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学者姓名：李晓理
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Abstract ：
Evolutionary algorithms have proven to be extremely effective at tackling multi-objective opti-mization problems (MOPs). However, when dealing with many-objective optimization problems (MaOPs), their performance frequently degrades, especially when the Pareto marking irregular shapes. The population pressure to choose the Pareto optimal front and address the generaliz-ability of different Pareto front shapes becomes more challenging as the number of objectives increases. We present a strength Pareto evolutionary algorithm based on adaptive reference points (SPEA/ARP) to address this problem. First, the reference points are updated using current and historical population information. The angles between the current demographic information and the predefined uniform reference points are used to select the active reference points, and the adaptive reference points are selected from the historical population information projected onto the reference plane. Second, the fitness function values are applied to classify the environmental selection criteria into two categories: 1) The angle distance scaling function using adaptive reference points is utilized to increase selection pressure, and the diversity of non-dominated solutions is balanced using the angle-based secondary selection technique. 2) Otherwise, the fitness function values are employed to choose the next generation of non-dominated solutions. Third, an aggregate fitness r-value generated by the angle distance scaling function is employed to construct matching pools that produce valid offsprings. Finally, extensive experiments are carried out to demonstrate SPEA/ARP performance by comparing it with six state-of-the-art many -objective evolutionary algorithms on 5-, 10-, 15-objective of 31 benchmark MaOPs. The experi-ments show that SPEA/ARP outperforms the compared algorithms.
Keyword ：
Irregular fronts Irregular fronts Strength Pareto evolutionary algorithm Strength Pareto evolutionary algorithm Matching pool Matching pool Angle distance scaling function Angle distance scaling function Adaptive reference points Adaptive reference points
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| GB/T 7714 | Li, Xin , Li, Xiaoli , Wang, Kang et al. A strength pareto evolutionary algorithm based on adaptive reference points for solving irregular fronts [J]. | INFORMATION SCIENCES , 2023 , 626 : 658-693 . |
| MLA | Li, Xin et al. "A strength pareto evolutionary algorithm based on adaptive reference points for solving irregular fronts" . | INFORMATION SCIENCES 626 (2023) : 658-693 . |
| APA | Li, Xin , Li, Xiaoli , Wang, Kang , Yang, Shengxiang . A strength pareto evolutionary algorithm based on adaptive reference points for solving irregular fronts . | INFORMATION SCIENCES , 2023 , 626 , 658-693 . |
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Abstract ：
The heating, ventilation, and air conditioning(HVAC) system consumes a large amount of energy in buildings. Accurate modeling of the HVAC refrigeration room system is crucial for building temperature control and optimization of energy consumption. In this paper, Levenberg Marquardt (LM) algorithm and particle swarm optimization (PSO) algorithm are used to establish a nonlinear autoregressive neural network model (PSO-NARX) for the modeling of water chillers in HVAC systems. NARX is a model used to describe nonlinear discrete systems. At the same time, using particle swarm optimization algorithm can improve the accuracy of the prediction model. The experimental results show that the PSO-NARX model can effectively model and predict the chiller model, and its performance is better compared to traditional DNN neural networks.
Keyword ：
Data-driven Data-driven Modeling Modeling chiller system chiller system NARX NARX PSO Algorithm PSO Algorithm
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| GB/T 7714 | Song, Zilong , Li, Xiaoli , Wang, Kang et al. Chiller System Modeling using PSO Optimization based NARX approach [J]. | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS , 2023 : 616-621 . |
| MLA | Song, Zilong et al. "Chiller System Modeling using PSO Optimization based NARX approach" . | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS (2023) : 616-621 . |
| APA | Song, Zilong , Li, Xiaoli , Wang, Kang , Li, Yang . Chiller System Modeling using PSO Optimization based NARX approach . | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS , 2023 , 616-621 . |
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Abstract ：
Due to the complex process of flue gas acid production and numerous processes, the controlled objects in the whole process have the characteristics of time-variation, randomness, nonlinearity and large lag. For SO2 fan, because the traditional PID control adopts the parameters set in advance, it is difficult to play an effective role in the complex flue gas acid production system, therefore, a fuzzy PID control method is proposed. The two-dimensional structure fuzzy controller model with two inputs and three outputs can dynamically adjust the PID controller parameters. By establishing a transfer function model for factory temperature data, we compare the fuzzy PID control with the traditional PID controller. The results demonstrate that the fuzzy PID controller possesses strong anti-interference ability, has a shorter adjustment time, and exhibits a smaller overshoot volume, which enables more effective control of the fan outlet temperature.
