Ali Dinler
Istanbul Medeniyet University
20 Papers
50 Citations
Ali Dinler is an academic researcher from Istanbul Medeniyet University. The author has contributed to research in topics: Nanopore & Curvature. The author has an hindex of 6, co-authored 20 publications. Previous affiliations of Ali Dinler include Daresbury Laboratory.
Chat about Author
Papers
Current status of wind energy forecasting and a hybrid method for hourly predictions
İnci Okumuş,Ali Dinler +1 more
TL;DR: In this article, the adaptive neuro-fuzzy inference system (ANFIS) and an artificial neural network (ANN) were combined for 1-h ahead wind speed forecasts, and the performance results showed the mean absolute percentage errors (MAPE) of 2.2598, 3.3530% and 3.8589% at three different locations for daily average wind speeds.
248
A new low-correlation MCP (measure-correlate-predict) method for wind energy forecasting
TL;DR: In this paper, a multiple principal least squares (MPLS) method was proposed for wind energy applications and tested using hourly wind data from four different regions, and the results show conclusively that the MPLS method is a strong competitor to the variance ratio method in the existence of concurrency.
33
Effect of Pore Geometry on Resistive-Pulse Sensing of DNA Using Track-Etched PET Nanopore Membrane
TL;DR: In this article, the effect of nanopore geometry on translocation properties of poly(ethylene terephthalate) (PET) membranes was investigated by adding different volume fractions of methanol to the alkali etching solution.
33
Reducing balancing cost of a wind power plant by deep learning in market data: A case study for Turkey
TL;DR: The present study initially casts the imbalance cost reducing problem as a binary classification problem and constructs a framework that consists of a long short term memory autoencoder and a blend of advanced classifiers that alters existing production forecasts and prevents abrupt rises in the imbalance costs.
15
Digital Breast Tomosynthesis imaging using compressed sensing based reconstruction for 10 radiation doses real data
Adem Polat,Adem Polat,Adem Polat,Nuno Matela,Ali Dinler,Yu Shrike Zhang,Isa Yildirim,Isa Yildirim +7 more
TL;DR: This work investigates if iterative reconstruction techniques applied without and with TV (ART and ART + TV3D) can help reduce the level of dose in DBT imaging and suggests that a compressed sensing based iterative reconstructing method (ART’+TV3d) could help decrease the radiation dose level that is one of the most critical limitations of DBT Imaging.
8