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Intelligent route planning model of industrial robot based on inertia moment parameter optimization

Intelligent route planning model of industrial robot based o...

2021 IEEE International Conference on computer Science, Electronic Information Engineering and Intelligent control technology, CEI 2021

作者： Lu, Xinghua Bao, Hanke He, Yinkang Huang, Jiahao Wang, Quanchun Mai, Kaijun Institute of computer control technology Guangzhou Huali College Guangzhou China College of Economics and Management Yunnan Agricultural University Kunming China

ISBN: (纸本)9780738146492

In order to improve the intelligent planning ability of monocular vision dynamic underactuated industrial robot's action route, an intelligent planning method of monocular vision dynamic underactuated industrial robot's action route based on inertia moment parameter optimization is proposed. Establishing a spatial grid area planning model of monocular vision dynamic underactuated industrial robot action route, adopting a cell moment parameter optimization method to carry out grid regionalization matching in the process of monocular vision dynamic underactuated industrial robot action route optimization planning, establishing optimization constraint parameters of monocular vision dynamic underactuated industrial robot action route distribution. Based on kinematic feature analysis method, the optimization planning and moment parameter optimization analysis of monocular vision dynamic underactuated industrial robot are carried out. Through automatic condition tracking feedback and moment parameter optimization analysis, the path optimization of monocular vision dynamic underactuated industrial robot is realized. Combined with unsupervised learning algorithm of fuzzy PID network, the intelligent planning model of action route is constructed to improve control stability. Tests show that this method has good adaptability and strong obstacle avoidance ability for monocular vision dynamic underactuated industrial robot. © 2021 IEEE.

关键词： Industrial robots

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technology Landscape for Epidemiological Prediction and Diagnosis of COVID-19

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computers, Materials & Continua 2021年第5期67卷 1679-1696页

作者： Siddhant Banyal Rinky Dwivedi Koyel Datta Gupta Deepak Kumar Sharma Fadi Al-Turjman Leonardo Mostarda Department of Instrumentation and Control Netaji Subhas University of TechnologyNew Delhi110078India Department of Computer Science&Engineering Maharaja Surajmal Institute of TechnologyNew Delhi110058India Department of Information Technology Netaji Subhas University of TechnologyNew Delhi110078India Research Center for AI and IoT Near East UniversityNicosiaMersin10Turkey Computer Science Department Camerino UniversityCamerino62032Italy

The COVID-19 outbreak initiated from the Chinese city of Wuhanand eventually affected almost every nation around the globe. From China,the disease started spreading to the rest of the world. After China, Italybecame the next epicentre of the virus and witnessed a very high death *** nations like the USA became severely hit by SARS-CoV-2 virus. TheWorld Health Organisation, on 11th March 2020, declared COVID-19 a pandemic. To combat the epidemic, the nations from every corner of the worldhas instituted various policies like physical distancing, isolation of infectedpopulation and researching on the potential vaccine of SARS-CoV-2. Toidentify the impact of various policies implemented by the affected countrieson the pandemic spread, a myriad of AI-based models have been presented toanalyse and predict the epidemiological trends of COVID-19. In this work, theauthors present a detailed study of different articial intelligence frameworksapplied for predictive analysis of COVID-19 patient record. The forecastingmodels acquire information from records to detect the pandemic spreadingand thus enabling an opportunity to take immediate actions to reduce thespread of the virus. This paper addresses the research issues and correspondingsolutions associated with the prediction and detection of infectious diseaseslike COVID-19. It further focuses on the study of vaccinations to cope withthe pandemic. Finally, the research challenges in terms of data availability,reliability, the accuracy of the existing prediction models and other open issuesare discussed to outline the future course of this study.

关键词： COVID-19 diagnosis deep learning forecasting models machine learning metaheuristics prediction big data pandemic

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The Impact of the Digital Transformation of Education on Literacy

The Impact of the Digital Transformation of Education on Lit...

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International IEEE Conference and Workshop: Óbuda on Electrical and Power Engineering (CANDO-EPE)

作者： Imre Farkas Attila Kovari Mónika Rajcsanyi-Molnar Department of Computer Systems and Control Technology Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Digital Technology Faculty of Computer Science Eszterházy Károly Catholic University Eger Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemét Hungary Department of Economics and Management Institute of Social Sciences University of Dunaujvaros Dunaujvaros Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

During the COVID19 epidemic, people of all ages from all walks of life around the world have become inevitably familiar with and almost dependent on the digital tools of the age and the opportunities they offer. A change had to take place very quickly that was unprecedented. This process has also affected education. During the time of the reading lock-in, there was a proliferation of digital curricular content. The second time around, online education came to the fore, thanks to less or well-functioning online platforms, and has since become part of everyday teaching. What is the effectiveness and usefulness of sharing knowledge in this way? Is literacy still needed at all? Can online and hybrid (a controlled mix of face-to-face and online) education replace the well-established contact and face-to-face education? The study show that the respondents believe that online education has had an impact on literacy and that it is perceived to have a positive impact. The results indicate that online education has a positive impact on the literacy levels of students, as it facilitates knowledge acquisition and enhances digital literacy skills.

