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3rd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2024

作者： Miguel-Escrig, Oscar Sanchez-Moreno, Jose Romero-Perez, Julio-Ariel Dormido-Bencomo, Sebastian Universitat Jaume I Department of System Engineering and Design Castelló de la Plana Spain Department of Computer Science and Automatic Control Madrid Spain

ISBN: (纸本)9798331540319

In the current industrial environment, which is shifting towards an event-based control prone paradigm, sampling strategies in use must be revisited. Both temporal and magnitude dependent sampling strategies have been demonstrated to be suitable for control systems, reporting benefits in different categories. However, these sampling strategies also present some drawbacks, discussed in this contribution. Aiming to address some of these drawbacks, a new sampling strategy is proposed gathering characteristics of both predecessors. © 2024 IEEE.

关键词： Decentralized control

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Innovative Automatic Tool Changing Mechanism for Robotic Arms

Innovative Automatic Tool Changing Mechanism for Robotic Arm...

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2024 Intermountain engineering, Technology and Computing, IETC 2024

作者： Das, Shaibal Berry, Matthew Deemyad, Taher Idaho State University Department of Electrical and Computer Engineering PocatelloID United States Idaho State University Department of Mechanical and Measurement & Control Engineering PocatelloID United States

ISBN: (纸本)9798350373073

This paper presents the development and implementation of an automatic tool-changing mechanism for industrial and collaborative robotic arms. The primary objective is to enable integration with a variety of end effectors, both active and passive, allowing the robot to autonomously switch tools without manual intervention. The system comprises three key components: a universal end-effector connector, various tools, and a customized toolbox. The universal connector will be screwed to the end of the robotic arm. It features a mechanical lock mechanism and accommodates electrical and pneumatic inlets for diverse tools. A dedicated toolbox, situated within the robot's workspace range, houses all tools for easy access. The study focuses on the design, 3D printing, and testing of two tools - an active and a passive tool - along with the innovative mechanical lock mechanism, which has been successfully developed and tested. Additionally, two industrial tasks have been defined to demonstrate the mechanism's functionality in real-world scenarios, emphasizing non-singular path trajectories between tasks. Future work includes the integration of additional sensors for enhanced automatic robot control. Overall, this research will contribute to the advancement of fully autonomous robotic systems in industrial settings. © 2024 IEEE.

关键词： End effectors

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Facial Expression Recognition Model Depending on Optimized Support Vector Machine

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computers, Materials & Continua 2023年第7期76卷 499-515页

作者： Amel Ali Alhussan Fatma M.Talaat El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Mona Alnaggar Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Machine Learning&Information Retrieval Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Robotics and Intelligent Machines Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt

In computer vision,emotion recognition using facial expression images is considered an important research *** learning advances in recent years have aided in attaining improved results in this *** to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of *** is feasible and useful to convert face photos into collections of visual words and carry out global expression *** main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is *** uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos *** FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization *** discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously *** search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.

关键词： Facial expression recognition machine learning linear dis-criminant analysis(LDA) support vector machine(SVM) grid search

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A control Theoretic Study on Omnidirectional MAVs with Minimum Number of Actuators and No Internal Forces at Any Orientation 63

A Control Theoretic Study on Omnidirectional MAVs with Minim...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Ali, Ahmed Gabellieri, Chiara Franchi, Antonio Faculty Robotics and Mechatronics Department Enschede7500 AE Netherlands Sapienza University of Rome Department of Computer Control and Management Engineering Rome00185 Italy

ISBN: (纸本)9798350316339

We propose a new multirotor aerial vehicle class of designs composed of a multi-body structure in which a main body is connected by passive joints to links equipped with propellers. We have investigated some instances of such class, some of which are shown to achieve omnidirectionality while having a minimum number of inputs equal to the main body Degrees of Freedom DoF's, only uni-directional positive thrust propellers, and no internal forces generated at steady state. After dynamics are derived following the Euler-Lagrange approach, an I/O dynamic feedback linearization strategy is then used to show the controllability of any desired pose with stable zero dynamics. We finally verify the developed controller with closed-loop simulations. © 2024 IEEE.

关键词： Feedback linearization

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Error Dependent Sampling to Reduce Transient Events in Event Based control 3

Error Dependent Sampling to Reduce Transient Events in Event...

