A training device and a training method for a neural network model. The training method includes: obtaining a data set; completing, according to the data set, a plurality of artificial intelligence (AI) model trainings to generate a plurality of models corresponding to the plurality of AI model trainings respectively; selecting, according to a first constraint, a first model set from the plurality of models; and selecting, according to a second constraint, the neural network model from the first model set.

Methods and systems for using multiple hyperspectral cameras sensitive to different wavelengths to predict characteristics of objects for further processing, including recycling, are described. The multiple hyperspectral images can be used to predict higher resolution spectra by using a trained machine learning model. The higher resolution spectra may be more easily analyzed to sort plastics into a recyclability category. The hyperspectral images may also be used to identify and analyze dark or black plastics, which are challenging for SWIR, MWIR, and other wavelengths. The machine learning model may also predict the base polymers and contaminants of plastic objects for recycling. The hyperspectral images may be used to predict recyclability and other characteristics using a trained machine learning model.

The present disclosure relates to a method, device, system and computer-program product for setting a lighting condition when an object is checked and a storage medium. The method includes that: the object is lighted by light sources capable of changing lighting parameters, and the object is captured by an image sensor in such lighting parameters to obtain captured images, wherein the object has known label data; and a part of or all of the captured images and the corresponding label data of the object are applied to learning of a machine learning model, and the lighting condition and the check algorithm parameters of the machine learning model is set simultaneously by optimizing both the lighting parameters and the check algorithm parameters, on the basis of a comparison result between an estimation result of the machine learning model and the label data. Therefore, operations are simplified.

A laser-based detection and analysis system for detecting plural members of volatile organic compounds includes a measuring unit configured to simultaneously measure a spectrum of the plural members of the volatile organic compounds located in a measuring chamber, with a laser beam having a wavelength of about 3.3 μm, and a data processing unit including a deep neural network, DNN, configured to process the spectrum measured by the measuring unit and to output an individual concentration of each of the plural members of the volatile organic compounds. The DNN is configured to update a weight W.sub.k for each member of the plural members by using hidden layers having plural nodes, each node having an activation function and an optimizer.

The invention relates to a method for the spectrometric characterization of microorganisms, comprising: providing a test microorganism; acquiring spectrometric measurement data from the test microorganism under potential exposure to variance that is not based on taxonomic classification; selecting a classifier which is trained to determine the identity of a microorganism on a second taxonomic level; and applying the classifier to the measurement data in order to determine the identity of the test microorganism on the second taxonomic level, wherein the classifier is variance-conditioned in such a way that it largely or completely masks out the effect of variance in the characterization of the test microorganism on the second taxonomic level.

There is provided a technology that supports selection of a label to be used for analysis of target molecules.

The present technology provides a label selection support system including an information acquisition unit that obtains, via a network, information associated with a plurality of target molecules to be analyzed, an information processor that obtains, using the information associated with a plurality of target molecules, in vivo expression information of the plurality of target molecules from a database storing in vivo expression information of target molecules and generates support information associated with assignment of a label to each of the plurality of target molecules on the basis of the expression information, and a transmitter that transmits the generated support information via the network.

The embodiment of the present application relates to the field of substance ingredient detection, for example, relates to a substance ingredient detection method and apparatus, and a detection device. The method includes: obtaining spectral information of a substance to be detected; and matching the spectral information with a pre-obtained prediction model based on a machine learning algorithm to obtain the ingredients of the substance to be detected. In the embodiment of the present application, the spectral information of the substance to be detected is obtained, and then the spectral information is matched with the prediction model based on the machine learning algorithm to obtain the prediction result of the ingredients of the substance to be detected. In the embodiment of the present application, the machine learning algorithm is combined with spectral recognition, the traditional algorithm is abandoned, the recognition speed is improved, and the substance detection efficiency is greatly improved.

Provided is a near-infrared spectroscopy-based method for chemical pattern recognition of the authenticity of the traditional Chinese medicine Gleditsiae Spina. The method uses the combination of a near-infrared spectroscopy acquisition method, a 1st derivative pretreatment method and a successive projection algorithm, a Kennard-Stone algorithm and a marching algorithm to perform chemical pattern recognition on the authenticity of the Gleditsiae Spina. The results of the pattern recognition method are accurate and reliable, and Gleditsiae Spina and counterfeits thereof can be accurately distinguished. The present application is the first to establish a method for the chemical pattern recognition of the quality of Gleditsiae Spina based on near-infrared spectroscopy, and can accurately distinguish between Gleditsiae Spina and counterfeits thereof, and provides scientific basis for the quality evaluation of Gleditsiae Spina.

A signal acquisition apparatus includes a light source that irradiates a living tissue with light and a detector that acquires signals from light returned from the living tissue to generate output data on the basis of the signals. The detector includes circuitry that acquires the signals and characteristic data regarding the signals and generates the output data on the basis of the characteristic data. The circuitry is implemented in a single semiconductor chip. Further, the present technology also provides a signal acquisition system including the signal acquisition apparatus and an analysis unit configured to analyze output data output from the image acquisition apparatus.

A method for determining an optical constant of a material includes: acquiring ellipsometric parameters; obtaining a optical constant of the material corresponding to the ellipsometric parameters by a machine learning model; the machine learning model including a mapping relationship between the ellipsometric parameters and the material optical constant of the material corresponding to the ellipsometric parameters. The method uses the machine learning model to implement an automatic fitting of ellipsometric parameters. In the method, the optical constant of the material is calculated by a machine learning model, which no longer depends on the experiences of the experimenters, thereby reducing requirements for the operator, accelerating the fitting of the data curve when calculating the optical constants of the material and improving the operation efficiency.