公开(公告)号：US20160148137A1

公开(公告)日：2016-05-26

申请号：US14796704

申请日：2015-07-10

Applicant: International Business Machines Corporation

Inventor： Dung Phan ,  Jinjun Xiong

IPC: G06Q10/06

CPC classification number: G06Q10/06312 ,  G01M99/008 ,  G06Q10/20 ,  Y02P90/86

Abstract: There are provided a system, a method and a computer program product for generating an optimal preventive maintenance/replacement schedule for a set of assets. The method includes receiving data regarding an asset, said data including a failure rate function of said asset, a cost of preventative maintenance (PM) of said asset, a cost of an asset failure, and a cost of replacing an asset. An optimal number K of preventative maintenance time intervals tk and an indication of a possible replacement is computed and stored for each asset by minimizing a mean cost-rate value function with respect to an electrical age of the asset. A first PM schedule is formed without consideration of labor and budget resource constraints. The method further generates a second maintenance schedule for a system of assets by minimizing a deviation from the optimal PM time intervals subject to the labor and budget resource constraints.

Abstract translation: 提供了用于为一组资产生成最佳预防性维护/更换时间表的系统，方法和计算机程序产品。 该方法包括接收关于资产的数据，所述数据包括所述资产的故障率功能，所述资产的预防性维护成本（PM），资产失败的成本以及替换资产的成本。 通过最小化相对于资产的电气年龄的平均成本率值函数来计算并存储每个资产的预防性维护时间间隔t k的最佳数量K和可能的替换的指示。 在不考虑劳动力和预算资源限制的情况下，组建了第一个PM计划。 该方法通过最小化与受劳动力和预算资源限制的最佳PM时间间隔的偏差来进一步产生资产系统的第二维护计划。

公开(公告)号：US09260031B2

公开(公告)日：2016-02-16

申请号：US13839755

申请日：2013-03-15

Applicant: International Business Machines Corporation

Inventor： Soumyadip Ghosh ,  Dung Phan ,  Mayank Sharma ,  Chai Wah Wu ,  Jinjun Xiong

IPC: B60L11/18 ,  G06Q30/00 ,  H02J3/00

CPC classification number: B60L11/1846 ,  B60L11/1848 ,  B60L11/185 ,  B60L11/1861 ,  B60L2240/70 ,  B60L2240/80 ,  G06Q30/00 ,  G06Q50/06 ,  H02J3/00 ,  Y02T10/7005 ,  Y02T10/7044 ,  Y02T10/705 ,  Y02T10/7072 ,  Y02T10/7291 ,  Y02T90/121 ,  Y02T90/128 ,  Y02T90/14 ,  Y02T90/16 ,  Y02T90/169 ,  Y04S30/14

Abstract: The present disclosure relates generally to the field of distributed charging of electrical assets. In various examples, distributed charging of electrical assets may be implemented in the form of systems, methods and/or algorithms.

Abstract translation: 本公开一般涉及电力资产的分布式充电领域。 在各种示例中，电子资产的分布式计费可以以系统，方法和/或算法的形式实现。

公开(公告)号：US20150177762A1

公开(公告)日：2015-06-25

申请号：US14134351

申请日：2013-12-19

Applicant: International Business Machines Corporation

Inventor： Gary Ditlow ,  Dung Phan ,  Jinjun Xiong

IPC: G05F1/66 ,  G06F7/60

CPC classification number: G05F1/66 ,  G05B2219/35215 ,  G06F7/60 ,  G06Q50/06

Abstract: An exemplary method includes solving on a computing system an optimal power flow formulation for a plurality of generators in a power system. The solving includes computing using multi-threaded parallelism a plurality of constraints for the formulation, computing using multi-threaded parallelism a plurality of Jacobian functions of the constraints,and computing using multi-threaded parallelism a Hessian of Lagrangian functions. The method further includes outputting results of the solving, wherein the results comprise values of generation levels for the plurality of generators. Apparatus and program products are also disclosed.

Abstract translation: 一种示例性方法包括在计算系统上求解用于电力系统中的多个发电机的最优功率流公式。 解决方案包括使用多线程并行化来计算多个约束，用于制定，使用多线程并行化计算约束的多个雅可比函数，以及使用多线程并行度计算拉格朗日函数的Hessian。 该方法还包括输出求解的结果，其中结果包括多个发生器的产生电平的值。 还公开了装置和程序产品。

公开(公告)号：US11996083B2

公开(公告)日：2024-05-28

申请号：US17337518

申请日：2021-06-03

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Kaizhi Qian ,  Yang Zhang ,  Shiyu Chang ,  Jinjun Xiong ,  Chuang Gan ,  David Cox

IPC: G10L13/10 ,  G06N20/00 ,  G10L17/04 ,  G10L21/013 ,  G10L25/63

CPC classification number: G10L13/10 ,  G06N20/00 ,  G10L17/04 ,  G10L21/013 ,  G10L25/63

Abstract: A computer-implemented method is provided of using a machine learning model for disentanglement of prosody in spoken natural language. The method includes encoding, by a computing device, the spoken natural language to produce content code. The method further includes resampling, by the computing device without text transcriptions, the content code to obscure the prosody by applying an unsupervised technique to the machine learning model to generate prosody-obscured content code. The method additionally includes decoding, by the computing device, the prosody-obscured content code to synthesize speech indirectly based upon the content code.

