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Water quality prediction of copper-molybdenum miningbeneficiation wastewater based on the PSO-SVR model

Frontiers of Environmental Science & Engineering 2023年第8期17卷 81-94页

作者： Xiaohua Fu Qingxing Zheng Guomin Jiang Kallol Roy Lei Huang Chang Liu Kun Li Honglei Chen Xinyu Song Jianyu Chen Zhenxing Wang Ecological Environment Management and Assessment Center Central South University of Forestry and TechnologyChangsha 410004China State Environmental Protection Key Laboratory of Water Environmental Simulation and Pollution Control South China Institute of Environmental SciencesMinistry of Ecology and EnvironmentGuangzhou 510655China Chinese Non-Ferrous Industrial Engineering Center of Pollution Control Technology&Equipment Science Environment Protection Co.Ltd.Changsha 410036China Institute of Computer Science University of TartuTartu 51009Estonia School of Environmental Science and Engineering Guangzhou UniversityGuangzhou 510006China A.B Freeman School of Business Tulane UniversityNew OrleansLA 70118USA Guangzhou Huacai Environmental Protection Technology Co. Ltd.Guangzhou 511480China

The mining-beneficiation wastewater treatment is highly complex and *** factors like influent quality,flow rate,pH and chemical dose,tend to restrict the effluent effectiveness of miningbeneficiation wastewater *** oxygen demand(COD)is a crucial indicator to measure the quality of mining-beneficiation *** COD concentration accurately of miningbeneficiation wastewater after treatment is essential for achieving stable and compliant *** reduces environmental risk and significantly improves the discharge quality of *** paper presents a novel AI algorithm PSO-SVR,to predict water *** optimization of our proposed model PSO-SVR,uses particle swarm optimization to improve support vector regression for COD *** generalization capacity tested on out-of-distribution(OOD)data for our PSOSVR model is strong,with the following performance metrics of root means square error(RMSE)is 1.51,mean absolute error(MAE)is 1.26,and the coefficient of determination(R2)is *** compare the performance of PSO-SVR model with back propagation neural network(BPNN)and radial basis function neural network(RBFNN)and shows it edges over in terms of the performance metrics of RMSE,MAE and R2,and is the best model for COD prediction of mining-beneficiation *** is because of the less overfitting tendency of PSO-SVR compared with neural network *** proposed PSO-SVR model is optimum for the prediction of COD in copper-molybdenum mining-beneficiation wastewater *** addition,PSO-SVR can be used to predict COD on a wide variety of wastewater through the process of transfer learning.

关键词： Chemical oxygen demand Mining-beneficiation wastewater treatment Particle swarm optimization Support vector regression Artificial neural network

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Lateral Velocity Estimation Utilizing Transfer Learning Characteristics by a Hybrid Data-mechanism-driven Model

Lateral Velocity Estimation Utilizing Transfer Learning Char...

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IEEE Symposium on Intelligent Vehicle

作者： Guoying Chen Jun Yao Zhenhai Gao Yongqiang Zhao Changsheng Liu Shunhui Song Min Hua State Key Laboratory of Automotive Simulation and Control Jilin University Changchun China China FAW Group Corporation Changchun China College of Computer Science and Technology Zhejiang University Hangzhou China DeepRoute.ai Shenzhen China

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

This paper introduces an innovative hybrid approach for estimating vehicle lateral velocity, merging mechanism-based methods with a Long Short-Term Memory (LSTM) neural network. Traditional estimation techniques, which are often susceptible to drift and inaccuracies due to parameter mismatches, fail to effectively adapt to varying driving conditions. Our proposed approach leverages the accuracy of mechanism-based estimates in specific scenarios to feed the LSTM network, creating a data-mechanism-driven solution. To overcome the inherent challenges of data-driven models, particularly concerning data quality and volume, our lateral velocity estimation model incorporates a feature extraction layer alongside a regression output layer. This architecture not only facilitates efficient parameter optimization within the feature extraction phase but also enables targeted retraining of the regression layer, significantly boosting transfer learning capabilities. We validate the robustness and the practicality of transfer learning across different vehicle classes in a simulation environment, showcasing its broad applicability and effectiveness.

关键词： Solid modeling Adaptation models Transfer learning Neural networks Estimation Feature extraction Data models

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A study on the prediction model of dam seepage volume based on isolated forest-multiple stepwise linear regression

A study on the prediction model of dam seepage volume based ...

