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Bioinspired Multifunctional Self-Sensing Actuated Gradient Hydrogel for Soft-Hard Robot Remote Interaction

Nano-Micro Letters 2024年第4期16卷 139-152页

作者： He Liu Haoxiang Chu Hailiang Yuan Deliang Li weisi deng Zhiwei Fu Ruonan Liu Yiying Liu Yixuan Han Yanpeng Wang Yue Zhao Xiaoyu Cui Ye Tian College of Medicine and Biological Information Engineering Northeastern UniversityShenyang 110169People’s Republic of China Foshan Graduate School of Innovation Northeastern UniversityFoshan 528300People’s Republic of China

The development of bioinspired gradient hydrogels with self-sensing actuated capabilities for remote interaction with soft-hard robots remains a challenging endeavor. Here, we propose a novel multifunctional self-sensing actuated gradient hydrogel that combines ultrafast actuation and high sensitivity for remote interaction with robotic hand. The gradient network structure, achieved through a wettability difference method involving the rapid precipitation of MoO_(2) nanosheets, introduces hydrophilic disparities between two sides within hydrogel. This distinctive approach bestows the hydrogel with ultrafast thermo-responsive actuation(21° s^(-1)) and enhanced photothermal efficiency(increase by 3.7 ℃ s^(-1) under 808 nm near-infrared). Moreover, the local cross-linking of sodium alginate with Ca^(2+) endows the hydrogel with programmable deformability and information display capabilities. Additionally, the hydrogel exhibits high sensitivity(gauge factor 3.94 within a wide strain range of 600%), fast response times(140 ms) and good cycling stability. Leveraging these exceptional properties, we incorporate the hydrogel into various soft actuators, including soft gripper, artificial iris, and bioinspired jellyfish, as well as wearable electronics capable of precise human motion and physiological signal detection. Furthermore, through the synergistic combination of remarkable actuation and sensitivity, we realize a self-sensing touch bioinspired tongue. Notably, by employing quantitative analysis of actuation-sensing, we realize remote interaction between soft-hard robot via the Internet of Things. The multifunctional self-sensing actuated gradient hydrogel presented in this study provides a new insight for advanced somatosensory materials, self-feedback intelligent soft robots and human–machine interactions.

关键词： Self-sensing Gradient structure Bioinspired actuator Hydrogel sensor Remote interaction

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基于网格型数值天气预报的风电集群日前功率预测方法

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南方电网技术 2024年第6期18卷 51-57,78页

作者：邓韦斯车建峰汪明清鲁聪王皓怀田伟达乔宽龙中国南方电网电力调度控制中心广州510663 新能源与储能运行控制国家重点实验室(中国电力科学研究院有限公司) 北京100192 贵州电网有限责任公司电力调度控制中心贵阳550002

风电集群日前功率预测是省级及以上电网调控中心制定发电计划、促进风电消纳的重要基础之一。风电日前功率预测(次日0时至24时)本质上是构建数值天气预报与实际功率之间的映射模型。充分挖掘数值天气预报气象信息与功率之间的深层映射... 详细信息

风电集群日前功率预测是省级及以上电网调控中心制定发电计划、促进风电消纳的重要基础之一。风电日前功率预测(次日0时至24时)本质上是构建数值天气预报与实际功率之间的映射模型。充分挖掘数值天气预报气象信息与功率之间的深层映射关系是提升风电功率预测精度的重要途径。利用网格型的数值天气预报并采用残差网络建立风电集群预测模型,挖掘风电集群所属空间三维网格型的气象分布与功率的关联关系。以实际运行数据进行仿真,结果显示所提方法在先进性和适应性两个方面均优于现有成熟方法。

