Abstract: Electric heaters for heating a hydrocarbon process stream. The electric heater has a cavity with parallel walls which may provide a rectangular cross section to the cavity. The parallel side walls may be parallel to a longitudinal axis of the electrical heating elements which extend into the cavity to transfer heat to fluid passing there through. Various configurations and orientations of the electrical heating elements are provided.

Abstract: Processes and apparatuses for heating a hydrocarbon process stream, with an electric heater to provide a portion of the heat requirement necessary for a chemical reaction to occur to one of the components of the hydrocarbon process stream. The electric heater may be used between two reaction zones which are arranged on top of each other. Additionally, reaction zones may be in reactors which are arranged in a polygon.

Abstract: In various examples, a three-dimensional (3D) intersection structure may be predicted using a deep neural network (DNN) based on processing two-dimensional (2D) input data. To train the DNN to accurately predict 3D intersection structures from 2D inputs, the DNN may be trained using a first loss function that compares 3D outputs of the DNN—after conversion to 2D space—to 2D ground truth data and a second loss function that analyzes the 3D predictions of the DNN in view of one or more geometric constraints—e.g., geometric knowledge of intersections may be used to penalize predictions of the DNN that do not align with known intersection and/or road structure geometries. As such, live perception of an autonomous or semi-autonomous vehicle may be used by the DNN to detect 3D locations of intersection structures from 2D inputs.

Abstract: Heat exchangers (also referred to as exchangers herein) are provided that fit within a bottom sump of a distillation column. These heat exchangers may be at least partially submerged in the bottoms fluid of the distillation column so that the exterior surface of the heat exchanger can contribute to the total area of the heat exchanger. The internal configuration of the exchanger allows for annular coaxial flow of the hot fluid (condensing vapor stream) and eliminates the need for top and bottom channel heads.

Abstract: A drive system for raising and lowering a window covering includes a motor, a driven wheel configured to engage a continuous cord loop, a controller for the motor, a sensor, and a housing. The continuous cord loop includes an endless loop of flexible material and one or more sensor targets disposed on the endless loop of flexible material. The housing supports a guide rail adjacent the driven wheel. The sensor is mounted to the guide rail and is configured to generate a signal indicating presence of each sensor target when the target is located in proximity to or in contact with the sensor. The controller is calibrated to store an initial position of each sensor target along the continuous cord loop, and is configured to receive the signal indicating presence of the sensor target and to identify a drift from the initial position during continuing operation of the drive system.

Abstract: In various examples, path detection using machine learning models for autonomous or semi-autonomous systems and applications is described herein. Systems and methods are disclosed that use one or more machine learning models to determine a geometry associated with a path for a vehicle. To determine the geometry, the machine learning model(s) may process sensor data generated using the vehicle and, based at least on the processing, output points associated with the path. In some examples, the machine learning model(s) outputs a limited number of points, such as between five and twenty points. One or more algorithms, such as one or more Bezier algorithms, may then be used to generate the geometry based at least on the points. As such, in some examples, the geometry may correspond to a Bezier curve that represents the path.

Abstract: A drive system for raising and lowering a window covering using a continuous cord loop that includes a motor with an associated encoder, device controller for the motor, a speed sensor, and proportional-integral-derivative (PID) layer. The device controller stores or receives a desired motor speed and generates a speed command signal to the motor. A closed-loop system applies a PID speed control algorithm to an error representing a difference between actual motor speed and desired motor speed. The PID layer uses the error to calculate a new output via PID control terms to automatically correct signals to the motor to achieve desired motor speed. A hembar alignment function automates positional control of a plurality of motor drive systems to synchronize raising or lowering of window covering shades with the shades in positional alignment. A system controller includes a time-counter indexed to a position scale to synchronize hembar alignment positional commands.

Abstract: A method for determining a contour of the representation, on a photo of an object made up of one or several elements, referred to as an “actual contour.” The method includes the following steps: 1) application of a first treatment to the photo so as to obtain a first image representing a first contour of the object; 2) application of a second treatment to the photo so as to obtain a second image representing a second contour of the object; 3) association, according to a predetermined association algorithm, of each point of the second contour with a point of the first respective contour, then removal of the points of the first contour that have not been associated with a point of the second contour, the first image then representing a corrected contour of the object.

Abstract: In various examples, one or more object detectors may regress bounding polygons for detected objects in systems (e.g., autonomous or semi-autonomous driving systems and applications) that provide object awareness, object identification, object avoidance, and/or object localization. The object detector may determine regression data representing a regressed polygon associated with a given shape of a detected object represented by classification data determined from a scene. The object detector may determine regression data for different regressed angles between different pairs of successive vertices of the regressed polygon and regressed lengths of vectors from a regressed geometric center of the regressed polygon to vertices of the regressed polygon. The object detector may generate, based at least in part on the regression data, a bounding shape for a detected object in the scene. In some embodiments, the object detector may be trained by deforming a regressed polygon to match a ground truth polygon.

Abstract: A device for opening and closing a curtain on a rod or rail includes a driven wheel coupled to a motor within a housing. The driven wheel protrudes from the housing in frictional engagement with the rod or rail. The device further includes a suspension assembly including a support member and one or more linkages. The support member is in sliding or rolling contact with the rod or rail. The linkages extend along an independent suspension axis and connect the support member to the housing to suspend the housing from the support member, allowing the device to traverse bumps in the rod or rail. The driven wheel is rotated by the motor to advance the device along the rod or rail for opening and closing the curtain. A curtain opening system may further include one or more sensor targets disposed on the rod or rail, and an encoder and a sensor operatively connected to a controller of the device for auto-calibration of position tracking.

