Abstract: A construction management system includes a geological data acquisition unit configured to acquire geological data of a work target, and a three-dimensional data acquisition unit configured to acquire three-dimensional data of the work target, where the three-dimensional data acquisition unit is configured to acquire, based on the geological data, three-dimensional data including a boundary between geologies of the work target.

Abstract: Systems and methods are disclosed for optimal utilization of machines for performing tasks across a predetermined area. A request may be received to initiate a job on a predetermined area, the job comprising a plurality of tasks associated with a plurality of machines. In response to the request, a first machine of the plurality of machines may be deployed to the predetermined area to execute a first task of the plurality of tasks autonomously. A deployment event may be determined based on characteristics of the first machine and a second machine, and based on characteristics of the predetermined area. The second machine to the predetermined area, upon detection of the deployment event, to execute a second task of the plurality of tasks autonomously.

Abstract: Embodiments as disclosed provide data processing systems and methods for controlling an automated survey system. One embodiment comprises: a processor; a data store storing a plurality of transactions thereon, each transaction comprising transaction metadata and a voice session recording of an inbound call, the transaction metadata for each transaction comprising an identifier for that transaction; a non-transitory computer readable medium having instructions executable on the processor for: generating, by the processor, a worklist for the survey campaign to control conducting of surveys by an automated survey system that is configured to execute the worklist, the worklist comprising a work item for each of the set of transactions and each work item comprising transaction identification data for a transaction from the set of transactions and contact information for the survey target for the transaction corresponding to the transaction identification data in the work item.

Abstract: The present disclosure provides a method and system for predicting building energy consumption based on a Holt-Winters and an extreme learning machine, the method including: constructing a building simulation model based on actual operation parameters of a building to obtain an original energy consumption data set of the building; decomposing the original energy consumption data set to obtain a linear energy consumption data set and a nonlinear energy consumption data set; performing prediction on the linear energy consumption data set by using a trained Holt-Winters model to obtain a linear energy consumption prediction result; and inputting the nonlinear energy consumption data set, the original energy consumption data set, and the linear energy consumption prediction result into a trained extreme learning machine model to output a building energy consumption prediction value of the building simulation model.

Abstract: A signal relationship is defined between a granular data value and a target data value. At least a portion of the granular data value corresponds to a granular latency value that is smaller than a target data latency value corresponding to the target data value. Granular data corresponding to the granular data value is interpreted. The granular data is aggregated in response to the signal relationship. A value of the target data value for a selected time reference is estimated, and the estimated value of the target data value is provided as a nowcasting prediction of the target data value.

Abstract: A system may include a computer-readable storage medium configured to store a first set of instructions for determining one or more candidate driver terminals to accept a service request; and a processor in communication with the computer-readable storage medium, wherein when executing the first set of instructions, the processor is directed to: establish a communication with a passenger terminal; obtain a service request including a position of the passenger terminal from the passenger terminal: determine a wireless fidelity (WiFi) station based on the position of the passenger terminal; determine one or more driver terminals that access the WiFi station; determine an access instance of each of the one or more driver terminals; and determine one or more candidate driver terminals among the one or more driver terminals based on the one or more access instances of the one or more driver terminals.

Abstract: Methods and systems are described herein for risk-adjusted predictive bidding for electronic advertisements. A first computing device receives, from a requesting device loading a webpage, a first request for graphical display source code corresponding to a computerized graphical advertisement display to be inserted into one or more impression opportunities on the webpage. A second computing device coupled to the first computing device transmits a second request to a plurality of third-party computing devices for one or more bids for an impression opportunity of the one or more impression opportunities. The second computing device receives the one or more bids from the third-party computing devices and adjusts at least one of the one or more bids based upon a risk factor received from the third-party computing device that submitted the corresponding bid. The first computing device determines whether to select the impression opportunity based on the adjusted one or more bids.

Abstract: A controller for central plant equipment obtains a model of one or more sources configured to supply input resources, one or more subplants configured to convert the input resources to output resources, and one or more sinks configured to consume the output resources. The controller generates a resource balance constraint that requires balance between a first amount of each resource and a second amount of each resource. The first amount of each resource includes a sum of an amount of the resource supplied by the sources and an amount of the resource produced by the subplants. The second amount of each resource includes a sum of an amount of the resource consumed by the subplants and an amount of the resource consumed by the sinks. The controller performs an optimization of an objective function subject to the resource balance constraint to determine target amounts of each resource to be produced or consumed by the central plant equipment at a plurality of times within an optimization period.

Abstract: A system (100) may include a computer-readable storage medium (220) configured to store a first set of instructions for determining one or more candidate driver terminals (140) to accept a service request; and a processor (210) in communication with the computer-readable storage medium (220), wherein when executing the first set of instructions, the processor (210) is directed to: establish a communication with a passenger terminal (130); obtain a service request including a position of the passenger terminal (130) from the passenger terminal (410); determine a wireless fidelity (WiFi) station based on the position of the passenger terminal (420); determine one or more driver terminals (140) that access the WiFi station (430); determine an access instance of each of the one or more driver terminals (440); and determine one or more candidate driver terminals (140) among the one or more driver terminals (140) based on the one or more access instances of the one or more driver terminals (450).

Abstract: A central plant includes an asset allocator configured to determine an optimal allocation of energy loads across central plant equipment. The asset allocator identifies sources configured to supply input resources, subplants configured to convert the input resources to output resources, and sinks configured to consume the output resources. The asset allocator generates a cost function and a resource balance constraint. The resource balance constraint requires balance between a total amount of each resource supplied by the sources and the subplants and a total amount of each resource consumed by the subplants and the sinks. The asset allocator determines the optimal allocation of the energy loads across the central plant equipment by optimizing the cost function subject to the resource balance constraint. The asset allocator is configured to control the central plant equipment to achieve the optimal allocation of the energy loads.