Abstract: The road obstacle detection device includes a semantic label estimation unit that estimates a semantic label for each pixel of an image using a classifier learned in advance and generates a semantic label image, an original image estimation unit for reconstruction of the original image from the semantic label image, a difference calculating unit for calculating a difference between the original image and the reconstructed image from the original image estimation unit as a calculation result, and a road obstacle detection unit for detecting a road obstacle based on the calculation result.

Abstract: A road obstacle detection device which uses a pre-learned first identifier to associate a semantic label with each pixel of an image, uses a pre-learned second identifier to estimate a statistical distribution of a semantic label of a predetermined region of interest of the image from a statistical distribution of a semantic label of a peripheral region that surrounds the region of interest, and uses the statistical distribution of the semantic label associated with the region of interest and the statistical distribution of the semantic label estimated for the region of interest to estimate a likelihood that an object is a road obstacle.

Abstract: In a road obstacle detection device, a first derivation unit derives, for each of a plurality of local regions, a probability that a local region is the road, such that the probability is higher as the ratio of a road region in the local region is higher; and a second derivation unit derives a probability that a target local region is not a previously decided normal physical body, and derives a probability that a road obstacle exists at the target local region, based on the derived probability that the target local region is not the normal physical body and a probability that a peripheral local region is the road, the peripheral local region being a local region at a periphery of the target local region, the probability that the peripheral local region is the road being derived by the first derivation unit.

Abstract: An image processing device includes a processor including hardware, the processor being configured to: generate a semantic label image by estimating a semantic label for each pixel of an input image by using a discriminator trained in advance; generate a restored image by estimating an original image from the semantic label image; calculate a first difference between the input image and the restored image; and update an estimation parameter for estimating the semantic label or an estimation parameter for estimating the original image based on the first difference.

Abstract: The road obstacle detection device includes a semantic label estimation unit that estimates a semantic label for each pixel of an image using a classifier learned in advance and generates a semantic label image, an original image estimation unit for reconstruction of the original image from the semantic label image, a difference calculating unit for calculating a difference between the original image and the reconstructed image from the original image estimation unit as a calculation result, and a road obstacle detection unit for detecting a road obstacle based on the calculation result.

Abstract: A road obstacle detection device which uses a pre-learned first identifier to associate a semantic label with each pixel of an image, uses a pre-learned second identifier to estimate a statistical distribution of a semantic label of a predetermined region of interest of the image from a statistical distribution of a semantic label of a peripheral region that surrounds the region of interest, and uses the statistical distribution of the semantic label associated with the region of interest and the statistical distribution of the semantic label estimated for the region of interest to estimate a likelihood that an object is a road obstacle.

Abstract: According to an embodiment of the present invention, a storage management apparatus to manage a plurality of storage combinations which are combinations of storages included in a storage group, includes a combination failure probability calculator and an allocation ratio calculator. The combination failure probability calculator calculates a failure probability of the storage combination based on a failure probability of each of the storages. The allocation ratio calculator calculates an allocation ratio of received data with respect to the storage combination based on the failure probability of the storage combination.

Abstract: According to one embodiment, physical position information on errors on a recording medium is acquired, physical position relationship between the errors on the recording medium is calculated based on the position information, and a failure mode related to the errors is determined based on the position relationship.

Abstract: According to one embodiment, there is provided a quality controlling device including: a predictor, a frequency calculator, and an implementing signal creator. The predictor employs a prediction model that associates an inspection result value of a first inspection with a predicted value being a value relating to a possibility of pass or failure in a second inspection and calculates the predicted value from an inspection result value that is obtained for an inspection target in the first inspection. The frequency calculator calculates, for the inspection target, an implementation frequency to implement the second inspection in accordance with the predicted value calculated by the predictor. The implementing signal creator creates a signal that indicates, for the inspection target, necessity of implementing the second inspection in accordance with the implementation frequency.

Abstract: According to one embodiment, a failure prognosis device includes circuitry configured to determine whether a sign of failure exists in a head, based on a signal quality value and a floating quantity of the head, the signal quality value being based on an error between a reproducing signal acquired from the head when reading data stored on a storage surface of a disk and a predetermined target signal, and output a determination result.

Abstract: According to one embodiment, physical position information on errors on a recording medium is acquired, physical position relationship between the errors on the recording medium is calculated based on the position information, and a failure mode related to the errors is determined based on the position relationship.

