Abstract: A machine tool system is equipped with a machine tool and a controller. The machine tool is equipped with a machine tool body and at least one temperature sensor that acquires temperature data of the machine tool body. The controller is equipped with a storage that stores the temperature data acquired in time-series by the temperature sensor, and an auxiliary power supply that supplies power to the temperature sensor and the storage when the supply of power to the machine tool body is stopped. The controller controls the machine tool by using the temperature data over a predetermined time range stored in the storage, and selects, in response to a charge state of the auxiliary power supply, the temperature data to be used in first control after the supply of power to the machine tool body is resumed.

Abstract: A machine tool system is equipped with a machine tool and a controller. The machine tool is equipped with a machine tool body and at least one temperature sensor that acquires temperature data of the machine tool body. The controller is equipped with a storage that stores the temperature data acquired in time-series by the temperature sensor, and an auxiliary power supply that supplies power to the temperature sensor and the storage when the supply of power to the machine tool body is stopped. The controller controls the machine tool by using the temperature data over a predetermined time range stored in the storage, and selects, in response to a charge state of the auxiliary power supply, the temperature data to be used in first control after the supply of power to the machine tool body is resumed.

Abstract: Provided are a circuit configuration optimization apparatus and a machine learning device capable of reducing the occurrence frequency of a malfunction based on one of the current position and the current time of a FPGA device. The circuit configuration optimization apparatus includes: a state data acquisition section that acquires at least one of a current position and current time of the FPGA device as state data; and a circuit configuration determination section that determines a circuit configuration of the FPGA device based on the state data acquired by the state data acquisition section, and outputs a command value for reconfiguring the determined circuit configuration on the FPGA device.

Abstract: A storage unit stores first information on at least one of a machine tool and a peripheral device of the machine tool. A control unit includes a generation unit configured to generate a first light emitting pattern which is recognizable to human eyes and which includes a lighting-on state, a lighting-off state, or a lighting-on/off state of a light emitting element, a modulation unit configured to modulate the first information into a light communication signal based on lighting-on/off of the light emitting element unrecognizable to human eyes but recognizable to an electronic device, a superimposition unit configured to superimpose the light communication signal on the first light emitting pattern, thereby generating a second light emitting pattern including the light communication signal, and a light emitting element control unit configured to control the light emitting element on a basis of the second light emitting pattern generated by the superimposition unit.

Abstract: A circuit configuration optimization apparatus includes a machine learning device that learns a circuit configuration of a FPGA device. The machine learning device observes circuit configuration data of the FPGA device and FPGA error occurrence state data indicating an error occurrence state of the FPGA device as state variables that express a current state of an environment. In addition, the machine learning device acquires determination data indicating propriety determination results of an operating state of the FPGA device. Then, the machine learning device learns the circuit configuration of the FPGA device in association with the FPGA error occurrence state data, using the state variables and the determination data.

Abstract: A storage unit stores first information on at least one of a machine tool and a peripheral device of the machine tool. A control unit includes a generation unit configured to generate a first light emitting pattern which is recognizable to human eyes and which includes a lighting-on state, a lighting-off state, or a lighting-on/off state of a light emitting element, a modulation unit configured to modulate the first information into a light communication signal based on lighting-on/off of the light emitting element unrecognizable to human eyes but recognizable to an electronic device, a superimposition unit configured to superimpose the light communication signal on the first light emitting pattern, thereby generating a second light emitting pattern including the light communication signal, and a light emitting element control unit configured to control the light emitting element on a basis of the second light emitting pattern generated by the superimposition unit.

Abstract: Provided are a circuit configuration optimization apparatus and a machine learning device capable of reducing the occurrence frequency of a malfunction based on one of the current position and the current time of a FPGA device. The circuit configuration optimization apparatus includes: a state data acquisition section that acquires at least one of a current position and current time of the FPGA device as state data; and a circuit configuration determination section that determines a circuit configuration of the FPGA device based on the state data acquired by the state data acquisition section, and outputs a command value for reconfiguring the determined circuit configuration on the FPGA device.

Abstract: A circuit configuration optimization apparatus includes a machine learning device that learns a circuit configuration of a FPGA device. The machine learning device observes circuit configuration data of the FPGA device and FPGA error occurrence state data indicating an error occurrence state of the FPGA device as state variables that express a current state of an environment. In addition, the machine learning device acquires determination data indicating propriety determination results of an operating state of the FPGA device. Then, the machine learning device learns the circuit configuration of the FPGA device in association with the FPGA error occurrence state data, using the state variables and the determination data.