Mitsubishi Electric Research Laboratories, Inc.

Nokia Corporation

Core Wireless Licensing S.a.r.l.

Kyeong Jin Kim

Philip Orlik

Jianlin Guo

Toshiaki Koike-Akino

Stefano Di Cairano

Kieran Parsons

Hongbo Sun

Milutin Pajovic

Karl Berntorp

Ye Wang

Padam Kafle

Heejin Ahn

Koon Hoo Teo

Zafer Sahinoglu

Bingnan Wang

Prabodh Varshney

Andrei Kniazev

Chiori Hori

Toshiaki Koike Akino

Marcus Greiff

Yacong Ding

Zhengyu Peng

Talha Ahmed Khan

Fei Ye

Bryan Liu

Imtiaj Khan

Dongyang Xu

Gilsoo Lee

Hao Song

Raj Tejas Suryaprakash

Okuary Osechas

Shengyi Wang

Xiao Wang

Ashwin Shirsat

A diagnosis system is provided for diagnosing the partial discharge faults in a power transformer. The system includes a feature extractor, a fault classifier and a domain discriminator and whose parameters are optimized by minimizing a combination of feature loss, classifier loss and domain discrepancy discriminator loss. The feature loss is a weighted sum of cosine similarity distance loss, batch-based instance separation loss, and batch-based feature decorrelation loss. The measured applied voltages and partial discharge voltages are pre-processing using sliding window method to represent the each partial discharge event as a series of statistics based moments for a set of overlapped time blocks within the duration of fault event, wherein statistics based moments include an average value of applied voltage for representing the applied voltage magnitude and fault event occurring moment for each time block, and the mean, standard deviation, Kurtosis, and Skewness of partial discharge voltages for representing the variations of partial discharging within each time block.

Transfer learning-based power transformer partial discharge diagnosis

Disclosed is a method and system for identifying an existence, location and type of a weak-signal fault in an islanded inverter-based microgrid. The weak-signal fault includes a high impedance fault, an inverter DC-side short-circuit fault, and an inverter tripping fault, and usually fails to be detected by conventional relay methods due to small magnitude of fault current. Upon received voltage and current measurements from intelligent electronic devices installed in the microgrid, the variation mode decomposition algorithm is firstly applied to detect the existence of fault based on denoised time series of measurements using discrete wavelet transform algorithm. After detecting the presence of fault, the correlation-based matrix is applied to locate the suspicious fault locations, and then K-nearest neighbors model is utilized to identify the faulty branch among those locations using dynamic time warping algorithm to measure the distance between neighbors. Following fault localization, fault classification is done by observing sequence components and phasor measurements and feeding the observational inputs to a fault classification logic circuit model.

Weak-signal fault identification of inverter-based microgrids

A computer-implemented method is provided for performing energy disaggregation of a distribution system-level net-load measurements using continuous-point-on-wave (CPOW) measurement units. The method uses a processor coupled with a memory stored instructions implementing the method using neural networks including an encoder network, a feature extractor, a separator network, a decoder network stored in the memory, wherein the instructions, when executed by the processor carry out at steps of the method include generating net-load time series data from voltage and current measurements via the CPOW measurement units, generating a compressed latent space representation from the net-load time series, converting the net-load time series into time-frequency domain, passing time domain cotextual information with the converted time-frequency domain representation of net-load time series to the feature extractor, estimating two weight matrices to be multiplied with an output from the encoder network and learning temporal features of a native load and a photovoltaic (PV) generation, transforming weighted latent representation corresponding the native load and the PV generation into time-domain representations, and predicting the native load and the PV generation at distribution system-level from the transformed time domain representations corresponding to the native load and PV generations.

Extraction of instantaneous renewable generation from net load measurements of power distribution systems

A method for semi-persistent scheduling (SPS) based resource allocation in a private 5G network comprises receiving location data of a plurality of user equipment (UEs) in the private 5G network in a last subframe of respective current communication periods of the UEs and determining distance distribution of the UEs in a subsequent communication period of a candidate UE of the plurality of UEs, based on the respective location data of the UEs. The method further comprises determining signal to noise ratio expectation of the UEs for the subsequent communication period, solving an optimization problem optimizing an expectation of data to be carried by each of the one or more SPS channels in the subsequent communication period, to obtain scheduling result for each of the one or more SPS channels and generating resource allocation data for the candidate UE based on the scheduling result.