Abstract: According to one embodiment, a time-series data waveform analysis device implemented by a computer including at least one hardware processor is provided. The hardware processor configured to: add a shapelet being a part of a partial time series included in labeled time-series data to a shapelet set; randomly extract one or more labeled time-series data and calculate a feature value of the shapelet for the extracted labeled time-series data according to a TSS method; update a parameter, which includes the shapelet and a weight coefficient for the shapelet, based on the feature value according to a stochastic gradient descent method; remove the shapelet, the corresponding weight coefficient of which is 0, from the shapelet set; and create an evaluation function based on the shapelet in the shapelet set and the weight coefficient.

Abstract: According to one embodiment, a failure prognosis device includes circuitry configured to determine whether a sign of failure exists in a head, based on a signal quality value and a floating quantity of the head, the signal quality value being based on an error between a reproducing signal acquired from the head when reading data stored on a storage surface of a disk and a predetermined target signal, and output a determination result.

Abstract: According to an embodiment of the present invention, a storage management apparatus to manage a plurality of storage combinations which are combinations of storages included in a storage group, includes a combination failure probability calculator and an allocation ratio calculator. The combination failure probability calculator calculates a failure probability of the storage combination based on a failure probability of each of the storages. The allocation ratio calculator calculates an allocation ratio of received data with respect to the storage combination based on the failure probability of the storage combination.

Abstract: According to one embodiment, there is provided a quality controlling device including: a predictor, a frequency calculator, and an implementing signal creator. The predictor employs a prediction model that associates an inspection result value of a first inspection with a predicted value being a value relating to a possibility of pass or failure in a second inspection and calculates the predicted value from an inspection result value that is obtained for an inspection target in the first inspection. The frequency calculator calculates, for the inspection target, an implementation frequency to implement the second inspection in accordance with the predicted value calculated by the predictor. The implementing signal creator creates a signal that indicates, for the inspection target, necessity of implementing the second inspection in accordance with the implementation frequency.

Abstract: The motor driving circuit includes a first converting circuit that outputs an analog voltage proportional to a rotational speed of a motor, a differential voltage calculating circuit that calculates a differential voltage between the analog voltage and a rotation instruction voltage that prescribes the rotational speed of the motor and outputs a differential voltage signal including information on the differential voltage, a duty controlling circuit that outputs, based on the differential voltage signal, a duty controlling signal including information on a control duty that controls a duty of a PWM signal so as to bring the differential voltage between the rotation instruction voltage and the analog voltage close to zero, and a motor driving waveform controlling circuit that generates the PWM signal in response to a signal based on the duty controlling signal and that outputs the PWM signal.

Abstract: The characteristic is defined by a characteristic line that connects a lowest point at which the duty of the PWM signal is a minimum value and the rotation command signal is a minimum rotation command signal, and a highest point at which the duty of the PWM signal is a maximum value and the rotation command signal is a maximum rotation command signal. The duty calculating circuit updates the characteristic so that the characteristic line passes through a first control point at which the duty of the PWM signal is a value set based on the first setting signal and the rotation command signal is a first rotation command signal between the minimum rotation command signal and the maximum rotation command signal.

Abstract: The driver circuit includes a first controlling circuit that outputs, to a gate of the auxiliary pMOS transistor, a first controlling signal that rises in synchronization with a rising of the first pulse signal and falls after a delay from a falling of the first pulse signal. The driver circuit includes a second controlling circuit that outputs, to a gate of the auxiliary nMOS transistor, a second controlling signal that rises in synchronization with a rising of the second pulse signal and falls after a delay from a falling of the second pulse signal.

Abstract: The characteristic is defined by a characteristic line that connects a lowest point at which the duty of the PWM signal is a minimum value and the rotation command signal is a minimum rotation command signal, and a highest point at which the duty of the PWM signal is a maximum value and the rotation command signal is a maximum rotation command signal. The duty calculating circuit updates the characteristic so that the characteristic line passes through a first control point at which the duty of the PWM signal is a value set based on the first setting signal and the rotation command signal is a first rotation command signal between the minimum rotation command signal and the maximum rotation command signal.

Abstract: A motor driving circuit controls driving of a motor based on communications with an external MCU. The motor driving circuit has a first port that receives a first digital signal outputted from the MCU. The motor driving circuit has a duty measuring circuit that measures a duty of the first digital signal inputted through the first port and outputs a duty information signal corresponding to the measured duty. The motor driving circuit has a frequency measuring circuit that measures a frequency of the first digital signal and outputs a frequency information signal corresponding to the measured frequency of the first digital signal.