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X-WR-CALNAME:Faculty of Science and Technology | University of Macau
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X-WR-CALDESC:Events for Faculty of Science and Technology | University of Macau
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TZID:Asia/Macau
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TZOFFSETFROM:+0800
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DTSTART:20180101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20181114T110000
DTEND;TZID=Asia/Macau:20181114T120000
DTSTAMP:20260523T065020
CREATED:20181114T030054Z
LAST-MODIFIED:20220927T043403Z
UID:5983-1542193200-1542196800@www.fst.um.edu.mo
SUMMARY:A Peridynamic View of Classical Continuum Mechanics
DESCRIPTION:Instructors/Speakers\nProf. Xiaowei HE\nInstitute of Software\nChinese Academy of Sciences\nChina \nAbstract\nPeridynamics is a formulation of the classical elastic theory that was originally targeted at simulating deformable objects with discontinuities\, especially fractures. In this talk\, I will introduce how to reformulate classical continuum mechanics with the peridynamic theory. To get an intuitive model that can be easily controlled\, we formulate the strain energy density function as a function parameterized by the dilatation and bond stretches\, which can be decomposed into multiple one-dimensional functions independently. To account for nonlinear material behaviors\, we also propose a set of nonlinear basis functions to help design a nonlinear strain energy function more easily. For an anisotropic material\, we additionally introduce an anisotropic kernel to control the elastic behavior for each bond independently. Experiments show that our model is flexible enough to approximately regenerate various hyperelastic materials in classical elastic theory\, including St.Venant-Kirchhoff and Neo-Hookean materials. \nBiography\nProf. Xiaowei He is currently an associate professor at the Institute of Software\, Chinese Academy of Sciences. He received both his BS and MS degrees from Peking University\, and his Ph.D. from Institute of Software\, Chinese Academy of Sciences. His research interests are mainly focused on computer graphics\, computational physics\, smoothed particle hydrodynamics\, peridynamics and nolocal theory. In recent years\, he has published several papers in international journals/conferences including SIGGRAPH\, TVCG\, SCA\, etc. Among them\, he proposed an efficient phase-field-based fluid solver to simulate arbitrarily complex multi-phase flows\, which was adopted by Adobe to realize a real-time three-dimensional oil painting system. Recently\, he has been doing research on how to apply machine learning to help improve both the performance and accuracy over traditional numerical solvers. He received two grants as PI from the Natural Science Foundation of China (NSFC) in 2014 and 2018\, respectively. \n 
URL:https://www.fst.um.edu.mo/event/a-peridynamic-view-of-classical-continuum-mechanics/
LOCATION:E11-4045 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20181115T110000
DTEND;TZID=Asia/Macau:20181115T120000
DTSTAMP:20260523T065020
CREATED:20181115T030046Z
LAST-MODIFIED:20220927T043403Z
UID:5981-1542279600-1542283200@www.fst.um.edu.mo
SUMMARY:Dendritic Neuron Model-Based Learning Algorithms and Applications
DESCRIPTION:Instructors/Speakers\nProf. Mengchu ZHOU\nThe Helen and John C. Hartmann Department of Electrical and Computer Engineering\nNew Jersey Institute of Technology\nNewark\, NJ 07102\, USA \nAbstract\nAn artificial neural network (ANN) that mimics the information processing mechanisms and procedures of neurons in human brains has achieved great success in many fields\, e.g.\, classification\, prediction and control. However\, traditional ANNs suffer from many problems\, such as the hard understanding problem\, the slow and difficult training problem and the difficulty to scale them up. These drawbacks motivate us to develop a new dendritic neuron model (DNM) by considering the nonlinearity of synapses\, not only for a better understanding of a biological neuronal system\, but also for providing a more useful method for solving practical problems. To achieve its better performance for solving problems\, six learning algorithms including biogeography-based optimization\, particle swarm optimization\, genetic algorithm\, ant colony optimization\, evolutionary strategy and population-based incremental learning are for the first time used to train it. The best combination of its user-defined parameters has been systemically investigated by using the Taguchi’s experimental design method. The experiments on fourteen different problems involving classification\, approximation and prediction are conducted by using a multi-layer perceptron and the proposed DNM. The results suggest that the proposed learning algorithms are effective and promising for training DNM and thus make DNM more powerful in solving classification\, approximation and prediction problems. \nBiography\nMengChu Zhou received his B.S. degree in Control Engineering from Nanjing University of Science