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1st International Conference on Technology Enhanced Learning in Higher Education, TELE 2021

作者： Kondratyev, Sergey E. Pikalov, Vladimir V. Muravyev, Artem A. Lipetsk State Technical University Automation and Computer Science Faculty Lipetsk Russia

ISBN: (纸本)9781665443968

This paper discusses the application of the developed mathematical model for controlling the motion of the nonholonomic mobile robot with the differential drive based on the ROBOTIS BIOLOID® STEM KIT for further use in competitive robotics, where young people of different ages can participate, both preschool, school, and college or university students. This model allows the application of optimized parameters of linear and angular velocities to servo drives of robots, which task is to navigate through the colored line (usually the color of white or black) in the minimal time interval using an array of infrared sensors. Additionally, it discusses the planning of movement on different tracks, straight lines, and various polygonal chains or curves, of which the competition track is usually created. Models of both two-wheeled and four-wheeled nonholonomic mobile robots with differential drives, which are used for experiments by students in our lab, are also discussed. Experimental data were obtained on the time intervals of mobile robots on different track sections;the results were compared before and after applying the optimization model. © 2021 IEEE.

关键词： Mobile robots

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Augmented and virtual reality technologies in education 1

Augmented and virtual reality technologies in education

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1st International Conference on Technology Enhanced Learning in Higher Education, TELE 2021

作者： Muzyleva, Inna Yazykova, Liubov Gorlach, Alina Gorlach, Yakov Lipetsk State Technical University Automation and Computer Science Faculty Lipetsk Russia

ISBN: (纸本)9781665443968

The article examines the technologies of industrial revolution 4.0 - augmented reality and virtual reality. A detailed numerical analysis of known examples of the application of these technologies in the form of registered programs has been carried out. The possibilities of optimization of organizational and educational processes in higher technical education are analyzed. The methodology of using augmented reality in the organizational and educational process of a technical university is presented. A variant of augmented reality is proposed that allows you to 'examine' a 3D model of an object by turning a paper marker. To implement it, the EV Toolbox augmented and virtual reality constructor is used. © 2021 IEEE.

关键词： Augmented reality

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Deep and Spatio-Temporal Detection for Abnormal Traffic in Cloud Data Centers

Deep and Spatio-Temporal Detection for Abnormal Traffic in C...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Yuan, Haitao Wang, Shen Bi, Jing Zhang, Jia School of Automation Science and Electrical Engineering Beihang University Beijing China Beijing University of Technology Faculty of Information Technology Beijing China Lyle School of Engineering at Southern Methodist University Dept. of Computer Science DallasTX United States

ISBN: (纸本)9798350337020

Current interactions of network traffic through cloud data centers have become an important process of network services. Precise and real-time detection and prediction of network traffic can assist system operators in effectively allocating resources, and assessing network performance based on actual service requirements, and analyzing network health. However, sources and distribution of network traffic are different, which makes accurate warnings of network attack traffic become a difficult problem. In recent years, neural networks have been proven to be effective in predicting time series data, particularly long short-term memory networks for capturing temporal features and convolutional methods for capturing spatial features. This work proposes a Deep Hybrid Spatio-Temporal (DHST) network method for abnormal traffic detection in cloud data centers, which combines a cooperative temporal convolutional network, an attention mechanism and a random inactivation method to capture the network traffic data's spatio-temporal features. It improves accuracy of abnormal traffic detection, and realizes classification of normal traffic and abnormal one. It achieves higher accuracy than typical detection methods when applied to a real-life dataset collected from Yahoo Webscope S5. © 2023 IEEE.

关键词： anomaly detection Deep learning network traffic spatio-temporal modeling temporal convolutional networks

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Efficient Restoration for Binary Neural Networks

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Tien Tzu Hsueh Pao/Acta Electronica Sinica 2025年第2期53卷 568-580页

作者： Zeng, Kai Wan, Zi-Xin Wang, Ming-Tao Shen, Tao Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Kunming650500 China Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Yunnan Kunming650500 China

Restoring the weight distribution, activation distribution, and gradient to the original full precision network data as much as possible can greatly improve the inference ability of the binary network. However, existing methods directly apply the restoration operation in forward propagation to binary data, and the gradient approximation functions for back-propagation are fixed or manually determined, resulting in the need for improvement in the restoration efficiency of binary networks. To address this problem, the efficient restoration method is investigated for binary neural networks. Firstly, a distribution recovery method for maximizing information entropy is proposed. By shifting the original full precision weight mean and scaling the modulus, the quantized binary weight directly has the characteristic of maximum distribution restoration. At the same time, a simple statistical translation and scaling factor is used to greatly improve the restoration efficiency of weight and activation. Furthermore, it is proposed a gradient function based on adaptive distribution approximation, which dynamically determines the update range of the current gradient in the P-percentile according to the actual distribution of the current full precision data. It adaptively changes the shape of the approximation function to efficiently update the gradient during the training process, thereby improving the convergence ability of the model. On the premise of ensuring the improvement of execution efficiency, theoretical analysis has confirmed that the method proposed in this paper can achieve maximum restoration of binary data. Compared with the existing advanced binary network models, the experimental results of our method show excellent performance, with a 60% and 67% reduction in computational time for the distribution restoration operation quantization of ResNet-18 and ResNet-20, respectively. An accuracy of 93.0% is achieved for VGG-Small binary quantization on the CIFAR-10 dataset

关键词： Restoration

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Methods of damping of electromechanical oscillations in a multi-motor system 18

Methods of damping of electromechanical oscillations in a mu...

