Abstract: An anode passivation slurry of anti-dendritic lithium and a method of preparation are provided. The method comprises the steps of dissolving a divalent copper metal compound and a non-ionic polymer to obtain a first solution, dissolving trimesic acid to obtain a second solution, and mixing the first and second solutions to obtain a copper-based metal-organic framework. The dried precursor are mixed with ionic liquid, which has contained a first lithium salt, and then dried to obtain an anion impregnated copper-based metal-organic framework. Thereafter, an anion impregnated copper-based metal-organic framework, a second lithium salt, polymer materials, and a second solvent are mixed to obtain the anode passivation slurry. The anode passivation slurry homogenizes the concentration of conduction of lithium ions and improves ionic conductivity, reducing the formation of lithium dendrites, and improving the cycle life of batteries. A battery with the anode passivation slurry dried on an anode is also disclosed.

Abstract: A tensor accelerator includes two tile execution units and a bidirectional queue. Each of the tile execution units includes a buffer, a plurality of arithmetic logic units, a network, and a selector. The buffer includes a plurality of memory cells. The network is coupled to the plurality of memory cells. The selector is coupled to the network and the plurality of arithmetic logic units. The bidirectional queue is coupled between the selectors of the tile execution units.

Abstract: A nanocomposite layer includes a carbon nanotube composite material and a lithium salt polymer composite. The carbon nanotube composite material includes a surface modified carbon nanotube with a positively charged group and a plurality of nanoparticles with a negatively charged group. The plurality of nanoparticles are attached to the surface modified carbon nanotube. The lithium salt polymer composite wraps the carbon nanotube composite material, and includes a first polymer, a second polymer, and a lithium salt.

Abstract: A method for generating reconstruction a reconstructed image is adapted to an input image having a target object. The method comprises converting the input image into a feature map with vectors by an encoder; performing a training procedure according to training images of reference objects to generate feature prototypes associated with the training images and store the feature prototypes to a memory; selecting a part of feature prototypes from the feature prototypes stored in the memory according to similarities between the feature prototypes and the feature vectors; generating a similar feature map according the part of feature prototypes and weights, wherein the weights represents similarities between the part of feature prototypes and the feature vectors; and converting the similar feature map into the reconstructed image by a decoder; wherein the encoder, the decoder and the memory form an auto-encoder.

Abstract: A method for establishing a complex motion controller includes following steps: obtaining a source controller and a destination controller, wherein the source controller is configured to generate a source motion, and the destination controller is configured to generate a destination motion; determining a transition tensor between the source controller and the destination controller, wherein the transition tensor has a plurality of indices, one of the plurality of indices corresponds to a plurality of phases of the source motion; calculating a plurality of transition outcomes of the transition tensor and recording the plurality of transition outcomes according to the plurality of indices; calculating a plurality of transition qualities according to the plurality of transition outcomes; and searching for an optimal transition quality from the plurality of transition qualities to establish a complex motion controller for generating a complex motion corresponding to one of the plurality of phases.

Abstract: A tensor accelerator includes two tile execution units and a bidirectional queue. Each of the tile execution units includes a buffer, a plurality of arithmetic logic units, a network, and a selector. The buffer includes a plurality of memory cells. The network is coupled to the plurality of memory cells. The selector is coupled to the network and the plurality of arithmetic logic units. The bidirectional queue is coupled between the selectors of the tile execution units.

Abstract: A method for training a locomotion controller of a robotic animal comprising obtaining a motion data of a reference animal, an environmental parameter, and a disturbance parameter; generating a plurality of primitive distributions and a first primitive influence according to the motion data by a policy network; selecting a current state of the reference animal from the motion data and set an adapting state of the reference animal; generating a second primitive influence, by the policy network, according to the current state, the adapting state, and the plurality of primitive distributions at least; and training the policy network according to a result determined by a discriminator according to the first primitive influence and the second primitive influence.

Abstract: A method for generating a loop video comprises: obtaining an input video including multiple frames with a first frame, with each of frames having multiple pixels; extracting a moving object corresponding to a moving pixel region in the first frame with the moving pixel region having at least two pixels; inputting multiple candidate periods to a target function respectively to calculate multiple errors of the moving pixel region; determining a start frame and a loop period of the moving pixel region for each error, with the loop period being associated with one of the candidate periods; generating multiple output frames according to start frames and loop periods; and generating an output frame sequence from the output frames according to a loop parameter, wherein the output frame sequence corresponds to the loop video.

Abstract: A method of transmitting and merging data is adapted to a sender and a receiver that are in communication with each other. The method comprises a sending stage and a receiving stage. The sending stage comprises: transmitting a first block data, a second block data and a third block data to the receiver by the sender; obtaining a fourth block data and a fifth block data by the sender; and transmitting the third, fourth and fifth block data to the receiver by the sender. The receiving stage comprises: receiving the first, second, and third block data by the receiver; merging the first, second and third block data to perform a convolution operation by the receiver; receiving the third, fourth and fifth block data by the sender; and merging the third, fourth and fifth block data to perform another convolution operation.

Abstract: A method for generating reconstruction a reconstructed image is adapted to an input image having a target object. The method comprises converting the input image into a feature map with vectors by an encoder; performing a training procedure according to training images of reference objects to generate feature prototypes associated with the training images and store the feature prototypes to a memory; selecting a part of feature prototypes from the feature prototypes stored in the memory according to similarities between the feature prototypes and the feature vectors; generating a similar feature map according the part of feature prototypes and weights, wherein the weights represents similarities between the part of feature prototypes and the feature vectors; and converting the similar feature map into the reconstructed image by a decoder; wherein the encoder, the decoder and the memory form an auto-encoder.

