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学者姓名：汤健
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摘要 ：
Kernel learning based on structure risk minimum can be employed to build a soft measuring model for analyzing small samples. However, it is difficult to select learning parameters, such as kernel parameter (KP) and regularization parameter (RP). In this paper, a soft measuring method is investigated to select learning parameters, which is based on adaptive multi-layer selective ensemble (AMLSEN) and least-square support vector machine (LSSVM). First, candidate kernels and RPs with K and R numbers are preset based on prior knowledge, and candidate sub-sub-models with K*R numbers are constructed through utilizing LSSVM. Second, the candidate sub-sub-models with same KPs and different RPs are selectively fused by using the branch and bound SEN (BBSEN) to obtain K SEN-sub-models. Third, these SEN-sub-models are selectively combined through using BBSEN again to obtain SEN models with different ensemble sizes, and then a new metric index is defined to determine the final AMLSEN-LSSVM-based soft measuring model. Finally, the learning parameters and ensemble sizes of different SEN layers are obtained adaptively. Simulation results based on the UCI benchmark and practical DXN datasets are conducted to validate the effectiveness of the proposed approach.
关键词 ：
dioxins emission dioxins emission Multi-layer selective ensemble learning Multi-layer selective ensemble learning least square support vector machine least square support vector machine soft measuring model soft measuring model municipal solid waste incineration municipal solid waste incineration
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| GB/T 7714 | Yu, Gang , Tang, Jian , Zhang, Jian et al. Adaptive Multi-Layer Selective Ensemble Least Square Support Vector Machines with Applications [J]. | INTELLIGENT AUTOMATION AND SOFT COMPUTING , 2021 , 29 (1) : 273-290 . |
| MLA | Yu, Gang et al. "Adaptive Multi-Layer Selective Ensemble Least Square Support Vector Machines with Applications" . | INTELLIGENT AUTOMATION AND SOFT COMPUTING 29 . 1 (2021) : 273-290 . |
| APA | Yu, Gang , Tang, Jian , Zhang, Jian , Wang, Zhonghui . Adaptive Multi-Layer Selective Ensemble Least Square Support Vector Machines with Applications . | INTELLIGENT AUTOMATION AND SOFT COMPUTING , 2021 , 29 (1) , 273-290 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Key process parameters such as production qualities and environmental pollution indices are difficult to be measured online in complex industrial processes. High time and economic costs make only limited small sample data be obtained to build process models, while the deep neural network model requires massive training samples. Although the deep forest algorithm is based on nonneural network structure, it mainly is utilized to effectively address classification problems. Owing to the above problems, a new deep forest regression algorithm based on cross-layer full connection is proposed. First of all, sub-forest prediction values of the input layer forest module are processed to obtain the layer regression vector, which is combined with the raw feature vector as the input of the middle layer forest model. And then, a cross-layer full connection way connecting the former layer regression vector contributes to an augmented layer regression vector. Meanwhile, the deep layer's number is adaptively adjusted via verifying the validation error. In the end, the output layer forest model is trained by using the augmented layer regression vector originated from the middle layer forest model and the raw feature vector. Sequentially, the maximum information flow is effectively ensured by information sharing. Moreover, the proposed method has the advantages of simple hyper-parameter setting criterion. Simulation results based on benchmark and industrial data show that the proposed method has equal or better performance than several state-of-art methods.
关键词 ：
Augment layer regression vector Augment layer regression vector Cross-layers full connection Cross-layers full connection Deep forest regression Deep forest regression Small sample modeling Small sample modeling
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| GB/T 7714 | Tang, Jian , Xia, Heng , Zhang, Jian et al. Deep forest regression based on cross-layer full connection [J]. | NEURAL COMPUTING & APPLICATIONS , 2021 , 33 (15) : 9307-9328 . |
| MLA | Tang, Jian et al. "Deep forest regression based on cross-layer full connection" . | NEURAL COMPUTING & APPLICATIONS 33 . 15 (2021) : 9307-9328 . |
| APA | Tang, Jian , Xia, Heng , Zhang, Jian , Qiao, Junfei , Yu, Wen . Deep forest regression based on cross-layer full connection . | NEURAL COMPUTING & APPLICATIONS , 2021 , 33 (15) , 9307-9328 . |
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摘要 ：
Municipal solid waste incineration (MSWI) process produces a type of highly toxic and persistent pollutant, i.e., Dioxins (DXN), which has tremendous realistic and potential hazards to the ecological environment and human health. It is very important for optimizing operation of MSWI process and controlling urban pollution in terms of realization of continuous real-time measurement of DXN emission concentration. The generation mechanism of DXN is very complex. Thus, there is a complex non-linear mapping relationship between DXN and input/output variables of MSWI process. Aim at these problems, a soft measuring method of DXN emission concentration based on feature selection and selective ensemble strategy is proposed. Firstly, the process variables of easy-to-measure are matched to obtain the modeling sample with characteristics of small sample and high dimension. Then, the variable projection importance (VIP) value based on linear projection to latent structure algorithm and input feature selection ratio based on expert experience are used to select the input features. At last, by using ensemble construction strategy based on manipulating training sample with characteristic of adaptive select kernel parameter is constructed. The proposed method is simulated and validated by using the data of DXN emission concentration in reference and actual MSWI process. © 2021, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.
