Abstract: A method for transmitting security settings between a first automation engineering field device and a second automation engineering field device includes: identifying and authenticating an operator by means of a service unit; assigning an authorization group based on the identifying and authenticating of the operator; in the case, in which the operator is assigned the administrator authorization group: encrypting at least one security setting, which is present in cleartext, by the first field device; exporting the encrypted security setting; importing the encrypted security setting into the second field device; decrypting the encrypted security setting; loading the decrypted security setting into the data memory of the second field device and operating the second field device with the loaded security setting of the first field device.

Abstract: A method for generating comparison pairs and distributing them to individual comparison requests for experiment participants for subjective individual ratings of pair comparisons. The method includes: providing a data set comprising sensory samples; identifying potentially promising comparison samples for each sample based on a metric, wherein the metric is based on a relationship between a possible estimation of the rating by experiment participants and at least one quantitative characteristic of the sample; distributing a predetermined number of overall comparisons to the potentially promising comparison pairs; generating the comparison requests for experiment participants by assigning the comparison pairs to the comparison requests, taking into account the following conditions to the effect that each sample is not provided more frequently than once in a comparison request, each sample is equally often provided for predetermined subgroups of the entirety of all experiment participants.

Abstract: A method for adjusting a noise generated by a device. The method includes: ascertaining, for each device feature, a dependence of a contribution of the device feature to a noise evaluation characteristic number on the value of the device feature by training an EBM with combinations of values for the device features and, for each of the combinations, an associated value of the noise evaluation characteristic number, each of a plurality of decision trees of the EBM ascertaining a contribution to the noise evaluation characteristic number for a device that has the value for the device feature; ascertaining values of the device features for a device of which the noise is to be adjusted; ascertaining changes for one or more of the device features to the values of the device features for improving the noise evaluation characteristic number; and adjusting the device according to the ascertained changes.

Abstract: The invention relates to a field device of automation technology, wherein the field device has at least one sensor designed to detect a physical, chemical, or biological variable of a process engineering process and/or at least one actuator designed to influence a physical variable of a process engineering process, or such a sensor and/or actuator is assigned to the field device, wherein the field device has at least one electronics unit for operating the field device, wherein at least two operating phases are defined in the field device, wherein one or more authorization information items are assigned to each of the operating phases, wherein the authorization information items define at least one user authorized to access the field device and/or at least one permitted operating action on the field device, and wherein the electronics unit is designed to operate the field device in a current operating phase, wherein the current operating phase is one of the defined operating phases, as well as to a method for

Abstract: Machine learning is used to classify a pleasantness of a sound emitted from a device. A plurality of pleasantness ratings from human jurors are received, each pleasantness rating corresponding to a respective one of a plurality of sounds emitted by one or more devices. Differences between each pleasantness rating and each of the other pleasantness ratings is determined via pairwise comparisons. These differences are converted into binary values based on which pleasantness rating is higher or lower in each comparison. Measurable sound qualities are received associated with the sounds. Second differences between each of the measurable sound qualities and every other of the plurality of measured sound qualities is determined in pairwise fashion. A classification model is trained to classify sound pleasantness by comparing the binary values with the second differences.

Abstract: Machine learning is used to predict a pleasantness of a sound emitted from a device. A plurality of pleasantness ratings from human jurors are received, each pleasantness rating corresponding to a respective one of a plurality of sounds emitted by one or more devices. A microphone system detects a plurality of measurable sound qualities (e.g., loudness, tonality, sharpness, etc.) of these rated sounds. A regression prediction model is trained based on the jury pleasantness ratings and the corresponding measurable sound qualities. Then, the microphone system detects measurable sound qualities of an unrated sound that has not been rated by the jury. The trained regression prediction model is executed on the measurable sound quality of the unrated sound to yield a predicted pleasantness of the unrated sound.

Abstract: A module for a technical facility includes a technical hardware for executing a technical sub-process, a controller for locally controlling the technical hardware wherein the controller is configured to control the technical hardware autonomously and an external interface of the controller, wherein the external interface comprises an OPC-UA server. The OPC-UA server has a fixedly predetermined information structure having static information and dynamic information wherein the static information describes the technical hardware and the controller, and the controller writes the dynamic information as real-time values of the technical hardware into the information structure.

Abstract: A system includes a processor in communication with one or more sensors. The processor is programmed to receiving, from the one or more sensors, vibrational information and sound information associated with the vibrational information from a test device, generating a training data set utilizing at least the vibrational data and the sound information associated with the vibrational data, wherein the training data set is sent to a machine learning model configured to output sound predictions, receiving real-time vibrational data from a run-time device running an actuator or electric dive emitting the real-time vibrational data, and based on the machine learning model and the real-time vibrational data, output a sound prediction indicating a purported sound emitted from the run-time device.