and Technology\, Nanjing\, China in 1983\, M.S. degree in Automatic Control from Beijing Institute of Technology\, Beijing\, China in 1986\, and Ph. D. degree in Computer and Systems Engineering from Rensselaer Polytechnic Institute\, Troy\, NY in 1990. He joined New Jersey Institute of Technology (NJIT)\, Newark\, NJ in 1990\, and is now a Distinguished Professor of Electrical and Computer Engineering. His research interests are in Petri nets\, intelligent automation\, Internet of Things\, big data\, web services\, and intelligent transportation. He has over 800 publications including 12 books\, 460+ journal papers (360+ in IEEE transactions)\, 12 patents and 28 book-chapters. He is the founding Editor of IEEE Press Book Series on Systems Science and Engineering and Editor-in-Chief of IEEE/CAA Journal of Automatica Sinica. He is a recipient of Humboldt Research Award for US Senior Scientists from Alexander von Humboldt Foundation\, Franklin V. Taylor Memorial Award and the Norbert Wiener Award from IEEE Systems\, Man and Cybernetics Society. He is a life member of Chinese Association for Science and Technology-USA and served as its President in 1999. He is VP for Conferences and Meetings\, IEEE Systems\, Man and Cybernetics Society. He is a Fellow of The Institute of Electrical and Electronics Engineers (IEEE)\, International Federation of Automatic Control (IFAC)\, American Association for the Advancement of Science (AAAS) and Chinese Association of Automation (CAA). \n 
URL:https://www.fst.um.edu.mo/event/dendritic-neuron-model-based-learning-algorithms-and-applications/
LOCATION:E11-4045 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20181120T150000
DTEND;TZID=Asia/Macau:20181120T160000
DTSTAMP:20260523T065020
CREATED:20181120T070001Z
LAST-MODIFIED:20220927T043403Z
UID:5979-1542726000-1542729600@www.fst.um.edu.mo
SUMMARY:Formal design of embedded real-time systems
DESCRIPTION:Instructors/Speakers\nProf. Naijun ZHAN\nState Key Lab of Computer Science\nInstitute of Software\nChinese Academy of Sciences\nBeijing \nAbstract\nRecently we propose an approach to designing embedded real-time systems formally. Using our approach\, one can first build a graphical model of a system to be developed with Simulink/Stateflow (S/S)\, and then conduct extensive simulation. In order to verify the graphical model formally\, we translate S/S diagrams into HCSP automatically. HCSP is a formal modeling language for hybrid systems\, an extension of CSP by introducing differential equations to model continuous evolution and several kinds of interrupts to model the interaction between continuous evolution and discrete jumps. Using Hybrid Hoare Logic and its theorem prover\, the translated HCSP model can be verified. For justifying the correctness of the translation\, we give an inverse translation from HCSP to Simulink\, so that the consistency can be checked by co-simulation. By providing a set of refinement rules\, an HCSP process can be generated into a piece of SystemC code\, approximate bisimilar to the original HCSP process. All work can be supported by a developed tool MARS. Several real-world case studies have been investigated to check the feasibility of the approach. \nBiography\nProf. Naijun Zhan is a distinguished research professor of State Key Lab. of Computer Science\, Institute of Software\, the Chinese Academy of Sciences. He got his bachelor degree and master degree both from Nanjing University\, and his PhD from Institute of Software Chinese Academy of Sciences. Prior to join Institute of Software\, Chinese Academy of Sciences\, he worked at the Faculty of Mathematics and Informatics\, Mannheim University\, Germany as a research fellow. He is the winner of Outstanding Youth Fund of Natural Science Foundation of China of 2016. His research interests cover formal design of real-time\, embedded and hybrid systems\, program verification\, concurrent computation models\, modal and temporal logics\, and so on. Now\, he serves the editorial boards of Formal Aspects of Computing\, Journal of Logical and Algebraic Methods in Programming\, Journal of Software\, Journal of Computer Research and Development. \n 
URL:https://www.fst.um.edu.mo/event/formal-design-of-embedded-real-time-systems/
LOCATION:E11-4045 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20181121T100000
DTEND;TZID=Asia/Macau:20181121T110000
DTSTAMP:20260523T065020
CREATED:20181121T020037Z
LAST-MODIFIED:20220927T043402Z
UID:5977-1542794400-1542798000@www.fst.um.edu.mo
SUMMARY:Principal metabolic flux mode analysis
