IMPACT OF GRAPE POMACE MACERATION ON THE QUALITY OF RHINE RIESLING WINE

Vina Rajnskog rizlinga proizvedena standardnom tehnologijom za bijela vina te maceracijom masulja od 3 sata i maceracijom masulja od 6 sati analizirana su kako bi se utvrdile razlike u kemijskom sastavu i senzornim svojstvima. Kemijska analiza provedena je nakon fermentacije, a senzorno ocjenjivanje 3 mjeseca nakon fermentacije. Vina dobivena maceracijom masulja od 3 sata senzorno su ocijenjena najbolje dok su vina dobivena maceracijom od 6 sati te standardnom tehnologijom bila slabije kakvoće. Po kemijskom sastavu vina dobivena maceracijom masulja nisu se međusobno razlikovala, dok su vina dobivena standardnim postupkom proizvodnje za bijela vina imala niži ukupni ekstrakt, pepeo, hlapivu kiselost i ukupne fenole.Rhine riesling wines produced by usual technology for white wines and by maceration for 3 hours and maceration for 6 hours were investigated for differences in chemical composition and sensory properties. Chemical analyses were performed after fermentation and sensory testing was done 3 months after fermentation. Wine produced by maceration for 3 hours was evaluated as sensory the best while the wine produced by maceration for 6 hours and usual technology was of inferior quality. In chemical composition there was no difference between wine produced by maceration of grape pomace while wines produced by standard technology for white wine had lower total extract, ashes, volatyle acidity and total phenols

Genetic Algorithm to Evolve Ensembles of Rules for On-Line Scheduling on Single Machine with Variable Capacity

International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC (8th . 2019. Almería, Spain

Predictive and generative machine learning models for photonic crystals

The prediction and design of photonic features have traditionally been guided by theory-driven computational methods, spanning a wide range of direct solvers and optimization techniques. Motivated by enormous advances in the field of machine learning, there has recently been a growing interest in developing complementary data-driven methods for photonics. Here, we demonstrate several predictive and generative data-driven approaches for the characterization and inverse design of photonic crystals. Concretely, we built a data set of 20,000 two-dimensional photonic crystal unit cells and their associated band structures, enabling the training of supervised learning models. Using these data set, we demonstrate a high-accuracy convolutional neural network for band structure prediction, with orders-of-magnitude speedup compared to conventional theory-driven solvers. Separately, we demonstrate an approach to high-throughput inverse design of photonic crystals via generative adversarial networks, with the design goal of substantial transverse-magnetic band gaps. Our work highlights photonic crystals as a natural application domain and test bed for the development of data-driven tools in photonics and the natural sciences. </p

Predictive and generative machine learning models for photonic crystals

© 2020 Thomas Christensen et al., published by De Gruyter, Berlin/Boston 2020. The prediction and design of photonic features have traditionally been guided by theory-driven computational methods, spanning a wide range of direct solvers and optimization techniques. Motivated by enormous advances in the field of machine learning, there has recently been a growing interest in developing complementary data-driven methods for photonics. Here, we demonstrate several predictive and generative data-driven approaches for the characterization and inverse design of photonic crystals. Concretely, we built a data set of 20,000 two-dimensional photonic crystal unit cells and their associated band structures, enabling the training of supervised learning models. Using these data set, we demonstrate a high-accuracy convolutional neural network for band structure prediction, with orders-of-magnitude speedup compared to conventional theory-driven solvers. Separately, we demonstrate an approach to high-throughput inverse design of photonic crystals via generative adversarial networks, with the design goal of substantial transverse-magnetic band gaps. Our work highlights photonic crystals as a natural application domain and test bed for the development of data-driven tools in photonics and the natural sciences

Predictive and generative machine learning models for photonic crystals

The prediction and design of photonic features have traditionally been guided by theory-driven computational methods, spanning a wide range of direct solvers and optimization techniques. Motivated by enormous advances in the field of machine learning, there has recently been a growing interest in developing complementary data-driven methods for photonics. Here, we demonstrate several predictive and generative data-driven approaches for the characterization and inverse design of photonic crystals. Concretely, we built a data set of 20,000 two-dimensional photonic crystal unit cells and their associated band structures, enabling the training of supervised learning models. Using these data set, we demonstrate a high-accuracy convolutional neural network for band structure prediction, with orders-of-magnitude speedup compared to conventional theory-driven solvers. Separately, we demonstrate an approach to high-throughput inverse design of photonic crystals via generative adversarial networks, with the design goal of substantial transverse-magnetic band gaps. Our work highlights photonic crystals as a natural application domain and test bed for the development of data-driven tools in photonics and the natural sciences

Predictive and generative machine learning models for photonic crystals

The prediction and design of photonic features have traditionally been guided by theory-driven computational methods, spanning a wide range of direct solvers and optimization techniques. Motivated by enormous advances in the field of machine learning, there has recently been a growing interest in developing complementary data-driven methods for photonics. Here, we demonstrate several predictive and generative data-driven approaches for the characterization and inverse design of photonic crystals. Concretely, we built a data set of 20,000 two-dimensional photonic crystal unit cells and their associated band structures, enabling the training of supervised learning models. Using these data set, we demonstrate a high-accuracy convolutional neural network for band structure prediction, with orders-of-magnitude speedup compared to conventional theory-driven solvers. Separately, we demonstrate an approach to high-throughput inverse design of photonic crystals via generative adversarial networks, with the design goal of substantial transverse-magnetic band gaps. Our work highlights photonic crystals as a natural application domain and test bed for the development of data-driven tools in photonics and the natural sciences. Cyber Securit