Abstract: Embodiments of the present invention provide a hybrid system and method for distributed virtual power plants integrated intelligent net zero. In this method, a cyber physical agent (CPA) is utilized to collect a carbon emission information and an energy management information, and then an artificial intelligence (AI) optimization model of an intelligent central dispatch platform is utilized to obtain a power dispatch manner of the distributed virtual power plants based on the carbon emission information and the energy management information, such that the power dispatch manner of the distributed virtual power plants meets the requirements of enterprise economic benefits and net zero carbon emissions at the same time.

Abstract: A golden path search method for manufacturing process provides a two-phase process to search for a golden path. A first phase step of the two-phase process includes preparing a search model based on a search algorithm, and selecting a plurality of key process stages of a plurality of process stages by feeding sets of final inspection values and the production paths of the workpieces into the searching model, and then generating a plurality of key paths according to the key process stages. A second phase step of the two-phase process includes building a plurality of prediction models of the key paths according to the production paths and the sets of final inspection values, and predicting a plurality of yield rates corresponding to the key paths according to the prediction models, and then searching for the golden path of the key paths according to the yield rates.

Abstract: Right decisions can only be made via understanding future trends. For example, energy consumption prediction is the basis of all Energy Management Systems (EMS). Any EMS decision-making must rely on the energy consumption prediction value for subsequent power grid planning or factory management. These prediction values play decisive roles in planning and management. A virtual metrology method based on a long sequence time-series prediction framework develops the Automatic Virtual Metrology (AVM) which can provide accurate predictive future trends to increase decision-making accuracy (such as production scheduling or facility control). The built-in period calculator module is used to automatically find out a best memorizing length, a best forecasting length and a best full-periodic pattern length for the long sequence time-series prediction framework.

Abstract: The present invention relates to a charging method and system for series-connected rechargeable battery cells. Specifically, it employs a first set and a second set of relays with a specific electrical coupling. Firstly, turning off all second relays, and turning on all first relays to charge the battery cells. When a specific battery reaches a preset charging condition, a corresponding first relay is deactivated to halt charging that particular battery. At the same time, a corresponding second relay is activated to continue charging the remaining battery cells.

Abstract: Embodiments of the present invention provide a multiple-variable predictive maintenance method for a component of a production tool and a computer program product thereof, in which a multiple-variable time series prediction (TSPMVA) and an information criterion algorithm are adapted to build a best vector autoregression model (VAR), thereby forecasting the complicated future trend of accidental shutdown of the component of the production tool. Therefore, the multiple-variable prediction of the present invention can improve the accuracy of prediction compared with the single-variable prediction.

Abstract: Methods for treating a disorder associated with immunoglobulin E (IgE) in a subject with antibodies capable of binding to the C?mx domain of a membrane-bound IgE. The subject can be administered with at least two doses of the antibody, the two doses being at least three months apart.

Abstract: A near-optimal scheduling system for considering processing-time variations and real-time data streaming includes a data parser module, a publish subscribe mechanism module, a processing time prediction module and a scheduling optimization module. In the processing time prediction module, an orthogonal greedy algorithm and a recurrent neural network are used to extract key features and then predict varying operation processing time. By formulating the scheduling problem in an integer programming form, an ordinal-optimization (OO) embedded decomposition and coordination method is established in the scheduling optimization module to provide dynamic and near-optimal schedules in a computationally efficient manner. Moreover, a publish subscribe mechanism is developed in the publish subscribe mechanism module by using subscribe and publish methods to realize real-time needs.

Abstract: The present invention provides a negative electrode active material modified by zwitterionic polymer, which comprises a negative electrode active powder and a nano-scaled layer of zwitterionic polymer coated on the negative electrode active powder. Besides, the present invention also relates to the preparation method and applications of the above-mentioned negative electrode active material. The replacement of the negative electrode active material used in conventional cells with the negative electrode active material modified by zwitterionic polymer of the present invention benefits ion insertion and extraction, reduces irreversible reaction and prolongs cycle life of cells, thereby enhancing overall effectiveness of cells.

Abstract: One embodiment provides a method comprising detecting at least one object displayed within at least one input frame of an input video. The method further comprises cropping, from the at least one input frame, at least one cropped image including the at least one object. The method further comprises generating at least one training image by overlaying simulated text on the at least one cropped image. The method further comprises providing the at least one training image to a pruned convolutional neural network (CNN). The pruned CNN learns, from the at least one training image, to reconstruct objects and textual regions during image super-resolution.

Abstract: A hybrid method for green intelligent manufacturing (GiM) combines the carbon reduction and the energy saving into the intelligent manufacturing based Industry 4.1 cloud platform. GiM assists companies to achieve the goal of net zero transition and help them advance to Industry 4.2 as soon as possible by simultaneously taking carbon footprint and energy issues into account. GiM collects large volumes of essential data (including carbon footprint) via cyber physical agents (CPAs), and sends them to two critical services of carbon management and intelligent energy management system (iEMS) deployed on the cloud platform. The two critical services optimize the energy dispatch schedule by strictly following the requirements of energy saving, carbon reduction, and net zero. Then, the state of zero defects of intelligent manufacturing achieved in Industry 4.1 can be upgraded to net zero of GiM in Industry 4.2.

Abstract: Embodiments of the present invention provide a hybrid system and method for distributed virtual power plants integrated intelligent net zero. In this method, a cyber physical agent (CPA) is utilized to collect a carbon emission information and an energy management information, and then an artificial intelligence (AI) optimization model of an intelligent central dispatch platform is utilized to obtain a power dispatch manner of the distributed virtual power plants based on the carbon emission information and the energy management information, such that the power dispatch manner of the distributed virtual power plants meets the requirements of enterprise economic benefits and net zero carbon emissions at the same time.

