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Distributed Optimization Algorithm for Multi-Robot Formation with Virtual Reference center

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 732-734页

作者： Jingyi Huang Shuaiyu Zhou Hua Tu Yuhong Yao Qingshan Liu the School of Mathematics Southeast University IEEE the Jiangsu Provincial Key Laboratory of Networked collective intelligence

Dear editor,In this letter,we use a distributed optimization approach to solve a class of multi-robot formation problem with virtual reference center.We investigate the design and analysis of the constrained consensus algorithm to solve the optimization problem with a sum of objective functions with some local constraints.In the multi-robot system with virtual reference center,each robot has messages on its own constraints and objective function,as well as the message about the formation that interacts with the virtual reference center.At the same time,all the robots collaborate to find the minimum value of the function defined by the formation.To find the optimal formation,we propose an algorithm with fixed step size with better performance.In addition,we use a combination of the Hungarian assignment algorithm and the proposed formation algorithm to get the optimal matching formation of the multi-robot system.

关键词： robot Robot editor

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“Towards Re-Inventing Psychohistory”:Predicting the Popularity of Tomorrow’s News from Yesterday’s Twitter and News Feeds

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Journal of Systems Science and Systems Engineering 2021年第1期30卷 85-104页

作者： Jiachen Sun Peter Gloor mit center for collective intelligence 245 First StreetCambridgeMA02142USA School of Electronics and Information Technology Sun Yat-Sen UniversityGuangzhou510275China

Rapid advances in machine learning combined with wide availability of online social media have created considerable research activity in predicting what might be the news of tomorrow based on an analysis of the past.In this work,we present a deep learning forecasting framework which is capable to predict tomorrow’s news topics on Twitter and news feeds based on yesterday’s content and topic-interaction features.The proposed framework starts by generating topics from words using word embeddings and K-means clustering.Then temporal topic-networks are constructed where two topics are linked if the same user has worked on both topics.Structural and dynamic metrics calculated from networks along with content features and past activity,are used as input of a long short-term memory(LSTM)model,which predicts the number of mentions of a specific topic on the subsequent day.Utilizing dependencies among topics,our experiments on two Twitter datasets and the HuffPost news dataset demonstrate that selecting a topic’s historical local neighbors in the topic-network as extra features greatly improves the prediction accuracy and outperforms existing baselines.

关键词： Topic’s popularity trend forecasting social media

Attention-Based Residual Dense Shrinkage Network for ECG Denoising

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Computer Modeling in Engineering & Sciences 2024年第3期138卷 2809-2824页

作者： Dengyong Zhang Minzhi Yuan Feng Li Lebing Zhang Yanqiang Sun Yiming Ling Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer and Artificial intelligence Huaihua UniversityHuaihua418000China School of Computer National University of Defense TechnologyChangsha410073China New Energy Centralized Control center CHN Energy Hunan Power New Energy Co.Ltd.Changsha410000China

Electrocardiogram(ECG)signal is one of the noninvasive physiological measurement techniques commonly usedin cardiac diagnosis.However,in real scenarios,the ECGsignal is susceptible to various noise erosion,which affectsthe subsequent pathological analysis.Therefore,the effective removal of the noise from ECG signals has becomea top priority in cardiac diagnostic research.Aiming at the problem of incomplete signal shape retention andlow signal-to-noise ratio(SNR)after denoising,a novel ECG denoising network,named attention-based residualdense shrinkage network(ARDSN),is proposed in this paper.Firstly,the shallow ECG characteristics are extractedby a shallow feature extraction network(SFEN).Then,the residual dense shrinkage attention block(RDSAB)isused for adaptive noise suppression.Finally,feature fusion representation(FFR)is performed on the hierarchicalfeatures extracted by a series of RDSABs to reconstruct the de-noised ECG signal.Experiments on the mit-BIHarrhythmia database and mit-BIH noise stress test database indicate that the proposed scheme can effectively resistthe interference of different sources of noise on the ECG signal.

