Hitachi, Ltd.

Memverge, Inc.

Haiyan Wang

Chetan Kumar Gupta

Abhay Mehta

Hsiu-Khuern Tang

Hiroaki Ozaki

Qiyao Wang

Shuang Feng

Yue Li

Susumu Serita

Wei Huang

Jie Yu

Takashi Saeki

Yue Zhao

Ahmed Khairy Farahat

Song Wang

Kunwu Huang

Wei Kang

Peiyu Zhuang

Yong Tian

Chenggong Fan

Hamed Khorasgani

Maojing Fu

Laleh Jalali

Huijuan Shao

Aniruddha Rajendra Rao

Takuya Kanazawa

Ashley Tung

Jingchao Sun

A method for reinforcement learning (RL) of continuous actions. The method may include receiving a state as input to at least one actor network to predict candidate actions based on the state, wherein the state is a current observation; outputting the candidate actions from the at least one actor network; receiving the state and the candidate actions as inputs to a plurality of distributional critic networks, wherein the plurality of distributional critic networks calculates quantiles of a return distribution associated with the candidate actions in relation to the state; outputting the quantiles from the plurality of distributional critic networks; and selecting an output action based on the candidate actions and the quantiles.

Uncertainty-aware continuous control system based on reinforcement learning

A method for detecting an anomaly in time series sensor data. The method may include identifying a noisiest cycle from the time series sensor data; for an evaluation of the noisiest cycle indicative of the anomaly being detected at a confidence level above a threshold, providing an output associated with the noisiest cycle as being the anomaly; and for the evaluation of the noisiest cycle indicative of the anomaly being detected at the confidence level not above the threshold: identifying a cycle from the time series sensor data having a most differing shape; and providing the output associated with the cycle having the most differing shape as being the anomaly.

Versatile anomaly detection system for industrial systems

To address loss of access to computing instances in a cloud computing environment, techniques are introduced for moving an application between computing instances in the cloud computing environment. A computing service captures baseline or delta snapshots of the state of the application running on a first computing instance. A baseline snapshot is indicative of the full state of the application, and a delta snapshot is indicative of changes in the state since a most recent snapshot was captured. Responsive to receiving an indication that the first computing instance is to stop servicing the application, the computing service stops the application from running on the first computing instance and moves data from the captured snapshots to a second computing instance. The computing service resume execution of the application on the second computing instances and captures snapshots of the state of the application at the second computing instance.

Efficiency and reliability improvement in computing service

In example implementations described herein, there are systems and methods for processing sensor data from an equipment over a period of time to generate sensor time series data; processing the sensor time series data in a kernel weight layer configured to generate weights to weigh the sensor time series data; providing the weighted sensor time series data to fully connected layers configured to conduct a correlation on the weighted sensor time series data with predictive maintenance labels to generate an intermediate predictive maintenance label; and providing the intermediate predictive maintenance label to an inversed kernel weight layer configured to inverse the weights generated by the kernel weight layer, to generate a predictive maintenance label for the equipment.

Predictive maintenance system for spatially correlated industrial equipment

In some examples, a system may generate a plurality of care path patient profile models based on a plurality of care path patterns for a plurality of past patient admissions. For example, each care path patient profile model may include a trained classifier. Further, the system may receive information related to a new patient admission, and may generate features from the received information related to the new patient admission. The system may input the features generated from the received information related to the new patient admission into the plurality of care path patient profile models to obtain a respective probability of being classified in a respective care path based on an amount of similarity to the patients who have gone through each care path. In addition, the system may present, on a display, information related to at least one care path pattern in a graphical user interface.

Care path analysis and management platform

A system is provided for operator profiling based on pre-installed sensor measurement. In example implementations, the system extracts a set of segmented time series data associated with a unit of operation and build models which distinguish the operators by machine learning algorithms. The system uses the models to output the evaluation score assigned to each operation, identify the key movements for skilled/non-skilled operators, and recommends appropriate actions to improve operation skill or adjust the scheduling of the operators.

System and methods for operator profiling for improving operator proficiency and safety

Example implementations described herein involve systems and methods involving a plurality of sensors monitoring one or more processes, the sensors providing sensor data, which can include determining a probability map of the sensor data from a database and a functional relationship between key performance indicators (KPIs) of the one or more processes and the sensor data; executing a search on the probability map to determine constrained and continuous ranges for the sensor data that optimize KPIs for the one or more processes based on the functional relationship; and generating a recommendation for the one or more processes that fit within the constrained and continuous range of the sensor data.

Operating envelope recommendation system with guaranteed probabilistic coverage

Disclosed herein is an apparatus and method for a shuffle manager for a distributed memory object system. In one embodiment, a method includes forming a system cluster comprising a plurality of nodes, wherein each node includes a memory, a processor and a network interface to send and receive messages and data, wherein the network interface operates on remote direct memory access; creating a plurality of sharable memory spaces having partitioned data, wherein each space is a distributed memory object having a compute node, wherein the sharable memory spaces are at least one of persistent memory or DRAM cache; and storing data in an in-memory data structure when there is available memory in a compute node; and if there is an out of memory condition, serializing at least some of the in-memory data and spilling it to a distributed memory object system to persist shuffled data outside the compute node.

Shuffle manager in a distributed memory object architecture

In some examples, a computing device may receive hierarchical data having a hierarchical structure including a plurality of levels. The computing device may determine a plurality of features based at least in part on the hierarchical data, and may select a subset of the features at a first level as candidates for consolidating to a next higher level in the hierarchical structure. The computing device may determine that a predicted loss of information from consolidating the subset of features is less than a threshold, and may revise the hierarchical structure to include a consolidated feature at the next higher level, rather than the subset of features. In some examples, a statistical model may be trained based on the revised hierarchical structure and used at least partially to make a determination, send a notification, and/or control a device.

Controlling devices based on hierarchical data

In some examples, a computing device may receive sensor data associated with a plurality of patient devices that are associated with a plurality of patients. The computing device may further receive caregiver records corresponding at least partially to the sensor data. At least two groups of indicators may be determined from the caregiver records, such a based on a selected subject. Further, the computing device may determine a plurality of clusters from the sensor data. Based on the plurality of clusters and the at least two groups, the computing device may determine an indication of a discrepancy in care for a patient of the plurality of patients. Based on the indication of the discrepancy, the computing device may send at least one of a notification to a caregiver computing device, a notification to a monitoring computing device, or a control signal to one of the patient devices.

Managing patient devices based on sensor data

In some examples, a computing device may receive sensor data for a target and at least one of: log data for the target, or historical log data and historical sensor data for a plurality of other targets. The computing device may determine at least one event classified as a non-uniform event in at least one of the log data or the historical log data, and may determine combined features, such as a feature vector, based on the sensor data and the non-uniform event(s). The computing device may determine an analysis result from the combined features. Further, based on the analysis result, the computing device may send a control signal to a device associated with the target for controlling the device, and/or may send a communication related to the target to another computing device.

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Fremont, CA, United States of America

Haiyan Wang