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4th IEEE SmartWorld, 15th IEEE International Conference on Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovations, SmartWorld/UIC/ATC/ScalCom/CBDCom/IoP/SCI 2018

作者： Hong, Chen Shi, Dianxi Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9781538693803

Unmanned aerial vehicle is considered one of the most promising technologies. In the multi-UAV systems, a great challenge is to control multiple UAVs remotely by one person without being limited by distance. In this paper, to address this problem, we propose a control system architecture based on cloud platform which has been developed in order to allow multiple users to control and monitor multiple UAVs simultaneously irrespective of the location. Besides, the cloud platform acts as a remote brain for UAVs by providing extra storage and computing resources. This system architecture not only simplifies the development of control station, but also enhances the capabilities of unmanned system. © 2018 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Kinematics and Dynamics Hessian Matrices of Manipulators Based on Screw Theory

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Chinese Journal of Mechanical Engineering 2015年第2期28卷 226-235页

作者： ZHAO Tieshi GENG Mingchao CHEN Yuhang LI Erwei YANG Jiantao Hebei Provincial Key Laboratory of Parallel Robot and Mechatronic System Yanshan University Key Laboratory of Advanced Forging & Stamping Technology and Science of Ministry of Education Yanshan University

The complexity of the kinematics and dynamics of a manipulator makes it necessary to simplify the modeling ***,the traditional representations cannot achieve this because of the absence of coordinate ***,the coordinate invariant method is an important research ***,the rigid-body acceleration,the time derivative of the twist,is proved to be a screw,and its physical meaning is *** on the twist and the rigid-body acceleration,the acceleration of the end-effector is expressed as a linear-bilinear form,and the kinematics Hessian matrix of the manipulator（represented by Lie bracket）is ***,Newton-Euler＇s equation is rewritten as a linear-bilinear form,from which the dynamics Hessian matrix of a rigid body is *** formulae and the dynamics Hessian matrix are proved to be coordinate *** to the principle of virtual work,the dynamics Hessian matrix of the parallel manipulator is gotten and the detailed dynamic model is *** index of dynamical coupling based on dynamics Hessian matrix is *** the end,a foldable parallel manipulator is taken as an example to validate the deduced kinematics and dynamics *** screw theory based method can simplify the kinematics and dynamics of a manipulator,also the corresponding dynamics Hessian matrix can be used to evaluate the dynamical coupling of a manipulator.

关键词： kinematics dynamics Hessian matrix parallel manipulator screw theory

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Iaso： an autonomous fault-tolerant management system for supercomputers

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Frontiers of Computer science 2014年第3期8卷 378-390页

作者： Kai LU Xiaoping WANG Gen LI Ruibo WANG Wanqing CHI Yongpeng LIU Hongwei TANG Hua FENG Yinghui GAO Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha 410073 China College of Computer National University of Defense Technology Changsha 410073 China ATR Laboratory National University of Defense Technology Changsha 410073 China

With the increase of system scale, the inherent reliability of supercomputers becomes lower and lower. The cost of fault handling and task recovery increases so rapidly that the reliability issue will soon harm the usability of supercomputers. This issue is referred to as the ＂reliability wall＂, which is regarded as a critical problem for current and future supercomputers. To address this problem, we propose an autonomous fault-tolerant system, named Iaso, in MilkyWay- 2 system. Iaso introduces the concept of autonomous management in supercomputers. By autonomous management, the computer itself, rather than manpower, takes charge of the fault management work. Iaso automatically manage the whole lifecycle of faults, including fault detection, fault diagnosis, fault isolation, and task recovery. Iaso endows the autonomous features with MilkyWay-2 system, such as self-awareness, self-diagnosis, self-healing, and self-protection. With the help of Iaso, the cost of fault handling in supercomputers reduces from several hours to a few seconds. Iaso greatly improves the usability and reliability of MilkyWay-2 system.