关键词：网格型数值天气预报离散型数值天气预报风电集群日前功率预测

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计及风速相关性的风电功率预测方法研究

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微型电脑应用 2024年第5期40卷 81-84页

作者：刘显茁邓韦斯谢恩彦中国南方电网有限责任公司电力调度控制中心广东广州510623 国电南瑞南京控制系统有限公司江苏南京215200

风电功率的准确预测对于风电场的运行控制及电力系统的安全稳定具有重要的意义,但风电功率具有典型的随机波动特性,风电功率预测存在较大的难度。针对风电功率预测准确率不理想的现状,提出一种计及风速相关性的风电功率预测模型,利用风... 详细信息

风电功率的准确预测对于风电场的运行控制及电力系统的安全稳定具有重要的意义,但风电功率具有典型的随机波动特性,风电功率预测存在较大的难度。针对风电功率预测准确率不理想的现状,提出一种计及风速相关性的风电功率预测模型,利用风速时空相关特性建立风电场间的功率相关性模型,由集合经验模态法和游程法求得不同变化趋势的模态分量,利用自回归条件异方差模型获得预测功率。通过风电功率预测实例的对比分析,表明考虑风速相关性可使预测误差降低35.67%,而自回归条件异方差模型可使预测误差进一步降低5.26个百分点,因此所提出的风电功率预测模型具有很好的的优越性。

关键词：风力发电功率预测风速相关性集合经验模态分解自回归条件异方差模型

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基于自适应神经网络的风光互补电站出力预测研究

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自动化与仪器仪表 2024年第3期 97-100,105页

作者：邓韦斯戴仲覆鲁聪张旭东王皓怀卢斯煜李崇浩刘显茁中国南方电网电力调度控制中心广州510663 直流输电技术国家重点实验室(南方电网科学研究院有限责任公司) 广州510663

为提升风光互补电站出力预测准确度,研究了风光互补电站的出力预测方法。分析了风光互补电站机组出力情况,建立了广义回归神经网络模型和径向基神经网络模型用于训练历史数据。提出改进动态组群合作优化求解算法,利用该算法对风光互补... 详细信息

为提升风光互补电站出力预测准确度,研究了风光互补电站的出力预测方法。分析了风光互补电站机组出力情况,建立了广义回归神经网络模型和径向基神经网络模型用于训练历史数据。提出改进动态组群合作优化求解算法,利用该算法对风光互补电站出力进行预测,并利用仿真分析论证了提出模型的有效性,说明提出的方法能够有效降低预测误差,改善预测精度。

关键词：神经网络风光互补出力预测自适应网络

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Deep Learning for Financial Time Series Prediction:A State-of-the-Art Review of Standalone and HybridModels

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 187-224页

作者： weisi Chen Walayat Hussain Francesco Cauteruccio Xu Zhang School of Software Engineering Xiamen University of TechnologyXiamen361024China Peter Faber Business School Australian Catholic UniversityNorth Sydney2060Australia Department of Information Engineering Polytechnic University of MarcheAncona60121Italy

Financial time series prediction,whether for classification or regression,has been a heated research topic over the last *** traditional machine learning algorithms have experienced mediocre results,deep learning has largely contributed to the elevation of the prediction ***,the most up-to-date review of advanced machine learning techniques for financial time series prediction is still lacking,making it challenging for finance domain experts and relevant practitioners to determine which model potentially performs better,what techniques and components are involved,and how themodel can be designed and *** review article provides an overview of techniques,components and frameworks for financial time series prediction,with an emphasis on state-of-the-art deep learning models in the literature from2015 to 2023,including standalonemodels like convolutional neural networks(CNN)that are capable of extracting spatial dependencies within data,and long short-term memory(LSTM)that is designed for handling temporal dependencies;and hybrid models integrating CNN,LSTM,attention mechanism(AM)and other *** illustration and comparison purposes,models proposed in recent studies are mapped to relevant elements of a generalized framework comprised of input,output,feature extraction,prediction,and related *** the state-of-the-artmodels,hybrid models like CNNLSTMand CNN-LSTM-AM in general have been reported superior in performance to stand-alone models like the CNN-only *** remaining challenges have been discussed,including non-friendliness for finance domain experts,delayed prediction,domain knowledge negligence,lack of standards,and inability of real-time and highfrequency *** principal contributions of this paper are to provide a one-stop guide for both academia and industry to review,compare and summarize technologies and recent advances in this area,to facilitate smooth and informed implementation,and t