关键词： Electrocardiogram signal denoising signal-to-noise ratio attention-based residual dense shrinkage network mit-BIH

Reynosin protects neuronal cells from microglial neuroinflammation by suppressing NLRP3 inflammasome activation mediated by NADPH oxidase

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Chinese Journal of Natural Medicines 2024年第6期22卷 486-500页

作者： YANG Yanqiu CHE Yue FANG Mingxia YAO Xiaohu ZHOU Di WANG Feng CHEN Gang LIANG Dong LI Ning HOU Yue Key Laboratory of Bioresource Research and Development of Liaoning Province College of Life and Health SciencesNorth-eastern UniversityShenyang 110000China National Frontiers Science center for Industrial intelligence and Systems Optimization Key Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang 110000China School of Traditional Chinese Materia Medica Key Laboratory of Innovative Traditional Chinese Medicine for Major Chronic Diseases of Liaoning provinceKey Laboratory for TCM Material Basis Study and Innovative Drug Development of Shenyang CityShenyang Pharmaceutical UniversityShenyang 110000China State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources School of Chemistry and Pharmaceutical SciencesGuangxi Normal UniversityGuilin 541000China

Neuroinflammation,mediated by the nucleotide-binding oligomerization domain-like receptor family pyrin domain-containing-3(NLRP3)inflammasome,is a significant contributor to the pathogenesis of neurodegenerative diseases(NDDs).Reynos-in,a natural sesquiterpene lactone(SL),exhibits a broad spectrum of pharmacological effects,suggesting its potential therapeutic value.However,the effects and mechanism of reynosin on neuroinflammation remain elusive.The current study explores the effects and mechanisms of reynosin on neuroinflammation using mice and BV-2 microglial cells treated with lipopolysaccharide(LPS).Our findings reveal that reynosin effectively reduces microglial inflammation in vitro,as demonstrated by decreased CD11b expression and lowered interleukin-1 beta(IL-1β)and interleukin-18(IL-18)mRNA and protein levels.Correspondingly,in vivo,results showed a re-duction in the number of Iba-1 positive cells and alleviation of morphological alterations,alongside decreased expressions of IL-1βand IL-18.Further analysis indicates that reynosin inhibits NLRP3 inflammasome activation,evidenced by reduced transcription of NLRP3 and caspase-1,diminished NLRP3 protein expression,inhibited apoptosis-associated speck-like protein containing a CARD(ASC)oli-gomerization,and decreased caspase-1 self-cleavage.Additionally,reynosin curtailed the activation of nicotinamide adenine dinuc-leotide phosphate(NADPH)oxidase,demonstrated by reduced NADP^(+)and NADPH levels,downregulation of gp91^(phox) mRNA,pro-tein expression,suppression of p47^(phox) expression and translocation to the membrane.Moreover,reynosin exhibited a neuroprotective effect against microglial inflammation in vivo and in vitro.These collective findings underscore reynosin’s capacity to mitigate mi-croglial inflammation by inhibiting the NLRP3 inflammasome,thus highlighting its potential as a therapeutic agent for managing neuroinflammation.

关键词： Microglia NLRP3 inflammasome Reactive oxygen species NADPH oxidase Neuron Reynosin

Complex network approach for the evaluation of asphalt pavement design and construction: a longitudinal study

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Science China(Information Sciences) 2022年第7期65卷 210-224页

作者： Hanjie LIU Jinde CAO Wei HUANG Xinli SHI Xudong WANG School of Mathematics Southeast University Jiangsu Provincial Key Laboratory of Networked collective intelligence Southeast University Research Institute of Highway Ministry of Transport Intelligent Transportation System Research center Southeast University School of Cyber Science and Engineering Southeast University

The assessment of asphalt pavement structures with diversified designs and constructions has been a critical challenge due to the practical limitations in testing and methodology. Here, we propose a complex network approach to the evaluation of longitudinal asphalt pavement performances in the Research Institute of Highway Ministry of Transport track(RIOHTrack). We add to the growing field in which network approaches are employed and indicate the profound application in understanding pavement structures.Specifically, similar and disparate pavement performances are revealed regardless of their predefined design and construction categories. Evidence shows that short-term pavement performance depends on the strength of the subbase structure, whereas long-term performance relies on the thickness of the asphalt layer. In addition, our results indicate that recycling materials could be an important substitute for reducing the thickness of asphalt concrete and maintaining a healthy service life.