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3rd IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2024

作者： Miguel-Escrig, Oscar Romero-Perez, Julio-Ariel Sanchez-Moreno, Jose Dormido-Bencomo, Sebastian Universitat Jaume I Department of System Engineering and Design Castelló de la Plana Spain Department of Computer Science and Automatic Control Madrid Spain

ISBN: (纸本)9798331540319

One of the objectives of event-based control is the reduction of generated events to perform an appropriate process control. Among the event generation techniques used to this end, those quantifying the error signal can be found more frequently for its simplicity of implementation. However, they entail a drawback in the form of trade-off between event generation and performance. Aiming to reduce the number of events, specifically in the transient response, without degrading the performance, an error dependent sampling scheme is studied in this work. © 2024 IEEE.

关键词： Electromagnetic transients

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A Real-Time Semantic Segmentation Method Based on Transformer for Autonomous Driving

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computers, Materials & Continua 2024年第12期81卷 4419-4433页

作者： Weiyu Hao Jingyi Wang Huimin Lu Donald Bren School of Information and Computer Sciences University of CaliforniaIrvineCA 92612USA Department of Control Engineering Kyushu Institute of TechnologyKitakyushu804-8550Japan School of Automation Southeast UniversityNanjing210096China

While traditional Convolutional Neural Network(CNN)-based semantic segmentation methods have proven effective,they often encounter significant computational challenges due to the requirement for dense pixel-level predictions,which complicates real-time *** address this,we introduce an advanced real-time semantic segmentation strategy specifically designed for autonomous driving,utilizing the capabilities of Visual *** leveraging the self-attention mechanism inherent in Visual Transformers,our method enhances global contextual awareness,refining the representation of each pixel in relation to the overall *** enhancement is critical for quickly and accurately interpreting the complex elements within driving sce-narios—a fundamental need for autonomous *** experiments conducted on the DriveSeg autonomous driving dataset indicate that our model surpasses traditional segmentation methods,achieving a significant 4.5%improvement in Mean Intersection over Union(mIoU)while maintaining real-time *** paper not only underscores the potential for optimized semantic segmentation but also establishes a promising direction for real-time processing in autonomous navigation *** work will focus on integrating this technique with other perception modules in autonomous driving to further improve the robustness and efficiency of self-driving perception frameworks,thereby opening new pathways for research and practical applications in scenarios requiring rapid and precise decision-making *** experimentation and adaptation of this model could lead to broader implications for the fields of machine learning and computer vision,particularly in enhancing the interaction between automated systems and their dynamic environments.

关键词： Visual transformer semantic segmentation autonomous driving

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Intelligent Proximate Analysis of Coal Based on Near-Infrared Spectroscopy and Multioutput Deep Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1398-1410页

作者： Zou, Liang Qiao, Jiahui Yu, Xinhui Chen, Xun Lei, Meng School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China

Proximate analysis of coal indicates the moisture, ash, volatile content, and calorific value, which has been widely utilized as the basis for coal characterization. It involves heating the coal under various conditions until a constant weight is obtained. Although it is a relatively simple process that does not require expensive analytical equipment, determining these characteristics is time consuming. An alternative way for proximate analysis is spectral analysis in combination with various machine learning methods. However, most previous works analyze individual characteristics and fail to explore the relationship among them. In this study, we propose a method for proximate analysis based on near-infrared spectroscopy and a multioutput attention Unet (MOA-Unet), which can predict multiple characteristics simultaneously. First, an attention-based Unet is designed as the shared feature extraction subnetwork, including an encoder, a decoder, convolutional block attention modules, and multiscale feature fusion modules, which can improve the representation power of the U-shape network through aggregating features of shallower layers and concatenating features of deeper layers. Second, four individual subnetworks with fully connected layers, designed for four outputs, are utilized for regressing those four characteristics. We employ the gradient normalization algorithm to alleviate the gradient magnitude masking effect caused by training imbalance among different tasks. The proposedMOA-Unet is compared with classical chemometric methods on 670 coal samples from on-site *** experimental results demonstrate that the proposedmodel achieves state-of-the-art performance with correlation coefficients of 0.9015, 0.9538, 0.8986, and 0.8884, corresponding to moisture, ash, volatile content, and calorific value, respectively. Impact Statement-The proximate analysis of coal has been widely utilized as the basis for determining the rank of coal which is in connection with coa