关键词： Financial time series prediction convolutional neural network long short-term memory deep learning attention mechanism finance

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一种基于DBSCAN+LAR的风电场数据清洗方法

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电力信息与通信技术 2024年第6期22卷 66-72页

作者：邓韦斯戴仲覆王皓怀周保荣鲁聪程铭刘显茁胡甲秋李崇浩张洋宁中国南方电网电力调度控制中心广东省广州市510663 直流输电技术国家重点实验室(南方电网科学研究院有限责任公司) 广东省广州市510663

针对因风电场机组异常数据而导致风电功率预测精度下降的问题,文章提出一种基于密度噪声应用空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法加上最小绝对残差(least absolute residual,LAR)法的风... 详细信息

针对因风电场机组异常数据而导致风电功率预测精度下降的问题,文章提出一种基于密度噪声应用空间聚类(density-based spatial clustering of applications with noise,DBSCAN)算法加上最小绝对残差(least absolute residual,LAR)法的风电场数据清洗方法。首先利用DBSCAN算法识别分散型异常数据,然后基于LAR方法构建堆积型异常数据识别模型,分别实现对风电场分散型异常数据和堆积型异常数据的清洗,最后通过Pearson相关系数和反向传播神经网络预测模型验证所提方法的效果。结果表明,基于DBSCAN+LAR的风电场数据清洗方法能有效减小风电功率预测误差。

关键词：风电场异常数据 DBSCAN LAR 数据清洗

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Deterministic and Robust Volt-var Control Methods of Power System Based on Convex Deep Learning

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 719-729页

作者： Qing Ma Changhong deng the School of Electrical Engineering and Automation Wuhan UniversityWuhanChina

Volt-var control(VVC)is essentially a non-convex optimization problem due to the non-convexity of power flow(PF)constraints,resulting in the difficulty in obtaining the optimum without convexity *** existing second-order cone method for the convexity conversion often leads to a sharp increase in PF constraints and optimization variables,which in turn increases the optimization difficulty or even leads to optimization *** paper first proposes a deterministic VVC method based on convex deep learning power flow(DLPF).This method uses the input convex neural network(ICNN)to establish a single convex mapping between state parameters and node voltage to complete the convexity conversion while the optimization variables only correspond to reactive power equipment,which can ensure the global optimum with extremely fast computation *** cope with the impact brought by the uncertainty of distributed energy and omit the additional worst scenario search of traditional robust VVC,this paper proposes robust VVC method based on convex deep learning interval power flow(DLIPF),which continues to adopt ICNN to establish another convex mapping between state parameters and node voltage *** DLIPF with DLPF,this method decreases the modeling and optimization difficulty of robust VVC *** results on 30-bus,118-bus,and 200-bus systems prove the correctness and rapidity of the proposed methods.

关键词： Volt-var control convexity conversion convex deep learning power flow

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Scattering of Water Waves by Dual Symmetric Inclined Floating Porous Barriers Using the DBEM

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China Ocean Engineering 2024年第1期38卷 156-168页

作者： WANG Li-xian deng Yan-wen YE Yang-sha deng Zheng-zhi Key Laboratory of High Performance Ship Technology(Wuhan University of Technology) Ministry of EducationWuhan 430063China School of Naval Architecture Ocean and Energy Power EngineeringWuhan University of TechnologyWuhan 430063China Key Laboratory of Water-Sediment Sciences and Water Disaster Prevention of Hunan Province Changsha 410114China Institute of Port Coastal and Offshore EngineeringZhejiang UniversityZhoushan 316021China

