Abstract: The present disclosure provides a process. In an embodiment, the process includes providing an aqueous pigment-polyolefin dispersion (P-P dispersion) and applying a grid-pattern of the aqueous P-P dispersion onto a rear encapsulant film. The process includes drying the grid-pattern into a grid layer to form a gridded rear encapsulant film. The process includes placing a plurality of photovoltaic cells and a front encapsulant film onto the gridded rear encapsulant film to form a stack, and laminating the stack to form a reflective photovoltaic (PV) module. The present disclosure also provides a reflective photovoltaic module produced by the process.

Abstract: The present disclosure relates to a curable composition for an encapsulant film, the curable composition comprising: (A) a polyolefin; (B) a fumed alumina; (C) an organic peroxide; (D) a silane coupling agent; (E) a crosslinking co-agent; and, optionally, (F) an additive component comprising a UV stabilizer, wherein the polyolefin has a volume resistivity of less than 4.0E+15 ohm·cm. The present disclosure further relates to an encapsulant film made from such a curable composition and a PV module containing such an encapsulant film.

Abstract: An encapsulant film is made from a composition comprising (A) a non-polar ethylene-based polymer having a density of 0.850 g/cc to 0.890 g/cc; (B) an organic peroxide; (C) a silane coupling agent; and (D) a co-agent comprising triallyl phosphate.

Abstract: The present disclosure relates to an encapsulant film made from a curable composition comprising: (A) a polyolefin polymer; (B) an organic peroxide; (C) a silane coupling agent; and (D) a co-agent comprising a monocyclic organosiloxane of formula (I).

Abstract: An encapsulant sheet includes a material formed from an ethylene/C4-C8?-olefin copolymer having a resin volume resistivity (VR) from greater than 1×1014? cm at 60° C. to less than 1×1016? cm at 60° C. and from 0.01 wt % to 0.2 wt % of an ion scavenger. The encapsulant sheet has a transmittance greater than 91%. A photovoltaic module (10) includes a front encapsulant sheet (12a) and a rear encapsulant sheet (12b) composed of the material. The photovoltaic module (10) has a power loss after potential induced degradation (PID) test from 0.05% to less than 5%.

Abstract: A system and method is disclosed having an end-to-end two-stage depth estimation deep learning framework that takes one spherical color image and estimate dense spherical depth maps. The contemplated framework may include a view synthesis (stage 1) and a multi-view stereo matching (stage 2). The combination of the two-stage process may provide the advantage of the geometric constraints from stereo matching to improve depth map quality, without the need of additional input data. It is also contemplated that a spherical warping layer may be used to integrate multiple spherical features volumes to one cost volume with uniformly sampled inverse depth for the multi-view spherical stereo matching stage. The two-stage spherical depth estimation system and method may be used in various applications including virtual reality, autonomous driving and robotics.

Abstract: A method and device for performing a perception task are disclosed. The method and device incorporate a dense regression model. The dense regression model advantageously incorporates a distortion-free convolution technique that is designed to accommodate and appropriately handle the varying levels of distortion in omnidirectional images across different regions. In addition to distortion-free convolution, the dense regression model further utilizes a transformer that incorporates an spherical self-attention that use distortion-free image embedding to compute an appearance attention and uses spherical distance to compute a positional attention.

Abstract: A system and method are disclosed herein for developing a machine perception model in the omnidirectional image domain. The system and method utilize the knowledge distillation process to transfer and adapt knowledge from the perspective projection image domain to the omnidirectional image domain. A teacher model is pre-trained to perform the machine perception task in the perspective projection image. A student model is trained by adapting the pre-existing knowledge of the teacher model from the perspective projection image domain to the omnidirectional image domain. By way of this training, the student model learns to perform the same machine perception task, except in the omnidirectional image domain, using limited or no suitably labeled training data in the omnidirectional image domain.

Abstract: An encapsulant film is made from a composition comprising (A) a non-polar ethylene-based polymer; (B) an organic peroxide; (C) a silane coupling agent; and (D) a co-agent comprising a compound of Structure I wherein R1-R6 each is independently selected from the group consisting of hydrogen, a C1-C8 hydrocarbyl group, a C1-C36 substituted hydrocarbyl group, and combinations thereof.

Abstract: An encapsulant film is made from a composition comprising (A) a non-polar ethylene-based polymer having a density of 0.850 g/cc to 0.890 g/cc; (B) an organic peroxide; (C) a silane coupling agent; and (D) a co-agent comprising triallyl phosphate.

