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Characteristics-based Estimation of distribution algorithm for the steelmaking-refining-continuous casting scheduling problem in the real-world steel plants

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Computers and Operations Research 2025年 181卷

作者： Zhang, Long Hu, Xi Wu, XiaoMing Guangxi Key Laboratory of Automatic Detection Technology and Instruments GuiLin541004 China School of Electronic Engineering and Automation GuiLin University of Electronic Technology GuiLin541004 China

As a key production process in the steel industry, excellent scheduling of Steelmaking-refining-Continuous Casting (SCC) manufacturing process can improve production efficiency, shorten the steel production cycle, and reduce the production cost for steel enterprises. This paper presents a Characteristics-based Estimation of Distribution Algorithm (CEDA) for the SCC scheduling problem in the real-world steel plants. Considering the processing characteristics of the continuous casting machine, a novel caster-based encoding scheme and an improved decoding scheme are proposed. Also, a distance concept is introduced to mitigate the impact of similar individuals on the probability model, and an importance-based probability model updating mechanism is designed to increase the impact of excellent individual on the probability model. Furthermore, an individual sampling scheme with enhanced probability is constructed to ensure continuous processing of the continuous casting machine as much as possible. Finally, this paper designs a limited insertion operation in the local search to address the exploitation of the proposed algorithm. Extensive numerical simulations demonstrate that the proposed CEDA for the SCC scheduling process is more efficient than some state-of-the-art algorithms in the literature. © 2025 Elsevier Ltd

关键词： Continuous casting

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Switchable terahertz polarization converter based on VO_(2)metamaterial

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Chinese Physics B 2022年第6期31卷 403-409页

作者： Haotian Du Mingzhu Jiang Lizhen Zeng Longhui Zhang Weilin Xu Xiaowen Zhang Fangrong Hu Guangxi Key Laboratory of Automatic Detecting Technology and Instrument Guilin University of Electronic TechnologyGuilin 541004China Guilin Institute of Information Technology Guilin 541004China

A switchable terahertz(THz)polarization converter based on vanadium dioxide(VO_(2)) metamaterial is *** is a 5-layer structure which containing metal split-ring-resonator(SRR),the first polyimide(PI)spacer,VO_(2) film,the second PI spacer,and metal *** is an array structure and the period in x and y directions is 100μ*** performance is simulated by using finite integration *** simulation results show that,when the VO_(2) is in insulating state,the device is a transmission polarization *** cross-linear polarization conversion can be realized in a broadband of0.70 THz,and the polarization conversion rate(PCR)is higher than 99%.Under thermal stimulus,the VO_(2) changes from insulating state to metallic state,and the device is a reflective polarization *** linear-to-circular polarization conversion can be successfully realized in a broadband of 0.50 THz,and the PCR is higher than 88%.

关键词： switchable terahertz(THz)metamaterial polarization conversion vanadium dioxide(VO_(2))

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Research on test scheduling of 3D NoC based the strategy of bandwidth division multiplexing and multi-clock 13

Research on test scheduling of 3D NoC based the strategy of ...

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2017 13th IEEE International Conference on Electronic Measurement & instruments (ICEMI)

作者： Xu Chuanpei Guo Rong Zhao Tongzhou School of Electronic Engineering and Automation Guilin University of Electronic Technology Guangxi Key Laboratory of Automatic Detection Technology and Instrument

ISBN: (纸本)9781509050369;9781509050352

To improve the testing efficiency of IP core in 3-dimensional network-on-chip(3D NoC), research on test scheduling of 3D NoC under multi-constraint is conducted. In this paper, we use the strategy of dynamic bandwidth division multiplexing to increase the utilization of 3D NoC. The strategy of multi-clock is used to determine the clock frequency of each IP core according to the ratio of power consumption and test time to shorten its test time. The improved particle swarm optimization(PSO) algorithm is used to find the optimal solution under the dual constraints of power consumption and bandwidth, which combined with the characteristics of 3D NoC. The location and order of transmission of the IP core's test data are viewed as two variables to optimize. The optimal speed and accuracy of the algorithm are advanced by adding the Explosion Operator(EO) and Specific Introductory Formula(SIF) in early phase, and the strategy of local optimal monitoring is added in the later phase. The circuit in international standard test set ITC'02 is used as the experimental object. The experiment results show that the work in this paper can shorten the test time and improve the utilization of on-chip resources effectively.