DESCRIPTION:Instructors/Speakers\nDr. Sahely BHADRA\nComputer Science and Engineering\nIndian Institute of Technology (IIT)\nPalakkad \nAbstract\nMotivation:\nIn the analysis of metabolism\, two distinct and complementary approaches are frequently used: Principal component analysis (PCA) and stoichiometric flux analysis. PCA is able to capture the main modes of variability in a set of experiments and does not make many prior assumptions about the data\, but does not inherently take into account the flux mode structure of metabolism. Stoichiometric flux analysis methods\, such as Flux Balance Analysis (FBA) and Elementary Mode Analysis\, on the other hand\, are able to capture the metabolic flux modes\, however\, they are primarily designed for the analysis of single samples at a time\, and not best suited for exploratory analysis on a large sets of samples. \nResults:\nWe propose a new methodology for the analysis of metabolism\, called Principal Metabolic Flux Mode Analysis (PMFA)\, which marries the PCA and stoichiometric flux analysis approaches in an elegant regularized optimization framework. In short\, the method incorporates a variance maximization objective form PCA coupled with a stoichiometric regularizer\, which penalizes projections that are far from any flux modes of the network. For interpretability\, we also introduce a sparse variant of PMFA that favours flux modes that contain a small number of reactions. Our experiments demonstrate the versatility and capabilities of our methodology. The proposed method can be applied to genome-scale metabolic network in efficient way as PMFA does not enumerate elementary modes. In addition\, the method is more robust on out-of-steady steady-state experimental data than competing flux mode analysis approaches. \nBiography\nDr. Sahely Bhadra is assistant Professor in Indian Institute of Technology\, Palakkad since July\, 2017. She has received her PhD from Computer Science and Automation department of Indian Institute of Science in 2012. Before joining IIT Palakkad she did postdoctoral research in Max Planck Institute for Informatics (2012-2014) \, Helsinki Institute for Information Technology (2014-2016) and Northeastern University (2017). Her research interest is Machine Learning and Optimization for multi view \, structured and  noisy data. She is interested in learning models to solve problem in biology. \n 
URL:https://www.fst.um.edu.mo/event/principal-metabolic-flux-mode-analysis/
LOCATION:E11-4045 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20181126T091500
DTEND;TZID=Asia/Macau:20181126T091500
DTSTAMP:20260523T065020
CREATED:20181126T011503Z
LAST-MODIFIED:20220927T043402Z
UID:5972-1543223700-1543223700@www.fst.um.edu.mo
SUMMARY:Sustainable Technology for Smart City Seminar
DESCRIPTION:
URL:https://www.fst.um.edu.mo/event/sustainable-technology-for-smart-city-seminar/
CATEGORIES:cee_events,event_list,seminarslectures
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Macau:20181127T110000
DTEND;TZID=Asia/Macau:20181127T120000
DTSTAMP:20260523T065020
CREATED:20181127T030029Z
LAST-MODIFIED:20220927T043401Z
UID:5968-1543316400-1543320000@www.fst.um.edu.mo
SUMMARY:Sensing and Analytics for Human Machine Systems
DESCRIPTION:Instructors/Speakers\nProf. Honghai LIU\nUniversity of Portsmouth\nUK \nAbstract\nIt requires innovative technologies and theoretical foundation of sensing and analytics as increasing complexity of modern systems with humans involved actively. The state of the art in human machine interface is largely dominant by solutions that are ad-hoc and application dependent. This talk attempts to summarize challenges for sensing and analytics from the perspective of human-machine systems\, and presents a computational framework aiming at anchoring behaviors to hardcoded features. Two projects will be introduced to showcase the framework: human hand skill transfer and interaction with children with autism spectrum disorders. The talk will conclude with comments on open issues and challenges in human-machine systems. \nBiography\nHonghai Liu received his Ph.D from King’s College\, University London\, UK. He is a Chair in Human Machine Systems at the University of Portsmouth\, UK. He previously held research appointments at the Universities of London\, University of Aberdeen\, and project leader appointments in large-scale industrial control and system integration industry. He is interested in intelligent sensing\, biomechatronics\, pattern recognition\, intelligent video analytics\, intelligent robotics and their practical applications with an emphasis on approaches that could make contribution to the intelligent connection of perception to action using contextual information. His research has been funded by UK research councils\, EU FP7\, the Leverhulme Trust\, the Royal Society and industry partners. He has authored/co-authored more than 200 per-reviewed journals and conference papers. He is an IET Fellow and JSPS Fellow. \nHonghai is an energetic contributor to our research community. He was the chair for IEEE Systems\, Man and Cybernetics Society’s Technical Committees\, also a Member of the IEEE Society’s Board of Governors\, leading the research theme on human machine systems. He is a Co-Editor-in-Chief for the Springer Journal of Intelligent Robotics and Applications and Associate Editor for IEEE Transactions on Human Machine Systems\, IEEE Transactions on Industrial Electronics and IEEE Transactions on Industrial Informatics. \n 
URL:https://www.fst.um.edu.mo/event/sensing-and-analytics-for-human-machine-systems/
LOCATION:E11-4045 (University of Macau)
CATEGORIES:event_list,seminarslectures
END:VEVENT
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