International Business Machines Corporation

Beijing Information Science & Technology University

Beijing Tashan Technology Co., Ltd.

Wei Zhuang

Liang Liu

Junmei Qu

Wen Jun Yin

Hong Zhou Sha

Jun Mei Qu

Zhuo Liu

Wei He

Li Li

Feng Liu

Jin Dong

Chao Zhu

Fei Luo

Mingli Dong

Lianqing Zhu

Xiaoping Lou

ChaoQiang Zhu

Dahua Zhang

Tengchen Sun

Chao Zhang

Hong Li

Fan Zhang

Yi Ge

Xiao Guang Rui

Lingyun Wang

Tao Tong

Long Sheng Bai

Kuo Meng

This disclosure provides a computer-implemented method. The method may comprise estimating a value range of a water body parameter based on measured data for a water quality indicator of a first set of time-spatial points and measured data for the water quality indicator of a second set of time-spatial points; and determining an optimal value of the water body parameter from the estimated value range by comparing the measured data for the water quality indicator of the second set and simulated data for the water quality indicator of the second set, wherein the simulated data for the water quality indicator of the second set is obtained based on a fluid dynamic model using the measured data for the water quality indicator of the first set as an input of the fluid dynamic model and using a value in the estimated value range as a parameter of the fluid dynamic model.

Water quality prediction

The present application relates to a sensor with a time-sharing regional shielding function and a robot. The sensor comprises a plurality of sensor units, each of which comprises regions contained in four multifunctional layers. Four parallel-plate capacitors are contained in the multifunctional layers. The multifunctional layers realize the regional shielding function through the time-sharing switching of analog switches and the control of a bus.

Sensor with time-sharing regional shielding function, electronic skin and robot

An input data set for a model for estimating a missing value of a sensor in a sensor network is determined. The input data set includes one or more sensor readings selected according to a temporal-spatial parameter which is dynamic and specific to the sensor. Then the missing value of the sensor is estimated using the determined input data set as an input to the model.

Missing sensor value estimation

The present application relates to a touch sensor with multifunctional layers and an intelligent robot. The touch sensor comprises a plurality of sensor units. Each of the sensor units comprises regions contained in four multifunctional layers. The first multifunctional layer and the third multifunctional layer are higher than the second multifunctional layer and the fourth multifunctional later, and the distance from the center of the first multifunctional layer to the center of the third multifunctional layer is greater than the distance from the center of the second multifunctional layer to the center of the fourth multifunctional layer. The first multifunctional layer and the third multifunctional layer form a capacitor C, and the second multifunctional layer and the fourth multifunctional layer form a capacitor C

Touch sensor with multifunctional layers, electronic skin and intelligent robot

Embodiments of the present disclosure allow accuracy of prediction of pollution to be improved. In operation, a prediction of pollution in a future time period is determined. The prediction of pollution indicates predicted data related to a pollution index. Then, matching historical data for the predicted data is determined from historical data related to the pollution index. The matching historical data is obtained in a historical time period corresponding to the future time period. Based on the matching historical data, the prediction of pollution is refined.

Pollution prediction

In one embodiment, a method for managing a data storage system includes: in response to receiving a data object, sorting data records in the data object on the basis of a first query so as to form a first backup; causing the first backup to be stored in the data storage system; and cause to be stored, in an index of the data storage system, the first query and a first address of the first backup in the data storage system.

Managing data storage system

A method, system, and computer program product, include identifying a plurality of pollution process sets and determining pollution sources based on pollution start times of target pollution processes with matched features in the plurality of pollution process sets within a time window.

System, method and computer program product for pollution source attribution

Disclosed is a novel system, computer program product, and method to compute an air quality forecast. An air quality forecast model, air quality real-time monitoring data, and air quality forecast data is accessed. A deviation in air pollution emission is monitored by classifying a difference between the air quality monitoring data and the air quality forecast data. This monitoring includes classifying any weather differences which are caused by weather, classifying any terrain differences which are caused by a geographic terrain; and, filtering the difference caused by inaccurate pollution emission inventory. The monitoring may repeat in response to a given time period elapsing or a chance in air quality forecast data received.

