International Business Machines Corporation

Lenovo (singapore) Pte. Ltd.

Globalfoundries Inc.

Infoprintsolutions Company, LLC

Tadanobu Inoue

Alan Edward Rosenbluth

Kehan Tian

David Osmond Melville

Masaharu Sakamoto

Ryuki Tachibana

Noboru Kamijo

Asim Munawar

Hidemasa Muta

Phongtharin Vinayavekhin

Toru Aihara

Guillaume Jean Victor Marie Le Moing

Michiaki Tatsubori

Seiichi Kawano

Akira Koseki

Takayuki Katsuki

Kohji Takano

Takao Moriyama

Shunichi Amano

Jayakorn Vongkulbhisal

Subhajit Chaudhury

Giovanni De De Magistris

Don Joven Ravoy Agravante

Haoxiang Qiu

Masahura Sakamoto

Shu Morikuni

David Carroll Challener

Mikel J Stanich

Chai Wah Wu

Larry Mason Ernst

Philip Lee Childs

Saeed Reza Bagheri

Sakyasingha Dasgupta

Junya Shimizu

Kazuo Sekiya

Kei Kawase

Daiki Kimura

Takeo Nakada

Kazutoshi Sugimoto

Jonathan Lee

Don Joven R Agravante

Norman Arthur Dion, Ii

Marc Alan Szeto-Millstone

Andreas Waechter

Kohichiro Kishimoto

Shin Kurogi

One or more systems, devices, computer program products, and/or computer-implemented methods provided herein relate to accurate anomaly detection in images using patched features. According to an embodiment, an extraction component can extract multiple layers of features from one or more patches of an image using a pretrained convolutional neural network (CNN). A feature mapping component can concatenate the features from the multiple layers to generate a tensor feature map comprising a one-dimensional feature vector for respective patches. A cropping component can perform center cropping on the tensor feature map. A calculation component can calculate a distance to a feature distribution mean for respective patches.

Image anomaly detection by enhancing patched features

A computerized machine learning anomaly detection model trained on a plurality of samples of one or more source domains (optionally, one or more source domains and the target domain) is accessed. Using online deep sets, one or more domain vectors are computed for each target domain sample at an observation point, each target domain sample corresponding to a given target domain, where the one or more domain vectors represent a similarity and difference among the source and target domains. The target domain sample is processed using the anomaly detection model trained on the plurality of samples of the source to generate an anomaly score, the processing being based on the computed one or more domain vectors.

Transfer anomaly detection using online deep sets

Methods and computer program products for training a neural network perform multiple forms of data augmentation on sample waveforms of a training dataset that includes both normal and abnormal samples to generate normal data augmentation samples and abnormal data augmentation samples. The normal data augmentation samples are labeled according to a type of data augmentation that was performed on each respective normal data augmentation sample. The abnormal data augmentation samples are labeled according to a type of data augmentation other than that which was performed on each respective abnormal data augmentation sample. A neural network model is trained to identify a form of data augmentation that has been performed on a waveform using the normal data augmentation samples and the abnormal data augmentation samples.

Sound anomaly detection with mixed augmented datasets

A computer-implemented method is provided for training a multi-source sound localization model using labeled simulation data and unlabeled real data. The method includes inputting the labeled simulation data and the unlabeled real data respectively into a multi-source sound localization model of a neural network to obtain a localization heatmap from an output layer of the multi-source sound localization model for each of the labeled simulation data and the unlabeled real data. The method further includes inputting the localization heatmap for each of the labeled simulation data and the unlabeled real data into an output discriminator. The method also includes training the output discriminator so that the output discriminator assigns a domain class label to distinguish simulation data from real data. The method additionally includes training, by a hardware process, the multi-source sound localization model by a first adversarial loss for the output discriminator with an original localization model loss.

Sim-to-real learning of 2D multiple sound source localization

A computer-implemented method is provided for learning an action policy. The method includes obtaining, by a processor, environment dynamics including triplets of a state, an action, and a next state. The state in each of the triplets is an expert state. The method further includes training, by the processor using the environment dynamics as training data, a dynamics model which obtains a pair of the state and the action as an input and outputs, for each next state, state-transition probabilities. The method also includes learning, by the processor, the action policy using trajectories of expert states according to a supervised learning technique by back-propagating error gradients through the trained dynamics model.