关键词： complex network Complex network asphalt pavement structure evaluation longitudinal RIOHTrack

Variable-order fractional derivative rutting depth prediction of asphalt pavement based on the RIOHTrack full-scale track

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Science China(Information Sciences) 2023年第5期66卷 196-214页

作者： Yu WANG Jiaojiao YAN Wei HUANG Leszek RUTKOWSKI Jinde CAO School of Mathematics Southeast University Jiangsu Provincial Key Laboratory of Networked collective intelligence Southeast University Intelligent Transportation System Research center Southeast University Systems Research Institute of the Polish Academy of Sciences Institute of Computer Science AGH University of Science and Technology Information Technology Institute University of Social Sciences Yonsei Frontier Lab Yonsei University

Rutting deformation is typical damage to asphalt pavement and can be quantitatively described as the time-varying mechanical deformation feature of the asphalt mixture during the creep process. First,a nonlinear viscoelastic creep model is established based on the Caputo variable-order fractional derivative.The fractional order in this model is not fixed and is defined as a time-varying function. The time-dependent mechanical properties of asphalt mixtures are efectively reflected. Based on the modified Burgers model, the variable-order function is revised to a linear form. Second, the variable-order fractional Burgers rutting model is obtained by fitting the test data of the RIOHTrack full-scale track. The Levenberg-Marquardt optimization algorithm is used to analyze the test data and the corresponding parameters to obtain more accurate and applicable variable-order fractional rutting mechanics and empirical models for diferent asphalt pavement structures. Finally, according to the classification of the pavement structure, structures III and VI are taken as examples. The rutting prediction and comparison results of several representative pavement structures are obtained to verify that the proposed rutting prediction model can also accurately predict all road sections of RIOHTrack. The new model is also compared with the rutting model of the mechanistic-empirical pavement design guide, the fractional-order Burgers rutting model, and the modified Burgers rutting model. The fitting results are evaluated using the determination coefficient R2. The results show that the variable-order fractional Burgers rutting model proposed in this study exhibits a better fit and is more applicable to describe the rutting deformation of asphalt pavement.

关键词： RIOHTrack full-scale track Caputo fractional derivative variable-order fractional modified Burgers model rutting prediction

Distributed online bandit tracking for Nash equilibrium under partial-decision information setting

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Science China(Technological Sciences) 2023年第11期66卷 3129-3138页

作者： FENG ZhangCheng XU WenYing CAO JinDe YANG ShaoFu RUTKOWSKI Leszek Jiangsu Provincial Key Laboratory of Networked collective intelligence School of MathematicsSoutheast UniversityNanjing 211189China Frontiers Science center for Mobile Information Communication and Security Nanjing 210096China School of Computer Science and Engineering Southeast UniversityNanjing 211189China The Systems Research Institute of the Polish Academy of Sciences Warsaw 01-447Poland AGH University of Science and Technology Krakow 30-059Poland

This paper is concerned with a Nash equilibrium(NE)tracking issue in online games with bandit feedback,where cost functions vary with time and agents only have access to the values of these functions at two points during each round.A partial-decision information setting is considered,in which agents have only access to the decisions of their neighbors.The primary objective of this paper is to develop a distributed online NE tracking algorithm that ensures sublinear growth of regret with respect to the total round T,under both the bandit feedback and partial-decision information setting.By utilizing a two-point estimator together with the leader-following consensus method,a new distributed online NE tracking algorithm is established with the estimated gradient and local estimated decisions based on the projection gradient-descent method.Moreover,sufficient conditions are derived to guarantee an improved upper bound of dynamic regret compared to existing bandit algorithms.Finally,a simulation example is presented to demonstrate the effectiveness of the proposed algorithm.

