Abstract: A method of providing optimal and personalized business decision making that leverages behavioral economics principles and machine learning techniques is discussed herein. The method may include collecting or simulating data relating to behavioral characteristics of a plurality of stakeholders and analyzing the collected data to construct behavioral economics and machine learning based models related to a business problem. These models can be used to optimize and personalize business interventions to influence consumers' purchasing behavior to achieve the best business outcome (in B2C use cases) and de-bias distorted information sharing in supply chains (in B2B use cases). By contrast, traditional consumer and supply chain analytics solutions lack behavioral insights and often lead to sub-optimal decision making because economic optimization approach alone is not adequate for decision making where behavioral biases are present.

Abstract: Example implementations described herein are directed to an asset management system configured to facilitate real-time inventory recognition with image analysis tagged with positional information. The example implementations described herein also provide the method and process to improve the accuracy of the pipe detection for counting with various approaches. Referring the expected number of the pipes, example implementations utilize the pipe detection algorithm, as well as the bounding box of the pipe stack, use the knowledge of the physical size of the pipe stack, and analyze a pipe stack from the images of two directions at the end.

Abstract: Example implementations described herein are directed to a solution to the problem of accurate real-time inventory counting and industrial inspection. The solution, involves a device such as a mobile device that assists a human operator on the field to quickly achieve high quality inspection results. The example implementations detect objects of interest in individual image snapshots and use location and orientation sensors to integrate the snapshots to reconstruct a more accurate virtual representation of the inspection area. This representation can then be reorganized in various ways to derive inventory counts and other information that are not planned originally.

Abstract: Example implementations described herein are directed to a solution to the problem of accurate real-time inventory counting and industrial inspection. The solution, involves a device such as a mobile device that assists a human operator on the field to quickly achieve high quality inspection results. The example implementations detect objects of interest in individual image snapshots and use location and orientation sensors to integrate the snapshots to reconstruct a more accurate virtual representation of the inspection area. This representation can then be reorganized in various ways to derive inventory counts and other information that are not planned originally.

Abstract: Example implementations described herein are directed to the projection of two dimensional (2D) image recognition results to three dimensional (3D) space by using 3D reconstructed data to realize accurate object counting, identification, scene re-organization, and so on in accordance with the desired implementation. Through the example implementations described herein, more accurate objection detection can be provided than regular 2D object detection.

Abstract: Example implementations described herein are directed to an asset management system configured to facilitate real-time inventory recognition with image analysis tagged with positional information. The example implementations described herein also provide the method and process to improve the accuracy of the pipe detection for counting with various approaches. Referring the expected number of the pipes, example implementations utilize use the pipe detection algorithm as well as the bounding box of the pipe stack, use the knowledge of the physical size of the pipe stack, and analyze a pipe stack from the images of two directions at the end.

Abstract: In some examples, a system receives first sensor data from respective wind turbines of a plurality of wind turbines. For instance, the first sensor data may include at least a power output and a wind speed per time interval. The system trains at least one respective model for each respective wind turbine based on the first sensor data received from that respective wind turbine. Further, the system receives, for a second time period, respective second sensor data from the respective wind turbines. The system executes, using the respective second sensor data, the respective model trained using the first sensor data received from that respective wind turbine to determine, for each respective wind turbine, a predicted power output for an upcoming period. The predicted power outputs may be aggregated to determine a total predicted power output and at least one action is performed based on the total predicted power output.

Abstract: In some examples, a system receives first sensor data from respective wind turbines of a plurality of wind turbines. For instance, the first sensor data may include at least a power output and a wind speed per time interval. The system trains at least one respective model for each respective wind turbine based on the first sensor data received from that respective wind turbine. Further, the system receives, for a second time period, respective second sensor data from the respective wind turbines. The system executes, using the respective second sensor data, the respective model trained using the first sensor data received from that respective wind turbine to determine, for each respective wind turbine, a predicted power output for an upcoming period. The predicted power outputs may be aggregated to determine a total predicted power output and at least one action is performed based on the total predicted power output.

Abstract: A biological marker identification method identifies biological markers within broad sets of biological data containing many more measurements than observations. For example, the data can contain thousands of measurements on each blood sample obtained from fewer than 100 subjects, each of which falls into one of a set of clinical classes or is associated with a value of a continuous clinical response variable. At least one biomarker, containing a small subset of measurements, is found that is capable of predicting a clinical endpoint. The biomarker can be used for, e.g., diagnosing disease or assessing response to a drug. First, the set of measurements is reduced to a smaller set of candidate measurements by eliminating measurements that either cannot distinguish among classes or are redundant. Biomarker subsets are then selected from the remaining set of measurements, either by an exhaustive search or a heuristic method that finds good but not necessary globally optimal biomarkers.