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Nonlinear control of Crane Systems Based on Intelligence computing of Disturbance Observer Under Mismatched Disturbance 22

Nonlinear Control of Crane Systems Based on Intelligence Com...

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22nd IEEE International symposium on Parallel and Distributed Processing with applications, ISPA 2024

作者： Wang, Tianlei Wang, Yiyan Xu, Meng Tian, Jiajie Zhang, Jingling Hong, Zhiyong Wuyi University School of Electronic and Information Engineering Jiangmen China Jiangmen Key Laboratory of Kejie Semiconductor Bonding Technology and Control System Jiangmen China Wuyi University School of Mechanical and Automation Engineering Jiangmen China Guangdong Kejie Technology Co. Ltd Jiangmen529020 China

ISBN: (纸本)9798331509712

A control method based on disturbance observer is proposed to address the positioning drift issue of the gantry crane under unmatched disturbances, aiming at precise positioning of the trolley and effective payload swing angle attenuation in a variable-length two-dimensional lifting system. The control scheme comprises a combination of a proportional-derivative controller and a disturbance observer. Firstly, the design of the coupling function in this paper is based on the observation of the natural characteristics of the dynamical model. It cleverly designs the coupling function, which not only helps to effectively reduce the swing angle but also prevents the problem of positioning drift under non-matching disturbances, subsequently, design a controller by integrating intelligent computing with control theory. Secondly, the designed disturbance observer observes and compensates for the system output to achieve disturbance suppression. Thirdly, a thorough stability analysis has been conducted to demonstrate that the proposed controller satisfies the desired conditions. Finally, simulation results illustrate that the pro-posed method success-fully addresses the limitations of existing methods and exhibits superior performance and robustness. © 2024 IEEE.

关键词： Gantry cranes

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Predictive Queue-based Low Latency Congestion Detection in Data Center Networks 21

Predictive Queue-based Low Latency Congestion Detection in D...

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21st IEEE International symposium on Parallel and Distributed Processing with applications, 13th IEEE International Conference on Big Data and Cloud computing, 16th IEEE International Conference on Social computing and Networking and 13th International Conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Dong, Pingping Lu, Xiaojuan Huang, Tairan Chen, Liying Yang, Yang Zhang, Lianming Hunan Normal University College of Information Science and Engineering Changsha China

ISBN: (纸本)9798350329223

End-to-end congestion control in lossless data center networks (DCN) depends on congestion detection. However, many current congestion control mechanisms ignore the mutually reinforcing relationship with congestion detection and flow identification, resulting in wasted link resources. For accurate congestion detection, the currently popular ternary congestion detection (TCD) technique sets three separate port states. However, the flow needs to go through multiple hops before deciding the flow category, which leads to serious delays. In order to achieve accurate congestion detection and low delay flow identification, we propose a predictive queue-based low latency congestion detection, called hop-by-hop flow identification (HFI) based on congestion detection. HFI predicts the dynamic queueing threshold based on the data transmission rate to provide timely and proactive congestion detection, then combined it with the congestion information notification from the next hop to identify the congested flow hop by hop. Experimental results show that DCQCN+HFI obtains shorter flow completion time, and improves link utilization by 55%, 7.5% compared to DCQCN, DCQCN+TCD. © 2023 IEEE.

关键词： Queueing theory

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SHOWAR: Right-Sizing And Efficient Scheduling of Microservices 12

<i>SHOWAR</i>: Right-Sizing And Efficient Scheduling of Micr...

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12th Annual ACM symposium on Cloud computing (SoCC)

作者： Baarzi, Ataollah Fatahi Kesidis, George Penn State Univ University Pk PA 16802 USA

ISBN: (纸本)9781450386388

Microservices architecture have been widely adopted in designing distributed cloud applications where the application is decoupled into multiple small components (i.e. "microservices"). One of the challenges in deploying microservices is finding the optimal amount of resources (i.e. size) and the number of instances (i.e. replicas) for each microservice in order to maintain a good performance as well as prevent resource wastage and under-utilization which is not cost-effective. This paper presents SHOWAR, a framework that configures the resources by determining the number of replicas (horizontal scaling) and the amount of CPU and Memory for each microservice (vertical scaling). For vertical scaling, SHOWAR uses empirical variance in the historical resource usage to find the optimal size and mitigate resource wastage. For horizontal scaling, SHOWAR uses basic ideas from control theory along with kernel level performance metrics. Additionally, once the size for each microservice is found, SHOWAR bridges the gap between optimal resource allocation and scheduling by generating affinity rules (i.e. hints) for the scheduler to further improve the performance. Our experiments, using a variety of microservice applications and real-world workloads, show that, compared to the state-of-the-art autoscaling and scheduling systems, SHOWAR on average improves the resource allocation by up to 22% while improving the 99th percentile end-to-end user request latency by 20%.

