公开(公告)号：US12112104B2

公开(公告)日：2024-10-08

申请号：US18407170

申请日：2024-01-08

Applicant: BEIJING UNIVERSITY OF TECHNOLOGY

Inventor： Jian Tang ,  JiaKun Chen ,  Heng Xia ,  Junfei Qiao

IPC: G06F30/20 ,  A62D3/00 ,  B09B5/00 ,  G16C20/30

CPC classification number: G06F30/20 ,  A62D3/00 ,  G16C20/30 ,  B09B5/00 ,  F23G2200/00

Abstract: A simulation analysis system for dioxin concentration in furnace of municipal solid waste incineration process includes an area division module, the area division module is connected with a numerical simulation module, the numerical simulation module is connected with a single-factor analysis module, the single-factor analysis module includes an orthogonal test analysis module, and the orthogonal test analysis module is connected with a control module; the area division module is used for dividing areas in the incinerator, the numerical simulation module is used for conducting modeling simulation on the divided areas, the single-factor analysis module is used for conducting single-factor analysis according to the output of the numerical simulation module, and the orthogonal test analysis module is used for conducting orthogonal test analysis according to the output of the numerical simulation module.

公开(公告)号：US20240078410A1

公开(公告)日：2024-03-07

申请号：US18450945

申请日：2023-08-16

Applicant: Beijing University of Technology

Inventor： Junfei Qiao ,  Haoshan Duan ,  Xi Meng ,  Jian Tang

IPC: G06N3/045 ,  F23G5/50

CPC classification number: G06N3/045 ,  F23G5/50 ,  F23G2207/30 ,  F23G2207/60

Abstract: A dynamic modular neural network (DMNN) for NOx emission prediction in MSWI process is provided. First, the input variables are smoothed and normalized. Then, a feature extraction method based on principal component analysis (PCA) was designed to realize the dynamic division of complex conditions, and the prediction task to be processed was decomposed into sub-tasks under different conditions. In addition, aiming each sub-tasks, a long short-term memory (LSTM)-based sub-network is constructed to achieve accurate prediction of NOx emissions under various working conditions. Finally, a cooperative strategy is used to integrate the output of the sub-networks, further improving the accuracy of prediction model. Finally, merits of the proposed DMNN are confirmed on a benchmark and real industrial data of a municipal solid waste incineration (MSWI) process. The problem that the NOx emission of MSWI process is difficult to be accurately predicted due to the sensor limitation is effectively solved.

公开(公告)号：US11461482B2

公开(公告)日：2022-10-04

申请号：US16822154

申请日：2020-03-18

Applicant: Beijing University of Technology

Inventor： Jian Tang ,  Dandan Wang ,  Xiaozhong Zhou

IPC: G06F21/60 ,  G06F11/14 ,  G06F21/62 ,  G06Q10/00

Abstract: According to aspects of the inventive concepts, provided is a method for erasing information based on a dual-security mechanism. A storage medium feature database, an information erasure feature database, and a firmware system feature database are built to match cases for to-be-erased electronic scrap. An erasure solution and a native system data package are generated based on the matching results. The information is erased and an erasure result is evaluated; and the information is recovered on the erased electronic scrap, and a recovery result is evaluated, to implement comprehensive double security evaluation. The information erasure validity of the electronic scrap is checked based on the evaluation results. If an erasure result is invalid, erasure solutions are corrected online based on the evaluation result, until the erasure result is valid and the electronic scrap with a native system recovered is obtained.

公开(公告)号：US12056426B2

公开(公告)日：2024-08-06

申请号：US18201578

申请日：2023-05-24

Applicant: BEIJING UNIVERSITY OF TECHNOLOGY

Inventor： Jian Tang ,  TianZheng Wang ,  Heng Xia ,  Junfei Qiao

IPC: G06F30/20 ,  G06Q10/30 ,  G06F111/10 ,  G06N20/20

CPC classification number: G06F30/20 ,  G06Q10/30 ,  G05B2219/23446 ,  G06F2111/10 ,  G06N20/20

Abstract: A hardware-in-loop simulation experiment platform of multiple input and multiple output loop control for MSWI process includes a real equipment layer and a virtual object layer, where in the real equipment layer and the virtual object layer realize communication through hard wirings and data acquisition cards, the real equipment layer and virtual object layer realize communication in OPC mode through Ethernet; the real equipment layer comprises monitoring equipment and control equipment, and the virtual object layer comprises an MSWI actuator model, an MSWI instrument device model and an MSWI process object model which are respectively operated in different industrial personal computers. The hardware-in-loop simulation experiment platform of multiple input and multiple output loop control for MSWI process provided by the invention is used for providing a reliable engineering verification environment for MSWI process control.