Systems, apparatuses, and methods for semi-persistent scheduling in communication networks

A computer-implemented pre-processed time-delay autoencoder based anomaly detection method are provided for detecting anomalous states of machines arranged in a factory automation (FA) system or a manufacturing production line. The method includes acquiring source signals from the machines via an interface performing a data pre-processing process for the acquired source signals by normalizing value ranges of the acquired source signals and filtering undesired features from the acquired source signals performing a time-delayed data reform process for the pre-processed source signals based on a time-delay window to generate pre-processed time-delay data submitting pre-processed time-delay testing data to a pre-processed time-delayed autoencoder (Prep-TDAE) neural network, wherein the pre-processed time-delay testing data are collected online while the machines are operated, wherein the Prep-TDAE neural network has been pre-trained by using the pre-processed time-delay training data detecting, if an anomaly state is encountered with respect to the machines, by computing anomaly scores of the pre-processed time-delay testing data, and determining, when the anomaly state is detected, anomaly occurrence time, duration and severity with respect to the anomaly state of each of the machines.

Anomaly detection and diagnosis in factory automation system using pre-processed time-delay neural network with loss function adaptation

A method for communication between a communication device and a base station in a private 5G network where the communication device and the base station have a base period of transmission defined there between as a minimal scheduling period, comprises transmitting location data of the communication device in a last subframe of a current communication period of the communication device. The method further comprises receiving resource allocation data including a transmission period widening factor for one or more subsequent communication periods of the communication device and computing a semi-persistent scheduling (SPS) period as a subsequent communication period of the one or more subsequent communication periods, based on the transmission period widening factor and the base period of transmission. Uplink communication is performed in the one or more subsequent periods, based on the resource allocation data and the SPS period.

A server jointly tracks states of multiple vehicles using measurements of satellite signals received at each vehicle and parameters of the probabilistic distribution of the state of each vehicle. The server fuse states and measurements into an augmented state of the multiple vehicles and an augmented measurement of the augmented state subject to augmented measurement noise defined by a non-diagonal covariance matrix with non-zero off-diagonal elements, each non-zero off-diagonal elements relating errors in the measurements of a pair of corresponding vehicles. The server executes a probabilistic filter updating the augmented state and fuses the state of at least some of the multiple vehicles with a corresponding portion of the updated augmented state.

Cooperative state tracking of multiple vehicles using individual and joint estimations

An anomaly detector for detecting anomaly in input data comprises an auto-encoder trained to encode the input data and decode the encoded input data to reconstruct the input data. Further, the anomaly detector comprises a classifier trained to determine a reconstruction loss indicative of a difference between the accepted input data and the reconstructed input data, where the reconstruction loss includes a weighted combination of a plurality of loss functions evaluating reconstruction losses of a plurality of parts of the reconstructed input data, different types of loss functions, or both. The classifier is further configured to detect an anomaly in the reconstructed input data when the reconstruction loss is above a threshold.

Apparatus and method for anomaly detection

A probabilistic system for tracking a state of a vehicle using unsynchronized cooperation of information includes a probabilistic multi-head measurement model relating incoming measurements with the state of the vehicle. The first head of the model relates measurements of the satellite signals subject to measurement noise with a belief on the state of the vehicle, and a second head relates an estimation of the state of the vehicle subject to estimation noise with the belief on the state of the vehicle. A probabilistic filter of the system updates recursively the belief on the state of the vehicle based on the multi-head measurement model accepting one or a combination of the measurements of the satellite signals subject to the measurement noise and the estimation of the state of the vehicle subject to the estimation noise.

