One variation of a method for deploying sensors within an agricultural facility includes: accessing scan data of a set of modules deployed within the agricultural facility; extracting characteristics of plants occupying the set of modules from the scan data; selecting a first subset of target modules from the set of modules, each target module in the set of target modules containing a group of plants exhibiting characteristics representative of plants occupying modules neighboring the target module; for each target module, scheduling a robotic manipulator within the agricultural facility to remove a particular plant from a particular plant slot in the target module and load the particular plant slot with a sensor pod from a population of sensor pods deployed in the agricultural facility; and monitoring environmental conditions at target modules in the first subset of target modules based on sensor data recorded by the first population of sensor pods.

One variation of a method for deploying sensors within an agricultural facility includes: accessing scan data of a set of modules deployed within the agricultural facility; extracting characteristics of plants occupying the set of modules from the scan data; selecting a first subset of target modules from the set of modules, each target module in the set of target modules containing a group of plants exhibiting characteristics representative of plants occupying modules neighboring the target module; for each target module, scheduling a robotic manipulator within the agricultural facility to remove a particular plant from a particular plant slot in the target module and load the particular plant slot with a sensor pod from a population of sensor pods deployed in the agricultural facility; and monitoring environmental conditions at target modules in the first subset of target modules based on sensor data recorded by the first population of sensor pods.

A fluid analysis simulation apparatus based on smoothed particle hydrodynamics (SPH) comprises a structure model generation unit that generates a structure model, a polyhedron generation unit that generates a polyhedron model surrounding the structure model and including a plurality of faces, a particle generation unit that generates a plurality of particles and arranges the plurality of particles inside the structure model using the structure model and the polyhedron model, and a flow data calculation unit that calculates flow data of the plurality of particles and performs a fluid analysis simulation based on the flow data.

A fluid analysis simulation apparatus based on smoothed particle hydrodynamics (SPH) comprises a structure model generation unit that generates a structure model, a polyhedron generation unit that generates a polyhedron model surrounding the structure model and including a plurality of faces, a particle generation unit that generates a plurality of particles and arranges the plurality of particles inside the structure model using the structure model and the polyhedron model, and a flow data calculation unit that calculates flow data of the plurality of particles and performs a fluid analysis simulation based on the flow data.

The present invention discloses a networked control system (NCS) time-delay compensation method based on predictive control. The method comprises the following steps: (1) acquiring random time-delay data in an NCS, and preprocessing the data; (2) predicting the current time-delay by using a fuzzy neural network (FNN) optimized by a particle swarm optimization (PSO) algorithm; (3) compensating the predicted time-delay by using an implicit proportional-integral-based generalized predictive control (PIGPC) algorithm; (4) determining whether a preset work end time is up according to a clock in the NCS; if yes, ending the process; if no, returning to step (2). The method disclosed by the present invention can accurately predict and effectively compensate the NCS time-delay and has excellent development prospect.

The present invention discloses a networked control system (NCS) time-delay compensation method based on predictive control. The method comprises the following steps: (1) acquiring random time-delay data in an NCS, and preprocessing the data; (2) predicting the current time-delay by using a fuzzy neural network (FNN) optimized by a particle swarm optimization (PSO) algorithm; (3) compensating the predicted time-delay by using an implicit proportional-integral-based generalized predictive control (PIGPC) algorithm; (4) determining whether a preset work end time is up according to a clock in the NCS; if yes, ending the process; if no, returning to step (2). The method disclosed by the present invention can accurately predict and effectively compensate the NCS time-delay and has excellent development prospect.

An information processing method includes displaying a graph on a display screen of a display, in response to a user operation of specifying at least part of the graph, displaying an icon corresponding to a numerical value which is associated with the at least part of the graph on the display screen, in response to a user operation of selecting the icon, as at least part of a mathematical expression to execute calculation using the numerical value which is associated with the icon selected, displaying the numerical value or a variable indicating the numerical value which is associated with the icon on the display screen.

An information processing method includes displaying a graph on a display screen of a display, in response to a user operation of specifying at least part of the graph, displaying an icon corresponding to a numerical value which is associated with the at least part of the graph on the display screen, in response to a user operation of selecting the icon, as at least part of a mathematical expression to execute calculation using the numerical value which is associated with the icon selected, displaying the numerical value or a variable indicating the numerical value which is associated with the icon on the display screen.

In implementations of item transfer control systems, a computing device implements a transfer system to receive input data describing types of requested items and corresponding quantities of the types of requested items to receive at each of a plurality of destination sites and types of available items and corresponding quantities of the types of available items that are available at each of a plurality of source sites. The transfer system constructs a flow network having a source node for each of the plurality of the source sites and a destination node for each of the plurality of the destination sites. An integral approximate solution is generated that transfers the corresponding quantities of the types of requested items to each of the plurality of the destination sites using a maximum flow solver and the flow network. The transfer system causes transferences of the corresponding quantities of the types of requested items to each of the plurality of the destination sites based on the integral approximate solution.

In implementations of item transfer control systems, a computing device implements a transfer system to receive input data describing types of requested items and corresponding quantities of the types of requested items to receive at each of a plurality of destination sites and types of available items and corresponding quantities of the types of available items that are available at each of a plurality of source sites. The transfer system constructs a flow network having a source node for each of the plurality of the source sites and a destination node for each of the plurality of the destination sites. An integral approximate solution is generated that transfers the corresponding quantities of the types of requested items to each of the plurality of the destination sites using a maximum flow solver and the flow network. The transfer system causes transferences of the corresponding quantities of the types of requested items to each of the plurality of the destination sites based on the integral approximate solution.