Abstract: The memory error determination device includes a processor configured to: detect a memory element in which an error has occurred in each of a plurality of layers included in a memory being three-dimensionally stacked, specify a position of each memory element in which the error has occurred in each of the plurality of layers, and determine that, when the position of each memory element in which the error has occurred is linearly aligned across a predetermined number of layers among the plurality of layers, the predetermined number being two or more, an error that has occurred in the memory is a soft error due to radiation incident on the memory.

Abstract: The memory error determination device includes a processor configured to: detect a memory element in which an error has occurred in each of a plurality of layers included in a memory being three-dimensionally stacked, specify a position of each memory element in which the error has occurred in each of the plurality of layers, and determine that, when the position of each memory element in which the error has occurred is linearly aligned across a predetermined number of layers among the plurality of layers, the predetermined number being two or more, an error that has occurred in the memory is a soft error due to radiation incident on the memory.

Abstract: To enable adjustment of digital filters suited to disturbances occurring in the surroundings. A receiving device includes: a digital filter that eliminates or attenuates a disturbance included in a signal received through a communication line; a coefficient adjusting unit that adjusts a coefficient of the digital filter based on operation schedule information of a device causing the disturbance in the communication line; and an information table that records a combination of operation information included in the operation schedule information and a coefficient of the digital filter corresponding to the operation information or correction information of the coefficient, in which the coefficient adjusting unit calculates the coefficient of the digital filter or the correction information of the coefficient from the information table based on the operation information included in the operation schedule information, and adjusts the coefficient of the digital filter.

Abstract: A machining apparatus is provided with a machine learning device that performs machine learning. The machine learning device performs the machine learning by receiving the input of machining accuracy between a machining shape of a workpiece measured on-machine and design data on the workpiece and machining time of the workpiece measured by a measurement device. Based on a result of the machine learning, the machining apparatus changes machining conditions such that the machining accuracy increases and the machining time becomes as short as possible.

Abstract: A control device that performs encoding of data to a code word and decoding from the code word to data by an error correction code is disclosed. The control device has an error correction function by which a correctable error is automatically corrected in the decoding. The control device includes an error correction processing unit which switches error correction codes based on a correction rate, which is outputted from a correction rate calculation unit, of every state so as to perform encoding from data to a code word and decoding from the code word to data and output correction information related to error correction. A lifetime calculation unit calculates a device lifetime which is a current remaining lifetime of a device based on a correction capability value of a current error correction code, a current correction rate, and a failure factor coefficient of a current operation state.

Abstract: A controller has an error correction capability by including: a state monitoring unit that analyzes a state of a monitoring target and outputs state information; an error correction processing unit that switches error correction codes so that a correction rate for the respective states becomes a value within a predetermined range; and a correction rate calculation unit that calculates the correction rate for the respective states based on the correction result by the error correction processing unit.

Abstract: A machining apparatus is provided with a machine learning device that performs machine learning. The machine learning device performs the machine learning by receiving the input of machining accuracy between a machining shape of a workpiece measured on-machine and design data on the workpiece and machining time of the workpiece measured by a measurement device. Based on a result of the machine learning, the machining apparatus changes machining conditions such that the machining accuracy increases and the machining time becomes as short as possible.

Abstract: A control device includes an error correction processing unit which switches error correction codes based on a correction rate, which is outputted from a correction rate calculation unit, of every state so as to perform encoding from information data to a code word and decoding from a code word to information data and output correction information related to error correction, and a lifetime calculation unit which calculates a device lifetime which is a current remaining lifetime of a device based on a correction capability value of a current error correction code, a current correction rate, and a failure factor coefficient of a current operation state.

Abstract: A controller has an error correction capability by including: a state monitoring unit that analyzes a state of a monitoring target and outputs state information; an error correction processing unit that switches error correction codes so that a correction rate for the respective states becomes a value within a predetermined range; and a correction rate calculation unit that calculates the correction rate for the respective states based on the correction result by the error correction processing unit.

Abstract: An injection molding machine having a simple structure and adapted to quickly operate for injection molding without taking in air bubbles is provided. The injection molding machine comprises a plunger and a nozzle for injecting the molten metal fed from the plunger into a metal mold. The plunger has a cylinder section with a through hole bored through an upper portion of its lateral wall for introducing the molten metal, and a piston for slidingly reciprocating within the cylinder section. A communication hole is bored through the piston and communicates with the through hole at a predetermined position. The communication hole runs from the lateral wall of the piston through the inside thereof and has a non-return valve arranged therein to allow only a flow from the lateral wall of the piston.

Abstract: An injection molding machine having a simple structure and adapted to quickly produce molded products without significant flaws on the surface is provided. It comprises a plunger being adapted to introduce the molten metal into the cylinder section, a nozzle for injecting the molten metal fed from the plunger into a metal mold and a piston arranged within a cylinder section of the plunger to slidingly reciprocate. It includes a piston drive unit for driving the piston to slidingly reciprocate, a stopper member for controlling the retracted position of the piston drive unit and a position selecting unit for defining a projected position and a retracted position for the stopper member.

Abstract: A route generating device has an elevation map memory storing elevation data for subareas defined by division of an operations zone. A gradient map creating section calculates gradient data based on the elevation data and creates a gradient map from the gradient data. An obstruction region extracting section extracts an inclination obstruction region based on the gradient map. A traveling route generating section obtains a travelable region based on the inclination obstruction region. All possible traveling routes is calculated based on the travelable region. A route evaluating section evaluates the traveling routes and selects an optimum route from the current position to the destination. The gradient map is created based on the elevation data, it is possible to generate an optimum route without three dimensional obstruct such as a fall, a tumble and any other inability in movement of the movable object.