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arXiv 2024年

作者： Ortiz-Haro, Joaquim TU Berlin Faculty IV - Electrical Engineering and Computer Science Learning and Intelligent Systems Germany

Modern robots excel at performing simple and repetitive tasks in controlled environments;however, future applications, such as robotic construction and assistance, will require long-term planning of physical interactions. These problems can be formulated as Task and Motion Planning (TAMP). The goal is to find how the robot should move to solve complex tasks requiring multiple interactions with objects in the environment, such as building furniture or cleaning and organizing the kitchen. However, TAMP is notoriously difficult to solve because it involves a tight combination of task planning and motion planning, considering geometric and physical constraints. In this thesis, we aim to improve the performance of TAMP algorithms from three complementary perspectives. First, we investigate the integration of discrete task planning with continuous trajectory optimization. Our main contribution is a conflict-based solver that automatically discovers why a task plan might fail when considering the constraints of the physical world. This information is then fed back into the task planner, resulting in an efficient, bidirectional, and intuitive interface between task and motion, capable of solving TAMP problems with multiple objects, robots, and tight physical constraints. Traditionally, there have been two competing approaches to solving TAMP problems: sample-based and optimization-based methods. In the second part, we first illustrate that, given the wide range of tasks and environments within TAMP, neither sampling nor optimization is superior in all settings. To combine the strengths of both approaches, we have designed meta-solvers for TAMP, adaptive solvers that automatically select which algorithms and computations to use and how to best decompose each problem to find a solution faster. A third promising direction to improve TAMP algorithms is to learn from previous solutions to similar problems. In the third part, we combine deep learning architectures with model-base

关键词： Motion planning

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intelligent Control Based in Neural-Fuzzy of Adaptive Backstepping for The Double Stator IPMSM Powered by a Five-Level NPC Inverter 13

Intelligent Control Based in Neural-Fuzzy of Adaptive Backst...

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13th IEEE International Conference and Expositions on Electric and Power engineering, EPEi 2024

作者： Benaissa, Rabie Mansouri, Smail Ouledali, Omar University of Adrar Faculty of Sciences and Technology Department of Electrical Engineering Laboratory for Sustainable Development and Computer Science 01000 Algeria University of Adrar Faculty of Sciences and Technology Department of Hydrocarbons and Renewable Energies Laboratory for Energy Environment and Information Systems 01000 Algeria

ISBN: (纸本)9798350356199

This article supplies a proposed approach Neuro Fuzzy Controller (NFC)-Adaptive Backstepping Controller (ABC)-Space Vector Modulation (SVM) for a five-level NPC inverter-Double Stator Interior Permanent Magnet Synchronous Motor (DSIPMSM). This paper's primary objective is to enhance DSIPMSM's performance by the use of strong control using the proposed approach. Given its benefits - including high torque density, high efficiency, high power density, and minimal maintenance - DSIPMSM, have recently been the focus of much research. Using the input-output linearization approach and Lyapunov stability theory, asymptotically stable trajectory-following dynamics are demonstrated using nonlinear adaptive backstepping control. The use of fuzzy logic and intelligence-based controllers are increasingly being used to enhance traditional control's performance and output. In addition, the fuzzy logic controller has a lot of flaws that are presently being fixed by the extremely effective NFC tool. The proposed method's worth was demonstrated by the simulation results in terms of durability, effective tracking dynamics, precision, and good disturbance rejection with little ripple. © 2024 IEEE.

关键词： Disturbance rejection

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Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 18-36页

作者： Ding Wang Ning Gao Derong Liu Jinna Li Frank L.Lewis IEEE the Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protectionand Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China the School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China the Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA the School of Information and Control Engineering Liaoning Petrochemical UniversityFushun 113001China the UTA Research Institute the University of Texas at ArlingtonArlington TX 76118 USA

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, ***, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation ***, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

关键词： Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(RL)

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Automated Access Control via License Plate Recognition using Neocognitron Neural Network 36

Automated Access Control via License Plate Recognition using...

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36th International Conference on Information Technologies, InfoTech 2022

作者： Rothkrantz, Leon Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Department of Intelligent Systems Delft2628 CD Netherlands

ISBN: (数字)9781665468701

ISBN: (纸本)9781665468701

In 1979 Fukushima developed a hierarchical, multilayered neural network called Neocognitron and used it for the automatic recognition of handwritten Japanese symbols. We combined the Neocognitron classifier with a special image and segment processor and applied the system in 2001 for automatic recognition of license plates in laboratory experiments. In this paper we report about a special image acquisition module and a postprocessor. We tested the system in real life conditions in an application of automated access control. © 2022 IEEE.