关键词： Three-dimensional network-on-chip(3D NoC) Band division multiplexing Multi-clock Particle swarm optimization Test scheduling

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Embedding Implicit User Importance for Group Recommendation

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Computers, Materials & Continua 2020年第9期64卷 1691-1704页

作者： Qing Yang Shengjie Zhou Heyong Li Jingwei Zhang Guangxi Key Laboratory of Automatic Measurement Technology and Instrument Guilin University of Electronic TechnologyGuilin541004China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin541004China Centre for Applied Informatics Victoria UniversityMelbourne8001Australia

Group recommendations derive from a phenomenon in which people tend to participate in activities together regardless of whether they are online or in reality,which creates real scenarios and promotes the development of group recommendation *** from traditional personalized recommendation methods,which are concerned only with the accuracy of recommendations for individuals,group recommendation is expected to balance the needs of multiple *** a proper model for a group of users to improve the quality of a recommended list and to achieve a better recommendation has become a large challenge for group recommendation *** studies often focus on explicit user characteristics,such as gender,occupation,and social status,to analyze the importance of users for modeling group ***,it is usually difficult to obtain extra user information,especially for ad hoc *** this end,we design a novel entropy-based method that extracts users’implicit characteristics from users’historical ratings to obtain the weights of group *** weights represent user importance so that we can obtain group preferences according to user weights and then model the group decision process to make a *** evaluate our method for the two metrics of recommendation relevance and overall ratings of recommended *** compare our method to baselines,and experimental results show that our method achieves a significant improvement in group recommendation performance.

关键词： Group recommendation preference aggregation user importance

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Residualpath-res-dense-net for retinal vessel segmentation 2

Residualpath-res-dense-net for retinal vessel segmentation

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2nd International Conference on Computer Vision, Image, and Deep Learning

作者： Huang, Xiaoming He, Fuyun Tang, Xiaohu Wang, Xun Qiu, Senhui Hu, Cong College of Electronic Engineering Guangxi Normal University Guilin541004 China Guangxi Key Laboratory of Automatic Detection Technology and Instrument Guilin541004 China Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin541004 China

ISBN: (数字)9781510646827

ISBN: (纸本)9781510646810

Retinal blood vessels segmentation acts as an important part to the treatment of ocular disease. Lately, automatic segmentation based on deep learning can solves problems of low efficiency and strong subjectivity of manual segmentation, and attracts the attention of researchers. In this paper, a novel segmentation model called Residualpath-Res-Dense Net (RRD-Net) is proposed to achieve vessel segmentation. In RRD-Net, Res-block and Dense-block are used to help speeding up the convergence of the network and learning more intrinsic features. In addition, the introduction of Residual Path can cut down the semantic difference between the connected feature and eliminate the potential impact of semantic difference on segmentation accuracy. We apply benchmark datasets DRIVE and CHASE_DB1 to evaluate effectiveness of the proposed network. Accuracy, sensitivity and F1 score demonstrate the effectiveness of RRD-Net. © 2021 SPIE.

关键词： Deep learning

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Hybrid CF on Modeling Feature Importance with Joint Denoising AutoEncoder and SVD++ 16th

Hybrid CF on Modeling Feature Importance with Joint Denoisin...

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16th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2020

作者： Yang, Qing Li, Heyong Zhou, Ya Zhang, Jingwei Fuentes, Stelios Guangxi Key Laboratory of Automatic Detection Technology and Instrument Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China Leicester University Leicester United Kingdom

ISBN: (纸本)9783030675363

AutoEncoder is an unsupervised learning approach that can maps inputs to useful intermediate features, which can be used to build recommendation. Intermediate features of different entities obtained by AutoEncoder may have different weight for predicting users behavior. However, existing research typically uses a uniform weight on intermediate features to make a fast learning algorithm, this general approach may lead to the limited performance of the model. In this paper, we proposes a novel approach by using SGD to dynamically learn the intermediate features importance, which can integrate the intermediate features into matrix factorization framework seamlessly. In the previous works, the entities intermediate features learned by AutoEncoder are modeled as a whole. On this basis, we proposes to use attention parameters in entity intermediate feature to dynamically learn the intermediate features importance and build fine-grained model. By learning unique attention unit for each entity intermediate feature, the entities intermediate features are integrated into the matrix factorization framework better. Extensive experiments conducted over two real-world datasets demonstrate our proposed approach outperforms the compared models. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Matrix factorization

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HGeoHashBase: an optimized storage model of spatial objects for location-based services

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Frontiers of Computer Science 2020年第1期14卷 208-218页

作者： Jingwei ZHANG Chao YANG Qing YANG Yuming LIN Yanchun ZHANG Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic TechnologyGuilin 541004China Guangxi Key Laboratory of Automatic Measurement Technology and Instrument Guilin University of Electronic TechnologyGuilin 541004China Centre for Applied Informatics College of Engineering and ScienceVictoria UniversityMelbourne 8001Australia

Many location-based services need to query objects existing in a specific space,such as location-based tourism resource *** a large number of spatial objects and the real-time object access requirements of location-based services pose a big challenge for spatial object storage and query *** this paper,we propose HGeoHashBase,an improved storage model by integrating GeoHash with key-value structure,to organize spatial objects for efficient range *** is responsible for spatial encoding and key-value structure as underlying data *** the similarity of the encodings for objects in the close geographical locations and the multi-version data mechanism are blended into the proposed model *** the tradeoff between encoding precision and query performance,a theoretical proof is *** experiments are designed and conducted,whose results show that the proposed model can gain significant performance improvement.