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18th International Scientific Technical Conference Alternating Current Electric Drives, ACED 2021

作者： Sinyukova, Tatyana Gladyshev, Vladislav Sinyukov, Alexey Lesnikova, Victoria Automation and computer science faculty Lipetsk State Technical University Lipetsk Russia

ISBN: (纸本)9781665412483

A common problem that occurs in modern production is the presence of electromechanical vibrations, which are a source of reducing the reliability of both an individual mechanism and the entire object as a whole. Therefore, in order to ensure reliable operation of the unit, it is necessary to ensure the process of vibration damping. The paper considers several methods of damping electromechanical oscillations in a multi-motor system, and provides a comparative analysis of the proposed methods. The object of the study is a belt conveyor, which is a continuous mechanism. The conveyor is installed in the blast furnace shop of the Novolipetsk Metallurgical Plant. © 2021 IEEE.

关键词： Damping

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Application of Kendall's W Coefficient to Identify Groups of Statistically Related Variables 3

Application of Kendall's W Coefficient to Identify Groups of...

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3rd International Conference on Control Systems, Mathematical Modeling, automation and Energy Efficiency, SUMMA 2021

作者： Zhuravlyova, Marina Vedishchev, Vitalii Lipetsk State Technical University Faculty of Automation and Computer Science Lipetsk Russia

ISBN: (纸本)9781665439817

Original algorithm for identifying groups of statistically interrelated variables based on Kendall's W statistics is considered. A formal description of the algorithm and assessing of the computational complexity are presented. The results of implementation of proposed algorithm for data of manufacturing process are shown. Comparison of the results obtained with the results of calculating Pearson and Spearman correlations for the same data is carried out. © 2021 IEEE.

关键词： Manufacture

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Research and analysis of a two-circuit and three-circuit system for automatic regulation of the thickness of a reversing cold rolling mill, PJSC 'Novolipetsk Metallurgical Plant' 18

Research and analysis of a two-circuit and three-circuit sys...

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18th International Scientific Technical Conference Alternating Current Electric Drives, ACED 2021

作者： Sinyukova, Tatyana Levin, Pavel Sinyukov, Alexey Automation and computer science faculty Lipetsk State Technical University Lipetsk Russia

ISBN: (纸本)9781665412483

Single-stand cold rolling mills are units involved in the complex technological process of manufacturing finished products of a metallurgical enterprise. The quality and demand for the finished product directly depends on the high-quality work of the mill. The control system applied at the mill directly affects the accuracy of its work. The study of the existing control system is aimed at identifying its shortcomings. The facility uses a two-circuit control system, the improvement of which is proposed by introducing a third circuit operating on the basis of data on the deviation in the deformation zone. © 2021 IEEE.

关键词： Cold rolling

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Artificial Neural Network Modeling for Predicting Thermal Conductivity of EG/Water-Based CNC Nanofluid for Engine Cooling Using Different Activation Functions

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Frontiers in Heat and Mass Transfer 2024年第2期22卷 537-556页

作者： Munirul Hasan Mustafizur Rahman Mohammad Saiful Islam Wong Hung Chan Yasser M.Alginahi Muhammad Nomani Kabir Suraya Abu Bakar Devarajan Ramasamy Faculty of Computing Universiti Malaysia Pahang Al-Sultan AbdullahPekanPahang26600Malaysia Faculty of Mechanical and Automotive Engineering Technology Universiti Malaysia Pahang Al-Sultan AbdullahPekanPahang26600Malaysia Department of Management and Information Technology St.Francis College179 Livingston StBrooklynNY11201USA Dell Technologies Data Analyticsand AutomationBukit TengahBukit Mertajam14000Malaysia Department of Computer Science Adrian CollegeAdrianMIUSA Department of Computer Science&Engineering United International University BangladeshUnited CityMadani AvenueBaddaDhaka1212Bangladesh

