Abstract: A material design support apparatus for supporting optimization of a design condition for a material, the material design support apparatus including: a design condition setting unit configured to set a range of a design condition for a material; a required characteristic setting unit configured to set a range of a required characteristic of the material; an exhaustive prediction point generation unit configured to generate a plurality of exhaustive prediction points within the range of the design condition; a prediction unit configured to input the exhaustive prediction points into a trained model in which a correspondence between a design condition for the material and a characteristic value of the material is learned, thereby to predict a characteristic value of the material; and a design condition adjustment unit configured to adjust a range of the design condition for the material in which a plurality of exhaustive prediction points are subsequently generated.

Abstract: An evaluation apparatus, an evaluation method, and an evaluation program applicable to a quality evaluation of sintered bodies are provided. The evaluation apparatus includes: an acquisition unit configured to acquire a cross-sectional image obtained by photographing a cross-section of a sintered body group stained with a staining solution; a generation unit configured to extract a saturation component for each of sintered bodies from the cross-sectional image, thereby to generate a saturation component image; and a visualization unit configured to visualize the saturation component image.

Abstract: Technical knowledge which is difficult for the expert to express in words or numerical form is predicted and reproduced. A technical knowledge prediction apparatus according to one embodiment of the present invention includes a problem setting unit configured to automatically create and set a problem which includes a control factor of a manufacturing step, and a variable of the control factor, an answer acquiring unit configured to acquire an answer with respect to the problem and indicating a decision of the manufacturing step, a query acquiring unit configured to acquire a query, and a prediction output unit configured to predict and output an answer corresponding to the query acquired by the query acquiring unit, based on a corresponding relationship between the problem and the answer.

Abstract: A physical property prediction device for predicting a physical property of a compound including: a generation unit configured to generate a first stage trained model, by using, as training data, first stage synthesis information and synthesis result information; a generation unit configured to generate second stage through last stage trained models, by using, as training data, n-th stage (n?2) synthesis information, n-th stage synthesis result information, and n?1-th stage synthesis result information; a reception unit configured to receive a synthesis information setting for the compound for which the physical property is predicted; a prediction unit configured to predict a physical property value of a product synthesized by the first stage chemical reaction of the compound; a prediction unit configured to repeat, from a second stage chemical reaction to a last stage chemical reaction of the compound, a process of predicting a physical property value of a product synthesized by the n-th stage chemical react

Abstract: Prediction accuracy is increased in a prediction device using a trained model. A prediction device includes: a first trained model and a second trained model configured to respectively output first output data and second output data in response to input of input data of a prediction target; and an output portion configured to obtain the first output data and the second output data and calculate a weighted average value or take a weighted majority, thereby outputting prediction data. The first trained model is configured such that prediction accuracy for the input data of an interpolation region becomes higher than in the second trained model. The second trained model is configured such that prediction accuracy for the input data of an extrapolation region becomes higher than in the first trained model.

Abstract: Model setting step sets a trained model acquired by machine learning of a correspondence relationship between an explanatory variable including information related to a material composition or a manufacturing condition of a target material, and an objective variable including information related to the material characteristics of the target material, and prediction step inputs an explanatory variable related to a target material whose material characteristics is to be predicted to the trained model set in the model setting step, and outputs an objective variable related to information of the explanatory variable, so as to predict the material characteristics of the target material to be predicted based on the objective variable.

Abstract: Information related to a factor affecting a lifetime characteristic of a battery is presented. The analyzing device includes an acquiring unit configured to acquire lifetime data from cycle measurement data of a target battery, a calculating unit configured to calculate, by factoring a relationship between a voltage and a current capacity calculated from the cycle measurement data of the target battery, factor intensity transition data indicating a change in intensity of each factor affecting the current capacity and factor data indicating a relationship between a voltage and a current capacity of each factor, and an output unit configured to output the lifetime data, the factor data, and the factor intensity transition data.

Abstract: Model setting step sets a trained model acquired by machine learning of a correspondence relationship between an explanatory variable including information related to a material composition or a manufacturing condition of a target material, and an objective variable including information related to the material characteristics of the target material, and prediction step inputs an explanatory variable related to a target material whose material characteristics is to be predicted to the trained model set in the model setting step, and outputs an objective variable related to information of the explanatory variable, so as to predict the material characteristics of the target material to be predicted based on the objective variable.

Abstract: A document retrieval apparatus includes a processor which receives an input of a keyword, acquires an author's name and a document file from a digital document database which stores document files of text data obtained by performing a character recognition process with respect to document image data of handwritten documents, and names of authors who wrote the handwritten documents, references an associating keyword database which stores information associating the authors' names, keywords, and associating keywords, to acquire an associating keyword of the input keyword, from the received input keyword and the acquired author's name, searches the acquired document file, using the input keyword and the acquired associating keyword, and outputs a search result of the searching.

Abstract: A material design apparatus is provided that can derive an optimal solution of a design condition of a polymer that satisfies desired and required physical properties.

Abstract: A material design system includes an expert terminal capable of using a model learning interface for performing machine learning of a model that inputs and outputs a correspondence between a design condition and a material property value of the material to be designed, and a plurality of general-purpose terminals configured to use a material design interface for estimating the material property value based on the design condition or estimating the design condition based on the material property value, by using a learned model that is created by the expert terminal and is for the material to be designed.

Abstract: Technical knowledge which is difficult for the expert to express in words or numerical form is predicted and reproduced. A technical knowledge prediction apparatus according to one embodiment of the present invention includes a problem setting unit configured to automatically create and set a problem which includes a control factor of a manufacturing step, and a variable of the control factor, an answer acquiring unit configured to acquire an answer with respect to the problem and indicating a decision of the manufacturing step, a query acquiring unit configured to acquire a query, and a prediction output unit configured to predict and output an answer corresponding to the query acquired by the query acquiring unit, based on a corresponding relationship between the problem and the answer.

Abstract: A document retrieval apparatus includes an input reception unit configured to receive an input of a keyword, a document acquisition unit configured to acquire an author's name and a document file from a digital document database which stores document files of text data obtained by performing a character recognition process with respect to document image data of handwritten documents, and names of authors who wrote the handwritten documents, a keyword acquisition unit configured to reference an associating keyword database which stores information associating the authors' names, keywords, and associating keywords, and acquire an associating keyword of the input keyword, from the input keyword received by the input reception unit and the author's name acquired by the document acquisition unit, a document search unit configured to search the document file acquired by the document acquisition unit, using the input keyword and the acquired associating keyword, and a search result output unit configured to output a

Abstract: A material design device derives the optimal solution for a design condition satisfying a desired material property. A design condition setting unit for setting a specified range of a design condition of a material to be designed. A comprehensive prediction point generation unit generates a plurality of comprehensive prediction points within the specified range set by the design condition setting unit. A design condition-material property table stores data sets in which each point of the comprehensive prediction points is associated with a material property value calculated by inputting the comprehensive prediction points generated by the comprehensive prediction point generation unit, to a learned model. A required property setting unit sets a specified range of a required property of the material. A design condition extraction unit extracts, from the design condition-material property table, a data set satisfying the required property set by the required property setting unit.