Nec Corporation

Jeol Ltd.

Yuki Chiba

Kenji Sobata

Asako Fujii

Yoji Miyazaki

Masayuki Sakata

Ryohei Fujimaki

Satoshi Morinaga

Yousuke Motohashi

Erik Kruus

Yasunori Futatsugi

Iain Melvin

Atsushi Fukuzato

Kazunori Tsukamoto

Jun Nakayamada

Yoshihiro Mishima

Yusuke Koitabashi

Takuroh Kashima

Chris White

A classification apparatus according to the present disclosure includes an input unit configured to receive an operation performed by a user, an extraction unit configured to extract moving image data by using a predetermined rule, a display control unit configured to display an icon corresponding to the extracted moving image data on a screen of a display unit, a movement detection unit configured to detect a movement of the icon on the screen caused by the operation performed by the user, and a specifying unit configured to specify a classification of the moving image data corresponding to the icon based on a position of the icon on the screen.

Classification apparatus, classification method and computer readable medium

A dangerous scene prediction devicefor predicting a dangerous scene occurring during driving of a vehicle includes a learning model selection/synthesis unitand a dangerous scene prediction unit. The learning model selection/synthesis unitselects, from two or more learning models, a learning model used for predicting the dangerous scene, depending on a scene determined based on information obtained during the driving of the vehicle. The dangerous scene prediction unitpredicts the dangerous scene occurring during the driving of the vehicle, using the selected learning model.

Dangerous scene prediction device, dangerous scene prediction method, and dangerous scene prediction program

A comfort determination model learning unitlearns a comfort determination model, by using comfortable activity data where a comfort indicator, which is an indicator measuring whether an individual is comfortable or not when an activity classified as a comfortable activity is performed, is associated with a teacher label indicating comfort, and uncomfortable activity data where the comfort indicator when an activity classified as an uncomfortable activity is performed, is associated with a teacher label indicating discomfort, as first training data, taking an objective variable for a comfort value indicating a degree of comfort, and taking an explanatory variable for each of the comfort indicators. An individual data generation unitgenerates individual data including explanatory variables, which are used in the comfort determination model, generated based on the comfort indicators of the subject during riding on a vehicle, and driving situations of the vehicle when the comfort indicators are obtained. A driving data generation unitgenerates comfortable driving data and uncomfortable driving data according to a comfort value calculated by applying the individual data to the comfort determination model.

Comfort driving data collection system, driving control device, method, and program

A focus adjustment method for a charged particle beam device having a magnetic field lens used for focus adjustment and an astigmatism corrector includes: acquiring a plurality of first images by varying an excitation current of the magnetic field lens within a focus search range, and determining a reference value of the excitation current; removing hysteresis from the magnetic field lens by setting the excitation current of the magnetic field lens outside the focus search range before and after varying the excitation current of the magnetic field lens within the focus search range; and acquiring a plurality of second images by varying the excitation current of the magnetic field lens using the reference value as a reference and varying a stigma correction value of the astigmatism corrector at each excitation current, and then determining optimum values of the excitation current and the stigma correction value.

Focus adjustment method for charged particle beam device and charged particle beam device

An apparatus that extracts an explanatory variable used as a condition from a classification model classified by the condition for selecting a component used for prediction, displays the explanatory variable in association with any of dimensional axes of a multi-dimensional space in which a prediction value is displayed, specifies the component that corresponds to a position in the multi-dimensional space specified by each of the explanatory variables associated with the dimensional axis, displays the prediction value calculated based on the specified component, on the same position and displays the multi-dimensional space that corresponds to the position in which the prediction value is displayed, in a mode that corresponds to the component used for calculating the prediction value.

Prediction result display system, prediction result display method, and prediction result display program

Provided are a motion recognition apparatus, a motion recognition system and a motion recognition method that enable 'event motions' to be recognized with a small number of calculations. The motion recognition system, which recognizes user motions by using sensor data, is configured to be provided with: a cyclical loss detection means for detecting cyclical losses of sensor data when a user is moving; and a recognition processing means for setting data intervals to be used for recognizing motions in accordance with the cyclical losses of sensor data that were detected, and for recognizing user motions on the basis of sensor data for the data intervals that have been set.

Motion recognition apparatus, motion recognition system, and motion recognition method

A position information providing apparatus includes a sensor data acquiring unit acquiring sensor data, a feature amount calculating unit calculating a feature amount from the sensor data, and a confidence level calculating unit calculating a confidence level using the feature amount, wherein the sensor data is more than one, and the feature amount includes a statistical amount of more than one piece of the sensor data and an amount showing a form of the sensor data.

Growing community of inventors

Tokyo, Japan

Yuki Chiba