Abstract: A device for assisting molding condition determination includes a molding state data adjustment amount obtaining unit and a molding condition element adjustment amount obtaining unit. The molding state data adjustment amount obtaining unit obtains, using a first learning model, a molding state data adjustment amount having a value equivalent to a difference between molding state data detected by a sensor and a molding state data target value. The molding condition element adjustment amount obtaining unit obtains, using a second learning model, an adjustment amount for a molding condition element corresponding to the molding state data adjustment amount.

Abstract: A degraded state estimation apparatus configured to estimate a degraded state of a battery mounted on a vehicle includes a detector, a storage, and an estimation processor. The detector detects state quantities including first, second, and third state quantities. The storage holds a maps of data including first and second maps of data. The first map of data indicates a degree of degradation of the battery based on the first and second state quantities. The second map of data indicates the degree of the degradation based on the second and third state quantities. The estimation processor estimates an amount of change in the degradation of the battery on the basis of the state quantities detected repetitively by the detector, the maps of data, and weighting coefficients each of which weights the degree of the degradation indicated by a corresponding one of the maps of data.

Abstract: A processing device includes one or more processors and one or more memories coupled to the one or more processors. The one or more processors are configured to perform a process including: acquiring pattern information, which is information indicating an operation pattern of at least one traffic light that is present on a traveling route of a vehicle; predicting, based on the pattern information, a number of stops, which is a number of times the vehicle is to stop at the at least one traffic light; and predicting, based on the number of stops, a total consumption amount of an energy source of the vehicle in a case where the vehicle travels on the traveling route.

Abstract: An injection molding device includes an injection device including a cylinder, a screw, and a nozzle provided at a front end of the cylinder, for discharging molten resin as the screw advances, a mold including a molded product cavity, and a resin channel between the molded product cavity and a portion abutting the nozzle, a channel pressure measuring device for acquiring channel pressure data in the resin channel, and a determination unit that determines presence/absence of clogging of a gate of the resin channel, based on features of the channel pressure data.

Abstract: There are provided a molding conditions determination assist device and a resin state estimation device including: a quality estimation unit which estimates quality of the mold products by a machine learning on the basis of a detection data; a quality transition storage unit which accumulates the estimated quality of the mold products and stores a quality transition with respect to the accumulated quality of the mold products; a tendency evaluation unit which evaluates a quality change tendency with respect to a prescribed standard quality on the basis of the quality transition; a relationship storage unit which stores a relationship between the quality change tendency and a modification amount of the molding conditions for returning the quality to the standard quality; and a modification conditions determination unit which determines the modification amount of the molding conditions on the basis of the quality change tendency and the relationship.

Abstract: A hinge cap includes a body and an upper lid connected to each other by a hinge. The body includes a top board and a side wall extending perpendicularly downward from a periphery of the top board. The top board includes a planned opening defined by a weakened portion and a discharge nozzle formed to surround the planned opening. The upper lid includes a top panel and a skirt extending perpendicularly downward from a periphery of the top panel. A protrusion extending in an axial direction of the cap is formed in the planned opening of the body on a side opposite to the hinge such that an end of the protrusion is fixed to an inner surface of the top panel of the upper lid in a closed state.

Abstract: A power storage apparatus includes a first storage battery, a first flow path, a connector, a pump, and a controller. The first flow path allows a coolant to flow therein to the first storage battery. The connector couples the first storage battery to an electric power device of an external apparatus including the electric power device and a second flow path. The connector couples the first flow path to the second flow path and thereby allows the first and second flow paths to form a circulatory flow path. The second flow path allows the coolant to flow therein. The pump is provided on the first flow path and controls a flow rate and a flowing direction of the coolant. The controller controls an operation of the pump. The controller switches the flowing direction of the coolant multiple times after the first and second flow paths are coupled to each other.

Abstract: A charging system includes a charging control unit, a detector, an acquisition unit, a calculation unit, and an estimation unit. The charging control unit controls a charging operation performed from a power feeding system including a power storage device to a storage battery. The detector detects, multiple times, charging electric power supplied to the storage battery. The acquisition unit acquires pieces of charging data each including data indicating a temporal change in the charging electric power supplied from the power feeding system to the storage battery. The calculation unit calculates respective approximate curves of the pieces of charging data. The estimation unit estimates a power feedable time during which the power feeding system is able to feed power, based on the approximate curves calculated based on the pieces of charging data, by using values of the charging electric power detected by the detector.

Abstract: A battery deterioration degree prediction apparatus comprising an obtaining unit and a controller. The obtaining unit obtains one or more histories of types of operation parameters for a target battery and reference batteries and a degree of deterioration of the reference batteries. The controller predicts a degree of deterioration of the target battery. Using map data where groups into which the reference batteries are classified and coefficients representing rates of change in the degrees of deterioration of the reference batteries are associated with each other. The groups in the map data are classified by a trend of histories of a first group parameter included in the types of operation parameters. The coefficients in the map data are derived based on the histories for the reference batteries and the degrees of deterioration of the reference batteries belonging to one of the groups that is associated with the coefficients.

Abstract: A vehicle includes a charging port, an in-vehicle battery, a control apparatus comprising one or more processors. The control apparatus is configured to control power supply in response to reception of a power supply request. The one or more processors perform setting a charge start time, starting the power supply to the power system if the power supply request is received before the charge start time, controlling the power of the power supply so that State of Charge in the in-vehicle battery becomes lower than or equal to a predetermined value before the charge start time, and controlling the power of the power supply so as to be lower than or equal to a predetermined upper limit at which increase in deterioration in the in-vehicle battery due to the power supply can be suppressed.