Abstract: A composition comprising the following: a) an ethylene/alpha-olefin/interpolymer; b) a peroxide; c) a “Si-containing compound” selected from the following (i) through (v): (i), where each R is independently selected from a C1-C3 alkyl, and n is from 1 to 16; (ii), where each R is independently methyl or ethyl, and n is from 1 to 18; (iii), where each R is independently methyl or ethyl; (iv) a vinyl oligomericsiloxane; or (v) any combination of (i) through (iv).

Abstract: This disclosed is an encapsulant film made from a curable composition comprising: (A) a polyolefin polymer; (B) an organic peroxide; (C) a silane coupling agent; and (D) a co-agent comprising a silane compound of formula (I). This further disclosed is a process for preparing said encapsulant film.

Abstract: The present disclosure relates to a curable composition for an encapsulant film, the curable composition comprising: (A) a polyolefin; (B) a fumed alumina; (C) an organic peroxide; (D) a silane coupling agent; (E) a crosslinking co-agent; and, optionally, (F) an additive component comprising a UV stabilizer, wherein the polyolefin has a volume resistivity of less than 4.0E+15 ohm·cm. The present disclosure further relates to an encapsulant film made from such a curable composition and a PV module containing such an encapsulant film.

Abstract: A system and method is disclosed having an end-to-end two-stage depth estimation deep learning framework that takes one spherical color image and estimate dense spherical depth maps. The contemplated framework may include a view synthesis (stage 1) and a multi-view stereo matching (stage 2). The combination of the two-stage process may provide the advantage of the geometric constraints from stereo matching to improve depth map quality, without the need of additional input data. It is also contemplated that a spherical warping layer may be used to integrate multiple spherical features volumes to one cost volume with uniformly sampled inverse depth for the multi-view spherical stereo matching stage. The two-stage spherical depth estimation system and method may be used in various applications including virtual reality, autonomous driving and robotics.

Abstract: A composition for use as a film layer comprises (A) an olefin-based polymer having a volume resistivity greater than 5.0*1015 ohm·cm; (B) a hydrophobic-treated fumed silica; (C) an alkoxysilane; (D) an organic peroxide; and (E) from 0 wt % to 1.5 wt % of a crosslinking co-agent.

Abstract: A composition for use as a film layer comprises (A) an olefin-based polymer having a volume resistivity greater than 5.0*1015 ohm·cm; (B) a hydrophilic fumed silica; (C) an alkoxysilane; (D) an organic peroxide; and, optionally, (E) from 0 wt % to 1.5 wt % of a crosslinking co-agent.

Abstract: A system and method is disclosed having an end-to-end two-stage depth estimation deep learning framework that takes one spherical color image and estimate dense spherical depth maps. The contemplated framework may include a view synthesis (stage 1) and a multi-view stereo matching (stage 2). The combination of the two-stage process may provide the advantage of the geometric constraints from stereo matching to improve depth map quality, without the need of additional input data. It is also contemplated that a spherical warping layer may be used to integrate multiple spherical features volumes to one cost volume with uniformly sampled inverse depth for the multi-view spherical stereo matching stage. The two-stage spherical depth estimation system and method may be used in various applications including virtual reality, autonomous driving and robotics.

Abstract: A system and method is disclosed having an end-to-end two-stage depth estimation deep learning framework that takes one spherical color image and estimate dense spherical depth maps. The contemplated framework may include a view synthesis (stage 1) and a multi-view stereo matching (stage 2). The combination of the two-stage process may provide the advantage of the geometric constraints from stereo matching to improve depth map quality, without the need of additional input data. It is also contemplated that a spherical warping layer may be used to integrate multiple spherical features volumes to one cost volume with uniformly sampled inverse depth for the multi-view spherical stereo matching stage. The two-stage spherical depth estimation system and method may be used in various applications including virtual reality, autonomous driving and robotics.

Abstract: The present disclosure relates to an encapsulant film made from a curable composition comprising: (A) a polyolefin polymer; (B) an organic peroxide; (C) a silane coupling agent; and (D) a co-agent comprising a monocyclic organosilazane of formula (I).

Abstract: An artificial neural network is trained to produce spatial labelling for a three-dimensional environment based on image data. A two-dimensional image representation is produced of omni-direction image data captured by one or more cameras of the three-dimensional environment. The artificial neural network is applied using the two-dimensional image representation as input and producing a first predicted label as output. A rotated two-dimensional image is generated by shifting image pixels of the two-dimensional image representation in a horizontal direction. The artificial neural network is then applied again using the rotated two-dimensional image as input and producing a second predicted label as its output. The artificial neural network is trained based at least in part on a difference between the first predicted label and the second predicted label.