The scattering of normally incident water waves by two surface-piercing inclined perforated barriers in water with a uniform finite depth is investigated within the framework of linear water wave *** that thin barriers are zero-thickness,a novel numerical method involving the the coupling of the dual boundary element method(DBEM)with damping layers is *** order to effectively damp out the reflected waves,two damping layers,instead of pseudoboundaries are implemented near the two side boundaries of the computational ***,the modified linearized free surface boundary conditions are formulated and used for solving both the ordinary boundary integral equation as well as the hypersingular boundary integral equation for degenerate *** newly developed numerical method is validated against analytical methods using the matched eigenfunction expansion method for the special case of two vertical barriers or the inclined angle to the vertical being *** influence of the length of the two damping layers has been ***,these findings are also validated against previous results for several *** validation,the numerical results for the reflection coefficient,transmission coefficient and dissipation coefficient are obtained by varying the inclination angle and porosity-effect *** effects of both the inclination angle and the porosity on the amplitudes of wave forces acting on both the front and rear barriers are also *** is found that the effect of the inclination angle mainly shifts the location of the extremal values of the reflection and the transmission ***,a moderate value of the porosity-parameter is quite effective at dissipating wave energy and mitigating the wave loads on dual barriers.

关键词： dual boundary element method inclined perforated floating breakwater reflection coefficient transmission coefficient damping layer

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INVARIANTS-PRESERVING DU FORT-FRANKEL SCHEMES AND THEIR ANALYSES FOR NONLINEAR SCHRÖDINGER EQUATIONS WITH WAVE OPERATOR

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Journal of Computational Mathematics 2024年第3期42卷 814-850页

作者： Dingwen deng Zhijun Li College of Mathematics and Information Science Nanchang Hangkong UniversityNanchang 330063China

Du Fort-Frankel finite difference method(FDM)was firstly proposed for linear diffusion equations with periodic boundary conditions by Du Fort and Frankel in *** is an explicit and unconditionally von Neumann stable ***,there has been no research work on numerical solutions of nonlinear Schrödinger equations with wave operator by using Du Fort-Frankel-type finite difference methods(FDMs).In this study,a class of invariants-preserving Du Fort-Frankel-type FDMs are firstly proposed for one-dimensional(1D)and two-dimensional(2D)nonlinear Schrödinger equations with wave *** using the discrete energy method,it is shown that their solutions possess the discrete energy and mass conservative laws,and conditionally converge to exact solutions with an order of for ofο(T^(2)+h_(x)^(2)+(T/h_(x))^(2))1D problem and an order ofο(T^(2)+h_(x)^(2)+h_(Y)^(2)+(T/h_(X))^(2)+(T/h_(y))^(2))for 2D problem in ***,τdenotes time-step size,while,hx and hy represent spatial meshsizes in x-and y-directions,***,by introducing a stabilized term,a type of stabilized invariants-preserving Du Fort-Frankel-type FDMs are *** not only preserve the discrete energies and masses,but also own much better stability than original ***,numerical results demonstrate the theoretical analyses.

关键词： Nonlinear Schrodinger equations with wave operator Du Fort-Frankel finite difference methods Discrete energy and mass conservative laws Numerical convergence

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Broadband angular spectrum analog processors based on all-dielectric metasurfaces

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Science China(Physics,Mechanics & Astronomy) 2024年第7期67卷 188-190页

作者： Lin deng Yongmin Liu Department of Electrical and Computer Engineering Northeastern UniversityBoston 02115USA Department of Mechanical and Industrial Engineering Northeastern UniversityBoston 02115USA

Digital signal processors are extensively used to execute mathematical operations and advanced computational tasks on digital ***,they suffer from several inherent limitations,including low speed,high energy consumption,and large memory requirements,because of the hardware bottleneck and the imperative conversion between digital and analogue signals.

关键词： dielectric spectrum hardware

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