Abstract: A system includes a processor, wherein the processor is programmed to receive sound information and vibrational information from a device in a first environment, generate a training data set utilizing at least the vibrational information and a sound perception score associated with the corresponding sound of the vibrational information, wherein the training data set is fed into an un-trained machine learning model, in response to meeting a convergence threshold of the un-trained machine learning model, outputting a trained machine learning model, receive real-time vibrational information from the device in a second environment, and based on the real-time vibrational information as an input to the trained machine learning model, output a real-time sound perception score indicating characteristics associated with sound emitted from the device.

Abstract: A method includes, in response to at least one convergence criterion not being met: receiving a labeled dataset that includes a plurality of labeled samples; receiving an unlabeled dataset that includes a plurality of unlabeled samples; identifying a plurality of labeled-unlabeled sample pairs; applying a data augmentation transformation to each labeled sample and each corresponding unlabeled sample; computing, for each least one labeled-unlabeled sample pair, latent representation spaces using the machine learning model; generating, using the machine learning model, a label prediction for each unlabeled sample for each labeled-unlabeled sample pair; computing a loss function for each labeled-unlabeled sample pair of the plurality of labeled-unlabeled sample pairs based on respective latency representation spaces and respective label predictions; applying an optimization function to each respective loss function; and updating a weight value for each labeled-unlabeled sample pair of the plurality of labeled-un

Abstract: A method includes receiving a labeled dataset that includes a plurality of labeled samples and initially training the machine learning model using the labeled dataset. The method also includes receiving an unlabeled dataset that includes a plurality of unlabeled samples. The method also includes computing latent representation spaces for each respective sample of the plurality of labeled samples and each respective sample of the plurality of the unlabeled samples. The method also includes generating a k-nearest neighbor similarity graph based on the latent representation spaces, generating a combined similarity graph by augmenting the k-nearest neighbor similarity graph using an expert-derived similarity graph, and propagating, using the combined similarity graph, labels to each respective sample of the plurality of unlabeled samples.

Abstract: A module for a technical facility includes a technical hardware for executing a technical sub-process, a controller for locally controlling the technical hardware wherein the controller is configured to control the technical hardware autonomously and an external interface of the controller, wherein the external interface comprises an OPC-UA server. The OPC-UA server has a fixedly predetermined information structure having static information and dynamic information wherein the static information describes the technical hardware and the controller, and the controller writes the dynamic information as real-time values of the technical hardware into the information structure.

Abstract: Disclosed is a method for obtaining emergency device access for field devices in process automation technology by means of a security token. The method includes the field device receiving and storing a public key before an emergency occurs; connecting the security token to the field device; sending a challenge from the field device to the security token; calculating a response to the challenge by means of a private key on the security token and sending the response from the security token to the field device; and granting emergency access if the response is correct.

Abstract: The invention belongs to the field of personalized cosmetics which, in terms of their composition, are determined by the end user (personalized), and it relates to a method for producing said cosmetics, and to dosing containers for the production thereof, and also to the use of cosmetic mixtures in the dosing containers and the use of the dosing containers for carrying out the method.

Abstract: A measuring apparatus for determining at least one measurand of a measuring medium includes a first measuring device including a first measuring sensor structured to contact the measuring medium and configured to detect measured values of the at least one measurand, the first measuring device embodied to determine a first measured value that is dependent on the at least one measurand of the measuring medium, a sampling device structured to remove a sample from the measuring medium, a second measuring device including a second measuring sensor and embodied to determine a second measured value that is dependent on the least one measurand of the sample, and an electronic control apparatus configured to receive and process the first and second measured value and to perform a verification, calibration and/or adjustment of the first measuring device using the second measured value.

Abstract: The present disclosure relates to a method for determining at least one process variable of a medium. The method includes steps of recording a first value for the process variable by means of a first method for determining the process variable and recording a second value for the process variable by means of a second method for determining the process variable. The method also includes steps of selecting at least one of the detected values for the process variable by means of a classifier and outputting the selected value for the process variable. The present disclosure further relates to a computer program designed for executing a method according to the present disclosure, and to a computer program product having a computer program according to the present disclosure.

Abstract: A facility for treating or processing or transporting products, using materials made Of wood composite, paperboard or plastic, the facility includes at least: a machine and at least one sensor assigned to the machine which has a detection surface oriented essentially in a vertical direction V, and is subject to a contamination of the detection surface during the treatment or processing process. The at least one sensor is assigned its own blow-off device operating with compressed air which includes at least of: a compressed air generator, an overpressure container, a blow-off nozzle and an electrically controllable switching valve. The compressed air generator is connected to the overpressure container via a first line and the overpressure container is connected to the blow-off nozzle via a second line.