关键词： Feature extraction

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Interference Suppression and Jitter Elimination Ability-Based Adaption Tracking Guidance for Robotic Fishes

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 126-137页

作者： Dongfang Li Jie Huang Rob Law Xin Xu Limin Zhu Edmond Q.Wu IEEE the School of Electrical Engineering and Automation Fuzhou University the Key Laboratory of System Control and Information Processing Ministry of Education the School of Electrical Engineering and Automation Fuzhou University 5G+Industrial Internet Institute Fuzhou University the University of Macau the College of Mechatronics and Automation National University of Defense Technology the School of Mechanical Engineering Shanghai Jiao Tong University the Department of Computer Science and Engineering Shanghai Jiao Tong University

This work presents an adaptive tracking guidance method for robotic fishes. The scheme enables robots to suppress external interference and eliminate motion jitter. An adaptive integral surge line-of-sight guidance rule is designed to eliminate dynamics interference and sideslip issues. Limited-time yaw and surge speed observers are reported to fit disturbance variables in the model. The approximation values can compensate for the system's control input and improve the robots' tracking ***, this work develops a terminal sliding mode controller and third-order differential processor to determine the rotational torque and reduce the robots' run jitter. Then, Lyapunov's theory proves the uniform ultimate boundedness of the proposed method. Simulation and physical experiments confirm that the technology improves the tracking error convergence speed and stability of robotic fishes.

关键词： Adaptive integral surge line-of-sight approximation robotic fish speed observer

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Real-Time Notifier for Blockchain Events 22

Real-Time Notifier for Blockchain Events

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22nd RoEduNet Conference: Networking in Education and Research, RoEduNet 2023

作者： Tanase-Paris, Cristian Carabas, Costin Tapus, Nicolae University Politehnica of Bucharest Faculty of Automatic Control and Computers Computer Science and Engineering Department Bucharest Romania

ISBN: (纸本)9798350393651

Blockchain technology gained much traction in the last few years. These decentralized databases offer security, immutability, and scalability across various applications. Decentralized applications generate vast amounts of data, known as events, that are recorded on the blockchain and are public to anyone. Some people may see opportunities for financial gains in these events and would like to know when they occur. This paper proposes a solution to process and deliver those events as real-time alerts to the users. It uses existing technologies such as message queues, multithreading, and asynchronous processing and integrates them into a scalable architecture. The results we achieved in this paper show that for an evenly distributed network traffic, which does not entirely consists of transaction bursts, the proposed solution offers reliability, efficiency, and a suitable delivery time to those wishing to integrate it into their projects. With time, this solution, or improved architectures, may form the basis of the following big-data architectures for processing blockchain events. © 2023 IEEE.

关键词： Blockchain

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A Distributed Automatic Domain-Specific Multi-Word Term Recognition Architecture using Spark Ecosystem 22

A Distributed Automatic Domain-Specific Multi-Word Term Reco...

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22nd International Symposium on Parallel and Distributed Computing, ISPDC 2023

作者： Truica, Ciprian-Octavian Istrate, Neculai-Ovidiu Apostol, Elena-Simona University Politehnica of Bucharest Faculty of Automatic Control and Computers Computer Science and Engineering Department Bucharest Romania

ISBN: (纸本)9798350341270

Automatic Term Recognition is used to extract domain-specific terms that belong to a given domain. In order to be accurate, these corpus and language-dependent methods require large volumes of textual data that need to be processed to extract candidate terms that are afterward scored according to a given metric. To improve text preprocessing and candidate terms extraction and scoring, we propose a distributed Spark-based architecture to automatically extract domain-specific terms. The main contributions are as follows: (1) propose a novel distributed automatic domain-specific multi-word term recognition architecture built on top of the Spark ecosystem;(2) perform an in-depth analysis of our architecture in terms of accuracy and scalability;(3) design an easy-to-integrate Python implementation that enables the use of Big Data processing in fields such as Computational Linguistics and Natural Language Processing. We prove empirically the feasibility of our architecture by performing experiments on two real-world datasets. © 2023 IEEE.

关键词： Ecosystems

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