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| 1 | Surrogate-Assisted Particle Swarm Optimization Algorithm With Pareto Active Learning for Expensive Multi-Objective Optimization显示文摘For multi-objective optimization problems, particle swarm optimization(PSO) algorithm generally needs a large number of fitness evaluations to obtain the Pareto optimal solutions. However, it will become substantially time-consuming when handling computationally expensive fitness functions. In order to save the computational cost, a surrogate-assisted PSO with Pareto active learning is proposed. In real physical space(the objective functions are computationally expensive), PSO is used as an optimizer, and its optimization results are used to construct the surrogate models. In virtual space, objective functions are replaced by the cheaper surrogate models, PSO is viewed as a sampler to produce the candidate solutions. To enhance the quality of candidate solutions, a hybrid mutation sampling method based on the simulated evolution is proposed, which combines the advantage of fast convergence of PSO and implements mutation to increase diversity. Furthermore, ε-Pareto active learning(ε-PAL)method is employed to pre-select candidate solutions to guide PSO in the real physical space. However, little work has considered the method of determining parameter ε. Therefore, a greedy search method is presented to determine the value ofεwhere the number of active sampling is employed as the evaluation criteria of classification cost. Experimental studies involving application on a number of benchmark test problems and parameter determination for multi-input multi-output least squares support vector machines(MLSSVM) are given, in which the results demonstrate promising performance of the proposed algorithm compared with other representative multi-objective particle swarm optimization(MOPSO) algorithms. | Zhiming Lv Linqing Wang Zhongyang Han Jun Zhao Wei Wang | 2019 | IEEE/CAA Journal of Automatica Sinica2019，6，3： | 13 |
| 2 | An Optimized Oxygen System Scheduling With Electricity Cost Consideration in Steel Industry显示文摘As an essential energy resource in steel industry,oxygen is widely utilized in many production procedures.With different demands of the oxygen amount,a gap between the generation and consumption always occurs.Therefore,its related optimization and scheduling work along with the electricity cost to fill the gap has a great impact on daily production and efficient energy utilization in steel plant.Considering an oxygen system in a steel plant in China,a nonlinear programming model for oxygen system scheduling is proposed in this study,which concerns not only the practical characteristics of the energy pipeline network,but also the electricity cost acquired by a fitting regression modeling between the load of air separation units(ASU)and its corresponding electricity consumption.A set of constraints is formulated for restricting the practical adjusting capacity and filling the imbalance gap of oxygen.To solve the proposed scheduling model with electricity cost consideration,a particle swarm optimization(PSO)algorithm is then adopted.To verify the effectiveness of the proposed approach,a large number of experiments employing the real data from this plant are carried out,both for the fitting regression and the scheduling optimization phases.And the results demonstrate that such a practice-based solution successfully resolves the oxygen scheduling problem and simultaneously minimizes the electricity cost,which will be beneficial for the enterprise. | Zhongyang Han Jun Zhao Wei Wang | 2017 | IEEE/CAA Journal of Automatica Sinica2017，4，2： | 4 |
| 3 | Homotypic clustering of L1 and B1/Alu repeats compartmentalizes the 3D genome显示文摘Organization of the genome into euchromatin and heterochromatin appears to be evolutionarily conserved and relatively stable during lineage differentiation.In an effort to unravel the basic principle underlying genome folding,here we focus on the genome itself and report a fundamental role for L1(LINE1 or LINE-1)and B1/Alu retrotransposons,the most abundant subclasses of repetitive sequences,in chromatin compartmentalization.We find that homotypic clustering of L1 and B1/Alu demarcates the genome into grossly exclusive domains,and characterizes and predicts Hi-C compartments.Spatial segregation of L1-rich sequences in the nuclear and nucleolar peripheries and B1/Alu-rich sequences in the nuclear interior is conserved in mouse and human cells and occurs dynamically during the cell cycle.In addition,de novo establishment of L1 and B1 nuclear segregation is coincident