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International Conference on computer Engineering and Applications (ICCEA)

作者： Guanyu Chen Suli Zhang Chenlin Zhang XiaoChun Qian Jilin Provincial Key Laboratory of Changbai Historical Culture and VR Reconstruction Technology Jilin Province Changbai Mountain Ecological Culture and Virtual Reality Simulation Engineering Research Center Changchun University of Technology School of Computer Science & Engineering Changchun China Jilin Provincial Key Laboratory of Changbai Historical Culture and VR Reconstruction Technology Jilin Province Changbai Mountain Ecological Culture and Virtual Reality Simulation Engineering Research Center Changchun Institute of Technology School of Computer Technology & Engineering Changchun China Jilin Provincial Key Laboratory of Changbai Historical Culture and VR Reconstruction Technology Jilin Province Changbai Mountain Ecological Culture and Virtual Reality Simulation Engineering Research Center Jilin Institute of Chemical Technology School of Information and Control Engineering Changchun China

In the study of hydraulic seepage prediction, it is often necessary to target multiple feature dimensions. In this paper, a variety of traditional machine learning algorithms based on stepwise regression filtering features are used to train a dam seepage prediction model based on isolated forest processing of data outliers. The results show that the algorithm can be applied to predict the seepage data of a reservoir more accurately, and further suggest improvements to the dam's seepage control function and observation facilities. The experimental results show that the correlation coefficient of the linear regression model is significantly improved when the training sample ratio is 0.8 after the isolated forest treatment of outliers and stepwise regression screening features.

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Prophet: Prompting Large Language Models with Complementary Answer Heuristics for Knowledge-based Visual Question Answering

arXiv

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arXiv 2023年

作者： Yu, Zhou Ouyang, Xuecheng Shao, Zhenwei Wang, Meng Yu, Jun The Key Laboratory of Complex Systems Modeling and Simulation the School of Computer Science Hangzhou Dianzi University China The HDU-ITMO Joint Institute Hangzhou Dianzi University China The School of Computer Science and Information Engineering Hefei University of Technology China

Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. Early studies retrieve required knowledge from explicit knowledge bases (KBs), which often introduces irrelevant information to the question, hence restricting the performance of their models. Recent works have resorted to using a powerful large language model (LLM) as an implicit knowledge engine to acquire the necessary knowledge for answering. Despite the encouraging results achieved by these methods, we argue that they have not fully activated the capacity of the blind LLM as the provided textual input is insufficient to depict the required visual information to answer the question. In this paper, we present Prophet—a conceptually simple, flexible, and general framework designed to prompt LLM with answer heuristics for knowledge-based VQA. Specifically, we first train a vanilla VQA model on a specific knowledge-based VQA dataset without external knowledge. After that, we extract two types of complementary answer heuristics from the VQA model: answer candidates and answer-aware examples. Finally, the two types of answer heuristics are jointly encoded into a formatted prompt to facilitate the LLM’s understanding of both the image and question, thus generating a more accurate answer. By incorporating the state-of-the-art LLM GPT-3 [1], Prophet significantly outperforms existing state-of-the-art methods on four challenging knowledge-based VQA datasets. To demonstrate the generality of our approach, we instantiate Prophet with the combinations of different VQA models (i.e., both discriminative and generative ones) and different LLMs (i.e., both commercial and open-source ones). © 2023, CC BY.

关键词： Knowledge based systems

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A Review of Intelligent Vision Enhancement technology for Battlefield

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Wu, Yufeng He, Yanjun Li, Ning Wang, Guowei Zhang, Longfei Zhang, Fuquan Zhu, Yifan The 15th Research Institute China Electronics Technology Group Corporation Beijing100083 China Key Laboratory of Digital Performance and Simulation Technology Beijing Institute of Technology Beijing100081 China College of Computer and Control Engineering Minjiang University Fuzhou350108 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China Department of Computer Science and Technology Tsinghua University Beijing100081 China

This paper reviews the application of intelligent vision enhancement technology in the battlefield environment and explores new research directions. This paper mainly introduces three parts. First, we introduce a solution for enhancing the battlefield situation and using head-mounted displays for soldier combat. Second, we summarize three core technologies supporting intelligent vision enhancement technology: 3D environment reconstruction, tracking and registration, and situational awareness. Third, we summarize three application directions of intelligent vision enhancement technology on the battlefield. Finally, the problems and challenges in the research work are proposed, including current issues such as accurate battlefield situational awareness by augmented reality technology, multiperson collaborative management and data flow, as well as challenges in future battlefield situation enhancement and perception. The future development trend of intelligent vision enhancement technology on the battlefield has been prospected. Two meanings of this paper are as follows: the first is to review the research status of intelligent vision enhancement technology from the technical level and identify the key technical points that may restrict development in the future;the second is to analyze the advantages and disadvantages of intelligent vision enhancement technology from the level of battlefield application and the roles of users. In addition, this paper proposes how to take the lead and take initiative in future wars. © 2023 Yufeng Wu et al.