关键词： Power engineering Epidemics Navigation Knowledge acquisition Digital transformation Education Information age Digital intelligence Cultural differences Problem-solving

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Determination of Hydrological Model Parameters by Newton Method 12th

Determination of Hydrological Model Parameters by Newton M...

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12th International Conference on Optimization and Applications, OPTIMA 2021

作者： Zasukhin, Sergey Zasukhina, Elena Moscow Institute of Physics and Technology Dolgoprudny Russia Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences Moscow Russia

ISBN: (纸本)9783030927103

The problem of determining parameters of a hydrological model is studied. The model describes vertical water transfer in unsaturated soil and includes some parameters that characterize hydrophysical properties of soil. The problem is formulated as a variational problem with differential constraints in the form of the Richards equation describing a process of water transfer in soil. The objective functional is proportional to the distance squared between measured and modeled values of the soil moisture. Numerical solution of the discretized problem is carried out using Newton method. Gradient and second order derivatives of the objective function are calculated using formulas obtained by fast automatic differentiation techniques. © 2021, Springer Nature Switzerland AG.

关键词： Newton-Raphson method

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Risk-Sensitive RL Using Sampling-Based Expectation-Maximization

Risk-Sensitive RL Using Sampling-Based Expectation-Maximizat...

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IEEE Conference on Decision and control

作者： Erfaun Noorani John S. Baras Karl H. Johansson Department of Electrical and Computer Engineering Institute for Systems Research (ISR) at the University of Maryland College Park MD USA Automatic Control Lab Royal Institute of Technology Stockholm Sweden

There is a need for robust Reinforcement Learning (RL) algorithms that can cope with model misspecification, parameter uncertainty, disturbances, etc. Risk-sensitive methods offer an approach to developing robust RL algorithms by hedging against undesirable outcomes in a probabilistic manner. The Probabilistic Graphical Model (PGM) framework offers systematic exploration for risk-sensitive RL. In this paper, we bridge the Markov Decision Process (MDP) and the PGM frameworks. We exploit the equivalence of optimizing a certain risk-sensitive criterion in the MDP formalism with optimizing a log-likelihood objective in the PGM formalism. By utilizing this equivalence, we offer an approach for developing risk-sensitive algorithms by leveraging the PGM framework. We explore the Expectation-Maximization (EM) algorithm under the PGM formalism. We show that risk-sensitive policy gradient methods can be obtained by applying sampling-based approaches to the EM algorithm, e.g., Monte-Carlo EM, with the log-likelihood. We show that Monte-Carlo EM leads to a risk-sensitive Monte-Carlo policy gradient algorithm. Our simulations illustrate the risk-sensitive nature of the resulting algorithm.

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Adaptive Sparse Deep Neural Network Inference on Resource-Constrained Cost-Efficient GPUs

Adaptive Sparse Deep Neural Network Inference on Resource-Co...

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IEEE Conference on High Performance Extreme Computing (HPEC)

作者： Ming Dun Xu Zhang Huawei Cao Yuan Zhang Junying Huang Xiaochun Ye State Key Lab of Processors Institute of Computing Technology CAS Beijing China School of Computer and Control Engineering UCAS Beijing China Nanjing Institute of InforSuperbahn Nanjing China

Sparse Deep Neural Networks (SpDNNs) has gained great popularity and been widely applied in various machine learning area. Compared to traditional dense DNNs, the unpre-dictable irregularity and sparsity in the sparse weight matrices of SpDNNs make them difficult to be efficiently parallelled. Moreover, most of the recent advanced efforts to optimize SpDNNs are based on high-end GPUs like NVIDIA V100, which may not be affordable to individuals and smaller re-search groups. However, migrating the SpDNNs to those cost-efficient but resource-constrained GPUs confronts enormous challenges, including limitations in both memory and computing resources, as well as the tiresome hyper-parameter tuning in batch parallelism. In this paper, we accelerate SpDNNs on GPUs with more restricted resources through exploiting the memory and computing resources. On one hand, we design the adaptive memory-aware data partition scheme to reduce memory consumption automatically. On the other hand, we propose the Tensor core/CUDA core fusion mechanism to efficiently utilize the hetergeneous computing resources on modern GPU architecture. To the best of our knowledge, we are the first to improve the performance on SpDNNs through adaptive memory tuning and utilizing hetergeneous computing core concurrency. We compare our implementation with the state-of-art previous champions, and the results demonstrate that our work achieves the highest speedup of 1.35 x and 1.39x compared to 2022 champion S&Z on single and multiple NVIDIA Tesla T4 GPUs, respectively. What's more, our work can reach similar or even better throughput compared to 2020 champion H&P on 6 V100 GPUs with only 4 T4 GPUs.

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The Emergence of Artificial Intelligence in Education and its Impact on Individual Literacy in Higher Education

The Emergence of Artificial Intelligence in Education and it...

