A new artificial intelligence and swarm intelligence method and system in simulated environments for autonomous drones and robots for suppression of forest fires, which foresees the simulation of swarm intelligence models in simulated environment for autonomous drones and robots for suppression of forest fires, performing the analysis of forest fires based on data and information on real time conditions or historical data, of a burning area, transforming same into a simulation environment to obtain a better strategy for firefighting, based on virtual reality environments for digital land, with a mixture of real and virtual images, using satellite/aerial images and combined maps of virtual reality, augmented and mixed. Thus, providing improvements and higher efficacy in combatting forest fires.

A system including a plurality of display panels, each display panel being configured to display a portion of a simulated fire such that together the plurality of display panels display a composite fire image. Each display panel has a display portion configured to display the portion of the simulated fire and at least one sensor configured to detect an extinguishant directed at the display portion. Each display panel also includes a display panel controller operatively coupled to the display portion, the at least one sensor, and the display panel controller of at least one other display panel. The display panel controller is configured to at least partially determine qualities of a modeled fire; and determine changes of the modeled fire based upon inputs received from the at least one sensor. The display panel controller is also configured to cause the display portion to display the portion of the simulated fire based upon data or properties of the modeled fire. The display panel controller is configured to provide data relating to the modeled fire, at or adjacent to an outer edge of the display portion, to at least one adjacent display panel, and the display panel controller is configured to receive data relating to a modeled fire, at or adjacent to an outer edge of the at least one adjacent display panel.

A method for providing task load-optimized computer-generated training experiences to a user of a training system that includes: a display, a training simulator, a prediction program (ML1), and a training optimization program (ML2). In response to receiving a predicted optimal task load, ML2 provides a first training experience recommendation related to the training content and/or training conditions that, if utilized in providing a training experience to the user, is predicted to result in the predicted actual task load of the user equaling the predicted optimal task load. In response to receiving biometric information or performance metric information, ML1 determines the predicted actual task load. If the predicted actual task load does not match the predicted optimal task load, ML2 provides a second training experience recommendation and a second training experience is provided where at least one of the training content or the training conditions is changed.

A mixed reality (MR) training system includes an identification algorithm (ML1) that identifies incidents of concern (IOCs) based on an incident report data set and related contextual data. Each IOC occurs in the data set with a frequency at least equal to a pre-determined threshold or the resulting consequence is at least equal to a different pre-determined threshold. The system also includes a prediction algorithm (ML2) configured to identify predicted changes in the frequency or contextual data of incidents, an experience generation algorithm (ML3) configured to generate an MR training experience based on IOCs identified by ML1 and the predictions of ML2. A fourth algorithm (ML4) tailors and optimizes MR generated training experiences based, in part, on (i) changes in the incident report data or contextual data or (ii) performance data or biometric response data received during or after a user's interaction with the MR training experience.

A firefighting training system for use in training firefighters in the extinguishment of a simulated fire. The system includes a burn area disposed in a pit structure. The burn area includes a multitude of individual burn zones such that each of the individual burn zones can support a fire there within. The system further includes a control system to control the fire in each individual burn zone and a multitude of thermal imaging devices positioned to monitor the multitude of individual burn zones and to report the temperature of each individual burn zone to the control system.

A fire-fighting virtual reality simulator allows the trainee to move in a virtual space in various complex virtual fire disaster situations and perform a suppression of a virtual fire and a confrontation training. An experience interactive simulator for providing a trainee with a floor movement that may occur in a fire disaster situation so that the trainee wearing HMD in a virtual fire-fighting training has the same sense of feeling as that in an actual fire-fighting training is provided. For the realistic content experience, a physical floor movement, such as tilting, falling, and shaking of the floor is reproduced using a base motion, thereby providing a floor movement that enables a sense of feeling similar to a fire disaster situation.

A tile system for a burn room includes a plurality of interlocking surface tiles, each surface tile having an upper portion and a lower portion. The lower portion extends beyond at least a portion of a perimeter of the upper portion to define a flange. The system further includes a plurality of interlocking corner tiles configured to interact with at least one of the plurality of surface tiles; and a bracket system for securing the plurality of surface tiles and the plurality of corner tiles to a surface.

A multi-dimensional space load and fire test system for a tunnel structure, which includes a multi-point loading self-balancing reaction force system having a rigid platform, two furnace body side-sealing apparatuses (22) and a model assembly and transport apparatus (23) for transporting and situating a tunnel model are on a track on the rigid platform (9), the two furnace body side-sealing apparatuses (22) are respectively used for sealing two end openings of the tunnel model, a tower-type combustion vehicle can be placed within an inner cavity of the tunnel model, a plurality of sets of evenly distributed self-adaptive loading apparatuses (3) used for exerting loading forces on an outer wall of the tunnel model are connected between two reaction force frames (1) of the multi-point loading self-balancing reaction force system. The present system is able to perform loading on tunnel models having different cross section shapes, can be adapted to testing requirements of tunnel structures having different cross section shapes, and with respect to tunnel structure fire testing in particular, a camera of the present system has a large imaging angle of view, the present invention has good heat resistance, possesses both terminal imaging and distance measurement, and can amply satisfy a use requirement for the high temperature environment of a tunnel fire.

A mixed reality (MR) training system includes an identification algorithm (ML1) that identifies incidents of concern (IOCs) based on an incident report data set and related contextual data. Each IOC occurs in the data set with a frequency at least equal to a pre-determined threshold or the resulting consequence is at least equal to a different pre-determined threshold. The system also includes a prediction algorithm (ML2) configured to identify predicted changes in the frequency or contextual data of incidents, an experience generation algorithm (ML3) configured to generate an MR training experience based on IOCs identified by ML1 and the predictions of ML2. A fourth algorithm (ML4) tailors and optimizes MR generated training experiences based, in part, on (i) changes in the incident report data or contextual data or (ii) performance data or biometric response data received during or after a user's interaction with the MR training experience.

Disclosed herein are a slim immersive display device and a slim visualization device. The slim immersive display device includes a slim visualization module for forming an image on a retina of an eyeball of a user based on an externally input image signal, a state information acquisition unit for acquiring a state of an external device as a state image, and a content control unit for analyzing the state image, generating an image corresponding to virtual-reality environment information, and inputting the image to the slim visualization module.