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Four development stages of collective intelligence

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Frontiers of information Technology & Electronic engineering 2024年第7期25卷 903-916页

作者： Renbin xiao school of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Image information Processing and Intelligent Control Ministry of EducationWuhan 430074China

The new generation of artificial intelligence(AI)research initiated by Chinese scholars conforms to the needs of a new information environment changes,and strives to advance traditional artificial intelligence(AI 1.0)to a new stage of AI *** one of the important components of AI,collective intelligence(CI 1.0),i.e.,swarm intelligence,is developing to the stage of CI 2.0(crowd intelligence).Through in-depth analysis and informative argumentation,it is found that an incompatibility exists between CI 1.0 and CI ***,CI 1.5 is introduced to build a bridge between the above two stages,which is based on biocollaborative behavioral *** 1.5 is the transition from CI 1.0 to CI 2.0,which contributes to the compatibility of the two ***,a new interpretation of the meta-synthesis of wisdom proposed by Qian Xuesen is *** meta-synthesis of wisdom,as an improvement of crowd intelligence,is an advanced stage of bionic intelligence,i.e.,CI *** is pointed out that the dual-wheel drive of large language models and big data with deep uncertainty is an evolutionary path from CI 2.0 to CI 3.0,and some elaboration is *** a result,we propose four development stages(CI 1.0,CI 1.5,CI 2.0,and CI 3.0),which form a complete framework for the development of *** different stages are progressively improved and have good *** to the dominant role of cooperation in the development stages of CI,three types of cooperation in CI are discussed:indirect regulatory cooperation in lower organisms,direct communicative cooperation in higher organisms,and shared intention based collaboration in *** division is the main form of achieving cooperation and,for this reason,this paper investigates the relationship between the complexity of behavior and types of labor ***,based on the overall understanding of the four development stages of CI,the future development direction and research issues of CI are explored.

关键词： Collective intelligence Meta-synthesis of wisdom Incompatibility Labor division Cooperative behavior Collective intelligence emergence Large language model

Influence of roughness on the detection of mechanical characteristics of low-k film by the surface acoustic waves

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Chinese Physics B 2014年第10期23卷 424-428页

作者：肖夏陶冶孙远 school of Electronic and information engineering Tianjin University

The surface acoustic wave （SAW） technique is a precise and nondestructive method to detect the mechanical charac- teristics of the thin low dielectric constant （low-k） film by matching the theoretical dispersion curve with the experimental dispersion curve. In this paper, the influence of sample roughness on the precision of SAW mechanical detection is inves- tigated in detail. Random roughness values at the surface of low-k film and at the interface between this low-k film and the substrate are obtained by the Monte Carlo method. The dispersive characteristic of SAW on the layered structure with rough surface and rough interface is modeled by numerical simulation of finite element method. The Young＇s moduli of the Black DiamondTM samples with different roughness values are determined by SAWs in the experiment. The results show that the influence of sample roughness is very small when the root-mean-square （RMS） of roughness is smaller than 50 nm and correlation length is smaller than 20 μm. This study indicates that the SAW technique is reliable and precise in the nondestructive mechanical detection for low-k films.

关键词： low-k film mechanical character detection rough surface rough interface surface acoustic wave

Variational Neural Inference Enhanced Text Semantic Communication System

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China Communications 2024年第7期21卷 50-64页

作者： Zhang Xi Zhang Yiqian Li Congduan Ma xiao school of Electronics and Communication engineering Sun Yat-Sen UniversityShenzhen 518107China Guizhou University State Key Laboratory of Public Big Data Guiyang 550025China Shenzhen Key Laboratory of Navigation and Communication Integration Shenzhen 518107China school of Computer Science and engineering Sun Yat-Sen UniversityGuangzhou 510006China

Recently,deep learning-based semantic communication has garnered widespread attention,with numerous systems designed for transmitting diverse data sources,including text,image,and speech,*** efforts have been directed toward improving system performance,many studies have concentrated on enhancing the structure of the encoder and ***,this often overlooks the resulting increase in model complexity,imposing additional storage and computational burdens on smart ***,existing work tends to prioritize explicit semantics,neglecting the potential of implicit *** paper aims to easily and effectively enhance the receiver's decoding capability without modifying the encoder and decoder *** propose a novel semantic communication system with variational neural inference for text ***,we introduce a simple but effective variational neural inferer at the receiver to infer the latent semantic information within the received *** information is then utilized to assist in the decoding *** simulation results show a significant enhancement in system performance and improved robustness.