Abstract: A virtual metrology method using a convolutional neural network (CNN) is provided. In this method, a dynamic time warping (DTW) algorithm is used to delete unsimilar sets of process data, and adjust the sets of process data to be of the same length, thereby enabling the CNN to be used for virtual metrology. A virtual metrology model of the embodiments of the present invention includes several CNN models and a conjecture model, in which plural inputs of the CNN model are sets of time sequence data of respective parameters, and plural outputs of the CNN models are inputs to the conjecture model.

Abstract: A virtual metrology method based on convolutional autoencoder and transfer learning includes performing a data alignment operation, a modeling operation and a calculating operation. The data alignment operation includes performing a data-length adjusting operation onto a plurality of sets of process data. The modeling operation includes classifying paired data and unpaired process data; creating a pre-trained model by using the unpaired process data, and then inputting the paired data to the pre-trained model to create a virtual metrology model based on convolutional autoencoder. The virtual metrology model based on convolutional autoencoder includes at least one convolutional neural network model. In addition, the calculating operation includes executing one of a predicting step and a transfer learning step according to whether the actual metrology data is obtained, thereby calculating one of a phase-one virtual metrology value and a phase-two virtual metrology value.

Abstract: Embodiments of the present disclosure provide a method for predicting an occurrence of a tool processing event, thereby determining whether to activate a virtual metrology. In a model-building stage, plural sets of model-building data are used to create at least one classification model in accordance with at least one classification algorithm, in which each classification model includes plural decision trees. Then, probabilities of the decision trees are used to create at least one reliance index model, and the sets of model-building data are used to create at least one similarity index model in accordance with a statistical distance algorithm. In a conjecture stage, a set of processing data of a workpiece is inputted into each classification model, each reliance index model and each similarity index model to determine whether to activate (start) virtual metrology.

Abstract: An additive manufacturing (AM) method is provided. The method includes performing a laser powder bed fusion (L-PBF) process on the powder layer. Then, a first surface roughness value of the powder layer after the L-PBF process is obtained to generate a first surface profile. An absorptivity and a set of re-melting process parameters data are used to perform a heat transfer simulation. A second surface profile of the powder layer after laser re-melting is obtained by using the first surface profile and a low-pass filter. Then, the set of re-melting process parameters data is adjusted iteratively to perform the heat transfer simulation until a second surface roughness value predicted from the second surface profile is smaller than or equal to a surface roughness threshold, thereby obtaining optimal values of re-melting process parameters for performing a re-melting process to reduce a surface roughness of a powder layer after the L-PBF process.

Abstract: An additive manufacturing (AM) method includes using an AM tool to fabricate a plurality of workpiece products; measuring qualities of the first workpiece products respectively; performing a temperature measurement on each of the melt pools on the powder bed during a fabrication of each of the workpiece products; performing photography on each of the melt pools on the powder bed during the fabrication of each of the workpiece products; extracting a length and a width of each of the melt pools; performing a melt-pool feature processing operation; building a conjecture model by using a plurality of sets of first process data and the actual metrology values of the first workpiece products in accordance with a prediction algorithm; and predicting a virtual metrology value of the second workpiece product by using the conjecture model based on a set of second process data.

Abstract: An additive manufacturing (AM) method includes using an AM tool to fabricate a plurality of workpiece products; measuring qualities of the first workpiece products respectively; performing a temperature measurement on each of the melt pools on the powder bed; performing photography on each of the melt pools on the powder bed; extracting a length and a width of each of the melt pools; performing a melt-pool feature processing operation; first converting each of the workspace images to a gray level co-occurrence matrix (GLCM); building a conjecture model by using a plurality of sets of first process data and the actual metrology values of the first workpiece products in accordance with a prediction algorithm; and predicting a virtual metrology value of the second workpiece product by using the conjecture model based on a set of second process data.

Abstract: A golden path search method for manufacturing process provides a two-phase process to search for a golden path. A first phase step of the two-phase process includes preparing a search model based on a search algorithm, and selecting a plurality of key process stages of a plurality of process stages by feeding sets of final inspection values and the production paths of the workpieces into the searching model, and then generating a plurality of key paths according to the key process stages. A second phase step of the two-phase process includes building a plurality of prediction models of the key paths according to the production paths and the sets of final inspection values, and predicting a plurality of yield rates corresponding to the key paths according to the prediction models, and then searching for the golden path of the key paths according to the yield rates.

Abstract: Methods for treating a disorder associated with immunoglobulin E (IgE) in a subject with antibodies capable of binding to the C?mx domain of a membrane-bound IgE. The subject can be administered with at least two doses of the antibody, the two doses being at least three months apart.

Abstract: One embodiment provides a method comprising receiving an input video comprising low-resolution (LR) frames and corresponding super-resolution (SR) frames, and generating a motion-compensated previous SR frame based on a current LR frame of the video and a motion-compensated previous residual frame of the video. The previous SR frame aligns with a current SR frame corresponding to the current LR frame. The method further comprises, in response to determining there is a mismatch between the previous SR frame and the current SR frame, correcting in the current SR frame errors that result from motion compensation based on the motion-compensated previous SR frame. The method further comprises restoring details to the current SR frame that were lost as a result of the correcting, and suppressing flickers of the current SR frame on the frequency domain, resulting in a flicker-suppressed current SR frame for presentation on a display.