A vehicle engine cooling system is of utmost importance to ensure that the engine operates in a safe temperature *** most radiators that are used to cool an engine,water serves as a cooling *** performance of a radiator in terms of heat transmission is significantly influenced by the incorporation of nanoparticles into the cooling *** and uniformity of nanoparticle distribution are the two major factors for the practical use of *** shape and size of nanoparticles also have a great impact on the performance of heat *** researchers are investigating the impact of nanoparticles on heat *** study aims to develop an artificial neural network(ANN)model for predicting the thermal conductivity of an ethylene glycol(EG)/waterbased crystalline nanocellulose(CNC)nanofluid for cooling internal combustion *** implementation of an artificial neural network considering different activation functions in the hidden layer is made to find the bestmodel for the cooling of an engine using the *** of the model with different activation functions in artificial neural networks are analyzed for different nanofluid concentrations and *** artificial neural networks,Levenberg–Marquardt is an optimization approach used with activation functions,including Tansig and Logsig functions in the training *** findings of each training,testing,and validation phase are presented to demonstrate the network that provides the highest level of *** best result was obtained with Tansig,which has a correlation of 0.99903 and an error of 3.7959×10^(–8).It has also been noticed that the Logsig function can also be a good model due to its correlation of 0.99890 and an error of 4.9218×10^(–8).Thus ourANNwith Tansig and Logsig functions demonstrates a high correlation between the actual output and the predicted output.

关键词： Artificial neural network activation function thermal conductivity nanocellulose

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An Efficient Method for Covid-19 Detection Using Light Weight Convolutional Neural Network

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computers, Materials & Continua 2021年第11期69卷 2475-2491页

作者： Saddam Bekhet Monagi H.Alkinani Reinel Tabares-Soto M.Hassaballah Faculty of Commerce South Valley UniversityQenaEgypt Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Department of Electronics and Automation Universidad Autónoma de ManizalesManizales170001Colombia Department of Computer Science Faculty of Computers and InformationSouth Valley UniversityQenaEgypt

The COVID-19 pandemic is a significant milestone in the modern history of civilization with a catastrophic effect on global wellbeing and *** situation is very complex as the COVID-19 test kits are limited,therefore,more diagnostic methods must be developed urgently.A significant initial step towards the successful diagnosis of the COVID-19 is the chest X-ray or Computed Tomography(CT),where any chest anomalies(e.g.,lung inflammation)can be easily *** hospitals possess X-ray or CT imaging equipments that can be used for early detection of *** by this,various artificial intelligence(AI)techniques have been developed to identify COVID-19 positive patients using the chest X-ray or CT ***,the advance of these AI-based systems and their highly tailored results are strongly bonded to high-end GPUs,which is not widely available in several *** paper introduces a technique for early COVID-19 diagnosis based on medical experience and light-weight Convolutional Neural Networks(CNNs),which does not require a custom hardware to run compared to currently available CNN *** proposed deep learning model is built carefully and fine-tuned by removing all unnecessary parameters and layers to achieve the light-weight attribute that could run smoothly on a normal CPU(0.54%of AlexNet parameters).This model is highly beneficial for countries where high-end GPUs are *** outcomes on some new benchmark datasets shows the robustness of the proposed technique robustness in recognizing COVID-19 with 96%accuracy.

关键词： Artificial intelligence COVID-19 chest CT chest X-ray deep learning

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From Data to Commonsense Reasoning: The Use of Large Language Models for Explainable AI

arXiv

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arXiv 2024年

作者： Krause, Stefanie Stolzenburg, Frieder Faculty of Automation and Computer Science Harz University of Applied Sciences Friedrichstr. 57-59 Wernigerode38855 Germany

Commonsense reasoning is a difficult task for a computer, but a critical skill for an artificial intelligence (AI). It can enhance the explainability of AI models by enabling them to provide intuitive and human-like explanations for their decisions. This is necessary in many areas especially in question answering (QA), which is one of the most important tasks of natural language processing (NLP). Over time, a multitude of methods have emerged for solving commonsense reasoning problems such as knowledge-based approaches using formal logic or linguistic analysis. In this paper, we investigate the effectiveness of large language models (LLMs) on different QA tasks with a focus on their abilities in reasoning and explainability. We study three LLMs: GPT-3.5, Gemma and Llama 3. We further evaluate the LLM results by means of a questionnaire. We demonstrate the ability of LLMs to reason with commonsense as the models outperform humans on different datasets. While GPT-3.5's accuracy ranges from 56% to 93% on various QA benchmarks, Llama 3 achieved a mean accuracy of 90% on all eleven datasets. Thereby Llama 3 is outperforming humans on all datasets with an average 21% higher accuracy over ten datasets. Furthermore, we can appraise that, in the sense of explainable artificial intelligence (XAI), GPT-3.5 provides good explanations for its decisions. Our questionnaire revealed that 66% of participants rated GPT-3.5's explanations as either"good" or"excellent". Taken together, these findings enrich our understanding of current LLMs and pave the way for future investigations of reasoning and explainability. © 2024, CC BY.

关键词： Formal logic

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