关键词 ：
Air pollution Air pollution Feature extraction Feature extraction Health hazards Health hazards Municipal solid waste Municipal solid waste Organic pollutants Organic pollutants Waste incineration Waste incineration
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| GB/T 7714 | Tang, Jian , Qiao, Jun-Fei , Xu, Zhe et al. Soft measuring approach of dioxin emission concentration in municipal solid waste incineration process based on feature reduction and selective ensemble algorithm [J]. | Control Theory and Applications , 2021 , 38 (1) : 110-120 . |
| MLA | Tang, Jian et al. "Soft measuring approach of dioxin emission concentration in municipal solid waste incineration process based on feature reduction and selective ensemble algorithm" . | Control Theory and Applications 38 . 1 (2021) : 110-120 . |
| APA | Tang, Jian , Qiao, Jun-Fei , Xu, Zhe , Guo, Zi-Hao . Soft measuring approach of dioxin emission concentration in municipal solid waste incineration process based on feature reduction and selective ensemble algorithm . | Control Theory and Applications , 2021 , 38 (1) , 110-120 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
With the increase of the replacement frequency of mobile phones, the recycling of used mobile phones (UMPs) has gradually become a hot topic. UMP recycling equipment based on the internet of things technology has become one type of the main devices to recycle UMPs, but there are still some problems such that low recognition success rates and tedious recycling steps. The key to solving these problems is to improve the accuracy of UMP recognition (UMPR). In view of the above problems, this paper reviews the methods of UMPR for UMP recycling equipment, which provides support for improving recycling efficiency. First, we briefly introduce the structure and recycling process of UMP recycling equipment. Next, the four typical methods of UMPR are addressed in detail and their respective shortcomings are analyzed. Then, these methods are summarized and analyzed, and the shortcomings and deficiencies of the current UMPR methods based on artificial intelligence, especially the image recognition method are addressed. Finally, the directions for future research on UMPR are given out.
关键词 ：
Used mobile phone (UMP) Used mobile phone (UMP) Artificial Intelligence Artificial Intelligence Image recognition Image recognition Used mobile phone recognition (UMPR) Used mobile phone recognition (UMPR) Recycling equipment Recycling equipment
引用：
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| GB/T 7714 | Wang, Zixuan , Tang, Jian , Cui, Chengyu et al. Review of Used Mobile Phone Recognition Method for Recycling Equipment [C] . 2020 : 1105-1110 . |
| MLA | Wang, Zixuan et al. "Review of Used Mobile Phone Recognition Method for Recycling Equipment" . (2020) : 1105-1110 . |
| APA | Wang, Zixuan , Tang, Jian , Cui, Chengyu , Li, Weitao , Xu, Zhe , Han, Honggui . Review of Used Mobile Phone Recognition Method for Recycling Equipment . (2020) : 1105-1110 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
The incineration process for municipal solid waste (MSW) has the advantages of fast speed, in-situ treatment, high capacity reduction rate, and energy recyclability. Among them, the MSW incineration (MSWI) based on mechanical grate furnaces is widely used. However, the diversity of MSW components and living habits, and the time-varying characteristic due to differences of natural environment, and frequent fluctuations in incineration conditions due to operating differences and equipment failures, etc., making it difficult to maintain the MSWI process operation at optimization status. Moreover, it is difficult to detect the combustion conditions in the furnace in real time by experimental methods and to verify the optimized control algorithm developed offline in terms of ensuring the safety of MSWI process. Thus, it is very important to construct a numerical simulation model of MSWI process close to actual operating conditions. Aim at the above problems, this paper reviews the numerical simulation method of MSWI process based on grate furnace. First, the MSWI process is described. Then, the numerical simulation methods of the MSWI process based on commercial software and self-developed software are summarized, and the advantages and disadvantages of each method are pointed out. Then, a comparative analysis of the above methods is given, and the difficulties are given out. Finally, the current status of numerical simulation is summarized, and future research directions and development prospects are given.
