhancement of video demosaicing techniques using optimization algorithm

Contact Info

Product

Resources

About