The proceedings contain 32 papers. The topics discussed include: extreme learning ANFIS for control applications;collaborative fuzzy rule learning for Mamdani type fuzzy inference system with mapping of cluster center...
ISBN:
(纸本)9781479945313
The proceedings contain 32 papers. The topics discussed include: extreme learning ANFIS for control applications;collaborative fuzzy rule learning for Mamdani type fuzzy inference system with mapping of cluster centers;real-time nonlinear modeling of a twin rotor MIMO system using evolving neuro-fuzzy network;adaptive dynamic output feedback control of Takagi-Sugeno fuzzy systems with immeasurable premise variables and disturbance;optimal robust control for generalized fuzzy dynamical systems: a novel use on fuzzy uncertainties;quadrotor control using dynamic feedback linearization based on piecewise bilinear models;ultra high frequency polynomial and sine artificial higher order neural networks for control signal generator;dissolved oxygen control of activated sludge biorectors using neural-adaptive control;and cascaded free search differential evolution applied to nonlinear system identification based on correlation functions and neural networks.
The proceedings contain 64 papers. The topics discussed include: noise-based logic versus quantum supremacy;modeling cerebro-vascular autoregulation after postural change perturbations;a review on emotion based harmfu...
ISBN:
(纸本)9798350398823
The proceedings contain 64 papers. The topics discussed include: noise-based logic versus quantum supremacy;modeling cerebro-vascular autoregulation after postural change perturbations;a review on emotion based harmful speech detection using machine learning;comparison of automatic question generation techniques;transparent slide detection and gripper design for slide transport by robotic arm;proposing a new model for estimation of oil rate passing through wellhead chokes in an Iranian heavy oil field;real-time multi-user 3D visualization software in medicine;evaluation of the use of an intelligent system in the calibration of a refined car-following model;inertial sensor-based movement classification with dimension reduction based on feature aggregation;and on the simulation of lower order control strategies for higher order systems.
computational knowledge vision [1] is emphasized as a novel perspective or field in this paper. It first proposes the visual hierarchy and its connection to knowledge, stating that knowledge is a justified true belief...
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computational knowledge vision [1] is emphasized as a novel perspective or field in this paper. It first proposes the visual hierarchy and its connection to knowledge, stating that knowledge is a justified true belief. To further the previous research, we concisely summarize our recent works and suggest a new direction that knowledge is also a thought framework in vision.
Traditional cognitive travel modeling typically employs a unified cognitive model to simulate representative travel behaviors, which may usually result in a weak characterization of user heterogeneity in paths, modes,...
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Traditional cognitive travel modeling typically employs a unified cognitive model to simulate representative travel behaviors, which may usually result in a weak characterization of user heterogeneity in paths, modes, and other factors. Large language model (LLM), by contrast, has significantly enhanced the anthropomorphic and personalized features of intelligent systems. To integrate their advantages, this article proposes LLM-driven cognitive modeling to generate more diverse and personalized travel demands. The new method sufficiently exploits LLM such as the llama as a basis and provides personalized travel plans so that more heterogenous travel demands could be generated. Additionally, introducing LLM into cognitive modeling can significantly reduce the time of model development, thus accelerating the research or engineering deployment. By calibrating and testing with one month's data from public transportation (buses and subways) in Beijing, our method, compared to traditional cognitive models, not only achieves better accuracy in reproducing typical travel patterns, but also generates more diverse ones, providing a more comprehensive input for computational experiments on traffic management and control strategies.
Powered by advanced information technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).In this context,computational experiments method has emerged as a nove...
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Powered by advanced information technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).In this context,computational experiments method has emerged as a novel approach for the design,analysis,management,control,and integration of CPSS,which can realize the causal analysis of complex systems by means of“algorithmization”of“counterfactuals”.However,because CPSS involve human and social factors(e.g.,autonomy,initiative,and sociality),it is difficult for traditional design of experiment(DOE)methods to achieve the generative explanation of system *** address this challenge,this paper proposes an integrated approach to the design of computational experiments,incorporating three key modules:1)Descriptive module:Determining the influencing factors and response variables of the system by means of the modeling of an artificial society;2)Interpretative module:Selecting factorial experimental design solution to identify the relationship between influencing factors and macro phenomena;3)Predictive module:Building a meta-model that is equivalent to artificial society to explore its operating ***,a case study of crowd-sourcing platforms is presented to illustrate the application process and effectiveness of the proposed approach,which can reveal the social impact of algorithmic behavior on“rider race”.