Keyword ：
Fuzzy PID Control Fuzzy PID Control PID Control PID Control Outlet Temperature of Fan Outlet Temperature of Fan
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| GB/T 7714 | Wang, Zihao , Li, Xiaoli , Wang, Kang et al. Fan flue gas temperature control system based on fuzzy PID control [J]. | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS , 2023 : 394-399 . |
| MLA | Wang, Zihao et al. "Fan flue gas temperature control system based on fuzzy PID control" . | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS (2023) : 394-399 . |
| APA | Wang, Zihao , Li, Xiaoli , Wang, Kang , Li, Yang . Fan flue gas temperature control system based on fuzzy PID control . | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS , 2023 , 394-399 . |
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Abstract ：
The sulfur dioxide (SO2) concentration of flue gas in metal smelting industry is high and difficult to recycle. The commonly used SO2 desulfurization treatment is acid production from flue gas, however, it is difficult to directly measure the conversion rate of SO2. In this paper, we present a soft sensor method based on Long Short-Term Memory (LSTM) which integrates the attention mechanism for predicting SO2 conversion rate to tackle such problem. Considering that the change of SO2 conversion rate is affected by many external factors, the attention mechanism is used to quickly and accurately predict the change results by considering the system data of several past moments. The proposed attention mechanism uses LSTM units to encode the hidden state of the look back time data, obtains different attention weights, and then decodes and predicts SO2 conversion rate according to the hidden state. The experimental results indicate that LSTM model with attention mechanism has lower training cost compared with LSTM model. The training accuracy and soft sensor accuracy are also improved owing to the attention mechanism. It is instructive for SO2 conversion rate soft sensor in acid production from flue gas.
Keyword ：
soft sensor soft sensor Long Short-Term Memory neural network Long Short-Term Memory neural network attention mechanism attention mechanism Acid production from flue gas Acid production from flue gas
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| GB/T 7714 | Zhang, Yongzheng , Li, Xiaoli , Wang, Kang et al. A LSTM Model with Attention Mechanism for Soft Sensor of SO2 Conversion Rate in Flue Gas Acid Production Process [J]. | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS , 2023 : 27-32 . |
| MLA | Zhang, Yongzheng et al. "A LSTM Model with Attention Mechanism for Soft Sensor of SO2 Conversion Rate in Flue Gas Acid Production Process" . | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS (2023) : 27-32 . |
| APA | Zhang, Yongzheng , Li, Xiaoli , Wang, Kang , Li, Yang . A LSTM Model with Attention Mechanism for Soft Sensor of SO2 Conversion Rate in Flue Gas Acid Production Process . | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS , 2023 , 27-32 . |
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Abstract ：
The effective control of converter inlet temperature in the process of acid production with flue gas is an effective means to improve the conversion rate of sulfur dioxide and reduce environmental pollution. According to the characteristics of the process of acid production with flue gas, the control process of converter inlet temperature is studied in this paper. Firstly, the CARIMA (Controlled auto-regressive integrated moving average, CARIMA) model of converter inlet temperature is established. Then, a generalized predictive controller based on CARIMA model is designed. Finally, the proposed method is verified by experiment and compared with PID controller. Experimental results show that the proposed method has a better tracking effect and smaller error. The effectiveness of the proposed method is verified.
Keyword ：
CARIMA model CARIMA model tracking control tracking control acid production acid production generalized predictive control generalized predictive control
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| GB/T 7714 | Liu, Minghua , Li, Xiaoli , Wang, Kang . Generalized Predictive Control of Converter Inlet Temperature in the Process of Acid Production with Flue Gas [J]. | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS , 2023 : 782-786 . |
| MLA | Liu, Minghua et al. "Generalized Predictive Control of Converter Inlet Temperature in the Process of Acid Production with Flue Gas" . | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS (2023) : 782-786 . |
| APA | Liu, Minghua , Li, Xiaoli , Wang, Kang . Generalized Predictive Control of Converter Inlet Temperature in the Process of Acid Production with Flue Gas . | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS , 2023 , 782-786 . |
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Abstract ：
Heating, Ventilation and Air Conditioning (HVAC) system is a highly nonlinear system with a large amount of complex, coupled inputs. This paper presents a novel prediction method for HVAC energy consumption based on deep neural network (DNN). In order to solve the problem that traditional neural networks tend to fall into local optima, batch normalization and Adam optimization algorithm are significantly incorporated in the DNN. In addition, particle swarm optimization (PSO) is utilized to search for the optimal number of hidden layer nodes and increase the accuracy of prediction model. The cooling tower data of HVAC is used to validate the network. The results show the mean absolute error and the mean square error of the PSO-DNN model, from which it can be seen obviously that our proposed prediction model performs better than traditional ones. Accordingly, DNN optimized by PSO algorithm is of great validity and superiorities for energy consumption prediction of HVAC system.