关键词 ：
numerical simulation numerical simulation municipal solid waste incineration (MSWI) municipal solid waste incineration (MSWI) optimal control optimal control Grate furnace Grate furnace
引用：
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| GB/T 7714 | Zhuang, Jiabin , Tang, Jian , Jia, Mingying et al. Numerical Simulation Method of Municipal Solid Waste Incineration Process Based on Grate Furnace: A Survey [C] . 2020 : 1156-1161 . |
| MLA | Zhuang, Jiabin et al. "Numerical Simulation Method of Municipal Solid Waste Incineration Process Based on Grate Furnace: A Survey" . (2020) : 1156-1161 . |
| APA | Zhuang, Jiabin , Tang, Jian , Jia, Mingying , Zhu, Hongjun . Numerical Simulation Method of Municipal Solid Waste Incineration Process Based on Grate Furnace: A Survey . (2020) : 1156-1161 . |
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摘要 ：
Magnetic storage medium such as hard disk is the main carrier of electronic information at present. How to completely remove the data information on storage medium such as used hard disk and prevent information leakage is a very important problem in the field of information security research. Based on the current research status of magnetic storage media degaussing technology, this article analyzes and summarizes the composition of degaussing equipment, degaussing process and each module of degaussing equipment. Then the article summarizes the research difficulties of magnetic storage media degaussing technology. Finally the article points out the possible future of magnetic storage media degaussing technology research direction.
关键词 ：
degaussing degaussing data destruction data destruction magnetic field magnetic field magnetic storage media magnetic storage media
引用：
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| GB/T 7714 | Xu, Zhe , Zhang, Ziying , Li, Pengsheng et al. Review of Research on Degaussing Technology of Magnetic Storage Media [C] . 2020 : 3400-3405 . |
| MLA | Xu, Zhe et al. "Review of Research on Degaussing Technology of Magnetic Storage Media" . (2020) : 3400-3405 . |
| APA | Xu, Zhe , Zhang, Ziying , Li, Pengsheng , Liu, Xiaoge , Tang, Jian . Review of Research on Degaussing Technology of Magnetic Storage Media . (2020) : 3400-3405 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
The mapping relationship between the mill load and the multi-component mechanical signals generated by the ball mill of the mineral grinding process is non-deterministic and complex. With the inherent filtering function of the human ear, the operating expert can effectively estimate the mill load and its internal parameters for their familiar mill in the actual industrial process. In order to obtain multiple single-mode sub-signals with physical meaning and complementary characteristic, this paper proposes a single-mode sub-signal selection method based on variational modal decomposition (VMD) and predictive performance. At first, based on prior knowledge, the value of decomposition layers required to perform VMD is determined. Then, VMD is used to decompose the original mechanical signal into multiple time-domain single-mode sub-signals with different bandwidths and time scales, and further are transformed to the frequency domain to obtain candidate single-mode sub-signal frequency spectrum. Finally, based on these candidate spectral data, a serial of candidate sub-models for mill load parameter prediction are constructed, and a series of selective ensemble models are built for obtaining reduced single-mode sub-signal frequency spectrum, The final single-mode sub-signals with the biggest complementary characteristics are selected based on the practical requirement. The effectiveness of the method is demonstrated by comparative experiment simulations based on the shell vibration signal of a laboratory-scale ball mill.
关键词 ：
Variational modal decomposition (VMD) Variational modal decomposition (VMD) decomposition layer decomposition layer prediction performance prediction performance single-mode sub-signal single-mode sub-signal
引用：
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| GB/T 7714 | Tang, Jian , Zhu, Hongjuan , Li, Dong et al. Single-mode Sub-signal Selection Method Based on VMD and Prediction Performance for Multi-component Mechanical Signal [C] . 2020 : 5730-5735 . |
| MLA | Tang, Jian et al. "Single-mode Sub-signal Selection Method Based on VMD and Prediction Performance for Multi-component Mechanical Signal" . (2020) : 5730-5735 . |
| APA | Tang, Jian , Zhu, Hongjuan , Li, Dong , Zhang, Jian , Yu, Gang . Single-mode Sub-signal Selection Method Based on VMD and Prediction Performance for Multi-component Mechanical Signal . (2020) : 5730-5735 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
The dioxin (DXN) emission concentration is an important indicator related to le stability and harmless operation of municipal solid waste incinerator (MSWI) process, and also one of the key parameters for MSWI process to realize optimal operation control. In the actual MSWI process, the process variables such as furnace temperature, grate speed and air pressure are hundreds. However, the modeling samples containing DXN emission concentrations are small, which makes it impossible to establish accurate DXN emission concentration forecasting model. In this study, a forecasting model construction method by using random forest (RF)-based transfer learning was proposed. At first, initial weights are assigned to the source domain and target domain samples. Then, an RF-based DXN emission concentration transfer learning model is established, and the prediction errors are used as the indicator to calculate the weight adjustment coefficients. Finally, the sample weights are adjusted through iteration loop in terms of increase the source domain instance weights that related to the target domain. The method proposed in this paper can transfer the source domain sample to enhance the prediction performance of the DXN emission concentration forecasting model. The experimental results based on the actual industrial data show the effectiveness of the proposed method.
