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Leader-follower: Human-centered intention-guided controller for novel SuperLimb with application to load-carrying scenarios

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Science China Technological Sciences 2025年第1期 325-338页

作者： Qinghua ZHANG Xianglong LI Hongwei JING Kerui SUN Yuancheng LI Sikai ZHAO Lele LI Haotian JU Ziqi WANG Rui WU Tianjiao ZHENG Jie ZHAO Yanhe ZHU The State Key Laboratory of Robotics and system Harbin Institute of Technology The Learning Algorithms and systems Laboratory School of Engineeringécole Polytechnique Fédérale de Lausanne

Supernumerary robotic arms(SuperLimb) are a new type of wearable robot that works closely with humans as a third hand to augment human operation capability. Accurate conveyance of wearers' intentions, allocation of roles, and humancentered interaction considerations are the key points in the process of human–SuperLimb collaboration. This paper proposes a human-centered intention-guided leader–follower controller that relies on the dynamic modeling of SuperLimb with application to load-carrying scenarios. The proposed leader–follower controller takes the human as the leader and the SuperLimb as the follower, achieving effective information communication, autonomous coordination, and good force compliance between SuperLimb, humans, and the environment under human safety assurance. First, the human–SuperLimb dynamic system is modeled to achieve force interaction with the environment and wearer. Second, to achieve the precise intention extraction of humans, pose data from five visual odometry sensors are fused to capture the human state, the generalized position, the velocity of hands, and the surface electromyography signals from two myoelectric bracelets sensors are processed to recognize the natural hand gestures during load-carrying scenarios by a designed Swin transformer network. Then, based on the real-time distance detection between human and mechanical limbs, the security assurance and force-compliant interaction of the human–SuperLimb system are realized. Finally, the human hand muscle intention recognition, human–robot safety strategy verification,and comparative load-carrying experiments with and without the proposed method are conducted on the SuperLimb *** showed that the task parameters are well estimated to produce more reasonable planning trajectories, and SuperLimb could well understand the wearer's intentions to switch different SuperLimb actions. The proposed sensor-based human–robot communication framework motivates future studies of o

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A Novel Model for Describing Rail Weld Irregularities and Predicting Wheel-Rail Forces Using a Machine Learning Approach

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Computer Modeling in engineering & Sciences 2025年第1期142卷 555-577页

作者： Linlin Sun Zihui Wang Shukun Cui Ziquan Yan Weiping Hu Qingchun Meng State Key Laboratory of High-Speed Railway Track system China Academy of Railway Sciences Corporation LimitedBeijing100081China Railway engineering Research Institute China Academy of Railway Sciences Corporation LimitedBeijing100081China School of Aeronautic Science and engineering Beihang UniversityBeijing100191China

Rail weld irregularities are one of the primary excitation sources for vehicle-track interaction dynamics in modern high-speed *** can cause significant wheel-rail dynamic interactions,leading to wheel-rail noise,component damage,and *** researchers have employed the vehicle-track interaction dynamic model to study the dynamic interactions between wheel and rail induced by rail weld geometry ***,the cosine wave model used to simulate rail weld irregularities mainly focuses on the maximum value and neglects the geometric *** this study,novel theoretical models were developed for three categories of rail weld irregularities,based on measurements of the high-speed railway from Beijing to *** vertical dynamic forces in the time and frequency domains were compared under different running *** forces generated by the rail weld irregularities that were measured and modeled,respectively,were compared to validate the accuracy of the proposed ***,based on the numerical study,the impact force due to rail weld irrregularity is modeled using an Artificial Neural Network(ANN),and the optimum combination of parameters for this model is *** results showed that the proposed model provided a more accurate wheel/rail dynamic evaluation caused by rail weld irregularities than that established in the *** ANN model used in this paper can effectively predict the impact force due to rail weld irrregularity while reducing the computation time.

关键词： Rail weld irregularity high-speed railway vehicle-track coupled dynamics wheel/rail dynamic vertical force artificial neural networks

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engineering system Theory─A New engineering Meta-Discipline

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Journal of systems engineering and Electronics 1999年第1期10卷 1-3页

作者： Wang Liancheng(Beijing Institute of Electronic system engineering, 100854, P. R. China)(Received July 2, 1998) Beijing Institute of Electronic system engineering 100854 P. R. China

This paper proposes a new engineering disciplin─engineering system Theory. It dis cusses rendered background, research objects and contents of the engineering system theory briefly. Finally, the met-discipline standing of the engineering system theory in the whole knowledge system of engineering science and its development potential are pointed out.

关键词： engineering system Discipline systems theory.

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Decision Making Support system(DSS)——A Synthesized and Integrated Crystallization of systems engineering, Artificial Intelligence and Electronic Technologies

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Journal of systems engineering and Electronics 1993年第1期4卷 1-2页

作者：汪成为 Beijing Institute of system engineering Chair

I firmly believe that of systems engineering is the requirement-driven force for the progress ofsoftware engineering, artificial intelligence and electronic technologies. The development ofsoftware engineering, artificial intelligence and electronic technologies is the technical supportfor the progress of systems engineering. INTEGRATION can be considered as "bridging" the ex-isting technologies and the People together into a coordinated system.

