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公开(公告)号：EP4428866A1
公开(公告)日：2024-09-11
申请号：EP22886869.1
申请日：2022-10-20
摘要： A synthetic material selection method according to the present disclosure performs material selection for selecting synthesis target materials based on material physical property information and execution possibility information of a database, instructs a control device to perform synthesis processing for the selected materials, and updates the execution possibility information of the database based on a result of the synthesis processing from the control device.
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2.发明公开
公开(公告)号：EP4394782A1
公开(公告)日：2024-07-03
申请号：EP23204948.6
申请日：2023-10-20
IPC分类号： G16C60/00
摘要： State of the art mechanisms for edible food coating design have the disadvantage that they fail to accommodate various factors such as type of compounds, chemical composition, and processing parameters such as pH, drying rate, temperature affect the nano-microstructure of the film. The disclosure herein generally relates to edible food coating, and, more particularly, to a method and system for developing and characterizing edible films using molecular dynamic simulations. The molecular dynamic simulations help design the edible food coatings, which may be further used for practical applications.
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3.发明公开
公开(公告)号：EP4364156A1
公开(公告)日：2024-05-08
申请号：EP22737619.1
申请日：2022-06-23
申请人： Norsk Hydro ASA
发明人： FURU, Trond , MYHR, Ole Runar
IPC分类号： G16C60/00
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公开(公告)号：EP4434041A1
公开(公告)日：2024-09-25
申请号：EP22809227.6
申请日：2022-11-17
IPC分类号： G16C60/00 , G06F30/367
CPC分类号： G16C60/00 , H10K71/00 , G06F30/367
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公开(公告)号：EP4399713A1
公开(公告)日：2024-07-17
申请号：EP22785955.0
申请日：2022-09-12
申请人： Firmenich SA
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公开(公告)号：EP4396829A1
公开(公告)日：2024-07-10
申请号：EP21773521.6
申请日：2021-09-04
申请人： CINCAP GmbH
发明人： PUCHKOV, Maxim , SIVARAMAN, Guru , BERTOSSA, Carlo
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公开(公告)号：EP4381515A1
公开(公告)日：2024-06-12
申请号：EP22757660.0
申请日：2022-08-03
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公开(公告)号：EP4411602A1
公开(公告)日：2024-08-07
申请号：EP22875790.2
申请日：2022-09-12
申请人： Resonac Corporation
摘要： Prediction accuracy of trained prediction models is improved by setting an appropriate weight using a trained clustering model and classified clusters. A method of generating a model for predicting a material characteristic includes a step of acquiring a training dataset, a step of generating a trained clustering model using the training dataset and a clustering model, and classifying the training dataset into N clusters, a step of calculating a distance between centroids of the clusters, a step of calculating a weight between the clusters using the distance between the centroids of the clusters and a parameter representing a feature of the training dataset, and a step of generating, for the N clusters, respective trained prediction models {Mi} 1 ≤ i ≤ N using the clusters and the weight.
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10.发明公开
公开(公告)号：EP4394783A1
公开(公告)日：2024-07-03
申请号：EP23219067.8
申请日：2023-12-21
发明人： PILLAI, Prajith , PAL, Parama
IPC分类号： G16C60/00
CPC分类号： G16C60/00 , G16C20/70 , G06N3/0455
摘要： Currently approaches for inversely obtaining plasmonic meta unit dimensions for given structural color require a considerably large dataset for inverse prediction. Further, they also suffer from low accuracy in regions corresponding to x and y coordinates proximal to the 45-degree axis in a CIE 1931 colour space i.e., close to the white point. Present disclosure provides method and system for inversely predicting nanofeatures of plasmonic metasurface using remodeled variational autoencoder. The system uses a remodeled variational autoencoder (VAE) network that inversely predicts plasmonic metasurface nanofeatures while providing diversity as well as prediction accuracy. The remodeled VAE network has a probabilistic encoder that maps chromaticity coordinates to a latent space and a probabilistic decoder that maps this latent space back to the coordinates. Further, a multi-layer perceptron layer is added to the latent space that considers the reconstruction as well as dimensional prediction, thereby making training of the network versatile.
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