关键词 ：
Transfer learning Transfer learning Random forest (RF) Random forest (RF) Municipal solid waste incineration (MSWI) Municipal solid waste incineration (MSWI) dioxins (DXN) dioxins (DXN)
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| GB/T 7714 | Xia, Heng , Tang, Jian , Cong, Qiumei et al. Dioxin Emission Concentration Forecasting Model for MSWI Process with Random Forest-Based Transfer Learning [C] . 2020 : 5724-5729 . |
| MLA | Xia, Heng et al. "Dioxin Emission Concentration Forecasting Model for MSWI Process with Random Forest-Based Transfer Learning" . (2020) : 5724-5729 . |
| APA | Xia, Heng , Tang, Jian , Cong, Qiumei , Qiao, Junfei , Xu, Zhe . Dioxin Emission Concentration Forecasting Model for MSWI Process with Random Forest-Based Transfer Learning . (2020) : 5724-5729 . |
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摘要 ：
With the advent of big data era in industry, data-driven modeling methods have been applied widely. The concept drift problem in industrial process modeling has also attracted widespread attention. However, the current research on concept drift focuses on classification tasks and computer fields, with less work on regression modeling of industrial processes. Aiming at the above problems, this paper summarizes the concept drift detection methods for industrial process modeling, and guide for solving this problem. First, the general definition of concept drift and its existence in industrial processes are introduced. Then, the existing drift detection technologies based on process variables, based on prediction error of difficulty-to -measure parameter and based on combine multiple factors are addressed. Thirdly, these methods are discussed, and some research difficulties are given out. Finally, the conclusion and the future research directions for the existing concept detection difficulties arc presented.
关键词 ：
Industrial process Industrial process Prediction error Prediction error Sample distribution Sample distribution Concept drift detection Concept drift detection Process variable Process variable
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| GB/T 7714 | Sun, Zijian , Tang, Jian , Qiao, Junfei et al. Review of Concept Drift Detection Method for Industrial Process Modeling [C] . 2020 : 5754-5759 . |
| MLA | Sun, Zijian et al. "Review of Concept Drift Detection Method for Industrial Process Modeling" . (2020) : 5754-5759 . |
| APA | Sun, Zijian , Tang, Jian , Qiao, Junfei , Cui, Chengyu . Review of Concept Drift Detection Method for Industrial Process Modeling . (2020) : 5754-5759 . |
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摘要 ：
Ball mill is a key heavy energy consuming equipment in the grinding process. It is based on the closed rotation operation mode and the tens of thousands of steel balls loaded inside it to realize the crushing effect on ore. The vibration signals generated by the impact of steel balls with different size distributions on the mill shell with different amplitudes and frequencies contain rich information related to the mill load parameters. The inherent non-stationary and non-linear characteristics of shell vibration signals make it difficult to extract valuable features, and the physical meaning of different characteristic frequency bands is even more difficult to explain. Although the empirical mode decomposition (EMD) algorithm and its improved version can adaptively decompose the vibration signals of the mill shell, there are problems such as mode aliasing. This paper presents a method for analyzing vibration signals of mill shell based on variational mode decomposition (VMD). Firstly, the number of layers K of VMD is determined according to the existing research results. Then, time and frequency domain analysis are performed on the decomposed IMFs. Finally, the contribution of different IMFs is analyzed based on the predictive performance of simple linear models. The effectiveness is shown by the vibration signal of the experimental mill.
关键词 ：
Mill load parameters Mill load parameters Shell vibration signal Shell vibration signal Variational mode decomposition (VDM) Variational mode decomposition (VDM)
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| GB/T 7714 | Liu, Zhuo , Chai, Tianyou , Tang, Jian et al. Signal Analysis of Mill Shell Vibration Based on Variational Modal Decomposition [C] . 2020 : 1168-1173 . |
| MLA | Liu, Zhuo et al. "Signal Analysis of Mill Shell Vibration Based on Variational Modal Decomposition" . (2020) : 1168-1173 . |
| APA | Liu, Zhuo , Chai, Tianyou , Tang, Jian , Yu, Wen . Signal Analysis of Mill Shell Vibration Based on Variational Modal Decomposition . (2020) : 1168-1173 . |
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