Abstract: A body fluid volume estimation device includes a pre-training unit, a transfer learning unit, and an estimation unit. The pre-training unit performs pre-training by using, as supervised information, information indicating body fluid volumes of the multiple patients when face images of multiple patients are captured. The transfer learning unit further performs transfer learning on multiple face images of one specific patient after the pre-training, and constructs a trained model. The estimation unit estimates, by inputting a face image of the one specific patient to the trained model, a body fluid volume at a point in time at which the face image of the one specific patient is captured. By estimating a body fluid volume from a face image by machine learning, the body fluid volume can be used for assistance such as decision making of a user.

Abstract: A body fluid volume estimation device includes a pre-training unit, a transfer learning unit, and an estimation unit. The pre-training unit performs pre-training by using, as supervised information, information indicating body fluid volumes of the multiple patients when face images of multiple patients are captured. The transfer learning unit further performs transfer learning on multiple face images of one specific patient after the pre-training, and constructs a trained model. The estimation unit estimates, by inputting a face image of the one specific patient to the trained model, a body fluid volume at a point in time at which the face image of the one specific patient is captured. By estimating a body fluid volume from a face image by machine learning, the body fluid volume can be used for assistance such as decision making of a user.

Abstract: A body fluid volume estimation device includes a pre-training unit, a transfer learning unit, and an estimation unit. The pre-training unit performs pre-training by using, as supervised information, information indicating body fluid volumes of the multiple patients when face images of multiple patients are captured. The transfer learning unit further performs transfer learning on multiple face images of one specific patient after the pre-training, and constructs a trained model. The estimation unit estimates, by inputting a face image of the one specific patient to the trained model, a body fluid volume at a point in time at which the face image of the one specific patient is captured. By estimating a body fluid volume from a face image by machine learning, the body fluid volume can be used for assistance such as decision making of a user.

Abstract: A body fluid volume estimation device includes a pre-training unit, a transfer learning unit, and an estimation unit. The pre-training unit performs pre-training by using, as supervised information, information indicating body fluid volumes of the multiple patients when face images of multiple patients are captured. The transfer learning unit further performs transfer learning on multiple face images of one specific patient after the pre-training, and constructs a trained model. The estimation unit estimates, by inputting a face image of the one specific patient to the trained model, a body fluid volume at a point in time at which the face image of the one specific patient is captured. By estimating a body fluid volume from a face image by machine learning, the body fluid volume can be used for assistance such as decision making of a user.

Abstract: The stress estimation device acquires the awakening degree of the subject and calculates the feature amount of the acquired awakening degree. The feature amount of the awakening degree is, for example, a ratio at which the temporal change of the awakening degree is within a predetermined range, information defining a histogram showing the distribution of the temporal change of the awakening degree, and the like. Then, the stress estimation device estimates the stress from the calculated feature amount using the stress model.

Abstract: An estimation apparatus (30) includes a first estimation unit (31) configured to estimate a first pulse rate from a first video data which is captured a body part and output a first feature data derived based on the first video data; a training unit (32) configured to train a determination model to determine a confidence value which indicates an reliability in the estimation of the first pulse rate and a physiological information measured from the body; an acquiring unit (33) configured to acquire a second feature data derived by the first estimation unit, and acquire the confidence value of the second pulse rate using the second feature data and the determination model trained by the training unit; and a second estimation unit (34) configured to estimate a third pulse rate based on the acquired confidence value.

Abstract: A stress estimation apparatus 1 acquires a biosignal of a measurement subject, which includes acceleration information of an action of at least one part of the measurement subject. A moving average is calculated from the acceleration information included in the acquired biosignal, and a threshold of an activity state of the measurement subject is calculated from the calculated moving average. Then, an activity state in a specific period is identified from the calculated moving average and the calculated threshold. Per-activity state data is generated by categorizing chronological data pieces of the acquired biosignal other than the acceleration information by identified activity state, and stress is estimated from the generated per-activity state data.

Abstract: A pulse determination apparatus determines a pulse rate of a person to be measured, determines whether the person to be measured is in a first state in which a pacemaker attached to the person to be measured is operating, a second state in which the pacemaker attached to the person to be measured is not operating, or a third state in which the person to be measured does not wear the pacemaker, based on variation of the pulse rate, and determines whether the pulse of the person to be measured is normal. The pulse determination apparatus does not make a determination, when it is determined that the person to be measured is in the first state, and the pulse determination apparatus makes the determination based on the pulse rate, when it is determined that the person to be measured is in the second state or the third state.

Abstract: The present invention relates to a thermally conductive resin composition containing a resin (A) having a melting point of 40 to 100° C. and an inorganic filler (B), wherein the resin (A) has a weight loss of less than 10% when the resin (A) is stored at 150° C. for 100 hours under an atmosphere. The thermally conductive resin composition of the present invention is suitably used for a thermal interface material (TIM), particularly TIM for a semiconductor module.

