Abstract: A calculation method for fractal dimensions of shale pores includes steps as follows. S1: multiple shale samples from a target stratum of a study area are obtained, parameter values of geological parameters of each of the multiple shale samples are obtained, followed by dividing the multiple shale samples into target shale samples and experimental shale samples. S2: PCA is performed on the parameter values to obtain principal components representing a variation of the geological parameters. fractal dimensions are calculated based on an existing fractal dimension calculation method. S3: the principal components are used as independent variables and the fractal dimensions are used as dependent variables, followed by performing regression analysis to obtain a quantitative calculation model. S4: the fractal dimensions of the target shale sample are calculated according to the parameter values and the quantitative calculation model for the fractal dimension based on the parameter values.

Abstract: A quantitative prediction method for gas content of deep marine shale includes: obtaining raw data of known wells; establishing relationship formulas between pore specific surface areas and adsorbed gas contents of a known well in an area as an adsorbed gas content quantitative prediction model; establishing relationship formulas between pore volumes and free gas contents of the known well as a free gas content quantitative prediction model; summing the adsorbed gas contents and corresponding free gas contents to obtain total gas contents; calculating adsorbed gas contents, free gas contents and total gas contents of the known wells; drawing a predicted adsorbed gas content contour map, a predicted free gas content contour map and a predicted total gas content contour map; and reading an adsorbed gas content, a free gas content and a total gas content of an unknown well in the area from the above contour maps.

Abstract: A quantitative prediction method for gas content of deep marine shale includes: obtaining raw data of known wells; establishing relationship formulas between pore specific surface areas and adsorbed gas contents of a known well in an area as an adsorbed gas content quantitative prediction model; establishing relationship formulas between pore volumes and free gas contents of the known well as a free gas content quantitative prediction model; summing the adsorbed gas contents and corresponding free gas contents to obtain total gas contents; calculating adsorbed gas contents, free gas contents and total gas contents of the known wells; drawing a predicted adsorbed gas content contour map, a predicted free gas content contour map and a predicted total gas content contour map; and reading an adsorbed gas content, a free gas content and a total gas content of an unknown well in the area from the above contour maps.

Abstract: The present disclosure provides methods and techniques for evaluating and improving algorithms for autonomous driving planning and control (PNC), using one or more metrics (e.g., similarity scores) computed based on expert demonstrations. For example, the one or more metrics allow for improving PNC based on human, as opposed to or in addition to optimizing certain oversimplified properties, such as the least distance or time, as an objective. When driving in certain scenarios, such as taking a turn, people may drive in a distributed probability pattern instead of in a uniform line (e.g., different speeds and different curvatures at the same corner). As such, there can be more than one “correct” control trajectory for an autonomous vehicle to perform in the same turn. Safety, comfort, speeds, and other criteria may lead to different preferences and judgment as to how well the controlled trajectory has been computed.

Abstract: Disclosed is a microfluidic detection device including a circuit substrate and a transparent substrate. The circuit substrate is provided with at least one first light-emitting device used to emit a detection beam, a photodetector used to receive the detection beam and send out a sensing signal, and a control circuit electrically connected to the first light-emitting device and the photodetector. The transparent substrate overlaps the circuit substrate and is provided with a microfluidic channel and a light guide structure. The light guide structure has a light incident surface disposed corresponding to the first light-emitting device and a light exiting surface disposed corresponding to the photodetector. The light guide structure extends from each of the light incident surface and the light exiting surface to the microfluidic channel and is adapted to transmit the detection beam into and out of the microfluidic channel.

Abstract: A display apparatus includes a driving backplane, a transparent metal oxide pattern layer, light-emitting elements and transparent structures. The transparent metal oxide pattern layer has a solid portion and openings defined by the solid portion. The light-emitting elements are respectively located in the openings of the transparent metal oxide pattern layer. The transparent structures respectively cover the light emitting elements, respectively overlap the openings of the transparent metal oxide pattern layer, and expose electrodes of the light emitting elements. The transparent structures are located between the light emitting elements and the driving backplane. Moreover, a manufacturing method of the display apparatus is also provided.

Abstract: In one embodiment, a system configures an image signal processor (ISP) of an autonomous driving vehicle (ADV) with a first set of ISP configuration parameters, where the ISP is used to process raw image data of an image sensor of the ADV based on the first set of ISP configuration parameters. The system determines whether one or more criteria is satisfied, where the one or more criteria corresponds to an expected change in a characteristic of ambient light being perceived by the image sensor of the ADV. In response to determining that the one or more criteria is satisfied, the system configures the ISP of the ADV with a second set of ISP configuration parameters, where the ISP is used to apply an image processing algorithm to raw image data based on the second set of ISP configuration parameters to generate an image.

Abstract: The technologies described herein are generally directed toward retrieving data from streaming storage. In an embodiment, a method can include receiving an application data request that identifies application data to be retrieved from a sequence of stored data chunks that correspond to a stored stream of data. The method can further include, based on the application data request, estimating a first estimated location of the application data, with the first estimated location including an identified chunk of a sequence of chunks. Further, the method can include, based on the application data request and a characteristic of the identified chunk, retrieving, by the system, a first data block that is estimated to comprise the application data, resulting in a first retrieved data block.

Abstract: A display device includes a substrate, subpixel units and a thin film encapsulation structure. The subpixel units are disposed on the substrate, and each of the subpixel units includes a light emitting part and a pixel defining part surrounding the light emitting part. The thin film encapsulation structure covers the subpixel units, and includes a first encapsulation layer and a second encapsulation layer covering and in contact with the first encapsulation layer. The refractive index of the first encapsulation layer is different from that of the second encapsulation layer. The first encapsulation layer includes first microstructures, and the first microstructures are respectively disposed on the pixel defining parts. The second encapsulation layer includes second microstructures, the second microstructures are respectively disposed on the pixel defining parts, and the second microstructures are respectively fitted with the first microstructures to form distributed Bragg reflectors.

