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2023 China Semiconductor Technology International Conference, CSTIC 2023

作者： Chen, Donglin Tang, Lixin Cong, Dehong Qiao, Jingchao Northeastern University National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Ministry of Education Shenyang110819 China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Shenyang110819 China

ISBN: (纸本)9798350311006

In the field of semiconductor manufacturing, highly automated production methods have become an inevitable trend of development thanks to their precise, stable and reliable characteristics. This paper focuses on the handling link in the semiconductor production process, designed a novel handling robot. Minimum Snap method is used to plan the end-effector trajectory of the robot, the control method of the robot is based on the position control, and the experiment environment is the co-simulation environment of Simulink and Adams. All the simulation testing of the planning and the control method has been done on the virtual prototype. © 2023 IEEE.

关键词： Position control

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Electrochemical response of MoS_(2) nanosheets/SiO_(2)-NH_(2) nanoparticles modified glassy carbon electrode to phenylacetic acid

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Rare Metals 2023年第12期42卷 4142-4152页

作者： Tian-Yao Qi Zhen-Yu Yuan Fan-Li Meng College of Information Science and Engineering Northeastern UniversityShenyang 110819China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao 066004China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern UniversityShenyang 110819China Key Laboratory of Data Analytics and Optimization for Smart Industry(Northeastern University) Ministry of EducationShenyang 110819China

Phenylacetic acid(PAA)is a primary raw material for illegal Methamphetamine(MATM)synthesis under the strong precursor chemicals supervisions of safrole and ***,trace detection of PAA at ultra-low concentration is a strategic technique and an urgent issue in the field of drug *** this paper,trace determination of PAA at sub-nmol-L-1 concentration level is achieved by hydrogen bond adsorption and electrochemical catalysis through the prepared aminated SiO_(2)nanoparticles(SiO_(2)-NH_(2) NPs)and MoS_(2) nanosheets(NSs)modified glassy carbon electrode(GCE).The prepared MoS_(2) NS s/SiO_(2)-NH_(2) NPs modified electrode represents a detecting limit of 0.0989 nmol·L^(-1)and an obvious increasing linear range before the concentration increasement up to 60 nmol·L^(-1)in square wave voltammetry(SWV)responses of *** SWV response of the modified electrode to PAA in the concentration range within 100 nmol·L^(-1)is higher than phenol,acetic acid(HOAc)and benzoic Acid(BEN).This electrochemical method for trace detection of PAA in aqueous solution with desired performance provides a feasible scheme for the detection of other drugs and aromatic precursor chemicals.

关键词： Phenylacetic acid detection Modified electrode MoS_(2)nanosheet Square wave voltammetry

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ETO Meets Scheduling: Learning Key Knowledge from Single-Objective Problems to Multi-Objective Problem

ETO Meets Scheduling: Learning Key Knowledge from Single-Obj...

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2021 China Automation Congress, CAC 2021

作者： Xu, Wendi Wang, Xianpeng College of Information Science and Engineering Northeastern University China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education China

ISBN: (纸本)9781665426473

Evolutionary transfer optimization(ETO) serves as "a new frontier in evolutionary computation research", which will avoid zero reuse of experience and knowledge from solved problems in traditional evolutionary computation. In schedule-ing applications via ETO, a highly competitive "meeting" framework between them could be constituted towards both intelligent scheduling and green scheduling, especially for carbon neutrality within the context of China. To the best of our knowledge, our study on scheduling here, is the 1st work of ETO for complex optimization when multi-objective problem "meets" single-objective problems in combinatorial case (not multitasking optimization). More specifically, key knowledge like positional building blocks clustered, could be learned and transferred for permutation flow shop scheduling problem (PFSP). Empirical studies on well-studied benchmarks validate relatively firm effectiveness and great potential of our proposed ETO-PFSP framework. © 2021 IEEE

关键词： data analytics

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A Multi-Objective Evolutionary Machine Learning Method for the Yield Strength Prediction of Strip Steel in Continuous Annealing

A Multi-Objective Evolutionary Machine Learning Method for t...