关键词： Image segmentation

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Recent Developments in Event-Triggered Control of Nonlinear systems:An Overview

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无人系统（英文） 2023年第1期11卷 27-56页

作者： Pengpeng Zhang Tengfei Liu Jie Chen Zhong-Ping Jiang Department of Control Science & Engineering Frontiers Science Center for Intelligent Autonomous SystemsTongji UniversityShanghai 201804P.R.China State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819P.R.China Department of Electrical and Computer Engineering New York University370 Jay StreetBrooklynNY 11201USA

Event-triggered control is a most popular paradigm for transferring feedback information in an economical"as needed"*** study of event-triggered control can be traced back to the *** significant advances on the topic of control over networks and the topic of nonlinear control systems over the last two decades,event-triggered control has quickly emerged as a major theoretical subject in control *** of event-triggered control are wide-spread ranging from embedded control systems and industrial control processes to unmanned systems and cyber-physical transportation *** this paper,we first review developments in the synthesis of event-triggered sampling *** event triggering mechanisms,such as static event trigger,dynamic event trigger,time-regularized event trigger,and event trigger with positive threshold offsets,are systematically ***,we study how to design a stabilizing controller that is robust with respect to the sampling ***,we review some recent results in the directions of self-triggered control,event-triggered tracking control and cooperative control,and event-triggered control of stochastic systems and partial differential equation *** applications of event-triggered control are also discussed.

关键词： Event-triggered control event-triggered sampling mechanism event-triggered controller

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Low-Cost, High-Performance Algorithm and Hardware Architecture for SAO Mode Decision 2

Low-Cost, High-Performance Algorithm and Hardware Architectu...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Hou, Bingjing Liu, Chang Zhang, Yuejun Huang, Leilei Jing, Minge Fan, Yibo Fudan University State Key Laboratory of Integrated Chips and Systems Shanghai China Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China East China Normal University Institute of Microelectronic Circuits and Systems Shanghai China

ISBN: (纸本)9798331515669

Sample adaptive offset (SAO) is applied for reducing sample distortion and attenuating ringing artifacts in both HEVC and VVC standards. The rate-distortion optimization process is used to select the best SAO parameter. It accounts for the majority of SAO complexity and determines the SAO filtering performance. Therefore, the rate-distortion cost calculation should be well handled to achieve both low cost and high coding performance. In this paper, we propose a simplified and optimized rate estimation algorithm based on the characteristics of SAO parameter syntax and entropy coding process. And we propose a hierarchical flow for rate-distortion cost calculation to further reduce the computational cost and storage cost for SAO mode decision hardware. The results show that the proposed algorithm achieves an average YUV loss of -0.10%, and the proposed SAO mode decision hardware architecture can be implemented by only 21.8k gates using TSMC 65nm process under 400 MHz. © 2024 IEEE.

关键词： Electric distortion

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Brain-inspired artificial intelligence research: A review

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science China(Technological sciences) 2024年第8期67卷 2282-2296页

作者： WANG GuoYin BAO HuaNan LIU Qun ZHOU TianGang WU Si HUANG TieJun YU ZhaoFei LU CeWu GONG YiHong ZHANG ZhaoXiang HE Sheng Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer and Information Science Chongqing Normal UniversityChongqing 401331China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing 100101China School of Psychological and Cognitive Sciences Peking UniversityBeijing 100871China State Key Laboratory of Multimedia Information Processing School of Computer SciencePeking UniversityBeijing 100871China Department of Computer Science School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China The Center for Research on Intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China

Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.