关键词： supercomputer autonomous management fault tolerant fault management MilkyWay-2 system

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Searching Latent Sub-Goals in Hierarchical Reinforcement Learning as Riemannian Manifold Optimization

Searching Latent Sub-Goals in Hierarchical Reinforcement Lea...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Liu, Sidun Qiao, Peng Dou, Yong Jin, Ruochun National University of Defense Technology Science and Technology on Parallel and Distributed Laboratory Hunan China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Hierarchical Reinforcement Learning (HRL) is promising to tackle the long-term sparse reward problem. However, goal conditioned HRL, which decomposes the goal into a series of sub-goals, suffers from sub-goal search inefficiency problems when the observation space is too large. This problem is more severe in a visual observation space, since its high latent dimensions, where the complete dynamics information is preserved, exponentially increase the difficulty of sub-goal search. In view of this, we propose to treat the latent space as a manifold, i.e., a Riemannian manifold. Assisted by the Riemannian manifold optimization, sub-goals can be efficiently searched in the higher-dimensional latent space, with the help of preserving the dynamics information efficiently. Experiments on a series of MuJoCo tasks with visual observation show that the proposed Riemannian manifold optimization, compared with the baseline that directly searches for sub-goals in bounded latent space, improves the success rate by 1.5 times on average. In much higher dimensions where the baseline no longer converges, the success rate of the proposed method is maintained. © 2022 IEEE.

关键词： Reinforcement learning

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NVLAN: A Novel VLAN technology for Scalable Multi-tenant Datacenter Networks 2

NVLAN: A Novel VLAN Technology for Scalable Multi-tenant Dat...

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2nd International Conference on Advanced Cloud and Big Data, CBD 2014

作者： Hu, Yang Li, Huiba Peng, Yuxing Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Computer Science Department China

ISBN: (纸本)9781479980857

Nowadays, cloud providers of 'Infrastructure as a service' require datacenter networks to support virtualization and multi-tenancy at large scale, while it brings a grand challenge to datacenters. Traditional network technologies of datacenters and common commodity devices of network can not meet these new needs. In this paper, we present NVLAN, a novel VLAN technology for scalable multi-tenant datacenter networks. NVLAN eliminates the quantity limitation of logic isolated networks on VLAN through a slight modification. NVLAN can be applied to very large-scale datacenter networks, without the need of expensive and dedicated switches. Through encapsulation of tenant's packets, NVLAN provides tenants with full address space virtualization to configure their own logic network. We implemented NVLAN on a test bed, and demonstrated that it can be scaled up to large numbers of multi-tenant networks with negligible overhead. © 2014 IEEE.

关键词： Virtualization

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Optimizing guest swapping using elastic and transparent memory provisioning on virtualization platform

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Frontiers of Computer science 2016年第5期10卷 908-924页

作者： Xi LI Pengfei ZHANG Rui CHU Huaimin WANG School of Information Science and Engineering Central South University Changsha 410083 China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha 410008 China

On virtualization platforms, peak memory de- mand caused by hotspot applications often triggers page swapping in guest OS, causing performance degradation in- side and outside of this virtual machine （VM）. Even though host holds sufficient memory pages, guest OS is unable to utilize free pages in host directly due to the semantic gap between virtual machine monitor （MM） and guest operat- ing system （OS）. Our work aims at utilizing the free memory scattered in multiple hosts in a virtualization environment to improve the performance of guest swapping in a transparent and implicit way. Based on the insightful analysis of behav- ioral characteristics of guest swapping, we design and im- plement a distributed and scalable framework HybridSwap. It dynamically constructs virtual swap pools using various policies, and builds up a synthetic swapping mechanism in a peer-to-peer way, which can adaptively choose different vir- tual swap pools. We implement the prototype of HybridSwap and evaluate it with some benchmarks in different scenar- ios. The evaluation results demonstrate that our solution has the ability to promote the guest swapping efficiency indeed and shows a double performance promotion in some cases. Even in the worst case, the system overhead brought by Hy- bridSwap is acceptable.

关键词： virtualization memory management guestswapping performance degradation

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Kinematics and Dynamics Analysis of a Quadruped Walking Robot with parallel Leg Mechanism

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Chinese Journal of Mechanical Engineering 2013年第5期26卷 881-891页

作者： WANG Hongbo SANG Lingfeng HU Xing ZHANG Dianfan YU Hongnian Parallel Robot and Mechatronic System Laboratory of Hebei Province Yanshan University Key Laboratory of Advanced Forging & Stamping Technology and Science of Ministry of Education Yanshan University Xi’an University of Architecture and Technology Science and Technology Research Institute Yanshan University School of Design Engineering and ComputingBournemouth University