关键词： autoscaling microservices cloud computing

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Investigating the variance increase of readout error mitigation through classical bit-flip correction on ibm and Rigetti quantum computers 38

Investigating the variance increase of readout error mitigat...

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38th International symposium on Lattice Field theory, LATTICE 2021

作者： Alexandrou, Constantia Funcke, Lena Hartung, Tobias Jansen, Karl Kühn, Stefan Polykratis, Georgios Stornati, Paolo Wang, Xiaoyang Weber, Tom Department of Physics University of Cyprus P.O. Box 20537 Nicosia1678 Cyprus Computation-Based Science and Technology Research Center The Cyprus Institute 20 Kavafi Street Nicosia2121 Cyprus Center for Theoretical Physics Co-Design Center for Quantum Advantage NSF AI Institute for Artificial Intelligence and Fundamental Interactions Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Perimeter Institute for Theoretical Physics 31 Caroline Street North WaterlooONN2L 2Y5 Canada Department of Mathematical Sciences University of Bath Bath United Kingdom Deutsches Elektronen-Synchrotron DESY Platanenallee 6 Zeuthen15738 Germany School of Physics Peking University 5 Yiheyuan Rd Haidian District Beijing100871 China Department of Computer Science Universität Hamburg Vogt-Kölln-Str. 30 Hamburg22527 Germany

Readout errors are among the most dominant errors on current noisy intermediate-scale quantum devices. Recently, an efficient and scaleable method for mitigating such errors has been developed, based on classical bit-flip correction [1, 2]. In this talk, we compare the performance of this method for ibm's and Rigetti's quantum devices, demonstrating how the method improves the noisy measurements of observables obtained on the quantum hardware. Moreover, we examine the variance amplification to the data after applying of our mitigation procedure, which is common to all mitigation strategies. We derive a new expression for the variance of the mitigated Pauli operators in terms of the corrected expectation values and the noisy variances. Our hardware results show good agreement with the theoretical prediction, and we demonstrate that the increase of the variance due to the mitigation procedure is only moderate. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

关键词： Errors

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Enabling Highly Efficient Capsule Networks Processing Through A PIM-Based Architecture Design 26

Enabling Highly Efficient Capsule Networks Processing Throug...

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26th IEEE International symposium on High Performance Computer Architecture (HPCA)

作者： Zhang, Xingyao Song, Shuaiwen Leon Xie, Chenhao Wang, Jing Zhang, Weigong Fu, Xin Univ Houston ECE Dept ECOMS Lab Houston TX 77004 USA Univ Sydney Future Syst Architecture FSA Lab Sydney NSW Australia Pacific Northwest Natl Lab PNNL Richland WA USA Capital Normal Univ Coll Informat Engn Beijing Peoples R China Beijing Adv Innovanon Ctr Imaging Theory & Techno Beijing Peoples R China

ISBN: (纸本)9781728161495

In recent years, the CNNs have achieved great successes in the image processing tasks, e.g., image recognition and object detection. Unfortunately, traditional CNN's classification is found to be easily misled by increasingly complex image features due to the usage of pooling operations, hence unable to preserve accurate position and pose information of the objects. To address this challenge, a novel neural network structure called Capsule Network has been proposed, which introduces equivariance through capsules to significantly enhance the learning ability for image segmentation and object detection. Due to its requirement of performing a high volume of matrix operations, CapsNets have been generally accelerated on modern GPU platforms that provide highly optimized software library for common deep learning tasks. However, based on our performance characterization on modern GPUs, CapsNets exhibit low efficiency due to the special program and execution features of their routing procedure, including massive unshareable intermediate variables and intensive synchronizations, which are very difficult to optimize at software level. To address these challenges, we propose a hybrid computing architecture design named PIM-CapsNet. It preserves GPU's on-chip computing capability for accelerating CNN types of layers in CapsNet, while pipelining with an off-chip in-memory acceleration solution that effectively tackles routing procedure's inefficiency by leveraging the processingin -memory capability of today's 31) stacked memory. Using routing procedure's inherent parallellization feature, our design enables hierarchical improvements on CapsNet inference efficiency through minimizing data movement and maximizing parallel processing in memory. Evaluation results demonstrate that our proposed design can achieve substantial improvement on both performance and energy savings for CapsNet inference, with almost zero accuracy loss. The results also suggest good performance scalability

关键词： Accelerators domain-specific architectures Architecture applications of Machine Learning Emerging technologies

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RoboX: An End-to-End Solution to Accelerate Autonomous control in Robotics 18

<i>RoboX</i>: An End-to-End Solution to Accelerate Autonomou...