关键词： deep learning semantic communication variational neural inference

Study on the vertical deformations induced by terrestrial water storage changes in Huang-Huai-Hai river basin

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Geodesy and Geodynamics 2024年第4期15卷 352-365页

作者： Liansheng Deng Yugang xiao Qusen Chen Feifei Liao Zhao Li school of electrical and Electronic information engineering Hubei Polytechnic UniversityHuangshi 435003China Hubei Luojia Laboratory GNSS Research CenterWuhan UniversityWuhan 430079.China Changjiang Spatial information Technology engineering Co. Ltd.Wuhan 430010China

Terrestrial water storage(TWs)variations are associated with water mass movements,which may cause the deformation displacements of the Global Navigation Satellite System(GNSS)*** study investigates the spatio-temporal Tws variations and addresses the relationship between deformation variations observed in the Huang-Huai-Hai River Basin(HHHRB)and local hydrological *** indicate that the vertical velocities at the GNSS stations induced by TWS changes are relatively small,and the impacts of the terrestrial water storage changes are mainly reflected in the changes of seasonal *** there is a downward TWS trend from 2011 to 2022 in most HHHRB areas,velocities from the vertical displacements of both Gravity Recovery and Climate Experiment(GRACE)and GRACE Follow-On(GFO)and the GNSS reflect that the HHHRB is undergoing an uplift process,while the magnitude of the GRACE/GFO derived velocities is much smaller than that of the GNSS *** hydrological deformations estimated from GRACE/GFO and GNSS measurements reveal that the TWS-derived displacements can explain 54.5%of the GNSS seasonal variations,with the phases of terrestrial water storage advancing by about one month relative to GNss common signal ***,the decrease of the groundwater storage in the HHHRB has been accelerating since *** reaching its lowest level around mid-2020,it began to rise rapidly,which might be closely related to the implementation of the South-North Water Transfer Central Project.

关键词： Terrestrial waterstorage GRACE GNSS Vertical deformations Huang-Huai-Hairiverbasin

A novel method for atomization energy prediction based on natural-parameter network

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Chinese Chemical Letters 2024年第1期35卷 505-509页

作者： Chaoqin Chu Qinkun xiao Chaozheng He Chen Chen Lu Li Junyan Zhao Jinzhou zheng Yinhuan Zhang school of Mechanical and electrical engineering Xi’an Technological UniversityXi’an 710021China school of electrical and information engineering Xi’an Technological UniversityXi’an 710021China school of Materials Science and Chemical engineering Xi’an Technological UniversityXi’an 710021China

Atomization energy(AE)is an important indicator for measuring material stability and reactivity,which refers to the energy change when a polyatomic molecule decomposes into its constituent *** AE based on the structural information of molecules has been a focus of researchers,but existing methods have limitations such as being time-consuming or requiring complex preprocessing and large amounts of training *** learning(DL),a new branch of machine learning(ML),has shown promise in learning internal rules and hierarchical representations of sample data,making it a potential solution for AE *** address this problem,we propose a natural-parameter network(NPN)approach for AE *** method establishes a clearer statistical interpretation of the relationship between the network’s output and the given *** use the Coulomb matrix(CM)method to represent each compound as a structural information ***,we also designed an end-to-end predictive *** results demonstrate that our method achieves excellent performance on the QM7 and BC2P datasets,and the mean absolute error(MAE)obtained on the QM7 test set ranges from 0.2 kcal/mol to 3 kcal/*** optimal result of our method is approximately an order of magnitude higher than the accuracy of 3 kcal/mol in published ***,our approach significantly accelerates the prediction ***,this study presents a promising approach to accelerate the process of predicting structures using DL,and provides a valuable contribution to the field of chemical energy prediction.

关键词： Structure prediction Atomization energy Deep learning Coulomb matrix NPN End-to-end

Sparse-view neutron CT 3D image reconstruction algorithm based on split Bregman method

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Nuclear Science and Techniques 2024年第9期35卷 41-55页

作者： Teng-Fei Zhu Yang Liu Zhi Luo xiao-Ping Ouyang school of Nuclear Science and engineering North China Electric Power UniversityBeijing 102206China State Key Laboratory of Nuclear Resources and Environment East China University of TechnologyNanchang 330013China State Key Laboratory of Intense Pulsed Radiation Simulation and Effect Xi’an 710024China

As a complement to X-ray computed tomography(CT),neutron tomography has been extensively used in nuclear engineer-ing,materials science,cultural heritage,and industrial *** of the attenuation matrix for neutron tomography with a traditional analytical algorithm requires hundreds of projection views in the range of 0°to 180°and typically takes several hours to *** a low time-resolved resolution degrades the quality of neutron *** the number of projection acquisitions is an important approach to improve the time resolution of images;however,this requires efficient reconstruction ***,sparse-view reconstruction algorithms in neutron tomography need to be *** this study,we investigated the three-dimensional reconstruction algorithm for sparse-view neu-tron CT *** enhance the reconstructed image quality of neutron CT,we propose an algorithm that uses OS-SART to reconstruct images and a split Bregman to solve for the total variation(SBTV).A comparative analysis of the performances of each reconstruction algorithm was performed using simulated and actual experimental *** to the analyzed results,OS-SART-SBTV is superior to the other algorithms in terms of denoising,suppressing artifacts,and preserving detailed structural information of images.

