Abstract: A display control device for performing control on a display of a display configured to display an image of content laid out in a virtual space as viewed from a predetermined position, the display being mounted on portions of eyes by a user. The device detects an orientation, with respect to the display, of a head portion of a person other than the user and determines whether a position of the content needs to be changed based on a detection-result and the position of the content in the virtual space. A position change destination of the content in the virtual space is set based on a distance between the position change destination of the content in the virtual space and a position at a current point in time of the content and to change the position of the content when the position of the content is to be changed.

Abstract: An estimation device includes an observation system constraint determination unit configured to determine data within constraints that is data within a first constraint based on time series data of each observation value of a first observation value observed by a first observation system and a second observation value observed by a second observation system, a model estimation unit configured to estimate parameters of a plurality of models including an observation model that is a model of each of the observation systems and a physical model that is a model within an apparatus provided with each of the observation systems based on the data within constraints, and a consistency determination unit configured to determine consistency of the models based on a deviation between a first predicted observation value predicted from the second observation system based on the estimated parameters and the first observation value.

Abstract: An abnormality diagnosis device includes an abnormality determination unit configured to determine whether or not there is an abnormality with respect to a state quantity acquired from equipment and a cause estimation unit configured to estimate a cause of the abnormality in the equipment from a state quantity determined to be abnormal by the abnormality determination unit using a cause correspondence table in which a cause of an abnormal mode of the equipment identified in fault tree analysis is associated with the state quantity that is abnormal when the cause has occurred.

Abstract: A monitoring device includes: an operation record acquisition unit configured to acquire operation records indicating a transition in a load factor or a load change factor with respect to operation time leading up to failure in failure cases; a threshold setting unit configured to set a threshold to be contrasted with the load factor or the load change factor in the operation records of the failure cases; a wear rate acquisition unit configured to acquire a wear rate that is a value derived from a relation between the load factor or the load change factor and the threshold and indicates a degree of progress of wear of a device according to operation time; and a failure rate cumulative frequency acquisition unit configured to acquire a transition in a failure rate cumulative frequency of the failure cases with respect to the wear rate.

Abstract: The purpose of the present invention is to provide an abnormality detection system and refrigerator, an abnormality detection method, and an abnormality detection program which can improve the accuracy of abnormality detection. The abnormality detection system comprises: an acquisition unit which acquires driving data of a refrigerator (11); a creation unit which creates a model capable of estimating a prescribed driving state of the refrigerator (11) on the basis of the driving data, when the driving data is non-trained data; a determination unit which compares an estimation value of the driving state, the estimation value being estimated by the model, with an actual measurement value corresponding to the driving state, and determines whether the driving data is an outlier; and an abnormality detection unit which performs, on the basis of the model, abnormality detection on the refrigerator (11), when the driving data is not determined to be the outlier.