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45th ACM/IEEE Annual International symposium on Computer Architecture (ISCA)

作者： Sacks, Jacob Mahajan, Divya Lawson, Richard C. Esmaeilzadeh, Hadi Georgia Inst Technol Alternat Comp Technol ACT Lab Atlanta GA 30332 USA Univ Calif San Diego Alternat Comp Technol ACT Lab La Jolla CA 92093 USA

ISBN: (纸本)9781538659847

Novel algorithmic advances have paved the way for robotics to transform the dynamics of many social and enterprise applications. To achieve true autonomy, robots need to continuously process and interact with their environment through computationally-intensive motion planning and control algorithms under a low power budget. Specialized architectures offer a potent choice to provide low-power, high-performance accelerators for these algorithms. Instead of taking a traditional route which profiles and maps hot code regions to accelerators, this paper delves into the algorithmic characteristics of the application domain. We observe that many motion planning and control algorithms are formulated as a constrained optimization problems solved online through Model Predictive control (MPC). While models and objective functions differ between robotic systems and tasks, the structure of the optimization problem and solver remain fixed. Using this theoretical insight, we create RoboX, an end-to-end solution which exposes a high-level domain-specific language to roboticists. This interface allows roboticists to express the physics of the robot and its task in a form close to its concise mathematical expressions. The RoboX backend then automatically maps this high-level specification to a novel programmable architecture, which harbors a programmable memory access engine and compute-enabled interconnects. Hops in the interconnect are augmented with simple functional units that either operate on in-fight data or are bypassed according a micro-program. Evaluations with six different robotic systems and tasks show that RoboX provides a 29.4x (73 x) speedup and 22.1 x (79.4 x) performance-per-watt improvement over an ARM Cortex A57 (Intel Xeon E3). Compared to GPUs, RoboX attains 7.8 x, 65.5 x, and 71.8x higher Performance-per Watt to Tegra X2, GTX 650 Ti, and Tesla K40 with a power envelope of only 3.4 Watts at 45 mn.

关键词： Accelerators domain-specific languages DSL in-network computing model predictive control MPC robotics

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Processing-in-Memory Enabled Graphics Processors for 3D Rendering 23

Processing-in-Memory Enabled Graphics Processors for 3D Rend...

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23rd IEEE International symposium on High Performance Computer Architecture (HPCA)

作者： Xie, Chenhao Song, Shuaiwen Leon Wang, Jing Zhang, Weigong Fu, Xin Univ Houston ECE Dept Houston TX 77004 USA PNNL HPC Grp Richland WA USA Capital Normal Univ Coll Informat Engn Beijing Adv Innovat Ctr Imaging Technol Beijing Peoples R China

ISBN: (纸本)9781509049851

The performance of 3D rendering of Graphics Processing Unit that converts 3D vector stream into 2D frame with 3D image effects significantly impacts users gaming experience on modern computer systems. Due to its high texture throughput requirement, main memory bandwidth becomes a critical obstacle for improving the overall rendering performance. 3D-stacked memory systems such as Hybrid Memory Cube provide opportunities to significantly overcome the memory wall by directly connecting logic controllers to DRAM dies. Although recent works have shown promising improvement in performance by utilizing HMC to accelerate special-purpose applications, a critical challenge of how to effectively leverage its high internal bandwidth and computing capability in GPU for 3D rendering remains unresolved. Based on the observation that texel fetches greatly impact off-chip memory traffic, we propose two architectural designs to enable Processing-In-Memory based GPU for efficient 3D rendering. Additionally, we employ camera angles of pixels to control the performance-quality tradeoff of 3D rendering. Extensive evaluation across several real-world games demonstrates that our design can significantly improve the performance of texture filtering and 3D rendering by an average of 3.97X (up to 6.4X) and 43% (up to 65%) respectively, over the baseline GPU. Meanwhile, our design provides considerable memory traffic and energy reduction without sacrificing rendering quality.

关键词： GPU Processing-In-Memory 3D-Stacked Memory Approximate computing 3D Rendering

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Onboard autonomous management system of SPARK Earth observation microsatellites 68

Onboard autonomous management system of SPARK Earth observat...

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68th International Astronautical Congress: Unlocking Imagination, Fostering Innovation and Strengthening Security, IAC 2017

作者： Xu, Rui Wang, Hui Xu, Wenming Cui, Pingyuan Feng, Zhun School of Aerospace Engineering Beijing Institute of Technology Beijing100081 China Key Laboratory of Autonomous Navigation and Control for Deep Space Exploration Ministry of Industry and Information Technology Beijing100081 China Shanghai Engineering Center for Microsatellites Shanghai201210 China