关键词： Neutron CT OS-SART Sparse-view 3D reconstruction Split Bregman Total variation

A LESO Based Backstepping Controller Considering Coal Seam Hardness for Rotary Speed in Coal Mine Tunnel Drilling Process

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Journal of Systems Science & Complexity 2024年第5期37卷 1789-1808页

作者： CHEN Luefeng LIU xiao WU Min LU Chengda PEDRYCZ Witold HIROTA Kaoru school of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Department of electrical and Computer engineering University of Alberta The Tokyo Institute of Technology

In the process of coal mine drilling, controlling the rotary speed is important as it determines the efficiency and safety of drilling. In this paper, a linear extended state observer(LESO) based backstepping controller for rotary speed is proposed, which can overcome the impact of changes in coal seam hardness on rotary speed. Firstly, the influence of coal seam hardness on the drilling rig's rotary system is considered for the first time, which is reflected in the numerical variation of load torque, and a dynamic model for the design of rotary speed controller is established. Then an LESO is designed to observe the load torque, and feedforward compensation is carried out to overcome the influence of coal seam hardness. Based on the model of the compensated system, a backstepping method is used to design a controller to achieve tracking control of the rotary speed. Finally, the effectiveness of the controller designed in this paper is demonstrated through simulation and field experiments, the steady-state error of the rotary speed in field is 1 r/min, and the overshoot is reduced to 5.8%. This greatly improves the stability and security, which is exactly what the drilling process requires.

关键词： Backstepping extended state observer hydraulic tunnel drilling machine rotary speed

Neural Dynamics for Cooperative Motion Control of Omnidirectional Mobile Manipulators in the Presence of Noises: A Distributed Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1605-1620页

作者： Yufeng Lian Xingtian xiao Jiliang Zhang Long Jin Junzhi Yu Zhongbo Sun the Department of Control engineering Changchun University of TechnologyChangchun 130012China IEEE the College of information Science and engineering Northeastern UniversityShenyang 110819 the National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China the school of information Science and engineering Lanzhou UniversityLanzhou 730000China the State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China

This paper presents a distributed scheme with limited communications, aiming to achieve cooperative motion control for multiple omnidirectional mobile manipulators(MOMMs).The proposed scheme extends the existing single-agent motion control to cater to scenarios involving the cooperative operation of MOMMs. Specifically, squeeze-free cooperative load transportation is achieved for the end-effectors of MOMMs by incorporating cooperative repetitive motion planning(CRMP), while guiding each individual to desired poses. Then, the distributed scheme is formulated as a time-varying quadratic programming(QP) and solved online utilizing a noise-tolerant zeroing neural network(NTZNN). Theoretical analysis shows that the NTZNN model converges globally to the optimal solution of QP in the presence of noise. Finally, the effectiveness of the control design is demonstrated by numerical simulations and physical platform experiments.

关键词： Cooperative motion control noise-tolerant zeroing neural network(NTZNN) omnidirectional mobile manipulator(OMM) repetitive motion planning

Optimization of Generator Based on Gaussian Process Regression Model with Conditional Likelihood Lower Bound Search

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CES Transactions on electrical Machines and Systems 2024年第1期8卷 32-42页

作者： xiao Liu Pingting Lin Fan Bu Shaoling Zhuang Shoudao Huang CES&IEEE the College of electrical and information engineering Hunan UniversityChangsha 410082China CES

The noise that comes from finite element simulation often causes the model to fall into the local optimal solution and over fitting during optimization of ***,this paper proposes a Gaussian Process Regression(GPR)model based on Conditional Likelihood Lower Bound Search(CLLBS)to optimize the design of the generator,which can filter the noise in the data and search for global optimization by combining the Conditional Likelihood Lower Bound Search *** the efficiency optimization of 15 kW Permanent Magnet Synchronous Motor as an ***,this method uses the elementary effect analysis to choose the sensitive variables,combining the evolutionary algorithm to design the super Latin cube sampling plan;Then the generator-converter system is simulated by establishing a co-simulation platform to obtain data.A Gaussian process regression model combing the method of the conditional likelihood lower bound search is established,which combined the chi-square test to optimize the accuracy of the model ***,after the model reaches the accuracy,the Pareto frontier is obtained through the NSGA-II algorithm by considering the maximum output torque as a ***,the constrained optimization is transformed into an unconstrained optimizing problem by introducing maximum constrained improvement expectation(CEI)optimization method based on the re-interpolation model,which cross-validated the optimization results of the Gaussian process regression *** above method increase the efficiency of generator by 0.76%and 0.5%respectively;And this method can be used for rapid modeling and multi-objective optimization of generator systems.

关键词： Generator optimization Gaussian Process Regression(GPR) Conditional Likelihood Lower Bound Search(CLLBS) Constraint improvement expectation(CEI) Finite element calculation