It is desired to require a walking robot for the elderly and the disabled to have large capacity,high stiffness,stability,***,the existing walking robots cannot achieve these requirements because of the weight-payload ratio and simple ***,Improvement of enhancing capacity and functions of the walking robot is an important research *** to walking requirements and combining modularization and reconfigurable ideas,a quadruped/biped reconfigurable walking robot with parallel leg mechanism is *** proposed robot can be used for both a biped and a quadruped walking *** kinematics and performance analysis of a 3-UPU parallel mechanism which is the basic leg mechanism of a quadruped walking robot are conducted and the structural parameters are *** results show that performance of the walking robot is optimal when the circumradius R,r of the upper and lower platform of leg mechanism are 161.7 mm,57.7 mm,*** on the optimal results,the kinematics and dynamics of the quadruped walking robot in the static walking mode are derived with the application of parallel mechanism and influence coefficient theory,and the optimal coordination distribution of the dynamic load for the quadruped walking robot with over-determinate inputs is analyzed,which solves dynamic load coupling caused by the branches’ constraint of the robot in the walk *** laying a theoretical foundation for development of the prototype,the kinematics and dynamics studies on the quadruped walking robot also boost the theoretical research of the quadruped walking and the practical applications of parallel mechanism.

关键词： walking robot parallel leg mechanism kinematics dynamics over-determinate inputs

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Factorization Machine-based Unsupervised Model Selection Method

Factorization Machine-based Unsupervised Model Selection Met...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Zhang, Ruyi Wang, Yijie Xu, Hongzuo Zhou, Haifang National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Machine learning is broadly used in many intelligent cybernetic systems. With the burgeoning of the communities of AI, the number of machine learning-based models is rapidly increasing, but picking a suitable and optimal (or relatively good) model from overwhelming options has become a conundrum when deploying a new system. Therefore, we are motivated by an intriguing question: Can we automatically select a proper model for new data? However, unsupervised model selection poses two main challenges: (i) Evaluation and comparison of candidate models on the new data are infeasible due to the lack of labels;and (ii) It is non-trivial to build relationships between model performance and data characteristics when the interaction between these characteristics should be considered. In light of these limitations, this paper proposes a factorization machine-based unsupervised model selection method. Following mainstream model selection protocols, we also leverage model performance on prior known datasets. Differently, we learn higher-order complex relationships between model performance and dataset characteristics. Specifically, our method transfers the historical performance into a second-order function of meta-features and embedding weights by harnessing the power of factorization machine. This function can be subsequently used to select a proper model when given a new dataset. Extensive experiments show that our method obtains more superior model selection performance than five state-of-the-art approaches, and our method executes faster than its competitors by approximate three magnitudes. © 2022 IEEE.

关键词： Factorization

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Transfer learning for deep neural network-based partial differential equations solving

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Advances in Aerodynamics 2021年第1期3卷 635-648页

作者： Xinhai Chen Chunye Gong Qian Wan Liang Deng Yunbo Wan Yang Liu Bo Chen Jie Liu Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense TechnologyChangshaChina China Aerodynamics Research and Development Center MianyangChina.

Deep neural networks(DNNs)have recently shown great potential in solving partial differential equations(PDEs).The success of neural network-based surrogate models is attributed to their ability to learn a rich set of solution-related ***,learning DNNs usually involves tedious training iterations to converge and requires a very large number of training data,which hinders the application of these models to complex physical *** address this problem,we propose to apply the transfer learning approach to DNN-based PDE solving *** our work,we create pairs of transfer experiments on Helmholtz and Navier-Stokes equations by constructing subtasks with different source terms and Reynolds *** also conduct a series of experiments to investigate the degree of generality of the features between different *** results demonstrate that despite differences in underlying PDE systems,the transfer methodology can lead to a significant improvement in the accuracy of the predicted solutions and achieve a maximum performance boost of 97.3%on widely used surrogate models.

关键词： Deep neural network Partial differential equation Surrogate model Transfer learning

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Detection of regions with the least impact on true and fake image classification through reinforcement learning 3

Detection of regions with the least impact on true and fake ...

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2020 3rd International Conference on Computer Information science and Artificial Intelligence, CISAI 2020

作者： Li, Wei Qiao, Peng Dou, Yong Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha410005 China

With the development of artificial intelligence [1-3], convolutional neural networks (CNNs) have made significant progress in image generation and manipulation. The generated images using facial image synthesis methods, e.g., Faceswap [4], Deepfakes [5], Face2face [6], NeuralTextures [7], are very realistic and even difficult to distinguish by human eyes. It poses serious challenges to social and personal security. There are many methods that can detect synthetic facial images with an accuracy rate of over 99%. But the synthetic facial image classification model can only give a true or fake result, and it is impossible to know which part is the discordant area. Therefore, we propose and experimentally verify a method that using reinforcement learning to select the region in the image that has the least impact on the classification model. © Published under licence by IOP Publishing Ltd.

关键词： Image classification

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