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Automated Droplet Manipulation Enabled by a Machine-Vision-Assisted Acoustic Tweezer

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SSRN 2024年

作者： Huan, Zhijie Zhou, Jie Xie, Yu Xu, Jinbin Wang, Haozheng Ma, Weicheng Li, Xiaowei Zhou, Wei Luo, Tao School of Electrical Engineering and Automation Xiamen University of Technology Xiamen361024 China Pen-Tung Sah Institute of Micro-Nano Science and Technology Xiamen University Xiamen361102 China Micro-/Nano-technology Research Center State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong University Xi’an710049 China Qingdao266580 China Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control Xiamen361024 China

Automated droplet manipulation holds great significance for various biochemical applications, including but limited to heavy metal ions detection. Despite notable progress, contactless-acoustic-tweezer-based automated droplet manipulation on superhydrophobic surfaces is rarely reported. In this work, we introduce a machine-vision-assisted acoustic tweezer (MVAAT) for automated and contactless manipulation of droplets on a superhydrophobic surface. The MVAAT generates ultrasound standing waves between an ultrasonic transducer (UST) and a superhydrophobic surface, inducing acoustic radiation force to facilitate contactless droplet manipulation on the superhydrophobic surface. An industrial-camera-based machine vision system is employed for real-time detection and tracking of droplets, providing precise droplet positions for automated droplet transportation and merging via a UST equipped on a Cartesian robot. Experimental results demonstrate that the proposed MVAAT can transport droplets of various volumes on a superhydrophobic surface along planned paths at considerably high velocities exceeding one centimeter per second. Moreover, automated merging and patterning of multiple droplets are achieved, showcasing the versatility of the MVAAT. Last, the MVAAT is applied for fluorescence-based Cu2+ detection to showcase its potential for practical biochemical analyses. The proposed MVAAT significantly expands the capability of ultrasound for droplet manipulation on superhydrophobic surfaces, promising numerous practical applications in biology and chemistry. © 2024, The Authors. All rights reserved.

关键词： Drops

Quantification-Based Scheduling Under Heterogeneous Platform for Industrial Internet

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SSRN 2023年

作者： Xia, Changqing Xia, Tianhao Wang, Renjun Jin, Xi Xu, Chi Li, Dong Zeng, Peng State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China Shenyang University of Chemical Technology Shenyang110142 China Shenyang University of Technology Shenyang110870 China

Satisfying the deterministic demand of industrial tasks is difficult because of the heterogeneity of devices and unclear affinity between tasks and platforms in industrial edge computing scenarios. To address this problem, this study proposes an entropyweight-scheduling method based on resource quantification. First, we analyzed the affinity problem in cross-platform operation of tasks and expanded the scheduling evaluation indicators in the existing real-time systems by analyzing the matching relationship between the attributes of various tasks and characteristics of the platform based on resource quantification. Next, we combined the entropy weight method to process the information entropy of all scheduling evaluation indicators and calculated the weighted summation results to assign the optimal scheduling device to each task. Finally, the entropy-weight-scheduling algorithm based on resource quantification was designed to analyze the scheduling performance of the algorithm under various parameter configurations. The experimental analysis indicated that the scheduling method based on resource quantification could effectively optimize the resource demand relationship between tasks and platforms, and the scheduling success rate of the proposed algorithm was 5.1%,7.7%, and 34.5% higher than those of MTT-SS, D-Quantify, and RRA algorithms, respectively. © 2023, The Authors. All rights reserved.

关键词： Scheduling algorithms

Privacy-Preserving Brain-Computer Interfaces: A Systematic Review

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arXiv

arXiv 2024年

作者： Xia, Kun Duch, Wlodzislaw Sun, Yu Xu, Kedi Fang, Weili Luo, Hanbin Zhang, Yi Sang, Dong Xu, Xiaodong Wang, Fei-Yue Wu, Dongrui The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Zhejiang Lab Hangzhou311121 China The Department of Informatics School of Physics Astronomy and Informatics Neurocognitive Laboratory Center for Modern Interdisciplinary Technologies Nicolaus Copernicus University Torun Poland The Ministry of Education Key Laboratory for Biomedical Engineering Zhejiang University Hangzhou China The Zhejiang University School of Medicine National Clinical Research Center for Child Health Hangzhou China Department of Biomedical Engineering Zhejiang University Hangzhou China The School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China The College of Public Administration Huazhong University of Science and Technology Wuhan430074 China The NetsUnion Clearing Corporation Beijing100045 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100049 China

A brain-computer interface (BCI) establishes a direct communication pathway between the human brain and a computer. It has been widely used in medical diagnosis, rehabilitation, education, entertainment, etc. Most research so far focuses on making BCIs more accurate and reliable, but much less attention has been paid to their privacy. Developing a commercial BCI system usually requires close collaborations among multiple organizations, e.g., hospitals, universities, and/or companies. Input data in BCIs, e.g., electroencephalogram (EEG), contain rich privacy information, and the developed machine learning model is usually proprietary. Data and model transmission among different parties may incur significant privacy threats, and hence privacy protection in BCIs must be considered. Unfortunately, there does not exist any contemporary and comprehensive review on privacy-preserving BCIs. This paper fills this gap, by describing potential privacy threats and protection strategies in BCIs. It also points out several challenges and future research directions in developing privacy-preserving BCIs. © 2024, CC BY.