Abstract: In various examples, live perception from sensors of a vehicle may be leveraged to generate potential paths for the vehicle to navigate an intersection in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute various outputs—such as heat maps corresponding to key points associated with the intersection, vector fields corresponding to directionality, heading, and offsets with respect to lanes, intensity maps corresponding to widths of lanes, and/or classifications corresponding to line segments of the intersection. The outputs may be decoded and/or otherwise post-processed to reconstruct an intersection—or key points corresponding thereto—and to determine proposed or potential paths for navigating the vehicle through the intersection.

Abstract: In various examples, live perception from sensors of a vehicle may be leveraged to generate potential paths for the vehicle to navigate an intersection in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute various outputs—such as heat maps corresponding to key points associated with the intersection, vector fields corresponding to directionality, heading, and offsets with respect to lanes, intensity maps corresponding to widths of lanes, and/or classifications corresponding to line segments of the intersection. The outputs may be decoded and/or otherwise post-processed to reconstruct an intersection—or key points corresponding thereto—and to determine proposed or potential paths for navigating the vehicle through the intersection.

Abstract: In various examples, live perception from sensors of a vehicle may be leveraged to detect and classify intersection contention areas in an environment of a vehicle in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute outputs—such as signed distance functions—that may correspond to locations of boundaries delineating intersection contention areas. The signed distance functions may be decoded and/or post-processed to determine instance segmentation masks representing locations and classifications of intersection areas or regions. The locations of the intersections areas or regions may be generated in image-space and converted to world-space coordinates to aid an autonomous or semi-autonomous vehicle in navigating intersections according to rules of the road, traffic priority considerations, and/or the like.

Abstract: In various examples, live perception from sensors of a vehicle may be leveraged to detect and classify intersections in an environment of a vehicle in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute various outputs—such as bounding box coordinates for intersections, intersection coverage maps corresponding to the bounding boxes, intersection attributes, distances to intersections, and/or distance coverage maps associated with the intersections. The outputs may be decoded and/or post-processed to determine final locations of, distances to, and/or attributes of the detected intersections.

Abstract: In various examples, live perception from sensors of a vehicle may be leveraged to detect and classify intersection contention areas in an environment of a vehicle in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute outputs—such as signed distance functions—that may correspond to locations of boundaries delineating intersection contention areas. The signed distance functions may be decoded and/or post-processed to determine instance segmentation masks representing locations and classifications of intersection areas or regions. The locations of the intersections areas or regions may be generated in image-space and converted to world-space coordinates to aid an autonomous or semi-autonomous vehicle in navigating intersections according to rules of the road, traffic priority considerations, and/or the like.

Abstract: A drive system for raising and lowering a window covering includes a motor, a driven wheel configured to engage a continuous cord loop, a controller for the motor, a sensor, and a housing. The continuous cord loop includes an endless loop of flexible material and one or more sensor targets disposed on the endless loop of flexible material. The housing supports a guide rail adjacent the driven wheel. The sensor is mounted to the guide rail and is configured to generate a signal indicating presence of each sensor target when the target is located in proximity to or in contact with the sensor. The controller is calibrated to store an initial position of each sensor target along the continuous cord loop, and is configured to receive the signal indicating presence of the sensor target and to identify a drift from the initial position during continuing operation of the drive system.

Abstract: A method for object detection includes: extracting a plurality of identification features from a plurality of reference images that are related to a target object; selecting a plurality of selected identification features respectively from the identification features so as to obtain a first feature dataset, and storing the first feature dataset, a quantity of selected identification features being smaller than a quantity of the identification features; in response to receipt of a to-be-detected image, performing a feature extraction operation on the to-be-detected image to obtain a second feature dataset; performing a similarity determination operation with respect to the to-be-detected image based on the first feature dataset and the second feature dataset, and calculating a quantity of instances of the target object in the to-be-detected image.

Abstract: In various examples, live perception from sensors of a vehicle may be leveraged to detect and classify intersections in an environment of a vehicle in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute various outputs—such as bounding box coordinates for intersections, intersection coverage maps corresponding to the bounding boxes, intersection attributes, distances to intersections, and/or distance coverage maps associated with the intersections. The outputs may be decoded and/or post-processed to determine final locations of, distances to, and/or attributes of the detected intersections.

Abstract: In various examples, feature values corresponding to a plurality of views are transformed into feature values of a shared orientation or perspective to generate a feature map—such as a Bird's-Eye-View (BEV), top-down, orthogonally projected, and/or other shared perspective feature map type. Feature values corresponding to a region of a view may be transformed into feature values using a neural network. The feature values may be assigned to bins of a grid and values assigned to at least one same bin may be combined to generate one or more feature values for the feature map. To assign the transformed features to the bins, one or more portions of a view may be projected into one or more bins using polynomial curves. Radial and/or angular bins may be used to represent the environment for the feature map.

Abstract: A drive system for raising and lowering a window covering includes a motor, a driven wheel configured to engage a continuous cord loop, a controller for the motor, a sensor, and a housing. The continuous cord loop includes an endless loop of flexible material and one or more sensor targets disposed on the endless loop of flexible material. The housing supports a guide rail adjacent the driven wheel. The sensor is mounted to the guide rail and is configured to generate a signal indicating presence of each sensor target when the target is located in proximity to or in contact with the sensor. The controller is calibrated to store an initial position of each sensor target along the continuous cord loop, and is configured to receive the signal indicating presence of the sensor target and to identify a drift from the initial position during continuing operation of the drive system.