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A study on 3 d printing automatic control system based on major metal components

A study on 3 d printing automatic control system based on ma...

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2021 international conference on Information Systems and Computer engineering, ISCE 2021

作者： Han, Zhongguan Li, Xiaocheng Duan, Xianyong Anhui Technical College of Mechanical and Electrical Engineering Wuhu ANHUI241002 China

3D printing has been developing for nearly 40 years since the 1980s. This technology from the present point of view is an important complement to the traditional mechanical manufacturing way. At present, it is widely used in education, medical treatment, military industry, automobile, traditional manufacturing, construction, film and television and other fields. Generally, it mainly produces models for demonstration as well as some test parts and functional parts used in the early stage of development. 3D printing belongs to additive manufacturing technology. Compared with traditional processing technology, 3D printing has the advantage of saving time, labor and labor. Therefore, it is of great benefit to establish a 3D printing automatic control system for maj-or metal components. Based on this, this paper explores the 3D printing automatic control system of major metal components. © Published under licence by IOP Publishing Ltd.

关键词： 3D printers

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Trajectory Planning for Landing Phase of Reusable Rocket with High Thrust-to-Weight Ratio

Trajectory Planning for Landing Phase of Reusable Rocket wit...

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international conference on Guidance, Navigation and control, ICGNC 2020

作者： Wang, Cong Song, Zhengyu Shi, Guoxing Gong, Qinghai Beijing Aerospace Automatic Control Institute Beijing100854 China National Key Laboratory of Science and Technology on Aerospace Intelligent Control Beijing100854 China China Academy of Launch Vehicle Technology Beijing100076 China

ISBN: (纸本)9789811581540

This paper studies the online trajectory planning method of the rocket vertical landing under the condition of high thrust-to-weight ratio (HTWR). This is common for most of the existing rockets which are configured with high-thrust engines but only few are installed. The HTWR leads to a substantial reduction in the physical feasible region of the optimal solution. Moreover, the traditional fuel optimal strategy has the characteristics of bang-bang control, which makes the rocket weak adaptability to disturbance or model deviation in the state of maximum or minimum thrust. Thus the online planning could be unsolvable or violate the feasible boundary. This paper first describes the equations of motion and constraints during the landing process, and then the influence of the HTWR is analyzed from the perspectives of physical feasible region and deviation adaptability. In this paper, an optimal objective function with larger feasible region is proposed, so that the online planning trajectory under the condition of HTWR can be far from the boundary of the feasible region. The nonlinearity of the problem is reduced by the method of compensation model, and a second-order cone programming problem with free terminal time suitable for the primal dual interior point method is constructed. The simulation results show that the landing trajectory planned by the proposed algorithm is adaptive to the model uncertainty and disturbances, and is more robust to achieving a safe landing with a HTWR. © 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Uncertainty analysis

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Machine Learning Based Intrusion Detection Scheme to Detect Replay Attacks in Smart Grid

Machine Learning Based Intrusion Detection Scheme to Detect ...

引用

IEEE Students' conference on electrical, Electronics and Computer science (SCEECS)

作者： R. Sriranjani Bharath Kumar. M Paramesh A. K MD Saleem N. Hemavathi A. Parvathy School of Electrical and Electronics Engineering SASTRA Deemed to be University Thanjavur TamilNadu India

Smart grid involves bi-directional communication among producer and consumer. Further, advanced metering infrastructure in smart grid facilitates transformation of consumer as prosumer through intelligent scheduling. As smart grid incorporates automatic control, communication techniques and information technology, protection against cyber attack is mandate. Though variety of cyber attacks is feasible in smart grid, the proposal addresses the detection mechanism for one of the predominant attacks namely replay attack using machine learning. In replay attack, legitimate node is compromised by the attacker and hence, the node will be sending older messages repeatedly at frequent intervals. In such scenario, the receiving node is busy and is not able to provide service for other legitimate nodes. Further, the older message may cause malfunctioning of control system which is undesirable. To evade this, machine learning based intrusion detection scheme for replay attack detection is proposed. The proposal is implemented in real time using voltage and current sensors, Arduino, Zigbee and Raspberry Pi and the data is monitored through Thing Speak. Further, the data from the Zigbee is collected through XCTU and is fed as input to the pool of machine learning algorithms implemented in MATLAB. The time stamp of the node is used to classify the data either as normal or malicious. Among the algorithms, fine Gaussian Support Vector Machine exhibits better performance metrics when compared with rest of the algorithms which clearly depicts the suitability of the model.

关键词： Support vector machines Machine learning algorithms Computational modeling Zigbee Intrusion detection Mathematical models Smart grids

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Sparse Sensing and Optimal Precision: Robust H8 Optimal Observer Design with Model Uncertainty

Sparse Sensing and Optimal Precision: Robust H8 Optimal Obse...