关键词： Decision Making Support system Artificial Intelligence and Electronic Technologies DSS A Synthesized and Integrated Crystallization of systems engineering

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engineering a customized nanodrug delivery system at the cellular level for targeted cancer therapy

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Science China Chemistry 2018年第4期61卷 497-504页

作者： Jin Li Liping Qiu Sitao Xie Jing Zhang Liqin Zhang Honglin Liu Juan Li Xiaobing Zhang Weihong Tan Molecular Science and biomedicine Laboratory State Key Laboratory of Chemo/Biosensing and ChemometricsCollege of Chemistry and Chemical EngineeringCollege of Life SciencesAptamer Engineering Center of Hunan ProvinceHunan University Center for Research at bio/nano Interface Department of Chemistry and Department of Physiology and Functional GenomicsHealth Cancer CenterUF Genetics Institute and McKnight Brain InstituteUniversity of Florida key Laboratory for Analytical Science of Food Safety and biology (Ministry of Education) College of ChemistryFuzhou University

Drug administration customized to individual cells could intrinsically address cancer heterogeneity and provide a safe and effective method for delivering personalized treatment. To accomplish this, we developed a smart nanodrug delivery system characterized by cancer cell-targeted drug delivery and intracellular biomarker-responsive drug activation. This system was composed of a long-nicked DNA duplex formed by tandem hybridization of two extended antisense oligonucleotides whose ends were separately blocked with a cancer cell-specific aptamer, AS1411,and a replaceable anti-biomarker probe(ABP). We demonstrated that this DNA nanodrug was directed to cancer cells with the guidance power of AS 1411 and then activated by the presence of a given intracellular biomarker. By using such a belt-and-braces strategy, this DNA nanodrug system could safely and efficiently accelerate apoptosis of target cancer cells. Moreover, since the expression level of biomarkers tends to indicate the specific physiological state of individual cells, biomarker-responsive activation of the nanodrug is expected to enable customized drug administration at the cellular level.

关键词： aptamer drug delivery personalized medicine antisense oligonucleotides

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Dynamic Multi-Graph Spatio-Temporal Graph Traffic Flow Prediction in Bangkok:An Application of a Continuous Convolutional Neural Network

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Computer Modeling in engineering & Sciences 2025年第1期142卷 579-607页

作者： Pongsakon Promsawat Weerapan Sae-dan Marisa Kaewsuwan Weerawat Sudsutad Aphirak Aphithana Department of Civil engineering Faculty of EngineeringRamkhamkaeng UniversityBangkok10240Thailand Department of Computer engineering Faculty of EngineeringRamkhamkaeng UniversityBangkok10240Thailand Department of Statistics Faculty of ScienceRamkhamkaeng UniversityBangkok10240Thailand

The ability to accurately predict urban traffic flows is crucial for optimising city ***,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility *** learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal ***,these models often become overly complex due to the large number of hyper-parameters *** this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction *** comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest *** the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 ***,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer *** Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time *** numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.

关键词： Graph neural networks convolutional neural network deep learning dynamic multi-graph spatio-temporal

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A real-time multi-head mixed attention mechanism-based prediction method for tunnel boring machine disc cutter wear

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Science China Technological Sciences 2025年第1期 290-311页

作者： Haodi WANG Chengjin QIN Honggan YU Zhinan ZHANG Guoqiang HUANG Zhengyang LIU Chengliang LIU State Key Laboratory of Mechanical system and Vibration School of Mechanical EngineeringShanghai Jiao Tong University

In the process of hard rock tunnel excavation, workers often need to enter the tunnel boring machine(TBM)cutterhead at regular intervals to measure cutter wear. However, this method is time-consuming and labor-intensive. Existing cutter prediction models primarily rely on geological parameters to predict overall cutter wear before construction, making it challenging to monitor real-time wear at different locations of the cutter and obtain accurate geological parameters. To address these challenges, this paper proposes a multi-head mixed attention mechanism-based method for real-time wear prediction of TBM disc cutter. First, a method of cutter wear normalization to eliminate measurement noise is explored. Then, considering the complex correlation of TBM operating parameters in feature and time dimensions, a new multi-head mixed attention mechanism model is designed to establish the dependency between different features and different moments, to better establish the mapping model between operating parameters and cutter wear. Finally, the current cutter wear state can be calculated by accumulating the wear amount of all previous small excavation sections. The effectiveness of the method is verified by using field data from the Mumbai tunnel. The results demonstrate that the method is capable of real-time prediction of front cutter and edge cutter wear on the test set, achieving an average accuracy rate of 95.75%. Moreover, the method can update the cutter wear status after every meter of excavation, which has good real-time performance. In addition, the average accuracy of cutter wear prediction of the proposed method is 11.75%, 10.375%, 3.875%, 3.625%, 1.375%, 3.125%, 2.25%, and 0.75%higher than that of LSTM, CNN, LSTM-CNN, CACNN, SACNN, MMADNN, MTACNN, and MFACNN. In summary, this approach offers an accurate prediction of cutter wear state while reducing inspection time and costs and has high application value.

