Abstract: A feedback device includes an acquisition unit configured to acquire a BCS evaluation value obtained by evaluating a BCS value representing a body condition score of livestock with a reference value for evaluating the BCS value and a feedback unit configured to output feedback information concerning the livestock based on the acquired BCS evaluation value.

Abstract: A method of making a single processor or a plurality of processors perform classification processing of classification target data using a machine learning model includes the steps of (a) preparing N machine learning models in a memory assuming N as an integer no smaller than 2, and (b) performing the classification processing of the classification target data using the N machine learning models. Each of the N machine learning models is configured so as to classify input data into any of a plurality of classes with learning using training data, and is configured so as to have at least one class different from a class of another of the N machine learning models.

Abstract: Provided is a determination method that includes obtaining measurement data, selecting one or more second wavelengths from a plurality of first wavelengths including at least one of a plurality of measurement wavelengths to generate a plurality of individuals, by using a genetic algorithm, inputting, to a first model learned to reproduce a correct answer label of a target object, the measurement data of the target object belonging to a remaining group and a second spectroscopic spectrum determined by the second wavelength to discriminate a label of the target object belonging to the remaining group, for each of the plurality of individuals, and determining whether or not to use the second wavelength as the wavelength of the spectroscopic spectrum for discrimination based on a rate at which the label is correctly discriminated.

Abstract: A feedback device includes a first acquisition unit configured to acquire first information including current information indicating a current rearing state of a cow, a second acquisition unit configured to acquire second information including at least one of future information indicating a future rearing state of the cow and an ideal condition indicating a condition ideal for rearing of the cow, and a feedback execution unit configured to execute feedback relating to the rearing to at least one of a user and a rearing device for rearing the cow. The feedback execution unit includes a generation unit configured to generate feedback information for the feedback by using the first information and the second information, and an output unit configured to output the feedback information.

Abstract: An evaluation method for evaluating target data includes: inputting a plurality of training sets to a vector neural network machine learning model having a plurality of vector neuron layers to train the machine learning model, the training sets including of general-purpose training data having a type different from the target data and a label corresponding to the general-purpose training data; acquiring a reference feature spectrum; acquiring a target feature spectrum; calculating a spectral similarity that is a similarity between the reference feature spectrum and the target feature spectrum; and evaluating the target data using the spectral similarity.

Abstract: A load specifying method for a muscle strength training device includes: acquiring a known feature spectrum for each of a plurality of pieces of time-series waveform data; acquiring target time-series waveform data; acquiring a target feature spectrum for each of a plurality of pieces of target time-series waveform data; and specifying a spectrum similarity satisfying a predetermined extraction condition and specifying a candidate load corresponding to the target time-series waveform data as a calculation source of the specified spectrum similarity.

Abstract: A production system includes a first manufacturing machine and a second manufacturing machine. The production system also includes a production control unit configured to control productivities of the first and second manufacturing machines. In response to detecting a breakdown sign in the first manufacturing machine, a stop time of the first manufacturing machine is predicted. After the detection of the breakdown sign and before the first manufacturing machine stops, the productivity control unit updates the productivities of the first and/or second manufacturing machines, causing the first manufacturing machine and/or the second manufacturing machine to have an updated productivity equal to or greater than its original productivity, such that a total productivity of the first manufacturing machine and the second manufacturing machine increases.

Abstract: Provided is a learning method including (a) preparing a plurality of pieces of data for learning; (b) dividing the plurality of pieces of data for learning into one or more groups to generate one or more input learning data groups; and (c) training M number of machine learning models, wherein (b) includes (b1) dividing the plurality of pieces of data for input into one or more regions to generate, as one of the input learning data groups, a collection of first type divided input data after division belonging to the same region, or (b2) dividing the plurality of pieces of data for learning belonging to one class into one or more groups to generate, as one of the input learning data groups, a collection of second type divided input data after division.

Abstract: An evaluation method for a trained machine learning model includes the steps of (a) inputting evaluation data to the trained machine learning model to generate first explanatory information used for an evaluation of the machine learning model, (b) using a value indicated by each piece of information included in the first explanatory information to generate second explanatory information indicating an evaluation of the trained machine learning model, and (c) outputting the generated second explanatory information.

Abstract: A method according to the present disclosure includes (a) generating N pieces of input data from one target object, (b) inputting the input data to a machine learning model and obtaining M classification output values, one determination class, and a feature spectrum, (c) obtaining a similarity degree between a known feature spectrum group and the feature spectrum for the input data, and obtaining a reliability degree with respect to the determination class as a function of the reliability degree, and (d) executing a vote for the determination class, based on the reliability degree with respect to the determination class, and determining a class determination result of the target object, based on a result of the vote.

