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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Sayyad, Javed Jatti, Ashwini Attarde, Khush Ramesh, B.T. Deokar, Shubhangi Robotics and Automation Department Lavale Maharashtra Pune India Computer Science and Engineering Lavale Maharashtra Pune India

ISBN: (纸本)9781665474511

Real-Time Operating System (RTOS) is an Operating System (OS) that does a task in a constrained time. The RTOS is integrated with the microprocessor or microcontroller for specialized applications where the time-bound completion of the task is very much substantial. ESP32 is a microcontroller that comes with a package of advantageous and suitable features for RTOS. The ability of equipment like motors, sensors, and actuators to multitask is critical in the Robotics and automation (R&A) industry. In industries, a dedicated/centralized system allocated for a particular activity is not economically feasible;yet, when performing several tasks, some delay or lag is introduced in the task. Consequently, the proposed solution for industrial multitasking is cost-effective and efficient. The RTOS is suitable for machines constrained to do painstaking work, like a robotic arm or CNC drilling. In the R&A industry, a hardware interruption is often critical for an emergency, which may also be accomplished using RTOS. In such applications, implementing a software timer is also essential to delay a task for a period of time. Stepper and servo motors are widely employed in the R&A industries for various applications. This article presents the multitasking of four stepper motors and the introduction of a software timer, which will be developed when the stepper motor multitasking is implemented. For multitasking, the FreeRTOS library will be utilized. © 2023 IEEE.

关键词： Microcontrollers

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Performance-Guaranteed Finite-Time Tracking of Strict-Feedback Systems with Unknown Control Directions: A Novel Switching Mechanism

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IEEE Transactions on Automatic Control 2025年第6期70卷 4061-4068页

作者： Mao, Bing Wu, Xiaoqun Fan, Ziye Lu, Jinhu Chen, Guanrong Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Wuhan University School of Mathematics and Statistics Hubei430072 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Electrical Engineering Hong Kong Hong Kong

The problem of achieving performance-guaranteed finite-time exact tracking for uncertain strict-feedback nonlinear systems with unknown control directions is addressed. A novel logic switching mechanism with monitoring functions is designed to deal with unknown control directions. This approach is different from existing methods like the Nussbaum gain technique and smooth orientation functions, which are inapplicable to finite-time exact tracking. The main challenge lies in designing monitoring functions and determining switching rules, which is well resolved through three steps. Most significantly, in contrast to existing works that solely guarantee bounded tracking or asymptotic exact tracking, the new mechanism incorporates novel integral sliding mode techniques to achieve finite-time exact tracking with guaranteed performance. Finally, simulations are presented with comparison to demonstrate the effectiveness of the proposed methods. © 1963-2012 IEEE.

关键词： Feedback

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Geometric prior guided hybrid deep neural network for facial beauty analysis

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CAAI Transactions on Intelligence Technology 2024年第2期9卷 467-480页

作者： Tianhao Peng Mu Li Fangmei Chen Yong Xu David Zhang The School of Computer Science and Technology Guizhou UniversityGuiyangChina Department of Automation Moutai InstituteRenhuaiGuizhouChina The School of Computer Science and Technology Harbin Institute of TechnologyShenzhenShenzhenChina The Information and Communication Engineering Department Dalian Minzu UniversityDalianChina The School of Data Science The Chinese University of Hong KongShenzhenShenzhenChina