关键词： Differential privacy

Distributed Constrained Online Nonconvex Optimization with Compressed Communication

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arXiv

arXiv 2025年

作者： Zhang, Kunpeng Xu, Lei Yi, Xinlei Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Mechanical Engineering University of Victoria VictoriaBCV8W 2Y2 Canada Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai201210 China Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online nonconvex optimization with time-varying inequality constraints over a network of agents. For a time-varying graph, we propose a distributed online primal–dual algorithm with compressed communication to efficiently utilize communication resources. We show that the proposed algorithm establishes an O(T max{1−θ1,θ1}) network regret bound and an O(T 1−θ1/2) network cumulative constraint violation bound, where T is the number of iterations and θ1 ∈ (0, 1) is a user-defined trade-off parameter. When Slater’s condition holds (i.e, there is a point that strictly satisfies the inequality constraints at all iterations), the network cumulative constraint violation bound is reduced to O(T 1−θ1). These bounds are comparable to the state-of-the-art results established by existing distributed online algorithms with perfect communication for distributed online convex optimization with (time-varying) inequality constraints. Finally, a simulation example is presented to validate the theoretical results. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

Muscle Synergy Analysis Based on NMF for Lower Limb Motor Function Assessment

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Muscle Synergy Analysis Based on NMF for Lower Limb Motor Fu...

IEEE International Conference on Robotics and Biomimetics

作者： Kexin Xiang Weiqun Wang Zeng-Guang Hou Chutian Zhang Jiaxing Wang Weiguo Shi Yuze Jiao Tianyu Lin State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Artifificial Intelligence University of Chinese Academy of Sciences Beijing China CASIA-MUST Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology Macao Macau Institute of Systems Engineering Macau University of Science and Technology Macau China CASIA-MUST Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology China

ISBN: (纸本)9781665481106

Accurate rehabilitation assessments are essential for designing effective rehabilitation methods and helping patients recover better. It's well known that commonly used scale assessment methods for neurorehabilitation suffer from the issue of subjectivity, thus investigation of objective assessment methods is very necessary. Muscle synergy analysis can be uesd to assess limb motor functions from the perspective of neuromuscular control. In this paper, a method for evaluation of human lower limb motor functions based on muscle synergy analysis is presented. Muscle synergy modules are designed using surface electromyography (sEMG) signals of the subjects' lower limbs by non-negative matrix factorizations (NMF). By comparing the cosine similarities of these synergy modules, it can be seen that muscle synergies of healthy subjects and patients are significantly different, while they are similar among healthy subjects. Therefore, a reference synergy module (RSM) is designed by averaging the muscle synergy modules for healthy subjects, and the similarities can be calculated by comparing the synergy modules for healthy subjects or patients with the RSM. In the experiment carried out in this study, average similarities of the three synergy modules for healthy subjects are respectively 0.97166, 0.87368 and 0.84932, and on the other hand, the average similarities for the three synergy modules for patients are respectively 0.59979, 0.56426 and 0.69042. Therefore, the similarities for healthy subjects are much higher than those for SCI patients, which denotes that the similarity between an individual synergy module and the RSM can be used as an objective assessing index for evaluating patients' motor function.

关键词： Neuromuscular Biomimetics Stability criteria Muscles Electromyography Indexes Robots

Anomaly Detection Based on Principle of Justifiable Granularity and Probability Density Estimation

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Anomaly Detection Based on Principle of Justifiable Granular...

Chinese automation Congress (CAC)

作者： Sheng Du Xian Ma Xiang Li Min Wu Weihua Cao Witold Pedrycz School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Faculty of Engineering and Natural Sciences Istinye University Sariyer/Istanbul Turkiye

Anomaly detection is essential to ensure the safety of industrial processes. This paper presents an anomaly detection approach based on the probability density estimation and principle of justifiable granularity. First, time series data are transformed into a two-dimensional information granule by the principle of justifiable granularity. Then, the test statistic is constructed, and the probability density and cumulative distribution functions of the test statistic are calculated. Next, the confidence level determines the test threshold. Finally, the time series data of a key parameter in the sintering process is used as a case study. The experimental result demonstrates that the proposed approach can detect abnormal time series data effectively, providing an accurate and effective solution for detecting time series anomalies in industrial processes.