关键词： Augmented reality

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MP-DPCC: A Motion Proxy-Based Dynamic Point Cloud Compression Framework

MP-DPCC: A Motion Proxy-Based Dynamic Point Cloud Compressio...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： ZhaoYi Jiang Dong Han Cao Song FangZhe Nan Bailin Yang School of Computer Science and Technolgy Zhejiang Gongshang University School of Statistics and Mathematics Zhejiang Gongshang University Economic Forecasting and Policy Simulation Laboratory Zhejiang Gongshang University Collaborative Innovation Center of Statistical Data Engineering Technology & Application Zhejiang Gongshang University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The increasing data volume and the demand for real-time transmission highlight the necessity for efficient compression of dynamic point cloud data. Existing methods primarily focus on reducing inter-frame redundancy by calculating per-point motion information, overlooking the computational and storage costs involved. In this paper, we propose a novel motion proxy-based dynamic point cloud compression framework to enhance the efficiency and accuracy of motion information utilization. Specifically, we introduce a feature proxy module to adaptively locate proxy points, which represent the overall motion through the motion of proxy points. Additionally, a motion enhancement module is employed to refine motion details and prevent local information loss caused by dense motion trajectories. Extensive experiments demonstrate the superiority of our approach. Compared with baseline methods, it achieves an average BD-rate improvement of 12.07% (D1) and 11.90% (D2).

关键词： Point cloud compression Costs Accuracy Dynamics Termination of employment Signal processing Real-time systems Acoustics Trajectory Speech processing

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Strain-Relief Patterns for Flexible Substrate-Supported Optimized Serpentine Configurations

Strain-Relief Patterns for Flexible Substrate-Supported Opti...

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Electronic Components and technology Conference (ECTC)

作者： Rui Chen Chong Ye Colin J. Stewart Suresh K. Sitaraman Computer-Aided Simulation of Packaging Reliability (CASPaR) Laboratory George W. Woodruff School of Mechanical Engineering Flexible and Wearable Electronics Advanced Research (FlexWEAR) Georgia Institute of Technology Atlanta GA

Serpentine printed conductors have been explored in flexible and stretchable electronics as a way to reduce strains in the conductors and thus, the overall normalized resistance increase under stretching and other mechanical loading conditions. In this study, it is shown that the substrate stiffness to conductor stiffness ratio plays an important role in the effectiveness of the serpentine geometry to reduce strains under axial stretching. The benefit of serpentine structure is evident when the substrate stiffness is significantly reduced. Also, it is shown through experimental resistance measurements that the normalized resistance increases less for a serpentine conductor compared to a straight-line conductor under axial stretching. When serpentine conductors are printed on stiffer flexible substrates, the benefits of the serpentine structure can be leveraged through introducing cut-out regions that can reduce localized strains.

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A homogenization method for nonlinear inhomogeneous elastic materials

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Virtual Reality & Intelligent Hardware 2021年第2期3卷 156-170页

作者： Jing ZHAO Fei ZHU Liyou XU Yong TANG Sheng LI College of Information Science and Engineering Yanshan UniversityQinhuangdaoHebei 066004China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province QinhuangdaoHebei 066004China School of Electronics Engineering and Computer Science Peking UniversityBeijing 100871China Beijing Engineering Research Center for Virtual Simulation and Visualization Beijing 100871China

