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International conference on Unmanned Aircraft Systems (ICUAS)

作者： Luis F. Recalde Bryan S. Guevara Victor Andaluz José Varela Javier Gimenez Daniel Gandolfo SISAu Research Group Universidad Tecnol&#x00F3 gica Indoam&#x00E9 rica Ambato Ecuador Instituto de Autom&#x00E1 tica Universidad Nacional de San Juan-CONICET Argentina

ISBN: (数字)9781665405935

ISBN: (纸本)9781665405942

Accurate trajectory tracking for multiple quadrotors is essential for safe navigation in dynamic environments. However, this can be a challenge due to high nonlinearities of the systems, environment’s obstacles and constraints in control actions. This article presents a nonlinear model predictive control (NMPC) for tracking a variety of trajectories using multiple quadrotors in a unknown environment with dynamic and static obstacles. The controller is formulated using kinematic and dynamic models of multiple commercial aerial robots (Mavic Pro 2), actuators limitations, and finally converting obstacles objects to states constraints. It was developed through CasADI, an open-source software for numerical optimization that provides a fast solution to keep each quadrotor on the desired trajectory while nonlinear constraints guarantee collision avoidance and smooth control signals. The controller is implemented in simulations to systematically evaluate the performance, considering tracking accuracy and computational time. The results of the simulations are presented to confirm that the proposed NMPC generates a smooth control values ensuring safe states for each aerial robot while following a desired trajectory in unknown environment.

关键词： Trajectory tracking Computational modeling Kinematics Autonomous aerial vehicles Real-time systems Trajectory Numerical models

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FDRC: Flow-Driven Rule Caching optimization in software Defined Networking

FDRC: Flow-Driven Rule Caching Optimization in Software Defi...

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IEEE International conference on Communications

作者： He Li Song Guo Chentao Wu Jie Li School of Computer Science and Engineering The University of Aizu Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science & Engineering Shanghai Jiao Tong University

ISBN: (纸本)9781467364300

With the sharp growth of cloud services and their possible combinations, the scale of data center network traffic has an inevitable explosive increasing in recent years. software defined network (SDN) provides a scalable and flexible structure to simplify network traffic management. It has been shown that Ternary Content Addressable Memory (TCAM) management plays an important role on the performance of SDN. However, previous literatures, in point of view on rule placement strategies, are still insufficient to provide high scalability for processing large flow sets with a limited TCAM size. So caching is a brand new method for TCAM management which can provide better performance than rule placement. In this paper, we propose FDRC, an efficient flow-driven rule caching algorithm to optimize the cache replacement in SDN-based networks. Different from the previous packet-driven caching algorithm, FDRC is characterized by trying to deal with the challenges of limited cache size constraint and unpredictable flows. In particular, we design a caching algorithm with low-complexity to achieve high cache hit ratio by prefetching and special replacement strategy for predictable and unpredictable flows, respectively. By conducting extensive simulations, we demonstrate that our proposed caching algorithm significantly outperforms FIFO and least recently used (LRU) algorithms under various network settings.

关键词： Flexible structures Prefetching Switches Silicon

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Wind Direction Prediction Based on nonlinear Autoregression and Elman Neural Networks for the Wind Turbine Yaw System

SSRN

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SSRN 2022年

作者： Yang, Yusong Solomin, Evgeny V. Shishkov, Aleksandr N. Department of Electric Stations Grids and Systems of Power Supply South Ural State University 76 Lenin prospekt Chelyabinsk454080 Russia Department of Electrical Engineering Moscow Polytechnic University Moscow Russia

At present, about 90% of wind power is represented by big megawatt class horizontal axis wind turbines (HAWT). One of the permanent modern trends is the optimization and improvement of the HAWT yaw control algorithms. However, still none of the proposed yaw control strategies can give a hundred percent result of power output. Thus, the research and development in this area *** this article we proposed a model for predicting a wind direction based on nonlinear autoregression (NAR) and Elman neural networks (ENN), which is designed to reduce the error in determination of wind *** model has demonstrated a high prediction accuracy of wind direction determination (up to 1.79°), comparing with the results obtained by traditional instruments (exceeding 5°). The modeling in Matlab/Simulink software package confirmed that the neural network based strategy can accurately and quickly predict changes in wind direction, which significantly improves the accuracy and performance of the HAWT yaw system. This not only reduces the yawing process time to a certain extent, but also allows to effectively reduce the number of yawing in time and extend the lifetime of wind turbine mechanical components, at the same time increasing the output power on more than 6%. © 2022, The Authors. All rights reserved.

关键词： Wind power

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A high-performance RISC-V SoC with a DNN Accelerator for Energy Applications

A High-Performance RISC-V SoC with a DNN Accelerator for Ene...