公开(公告)号：US11880765B2

公开(公告)日：2024-01-23

申请号：US17074054

申请日：2020-10-19

Applicant: International Business Machines Corporation ,  University of Illinois at Urbana-Champaign

Inventor： Pin-Yu Chen ,  Yada Zhu ,  Jinjun Xiong ,  Kumar Bhaskaran ,  Yunan Ye ,  Bo Li

IPC: G06Q30/00 ,  G06N3/08 ,  G06F40/279 ,  G06Q40/06

CPC classification number: G06N3/08 ,  G06F40/279 ,  G06Q40/06

Abstract: A processor training a reinforcement learning model can include receiving a first dataset representing an observable state in reinforcement learning to train a machine to perform an action. The processor receives a second dataset. Using the second dataset, the processor trains a machine learning classifier to make a prediction about an entity related to the action. The processor extracts an embedding from the trained machine learning classifier, and augments the observable state with the embedding to create an augmented state. Based on the augmented state, the processor trains a reinforcement learning model to learn a policy for performing the action, the policy including a mapping from state space to action space.

公开(公告)号：US20230214705A1

公开(公告)日：2023-07-06

申请号：US17566624

申请日：2021-12-30

Applicant: International Business Machines Corporation ,  The Board of Trustees of the University of Illinois

Inventor： Pin-Yu Chen ,  Nandhini Chandramoorthy ,  Karthik V Swaminathan ,  Jinjun Xiong ,  Devansh Paresh Shah ,  Bo Li

IPC: G06N20/00 ,  G06N5/04

CPC classification number: G06N20/00 ,  G06N5/04

Abstract: An input transformation function that transforms input data for a second machine learning system is learned using a first machine learning system, the learning being based on minimizing a summation of a task loss and a post-activation density loss. The input data is transformed using the learned input transformation function to alter the post-activation density to reduce an amount of energy consumed for an inferencing task and the inferencing task is carried out on the transformed input data using the second machine learning system.

公开(公告)号：US20220171923A1

公开(公告)日：2022-06-02

申请号：US17109008

申请日：2020-12-01

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION ,  THE BOARD OF TRUSTEES OF THE UNIVERSITY OF ILLINOIS

Inventor： Lingfei Wu ,  Jinjun Xiong ,  Hongyu Gong ,  Suma Bhat ,  Wen-Mei Hwu

IPC: G06F40/211 ,  G06N3/04 ,  G06N3/08 ,  G06K9/62 ,  G06N7/00

Abstract: A computer-implemented method for generating an abstract meaning representation (“AMR”) of a sentence, comprising receiving, by a computing device, an input sentence and parsing the input sentence into one or more syntactic and/or semantic graphs. An input graph including a node set and an edge set is formed from the one or more syntactic and/or semantic graphs. Node representations are generated by natural language processing. The input graph is provided to a first neural network to provide an output graph having learned node representations aligned with the node representations in the input graph. The method further includes predicting via a second neural network, node label and predicting, via a third neural network, edge labels in the output graph. The AMR is generated based on the predicted node labels and predicted edge labels. A system and a non-transitory computer readable storage medium are also disclosed.

公开(公告)号：US11314950B2

公开(公告)日：2022-04-26

申请号：US16830106

申请日：2020-03-25

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION ,  The Board of Trustees of the University of Illinois

Inventor： Lingfei Wu ,  Jinjun Xiong ,  Hongyu Gong ,  Suma Bhat ,  Wen-Mei Hwu

IPC: G06F40/56 ,  G06F40/253 ,  G06F40/35 ,  G06N3/04

Abstract: A computer-implemented method is provided for transferring a target text style using Reinforcement Learning (RL). The method includes pre-determining, by a Long Short-Term Memory (LSTM) Neural Network (NN), the target text style of a target-style natural language sentence. The method further includes transforming, by a hardware processor using the LSTM NN, a source-style natural language sentence into the target-style natural language sentence that maintains the target text style of the target-style natural language sentence. The method also includes calculating an accuracy rating of a transformation of the source-style natural language sentence into the target-style natural language sentence based upon rewards relating to at least the target text style of the source-style natural language sentence.

公开(公告)号：US11048447B2

公开(公告)日：2021-06-29

申请号：US16656295

申请日：2019-10-17

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Zaid Qureshi ,  I-Hsin Chung ,  Wen-Mei Hwu ,  Jinjun Xiong

IPC: G06F3/06 ,  G06F12/1009

Abstract: Embodiments for providing direct access to non-volatile memory by a processor. One or more accelerators may be provided, via an application programming interface (“API”), direct access to non-volatile storage independent of a host central processing unit (“CPU”) on a control path or data path to perform a read operation and write operation of data.

公开(公告)号：US20210141863A1

公开(公告)日：2021-05-13

申请号：US16678341

申请日：2019-11-08

Applicant: International Business Machines Corporation ,  The Board of Trustees of the University of Illinois

Inventor： Lingfei Wu ,  Jinjun Xiong ,  Julia Constanze Hockenmaier ,  Rajarshi Haldar

IPC: G06F17/27 ,  G06N3/08 ,  G06N3/04 ,  G06F16/2452

Abstract: Embodiments of the invention describe a computer-implemented method that includes receiving a query that includes a query sequence having query characters grouped into query words. A segment of program code is retrieved from a database for evaluation. The program code includes a program code sequence including program code characters grouped into program code words. The query sequence, the query word, the program code sequence, and the program code word are each converted to sequence and word representations. Query sequence-level features, query word-level features, program code sequence-level features, and program code word-level features are extracted from the sequence and word representation. Similarity between the query and the segment of program code is determined by applying a similarity metric technique to the query sequence-level features, the query word-level features, the program code sequence-level features, and the program code word-level features.