Abstract: Provided an area-specific environment management system, which manages an environment in an individual one of a plurality of areas, the system including: a biological information sensor that acquires biological information about at least one of an individual and a group who belongs to an individual one of the areas; an environmental information sensor that acquires environmental information about an individual one of the areas; an arousal level estimation section that estimates an arousal level with respect to the biological information by using an arousal level estimation model; and an environment provision section that provides an environment to an individual one of the areas based on the arousal level and the environmental information, wherein the arousal level includes, as a state to which the arousal level belongs, a state from sleepy until awakened; a wakeful state; and an excessively wakeful state, and wherein the environment provision section includes an individual control mode corresponding to a stat

Abstract: A molded body includes: a first resin part; a metal part that is covered by the first resin part and that has a protrusion portion which protrudes from a first end portion of the first resin part; and a second resin part that has a second end portion which is positioned on an opposite side of the protrusion portion with respect to the first end portion in an extension direction of the protrusion portion and that covers the first resin part in a state where the first resin part protrudes in the extension direction.

Abstract: A speaker identification device includes: a primary speaker identification unit that computes, for each pre-stored registered speaker, a score that indicates the similarity between input speech and speech of the registered speakers; a similar speaker selection unit that selects a plurality of the registered speakers as similar speakers according to the height of the scores thereof; a learning unit that creates a classifier for each similar speaker by sorting the speech of a certain similar speaker among the similar speakers as a positive instance and the speech of the other similar speakers as negative instances; and a secondary speaker identification unit that computes, for each classifier, a score of the classifier with respect to the input speech, and outputs an identification result.

Abstract: A bus bar unit includes a plurality of bus bars that electrically connects coils of each phase of a rotary electric machine and an external power supply, a plurality of caps that separately covers portions of the plurality of bus bars and separately hold the plurality of bus bars, and an outer holding section that is formed of an insulating material, that collectively covers the plurality of caps, and that is formed in a state in which the plurality of bus bars are electrically insulated.

Abstract: An analysis object determination device includes a detection unit which detects a plurality of specific utterance sections using data related to a voice in a conversation, the specific utterance sections representing a plurality of specific events originating from one or a plurality of participants in the conversation, or a specific event originating from one of the conversation participants, and an object determination unit which determines, on the basis of the plurality of specific utterance sections detected by the detection unit, one or more cause analysis sections for the specific event originating from the conversation participant, the number of the cause analysis sections being fewer than the number of the plurality of specific utterance sections.

Abstract: The present invention relates to a thermally conductive resin composition containing a resin (A) having a melting point of 40 to 100° C. and an inorganic filler (B), wherein the resin (A) has a weight loss of less than 10% when the resin (A) is stored at 150° C. for 100 hours under an atmosphere. The thermally conductive resin composition of the present invention is suitably used for a thermal interface material (TIM), particularly TIM for a semiconductor module.

Abstract: A bus bar unit includes a plurality of bus bars that electrically connects coils of each phase of a rotary electric machine and an external power supply, a plurality of caps that separately covers portions of the plurality of bus bars and separately hold the plurality of bus bars, and an outer holding section that is formed of an insulating material, that collectively covers the plurality of caps, and that is formed in a state in which the plurality of bus bars are electrically insulated.

Abstract: An aliphatic-chain saturated hydrocarbon including an aliphatic hydrocarbon chain which is saturated and has an ion pair at each end of the aliphatic hydrocarbon chain.

Abstract: An analysis subject determination device includes: a demand period detection unit which detects, from data corresponding to audio of a dissatisfaction conversation, a demand utterance period which represents a demand utterance of a first conversation party among a plurality of conversation parties which are carrying out the dissatisfaction conversation; a negation period detection unit which detects, from the data, a negation utterance period which represents a negation utterance of a second conversation party which differs from the first conversation party; and a subject determination unit which, from the data, determines a period with a time obtained from the demand period utterance period as a start point and a time obtained from the negation utterance period after the demand utterance period as an end point to be an analysis subject period of a cause of dissatisfaction of the first conversation party in the dissatisfaction conversation.

Abstract: The voiced sound interval classification device comprises a vector calculation unit which calculates, from a power spectrum time series of voice signals, a multidimensional vector series as a vector series of a power spectrum having as many dimensions as the number of microphones, a difference calculation unit which calculates, with respect to each time of the multidimensional vector series, a vector of a difference between the time and the preceding time, a sound source direction estimation unit which estimates, as a sound source direction, a main component of the differential vector, and a voiced sound interval determination unit which determines whether each sound source direction is in a voiced sound interval or a voiceless sound interval by using a predetermined voiced sound index indicative of a likelihood of a voiced sound interval of the voice signal applied at each time.

Abstract: An analysis subject determination device includes: a demand period detection unit which detects, from data corresponding to audio of a dissatisfaction conversation, a demand utterance period which represents a demand utterance of a first conversation party among a plurality of conversation parties which are carrying out the dissatisfaction conversation; a negation period detection unit which detects, from the data, a negation utterance period which represents a negation utterance of a second conversation party which differs from the first conversation party; and a subject determination unit which, from the data, determines a period with a time obtained from the demand period utterance period as a start point and a time obtained from the negation utterance period after the demand utterance period as an end point to be an analysis subject period of a cause of dissatisfaction of the first conversation party in the dissatisfaction conversation.