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Electronic Information Engineering and Computer Communication (EIECC), International conference on

作者： Yang Ren Xinyu Liu Lewei Li Nianxiong Tan College of Integrated Circuits Zhejiang University Hangzhou China Research and Development Center Hangzhou Vango Technologies Inc Hangzhou China College of Integrated Circuits Zhejiang University Hangzhou Vango Technologies Inc Hangzhou China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

As in many applications, the energy sector is witnessing the migration of artificial intelligence (AI) algorithms from the cloud to the edge. This paper introduces a System-on-Chip (SoC) designed for energy control units (ECUs) and data concentrators (DCs) to support edge computing acceleration. The SoC is implemented on the Chipyard platform, utilizing the open-source out-of-order processor BOOM and the software-friendly accelerator Gemmini. Fabricated using a 22nm process, the core achieves a normal operating frequency of 1GHz and occupies a die area of 22.99mm 2 .

关键词： Out of order AI accelerators System-on-chip Smart grids optimization Next generation networking Edge computing

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Research on the Design of LLC Digital Power Supply Based on Microcontroller

Research on the Design of LLC Digital Power Supply Based on ...

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International Symposium on Environmentally Conscious Design and Inverse Manufacturing (EcoDesign)

作者： Chunguang Zhou Jian Liu School of Electrical and Electronic Engineering Shanghai Institute of Technology Shanghai China

ISBN: (数字)9798350363043

ISBN: (纸本)9798350363050

In this study, we have undertaken a comprehensive design of a digital power supply, leveraging the LLC resonant converter and controlled by a cost-effective STMicroelectronics’s microcontroller. Recognizing the limitations of traditional analog power systems in delivering high-efficiency, compact, and intelligent solutions, we have embraced digital control technology to address the challenges of output stability inherent in the LLC resonant converter. Our approach offers precise control, rapid response, and a reduced environmental footprint, significantly enhancing the stability and reliability of the power supply system. We have detailed the design and implementation of our LLC digital power supply. Our design includes self-tuning, cost-effectiveness, improved dynamic response, and self-monitoring capabilities through custom algorithms. We have calculated and selected the models of key components, such as optocouplers and transformers, in the inverter circuit for optimal performance. The software system is based on STMicroelectronics’s embedded microcontroller and is divided into PWM control, ADC reading, PID closed-loop control algorithm and error handling to achieve stable voltage output. The successful development of a 1000 W LLC digital power supply paves the way for future advancements in control strategies, component optimization, and environmental adaptability in power supply systems.

关键词： Power system measurements Power supplies Microcontrollers Density measurement Heuristic algorithms software algorithms Resonant converters Power system stability Stability analysis Circuit stability

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Real-time feasibility of nonlinear predictive control for large scale processes-a case study

Real-time feasibility of nonlinear predictive control for la...

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American Control conference (ACC)

作者： Z. Nagy R. Findeisen M. Diehl F. Allgower H.G. Bock S. Agachi J.P. Schloder D. Leineweber Babes Bolyai University Romania Institute for Systems Theory in Engineering University of Stuttgart Germany Interdisciplinary Center for Scientific Computing (IWR) University of Heidelberg Germany Bayer AG Leverkusen Germany

Despite many control theoretic and numerical advances, up to now there is no realistic feasibility study of modern nonlinear model predictive control (NMPC) schemes for the real-time control of large-scale processes. In this paper the application of NMPC to a nontrivial process control example, namely the control of a high-purity binary distillation column, is considered. Using models of different complexity and different control schemes, the computational load, resulting closed loop performance and the effort needed to design the controllers is compared. It is shown that a real-time application of modern NMPC schemes is feasible with existing techniques, even for a 164/sup th/ order model with a sampling time of 30 s, if a state of the art dynamic optimization algorithm and an efficient NMPC scheme are used.

关键词： Predictive control Large-scale systems Computer aided software engineering Predictive models Process control Distillation equipment Size control nonlinear control systems Sampling methods Power system modeling

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Research on Image Edge Detection Method Based on Wolf King Algorithm

Research on Image Edge Detection Method Based on Wolf King A...

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International conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Chang Jian Fan Yimeng School of Software Liaoning Technical University Huludao China

ISBN: (数字)9798350350760

ISBN: (纸本)9798350350777

Aiming at the problem that the current image edge detection algorithm is not accurate enough to capture the edge contour, an image edge detection algorithm based on Wolf king algorithm was proposed. Firstly, local principal component analysis and generalized Gaussian prior were used to improve the Gaussian filter, so that the image noise was effectively suppressed and the detail information was significantly enhanced. Secondly, multi-direction gradient calculation is proposed to reduce the influence of noise on gradient estimation and provide more information to locate the edge, so as to determine the position and direction of the edge more accurately. Finally, the Wolf king algorithm proposed in this paper is based on the grey Wolf algorithm, which is improved by using the nonlinear convergence factor and adaptive weight strategy. The improved algorithm is used to optimize Otsu to obtain the image threshold, realize the automatic acquisition of the image high and low threshold, and further improve the accuracy of the algorithm. More detailed edge contour information can be extracted from the image processed by the above method. The experimental results show that the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) of the images processed by the LPG-PCA image denoising algorithm are increased by 18.8% and 13.6% respectively compared with the traditional Gaussian filtering. In terms of threshold processing, compared with most existing edge detection algorithms, the Wolf king algorithm proposed in this paper can accurately detect the edge contour, and it is more coherent in edge extraction, with better unilateral response effect and better algorithm performance.