关键词： location-based services distributed storage model storage and access optimization

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GSSM: An Integration Model of Heterogeneous Factors for Point-of-Interest Recommendation 6th

GSSM: An Integration Model of Heterogeneous Factors for Po...

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6th International Conference on Artificial Intelligence and Security,ICAIS 2020

作者： Yang, Qing Chen, Yiheng Luo, Peicheng Zhang, Jingwei Guangxi Key Laboratory of Automatic Measurement Technology and Instrument Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9789811580857

Millions of users prefer to share their locations and social relationships on location-based social networks (LBSNs), such as Gowalla, Foursquare, and etc. It has become an important function to recommend points-of-interest (POIs) for users based on their check-in records. However, the user-location matrix generated by check-in records is often very sparse which causes those traditional methods to present a suboptimal recommendation performance. We proposed a new POI recommendation algorithm named GSSM, which combines multiple factors which can help solve problems in these recommendations and improve recommendation performance. We employ the gaussian mixture model (GMM) to extract geographical factor from user’s check-in records. Then we assign a concrete semantics to define social factor. The strength of the connection between users is indicated by the number of visits from associated users. In order to capture the similarity factor between users, we apply the popular cosine similarity to improve recommendation performance for the POI recommendation as the basis of calculating user similarity. Finally, an optimal fusion framework is designed to incorporate the three factors mentioned above, and GSSM is proposed. We conduct experiments on large-scale real data set which is crawled from Gowalla. The experimental results show that the performance of GSSM is better than the baseline and many current popular POI recommendation models. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Improved design and construction of an ionization chamber for the CSNS beam loss monitor (BLM)

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Chinese Physics C 2012年第4期36卷 329-333页

作者：田建民徐美杭赵中亮陈昌阮向东陈元柏徐韬光陆双桐 State Key Laboratory of Technologies of Nuclear Detection and Electronics Institute of High Energy Physics Chinese Academy of Sciences Physics Science and Engineering Technology Department Guangxi University Beijing Nuclear Instrument Factory China National Nuclear Corporation

Based on the first ionization chamber （IC） prototype, the structure, working gas component and electrode material of the IC are improved. The test of the improved IC shows that the plateau length is about 2000 V, the plateau slope is less than 0.2%/100 V, the sensitivity is 19.6 pA/rad·h 1, the up-limitation of the linearity can be up to 3.6×105 rad/h, and the applied voltage can be operated to 3500 V. The test results show that the performance of the improved IC meets the requirements of the beam loss monitor.

关键词： CSNS BLM detector ionization chamber plateau curve

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Dynamical Rating Prediction with Topic Words of Reviews: A Hierarchical Analysis Approach 15th

Dynamical Rating Prediction with Topic Words of Reviews: A H...

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15th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2019

作者： Zhang, Huibing Zhong, Hao Yang, Qing Jia, Fei Zhou, Ya Pan, Fang Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Automatic Measurement Technology and Instrument Guilin University of Electronic Technology Guilin541004 China Teaching Affairs Office Guangxi Normal University Guilin541004 China

ISBN: (纸本)9783030301453

Social commerce is an important part of the social network which contains a large number of user behaviors and user relationships. Users generate reviews, social relations, user-product or product-product mapping information that can reflect an evolution of product characteristics and user preferences in using social commerce. It is a popular topic by using these information to conduct rating prediction in the field of intelligent recommendation. In this paper, optimizing the rating prediction based on topic analysis in two aspects. On the one hand, in the process of data preprocessing, constructing a dynamic hierarchical tree of topic words (DHTTW), which can not only capture the change of users’ preferences for product property, but also reflect the impact of different product property on users’ preferences at the same time. Based on DHTTW, designing the mapping rules from user reviews to DHTTW to generate user preference vectors. On the other hand, in the process of prediction, proposing a prediction method named combination of gradient boosting decision tree and multi-class linear regression (GBDT-MCLR), which further improves the accuracy of rating prediction. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Forecasting

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