Abstract: A connection passage is formed by a first impeller housing member, a motor housing member, and a second impeller housing member. This prevents, for example, the formation of a gap between a housing and a pipe, which is separate from the housing, in a centrifugal compressor like in a configuration in which a first discharge port and a second suction port are connected to each other by attaching the pipe to the housing. The connection passage is formed only by sequentially overlapping and coupling the first impeller housing member, the motor housing member, and the second impeller housing member with each other in an axial direction of the rotary shaft. This eliminates, for example, the need to provide a structure in the pipe that absorbs the dimensional tolerances that occur between the housing and the pipe when attaching the pipe to the housing.

Abstract: A centrifugal compressor includes a rotary shaft, an electric motor, an impeller, a thrust bearing, and a housing having a passage that includes a motor cooling passage and a bearing cooling passage. The housing includes an inner housing and an outer housing. The motor cooling passage is defined by the inner housing and the outer housing. The passage includes a branching passage through which cooling fluid is divided and flows into the motor cooling passage and the bearing cooling passage and a joining passage through which the cooling fluid flowing through the bearing cooling passage joins the cooling fluid flowing through the motor cooling passage. The motor cooling passage includes a guide wall that guides the cooling fluid flowing through the motor cooling passage.

Abstract: First training data including a set value related to a molding machine, a measured value obtained by measuring a physical quantity related to molding, and a degree of quality of a molded product generated by the molding machine is collected, a first learning model for outputting a degree of quality of a molded product when a set value and a measured value are input is generated by machine learning based on collected first training data, second training data including a defect degree for each defect type of a molded product, a measured value, and a set value capable of reducing the defect degree is collected, and a second learning model for outputting a set value capable of reducing a defect degree when a defect degree and a measured value are input is generated by machine learning based on collected second training data and a degree of quality output from the first learning model.

Abstract: A centrifugal compressor that includes: a rotary shaft; a compressor impeller mounted on the rotary shaft and configured to rotate together with the rotary shaft to compress a fluid; a housing accommodating the rotary shaft and the compressor impeller; and a thrust bearing supporting the rotary shaft in a thrust direction such that the rotary shaft is rotatable. The housing includes: an impeller chamber in which the compressor impeller is accommodated; a thrust bearing accommodation chamber in which the thrust bearing is accommodated; and a partition wall separating the impeller chamber from the thrust bearing accommodation chamber. The partition wall has therein a cooling gas passage through which cooling gas flows to cool the thrust bearing and a cooling water passage through which cooling water flows to cool the partition wall.

Abstract: Provided is an injection molding machine system (1) that performs control of molding conditions in an injection molding machine (2) by an agent (6) including a machine learning device which performs reinforcement learning. In the present learning, physical data obtained from the injection molding machine (2) and a defect type indicating the type of a molding defect in a molded article are used as states, molding conditions are used as actions, and a defect state indicating the defect level of the molding defect is used as a reward.

Abstract: Dimensional limitation in attachment of a torque detection device in a molding machine is reduced. A molding machine system is provided, the molding machine system including: a molding machine including a cylinder, a first screw and a second screw embedded in the cylinder, and a motor rotationally driving the first screw and the second screw; a feature-amount identification apparatus detecting a torque of the motor as one example of a feature amount related to an operation state of the molding machine; a torque detection device attached to a rotational shaft of the first screw and detecting a torque of the first screw; and a torque derivation apparatus deriving a torque of the second screw from the torque of the motor detected by the feature-amount identification apparatus and the torque of the first screw detected by the torque detection device.

Abstract: A method of adjusting a molding condition parameter of a molding machine includes determining change values of a plurality of molding condition parameters set in a molding machine in a molding process cycle, determining change timings of the plurality of parameters determined as objects to be changed with reference to timing information when the change values of the plurality of molding condition parameters are determined, and sequentially changing the plurality of parameters determined as objects to be changed according to the determined change timings.

Abstract: A display device includes a housing, an electro-optic layer, a display to display at least a portion of a rearward image, and an actuator to switch the electro-optic layer between first and second postures. In the first posture, the display displays the portion of the rearward image. In the second posture, the reflectivity of the incident light varies between at least first and second values based on at least one of a first illuminance or a second illuminance, and the second value is larger than the first value. In the first posture, the reflectivity of the incident light is equal to or more than the second value. The first illuminance indicates brightness around a vehicle and is detected by a sensor. The second illuminance indicates intensity of light emitted on a front surface of the display device and is detected by another sensor.

Abstract: An operation quantity determination device for determining an operation quantity related to a molding machine, includes an observation unit configured to acquire observation data obtained by observing a physical quantity related to molding by the molding machine when the molding is executed, a state expression unit configured to generate a state expression map expressing a state of the molding machine based on the observation data acquired by the observation unit, and an operation quantity output unit configured to output the operation quantity based on the state expression map generated by the state expression unit.

Abstract: Physical quantity data indicating the state of a molded product produced by changing a first molding condition parameter set in a molding machine such that the quality of the molded product is degraded or the state of the molding machine is acquired, physical quantity data indicating the state of a molded product produced by changing a second molding condition parameter set in the molding machine or the state of the molding machine is acquired, the second molding condition parameter before change, the physical quantity data obtained at this time, the second molding condition parameter after change, and the physical quantity data obtained when setting the second molding condition parameter after change are stored in association with each other, and a dataset for machine learning is created by repeating the change of the first and second molding condition parameters and the acquisition of the physical quantity data.