Abstract: According to some embodiments, systems, methods and media for dynamically generating scenario parameters for an autonomous driving vehicles (ADV) are described. In one embodiment, when an ADV enters a driving scenario, the ADV can invoke a map-based scenario checker to determine the type of scenario, and invokes a corresponding neural network model to generate a set of parameters for the scenario based on real-time environmental conditions (e.g., traffics) and vehicle status information (e.g., speed). The set of scenario parameters can be a set of extra constraints for configuring the ADV to drive in a driving mode corresponding to the scenario.

Abstract: In one embodiment, a microcontroller unit (MCU) receives an expected state of an autonomous driving vehicle (ADV) from a controller of the ADV, where the controller controls motions of the ADV using a control algorithm. The MCU receives sensor data from one or more sensors of the ADV. The MCU determine an actual state of the ADV based on the sensor data. The MCU determines a performance metric of the control algorithm based on the expected state and the actual state. In response to determining the performance metric has satisfied a predetermined condition, the MCU determines a plurality of weight values for the control algorithm. The MCU sends the plurality of weight values to the control system to tune one or more weight parameters of the control algorithm using the plurality of weight values, where the controller controls the ADV using the tuned control algorithm.

Abstract: The technologies described herein are generally directed toward retrieving data from streaming storage. In an embodiment, a method can include receiving an application data request that identifies application data to be retrieved from a sequence of stored data chunks that correspond to a stored stream of data. The method can further include, based on the application data request, estimating a first estimated location of the application data, with the first estimated location including an identified chunk of a sequence of chunks. Further, the method can include, based on the application data request and a characteristic of the identified chunk, retrieving, by the system, a first data block that is estimated to comprise the application data, resulting in a first retrieved data block.

Abstract: A display element includes a first spacer, a second spacer, at least one first electrode, a second electrode, at least one LED structure, a reflective layer, a first transparent molding layer and a transparent conductive layer. The second spacer is located on one side of the first spacer. The first electrode is surrounded by the first spacer. The second electrode is surrounded by the second spacer. The LED structure is located on the first electrode. The reflective layer is located on a sidewall of the first spacer facing the LED structure. The first transparent molding layer is located on the reflective layer and surrounds the LED structure. The transparent conductive layer is located on the top surface of the second semiconductor layer and the top surface of the first transparent molding layer, and extends to the second electrode.

Abstract: A display device includes an array substrate, light emitting elements and light shielding units. The light emitting elements are disposed on the array substrate and electrically connected to the array substrate, where each of the light emitting elements has a first surface and a second surface opposite to the first surface. The second surfaces face the array substrate. The light shielding units are disposed on the array substrate and arranged alternately with the light emitting elements, where the light shielding units expose the first surfaces, and each of the light shielding units has a top and a bottom opposite to the top. The bottoms face the array substrate, and a cavity is existed between the bottoms and the array substrate.

Abstract: A display device includes a circuit substrate and at least one light emitting diode (LED) packaging structure electrically connected to the circuit substrate. Each of the at least one LED packaging structure includes a plurality of LEDs, a plurality of transparent packaging structures, a molding layer, a redistribution structure and a common electrode. Each LED includes a first electrode, a semiconductor stack structure and a second electrode stacked with each other. The transparent packaging structures respectively surround the LEDs. The molding layer surrounds the transparent packaging structures. The redistribution structure is located on a first side of the molding layer and is electrically connected to the first electrodes of the LEDs. The common electrode is located on a second side of the molding layer and is electrically connected to the second electrodes of the LEDs.

Abstract: In one embodiment, an exemplary method includes the operations of receiving, at a profiling application, a record file recorded by the ADV for a driving scenario in an area, and a high definition map matching the area; extracting planning messages and perception messages from the record file; and aligning the planning message and the perception messages based on their timestamps. The method further includes calculating an individual performance score for each planning cycle of the ADV for the driving scenario based on the planning messages; calculating a weight for each planning cycle based on the perception messages and the high definition map; and then calculating a weighted score for the driving scenario based on individual performance scores and their corresponding weights.

Abstract: Systems, methods, and media for factoring localization uncertainty of an ADV into its planning and control process to increase the safety of the ADV. The uncertainty of the localization can be caused by sensor inaccuracy, map matching algorithm inaccuracy, and/or speed uncertainty. The localization uncertainty can have negative impact on trajectory planning and vehicle control. Embodiments described herein are intended to increase the safety of the ADV by considering localization uncertainty in trajectory planning and vehicle control. An exemplary method includes determining a confidence region for an ADV that is automatically driving on a road segment based on localization uncertainty and speed uncertainty; determining that an object is within the confidence region, and a probability of collision with the ADV based on a distance of the object to the ADV; and planning a trajectory based on the probability of collision, and controlling the ADV based on the probability of collision.

Abstract: Disclosed are performance metrics for evaluating the accuracy of a dynamic model in predicting the trajectory of ADV when simulating the behavior of the ADV under the control commands. The performance metrics may indicate the degree of similarity between the predicted trajectory of the dynamic model and the actual trajectory of the vehicle when applied with identical control commands. The performance metrics measure deviations of the predicted trajectory of the dynamic model from the actual trajectory based on the ground truths. The performance metrics may include cumulative or mean absolute trajectory error, end-pose difference (ED), two-sigma defect rate (?2?), the Hausdirff Distance (HAU), the longest common sub-sequence error (LCSS), or dynamic time warping (DTW). The two-sigma defect rate represents the ratio of the number of points with true location error falling out of the 2? range of the predicted location error over the total number of points in the trajectory.