Abstract: Disclosed is an efficient nitrogen and phosphorus removal process system for mariculture tail water treatment. The process system comprises a physical filtering device, an efficient biological treatment unit, a flocculation sedimentation tank, a sand filtering tank, a clean water tank and a sludge tank, wherein the physical filtering device, the efficient biological treatment unit, the flocculation sedimentation tank, the sand filtering tank and the clean water tank are sequentially connected; the physical filtering device, the efficient biological treatment unit, the flocculation sedimentation tank and the sand filtering tank are all connected with the sludge tank through pipelines, and the physical filtering device and the sand filtering tank are both connected with the clean water tank through pipelines.

Abstract: A main board, a hot plug control signal generator, and a control signal generating method thereof are provided. The hot plug control signal generator includes a controller and a latch. The controller provides a control signal. The latch is operated based on an operation power to generate a hot plug control signal. The latch sets the hot plug control signal to a disabled first logic value, and latches the hot plug control signal at the first logic value.

Abstract: A solar loading-based system includes a memory, a solar load prediction module, and an eco-routing module. The memory is configured to store map information and environment information. The solar load prediction module is configured, based on the map information and the environment information, to (i) determine a route of a host vehicle, (ii) predict solar loading on the host vehicle along the route, and (iii) predict an amount of energy to be consumed by the host vehicle over the route based on the predicted solar loading. The eco-routing module is configured, based on the amount of energy to be consumed by the host vehicle over the route, to at least one of (i) determine whether to follow the route, or (ii) inform a user of the route and the predicted amount of energy to be consumed over the route.

Abstract: A solar loading-based system includes a memory, a disturbance prediction module, a cabin temperature estimation module and a thermal control module. The memory stores a cabin thermal load model of an interior cabin of a host vehicle and a solar load prediction model. The disturbance prediction module: receives signals indicative of states of cabin thermal actuators and comfort metrics; and predicts an effect of solar loading over a known portion of a predicted route including predicting cabin temperatures based on the solar load prediction model, the states of the cabin thermal actuators, and the comfort metrics. The cabin temperature estimation module, based on the cabin thermal load model, determines a first comfort metric based on the predicted cabin temperatures. The thermal control module controls cabin thermal actuators to adjust cabin states, including the first comfort metric, to respective target values based on the predicted effect of solar loading.

Abstract: A trip energy estimation system for a vehicle includes a traffic speed module configured to determine an average traffic speed along a projected route, a path information module configured to output path information indicating route features along the projected route, a perceived speed module configured to output a perceived vehicle speed along the projected route based on the average traffic speed and the path information, and a dynamic driving module configured to calculate and output a predicted driver speed based on the perceived vehicle speed and a feedback input indicative of the predicted driver speed. The dynamic driving module is configured to execute a machine learning algorithm to calculate the predicted driver speed.

Abstract: A distributed learning system of a vehicle includes: a control module configured to: control a plant of the vehicle using a policy; send signals to a learning module including information on an impact of the control on the plant; and selectively control the plant using exploratory control; and the learning module, where the learning module is separate from the control module and is configured to selectively update the policy based on (a) the signals from the control module, (b) state parameters resulting from the control of the plant using the policy, and (c) performance feedback determined based on the control of the plant using the policy and the selective control of the plant using exploratory control, where the control module is configured to receive the exploratory control from the learning module.

Abstract: A computer for an energy management system of an electric vehicle includes a processor. The computer further includes a memory including instructions such that the processor is programmed to determine a value function V based on a plurality of actions U in a plurality of states S. The processor is further programmed to select an action associated with a highest reward value at a current state S. The action U is an HVAC subsystem variable. The state S is a traction power drawn from a rechargeable energy storage system (RESS) to operate a traction subsystem, a base power input drawn from the RESS to operate an HVAC subsystem, a nominal reference cabin heat input set-point determined by the local HVAC processor, an acceleration of the electric vehicle, a current vehicle speed, an average vehicle speed, and a calibrated average vehicle speed estimate.

Abstract: A battery system includes: at least two battery modules, each including at least three strings of battery cells that are configured to, at different times be: connected in series and to a first positive terminal via first switches; connected in parallel and to a second positive terminal via second switches; and disconnected from both of the first and second positive terminals; and a switch control module configured to: during operation in a first power mode: determine periods of phases, respectively; and determine periods for the strings, respectively, to be connected to the second positive terminal during the phases; during operation in a second power mode: set the periods of the phases, respectively, to be equal in length; and set the periods for the strings, respectively, to be equal in length; and selectively actuate the switches based on the periods of the phases and the periods for the strings.

Abstract: A battery system includes: a first positive terminal, a second positive terminal, and a negative terminal; at least two battery modules, each including switches and at least three strings of battery cells configured to, via the switches, at different times be: connected in series and to the first positive terminal; connected in parallel and to the second positive terminal; and disconnected from both of the first and second positive terminals; and a switch control module configured to, when a fault is diagnosed in one of the strings of one of the battery modules: at least one of: actuate the switches and isolate the one of the battery modules from the first and second positive terminals; and actuate the switches and isolate the one of the strings from the first and second positive terminals; and selectively actuate the switches of the remainder of the battery modules using model predictive control.

