Abstract: A robot includes a base, a pivot base, an upper arm, and a lower arm. The base is fixed to an installation surface. The pivot base has a pivot base proximal end which is connected to the base such that the pivot base is rotatable around a first axis along a vertical direction. The lower arm includes a first extending portion, a second extending portion, and a connecting portion. The connecting portion has a hollow structure and connects the first extending portion and the second extending portion such that the first arm proximal end faces the second arm proximal end and such that the first arm distal end faces the second arm distal end. The connecting portion includes a reinforcing portion in the hollow structure. The reinforcing portion includes ribs which connect opposed inner walls of the connecting portion and which cross each other.

Abstract: Provided is a highly reliable technology for optimizing an action for controlling an environment in a target space.

Abstract: A prediction method of performing heat source demand prediction in a space having a predetermined air conditioning control region, includes predicting a required heat quantity in the air conditioning control region by using a predetermined parameter related to a surrounding environment of the air conditioning control region and a setting value of an air conditioner set in the air conditioning control region as inputs, and predicting a heat source demand of the entire space from the predicted required heat quantity for each air conditioning control region.

Abstract: A learning-type signal separation method performed using a model formulation unit, which performs learning processing based on a training-use signal including a specific component, and a training-use signal not including the specific component, the training-use signals including a common characteristic. The learning-type signal separation method includes: generating learned data by causing the model formulation unit to perform learning processing based on the training-use signal and information indicating whether or not the specific component is included in the training-use signal, to generate a data series signal in which the specific component has been separated and removed from a data series of the training-use signal; acquiring an arbitrary signal including the common characteristic; and generating, based on the acquired arbitrary signal and the generated learned data, a data series signal in which the specific component has been separated and removed from a data series of the arbitrary signal.

Abstract: Sufficient data extension can be achieved even if the starting date/time and ending date/time of the learning execution data have been uniquely defined. Based on entire learning data that is a set of time-series data, wherein the entire learning data is a set of minimum constitution unit data, each of which is time-series data having a first time interval that is a time interval required for learning, wherein the time-series data having the first time interval is each assigned a first label that indicates a feature in a time series of the first interval; a generation unit (103) generates learning execution data that is a set of time-series data to be used in learning, by combining the minimum constitution unit data included in the entire learning data such that regularity of the first label in a time series of the entire learning data is maintained.

Abstract: Provided is a highly reliable technology for optimizing an action for controlling an environment in a target space.

Abstract: A learning-type signal separation method performed using a model formulation unit, which performs learning processing based on a training-use signal including a specific component, and a training-use signal not including the specific component, the training-use signals including a common characteristic. The learning-type signal separation method includes: generating learned data by causing the model formulation unit to perform learning processing based on the training-use signal and information indicating whether or not the specific component is included in the training-use signal, to generate a data series signal in which the specific component has been separated and removed from a data series of the training-use signal; acquiring an arbitrary signal including the common characteristic; and generating, based on the acquired arbitrary signal and the generated learned data, a data series signal in which the specific component has been separated and removed from a data series of the arbitrary signal.