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Jun Zhao
Dalian University of Technology
61Publications
16H-index
546Citations
Publications 61
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#1Fan Zhou (DUT: Dalian University of Technology)
#2Zhongyang Han (DUT: Dalian University of Technology)H-Index: 5
Last.Wei Wang (DUT: Dalian University of Technology)H-Index: 35
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Embedded feature selection is an important branch in the field of feature engineering. However, due to the excessive computing time brought by the iterative mechanism, variational inference-based embedded feature selection cannot satisfy the real-time demand for practical application. In such a case, a Spark-based embedded feature selection approach is proposed in this study. Automatic relevance determination kernel-based variational relevance vector machine is selected as the basic model so tha...
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#1Ze Wang (DUT: Dalian University of Technology)
#2Zhongyang Han (DUT: Dalian University of Technology)H-Index: 5
Last.Feng Jin (DUT: Dalian University of Technology)H-Index: 1
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Named Entity Recognition (NER) is an important branch of Natural Language Processing (NLP). Among the existed NER methods, one of the most advanced and commonly deployed approach is the Long Short Term Memory with a Conditional Random Field layer (LSTM-CRF). However, this supervised method generally requires a large number of labeled corpuses, which is very limited regarding the texts in drug patent of this study. Bearing this in mind, a word similarity feature-based semi-supervised NER approach...
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#1Dong Wang (DUT: Dalian University of Technology)H-Index: 15
#2Yun Huang (DUT: Dalian University of Technology)
Last.Linqing Wang (DUT: Dalian University of Technology)H-Index: 1
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This paper focuses on the design of the event-triggered distributed optimal consensus control for a multi-agent system with saturation actuator under a strongly connected weight-balanced digraph. An event-triggered control protocol with a sigmoid function is presented, which reduces the communication workload among agents and overcomes the actuator saturation. The convergency to the optimal consensus point of the distributed multi-agent systems is transformed into stability analysis of the corre...
#1Zhiming Lv (DUT: Dalian University of Technology)
#2Linqing Wang (DUT: Dalian University of Technology)H-Index: 1
Last.Wei Wang (DUT: Dalian University of Technology)H-Index: 35
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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 &...
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#1Long Chen (DUT: Dalian University of Technology)H-Index: 2
#2Linqing Wang (DUT: Dalian University of Technology)H-Index: 1
Last.Wei Wang (DUT: Dalian University of Technology)H-Index: 35
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Considering that real-life time series mixed with missing points cannot be directly modeled by using most of the supervised machine learning methods, this paper proposes a novel time series prediction method based on relevance vector machines for incomplete training dataset. Given the regularity between the missing inputs and outputs constructed by the phase space reconstruction, this paper imputes the missing inputs during the learning process by the values of their corresponding missing output...
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#1Long ChenH-Index: 2
#2Linqing WangH-Index: 1
Last.Wei WangH-Index: 35
view all 5 authors...
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#1Jun Zhao (DUT: Dalian University of Technology)H-Index: 16
#2Tianyu Wang (DUT: Dalian University of Technology)H-Index: 1
Last.Wei Wang (DUT: Dalian University of Technology)H-Index: 35
view all 4 authors...
A timely and effective scheduling of the byproduct gas system plays a pivotal role in realizing intelligent manufacturing and energy conservation in the steel industry. In order to realize real-time dynamic scheduling of the blast furnace gas (BFG) system, a granular prediction and dynamic scheduling process based on adaptive dynamic programming is proposed in this paper. To reflect the specificity of production reflected in the fluctuation of data, a series of information granules is constructe...
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#1Jun Zhao (DUT: Dalian University of Technology)H-Index: 16
#2Wei Wang (DUT: Dalian University of Technology)H-Index: 35
Last.Chunyang Sheng (SDUST: Shandong University of Science and Technology)
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It is hard for raw industrial data accumulated by commonly implemented supervisory control and data acquisition (SCADA) system on-site to be directly employed to construct a prediction model, given that such data are always mixed with high level noise, missing points, and outliers due to the possible real-time database malfunction, data transformation, or maintenance. Thereby, the data preprocessing techniques have to be implemented, which usually contain anomaly data detection, data imputation,...
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#1Jun Zhao (DUT: Dalian University of Technology)H-Index: 16
#2Wei Wang (DUT: Dalian University of Technology)H-Index: 35
Last.Chunyang Sheng (SDUST: Shandong University of Science and Technology)
view all 3 authors...
Based on the results of a number of different forecasting modes introduced in the previous chapters, this chapter provides a practical case study related to the optimal scheduling for energy system in steel industry based on the prediction outcomes. As for the by-product gas scheduling problem, a two-stage scheduling method is introduced here. On the prediction stage, the states of the optimized objectives, the consumption of the outsourcing natural gas and oil, the power generation, and the gas...
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#1Jun Zhao (DUT: Dalian University of Technology)H-Index: 16
#2Wei Wang (DUT: Dalian University of Technology)H-Index: 35
Last.Chunyang Sheng (SDUST: Shandong University of Science and Technology)
view all 3 authors...
The selection of parameters or hyper-parameters gives great impact on the performance of a data-driven model. This chapter introduces some commonly used parameter optimization and estimation methods, such as the gradient-based methods (e.g., gradient descend, Newton method, and conjugate gradient method) and the intelligent optimization ones (e.g., genetic algorithm, differential evolution algorithm, and particle swarm optimization). In particular, in this chapter, the conjugate gradient method ...
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