Abstract: An adhesive composition includes an organic adhesive material and a sand. The organic adhesive material includes a water-soluble adhesive and a starch, wherein a weight of the organic adhesive material accounts for 4 wt % to 70 wt % of an overall weight of the adhesive composition, and a weight of the sand accounts for 30 wt % to 96 wt % of the overall weight of the adhesive composition. A water-soluble adhesive mixture includes the adhesive composition and an aqueous solution, which are mixed to form a viscous mixture (i.e., the water-soluble adhesive mixture). The water-soluble adhesive mixture has an adhering function for users to apply to and adhere to a surface of toys, so that the toys could be adhered together to form a toy model. As the water-soluble adhesive could be softened and dissolved when the water-soluble adhesive mixture is immersed in water, the toy model could be disassembled easily.

Abstract: A trip energy estimation system includes a memory and a control module. The memory stores average traffic speed, average traffic acceleration, driver speed, and driver acceleration data. The control module executes an algorithm to estimate an amount of energy for an electric vehicle to travel between two locations. The algorithm includes: determining, based on the average traffic speed data and the average traffic acceleration data, a baseline amount of energy for the electric vehicle to travel between the two locations; determining, based on the driver speed data and the driver acceleration data, a dynamic amount of energy corresponding to at least one of stop and go events, over speeding, or under speeding; and determining a total amount of energy based on the baseline amount of energy and the dynamic amount of energy. The control module, based on the total amount of energy, performs an operation including indicating a trip estimate.

Abstract: Computer-implemented techniques for repeatable and interpretable divisive analysis. In one embodiment, for example, a method comprises: identifying top-level cohorts of data items based on one or more characteristics of the data items in common; recursively or iteratively dividing a selected top-level cohort in a top-down manner resulting in a plurality of sub-level cohorts arranged in a hierarchy; detecting a particular data item that is a statistical outlier among data items of a leaf cohort in the hierarchy; and causing display of an indication in a computer user interface that the particular data item is an outlier.

Abstract: This document discloses system, method, and computer program product embodiments for detecting an object. For example, the method includes generating a plurality of cuboids by performing the following operations: defining a plurality of first cuboids each encompassing lidar data points that are plotted on a respective 3D graph of a plurality of 3D graphs; accumulating the lidar data points encompassed by the plurality of first cuboids; computing an extent using the accumulated lidar data points; and defining a second cuboid that has dimensions specified by the extent. The first cuboids and/or the second cuboid may be used to detect the object.

Abstract: Systems and methods for operating an autonomous vehicle. The methods comprising: obtaining, by a computing device, loose-fit cuboids overlaid on 3D graphs so as to each encompass LiDAR data points associated with a given object; defining, by the computing device, an amodal cuboid based on the loose-fit cuboids; using, by the computing device, the amodal cuboid to train a machine learning algorithm to detect objects of a given class using sensor data generated by sensors of the autonomous vehicle or another vehicle; and causing, by the computing device, operations of the autonomous vehicle to be controlled using the machine learning algorithm.

Abstract: A method for generating energy-optimized travel routes for a motor vehicle includes one or more of the following: receiving an origin destination (OD) of the motor vehicle and an encrypted energy consumption database of the motor vehicle; generating N candidate routes for the OD; evaluating encrypted energy consumption over a route using an encrypted energy consumption database; applying at least one of homomorphic addition function or homomorphic multiplication function to the encrypted energy consumption data; and returning N candidate routes and their encrypted energy consumption to a client.

Abstract: A control system of a vehicle includes: a target speed module configured to, using a parametric driver model and based on first driver parameters, second driver parameters, and vehicle parameters, determine a target vehicle speed trajectory for a future predetermined period; a driver parameters module configured to determine the first driver parameters based on conditions within a predetermined distance in front of the vehicle; and a control module configured to adjust at least one actuator of the vehicle based on the target vehicle speed trajectory and a present vehicle speed.

Abstract: A wireless communication system having a dual-band coexistence architecture is provided, and the system includes a processing circuit, a first transceiver, a first front-end module, a first switch circuit, a first filter, a second filter, a second switch circuit, a first antenna, a second transceiver, a second front-end module, a third switch circuit, a third filter, a fourth filter, a fourth switch circuit and a second antenna. The first filter and the second filter use a combination of a wideband filter and a narrowband filter, and the fourth filter and the third filter also use a combination of a wideband filter and a narrowband filter, so as to achieve the dual-band coexistence architecture.

Abstract: A headband structure of a headphone, including a headband assembly and a telescopic adjustment module, is provided. The headband assembly has a steel belt. The steel belt has multiple positioning slots. The telescopic adjustment module includes a fixing base, an elastic sheet, and a positioning ball. The fixing base is slidably disposed to the steel belt. The elastic sheet is disposed to the fixing base, and the elastic sheet has an accommodating groove. The positioning ball is disposed in the accommodating groove, and the positioning ball is located between the steel belt and the elastic sheet. The elastic sheet constantly pushes the positioning ball to keep the positioning ball in contact with the steel belt, and the positioning slots are located on moving paths of the positioning ball.

Abstract: Systems and methods for operating an autonomous vehicle. The methods comprising: obtaining, by a computing device, loose-fit cuboids overlaid on 3D graphs so as to each encompass LiDAR data points associated with a given object; defining, by the computing device, an amodal cuboid based on the loose-fit cuboids; using, by the computing device, the amodal cuboid to train a machine learning algorithm to detect objects of a given class using sensor data generated by sensors of the autonomous vehicle or another vehicle; and causing, by the computing device, operations of the autonomous vehicle to be controlled using the machine learning algorithm.