Eagle Industry Co., Ltd.

Hitachi, Ltd.

Tdk Corporation

Nippon Telegraph and Telephone Corporation

Honda Motor Co., Ltd.

Other

Mitsubishi Heavy Industries Limited

Asahi Glass Company, Limited

Hitachi Astemo Ltd.

Kabushiki Kaisha Toyota Jidoshokki

Paramount Bed Company, Limited

Yasuhiro Ikeda

Atsushi Sano

Yutaka Uematsu

Keishiro Watanabe

Keisuke Ishibashi

Ryoichi Kawahara

Yusuke Nakano

Satoshi Muraoka

Hayahito Ukai

Takeshi Yamada

Masaki Sobu

Takayuki Imai

Kazumasa Tanaka

Akinobu Nojima

Takehiro Horigome

Hironori Deno

Shota Kawanaka

Hiroshi Sakamoto

Masatoshi Itadani

Yuriko Kaida

Eiji Shidoji

Satoshi Yamanaka

Hiroshi Ebihara

Yasuyuki Kawanaka

Yasuhisa Arita

Tadahiro Kimura

Takahira Miyagi

Hitoshi Ito

Shinobu Munekata

Yuhei Matsuo

Takanobu Otani

Toshihiko Matsuo

Hiromi Sakurai

Yasushi Takayama

Tomoki Hanada

Takeshi Itagaki

Kazuki Toyooka

Hiroshi Kuzumi

Junji Ogawa

Hideyuki Sakamoto

Hidekazu Uehara

Takeshi Hosoe

Hideyuki Koseki

Norio Chujo

Tadanobu Toba

Kenichi Shimbo

Masaya Agata

Hikaru Katori

Masaru Sakai

Atsushi Arata

Masamitsu Sanada

Satoshi Sekiguchi

Kenji Kiryu

Yuji Harada

Toru Yazaki

Shinichi Nonaka

Takeo Yamashita

Yoichi Matsuo

Akihiko Umeda

Atsushi Ichige

Takahiko Iwasaki

Tetsuo Takaishi

Akihiro Inamura

Hideki Maruyama

Koichiro Oiyama

Nobuo Nakahara

Masako Kawamoto

Ken Yoshikawa

Kentarou Saeki

Masami Osako

Masatsugu Oshimi

Masato Shimizu

Koichi Mizunoya

Nobuyuki Hosaka

Tomoki Miyake

Masanobu Kimura

Hajime Ichiyasu

Ichiro Miyai

Tatuhiko Watanabe

Kengo Tajiri

Shungo Okabe

Norio Moteki

Yukikazu Fukumoto

Tomoko Yamaki

Hiroomi Sakuma

Nobushige Nakajima

Kimihiro Tani

Hiroyuki Tokumasu

An arithmetic operation device executes a test using a partially reconfigurable programmable logic unit, the programmable logic unit includes a test target circuit which is a user circuit, and a non-test circuit which is a user circuit which is not the test target circuit, and the arithmetic operation device includes a configuration control unit which causes the programmable logic unit to form, by partial reconfiguration, a test partition unit which separates the test target circuit and the non-test circuit, and a partition control unit which controls the test partition unit to test the test target circuit.

Arithmetic operation device, testing method

A feature value generation device includes a generator configured to generate vectors whose elements are feature values of data items collected at a plurality of timings from a target of anomaly detection, so as to normalize or standardize the vectors based on a set of predetermined vectors; a learning unit configured to learn the predetermined vectors so as to output a learning result; and a detector configured to detect, for each of the vectors normalized or standardized by the generator, an anomaly based on said each of the vectors and the learning result. The set of predetermined vectors is a set of vectors with which no anomaly is detected by the detector, and the set of vectors is updated in accordance with no anomaly being detected by the detector.

Anomaly detection method using an autoencoder learning from data items collected by measuring devices

A vehicle power supply system, mounted on a vehicle including an internal combustion engine, includes: a main power supply system including a main low-voltage power supply; and a backup power supply system including a backup low-voltage power supply. A starter motor that starts the internal combustion engine is connected to the main power supply system. At least one of a vehicle control device, configured to control the main power supply system, the backup power supply system, the internal combustion engine and the starter motor, and a backup power supply control device of the backup power supply system is configured to execute an abnormality determination processing of determining whether an abnormality occurs in the main power supply system, and does not execute the abnormality determination processing when the starter motor is in operation, or determines that no abnormality occurs in the main power supply system in the abnormality determination processing.

Vehicle power supply system

An electronic control device includes a receiving unit that receives a data signal from an external device via a transmission line, a determination criterion selection unit that determines a determination criterion based on transition information of a waveform in the data signal, and a condition determination unit that determines a condition of a communication system including the external device, the receiving unit, and the transmission line based on a transmission waveform characteristic in the data signal and the determination criterion.

Electronic control device and determination method

A wiring substrate is connected to a backplane, and includes: a first connector that is mounted on one surface of the wiring substrate and is connected to the backplane; an opening portion that is formed in the one surface on a side opposite to a side connected to the backplane of the first connector, and through which a cable having one end connected to the first connector is passed; an integrated circuit that is mounted on the one surface on a side opposite to a side on which the first connector is present relative to the opening portion; and a second connector that is mounted on the other surface on a side opposite to the one surface in the vicinity of the integrated circuit on the side opposite to the side on which the first connector is present relative to the opening portion, is connected to the integrated circuit via a through hole penetrating the wiring substrate, and is connected to the other end of the cable.

