Real-time Tracking Based on Neuromrophic Vision

Real-time tracking is an important problem in computer vision in which most methods are based on the conventional cameras. Neuromorphic vision is a concept defined by incorporating neuromorphic vision sensors such as silicon retinas in vision processing system. With the development of the silicon technology, asynchronous event-based silicon retinas that mimic neuro-biological architectures has been developed in recent years. In this work, we combine the vision tracking algorithm of computer vision with the information encoding mechanism of event-based sensors which is inspired from the neural rate coding mechanism. The real-time tracking of single object with the advantage of high speed of 100 time bins per second is successfully realized. Our method demonstrates that the computer vision methods could be used for the neuromorphic vision processing and we can realize fast real-time tracking using neuromorphic vision sensors compare to the conventional camera

Limit Analysis On Seismic Stability Of Anisotropic And Nonhomogeneous Slopes With Anti-slide Piles

This study employs the limit analysis method to evaluate the seismic stability of anisotropic and nonhomogeneous slopes stabilized with anti-slide piles. The pseudo-static approach is used to simplify the earthquake load. The yield seismic acceleration factor is obtained from the optimization procedure and the results are verified with the published data. Then, the seismically unstable slope is reinforced with anti-slide piles, and the seismic stability of the reinforced slope is explored. The results show that the anisotropy and Non homogeneity of soils have significant effects on the stabilizing force required from the anti-slide piles and the optimal location of the pile is near the toe of the slope

Novel design and simulation of a hybrid solar electricity system with organic Rankine cycle and PV cells

The proposed system mainly consists of flat-plate compound parabolic concentrators (CPCs) integrated with photovoltaic (PV) cells and organic Rankine cycle (ORC). The technologies of CPC, PV cell and ORC are analyzed, and feasibility of the hybrid solar electricity system is demonstrated. Novel configuration for the hybrid electricity generation is carefully designed to react to different operating conditions. Fundamentals of the innovative system are illustrated, and mathematical models are developed to study the heat transfer and energy conversion processes. The results indicate that the lowtemperature solar thermal power generation integrated PV cells can produce much more electric per unit surface area than side-by-side PV panels and CPC-ORC modules. © The Author 2010. Published by Oxford University Press. All rights reserved

Enabling controlling complex networks with local topological information

Complex networks characterize the nature of internal/external interactions in real-world systems including social, economic, biological, ecological, and technological networks. Two issues keep as obstacles to fulflling control of large-scale networks: structural controllability which describes the ability to guide a dynamical system from any initial state to any desired fnal state in fnite time, with a suitable choice of inputs; and optimal control, which is a typical control approach to minimize the cost for driving the network to a predefned state with a given number of control inputs. For large complex networks without global information of network topology, both problems remain essentially open. Here we combine graph theory and control theory for tackling the two problems in one go, using only local network topology information. For the structural controllability problem, a distributed local-game matching method is proposed, where every node plays a simple Bayesian game with local information and local interactions with adjacent nodes, ensuring a suboptimal solution at a linear complexity. Starring from any structural controllability solution, a minimizing longest control path method can efciently reach a good solution for the optimal control in large networks. Our results provide solutions for distributed complex network control and demonstrate a way to link the structural controllability and optimal control together.The work was partially supported by National Science Foundation of China (61603209), and Beijing Natural Science Foundation (4164086), and the Study of Brain-Inspired Computing System of Tsinghua University program (20151080467), and Ministry of Education, Singapore, under contracts RG28/14, MOE2014-T2-1-028 and MOE2016-T2-1-119. Part of this work is an outcome of the Future Resilient Systems project at the Singapore-ETH Centre (SEC), which is funded by the National Research Foundation of Singapore (NRF) under its Campus for Research Excellence and Technological Enterprise (CREATE) programme. (61603209 - National Science Foundation of China; 4164086 - Beijing Natural Science Foundation; 20151080467 - Study of Brain-Inspired Computing System of Tsinghua University program; RG28/14 - Ministry of Education, Singapore; MOE2014-T2-1-028 - Ministry of Education, Singapore; MOE2016-T2-1-119 - Ministry of Education, Singapore; National Research Foundation of Singapore (NRF) under Campus for Research Excellence and Technological Enterprise (CREATE) programme)Published versio