Abstract: A display device includes a housing, a variable reflectance element, a display to display at least a portion of a rearward image, a display device control circuit, and an actuator to switch the variable reflectance element between first and second postures. In the first posture, the display displays the portion of the rearward image. In the second posture, the display displays the rearward image. In the second posture, the reflectivity of the incident light varies between first and second values of a first and a second illuminance, respectively. The first illuminance indicates brightness around a vehicle and is detected by a sensor. The second illuminance indicates intensity of light emitted on a front surface of the display device and is detected by another sensor. When the display displays at least the portion of the rearward image, the display is visible to a driver through the variable reflectance element.

Abstract: A reinforcement learning method of a learning machine including a first agent adjusting a manufacture condition of a manufacturing device based on observation data obtained by observing a state of the manufacturing device and a second agent having a functional model or a functional approximator representing a relationship between the observation data and the manufacture condition in a different way from the first agent, comprises: adjusting the manufacture condition searched by the first agent that is performing reinforcement learning, using the observation data and the functional model or the functional approximator of the second agent; calculating reward data in accordance with a state of a product manufactured by the manufacturing device under the manufacture condition adjusted; and performing reinforcement learning on the first agent and the second agent based on the observation data and the reward data calculated.

Abstract: A reinforcement learning method of a learning machine including a first agent adjusting a manufacture condition of a manufacturing device based on observation data obtained by observing a state of the manufacturing device and a second agent having a functional model or a functional approximator representing a relationship between the observation data and the manufacture condition in a different way from the first agent, comprises: adjusting the manufacture condition searched by the first agent that is performing reinforcement learning, using the observation data and the functional model or the functional approximator of the second agent; calculating reward data in accordance with a state of a product manufactured by the manufacturing device under the manufacture condition adjusted; and performing reinforcement learning on the first agent and the second agent based on the observation data and the reward data calculated.

Abstract: An information processing method for detecting conditions of a plurality of manufacturing devices respectively used by a plurality of entities includes acquiring sensor value data obtained by detecting physical quantities related to the manufacturing devices; individually storing collected sensor value data in a plurality of databases; generating by machine learning a plurality of learning models based on the stored sensor value data; and calculating a condition of the manufacturing device of one of the entities by inputting sensor value data acquired from the manufacturing device of the one of the entities to the one or more of the learning models selected by the one of the entities.

Abstract: An abnormality detection system includes an abnormality detection apparatus that detects an abnormality of a manufacturing device, a control device that performs operation control of the manufacturing device and transmits operating data, and a sensor that detects a physical quantity related to operation of the manufacturing device or a product manufactured by the manufacturing device and outputs sensor value data. The abnormality detection apparatus receives operating data transmitted from the control device, acquires sensor value data output from the sensor, calculates statistics of the sensor value data acquired, determines a presence or an absence of an abnormality of the manufacturing device based on the statistics calculated and a threshold depending on the operating data received, and transmits a determination result and the statistics to the control device.

Abstract: An abnormality detection system includes an abnormality detection apparatus that detects an abnormality of a production device, a control device that performs operation control of the manufacturing device and transmits operating data, a sensor that detects a physical quantity related to operation or a product of the manufacturing device and outputs sensor value data, and a diagnostic apparatus. The abnormality detection apparatus determines a presence or an absence of an abnormality of the manufacturing device based on the operating data transmitted from the control device and the sensor value data output from the sensor and transmits the determination result to the control device. The abnormality detection apparatus further transmits the operating data and the sensor value data to the diagnostic apparatus.

Abstract: A computer program causing a computer to execute processing of detecting an abnormality of an industrial machine having a movable part is provided. The computer executes the processing oft acquiring physical quantity data on a time-series basis output from a sensor detecting a physical quantity related to a motion of the movable part; converting the physical quantity data on a time-series basis acquired to a time-series data image representing the physical quantity data; inputting the time-series data image converted to a learning model to calculate a feature of the time-series data image, the learning model being trained with a feature of the time-series data image related to the movable part in a normal condition; and determining a presence or an absence of an abnormality of the industrial machine based on the calculated feature.

Abstract: Faults in rotating machines can be diagnosed or detected using two radio frequency (RF) sensing modes. RF sensing phenomenon can be used to detect the presence of undesirable behavior in rotating machines including excessive bending, vibration, eccentricity, torsion, and longitudinal strain. RF based sensors represent a non-invasive solution. The sensing modes are based on RF metamaterials and Doppler effect influence and radar cross section evaluation all coupled with a machine learning algorithm. The system is based on monitoring resonance shift, negative permeability and return loss magnitudes. Electromagnetic numerical simulations showed a significant change in those magnitudes upon applied mechanical strains as compared to original reference unstrained cases. Metamaterial texturing design can be controlled by controlling the cells scale and substrate materials.

Abstract: A centrifugal compressor includes a rotary shaft, an electric motor, an impeller, a thrust bearing, and a housing having a passage that includes a motor cooling passage and a bearing cooling passage. The housing includes an inner housing and an outer housing. The motor cooling passage is defined by the inner housing and the outer housing. The passage includes a branching passage through which cooling fluid is divided and flows into the motor cooling passage and the bearing cooling passage and a joining passage through which the cooling fluid flowing through the bearing cooling passage joins the cooling fluid flowing through the motor cooling passage. The motor cooling passage includes a guide wall that guides the cooling fluid flowing through the motor cooling passage.

Abstract: A signal processing device includes: a processor; and a memory having instructions. When executed by the processor, the instructions cause the signal processing device to perform operations including: converting a first signal that is a time domain signal into a second signal that is a frequency domain signal in response to reception of the first signal, the first signal containing noise superimposed on a broadcast electric signal derived from a broadcast electromagnetic wave, the noise having peaks of amplitude at regular frequency intervals in the frequency domain; calculating a frequency interval between the peaks of the noise in the frequency domain based on a correlation of the second signal; determining a frequency shift amount in the frequency domain based on the frequency interval; and shifting a frequency of the second signal by the frequency shift amount to create a frequency-shifted signal.