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IISE Annual Conference and Expo 2024

作者： Liu, Chang Tang, Lixin Zhang, Kainan Guo, Jiajing National Frontiers Science Center for Industrial Intelligence and Systems Optimization North-eastern University Shenyang110819 China Ministry of Education Shenyang110819 China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Shenyang110819 China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Shenyang110819 China

ISBN: (纸本)9781713877851

In the continuous annealing process, the yield strength is the key property index to evaluate the quality of strip steel and guide the production. It is difficult to perform online quality measurement for numerous products in the steel industry, this brings significant challenges for process monitoring and operation optimization. In this paper, a multiobjective evolutionary machine learning method is proposed for the yield strength prediction of strip steel in continuous annealing. First, the factors affecting the yield strength of strip steel are analyzed and the prediction model based on a hybrid kernel support vector regression (HKSVR) is established. To improve the prediction accuracy, a multi-objective evolutionary optimization algorithm with a knee solution strategy is used to optimize the key parameters of the HKSVR. It is verified by the actual production data that the proposed method has high prediction accuracy and can meet the actual production demand. Moreover, the proposed method can help practitioners to construct quality analytics models for many other similar production lines, providing assurance for quality control and optimization with a good generalization. © IISE Annual Conference and Expo *** rights reserved.

关键词： Prediction models

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Dissipative Tracking Control of T-S Fuzzy Switched Systems under Bumpless Transfer Performance 41

Dissipative Tracking Control of T-S Fuzzy Switched Systems u...

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第41届中国控制会议

作者： Guangxu He Jun Zhao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education Collee of Information Science and Engineering Northeastern University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper studies the tracking control problem of the T-S fuzzy switched systems with the α-dissipativity and the bumpless transfer *** on the reference system,the fuzzy tracking controller will be designed by using the parallel distributed compensation *** sufficient conditions in terms of linear matrix inequalities are obtained to guarantee the adissipative tracking bumpless transfer performance via the co-design of the bumpless transfer controller and the state-dependent switching law,even if the tracking problem of each fuzzy subsystems is not ***,an example is given to validate the effectiveness of the results.

关键词： T-S fuzzy switched systems α-dissipative tracking bumpless transfer control

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Bumpless Transfer Control of Switched Time-Delay Linear Systems with State-dependent Switching 41

Bumpless Transfer Control of Switched Time-Delay Linear Syst...

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第41届中国控制会议

作者： Jinghan Li Jun Zhao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education College of Information Science and Engineering Northeastern University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper deals with the bumpless transfer control for switched time-delay *** time-delay bumpless transfer performance for switched linear system is provided,which can suppress control bumps caused by the current state and the time-delay state at the switching ***,by utilizing the multiple Lyapunov functions,under the co-design of the state-dependent switching law and the time-delay controller,a sufficient condition guaranteeing bumpless transfer control of switched time-delay systems is *** effectiveness of the result is validated by an *** result shows that our method reduces the bumpy amplitude while ensuring the stability of the switched time-delay system.

关键词： Switched Systems Time-delay State-dependent Bumpless Transfer

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Optical fiber triethylamine room temperature gas sensor based on HMS platform with DFT calculation and Experimental Demonstration

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Applied Surface Science 2025年

作者： Yan-nan Wang Jin Li Zhenyu Yuan Fanli Meng College of Mechanical and Electronic Engineering Northwest A&F University Yangling 712100 China College of Information Science and Engineering Northeastern University Shenyang 110819 China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao 066004 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang 110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education China

An optical fiber triethylamine (TEA) gas sensor based on a hollow malposition structure (HMS) platform with CQDs@BiOBr gas-sensing layer is proposed. The CQDs@BiOBr gas-sensing material is coated onto the surface of the hollow core fiber (HCF) using a dip-coating method. The misaligned structure is designed to guide light transmission within the cladding of the HCF and excite the evanescent field. The heterojunction formed between CQDs and BiOBr enhances the separation efficiency of electron-hole pairs, improving the transfer speed of charge carriers during the gas adsorption process. Experimental results show that the sensitivity of sensor at room temperature is 3.53 pm/ppm, which is 2.43 times higher compared to a sensor coated with BiOBr alone. Additionally, the response and recovery times of sensor are 12 s and 36 s, respectively, with a limit of detection (LOD) of 5.67 ppm. DFT calculations provide insight into the electronic transfer pathways and electron cloud distribution during the gas adsorption process, further validating the crucial role of the CQDs@BiOBr heterojunction in enhancing gas-sensing performance. The proposed optical fiber TEA gas sensor shows significant potential for applications in environmental monitoring and industrial safety.