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Synthesis and Electrical Properties of TiO_2 nanoparticles Embedded in Polyamide-6 nanofibers Via Electrospinning

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nano-Micro Letters 2011年第1期3卷 56-61页

作者： R.Nirmala Jin Won Jeong R.Navamathavan Hak Yong Kim bio-nano system engineering Chonbuk National University Department of Organic Materials and Fiber engineering Chonbuk National University Center for Healthcare Technology & Development Chonbuk National University School of Advanced Materials engineering Chonbuk National University

We report on the synthesis and characterizations of TiO2 nanoparticles embedded in polyamide-6composite nanofibers by using electrospinning technique. The influence of substrate on the electrical characteristics of polyamide-6/TiO2 composite nanofibers was investigated. The resultant nanofibers exhibit good incorporation of TiO2 nanoparticles. The doping of TiO2 nanoparticles into the polyamide-6 nanofibers were confirmed by high resolution transmission electron microscopy and energy dispersive X-ray spectroscopy. Photoluminescence（PL） and cathodoluminescence（CL） spectroscopy were also used to characterize the *** PL and CL spectra reveal that the as-spun polyamide-6/TiO2 composite nanofibers consisted of overlapping of two broad emission bands due to the contribution of polyamide-6（centered at about 475 nm）, which might originate from organic functional groups of polyamide-6 and TiO2 nanoparticles（centered around 550 nm）. The electrical conductivity of the polyamide-6/TiO2 composite nanofibers on different substrates was carried *** was found that the electrical conductivity of the polyamide-6/TiO2 composite nanofibers on silicon substrate was in the range of 13 μA, and about 1 to 20 p A for the paper and glass substrates.

关键词： Electrospinning Polyamide-6 nanoparticles nanofibers Electrical studies

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Ultra‑High Sensitivity Anisotropic Piezoelectric Sensors for Structural Health Monitoring and Robotic Perception

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nano-Micro Letters 2025年第2期17卷 432-446页

作者： Hao Yin Yanting Li Zhiying Tian Qichao Li Chenhui Jiang Enfu Liang Yiping Guo State Key Laboratory of Metal Matrix Composites School of Materials Science and EngineeringShanghai Jiao Tong UniversityShanghai 200240People’s Republic of China Beijing Vacuum Electronics Research Institute Beijing 100015People’s Republic of China Fundamental Science On Vibration Shock and Noise LaboratoryState Key Laboratory of Mechanical System and VibrationSchool of Mechanical EngineeringShanghai Jiao Tong UniversityShanghai 200240People’s Republic of China

Monitoring minuscule mechanical signals,both in magnitude and direction,is imperative in many application scenarios,e.g.,structural health monitoring and robotic sensing ***,the piezoelectric sensor struggles to satisfy the requirements for directional recognition due to the limited piezoelectric coefficient matrix,and achieving sensitivity for detecting micrometer-scale deformations is also ***,we develop a vector sensor composed of lead zirconate titanate-electronic grade glass fiber composite filaments with oriented arrangement,capable of detecting minute anisotropic *** as-prepared vector sensor can identify the deformation directions even when subjected to an unprecedented nominal strain of 0.06%,thereby enabling its utility in accurately discerning the 5μm-height wrinkles in thin films and in monitoring human pulse *** ultra-high sensitivity is attributed to the formation of porous ferroelectret and the efficient load transfer efficiency of continuous lead zirconate titanate ***,when integrated with machine learning techniques,the sensor’s capability to recognize multi-signals enables it to differentiate between 10 types of fine textures with 100%*** structural design in piezoelectric devices enables a more comprehensive perception of mechanical stimuli,offering a novel perspective for enhancing recognition accuracy.

关键词： Flexible piezoelectric filaments Anisotropic Ultra-high sensitivity Structural health detection Texture recognition

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Stability Prediction in Smart Grid Using PSO Optimized XGBoost Algorithm with Dynamic Inertia Weight Updation

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Computer Modeling in engineering & Sciences 2025年第1期142卷 909-931页

作者： Adel Binbusayyis Mohemmed Sha Department of Software engineering College of Computer engineering and Sciences in Al-Kharj Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia

Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid *** the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the *** also possesses a better impact on averting overloading and permitting effective energy *** though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized *** overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning *** accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data ***,the pre-processed data are taken for training and *** that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in *** PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed *** hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on *** results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.

关键词： Smart Grid machine learning particle swarm optimization XGBoost dynamic inertia weight update

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