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Intelligent proportioning model of converter scrap based on optimization algorithm

Progress in Natural Science:Materials International 2024年第1期34卷 37-44页

作者： Chaojie Zhang Jinjun Cheng Liqiang Zhang Yingxue Wang Wei Zhang Jianwei Yuan Haichuan Wang School of Metallurgical Engineering Anhui University of Technology National Engineering Research Center for Public Safety Risk Perception and Control by Big Data(RPP) China Academic of Electronics and Information Technology Wuhu Xinxing Pipes Co. Ltd.

Scrap is one of the main raw materials in converter steelmaking. Due to the wide variety of scrap and the significant differences in its prices, the proportioning of scrap presents a challenge in converter steelmaking. This paper explores the optimal proportioning method of scrap for converters using an optimization mathematical model. Initially, the melting characteristics of scrap after being added to the converter's molten iron were investigated using finite element numerical simulations. The study found that when the weight of the scrap is fixed, both the shape and type of the scrap significantly influence the melting time. Subsequently, employing mathematical optimization methods and considering comprehensive process parameters of the 210t converter smelting, including the composition and unit price of the scrap, as well as the requirements of the converter steelmaking process for scrap, an optimization model for the proportioning of scrap in converters was *** model was solved using computer programming languages. Through this optimization model, it is possible to obtain the lowest cost per ton of molten steel in the converter smelting process by determining the optimal proportions of various types of scrap.

关键词： scrap proportioning Optimization model Converter steelmaking mathematical model numerical simulation

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A Network Security Situation Prediction Model Enhanced by Multi Head Attention Mechanism

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第18期49卷 143-156页

作者： Chen, Jian Bian, Huanqiang Liang, Hao Laboratory and Information Technology Center Ningbo University of Finance & Economics Ningbo315175 China Ningbo City College of Vocational Technology Ningbo315175 China Big Data Division Ningbo University of Technology Ningbo315175 China

The Internet has grown as a result of information technology advancements, and cybercrime is becoming more and more common. To improve the network defense against all kinds of network attacks and reduce the success rate of cyber crimes, the research innovatively proposes to use the multi-tease attention mechanism to improve the gating cycle unit, and use the multi-head attention mechanism to obtain network security feature information at different locations, so as to improve the learning characteristics of network situation prediction and realize network security situation prediction. Three layers comprised the model: the prediction layer, the transform layer, and the circular network layer. The circular network layer was responsible for dimensionality reduction of network information data. information features were extracted via the transform layer. The outcomes of the predictions were output by the prediction layer. The study's model performed better when taught in both directions, according to the data, and its accuracy could reach roughly 93.5%. The highest level of model accuracy could be reached when other parameters were fixed and the neurons in the feed-forward layer was 28. Compared with other network security situation prediction models, the proposed model could improve the prediction accuracy to around 93.5% and the precision to around 91% on the UNSW-NB15 dataset, while maintaining the F1 value of the model at around 92%. The research-designed model can accurately predict the network security situation changes, which improves the Internet's defense against attacks and maintains the normal operation of the Internet community. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Prediction models

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Evaluating quality of motion for unsupervised video object segmentation

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Optoelectronics Letters 2024年第6期20卷 379-384页

作者： CHENG Guanjun SONG Huihui Jiangsu Key Laboratory of Big Data Analysis Technology Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment TechnologyNanjing University of Information Science&TechnologyNanjing 210044China12

Current mainstream unsupervised video object segmentation(UVOS) approaches typically incorporate optical flow as motion information to locate the primary objects in coherent video frames. However, they fuse appearance and motion information without evaluating the quality of the optical flow. When poor-quality optical flow is used for the interaction with the appearance information, it introduces significant noise and leads to a decline in overall performance. To alleviate this issue, we first employ a quality evaluation module(QEM) to evaluate the optical flow. Then, we select high-quality optical flow as motion cues to fuse with the appearance information, which can prevent poor-quality optical flow from diverting the network's attention. Moreover, we design an appearance-guided fusion module(AGFM) to better integrate appearance and motion information. Extensive experiments on several widely utilized datasets, including DAVIS-16, FBMS-59, and You Tube-Objects, demonstrate that the proposed method outperforms existing methods.