Author correction: Enabling controlling complex networks with local topological information

Correction to: Scientific Reports https://doi.org/10.1038/s41598-018-22655-5, published online 15 March 2018. The Acknowledgements section in this Article is incomplete.The work was partially supported by National Science Foundation of China (61603209, 61327902), and Beijing Natural Science Foundation (4164086), and the Study of Brain-Inspired Computing System of Tsinghua University program (20151080467), and SuZhou-Tsinghua innovation leading program 2016SZ0102, and Ministry of Education, Singapore, under contracts RG28/14, MOE2014-T2-1-028 and MOE2016-T2-1-119. Part of this work is an outcome of the Future Resilient Systems project at the Singapore-ETH Centre (SEC), which is funded by the National Research Foundation of Singapore (NRF) under its Campus for Research Excellence and Technological Enterprise (CREATE) program. (61603209 - National Science Foundation of China; 61327902 - National Science Foundation of China; 4164086 - Beijing Natural Science Foundation; 20151080467 - Study of Brain-Inspired Computing System of Tsinghua University program; 2016SZ0102 - SuZhou-Tsinghua innovation leading program; RG28/14 - Ministry of Education, Singapore; MOE2014-T2-1-028 - Ministry of Education, Singapore; MOE2016-T2-1-119 - Ministry of Education, Singapore; National Research Foundation of Singapore (NRF) under its Campus for Research Excellence and Technological Enterprise (CREATE) program)Published versio

Non-classical non-Gaussian state of a mechanical resonator via selectively incoherent damping in three-mode optomechanical systems

We theoretically propose a scheme for the generation of a non-classical single-mode motional state of a mechanical resonator (MR) in the three-mode optomechanical systems, in which two optical modes of the cavities are linearly coupled to each other and one mechanical mode of the MR is optomechanically coupled to the two optical modes with the same coupling strength simultaneously. One cavity is driven by a coherent laser light. By properly tuning the frequency of the weak driving field, we obtain engineered Liouvillian superoperator via engineering the selective interaction Hamiltonian confined to the Fock subspaces. In this case, the motional state of the MR can be prepared into a non-Gaussian state, which possesses the sub-Poisson statistics although its Wigner function is positive.Comment: 6 pages, 5 figure

Parametric and economic analysis of high-temperature cascade organic Rankine cycle with a biphenyl and diphenyl oxide mixture

High-temperature organic Rankine cycle (ORC) systems have the potential to improve the heat-to-power conversion efficiency and expand the temperature range for heat recovery, heat battery and solar power generation. Restricted by the critical temperature of the commonly used organic working fluids, the current ORC technology has a maximum working temperature of around 300 °C. This paper proposes a high-temperature cascade organic Rankine cycle (CORC) system using a biphenyl and diphenyl oxide (BDO) mixture as the top cycle fluid and conventional organic fluids for the bottom cycle. The BDO mixture has excellent heat stability over a wide operation condition from 12 °C to 400 °C in single-phase and binary-phase states. However, at present a detailed study on the ORC using the mixture is lacking. In this paper, a parametric analysis of the high-temperature CORC system is conducted. A mathematical model based on the equivalent hot side temperature is built to simulate the ORC efficiency. The thermodynamic and exergetic performances of the novel CORC system under different bottom ORC working fluids, mixing chamber temperatures, evaporation temperatures, and condensation temperatures are investigated. The results show the maximum thermal efficiency of the CORC system is 38.74 % and 40.26 % at top ORC evaporation temperatures of 360 °C and 400 °C. The largest exergy destruction takes place in the heat exchanger between the top and bottom ORCs. Besides, the heat regenerators have a significant impact on the thermodynamic performance and can elevate the CORC efficiency by about 4 %. The proposed system has a higher efficiency and a lower equipment cost than conventional steam Rankine cycle at 400 °C while eliminating the challenges of wet steam turbines