Wiring substrate and information processing device

An anomaly factor estimation apparatus includes a memory and a processor configured to execute training an anomaly detection model for performing anomaly detection based on an error of output data with respect to input data represented by a multidimensional numerical vector; inputting test data into the anomaly detection model, to determine whether the test data corresponds to an anomaly based on output data output from the anomaly detection model and the test data; and calculating a degree of contribution to the anomaly for each dimension of the test data based on a likelihood of said each dimension, in response to determining that the test data corresponds to the anomaly.

Anomaly factor estimation apparatus, anomaly factor estimation method and program

A vehicle power supply system, being mounted on a vehicle, includes: a main power supply system including a main low-voltage power supply and a normal load; and a backup power supply system including a backup low-voltage power supply and an emergency important load and connected to the main power supply system. A backup power supply control device of the backup power supply system is configured to execute a backup low-voltage power supply state estimation processing, and output a signal indicating that the backup low-voltage power supply is in a state allowing supplying electric power for operating the emergency important load based on an estimation result of the backup low-voltage power supply state estimation processing, in a case in which the state of the vehicle does not satisfy a predetermined condition.

A feature value generation device includes a generator configured to digitize non-numerical text data items collected at a plurality of timings from a target of anomaly detection, to generate vectors whose elements are feature values corresponding to the digitized data items; a learning unit configured to learn the vectors during a learning period so as to output a learning result; and a detector configured to detect, during a test period, for each of the vectors generated by the generator, an anomaly based on said each of the vectors and the learning result.

Feature value generation device, feature value generation method, and program

An anomaly detection apparatus having a function of an autoencoder that includes an input layer, hidden layers, and an output layer, and that learns parameters such that data of the input layer is reproduced in the output layer, the anomaly detection apparatus including: input means that input normal data of a plurality of types; learning means that learn parameters such that normal data of the input layer is reproduced in the output layer by learning a feature across data types using data of a dimension which is smaller than a dimension of the normal data; and anomaly degree calculation means that input test data to the autoencoder using the parameters learned by the learning means, and calculate anomaly degree of the test data based on output data of the autoencoder and the test data.

Anomaly detection apparatus, anomaly detection method, and program

An automatic driving executability determination unit restricts automatic driving when a charge amount determination unit (third determination unit) determines that a battery is in a low capacity state, and decides whether to restrict the automatic driving according to a determination result of a discharge performance determination unit (first determination unit) when a temperature determination unit (second determination unit) determines that the battery is in the low capacity state but a charge amount determination unit determines that the battery is not in the low capacity state.

Vehicle control device

A device for estimating a cause of an anomaly comprises: a detection unit to detect an anomaly in a detection target based on a learner trained on first numerical vectors obtained from a detection target when the detection target is under a normal condition and second numerical vectors to be obtained from the detection target at multiple time; and a first computing unit to compute, for each metric of a second numerical vector from which an anomaly has been detected, as information for estimating a metric of cause of the anomaly, a value obtained by subtracting, from a value of the metric, an average of the metric in the first numerical vectors, and dividing a result of the subtracting by standard deviation of the metric in the first numerical vectors. This device supports estimation of the cause of an anomaly detected in a target object for detecting an anomaly.

Anomaly factor estimation device, anomaly factor estimation method, and storage medium

An anomaly location identification device includes a determination unit configured to determine presence or absence of an anomaly by inputting part or all of information items output from a plurality of devices into an anomaly detection algorithm; a calculation unit configured to calculate, in response to a determination made by the determination unit that an anomaly is present, with respect to one of the information items, an index indicating a degree of contribution to the anomaly; and an identification unit configured to perform calculation by an analysis algorithm using a causal model receiving the index as input, to identify an anomalous device, to improve the precision and calculation speed related to identification of an anomaly location.

Anomaly location identification device, anomaly location identification method, and program

Provided is a vehicle control device that prevents a specific function of a traveling vehicle from being not able to operate due to a failure of a battery. The vehicle control device includes: a control section configured to execute predetermined function control for a vehicle system; a discharge performance determination section configured to determine whether or not a battery is able to discharge a predetermined amount of current in accordance with a predetermined function before the predetermined function control is started; and an execution availability determination section configured to permit the control section to execute the predetermined function when it is determined that the predetermined amount of current is able to be discharged and inhibit the control section from executing the predetermined function when it is determined that the predetermined amount of current is not able to be discharged.

When replacement of a battery is detected, an automatic driving executability determination unit determines whether the battery is in a low capacity state until a predetermined time elapses, using battery information (battery charge amount, discharge performance) excluding an estimation value of a temperature of the battery, and restricts automatic driving according to such a determination result.

A mechanical seal capable of preventing charge accumulation on a rotary shaft includes a fixed sealing element which is held at an inner periphery of housing members of machines to be installed and has at least a fixed seal ring, and a rotating sealing element which is held at an outer periphery of a rotary shaft rotatably supported in the housing members and has at least a rotating seal ring. A conductive element is provided between the fixed sealing elementand the rotary shaft or a rotary shaft member fixed to the rotary shaft.

Mechanical seal

Growing community of inventors

Tokyo, Japan

Yasuhiro Ikeda