Closed loop model-based action learning with model-free inverse reinforcement learning

Methods, systems, and computer program products for detecting anomalous behavior include reconstructing a waveform of a target device from an input waveform using a target autoencoder. A waveform of unrelated sound events is reconstructed from the input waveform using an environmental autoencoder. The input waveform is classified to determine that the input waveform is produced by anomalous behavior of the target device using a classifier, based on the reconstructed waveform of the target device and the reconstructed waveform of the unrelated sound events. An automatic response to the anomalous behavior is generated.

Anomalous sound detection with timbre separation

Methods and systems for localizing a sound source include determining a spatial transformation between a position of a reference microphone array and a position of a displaced microphone array. A sound is measured at the reference microphone array and at the displaced microphone array. A source of the sound is localized using a neural network that includes respective paths for the reference microphone array and the displaced microphone array. The neural network further includes a transformation layer that represents the spatial transformation.

Two-dimensional sound localization with transformation layer

A computer-implemented method is provided for multi-source sound localization. The method includes extracting, by a hardware processor, spectral features from respective pluralities of microphones comprised in each of two or more microphone arrays. The method further includes forming, by the hardware processor, respective sets of pairs of the spectral features from the respective pluralities of microphones within each of the two or more microphone arrays by rearranging and duplicating the spectral features from the respective pluralities of microphones included in each of the two or more microphone arrays. The method also includes inputting, by the hardware processor, the respective sets of pairs of the spectral features into a neural network to encode the spectral features into deep features and decode the deep features to output from the neural network at least one location representation of one or more sound sources.

Feature processing for multi-array sound applications with deep learning and limited data

Methods and a system are provided for detecting object pose. A method includes training, by a processor, a first autoencoder (AE) to generate synthetic output images based on synthetic input images. The method further includes training, by the processor, a second AE to generate synthetic output images, similar to the synthetic output images generated by the first AE, based on real input images. The method also includes training, by the processor, a neural network (NN) to detect the object pose using the synthetic output images generated by the first and second AEs. The method additionally includes detecting and outputting, by the processor, a pose of an object in a real input test image by inputting the real input test image to the second AE to generate a synthetic image therefrom, and inputting the synthetic image to the NN to generate an NN output indicative of the pose of the object.

Detecting object pose using autoencoders

A computer-implemented method executed by a robotic system for performing a positional search process in an assembly task is presented. The method includes decomposing, by the robotic system, a perturbation motion into a plurality of actions, the perturbation motion being a motion for an assembly position searched by the robotic system, each action of the plurality of actions related to a specific direction. The method further includes performing reinforcement learning by selecting an action among decomposed actions and assembly movement actions at each step of the positional search process based on corresponding force-torque data received from at least one sensor associated with the robotic system. The method also includes outputting a best action at each step for completion of the assembly task as a result of the reinforcement learning.

Decomposed perturbation approach using memory based learning for compliant assembly tasks

A computer-implemented method, a system, and a computer program product manage a blacklist. The method includes, for a detected improper device, transferring a blacklist or an identifier or identifiers, present in the blacklist, respectively, of one or more devices associated with information on at least one geographic position of associated with a position of the improper device to one or more relay devices which exist at or near the at least one geographic position associated with the position of the improper device. The blacklist is used for controlling commutation or communication from or to the improper device.

Management of a blacklist for controlling a communication

A computer-implemented method for inspecting a clearance size between a hole and an object inserted in the hole, includes: controlling a robot arm so that the robot arm performs a predetermined motion to move the object inserted in the hole; monitoring a response to the predetermined motion from the hole and the object; and calculating information on the clearance size between the hole and the object using the response to the predetermined motion.

Inspecting clearance size between mechanical parts

A method, a system, and a computer program product provide for changing a boundary between zones. This includes setting a location of a boundary which defines a part of a zone; detecting that an object moves into the zone across the boundary; and in response to the detection, changing the location of the boundary in a direction away from the object, compared to the location of the boundary before the change.

Dynamic boundary setting

The present invention can be a method, system, and computer program product. One embodiment of the present invention provides a computer-implemented method for identifying an artificial object. The method includes receiving capacitance data which is obtained by measuring with at least one electrodes in a sensor at least one predefined surfaces of the artificial object placed against the sensor; converting each of the obtained capacitance data into an evaluation level in an evaluation system having more than two evaluation levels, to obtain a capacitance distribution on the surface of the artificial object; determining whether the obtained capacitance distribution matches a pre-registered capacitance distribution or one of pre-registered capacitance distributions; and if the determination result is positive, concluding the artificial object is identified. The method can further include identifying or performing an authentication of a user associated with the artificial object when artificial object is identified.

Identification of artificail object and artifical object used therein

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Kanagawa-ken, Japan

Tadanobu Inoue