Probabilistic state tracking with multi-head measurement model

A system for a region including a processor to receive data including weather and component data. Generate a dataset for the region to identify weather events and a plurality of model forcings converted into tabular form. A machine learning (ML) model for each model forcing for each component uses dataset. Iteratively, for each weather event: identify for each component a corresponding model forcing with weather variables. Generate an output value for ML model corresponding to the identified components to the set of weather variables and update ML model. Receive observed data over time periods of an impending weather (IW) event. Iteratively, for each time period: identify, for each component a corresponding model forcing with weather variables and update ML model. Generate for the updated ML model, an output value predicting a component status as a failed or not failed for the time period.

Weather-related overhead distribution line failures online forecasting

A traffic control system for controlling traffic at interconnected intersections is provided, where the system comprises a receiver that receives traffic data that indicates states of vehicles approaching an intersection of the interconnected intersections and directions of the vehicles exiting the intersection. Further, the system comprises a processor that determines intersection crossing times and velocities of vehicles approaching the intersection by minimizing at least one of a total travel time or a maximum travel time of the vehicles for crossing the intersection. The contribution of each vehicle of the vehicles approaching the intersection in the at least one of a total travel time or a maximum travel time is weighted based on directions of the vehicles and traffic at next intersection. Further, the system comprises a transmitter that transmits the intersection crossing times and velocities to the vehicles exiting the intersection for controlling the traffic at the interconnected intersections.

Adaptive control of vehicular traffic

The communication system is provided, where the communication system comprises multiple cluster distributed antenna system (MC-DAS) network and a controller. Each cluster in the MC-DAS comprises a cluster master (CM) and remote radio units (RRU), which are in the coverage area of the controller. The controller and the DAS clusters are synchronized using a hierarchical precision time protocol (HPTP). Each DAS cluster is configured to transmit messages independently from other DAS clusters in the plurality of DAS clusters using a distributed cyclic delay diversity (CDD) scheme with a determined length of a cyclic prefix. The controller further comprises a controller configured to transmit a message from the controller to a receiver through one or more DAS clusters of the plurality of the DAS clusters.

Communication system and method for asynchronous joint transmissions using a cluster-based distributed cyclic delay diversity scheme

Systems and methods for scheduling data traffic in a wireless time sensitive network (TSN). A computer configured to synchronize clocks of all nodes with a common clock in the TSN. Obtain data traffic information for the TSN using a network scheduler to establish routing paths. Determine routing paths using the obtained data traffic information and stored routing information via a memory. Compute a link communication delay for each link of the one or more relay nodes connecting a source node to a destination node of the TSN for each TSN stream using a network scheduler. Determine interfering links for each wireless link using the network scheduler. Determining a scheduling period using the network scheduler. Determine optimal scheduling using an optimal scheduling module. Generate the gate control list for each egress port of the wired node and the wireless transmitter of the TSN, and begin transmission of the data.

Scheduling data traffic in wireless time sensitive networks

A system jointly controls vehicles according to traffic configuration of a zone of intersection of a first road and a second road. The intersection zone includes a sequencing zone and a control zone covering sections of the first and the second road in proximity to the intersection. The system groups vehicles traveling within the sequencing zone on the first and the second roads into a set of groups of the vehicles and prevents the vehicles of different groups to travel concurrently in the control zone such that the first vehicle of the following group cannot pass the last vehicle of the preceding group. The system determines motion trajectories for the vehicles of the same group traveling in the control zone on the first and the second roads to pass the intersection and transmits the motion trajectories to the corresponding vehicles.

Joint control of vehicles traveling on different intersecting roads

A positioning system for tracking a position of a vehicle includes a receiver configured to receive phase measurements of satellite signals received at multiple instances of time from multiple satellites, and a memory configured to store a recurrent neural network trained to determine a position of the vehicle from a set of phase measurements in a presence of noise caused by a multipath transmission of at least some of the satellite signals at some instances of time. A processor of the positioning system is configured to track the position of the vehicle over different instances of time by processing the set of phase measurements received at each instance of time with the recurrent neural network to produce the position of the vehicle at each instance of time.

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Lexington, MA, United States of America

Kyeong Jin Kim