This paper outlines the challenges and outcomes in developing an embedded brushed DC motor drive tailored for higher educational laboratory teaching. It covers requirement definition, component design, integration, as...
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ISBN:
(纸本)9798350329537;9798350329520
This paper outlines the challenges and outcomes in developing an embedded brushed DC motor drive tailored for higher educational laboratory teaching. It covers requirement definition, component design, integration, assembly, and testing, aiming to provide students with practical motor control system experience.
An investigation and outline of Metacontrol and Decontrol in Metaverses for controlintelligence and knowledge automation are *** control with prescriptive knowledge and parallel philosophy is proposed as the starting...
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An investigation and outline of Metacontrol and Decontrol in Metaverses for controlintelligence and knowledge automation are *** control with prescriptive knowledge and parallel philosophy is proposed as the starting point for the new control philosophy and technology,especially for computationalcontrol of metasystems in cyberphysical-social *** argue that circular causality,the generalized feedback mechanism for complex and purposive systems,should be adapted as the fundamental principle for control and management of metasystems with metacomplexity in ***,an interdisciplinary approach is suggested for Metacontrol and Decontrol as a new form of intelligent control based on five control metaverses:MetaVerses,MultiVerses,InterVerses,TransVerse,and DeepVerses.
The aim of my project is to offer an automation solution for examining solid-state passive detectors frequently used in particle physics. Since the detector surface often exceeds the field of view of microscopes, the ...
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The Industry 4.0 digital transformation envisages future industrial systems to be fully automated, including the control, upgrade, and configuration processes of a large number of heterogeneous wired/wireless intercon...
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The Industry 4.0 digital transformation envisages future industrial systems to be fully automated, including the control, upgrade, and configuration processes of a large number of heterogeneous wired/wireless interconnected devices in Industrial Internet of Things (IIoT) environments. Most of the industrial automation systems today are based on the traditional International Society of automation (ISA)-95 model, with some recently transitioned to Cloud automation systems. Latest developments in network connectivity technologies, Artificial intelligence (AI), and Cloud/Fog computing technologies have motivated us to rethink the ISA-95 model. In this paper, we propose a vision that aims to migrate most of the computational and automation tasks closer to the ground, which we term the collaborative "Cloud-Fog automation" paradigm. We perform a comprehensive survey of the state-of-the-art and formulate the three pillars of this vision: Deterministic Connectivity, Deterministic Connected intelligence, and Deterministic Networked Computing. In each of these pillars, we review their latency and reliability, security, and functional safety requirements and challenges. Finally, we articulate and highlight key future research directions to realize this vision.
作者：
Huang, WenjunCui, YunduanLi, HuiyunWu, XinyuUniv Chinese Acad Sci
Sch Artificial Intelligence Beijing 101408 Peoples R China Chinese Acad Sci
Shenzhen Inst Adv Technol Shenzhen 518055 Guangdong Peoples R China Chinese Acad Sci
Shenzhen Inst Adv Technol CAS Key Lab Human Machine Intelligence Synergy Sys Shenzhen 518055 Guangdong Peoples R China Chinese Acad Sci
Shenzhen Inst Adv Technol Guangdong Hong Kong Macao Joint Lab Human Machine Shenzhen 518055 Guangdong Peoples R China
Gaussian process (GP) offers a robust solution for modeling the dynamics of unmanned surface vehicles (USV) in model-based reinforcement learning (MBRL). However, the rapidly increasing computational complexity with a...
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Gaussian process (GP) offers a robust solution for modeling the dynamics of unmanned surface vehicles (USV) in model-based reinforcement learning (MBRL). However, the rapidly increasing computational complexity with a large sample capacity of GP limits its application in complex scenarios that require substantial samples to cover the state space. In this article, a novel probabilistic MBRL approach, probabilistic neural networks model predictive control (PNMPC) is proposed to tackle this issue. With an iterative learning framework, PNMPC properly models the USV dynamics using neural networks from a probabilistic perspective to avoid the computational complexity associated with sample capacity. Employing this model to effectively propagate system uncertainties, a model predictive control (MPC) policy is developed to robustly control the USV against external disturbances. Evaluated by position-keeping and multiple targets-tracking scenarios on a real USV data-driven simulation, the proposed method consistently demonstrates its significant superiority in both model accuracy and control performance compared to not only GP model-based approaches but also the probabilistic neural networks-based MBRL baselines, across various scales of external disturbances.
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