关键词： online game bandit feedback partial-decision two-point gradient estimator

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A MACHINE LEARNING APPROACH FOR MECHANISM SELECTION IN COMPLEX NEGOTIATIONS

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Journal of Systems Science and Systems Engineering 2018年第2期27卷 134-155页

作者： Reyhan Aydogan Ivan Marsa-Maestre Mark Klein Catholijn M. Jonker Department of Computer Science Ozyegin University Istanbul Turkey reyhan Interactive intelligence Group Delft University of Technology The Netherlands Department of Computer Engineering University of Alcala 28805 Alcala de Henares Madrid Spain ivan Interactive center for collective intelligence Massachusetts Institute of Technology Cambridge USA

Automated negotiation mechanisms can be helpful in contexts where users want to reach mutually satisfactory agreements about issues of shared interest, especially for complex problems with many interdependent issues. A variety of automated negotiation mechanisms have been proposed in the literature. The effectiveness of those mechanisms, however, may depend on the charaeteristics of the underlying negotiation problem （e.g. on the complexity of participant＇s utility functions, as well as the degree of conflict between participants）. While one mechanism may be a good choice for a negotiation problem, it may be a poor choice for another. In this paper, we pursue the problem of selecting the most effective negotiation mechanism given a particular problem by （1） defining a set of scenario metrics to capture the relevant features of negotiation problems, （2） evaluating the performance of a range of negotiation mechanisms on a diverse test suite of negotiation scenarios, （3） applying machine learning techniques to identify which mechanisms work best with which scenarios, and （4） demonstrating that using these classification rules for mechanism selection enables significantly better negotiation performance than any single mechanism alone.

关键词： Automated negotiation mechanism selection scenario metrics

ARISTOTLE SAID ＂HAPPINESS IS A STATE OF ACTIVITY＂ - PREDICTING MOOD THROUGH BODY SENSING WITH SMARTWATCHES

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Journal of Systems Science and Systems Engineering 2018年第5期27卷 586-612页

作者： Peter A Gloor Andrea Fronzetti Colladon Francesca Grippa Pascal Budner Joscha Eirich mit center for collective intelligence 245 First Street 02142 Cambridge MA USA Department of Enterprise Engineering University of Rome Tor Vergata Via del Politecnico n°1 00133 Rome Italy Northeastern University 360 Huntington Avenue 02115 Boston MA USA University of Cologne Albertus-Magnus-PIatz 50923 Koln Germany University of Bamberg Kapuzinerstraβe1696047 Bamberg Germany

We measure and predict states of Activation and Happiness using a body sensing applicationconnected to smartwatches. Through the sensors of commercially available smartwatches we collectindividual mood states and correlate them with body sensing data such as acceleration, heart rate, lightlevel data, and location, through the GPS sensor built into the smartphone connected to the smartwatchWe polled users on the smartwatch for seven weeks four times per day asking for their mood state. Wefound that both Happiness and Activation are negatively correlated with heart beats and with the levelsof light. People tend to be happier when they are moving more intensely and are feeling less activatedduring weekends. We also found that people with a lower Conscientiousness and Neuroticism andhigher Agreeableness tend to be happy more frequently. In addition, more Activation can be predictedby lower Openness to experience and higher Agreeableness and Conscientiousness. Lastly, we find thattracking people＇s geographical coordinates might play an important role in predicting Happiness andActivation. The methodology we propose is a first step towards building an automated mood trackingsystem, to be used for better teamwork and in combination with social network analysis studies.

关键词： Body sensing systems mood tracking smartwatch experience sampling happiness activation