Facial beauty analysis is an important topic in human *** may be used as a guidance for face beautification applications such as cosmetic *** neural networks(DNNs)have recently been adopted for facial beauty analysis and have achieved remarkable ***,most existing DNN-based models regard facial beauty analysis as a normal classification *** ignore important prior knowledge in traditional machine learning models which illustrate the significant contribution of the geometric features in facial beauty *** be specific,landmarks of the whole face and facial organs are introduced to extract geometric features to make the *** by this,we introduce a novel dual-branch network for facial beauty analysis:one branch takes the Swin Transformer as the backbone to model the full face and global patterns,and another branch focuses on the masked facial organs with the residual network to model the local patterns of certain facial ***,the designed multi-scale feature fusion module can further facilitate our network to learn complementary semantic information between the two *** model optimisation,we propose a hybrid loss function,where especially geometric regulation is introduced by regressing the facial landmarks and it can force the extracted features to convey facial geometric *** performed on the SCUT-FBP5500 dataset and the SCUT-FBP dataset demonstrate that our model outperforms the state-of-the-art convolutional neural networks models,which proves the effectiveness of the proposed geometric regularisation and dual-branch structure with the hybrid *** the best of our knowledge,this is the first study to introduce a Vision Transformer into the facial beauty analysis task.

关键词： deep neural networks face analysis face biometrics image analysis

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The Impact of Domain Name Server(DNS)over Hypertext Transfer Protocol Secure(HTTPS)on Cyber Security:Limitations,Challenges,and Detection Techniques

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computers, Materials & Continua 2024年第9期80卷 4513-4542页

作者： Muhammad Dawood Shanshan Tu Chuangbai Xiao Muhammad Haris Hisham Alasmary Muhammad Waqas Sadaqat Ur Rehman Faculty of Information Technology Beijing University of TechnologyBeijing100124China National Center of Robotics and Automation University of Engineering and TechnologyPeshawar25000Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia School of Computing and Engineering Sciences Faculty of Engineering and ScienceUniversity of GreenwichLondonSE109LSUnited Kingdom School of Engineering Edith Cowan UniversityPerthWA 6027Australia School of Computer Science and Engineering University of SalfordManchesterM54BRUnited Kingdom

The DNS over HTTPS(Hypertext Transfer Protocol Secure)(DoH)is a new technology that encrypts DNS traffic,enhancing the privacy and security of ***,the adoption of DoH is still facing several research challenges,such as ensuring security,compatibility,standardization,performance,privacy,and increasing user *** significantly impacts network security,including better end-user privacy and security,challenges for network security professionals,increasing usage of encrypted malware communication,and difficulty adapting DNS-based security ***,it is important to understand the impact of DoH on network security and develop newprivacy-preserving techniques to allowthe analysis of DoH traffic without compromising user *** paper provides an in-depth analysis of the effects of DoH on *** discuss various techniques for detecting DoH tunneling and identify essential research challenges that need to be addressed in future security ***,this paper highlights the need for continued research and development to ensure the effectiveness of DoH as a tool for improving privacy and security.

关键词： DNS DNS over HTTPS cybersecurity machine learning

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Trust Assessment Model for Visual Image-Based Human-Robot Interaction Under Known and Unknown Threats 16th

Trust Assessment Model for Visual Image-Based Human-Robot I...

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16th International Conference on Social Robotics, ICSR + BioMed 2024

作者： Li, Heqing Li, Xinde Liu, Erfeng Ge, Shuzhi Sam School of Cyber Science and Engineering Southeast University Nanjing China School of Automation Southeast University Nanjing China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore

ISBN: (纸本)9789819789627

Visual imagery is a critical perceptual source in human-robot interaction (HRI), extensively applied in various domains such as robotic navigation, target detection, and task planning. Aerial robots, also known as UAVs, are the typical representatives of teleoperation robots. The substantial distance between humans and remotely operated robots, coupled with the dynamic and open environments these robots navigate, exposes image-based HRI to significant security risks from both known and unknown threats. The trustworthiness of data sources is a fundamental prerequisite for ensuring the security of HRI systems. However, the multidimensionality of trust factors and the uncertainty of trust evidence have constrained its practical application. To advance this field, we have innovatively proposed a trust evaluation scheme based on multidimensional evidence fusion within the Belief Functions (BFs) framework. This scheme constructs an adaptive trust evaluation model capable of addressing both known and unknown threats. For known threats, we developed coarse-grained multidimensional trust elements and employed multiple lightweight SVM submodels to construct Basic Belief Assignments (BBAs), thereby achieving direct trust modeling and enabling rapid trust evaluation of images. Conversely, for unknown threats, we utilized pretrained models to establish fine-grained multidimensional trust elements, implementing indirect trust modeling through trust recommendation, thus facilitating indirect trust evaluation of images. We further combined direct and indirect trust to derive a overall trust assessment of the images, achieving adaptive dynamic trust evaluation in image-based HRI. Additionally, to more accurately represent image trustworthiness, we introduced a BBAs weighted fusion method, which aids in more rational trust aggregation. To validate the efficacy of our proposed method, we conducted experiments on a real aerial image dataset. The results demonstrate that our approach ef

关键词： Human robot interaction

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A Novel Deep Learning Model for Classifying Power Quality Problems in PV-integrated Microgrids using CNN-LSTM 1

A Novel Deep Learning Model for Classifying Power Quality Pr...

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1st International Conference on Innovative engineering Sciences and Technological Research, ICIESTR 2024

作者： Channa, Irfan Ali Li, Dazi Dahri, Fida Hussain Abro, Ghulam E. Mustafa Zahid, Faizan University of Chemical Technology Institute of Automation Beijing Beijing100069 China Southeast University School of Computer Science and Engineering Nanjing China Saudi Arabia University of Catania Department of Electrical Electronic and Computer Engineering Italy

ISBN: (纸本)9798350348637

Distributed generation based on photo-voltaic (PV) modules play a vital role to provide continuous power supply for consumers. Power quality (PQ) events caused by integration of distributed energy sources in microgrids (MG) deteriorate the operation of distribution and utility of power supply. In this work, a new CNN-LSTM based deep learning approach is proposed to identify power quality disturbances (PQDs) in PV-integrated microgrid. In a proposed framework, full closed-loop neural architecture containing different layers to facilitate automatic feature extraction and long-time short memory (LSTM) network provide temporal feature for intelligent classifier to improve the training speed. Moreover, comparison with other deep neural networks proves that proposed approach can simplify the procedure of PQ problems with the conventional signal analysis and feature selection methods. A typical PV-integrated microgid is simulated on PSCAD software to prove the robustness of classifier for classification of PQDs in renewable energy integrated distribution networks. © 2024 IEEE.

关键词： Microgrids

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Dynamic Modeling of Strip Rolling Process Using Probabilistic Neural Network

Dynamic Modeling of Strip Rolling Process Using Probabilisti...

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18th IEEE International Conference on Advanced Motion Control, AMC 2024

作者： Deng, Jifei Sierla, Seppo Sun, Jie Vyatkin, Valeriy Northeastern University State Key Laboratory of Rolling and Automation Shenyang China Aalto University Department of Electrical Engineering and Automation Espoo Finland Luleå University of Technology Department of Computer Science Electrical and Space Engineering Luleå Sweden

ISBN: (纸本)9798350342819

This paper proposes an intelligent dynamic modeling method for strip rolling process. Actuators of a cold rolling mill perform actions, including work roll bending, intermediate roll bending, and roll gap tilting, to regulate the product quality. Conventional first-principles modeling necessitates intricate mathematical equations derived from process analysis and action evaluation. However, the complexity of the model increases the difficulty of upgrading the model. To address this limitation, a novel approach involving a deep neural network with a Gaussian distribution layer is proposed to effectively capture the process dynamics. A negative log likelihood is established as the loss function for the model using real-world industrial data. Experiment outcomes demonstrate the efficacy of the proposed method in achieving an accurate dynamic model. © 2024 IEEE.

关键词： Deep neural networks

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Computing Technology Acoustic Inversion engineering Seismic Data and Its Application for Construction Purposes 16

Computing Technology Acoustic Inversion Engineering Seismic ...