关键词： artificial intelligence cognitive science brain science intelligence science large language model

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Exploring advanced feature selection techniques: an application to dialectal Arabic data

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International Journal of Information Technology (Singapore) 2024年第7期16卷 4637-4649页

作者： Abdellah, Ait Elouli Ouahi, Hassan Cherrat, El Mehdi Bekkar, Abdellatif Department of Computer Science Laboratory of Innovation in Mathematics and Intelligent Systems Faculty of Applied Sciences Ibn Zohr University Ait Melloul Morocco Laboratory of Innovation in Mathematics and Intelligent SystemsFaculty of Applied Sciences Ibn ZohrUniversity Ait Melloul Morocco Laboratory of Systems Engineering and Information Technology National School of Applied Sciences Ibn Zohr University Agadir Morocco Department of Computer ScienceLIM LaboratoryFaculty of Sciences and Technics Hassan II University of Casablanca Mohammedia Casablanca Morocco

In the field of automated language processing, distinguishing between Moroccan Arabic (Darija) in multilingual contexts is a major challenge. This study addresses this challenge by exploiting feature selection techniques to improve the accuracy of tongue detection. Using a comprehensive methodology integrating various feature selection methods, including TF-idf, CBOW, Word2Vec for feature extraction, LASSO regression, decision trees for machine learning techniques, and statistical feature selection such as ANOVA, Pearson correlation coefficient, and Mutual information, alongside semantic techniques by developing our semantic encoders adapted to Arabic data, we strive to uncover the most important, relevant language features while attenuating the noise. As a result, we have obtained robust results by applying the famous XGBOOST, with conventional extraction methods and SVD as a dimensional reduction method with a very reasonable execution time. Our experimental results highlight the effectiveness of feature selection techniques to reinforce supervised learning tasks. This research not only advances the field of natural language processing but also highlights the central role of feature selection in deciphering complex linguistic landscapes. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Comparative Analysis Dialect Identification Dimensionality Reduction Moroccan Dialect Arabic NLP Standard Arabic

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When Does Sora Show:The Beginning of TAO to Imaginative Intelligence and Scenarios engineering

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 809-815页

作者： Fei-Yue Wang Qinghai Miao Lingxi Li Qinghua Ni Xuan Li Juanjuan Li Lili Fan Yonglin Tian Qing-Long Han School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Department of Electrical and Computer Engineering Indiana University-Purdue University IndianapolisIndianapolisIN 46202USA Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China Virtual Reality Fundamental Research Laboratory Department of Mathematics and TheoriesPeng Cheng LaboratoryShenzhen 518000China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia

DURING our discussion at workshops for writing“What Does ChatGPT Say:The DAO from Algorithmic Intelligence to Linguistic Intelligence”[1],we had expected the next milestone for Artificial Intelligence(AI)would be in the direction of Imaginative Intelligence(II),i.e.,something similar to automatic wordsto-videos generation or intelligent digital movies/theater technology that could be used for conducting new“Artificiofactual Experiments”[2]to replace conventional“Counterfactual Experiments”in scientific research and technical development for both natural and social studies[2]-[6].Now we have OpenAI’s Sora,so soon,but this is not the final,actually far away,and it is just the beginning.

关键词： something Intelligence replace

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Improving Motor Imagery Classification in BCI systems Using EMD and Multi-Layer CNNs 14

Improving Motor Imagery Classification in BCI Systems Using ...

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14th International Conference on computer and Knowledge engineering, ICCKE 2024

作者： Arghand, Reza Chaibakhsh, Ali Radman, Moein University of Guilan Intelligent Systems and Advanced Control Lab Faculty of Mechanical Engineering Rasht Guilan41996-13776 Iran University of Essex Brain-Computer Interfacing and Neural Engineering Laboratory School of Computer Science and Electronic Engineering ColchesterCO4 3SQ United Kingdom

ISBN: (纸本)9798331511272

Brain-computer Interface (BCI) systems are relatively new technologies that could play a significant role in aiding the recovery of impaired activities resulting from neuromuscular disabilities in affected individuals. Accurate recognition and classification of motor imagery in BCI systems present a challenge, leading to extensive research in recent years aimed at improving the accuracy of these systems. In this study, a combination of the Empirical Mode Decomposition (EMD) method and a multi-layer Convolutional Neural Network (CNN) is employed. Initially, the signal is decomposed into Intrinsic Mode Functions (IMFs) using EMD, and all IMFs across all trials are analyzed to select the best ones, which are then fed into the CNN as inputs. Additionally, the study incorporates the fusion of three CNN networks, each corresponding to a different IMF. The features extracted from these networks are combined and used to train an SVM classifier. The proposed method achieved an accuracy of 86.1% on the BCI-2a 2008 dataset, outperforming other state-of-the-art approaches. ©2024 IEEE.

关键词： Brain computer interface

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