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公开(公告)号：US20180144233A1
公开(公告)日：2018-05-24
申请号：US15394275
申请日：2016-12-29
发明人： Yi-Yuan CHEN , Xin-Lan LIAO , Kun-Hsien LIN , Lih-Guong JANG , Chi-Neng LIU , Nien-Chu WU , Po-Yu HUANG
摘要： A ticket authentication method and a ticket authentication device are provided. The ticket authentication method includes the following steps. A first electronic device outputs an e-ticket. A second electronic device acquires the e-ticket. The second electronic device outputs a visible light verification code. The first electronic device acquires the visible light verification code and generates a composite code according to a certification data and the verification code. The second electronic device acquires the visible light composite code, and determines whether the composite code matches the certification data and the verification code. When the composite code matches the certification data and the verification code, the second electronic determines that the authentication of the e-ticket is successful.
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公开(公告)号：US20230153652A1
公开(公告)日：2023-05-18
申请号：US17830040
申请日：2022-06-01
摘要： A parameter optimization device includes a data acquisition module, a sampling function calculation module, a clustering module and a parameter recommendation module. The data acquisition module is configured to acquire several input parameter values and corresponding several measurement output values. The sampling function calculation module is configured to obtain several sampling function values according to the input parameter values and the measurement output values. The clustering module is configured to obtain several parameter groups according to the input parameter values and the sampling function values. The parameter recommendation module is configured to obtain several recommended parameter values from at least one of the parameter groups.
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3.发明申请公开(公告)号：US20190171776A1
公开(公告)日：2019-06-06
申请号：US15857148
申请日：2017-12-28
发明人： Po-Yu HUANG , Hong-Chi KU , Sen-Chia CHANG , Te-Ming CHEN , Pei-Yi LO
摘要： A parameter optimization method includes: a parameter search is performed on an input parameter, an output response value and a target value through a plurality of optimization schemes to search for a plurality of candidate recommended parameters. Each optimization scheme is assigned to a weight value according to user historical decision information. At least one recommended parameter is selected from the candidate recommended parameters according to the weight values. An user interface is provided for a user to input a decision instruction. A new input parameter is selected from the at least one recommended parameter according to the decision instruction; the new input parameter is inputted into the target system; and a new output response value is evaluated whether meets a specification condition. The user historical decision information is updated based on the decision instruction to adjust the weight values.
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4.发明申请公开(公告)号：US20210174261A1
公开(公告)日：2021-06-10
申请号：US16728759
申请日：2019-12-27
发明人： Po-Yu HUANG , Yu-Hsiuan CHANG , Hong-Chi KU
摘要： An optimum sampling search system and method with risk assessment, and a graphical user interface are provided. The optimum sampling search system includes a data acquisition unit, an objective satisfaction score calculation unit, a constraint satisfaction probability calculation unit, a sampling risk evaluation unit, and an adjusting unit. If the constraint satisfaction probability of a recommended sampling parameter is between a first predetermined value and a second predetermined value, the recommended sampling parameter is adjusted, by the adjusting unit, to optimize a constraint satisfaction probability model.
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公开(公告)号：US20220171349A1
公开(公告)日：2022-06-02
申请号：US17135349
申请日：2020-12-28
发明人： Po-Yu HUANG , Chun-Fang CHEN , Hong-Chi KU , Te-Ming CHEN , Chien-Liang LAI , Sen-Chia CHANG
摘要： A system and a method for parameter optimization with adaptive search space and a user interface using the same are provided. The system includes a data acquisition unit, an adaptive adjustment unit and an optimization search unit. The data acquisition unit obtains a set of executed values of several operating parameters and a target parameter. The adaptive adjustment unit includes a parameter space transformer and a search range definer. The parameter space transformer performs a space transformation on a parameter space of the operating parameters according to the executed values. The search range definer defines a parameter search range in a transformed parameter space based on the sets of the executed values. The optimization search unit takes the parameter search range as a limiting condition and takes optimizing the target parameter as a target to search for a set of recommended values of the operating parameters.
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公开(公告)号：US20200184346A1
公开(公告)日：2020-06-11
申请号：US16428531
申请日：2019-05-31
发明人： Po-Yu HUANG , Sen-Chia CHANG , Te-Ming CHEN , Hong-Chi KU
摘要： A dynamic prediction model establishment method, an electric device and a user interface are provided. The dynamic prediction model establishment method includes the following steps. An integration model is established by a processing device according to at least one auxiliary data set. The integration model is modified as a dynamic prediction model by the processing device according to a target data set. A sampling point recommendation information is provided by the processing device according to an error degree or an uncertainty degree between the at least one auxiliary data set and the target data set.
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公开(公告)号：US20200151610A1
公开(公告)日：2020-05-14
申请号：US16231732
申请日：2018-12-24
发明人： Chuang-Hua CHUEH , Jia-Min REN , Po-Yu HUANG , Yu-Hsiuan CHANG
摘要： An ensemble learning prediction method includes: establishing a plurality of base predictors based on a plurality of training data; initializing a plurality of sample weights of a plurality of sample data and initializing a processing set; in each iteration round, based on the sample data and the sample weights, establishing a plurality of predictor weighting functions of the predictors in the processing set and predicting each of the sample data by each of the predictors in the processing set for identifying a prediction result; evaluating the predictor weighting functions, and selecting a respective target predictor weighting function from the predictor weighting functions established in each iteration round and selecting a target predictor from the predictors in the processing set to update the processing set and to update the sample weights of the sample data.
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