with the formation of higher-order chromatin structures during early embryogenesis and appears to be critically regulated by L1 and B1 transcripts.Importantly,depletion of L1 transcripts in embryonic stem cells drastically weakens homotypic repeat contacts and compartmental strength,and disrupts the nuclear segregation of L1-or B1-rich chromosomal sequences at genome-wide and individual sites.Mechanistically,nuclear co-localization and liquid droplet formation of L1 repeat DNA and RNA with heterochromatin protein HP1αsuggest a phase-separation mechanism by which L1 promotes heterochromatin compartmentalization.Taken together,we propose a genetically encoded model in which L1 and B1/Alu repeats blueprint chromatin macrostructure.Our model explains the robustness of genome folding into a common conserved core,on which dynamic gene regulation is overlaid across cells. | J.Yuyang Lu Lei Chang Tong Li Ting Wang Yafei Yin Ge Zhan Xue Han Ke Zhang Yibing Tao Michelle Percharde Liang Wang Qi Peng Pixi Yan Hui Zhang Xianju Bi Wen Shao Yantao Hong Zhongyang Wu Runze Ma Peizhe Wang Wenzhi Li Jing Zhang Zai Chang Yingping Hou Bing Zhu Miguel Ramalho-Santos Pilong Li Wei Xie Jie Na Yujie Sun Xiaohua Shen | 2021 | Cell Research2021，31，6： | 1 |
| 4 | Real time tank levels based on multi- vector regressor 显示文摘 | HAN Zhongyang LIU Ying prediction for converter gas output least square support Engineering Practice 2012 ZHAO Jun | 2012 | Control2012，，20： | 1 |
| 5 | Variational Inference Based Kernel Dynamic Bayesian Networks for Construction of Prediction Intervals for Industrial Time Series With Incomplete Input显示文摘Prediction intervals(PIs)for industrial time series can provide useful guidance for workers.Given that the failure of industrial sensors may cause the missing point in inputs,the existing kernel dynamic Bayesian networks(KDBN),serving as an effective method for PIs construction,suffer from high computational load using the stochastic algorithm for inference.This study proposes a variational inference method for the KDBN for the purpose of fast inference,which avoids the timeconsuming stochastic sampling.The proposed algorithm contains two stages.The first stage involves the inference of the missing inputs by using a local linearization based variational inference,and based on the computed posterior distributions over the missing inputs the second stage sees a Gaussian approximation for probability over the nodes in future time slices.To verify the effectiveness of the proposed method,a synthetic dataset and a practical dataset of generation flow of blast furnace gas(BFG)are employed with different ratios of missing inputs.The experimental results indicate that the proposed method can provide reliable PIs for the generation flow of BFG and it exhibits shorter computing time than the stochastic based one. | Long Chen Linqing Wang Zhongyang Han Jun Zhao Wei Wang | 2020 | IEEE/CAA Journal of Automatica Sinica2020，7，5： | 1 |
| 6 | Reversible conductivity recovery of highly sensitive flexible devices by water vapor显示文摘With decreasing size of integrated circuits in wearable electronic devices,the circuit is more susceptible to aging or fracture problem,subsequently decreasing the transmission efficiency of electricity.Micro-healing represents a good approach to solve this problem.Herein,we report a water vapor method to repair microfiber-based electrodes by precise positioning and rapid healing at their original fracture sites.To realize this micro-level conducting healing,we utilize a bimaterial composed of polymeric microfibers as healing agents and electrically conductive species on its surface.This composite electrode shows a high-performance conductivity,great transparency,and ultra-flexibility.The transmittance of our electrode could reach up to 88 and 90%with a sheet resistance of 1 and 2.8Ωsq^(−1),respectively,which might be the best performance among Au-based materials as we know.Moreover,after tensile failure,water vapor is introduced to mediate heat transfer for the healing process,and within seconds the network electrode could be healed along with recovering of its resistance.The recovering process could be attributed to the combination of adhesion force and capillary force at this bimaterial interface.Finally,this functional network is fabricated as a wearable pressure/strain sensing device.It shows excellent stretchability and mechanical durability upon 1000 cycles. | Yuting Wang Yingchun Su Zegao Wang Zhongyang Zhang Xiaojun Han Mingdong Dong Lifeng Cui Menglin Chen | 2018 | npj Flexible Electronics2018，2，1： | 0 |
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