Abstract: A method for training a defect detector comprises: obtaining a first reference image of a first reference object, wherein the first reference object has a defect and the first reference image has a first label indicating the defect; training a reconstruction model according to a second reference image of a second reference object associated with the first reference object, wherein a defect level of the second reference object is in a tolerable range with an upper limit; obtaining a target image of a target object associated with the first reference object and the second reference object; generating a second label according to the target image, the reconstruction model and an error calculation procedure, wherein the second label comprises a defect of the target object; and training a defect detector by performing a machine learning algorithm according to the first reference image, the target image and the second label.

Abstract: A method for generating a loop video comprises: obtaining an input video including multiple frames with a first frame, with each of frames having multiple pixels; extracting a moving object corresponding to a moving pixel region in the first frame with the moving pixel region having at least two pixels; inputting multiple candidate periods to a target function respectively to calculate multiple errors of the moving pixel region; determining a start frame and a loop period of the moving pixel region for each error, with the loop period being associated with one of the candidate periods; generating multiple output frames according to start frames and loop periods; and generating an output frame sequence from the output frames according to a loop parameter, wherein the output frame sequence corresponds to the loop video.

Abstract: A method for training a locomotion controller of a robotic animal comprising obtaining a motion data of a reference animal, an environmental parameter, and a disturbance parameter; generating a plurality of primitive distributions and a first primitive influence according to the motion data by a policy network; selecting a current state of the reference animal from the motion data and set an adapting state of the reference animal; generating a second primitive influence, by the policy network, according to the current state, the adapting state, and the plurality of primitive distributions at least; and training the policy network according to a result determined by a discriminator according to the first primitive influence and the second primitive influence.

Abstract: A method for training a virtual animal to move based on control parameters comprises an imitation learning stage and an adaptive control stage. The imitation learning stage includes obtaining a first momentum, a second momentum, a current state and a target state of a reference animal associated with the virtual animal, analyzing the first and second momentum to generate primitive distributions, and training a first gating network to generate a first primitive influence so as to convert the current state to the target state. The adaptive control stage includes obtaining a control parameter set, training a second gating network to generate a second primitive influence so as to convert the current state to a combination of the current state and the control parameter set, and generating a determination result according to the first and second primitive influences to update the second gating network.

Abstract: A method for labeling image comprises: obtaining a target image of a target object; generating a reconstruction image according to the target image and a reconstruction model, wherein the reconstruction model is trained with a plurality of reference images and a machine learning algorithm, each of the reference images is an image of a reference object whose defect level is in a tolerable range with an upper limit, and each of the reference objects is associated with the target object; generating a first difference image and a second difference image respectively by performing a first difference algorithm and a second difference algorithm respectively according to the target image and the reconstruction image; and generating an output image by performing a pixel-scale operation according to the first difference image and the second difference image, wherein the output image includes a label indicating a defect of the target object.

Abstract: A method for training a defect detector comprises: obtaining a first reference image of a first reference object, wherein the first reference object has a defect and the first reference image has a first label indicating the defect; training a reconstruction model according to a second reference image of a second reference object associated with the first reference object, wherein a defect level of the second reference object is in a tolerable range with an upper limit; obtaining a target image of a target object associated with the first reference object and the second reference object; generating a second label according to the target image, the reconstruction model and an error calculation procedure, wherein the second label comprises a defect of the target object; and training a defect detector by performing a machine learning algorithm according to the first reference image, the target image and the second label.

Abstract: A method for labeling image comprises: obtaining a target image of a target object; generating a reconstruction image according to the target image and a reconstruction model, wherein the reconstruction model is trained with a plurality of reference images and a machine learning algorithm, each of the reference images is an image of a reference object whose defect level is in a tolerable range with an upper limit, and each of the reference objects is associated with the target object; generating a first difference image and a second difference image respectively by performing a first difference algorithm and a second difference algorithm respectively according to the target image and the reconstruction image; and generating an output image by performing a pixel-scale operation according to the first difference image and the second difference image, wherein the output image includes a label indicating a defect of the target object.

Abstract: A method for dynamically switching blood pressure measurement model, adapted to a wearable blood pressure measurement device with a biosignal sensing assembly and a processor, wherein said biosignal sensing assembly comprises two exposed electrodes, comprises: obtaining potential difference by said two exposed electrodes; determining whether potential difference is smaller than potential threshold by processor; obtaining first biosignal of specified user by biosignal sensing assembly when potential difference is smaller than the potential threshold; calculating first blood pressure value by processor according to at least first biosignal and first blood pressure model and outputting first blood pressure value; obtaining second biosignal of specified user by biosignal sensing assembly when potential difference is not smaller than the potential threshold, wherein types of first and second biosignal are different; and calculating second blood pressure value by processor according to second biosignal and second b

Abstract: A method of establishing blood pressure model comprising: obtaining a plurality of first physiologic data of a plurality of general users and a plurality of first blood pressure data of the plurality of general users; performing a deep learning algorithm to establish a general blood pressure model according to the plurality of first physiologic data and the plurality of first blood pressure data, wherein the general blood pressure model has a parameter set and a loss function; obtaining a second physiologic data of a specific user and a second blood pressure data of the specific user; generating a blood pressure estimation according to the second physiologic data and the parameter set; calculating an error according to the blood pressure estimation, the second blood pressure data and the loss function; and adjusting the parameter set to establish a specific blood pressure model according to the error.

Abstract: A surface defect detection method applied to a surface of an object is disclosed. The method includes obtaining an image of the surface, performing a deep learning algorithm by a computing device to set a plurality of bounding boxes in the image and to output a plurality of feature parameter sets associated with the plurality of bounding boxes, with each bounding box enclosing a possible defect of the surface, and performing a classifying algorithm by the computing device according to the bounding boxes and the feature parameters to determine whether the surface conforms to a specification.