Abstract: A machine learning device includes a sparse modeling processing unit and a selection unit. The sparse modeling processing unit acquires individual importance degrees for each of explanatory variable candidates, the individual importance degrees being acquired by using respective sparse modeling methods different from each other, each of the sparse modeling methods taking input data including a specified objective variable in a learning model used for industrial activity and the explanatory variable candidates that are candidates for an explanatory variable for explaining the specified objective variable. The selection unit calculates a comprehensive importance degree for each of the explanatory variable candidates based on the individual importance degrees of each of the explanatory variable candidates, and selects an explanatory variable of the learning model from among the explanatory variable candidates based on the comprehensive importance degree.

Abstract: A degraded state estimation apparatus configured to estimate a degraded state of a battery mounted on a vehicle includes a detector, a storage, and an estimation processor. The detector detects state quantities including first, second, and third state quantities. The storage holds a maps of data including first and second maps of data. The first map of data indicates a degree of degradation of the battery based on the first and second state quantities. The second map of data indicates the degree of the degradation based on the second and third state quantities. The estimation processor estimates an amount of change in the degradation of the battery on the basis of the state quantities detected repetitively by the detector, the maps of data, and weighting coefficients each of which weights the degree of the degradation indicated by a corresponding one of the maps of data.

Abstract: A vehicle includes at least one processor, at least one memory, and a storage. The processor functions as a current-value acquirer that acquires a current value of a battery mounted on the vehicle, a current-value frequency distribution deriver that increments an acquisition count for one of classes, to which the current value belongs, and derives a current-value frequency distribution representing acquisition counts for the respective classes, and a current-value rate distribution deriver that transforms the acquisition counts to acquisition rates each indicating a rate of the each of the acquisition counts to a total of the acquisition counts and derives a current-value rate distribution representing the acquisition rates.

Abstract: There are provided a molding conditions determination assist device and a resin state estimation device including: a quality estimation unit which estimates quality of the mold products by a machine learning on the basis of a detection data; a quality transition storage unit which accumulates the estimated quality of the mold products and stores a quality transition with respect to the accumulated quality of the mold products; a tendency evaluation unit which evaluates a quality change tendency with respect to a prescribed standard quality on the basis of the quality transition; a relationship storage unit which stores a relationship between the quality change tendency and a modification amount of the molding conditions for returning the quality to the standard quality; and a modification conditions determination unit which determines the modification amount of the molding conditions on the basis of the quality change tendency and the relationship.

Abstract: A metal cap in which a linear to be held to a cap body and separable from the cap body can be formed by a series of operations for forming the cap body and press molding the liner. The liner of the metal cap has a double-layer structure including a barrier layer on a top panel wall side and a sealing layer on the inner side of the cap body, and the barrier layer is temporarily bonded to the top panel wall with a weak adhesive at the time of molding the barrier layer and separated from the top panel wall by the natural cooling of the barrier layer after molding.

Abstract: A container lid is configured such that repeated movement of a grip piece and a connecting piece of the lid will not break the connecting piece so that the lid can be removed easily. An extension section extends upwardly from an upper end of the connecting section along an inner peripheral surface of a skirt wall of the lid. When the container lid is on the mouth-neck section of a container, an upper end part of the extension section is sandwiched between an inner peripheral surface of the lid skirt wall and an outer peripheral surface of a locking projection on the container mouth-neck section.

Abstract: A machine learning device includes a sparse modeling processing unit and a selection unit. The sparse modeling processing unit acquires individual importance degrees for each of explanatory variable candidates, the individual importance degrees being acquired by using respective sparse modeling methods different from each other, each of the sparse modeling methods taking input data including a specified objective variable in a learning model used for industrial activity and the explanatory variable candidates that are candidates for an explanatory variable for explaining the specified objective variable. The selection unit calculates a comprehensive importance degree for each of the explanatory variable candidates based on the individual importance degrees of each of the explanatory variable candidates, and selects an explanatory variable of the learning model from among the explanatory variable candidates based on the comprehensive importance degree.

Abstract: To provide a quality prediction system predicting a quality element of a molded item using machine learning. The quality prediction system includes a sensor disposed in the mold and configured to detect state data regarding the molten material supplied in the cavity, a learned-model storage unit configured to store a model which is a learned model generated by machine learning in which the state data detected by at least the sensor is used as a training data set and is a learned model related to the state data and a quality element of the molded item, and a quality prediction unit configured to predict the quality element of the molded item which is newly molded based on the state data newly detected by the sensor and the learned model.

Abstract: A device for assisting molding condition determination is used with a molding method that molds an article by feeding molten material into a mold. The device includes a learning model generating unit, an input unit, and an output unit. The learning model generating unit creates a learning model through machine learning in which a plurality of molding condition element items used to mold the article and a plurality of quality element items of the molded article are used as learning data. The learning model relates to a degree of influence of each molding condition element item on each quality element item. The input unit receives input of a subject quality element item to be checked, selected from the quality element items. The output unit outputs, using the learning model, the multiple molding condition element item that has the degree of influence on the subject quality element item.

Abstract: A deterioration determining apparatus includes: an operating condition acquirer to acquire an operating condition of a processing device; a processing state data acquirer to acquire processing state data detected by a sensor attached to the processing device; a learning model generator to conduct machine learning using, as learning data, the operating condition and the processing state data so as to preliminarily generate a learning model concerning the operating condition and the processing state data; an actual data acquirer to acquire actual data that is the processing state data at a determining time; a predicted data acquirer to acquire, using the learning model, predicted data that is the processing state data for the operating condition at the determining time; and a determiner to determine the degree of deterioration in the processing device in accordance with the degree of divergence between the actual data and the predicted data.