关键词： Evaluating quality of motion for unsupervised video object segmentation

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Three-Dimensional Modeling of Maize Canopies Based on Computational Intelligence

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Plant Phenomics 2024年第2期6卷 349-363页

作者： Yandong Wu Weiliang Wen Shenghao Gu Guanmin Huang Chuanyu Wang Xianju Lu Pengliang Xiao Xinyu Guo Linsheng Huang National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefei 230601China Information Technology Research Center Beijing Academy of Agriculture and Forestry SciencesBeijing 100097China Beijing Key Lab of Digital Plant National Engineering Research Center for Information Technology in AgricultureBeijing 100097China Nongxin Science&Technology(Beijing)Co. LtdBeijing 100097China

The 3-dimensional(3D)modeling of crop canopies is fundamental for studying functional-structural plant *** studies often fail to capture the structural characteristics of crop canopies,such as organ overlapping and resource *** address this issue,we propose a 3D maize modeling method based on computational *** initial 3D maize canopy is created using the t-distribution method to reflect characteristics of the plant architecture.

关键词： intelligence modeling based canopies computational dimensional maize three

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Interactive influence of urban heat island and urban pollution island in two major cities of India (Bangalore and Delhi)

Interactive influence of urban heat island and urban polluti...

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2024 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2024

作者： Dasgupta, Anindita Kumar, Uttam International Institute of Information Technology Spatial Computing Laboratory Center For Data Sciences Bangalore India

ISBN: (纸本)9798350389678

Rapid urbanization with the changes in land use patterns has resulted in the human induced urban heat island (UHI). UHI is a well-known phenomenon in which urban environments retain more heat than nearby rural environments effecting the urban population. City-heat interacts with rising summer temperatures and heat-waves and emission produced placing additional pressure on infrastructure. This study investigates the impact of interactive influence of Urban Heat Island and Urban Pollution Island (UPI) in two major cities of India (Delhi and Bangalore). The seasonal study for summer was performed for the two cities and so that interactive influence of aerosol, Nitrogen dioxide (NO2) and Land Surface Temperature (LST) is measured when the effect of UHI is the most. The study is performed for five years (2019, 2020, 2021, 2022 and 2023) and it was observed that in Delhi the overall aerosol index is greater than Bangalore. There was a considerable difference in the concentration of pollutant in 2021(during lock down because of pandemic) and 2022 (post-pandemic). Study was carried at a ward level in order to find out the interaction of pollutant and LST. Regression analysis was done taking the spatial metrics such as mean patch size, patch density, edge density, and shape of built-up patches at ward level (micro scale) of the city to measure the effect of anthropogenic activities on LST. © 2024 IEEE.

关键词： Atmospheric temperature

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Spatial-temporal Graph Transformer Network for Spatial-temporal Forecasting

Spatial-temporal Graph Transformer Network for Spatial-tempo...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Dao, Minh-Son Zetsu, Koji Hoang, Duy-Tang National Institute of Information and Communications Technology Big Data Integration Research Center Tokyo Japan

ISBN: (纸本)9798350362480

In the context of smart cities, there is a growing demand for risk-free navigation systems that help citizens avoid congestion and enjoy outdoor activities in clean environments. Such systems require the ability to predict traffic and environmental hotspots by analyzing spatial-temporal big data from various sources, including IoT devices, stations, CCTV, and personal device networks. However, analyzing and forecasting spatial-temporal data presents significant challenges due to the complex interplay of spatial and temporal dependencies. To address these challenges, we introduce a novel dual attention mechanism graph transformer that leverages both spatial and temporal information to capture intricate patterns in spatial-temporal data. We evaluate our model on two forecasting tasks: air pollution and traffic flow. Our results demonstrate superior performance compared to other graph neural network models. Consequently, this model has been integrated into a traffic-risk navigator application, which will be evaluated in Yokohama, Japan, using real data collected from air pollution stations and traffic monitors. © 2024 IEEE.

关键词： data flow graphs

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Research and application on visualization of ocean scalar field 6

Research and application on visualization of ocean scalar fi...

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6th International Conference on Geoscience and Remote Sensing Mapping, GRSM 2024

作者： He, Long Jiang, Xiaoyi Guo, Xue Sun, Hanrui Technology Innovation Center of Marine Information MNR Tianjin300171 China National Marine Data and Information Service Tianjin300171 China

ISBN: (纸本)9781510688018

The visualization of ocean scalar field is of great importance to scientists engaged in the analysis of marine extreme weather, the delineation of ship routes and the prediction of fish populations. This paper presents a text format transformation method for gridded marine environment data, and completed visualization with the assistance of ArcGIS API for JavaScript. This visualization method realizes the lightweight architecture of the ocean scalar field display and addresses the issues previously encountered, such as the occupation of numerous server resources and the slow response speed of the system. The visualization architecture proposed in this paper enables the effective display of diverse marine environment scalar elements, including sea temperature, salinity, sea surface height, and others, within the ODINWESTPAC system. This system provides information services for marine managers and researchers. © 2025 SPIE.