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International IEEE Conference and Workshop: Óbuda on Electrical and Power Engineering (CANDO-EPE)

作者： Imre Farkas Attila Kovari Mónika Rajcsanyi-Molnar Department of Computer Systems and Control Technology Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Digital Technology Faculty of Computer Science Eszterházy Károly Catholic University Eger Hungary Institute of Electronics and Communication Systems Kandó Kalmán Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemét Hungary Department of Economics and Management Institute of Social Sciences University of Dunaujvaros Dunaujvaros Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

In the last three years, a new partner has emerged for teachers and educators in the field of education. The mushrooming of applications based on large language models has greatly shaped the educational development fields of the present era. The use of various AI applications has become commonplace among students and teachers alike. Many questions are being raised by researchers in this field. What does the rapid development of AI applications bring to the field of pedagogy? Should the products of such applications be compared with human performance? The form presented in this thesis seeks to answer similar questions. What is the impact of frequent use of these applications on the literacy level of individuals? In which areas do students tend to use these applications? The results indicate that half of the students believe their literacy levels will decrease with the mass emergence of AI applications, while 36% feel it will not change. However, 88 % of students have already dealt with AI-based applications, with 64 % using them several times or more, suggesting a high level of integration into their educational processes.

关键词： Ethics Power engineering Large language models Education Collaboration Learning (artificial intelligence) Reliability Artificial intelligence Monitoring Guidelines

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Stabilization of hyperbolic reaction-diffusion systems on directed networks through the generalized Routh-Hurwitz criterion for complex polynomials

Stabilization of hyperbolic reaction-diffusion systems on di...

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SICE International Symposium on control Systems (ISCS)

作者： Riccardo Muolo Anthony Hastir Hiroya Nakao Department of Systems and Control Engineering Tokyo Institute of Technology Japan Department of Mathematics and Computer Science Bergische Universität Wuppertal Germany Department of Mathematics and naXys Namur Institute for Complex Systems University of Namur Belgium

The study of dynamical systems on complex networks is of paramount importance in engineering, given that many natural and artificial systems find a natural embedding on discrete topologies. For instance, power grids, chemical reactors and the brain, to name a few, can be modeled through the network formalism by considering elementary units coupled via the links. In recent years, scholars have developed numerical and theoretical tools to study the stability of those coupled systems when subjected to perturbations. In such framework, it was found that asymmetric couplings enhance the possibilities for such systems to become unstable. Moreover, in this scenario the polynomials whose stability needs to be studied bear complex coefficients, which makes the analysis more difficult. In this work, we put to use a recent extension of the well-known Routh-Hurwitz stability criterion, allowing to treat the complex coefficient case. Then, using the Brusselator model as a case study, we discuss the stability conditions and the regions of parameters when the networked system remains stable.

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Communication-Constrained STL Task Decomposition through Convex Optimization

arXiv

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arXiv 2024年

作者： Marchesini, Gregorio Liu, Siyuan Lindemann, Lars Dimarogonas, Dimos V. Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Thomas Lord Department of Computer Science University of Southern California Los AngelesCA United States

In this work, we propose a method to decompose signal temporal logic (STL) tasks for multi-agent systems subject to constraints imposed by the communication graph. Specifically, we propose to decompose tasks defined over multiple agents which require multi-hop communication, by a set of sub-tasks defined over the states of agents with 1-hop distance over the communication graph. To this end, we parameterize the predicates of the tasks to be decomposed as suitable hyper-rectangles. Then, we show that by solving a constrained convex optimization, optimal parameters maximising the volume of the predicate’s super-level sets can be computed for the decomposed tasks. In addition, we provide a formal definition of conflicting conjunctions of tasks for the considered STL fragment and a formal procedure to exclude such conjunctions from the solution set of possible decompositions. The proposed approach is demonstrated through simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Convex optimization

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Tiny CNN for feature point description for document analysis: approach and dataset

Computer Optics

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computer Optics 2022年第3期46卷 429-435页

作者： Sheshkus, Alexander Vladimirovich Chirvonaya, Anastasiya Nikolaevna Arlazarov, Vladimir Lvovich Moscow Institute for Physics Technology Moscow Region Institutskiy per. 9 Dolgoprudny141701 Russia Institute for Systems Analysis Federal Research Center "Computer Science Control" of Russian Academy of Sciences pr. 60-letiya Oktyabrya 9 Moscow117312 Russia Smart Engines Service LLC pr. 60-letiya Oktyabrya 9 Moscow117312 Russia National University of Science Technology "MISIS" Leninskiy prospect 4 Moscow119049 Russia

In this paper, we study the problem of feature points description in the context of document analysis and template matching. Our study shows that specific training data is required for the task especially if we are to train a lightweight neural network that will be usable on devices with limited computational resources. In this paper, we construct and provide a dataset of photo and syn-thetically generated images and a method of training patches generation from it. We prove the ef-fectiveness of this data by training a lightweight neural network and show how it performs in both general and documents patches matching. The training was done on the provided dataset in comparison with HPatches training dataset and for the testing, we solve HPatches testing framework tasks and template matching task on two publicly available datasets with various documents pic-tured on complex backgrounds: MIDV-500 and MIDV-2019. © 2022, Institution of Russian Academy of Sciences. All rights reserved.

关键词： Template matching

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