Keyword ：
HVAC HVAC PSO Algorithm PSO Algorithm Energy Consumption Energy Consumption Data-driven Data-driven Deep Neural Network Deep Neural Network
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| GB/T 7714 | Du, Linhui , Li, Xiaoli , Wang, Kang et al. Prediction of HVAC Energy Consumption Using PSO Optimized Deep Neural Network [J]. | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS , 2023 : 52-57 . |
| MLA | Du, Linhui et al. "Prediction of HVAC Energy Consumption Using PSO Optimized Deep Neural Network" . | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS (2023) : 52-57 . |
| APA | Du, Linhui , Li, Xiaoli , Wang, Kang , Li, Yang . Prediction of HVAC Energy Consumption Using PSO Optimized Deep Neural Network . | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS , 2023 , 52-57 . |
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Abstract ：
The quality of flotation conditions directly affects the flotation efficiency. Aiming at the problems of difficult online detection, strong subjective arbitrariness, and low recognition efficiency of various flotation conditions in actual flotation work, a flotation condition recognition method based on hypergraph neural network (HGNN) and dynamic feature of forth images is proposed in this paper. Firstly, an improved local binary mode (LBP-TOP) algorithm is introduced to extract the dynamic features of forth sequence containing time information, and then features such as kurtosis and skewness are extracted as supplements to integrate the dynamic features of forth with the supplementary features. By utilizing the aforementioned characteristics and constructing a hypergraph, we have developed an HGNN model that facilitates high-order complex data correlation encoding, thus accomplishing accurate identification of flotation conditions. Finally, simulation shows the effectiveness of the proposed method.
Keyword ：
Forth flotation Forth flotation LBP-TOP LBP-TOP Hypergraph neural network Hypergraph neural network Condition identification Condition identification
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| GB/T 7714 | Fan, Zunguan , Wang, Kang , Li, Xiaoli . Flotation Condition Recognition Based on HGNN and Forth Image Dynamic Feature [J]. | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS , 2023 : 423-428 . |
| MLA | Fan, Zunguan et al. "Flotation Condition Recognition Based on HGNN and Forth Image Dynamic Feature" . | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS (2023) : 423-428 . |
| APA | Fan, Zunguan , Wang, Kang , Li, Xiaoli . Flotation Condition Recognition Based on HGNN and Forth Image Dynamic Feature . | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS , 2023 , 423-428 . |
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Abstract ：
This paper proposes a data-driven, multi-objective optimization control method based on policy iteration for flotation process, addressing the limitations of existing control methods that rely on the system model and cannot satisfy multiple performance indices simultaneously. Firstly, The ordinary linear quadratic regulator algorithm is improved with process data, enabling the algorithm to obtain optimal feedback gain without relying on the internal dynamics model of the system. Then, This method is further extended to the situation where multiple objectives exist, minimizing the deviation of the final additive amount while meeting control requirements for both concentrate and tailings grade. Finally, the convergence and effectiveness of the proposed method are verified by simulation experiments.