Background Fast simulation techniques are strongly favored in computer graphics,especially for the nonlinear inhomogeneous elastic *** homogenization theory is a perfect match to simulate inhomogeneous deformable objects with its coarse discretization,as it reveals how to extract information at a fine scale and to perform efficient computation with much less *** existing homogenization method is not applicable for ubiquitous nonlinear materials with the limited input deformation *** In this paper,we have proposed a homogenization method for the efficient simulation of nonlinear inhomogeneous elastic *** approach allows for a faithful approximation of fine,heterogeneous nonlinear materials with very coarse *** analysis provides the basis of a linear deformation space and modal derivatives extend the space to a nonlinear regime;based on this,we exploited modal derivatives as the input characteristic deformations for *** also present a simple elastic material model that is nonlinear and anisotropic to represent the homogenized *** nonlinearity of material deformations can be represented properly with this *** material properties for the coarsened model were solved via a constrained optimization that minimizes the weighted sum of the strain energy deviations for all input deformation *** arbitrary number of bases can be used as inputs for homogenization,and greater weights are placed on the more important low-frequency *** Based on the experimental results,this study illustrates that the homogenized material properties obtained from our method approximate the original nonlinear material behavior much better than the existing homogenization method with linear displacements,and saves orders of magnitude of computational *** The proposed homogenization method for nonlinear inhomogeneous elastic materials is capable of capturing the nonlinear dynamics of the

关键词： Physical-based simulation Homogenization theory Heterogeneous material Modal basis

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Performance Creativity Enhancement Method Based on Emotional Analysis and Concentration

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第3期8卷 1019-1048页

作者： Liu, Chang Zhang, Longfei Huang, Tianyu Wu, Yufeng Zhang, Fuquan Lin, Qi Department of Computer Science and Technology Beijing Institute of Technology 100081 China Beijing Key Laboratory of Digital Performance and Simulation Technology 100081 China College of Computer and Control Engineering Minjiang University Fuzhou350108 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China Informatics King’s College London LondonWC2R 2LS United Kingdom

Intelligent performance creativity is a new research direction of the intersection of technology and art. At present, the cutting-edge technologies such as computer simulation, emotional computing and machine learning have been applied to the evaluation and enhancement of intelligent performance creativity. The application of these technologies has greatly increased the diversity of performance forms and the complexity of performance content. For the traditional stage performance, due to the lack of timely feedback from the audience, it is difficult for the director to enhance the creativity effec-tively. In order to solve this problem, this paper proposes concentration level enhancement method (CLEM). The CLEM extracts and analyzes the physiological and emotional characteristics of the audience by collecting the multimodal physiological signal data of the audience. Through the analysis of the features, the concentration information of the audience is obtained and the concentration level (CL) is defined. Based on the physiological and emotional induction mechanism, a performance creativity enhancement strategy is proposed, which enhances the performance creativity in two aspects: " Visual-based" and " Content-based". Through the concentration level enhancement method, compare the audience’s performance attention changes before and after the performance creativity enhancement, select the effective strategies to enhance the performance creativity, and provide the director with quantifiable performance creativity enhancement feedback results. The experimental results show that the CLEM proposed in this paper can effectively induce significant changes in the audience’s emotion, and the selected creative enhancement strategy can significantly improve the audience’s concentration and achieve the purpose of enhancing the creativity of the performance. © 2023, Journal of Network Intelligence.

关键词： Physiology

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Parallel Optimization of Computational Fluid Dynamics Application code_saturne Based on Next Generation Sunway Supercomputer

Parallel Optimization of Computational Fluid Dynamics Applic...

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International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)

作者： Anjun Liu Yin Liao Xihao Xu YanYan Chen Hongjun Zhong Jianming Wu Bo Kong Alexander Raikov Meng Guo Jinan Key Laboratory of High Performance Industrial Software Jinan Institute of Supercomputing Technology Jinan China Jinan Supercomputing-Zhejiang Yuansuan Industrial Simulation System Joint Laboratory Zhejiang Yuansuan Technology Co. Ltd Hangzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan ) Qilu University of Technology (Shandong Academy of Sciences) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

code_saturne is an open-source general large-scale computational fluid dynamics software independently developed by Electricite de France since 1997. Due to its high computing performance, code_saturne has been widely used in the nuclear industry, heavy fuel and biomass combustion, particle tracking and other computational fluid dynamics fields. In this work, we implemented code_saturne on the new generation Sunway supercomputer (SW26010pro) at the first time. By applying the data value-taking technology of double cache, combined with Sunway shared memory technology, we have adapted the data structure and Modular programming of code_saturne conducively to the new generation Sunway supercomputer (SW26010pro) and accelerated the software hotspots the software hotspots by 1–2 times. After optimizing and porting, this software has successfully served in the numerical simulation of the lateral flow of fuel assemblies in the nuclear industry.

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