关键词： Extreme weather

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LLM Driven Web Profile Extraction for Identical Names 24

LLM Driven Web Profile Extraction for Identical Names

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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： Sancheti, Prateek Karlapalem, Kamalakar Vemuri, Kavita Data Sciences and Analytics Center International Institute of Information Technology Hyderabad India Cognitive Science Lab International Institute of Information Technology Hyderabad India

ISBN: (纸本)9798400701726

The number of individuals having identical names on the internet is increasing. Thus making the task of searching for a specific individual tedious. The user must vet through many profiles with identical names to get to the actual individual of interest. The online presence of an individual forms the profile of the individual. We need a solution that helps users by consolidating the profiles of such individuals by retrieving factual information available on the web and providing the same as a single result. We present a novel solution that retrieves web profiles belonging to those bearing identical Full Names through an end-to-end pipeline. Our solution involves information retrieval from the web (extraction), LLM-driven Named Entity Extraction (retrieval), and standardization of facts using Wikipedia, which returns profiles with fourteen multi-valued attributes. After that, profiles that correspond to the same real-world individuals are determined. We accomplish this by identifying similarities among profiles based on the extracted facts using a Prefix Tree inspired data structure (validation) and utilizing ChatGPT’s contextual comprehension (revalidation). The system offers varied levels of strictness while consolidating these profiles, namely strict, relaxed, and loose matching. The novelty of our solution lies in the innovative use of GPT – a highly powerful yet an unpredictable tool, for such a nuanced task. A study involving twenty participants, along with other results, found that one could effectively retrieve information for a specific individual. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Extraction

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MART(Splitting-Merging Assisted Reliable)Independent Component Analysis for Extracting Accurate Brain Functional Networks

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Neuroscience Bulletin 2024年第7期40卷 905-920页

作者： Xingyu He Vince D.Calhoun Yuhui Du School of Computer and Information Technology Shanxi UniversityTaiyuan 030006China Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Georgia State UniversityGeorgia Institute of TechnologyEmory UniversityAtlanta 30303USA

Functional networks(FNs)hold significant promise in understanding brain *** component analysis(ICA)has been applied in estimating FNs from functional magnetic resonance imaging(fMRI).However,determining an optimal model order for ICA remains challenging,leading to criticism about the reliability of FN ***,we propose a SMART(splitting-merging assisted reliable)ICA method that automatically extracts reliable FNs by clustering independent components(ICs)obtained from multi-model-order ICA using a simplified graph while providing linkages among FNs deduced from different-model *** extend SMART ICA to multi-subject fMRI analysis,validating its effectiveness using simulated and real fMRI *** on simulated data,the method accurately estimates both group-common and group-unique components and demonstrates robustness to *** two age-matched cohorts of resting fMRI data comprising 1,950 healthy subjects,the resulting reliable group-level FNs are greatly similar between the two cohorts,and interestingly the subject-specific FNs show progressive changes while age ***,both small-scale and large-scale brain FN templates are provided as benchmarks for future *** together,SMART ICA can automatically obtain reliable FNs in analyzing multi-subject fMRI data,while also providing linkages between different FNs.

关键词： Independent component analysis Functional magnetic resonance imaging-Brain functional networks Clustering Multi-model-order

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Efficiency-optimized 6G:A virtual network resource orchestration strategy by enhanced particle swarm optimization

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Digital Communications and Networks 2024年第5期10卷 1221-1233页

作者： Sai Zou Junrui Wu Haisheng Yu Wenyong Wang Lisheng Huang Wei Ni Yan Liu College of Big Data and Information Engineering Guizhou UniversityGuiyangChina Prospective Technology Lab of the China Future Internet Engineering Center BeijingChina College of Computer Science and Engineering University of Electronic Science and TechnologyChengduChina International Institute for Next Generation Internet Macao University of Science and TechnologyMacaoChina Data61 Business Unit CSIROAustralia

The future Sixth-Generation (6G) wireless systems are expected to encounter emerging services with diverserequirements. In this paper, 6G network resource orchestration is optimized to support customized networkslicing of services, and place network functions generated by heterogeneous devices into available *** is a combinatorial optimization problem that is solved by developing a Particle Swarm Optimization (PSO)based scheduling strategy with enhanced inertia weight, particle variation, and nonlinear learning factor, therebybalancing the local and global solutions and improving the convergence speed to globally near-optimal *** show that the method improves the convergence speed and the utilization of network resourcescompared with other variants of PSO.

关键词： Virtualization Network function orchestration Network resource virtualized orchestration (NRVO) Particle swarm optimization(PSO)

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