公开(公告)号：US11961648B2

公开(公告)日：2024-04-16

申请号：US17541903

申请日：2021-12-03

Applicant: Beijing University of Technology

Inventor： Jian Tang ,  Zhe Xu ,  Pengsheng Li ,  Xiaoge Liu

IPC: H01F13/00 ,  G01R33/02

CPC classification number: H01F13/006 ,  G01R33/02

Abstract: A rapid demagnetization method based on characteristics of magnetic media. In the method, basic information is obtained by a recognition module of magnetic media by means of multi-source sensing collaboration. The magnetic medium is identified by using a data processing technology and a magnetic medium identification algorithm, and then the characteristic information is extracted. Optimized set values of demagnetization parameters are obtained by a demagnetization parameter optimizing and setting module based on a demagnetization optimizing model. Demagnetization parameter set values are tracked by a closed-loop control module of a demagnetization magnetic field in combination with domain expert knowledge by using a closed-loop control mechanism integrated with a magnetic field control algorithm, a charging-discharging device, a magnetic field generating device, a magnetic field sensor and an environmental sensor, completing the rapid demagnetization of the magnetic medium.

公开(公告)号：US11976817B2

公开(公告)日：2024-05-07

申请号：US17038723

申请日：2020-10-26

Applicant: Beijing University of Technology

Inventor： Junfei Qiao ,  Zihao Guo ,  Jian Tang

IPC: F23G5/00 ,  F23G5/50 ,  G01N33/00

CPC classification number: F23G5/50 ,  G01N33/0036 ,  F23G2207/10 ,  F23G2208/00

Abstract: A method for detecting a dioxin emission concentration of a municipal solid waste incineration process based on multi-level feature selection. A grate furnace-based MSWI process is divided into a plurality of sub-processes. A correlation coefficient value, a mutual information value and a comprehensive evaluation value between each of original input features of the sub-processes and the DXN emission concentration are obtained, thereby obtaining first-level features. The first-level features are selected and statistically processed by adopting a GAPLS-based feature selection algorithm and according to redundancy between different features, thereby obtaining second-level features. Third-level features are obtained according to the first-level features and statistical results of the second-level features. A PLS algorithm-based DXN detection model is established based on model prediction performance and the third-level features. The obtained PLS algorithm-based DXN detection model is applied to detect the DXN emission concentration of the MSWI process.

公开(公告)号：US12002014B2

公开(公告)日：2024-06-04

申请号：US16967408

申请日：2019-12-02

Applicant: BEIJING UNIVERSITY OF TECHNOLOGY

Inventor： Jian Tang ,  Junfei Qiao ,  Zihao Guo ,  Haijun He

IPC: G06Q10/30 ,  G05B13/02 ,  G05B13/04 ,  G06N20/10

CPC classification number: G06Q10/30 ,  G05B13/027 ,  G05B13/042 ,  G05B13/048 ,  G06N20/10

Abstract: Disclosed is a soft measurement method of DXN emission concentration based on multi-source latent feature selective ensemble (SEN) modeling. First, MSWI process data is divided into subsystems of different sources according to industrial processes, and principal component analysis (PCA) is used to separately extract the subsystems' latent features and conduct multi-source latent feature primary selection according to the threshold value of the principal component contribution rate preset by experience. Using mutual information (MI) to evaluate the correlation between the latent features of the primary selection and DXN, and adaptively determine the upper and lower limits and thresholds of the latent feature reselection; finally, based on the reselected latent features, a least squares-support vector machine (LS-SVM) algorithm with a hyperparameter adaptive selection mechanism is used to establish DXN emission concentration sub-models for different subsystems, and based on branch and bound (BB) and prediction error information entropy weighting algorithm to optimize the selection of sub-models and calculation weights coefficient, a SEN soft measurement model of DXN emission concentration is constructed.