Air quality forecast by adapting pollutant emission inventory

A method for determining a source of pollutant emissions for a selected area includes: mapping a grid onto a representation of the selected area; collecting monitoring data for the selected data; from the monitoring data, assigning air pollution values and weather values to each cell in the grid using an interpolation method to estimate values for gap cells; re-sizing the grid to mitigate the influence of atmospheric turbulence on the air pollution values; and using weather factor separation to minimize the influence of weather from the air pollution values, resulting in air pollution values that reflect the net pollutant emissions for the selected area.

Determining the net emissions of air pollutants

Embodiments of the present disclosure relates to data block processing in a distributed processing system. According to one embodiment of the present disclosure, a computer-implemented method is proposed. A first performance indicator for processing a data block by a first processing module is obtained, where the data block is loaded into the first processing module. Then, a second performance indicator for processing the data block by a second processing module is obtained, where the first and second processing modules being logical instances launched in a distributed processing system for processing data blocks. Next, one processing module is selected from the first and second processing modules for processing the data block based on a relationship between the first and second performance indicators.

Data block processing

Aspects of the present invention include a method, which includes updating, by a processor, one or more distributed memory datasets having data stored therein in response to a write data operation, the one or more distributed data memory datasets being located in a database. The method further includes splitting, by the processor, any one of the one or more distributed memory datasets into two distributed memory datasets when a size of the any one of the one or more distributed memory datasets exceeds a threshold value. The method further includes moving, by the processor, the stored data in any one of the one or more distributed memory datasets to regions within the database upon an occurrence of one or more conditions with respect to the one or more distributed memory datasets. Other aspects of the present invention include a system and a computer program product.

Data write/import performance in a database through distributed memory

This disclosure provides a computer-implemented method for data storage in a non-relational database. The method comprises receiving a data item to be inserted into a table in the non-relational database. The method further comprises determining a dynamic data section and a static data section of the received data item. The method further comprises in response to the static data section not existing in the static data storage area, storing the static data section in the static data storage area. The method further comprises storing the dynamic data section in the dynamic data storage area.

Data storage with improved efficiency

Technical solutions are described for optimizing operation of a server cluster. An example method includes receiving a job request that executes using a set of data blocks, the job request being associated with an expected completion time. The cluster server is used to identify a set of replica servers, wherein each server from the set of replica servers contains the set of data blocks. In response to each server from the set of replica servers estimating a completion time for the job request that is more than the expected completion time, a new server is initiated, the set of data blocks is relocated from a first server from the set of replica servers to the new server, and the job request is allocated to the new server.

Data and task reallocation in distributed computing platforms

A method, system, and computer program product, include determining a task resource consumption predicted for each of one or more tasks being executed on a node, wherein the task resource consumption is a function of time and predicting a node resource consumption of the node based at least on the predicted task resource consumption, wherein the node resource consumption is a function of time.

System, method and computer program product for resource management in a distributed computation system

A mechanism is provided for detecting air-pollution anomalies. A historical air-pollution pattern is identified for each of a plurality of air-pollution monitoring stations. For each of the plurality of air-pollution monitoring stations, responsive to receiving real-time data from a particular air-pollution monitoring station, the real-time data is compared to the historical air-pollution pattern associated with the particular air-pollution monitoring station. A density difference value is generated based on the comparison of the real-time data to the historical air-pollution pattern associated with the particular air-pollution monitoring station and a determination is made as to whether the density difference value is greater than a predetermined confidence threshold. For each of a subset of the plurality of air-pollution monitoring stations whose associated density difference value is greater than the predetermined confidence threshold, an indication of an anomaly in detected air-pollution levels is identified to an administrator.

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Beijing, China

Wei Zhuang