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2021 American control conference, ACC 2021

作者： Deshpande, Vedang M. Bhattacharya, Raktim Aerospace Engineering Texas Am University TX77843 United States Aerospace Engineering Electrical Computer Engineering Texas Am University TX77843 United States

ISBN: (纸本)9781665441971

We present a framework which incorporates three aspects of the estimation problem, namely, sparse sensor configuration, optimal precision, and robustness in the presence of model uncertainty. The problem is formulated in the H8 optimal observer design framework. We consider two types of uncertainties in the system, i.e. structured affine and unstructured uncertainties. The objective is to design an observer with a given H8 performance index with minimal number of sensors and minimal precision values, while guaranteeing the performance for all admissible uncertainties. The problem is posed as a convex optimization problem subject to linear matrix inequalities. Numerical simulations demonstrate the application of the theoretical results presented in this work. © 2021 American automatic control Council.

关键词： Convex optimization

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An Efficient Weed Detection using Adaboost and CNN Hybrid Model

An Efficient Weed Detection using Adaboost and CNN Hybrid Mo...

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Advancement in Computation & Computer Technologies (InCACCT), international conference on

作者： Divya R Heena SK Shailaja Y Ramesh S. M Ashok Kumar S Kumar V Department of Electrical and Electronics Engineering PSG College of Technology Coimbatore India Department of Computer Science Engineering QIS College of Engineering and Technology Ongole India Department of ME QIS College of Engineering and Technology Ongole India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering Karpagam Academy of Higher Education Coimbatore India Department of Electronics and Communication Engineering Builders Engineering College Kangeyam India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

The detection of weeds is an essential component of precision agriculture because it provides farmers with the tools necessary to efficiently manage and control weed infestation in agricultural areas. Manual work is laborious and error-prone in traditional weed-detecting methods. Computer vision and machine learning have shown promise in automating weed detection in recent years. This study introduces a hybrid model that combines AdaBoost and CNN to detect weeds. The hybrid model uses AdaBoost and CNN to boost weed detection accuracy. AdaBoost is used as a feature selector to identify input images with discriminative properties. CNNs, or convolutional neural networks, are popular deep learning models that can extract complex spatial features from images. This CNN receives the selected features. The CNN learns and improves features, enabling precise weed classification. A large dataset from Kaggle with crop field images of various weed species and backdrops is used to evaluate the hybrid model. The dataset is hand-annotated to provide ground truth labels for training and testing. The dataset trains the hybrid model, which is optimized for precision, recall, and F1 score. Experimental comparisons show that the proposed AdaBoost and CNN hybrid model outperforms individual models and other weed detection methods in accuracy and computing economy.

关键词： Precision agriculture Training Accuracy Automation Computational modeling Crops Production Feature extraction Data models Convolutional neural networks

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Adaptive Driving Models for Intelligent Traffic Cyber Physical Systems 3

Adaptive Driving Models for Intelligent Traffic Cyber Physic...

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3rd international conference on electrical, Communication and Computer engineering, ICECCE 2021

作者： Nae, Corneliu Andrei Dumitrache, Ioan University Politehnica Bucharest Faculty of Automatic Control and Computer Science Bucharest Romania

ISBN: (纸本)9781665438971

In current paper we introduce new developments related to the microscopic vehicular traffic flow simulations and specific modelling, where we also exploit latest achievements in state-of-the-art capabilities in HPC. Our major goal is to further extend concepts from CPS-Cyber Physical Systems for the future autonomous vehicles to be part of the ITS-Intelligent Transportation System of tomorrow. In our global vision, T-CPS-Transportation Cyber Physical Systems is the framework where we consider those technologies that are able to fully exploit capabilities at both vehicle level and at the infrastructure level, including synergies. Here we present our advanced driver model, adapted for major challenges in both urban and highway environment: safety dynamic longitudinal model, lane changing options, congestion mitigation solutions and passenger safety and comfort needs. The overall set of parameters defining our models are introduced and a complex test matrix is presented for our numerical experiments. We fully simulate our model in realistic environments and we assess performance, trying to suggest integration specifications for the next generation autonomous car computer system. © 2021 IEEE.

关键词： Cyber Physical System

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Comparative Analysis of STATCOM, SVC, TCSC, and UPFC for Power Flow Balance and Efficiency of Parallel Distribution Lines

Comparative Analysis of STATCOM, SVC, TCSC, and UPFC for Pow...