Abstract: For a plurality of types of print media, a pre-trained model is prepared, the pre-trained model being generated by machine learning based on spectroscopic information of an unprinted area on the print medium and an identifier indicating the type of the print medium and using the identifier as a trainer. The spectroscopic information of the unprinted area on the print medium to print on is acquired. The acquired spectroscopic information is applied to the pre-trained model. Using a result thereof, auxiliary information about the type of the print medium is outputted. A specification of the type of the print medium using the outputted auxiliary information is accepted. When the type of the print medium is thus specified, printing is performed using a print condition suitable for the print medium.

Abstract: A method of performing classification processing on classification target data includes: (a) a step of preparing N machine learning models; (b) a step of, when a plurality of pieces of training data are input into the N machine learning models, preparing a known feature vector group obtained from output of at least one specific layer of the plurality of vector neuron layers; and (c) a step of computing, using a selected machine learning model selected from the N machine learning models a similarity, for each class, between the known feature vector group and a feature vector obtained from output of the specific layer when the classification target data is input into the selected machine learning model, and determining a class for the classification target data using the similarity.

Abstract: A printing condition setting method is a printing condition setting method of setting printing conditions in a printing apparatus, including a learning step of performing machine learning by using ink physical characteristics and ink type information, a similarity score calculation step of calculating a similarity score indicating a similarity of a use ink used for printing in the printing apparatus with respect to a learned ink learned in the learning step, and a printing condition setting step of setting the printing conditions according to the use ink based on the similarity score.

Abstract: A sonic speed measurement device includes a reception array in which a plurality of reception elements which output reception signals in response to reception of an ultrasonic wave are disposed in one direction, a phase difference detection portion that detects a phase difference between the reception signals output from the reception elements adjacent to each other in a case where the plurality of reception elements receive the ultrasonic wave which propagates in a spherical wave shape from a target point, and a sonic speed calculation portion that calculates a sonic speed of the ultrasonic wave on the basis of the phase difference.

Abstract: A print condition setting method for setting a print condition in a printer includes: an ink type learning step of executing machine learning of an ink type discriminator using physical property information of ink and an ink type identifier; a medium type learning step of executing machine learning of a medium type discriminator using characteristic information of a medium and medium type identification information; and a print condition setting step of setting the print condition according to an ink type discriminated by the ink type discriminator and a medium type discriminated by the medium type discriminator.

Abstract: A class discrimination method includes: (a) a step of preparing, for each class, a known feature spectrum group obtained based on an output of a specific layer among a plurality of vector neuron layers when a plurality of pieces of training data are input to a machine learning model; and (b) a step of executing a class discrimination processing of the data to be discriminated using the machine learning model and the known feature spectrum group. The step (b) includes: (b1) a step of calculating a feature spectrum based on an output of the specific layer according to the data to be discriminated to the machine model; (b2) a step for each of the one or more classes; (b3) a step of creating an explanatory text of a class discrimination result for the data to be discriminated according to the similarity; and (b4) a step of outputting the explanatory text.

Abstract: An image processing method includes: an image pickup step of picking up an RGB image of a target object to be picked up, and picking up a spectroscopic image of the target object in a predetermined wavelength range and thus acquiring spectroscopic information peculiar to the target object in the wavelength range; and a display step of displaying a complemented image complemented by superimposing the spectroscopic information on the RGB image.

Abstract: A class determination method includes: step (a): preparing, for each of a plurality of classes, a known feature spectrum group obtained when a plurality of pieces of training data are input to a vector neural network type machine learning model; and step (b): executing, by using the machine learning model and the known feature spectrum group, a class determination processing on data to be determined. The step (b) includes step (b1), calculating a feature spectrum according to an input of the data to be determined to the machine learning model, step (b2), calculating a class similarity between the feature spectrum and the known feature spectrum group related to each of the plurality of classes, and step (b3), determining a class of the data to be determined according to the class similarity.

Abstract: An ultrasonic device includes an ultrasonic transceiver that transmits an ultrasonic wave to a target at a predetermined interval, and that receives the ultrasonic wave reflected on the target so as to output a reception signal, a signal integration unit that outputs an integrated signal obtained by integrating the reception signals output within a predetermined period, and a position detection unit that detects a position of the target, based on a magnitude relationship between signal intensity of the integrated signal and a predetermined reference value.

Abstract: A printing condition setting method is a printing condition setting method of setting printing conditions in a printing apparatus, including a learning step of performing machine learning by using ink physical characteristics and ink type information, a similarity score calculation step of calculating a similarity score indicating a similarity of a use ink used for printing in the printing apparatus with respect to a learned ink learned in the learning step, and a printing condition setting step of setting the printing conditions according to the use ink based on the similarity score.