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Prediction of Blast Furnace Temperature Based on Evolutionary optimization 11th

Prediction of Blast Furnace Temperature Based on Evolutionar...

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11th International Conference on Evolutionary Multi-Criterion optimization, EMO 2021

作者： Hu, Tenghui Wang, Xianpeng Wang, Yao Dong, Zhiming Zhuang, Xinyu Liaoning Key Laboratory of Manufacturing System and Logistics Shenyang110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang110819 China Ltd. Beijing100085 China

ISBN: (纸本)9783030720612

Blast furnace temperature, generally characterized by silicon content, is an important indicator to characterize the stability of furnace conditions in the blast furnace ironmaking process. To achieve its accurate prediction, a prediction model based on multi-objective evolutionary optimization and non-linear ensemble learning is proposed in this paper. First, the input features of each sub-learner are optimized through a modified discrete multi-objective evolutionary algorithm to obtain a set of highly accurate and diverse sub-learners. Subsequently, a nonlinear ensemble method based on extreme learning machine is employed to ensemble the obtained sub-learners to further improve the accuracy and robustness of the prediction model. Furthermore, the effectiveness of the proposed prediction method is verified by experiments based on actual production data. © 2021, Springer Nature Switzerland AG.

关键词： Forecasting

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Reinforcement Learning for Dynamic Mutation Process Control in Multi-Objective Differential Evolution

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IFAC-PapersOnLine 2022年第15期55卷 117-122页

作者： Lue Tao Gongshu Wang Yang Yang Yun Dong Lijie Su Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Northeastern University China

To improve the search performance of the multi-objective differential evolution algorithm, we use a reinforcement learning agent to control the dynamic mutation process. First, a novel multi-objective optimization framework with new mutation operators is developed. Then, a Q-learning algorithm is introduced to select mutation operators with different characteristics adaptively. Specifically, we design three feature functions to describe the state of the population observed by the agent. Based on the observed state, the agent dynamically selects the mutation operator in each generation, which strongly improved the searching ability. We compare the performance of the proposed method with two state-of-art algorithms on benchmark functions and find that the proposed algorithm has a better performance in multiple indicators. At the same time, the component validity analysis also shows the effectiveness of the Q-learning and the framework introduced in this paper. Finally, the learning process shows that the agent can achieve asymptotic convergence.

关键词： Reinforcement Learning Evolutionary Algorithm Multi-Objective Deferential Evolution Mutation Process Control Operator Selection

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An Enhanced Surrogate-Assisted Multi-Objective Differential Evolution Algorithm with Self-Adaptive Strategies for Order Planning in Hot Rolling Process: Surrogate-Assisted MODE with Self-Adaptive Strategies for OPHR Problem

An Enhanced Surrogate-Assisted Multi-Objective Differential ...

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2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion

作者： Che, Gelegen Tang, Lixin Zhao, Guodong Guo, Qingxin Guo, Jianqiang National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Liaoning Shenyang China Northeastern University National Frontiers Science Center for Industrial Intelligence and Systems Optimization Liaoning Shenyang China Northeastern University Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Shenyang China

ISBN: (纸本)9798400704956

This paper studies a crucial decision-making problem of order planning for a hot rolling process (OPHR). The OPHR problem is to determine the optimal sequence of orders, with the goals of minimizing penalties for earliness/tardiness, maximizing profits from special orders, and minimizing transition penalties of adjacent orders. A multi-objective differential evolution (MODE) method with self-adaptive strategies (SaMODE) is developed to tackle it. Then, a radial basis function neural network is integrated into the SaMODE (RBFNN-SaMODE) as a surrogate model to reduce the computational burden of evaluating objectives throughout evolution. The proposed method is extensively tested on real production data, and the results demonstrate its superiority over the classical MODE, yielding highly effective solutions. © 2024 held by the owner/author(s).

关键词： Radial basis function networks

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