International Business Machines Corporation

Deng Xin Luo

Xiang Yu Yang

Zhong Fang Yuan

Yong Wang

Wen Jie Wang

Ye Wy Wang

Yu Pan

Miao Guo

Tong Liu

Yi Ming Wang

Na Liu

Jing Du

Chen Yu Chang

Han Ying Song

Li Jia Lu

Wen Jie Hao

Chi Nan

Yu Ying Yy Wang

Yan Yu Zhang

Zhi Yong Jia

Jun Guo

Qin Dong Yin

Jia Wei He

Wang Hu Dang

Jia Yong Xie

Yu Ying Y Y Wang

Mechanisms are provided for automatically predicting vehicle weight of a moving vehicle. Vehicle operation data is obtained from one or more on-board sensors/systems of the vehicle and features are extracted. The features are filtered based on a required working condition of the vehicle to identify intervals of features having valid feature data to thereby generate a filtered valid data. The filtered valid data is processed by a first artificial intelligence (AI) computer model based on time slices of the filtered valid data to generate a first prediction of vehicle weight, and by a second AI computer model based on buckets of key variables to generate a second prediction of vehicle weight. The first prediction is fused with the second prediction to generate a final prediction of vehicle weight which is output to downstream computing logic.

Estimating moving vehicle weight based on driving data

From a set of point data, a set of scattered rays is constructed. From the set of scattered rays, a set of ray slopes is computed. The set of ray slopes is mapped to a corresponding set of trigonometric functions. Using an optimization method, a parameter of the set of trigonometric functions is selected. Using an inverse of the set trigonometric functions, a vehicle mass corresponding to the set of point data is computed. Based on the vehicle mass, a threshold braking distance of a collision avoidance system of the vehicle is adjusted, the threshold braking distance comprising a distance from an object predicted to collide with the vehicle. By braking the vehicle at least the threshold braking distance from the object, a predicted collision between the vehicle and the object is avoided.

Vehicle mass measurement for automated braking

Reducing an average give-away rate of a weighing device by determining a weight of a product of a weighing device that includes an article, determining one or more conditions of an environment of the weighing device, determining a state of the environment of the weighing device, wherein the state relates to an average give-away rate of the environment of the weighing device, determining a reward value for the state of the environment of the weighing device, wherein the reward value is based at least in part on the weight of the product, and generating a set of parameters for the weighing device based at least in part on the environment, the state, and the reward.

Adjusting parameters of weighing device for reducing average giveaway rate when packaging an article

In an approach, a processor obtains a configuration file of a distributed file system federation, the configuration file comprising a list of a plurality of subclusters within the distributed file system federation and migration trigger factors for the plurality of subclusters. A processor determines a list of one or more source subclusters and a list of to-be-migrated directories in the one or more source subclusters based on a scanning result of the plurality of subclusters and the migration trigger factors in the configuration file. A processor generates a migration plan to migrate the to-be-migrated directories from the one or more source subclusters to one or more target subclusters in the distributed file system federation.

Data migration in a distributed file system

A method, computer program product, and computer system for repairing a Dockerfile. Library versions containing initial version numbers of libraries are extracted from the Dockerfile. A Monte Carlo tree search (MCTS) is executed, using the extracted library versions as input, which generates a tree that includes multiple levels populated with noses. Each node in a level represents the generic library name of a library version in the Dockrerfile and an associated randomly selected version number. At least one of the randomly selected version numbers associated with at least one node in a level differs from the initial version number associated with a versionf. A best successful installation path is selected from the at least one successful installation path. The Dockerfile is repaired by inserting randomly selected version numbers into Dockerfile as replacements for some of the initial version numbers.

Modification of a Dockerfile to repair vulnerabilities existing in the Dockerfile

A method, system, and computer program product for implementing automated pressure level detection and correction is provided. The method includes retrieving from sensors, measurement attributes associated with geological conditions occurring during a mining process. Subsequently, threshold levels configured to activate an alarm associated with measurement attributes exceeding the threshold levels are determined and code is executed with respect to the threshold levels. A combined threshold severity level associated with the safety threshold levels is determined and code is executed with respect to the safety threshold levels. A combined clustering level associated with the safety threshold levels is determined and a difference value between the combined threshold severity level and combined clustering level is generated. A relationship between the difference value and a threshold value is determined and automated software and hardware control systems are enabled for controlling machinery associated with the mining process resulting in operation of the machinery.

Automated pressure level detection and correction

An example operation may include one or more of monitoring a plurality of brokers within a cluster to identify current workload attributes of the plurality of brokers, determining a health value of a lead broker within the cluster via execution of a machine learning model on current workload attributes of the lead broker, determining to modify resources assigned to the lead broker based on the determined health value of the lead broker, executing an optimization algorithm on the current workload attributes of the plurality of brokers within the cluster to determine an optimum task distribution, and reallocating tasks amongst the lead broker and the one or more other brokers within the cluster based on the optimum task distribution.

Balanced optimization within a broker cluster

A method, computer program product and computer system to provide topic guide during document drafting is provided. A processor retrieves at least one section of text from a document. A processor receives a target topic for the document. A processor extracts at least one local topic from the at least one section of text. A processor generates a semantic network comprising the at least one local topic and the target topic. A processor determines a deviation value for the at least one local topic based on a distance between the at least one local topic and the target topic in the semantic network. A processor, in response to the deviation value exceeding a threshold value, alerts a user that the at least one section of text from the document is off-topic from the target topic.

Computer-assisted topic guidance in document writing

Mechanisms are provided for generating an augmented reality representation of a real-world environment. An augmented reality (AR) system receives a captured digital image of the real-world environment and generates an initial estimate of a candidate point specifying an estimated location of an annotation point of a virtual object model within the captured digital image of the real-world environment. An accuracy of the initial estimate is calculated based on a function of characteristics of the annotation point and a function of characteristics of the candidate point and, in response to the evaluation of accuracy indicating that the initial estimate is not accurate, an annotation point location refinement operation is performed to generate a refined candidate point for aligning the annotation point with the captured digital image of the real-world environment. An AR representation of the real-world environment is generated based on the refined candidate point.

Automated refinement of augmented reality virtual object annotations

Methods, computer program products, and/or systems are provided that perform the following operations: obtaining pre-check data associated with specified data nodes; calculating outliers for each specified data node, wherein the outliers are calculated based on a unit of the pre-check data associated with each specified data node; backtracking the calculated outliers for each specified data node through an associated generating data link; selecting one or more data nodes associated with a set of largest outliers; selecting one or more data links associated with the set of largest outliers; and generating potential anomaly indications based on the one or more data nodes selected and the one or more data links selected.

Cognitive data outlier pre-check based on data lineage

Described are techniques for determining statistical properties of time series data. The techniques include a method comprising graphing, from time series data, a time series data graph. The method further comprises iteratively segmenting the time series data graph into respective pluralities of subgraphs using respective segmentation schemes until a first plurality of subgraphs generated by a first segmentation scheme exhibits a similarity between respective subgraphs of the first plurality of subgraphs satisfying a similarity threshold. The first segmentation scheme can be selected from: an equidistant segmentation scheme, a local extrema segmentation scheme, and a windowed segmentation scheme. The method further comprises associating a classification to the time series data based on the first segmentation scheme. The classification can be indicative of one selected from: stationarity of the time series data, periodicity of the time series data, and trending of the time series data.

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Xi'an, China

Deng Xin Luo