Keyword ：
Multi-objective optimization Multi-objective optimization Optimal control Optimal control Flotation process Flotation process Data-driven Data-driven
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| GB/T 7714 | Xu, Hang , Wang, Kang , Li, Xiaoli . Multi-objective Optimization Control of Flotation Process Based on Policy Iteration [J]. | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS , 2023 : 417-422 . |
| MLA | Xu, Hang et al. "Multi-objective Optimization Control of Flotation Process Based on Policy Iteration" . | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS (2023) : 417-422 . |
| APA | Xu, Hang , Wang, Kang , Li, Xiaoli . Multi-objective Optimization Control of Flotation Process Based on Policy Iteration . | 2023 IEEE 12TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS CONFERENCE, DDCLS , 2023 , 417-422 . |
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Abstract ：
Sulfur dioxide (SO2) can cause detrimental impacts on the ecosystem. It is well known that coal-fired power plants play a dominant role in SO2 emissions, and consequently industrial flue gas desulfurization (IFGD) systems are widely used in coal-fired power plants. To remove SO2 effectively such that ultra-low emission standard can be satisfied, IFGD modeling has become urgently necessary. IFGD is a chemical process with long-term dependencies between time steps, and it typically exhibits strong non-linear behavior. Furthermore, the process is rendered non-stationary due to frequent changes in boiler loads. The above-mentioned properties make IFGD process modeling a truly formidable problem, since the chosen model should have the capability of learning long-term dependencies, non-linear dynamics and non-stationary processes simultaneously. Previous research in this area fails to take all the above points into account at a time, and this calls for a novel modeling approach so that satisfactory modeling performance can be achieved. In this work, a novel bivariate empirical mode decomposition (BEMD)-based temporal convolutional network (TCN) approach is proposed. In our approach, BEMD is employed to generate relatively stationary processes, while TCN, which possesses long-term memory ability and uses dilated causal convolutions, serves to model each subprocess. Our method was validated using the operating data from the desulfurization system of a coal-fired power station in China. Simulation results show that our approach yields desirable performance, which demonstrates its effectiveness in the IFGD dynamic modeling problem.
Keyword ：
bivariate empirical mode decomposition bivariate empirical mode decomposition system modeling system modeling flue gas desulfurization flue gas desulfurization temporal convolutional network temporal convolutional network
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| GB/T 7714 | Liu, Quanbo , Li, Xiaoli , Wang, Kang . Dynamic Modeling of Flue Gas Desulfurization Process via Bivariate EMD-Based Temporal Convolutional Network [J]. | APPLIED SCIENCES-BASEL , 2023 , 13 (13) . |
| MLA | Liu, Quanbo et al. "Dynamic Modeling of Flue Gas Desulfurization Process via Bivariate EMD-Based Temporal Convolutional Network" . | APPLIED SCIENCES-BASEL 13 . 13 (2023) . |
| APA | Liu, Quanbo , Li, Xiaoli , Wang, Kang . Dynamic Modeling of Flue Gas Desulfurization Process via Bivariate EMD-Based Temporal Convolutional Network . | APPLIED SCIENCES-BASEL , 2023 , 13 (13) . |
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Abstract ：
In this article, based on the concept of an equivalent-input-disturbance (EID), a tunable nonlinear-function-based estimator is developed for fast and efficient control of unknown and mismatched disturbances of a tracking control system, and the control performances are analyzed. A great superiority over the conventional EID estimators is that the gain is varying and tunable with respect to an intermediate disturbance-rejection performance indicator. First, the control task is divided into the control of reference-tracking and disturbance-rejection, and the corresponding control mechanisms are clarified. For the disturbance-rejection control, explanation of the intermediate performance indicator is provided and a tunable nonlinear-function-based EID estimator is constructed. Then, the tunability condition for the varying gain of the developed EID estimator is given. A relation between control performances and control parameters is clarified a theoretical analysis. An input-to-state stability condition is derived for the closed-loop control system. Finally, comparisons with other methods through simulations and experiments demonstrate fast disturbance-rejection response, weak noise impact, and strong robustness of the developed method.
Keyword ：
Disturbance rejection Disturbance rejection performance analysis performance analysis function estimator function estimator equivalent input disturbance (EID) equivalent input disturbance (EID) nonlinear control nonlinear control mismatched disturbance mismatched disturbance performance synthesise performance synthesise
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| GB/T 7714 | Yu, Pan , Wu, Qiang , Liu, Kang-Zhi et al. Tunable Nonlinear-Function-Based Estimators for Mismatched Disturbances and Performance Analysis [J]. | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS , 2023 . |
| MLA | Yu, Pan et al. "Tunable Nonlinear-Function-Based Estimators for Mismatched Disturbances and Performance Analysis" . | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2023) . |
| APA | Yu, Pan , Wu, Qiang , Liu, Kang-Zhi , She, Jinhua , Li, Xiaoli . Tunable Nonlinear-Function-Based Estimators for Mismatched Disturbances and Performance Analysis . | IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS , 2023 . |
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