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international conference on Green Energy and Applications (ICGEA)

作者： Jan Michael Batilo Ambrosio Cultura Clark Darwin Gozon Electrical Engineering Department University of Science and Technology of Southern Philippines Cagayan de Oro City Philippines

ISBN: (数字)9798331530778

ISBN: (纸本)9798331530785

Efficient power delivery and reliable system operation are critical in modern electrical distribution networks. The 69 kV parallel distribution line of the Cagayan de Oro Power Distribution System faces challenges such as high losses, congestion, and fluctuating reliability due to increasing load demand and distributed generation (DG) integration. Flexible AC Transmission System (FACTS) devices, including Static Synchronous Compensator (STATCOM), Static VAR Compensator (SVC), Thyristor controlled Series Capacitor (TCSC), and Unified Power Flow controller (UPFC), offer technological solutions to optimize power flow, improve system reliability, and enhance overall efficiency. This paper presents a comparative analysis of the effects of these FACTS devices on the operational performance of the 69 kV parallel distribution line. Using MATLAB for simulation, the study evaluates the devices' impact on critical performance parameters, including power flow optimization, transmission efficiency, and system reliability under varied loading conditions. Results indicate that while all FACTS devices enhance system performance, UPFC demonstrates the most comprehensive improvement in terms of reducing line losses, balancing power flow, and ensuring operational reliability. This comparative analysis provides insights into selecting the most appropriate FACTS for future implementation in Cagayan de Oro Distribution System and as well as other distribution networks facing similar challenges.

关键词： Performance evaluation Thyristors Static VAr compensators Distribution networks automatic voltage control Power capacitors Power system reliability Reliability Impedance Load flow

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Adaptive Neural Tracking control for Nonlinear Servo System with Unknown Time-Varying Parameters and Uncertainties

Adaptive Neural Tracking Control for Nonlinear Servo System ...

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Chinese control conference (CCC)

作者： Faxiang Zhang Guanbin Gao Jing Na Jiande Wu Faculty of Mechanical and Electrical Engineering and Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment Kunming University of Science and Technology Kunming P. R. China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming P. R. China

This paper solves the neural network (NN) tracking control problem for uncertain nonlinear servo system with time-varying parameters and uncertainties. To address the uncertain nonlinear function with unknown time-varying parameters (i.e., unknown nonlinear spatiotemporal function), the time-varying parameter extraction method is used to separate the time-varying parameters from uncertain nonlinear spatiotemporal function, which yields an unknown state-based boundary function. By the tools of NN and adaptive technology, an adaptive neural tracking controller is designed, which guarantees the uniformly ultimately bounded (UUB) performance of the resulting closed-loop system. The effectiveness of the designed method is verified by simulations.

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Out-of-distribution Object Detection through Bayesian Uncertainty Estimation

Out-of-distribution Object Detection through Bayesian Uncert...

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international conference on Information Fusion

作者： Tianhao Zhang Shenglin Wang Nidhal Bouaynaya Radu Calinescu Lyudmila Mihaylova Automatic Control and Systems Engineering The University of Sheffield UK Department of Electrical and Computer Engineering Rowan University USA Department of Computer Science University of York UK

The superior performance of object detectors is often established under the condition that the test samples are in the same distribution as the training data. However, in many practical applications, out-of-distribution (OOD) instances are inevitable and usually lead to uncertainty in the results. In this paper, we propose a novel, intuitive, and scalable probabilistic object detection method for OOD detection. Unlike other uncertainty-modeling methods that either require huge computational costs to infer the weight distributions or rely on model training through synthetic outlier data, our method is able to distinguish between in-distribution (ID) data and OOD data via weight parameter sampling from proposed Gaussian distributions based on pre-trained networks. We demonstrate that our Bayesian object detector can achieve satisfactory OOD identification performance by reducing the FPR95 score by up to 8.19% and increasing the AUROC score by up to 13.94% when trained on BDD100k and VOC datasets as the ID datasets and evaluated on COCO2017 dataset as the OOD dataset.

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Solving General Ranking and Selection Problems with Risk-aversion

Solving General Ranking and Selection Problems with Risk-ave...

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IEEE international conference on Networking, Sensing and control

作者： Ming Liu Yecheng Zhao Feng Chu Mengchu Zhou Zhongzheng Liu School of Economics & Management Tongji University Shanghai China IBISC Univ Évry University of Paris-Saclay Évry France Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

In simulation optimization, a ranking and selection (R&S) problem aims to select the best from candidate solutions, subject to a limited budget of simulation runs. Existing R&S literature focuses on selecting the best solution, based on a ranking criterion defined by the mean performance. Ignoring performance variance in the ranking criterion definition, however, may lead to selecting a very risky solution, with low average performance but high variation. In this paper, we address a new risk-averse R&S problem, which is a generalization of the classic (risk-neutral) R&S problem, by ranking the solutions via the weighted sum of the mean and variance of the performance. For this novel problem, a new approach is developed based on Karush-Kuhn-Tucker conditions, which is a generalization of optimal computing budget allocation (OCBA). Numerical experiments are conducted to show its efficiency.

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