The arithmetic device configured to perform a calculation for controlling a motion of a grasping apparatus that performs work involving a motion of sliding a grasped object includes: an acquisition unit configured to acquire a state variable indicating a state of the grasping apparatus during the work; a storage unit storing a learned neural network that has been learned by receiving a plurality of training data sets composed of a combination of the state variable acquired in advance and correct answer data corresponding to the state variable; an arithmetic unit configured to calculate a target value of each of various actuators related to the work of the grasping apparatus by inputting the state variable to the learned neural network read from the storage unit; and an output unit configured to output the target value of each of the various actuators to the grasping apparatus.

A system for monitoring a condition of a machine includes an acoustic detector configured to capture an audio signal of the machine. A controller is communicatively coupled to the audio detector and configured to transmit the audio signal to a remote computing unit. The remote computing unit configured to generate a condition status signal based on at least one of an unsupervised machine learning process or a supervised machine learning process. The controller is configured to receive the condition status signal from the remote computing unit and communicate a condition status based on the received condition status signal.

An NC unit calculates the phase difference in chatter vibration during working on the basis of detection result of sound produced by working a workpiece by an end mill, increases the number of rotations of the end mill by a predetermined number if the phase difference is smaller than a first phase difference threshold, and decreases the number of rotations of the end mill by a predetermined number if the phase difference is larger than a second phase difference threshold. Further, if the phase difference is between the first phase difference threshold and the second phase difference threshold, the NC unit finds the resonance frequency of a machine tool by multiplying a chatter frequency by a correction factor that changes according to the chatter frequency, and calculates the number of rotations of the end mill on the basis of the resonance frequency, to obtain stable working with suppressed chatter vibration.

An automatic system level testing (ASLT) system for testing smart devices is disclosed. The system comprises a system controller coupled to a smart device in an enclosure, wherein the system controller comprises a memory comprising test logic and a processor. The enclosure comprises a plurality of components, wherein the processor is configured to automatically control the smart device and the plurality of components in accordance with the test logic. The plurality of components comprises: (a) a robotic arm comprising a stylus affixed thereto; and (b) a platform comprising a device holder affixed thereto, wherein the smart device is inserted into the device holder; and (c) a wireless access point. The processor is further configured to: (a) control the smart device to activate wireless mode; (b) receive wireless signals from the wireless access point using the smart device; (c) retrieve wireless scan results from the smart device; and (d) analyze the wireless scan results.

A machine learning device for detecting an indication of an occurrence of chatter in a tool for a machine tool, includes a state observation unit which observes at least one state variable of a vibration of the machine tool itself, a vibration of a building in which the machine tool is installed, an audible sound, an acoustic emission and a motor control current value of the machine tool, in addition to a vibration of the tool; and a learning unit which generates a learning model based on the state variable observed by the state observation unit.

An automatic system level testing (ASLT) system for testing smart devices is disclosed. The system comprises a system controller coupled to and operable to stress a smart device in an enclosure, wherein the enclosure comprises a plurality of components, and wherein the system controller comprises: (a) a memory comprising test logic; and (b) a processor configured to automatically control the plurality of components and test the smart device in accordance with the test logic. Further, the plurality of components comprises: (a) a robotic arm comprising a stylus affixed thereto, wherein the stylus is operable to manipulate the smart device; and (b) a platform comprising a device holder affixed thereto, wherein the device holder is operable to receive the smart device, and wherein the platform and the robotic arm are robotically controlled to move by the processor.

An abnormal-sound detection device has an imaging unit, an operation range identification unit, a sound collection unit, an abnormal-sound detection unit, an abnormal-sound generation position identification unit, and an abnormal-sound source determination unit. The operation range identification unit identifies and stores the operation range of a diagnosis object on the basis of the image captured by an imaging unit. The abnormal-sound detection unit detects abnormalities in sounds included in the sounds collected by the sound collection unit, the sounds arriving from the diagnosis object. When an abnormality in a sound is detected by the abnormal-sound detection unit, the abnormal-sound generation position identification unit identifies the position at which the abnormality of the sound was generated. The abnormal-sound source determination unit compares the operation range and the abnormal-sound generation position of the diagnosis object, and determines whether the abnormality of the sound is derived from an abnormality of the diagnosis object.

According to one embodiment described herein aim to provide a mobile assist device capable of comprehensively determining (or judging) data output from various sensors, thereby determining (or judging) an item which should be controlled next. The mobile assist device includes a movable body, a sensors mounted on the movable body, and a control module which determines the brightness of the surroundings from an output of the sensor, and also outputs a control signal for controlling the operation of the other instrument based on a result of the determination.

An automatic system level testing (ASLT) system for testing smart devices is disclosed. The system comprises a system controller operable to be coupled with a smart device in an enclosure, wherein the system controller comprises a memory comprising test logic and a processor. The system also comprises the enclosure, wherein the enclosure comprises a plurality of components, the plurality of components comprising: (i) a robotic arm comprising a stylus, wherein the stylus is operable to manipulate the smart device to simulate human interaction therewith; and (ii) a platform comprising a device holder, wherein the device holder is operable to receive a smart device inserted there into. The processor is configured to automatically control the smart device and the plurality of components in accordance with the test logic.

A processor is configured to perform signal output processing for outputting an instruction signal indicative of an operational content corresponding to sensor data which is output from a sensor configured to sense an external environment, first device-control processing for controlling an operation of a device which is configured to perform the operation, based on the instruction signal output in the signal output processing, and second device-control processing for controlling the operation of the device, based on an interrupt signal which is output when the sensor data satisfies a predetermined condition processing.