ISBN: (纸本)9781510855373

Space remote sensing has played a key role in numerous fields, such as resource exploration, disaster prevention, and environmental protection. Many countries have been attracted to research the Earth observation microsatellite because of its short development period, low cost, and high return. With the increasing number of in-orbit satellites, however, traditional ground management way (TT&C) has caused a heavy workload, and is error-prone and timeconsuming. That makes increasingly urgent demand for the onboard autonomous management ability of satellites. As the onboard resources of microsatellites are extremely limited, the implementation of onboard autonomous management is especially difficult. This paper addresses an onboard autonomous management system for Earth observation microsatellites with double processors. A concurrent CAN bus architecture is designed to avoid affecting original onboard systems. As a node on the CAN bus, the autonomous management system (AMS) runs independently on a single FPGA with extra SRAMs. By transmitting, receiving, and listening, the AMS can communicate with original housekeeping system and other subsystems. For Earth observation microsatellites, the AMS mainly focuses on the autonomous generation of command sequences of observation and data transmission tasks. To satisfy multiple complex constraints and low computational cost, a new dynamic value based heuristic planning (DVHP) method is proposed for the multi-area-target observation of microsatellites. Additionally, an efficient rotational-path decomposition based recursive planning (RDRP) method is adopted for the attitude reorientation under bounded and pointing constraints. The AMS has been successfully applied in two SPARK microsatellites launched on 22 December, 2016, which were developed by the Shanghai Engineering Center for Microsatellites. Microsatellite experiment results demonstrate that under very limited onboard computing resources (32MHz processor, 2MB RAM), the A

关键词： Disaster prevention

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Cloud elasticity: Going beyond demand as user load 3

Cloud elasticity: Going beyond demand as user load

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3rd Internatioal Workshop on Adaptive Resource Management and Scheduling for Cloud computing, ARMS-CC 2016

作者： Chilipirea, Cristian Constantin, Alexandru Popa, Dan Crintea, Octavian Dobre, Ciprian Faculty of Automatic Control and Computers University Politehnica of Bucharest Bucharest Romania

ISBN: (纸本)9781450342278

Cloud computing systems have become not only popular, but extensively used. They are supported and exploited by both industry and academia. Cloud providers have diversified and so did the software offered by their systems. Infrastructure as a Service (IaaS) clouds are now available from single virtual machine use cases, such as a personal server, to specialized high performance or machine learning engines. This popularity has been brought by the low-cost and risk-free feature of renting computing resources instead of buying them, in a large, one-time investment. Furthermore, clouds permit their clients the use of elasticity. Elasticity is the most relevant feature of cloud computing. It refers to the clients' ability to easily change the number of rented resources in a live environment. This permits the entire system to handle differences in load. Most cloud clients serve web applications or services to third parties. In these cases, load differences can be correlated to the number of users for the service and elasticity is used to handle differences in what is called user load. Most of the scientific literature approaches elasticity looking solely at user load. To give a clearer understanding, the majority of cloud frameworks in use today work as follows: they start a number of worker nodes, and tasks are assigned to them for execution. Only when the user load changes, the number of workers is adjusted, if any. In this paper, we propose an alternative approach, where the number of workers depends on the actual requirements coming from the different execution steps of an application. We show such an idea can be achieved for several workflows from different fields and that it can bring significant benefits to execution time and cost. © 2016 ACM.

关键词： Elasticity

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Preserving Statistical Validity in Adaptive Data Analysis 15

Preserving Statistical Validity in Adaptive Data Analysis

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47th Annual ACM symposium on theory of computing (STOC) held as part of the Federated computing Research Conference

作者： Dwork, Cynthia Feldman, Vitaly Hardt, Moritz Pitassi, Toniann Reingold, Omer Roth, Aaron Microsoft Res Redmond WA 98052 USA IBM Res Almaden San Jose CA USA Univ Toronto Toronto ON Canada Samsung Res Amer Mountain View CA USA Univ Penn Philadelphia PA 19104 USA

ISBN: (纸本)9781450335362

A great deal of effort has been devoted to reducing the risk of spurious scientific discoveries, from the use of sophisticated validation techniques, to deep statistical methods for controlling the false discovery rate in multiple hypothesis testing. However, there is a fundamental disconnect between the theoretical results and the practice of data analysis: the theory of statistical inference assumes a fixed collection of hypotheses to be tested, or learning algorithms to be applied, selected non-adaptively before the data are gathered, whereas in practice data is shared and reused with hypotheses and new analyses being generated on the basis of data exploration and the outcomes of previous analyses. In this work we initiate a principled study of how to guarantee the validity of statistical inference in adaptive data analysis. As an instance of this problem, we propose and investigate the question of estimating the expectations of m adaptively chosen functions on an unknown distribution given n random samples. We show that, surprisingly, there is a way to estimate an exponential in n number of expectations accurately even if the functions are chosen adaptively. This gives an exponential improvement over standard empirical estimators that are limited to a linear number of estimates. Our result follows from a general technique that counter-intuitively involves actively perturbing and coordinating the estimates, using techniques developed for privacy preservation. We give additional applications of this technique to our question.

关键词： differential privacy generalization bounds false discovery overfitting adaptive data analysis statistical estimation

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