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The Multi-Blockchain Based Data Trading Markets with Novel Pricing Mechanisms

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SSRN 2022年

作者： Li, Juanjuan Li, Junqing Wang, Xiao Qin, Rui Yuan, Yong Wang, Fei-Yue The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Qingdao Academy of Intelligent Industries Qingdao266109 China School of Mathematics Renmin University of China Beijing100872 China Macao Institute of Systems Engineering Macau University of Science and Technology Macau 999078 China

Aiming to deal with the challenge of data security and data pricing in data trading markets, we propose the novel multi-blockchain-based framework and design the related pricing mechanisms. Specifically, data recording and trading are conducted separately in the Data Blockchain (DChain) and the Value Blockchain (VChain). Based on these blockchains, two-layer data trading markets are established to manage the initial data trading in the primary market and the data resale in the secondary market. Pricing mechanisms are proposed to protect the formulated data trading markets against strategic trading behaviors and balance the payoffs of both suppliers and users. In the regular data trading on VChain-S2D, two auction models are used to deal with users' strategic bidding in terms of the demand scale, where the incentive-compatible Vickrey-Clarke-Groves (VCG) model is dedicated to the low-demand trading scenario, while the nearly incentive-compatible Monopolistic Price (MP) model is for the high-demand trading scenario. In the temporary data trading on VChain-D2S, a reverse auction mechanism named Two-Stage Obscure Selection (TSOS) is designed to regulate both suppliers' quoting and users' valuation strategies. Moreover, experiments are carried out to validate our theoretical analysis. Our work is expected to enhance the data security and improve the trading efficiency. © 2022, The Authors. All rights reserved.

关键词： Blockchain

Parallel Virtual Impedance Based Fluctuation Power Delivery Strategy For MMC Based SST 14

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Parallel Virtual Impedance Based Fluctuation Power Delivery ...

14th IEEE International Symposium on Power Electronics for Distributed Generation systems, PEDG 2023

作者： Li, Jaiyi Zhou, Jianqiao Zhang, Jianwen Shi, Gang Cai, Xu Wang, Yong Dong, Xiaofeng Bao, Wei Donghua University College of Information Science and Technology Shanghai China Shanghai Jiao Tong University Key Laboratory of Control of Power Transmission and Conversion Ministry of Education Shanghai China Beijing Sifang Automation CO. Ltd. Hvdc Transmission and Power Electronics Business Unit Beijing China Facilities Management Suzhou Power Supply Company Suzhou China State Grid Shanghai Municipal Electric Power Company Shanghai Electrical Apparatus Research Institute Group CO. Ltd. Shanghai China

ISBN: (纸本)9798350328233

Modular multilevel converter (MMC) based solid state transformer (MMC-SST) shows promise in hybrid AC/DC distribution grid. This paper introduces a topology consisting of cells made of directly coupled MMC sub-modules (SMs) and isolated SAB modules forming a multi-port SST for grid interconnection. Due to the large SM capacitors for low frequency fluctuation power handling, the device power density is low, which will limit its application. This paper proposes an MMC-SST SM capacitor optimization method based on fluctuation power delivery. It reduces the equivalent low frequency impedance of the SAB by parallel virtual impedance. Low frequency fluctuation power will be delivered into SAB circuit and naturally eliminated in low-voltage DC side due to three-phase symmetrical MMC-SST structure. Therefore, the SM capacitance could be optimized where the power fluctuation flowing into it are reduced. The feasibility of the proposed method is verified by a simulation model. © 2023 IEEE.

关键词： Capacitance

Data-Driven Interval Robust Optimization Method of Vpp Bidding Strategy in Spot Market Under Multiple Uncertainties

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SSRN 2024年

作者： Ma, Ying Li, Zhen Liu, Ruyi Liu, Bin Yu, Samson S. Liao, Xiaozhong Shi, Peng School of Automation Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology Beijing100081 China School of Mathematics and Statistics University of Sydney NSW2006 Australia Beijing102249 China School of Engineering Deakin University VIC3216 Australia School of Electrical and Electronic Engineering Univerty of Adelaide SA5005 Australia Research and Innovation Center Obuda University Budapest1034 Hungary

Virtual power plant (VPP) participating in the spot market can improve the flexibility of new-type power systems with the increasing penetration of renewable energy. However, multiple uncertainties on the market side, load side and generation side affect the bidding plans and operation efficiency of a VPP. In this paper, a two-stage interval robust optimization model is established to optimize the bidding plans of a VPP including gas turbine, energy storage, photovoltaic (PV) and electric vehicles (EVs). Interval numbers generated by the data-driven model are used to describe the uncertainty and correlation of electricity price in the spot market. Uncertainty sets are employed to describe the uncertainties in the number of EVs and PV power generation. The objective of the model is to jointly optimize the cost of VPP participating in the spot market considering arbitrage opportunity. The objective function containing interval numbers is transformed into a solvable form based on a ranking method considering pessimistic decisions. To solve this model, an improved column-and-constraint generation (C&CG) algorithm is developed based on the combination method of genetic algorithm (GA) and solver Gurobi. The results show that the interval numbers of electricity price generated by the data-driven model proposed in this paper can reduce the cost fluctuation of VPP by 3.2%. The two-stage interval robust model proposed in this paper can reduce the cost of VPP by 1.4% compared with the conventional single-stage robust method, and lessen the cost by 35.2% compared with the robust method that does not consider arbitrage opportunities in day-ahead and real-time markets. The improved C&CG algorithm presents superior performance in convergence and accuracy. Compared to the stochastic optimization method that generates n scenarios, the computational time of the model with the interval optimization method introduced in this paper can be reduced to 1∕n. Results from case studies illust

关键词： Digital storage