关键词： Accuracy PSNR Image edge detection Simulation Noise Data mining optimization Image denoising Convergence Principal component analysis

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Semi-Supervised Random Forests

Semi-Supervised Random Forests

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International conference on Computer Vision (ICCV)

作者： Christian Leistner Amir Saffari Jakob Santner Horst Bischof Institute for Computer Graphics and Vision Graz University of Technology Austria

Random Forests (RFs) have become commonplace in many computer vision applications. Their popularity is mainly driven by their high computational efficiency during both training and evaluation while still being able to achieve state-of-the-art accuracy. This work extends the usage of Random Forests to Semi-Supervised Learning (SSL) problems. We show that traditional decision trees are optimizing multi-class margin maximizing loss functions. From this intuition, we develop a novel multi-class margin definition for the unlabeled data, and an iterative deterministic annealing-style training algorithm maximizing both the multi-class margin of labeled and unlabeled samples. In particular, this allows us to use the predicted labels of the unlabeled data as additional optimization variables. Furthermore, we propose a control mechanism based on the out-of-bag error, which prevents the algorithm from degradation if the unlabeled data is not useful for the task. Our experiments demonstrate state-of-the-art semi-supervised learning performance in typical machine learning problems and constant improvement using unlabeled data for the Caltech-101 object categorization task.

关键词： Semisupervised learning Iterative algorithms Computer vision Application software Computational efficiency Decision trees Annealing Error correction Machine learning algorithms Degradation

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An Efficient Real-Time NMPC for Quadrotor Position Control under Communication Time-Delay

An Efficient Real-Time NMPC for Quadrotor Position Control u...

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International conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Bárbara Barros Carlos Tommaso Sartor Andrea Zanelli Gianluca Frison Wolfram Burgard Moritz Diehl Giuseppe Oriolo Sapienza Università di Roma Automation & Management Engineering (DIAG) Italy Belgium University of Freiburg Germany

ISBN: (数字)9781728177090

ISBN: (纸本)9781728177106

The advances in computer processor technology have enabled the application of nonlinear model predictive control (NMPC) to agile systems, such as quadrotors. These systems are characterized by their underactuation, nonlinearities, bounded inputs, and time-delays. Classical control solutions fall short in overcoming these difficulties and fully exploiting the capabilities offered by such platforms. This paper presents the design and implementation of an efficient position controller for quadrotors based on real-time NMPC with time-delay compensation and bounds enforcement on the actuators. To deal with the limited computational resources onboard, an offboard control architecture is proposed. It is implemented using the high-performance software package acadoff, which solves optimal control problems and implements a real-time iteration (RTI) variant of a sequential quadratic programming (SQP) scheme with Gauss-Newton Hessian approximation. The quadratic subproblems (QP) in the SQP scheme are solved with HPIPM, an interior-point method solver, built on top of the linear algebra library BLASFEO, finely tuned for multiple CPU architectures. Solution times are further reduced by reformulating the QPs using the efficient partial condensing algorithm implemented in HPIPM. We demonstrate the capabilities of our architecture using the Crazyflie 2.1 nano-quadrotor.

关键词： Real-time systems Computer architecture Delays Prediction algorithms software algorithms Position control Optimal control

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DCFMS: A chunk-based distributed file system for supporting multimedia communication

DCFMS: A chunk-based distributed file system for supporting ...

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Federated conference on Computer Science and Information Systems (FedCSIS)

作者： Cosmin Marian Poteras Mihai Mocanu Constantin Petrisor Cristian Marian Mihaescu Software Engineering Department University of Craiova Craiova Romania

It is well known that the main drawback of distributed applications that require high performance is related to the data transfer speed between system nodes. The high speed networks are never enough. The application has to come out with special techniques and algorithms for optimizing data availability. This aspect is increasingly needed for some categories of distributed applications such as computational steering applications, which have to permanently allow users to interactively monitor and control the progress of their applications. Following our previous research which was focused on the development of a set of frameworks and platforms for distributed simulation and computational steering, we introduce in this paper a new model for distributed file systems, supporting data steering, that is able to provide optimization for data acquisition, data output and load balancing while reducing the development efforts, improving scalability and flexibility of the system. Data partitioning is being performed at a logical level allowing multimedia applications to define custom data chunks like frames of a video, phrases of text, regions of an image, etc.

关键词： Distributed databases Availability Data models Peer to peer computing Load management Computational modeling Protocols

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