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16th IEEE International Scientific and Technical Conference Actual Problems of Electronic Instrument engineering, APEIE 2023

作者： Goshko, Elena Yu. Zerkal, Sergey M. Ivanov, Sergey S. Automation and Computer Engineering Novosibirsk State Technical University Novosibirsk Russia Department of Computer Science Siberian University of Consumer Cooperation Novosibirsk Russia

ISBN: (纸本)9798350330885

The Shallow-depth engineering seismic studies are carried out in the upper part of the geological section, which is characterized by sharp vertical and horizontal variability of the physical properties of rocks. Additional difficulties in observing and recording caused by seismic vibrations of the soil are associated with the influence of unavoidable interference: ground (wind, sound noise), geological interference (noise of rock cracking during unloading of tectonic stress), man-made interference (the influence of power lines on signal reception, trembling of the soil during the work of construction or quarry mechanisms, cars, trains). The allocation of a useful signal against the background of interference requires the organization of seismic work with repeated repetition of excitations - the accumulation of signals, as well as the development of special computational algorithms and the use of new technologies for processing engineering seismic data. The article considers a computational algorithm of acoustic inversion, which transforms a wave seismic section into a discrete section of pulsed reactions of the medium - effective reflection coefficients. It has a compact discrete appearance and is easier for visual observation and further physico-geological interpretation. The computer technology of application of the acoustic inversion algorithm is implemented. The application of this technology to real engineering seismic data obtained on the territory of the construction of a high-rise building made it possible to determine the parameters of the geological foundation lying under the ground surface of the construction site, namely: the depth of occurrence, relief, areas of increased disintegration. The established properties of the geological foundation are necessary for the analysis of the strength and stability of soils during the construction and operation of a high-rise structure for its safety. © 2023 IEEE.

关键词： Foundations

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Switching Classes: Characterization and Computation 49

Switching Classes: Characterization and Computation

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49th International Symposium on Mathematical Foundations of computer Science, MFCS 2024

作者： Antony, Dhanyamol Cao, Yixin Pal, Sagartanu Sandeep, R.B. Department of Computer Science and Automation Indian Institute of Science Bengaluru India Department of Computing Hong Kong Polytechnic University Hong Kong Hong Kong Department of Computer Science & Engineering Indian Institute of Technology Dharwad India

ISBN: (纸本)9783959773355

In a graph, the switching operation reverses adjacencies between a subset of vertices and the others. For a hereditary graph class G, we are concerned with the maximum subclass and the minimum superclass of G that are closed under switching. We characterize the maximum subclass for many important classes G, and prove that it is finite when G is minor-closed and omits at least one graph. For several graph classes, we develop polynomial-time algorithms to recognize the minimum superclass. We also show that the recognition of the superclass is NP-hard for H-free graphs when H is a sufficiently long path or cycle, and it cannot be solved in subexponential time assuming the Exponential Time Hypothesis. © Dhanyamol Antony, Yixin Cao, Sagartanu Pal, and R. B. Sandeep.

关键词： Graph algorithms

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Parallel intelligent education with ChatGPT

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Frontiers of Information Technology & Electronic engineering 2024年第1期25卷 12-18页

作者： Jiacun WANG Ying TANG Ryan HARE Fei-Yue WANG Department of Computer Science and Software Engineering Monmouth UniversityNew Jersey 07764USA Department of Electrical and Computer Engineering Rowan UniversityNew Jersey 08028USA Institute of Automation Chinese Academy of SciencesBeijing 100190China

This paper presents a framework for parallel intelligent education that involves physical and virtual learning for a personalized learning *** especially focus on Chat Generative Pre-trained Transformer(ChatGPT)owing to its considerable potential to supplement regular class *** address the strengths and weaknesses of learning with ***,we discuss the challenges and solutions of the proposed parallel intelligent education with ChatGPT.

关键词： education GPT intelligent

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