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Applied Artificial Intelligence and Computing (ICAAIC), international conference on

作者： Levina P. Kaliyamoorthi Vinjamuri S.N.CH. Dattu G Dineshnath S. Jaiganesh Dinesh Chandra Dobhal C. R. Rene Robin Department of Information Science & Engineering KNS Institute of Technology Bangalore Karnataka India Department of Computer Science and Engineering Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India Department of Mechanical Engineering Aditya Engineering College Surampalem Andhra Pradesh India Department of Computer Science and Engineering KL University Vaddeswaram Andhra Pradesh India Department of Master of Computer Applications PSNA College of Engineering and Technology (Autonomous) Dindigul Tamil Nadu India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Computer Science and Engineering Sri Sairam Engineering College Chennai Tamil Nadu India

The objective of this research study is to reduce the complexity and increase the flexibility to create and maintain computational solutions applied to health, where the scenarios are quite complex and dynamic. The main contributions of HealthDash are: (1) Provide dynamism in the process of creating IoT applications, both in the fog layer and in the cloud layer, through unification action of technologies adopted at both levels; (2) provide means to reduce the volume of information transmitted between the fog layer and the cloud layer, enabling the analysis and filtering of data closer to the edge of the network. This article proposes a solution for the development of IoT applications for health called HealthDash, based on the data flow-oriented programming paradigm, allowing to indicate in which layer of the architecture (fog or cloud) the data flows, which process data from the monitored patients, will be executed.

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Gaussian Function Representation of 2nd-Order Normal Cloud Model

Gaussian Function Representation of 2nd-Order Normal Cloud M...

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international conference on Intelligent System and Knowledge Engineering, ISKE

作者： Min Li Guoyin Wang Zeng Yu Hongjun Wang Jihong Wan Tianrui Li School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing China School of Computer Science and Technology Guangdong University of Technology Guangdong China

Natural language is a fruit of human cognition, and its basic units are linguistic terms. Linguistic terms' representation and processing have played a fundamental role in applications such as risk assessment. Cloud Model, which integrates the approaches of probability theory and fuzzy sets, is a model for expressing the uncertainties of linguistic terms. Its advantage is that it is a bidirectional cognitive model. The 2nd-order normal cloud (2Ord-NC) is a specific Cloud Model. It can represent the linguistic concept's intension using three statistics parameters (Expectation Ex, Entropy En, and Hyper-entropy He) and its extension using cloud drops (instances). A cognitive transformation between a qualitative concept and its quantitative instances can be implemented by probabilistic programming (FCT and BCT). In the current works, Upper and Lower membership functions characterize Cloud Model-s. However, scholars have yet to explain this representation method's rationality theoretically. This paper proposes the 2nd-order normal cloud model's Gaussian function representation. Firstly, it is proved that the 2nd-order normal cloud model (2Ord-NCM) can be determined by its outer envelope and expectation membership functions. The properties of the 2Ord-NCM's fuzzy membership functions are analyzed when the concept is clear and unclear, and the definitions of inner envelope membership function family, outer envelope membership function family, and representative membership function family are given. Secondly, it is proved that the 2Ord-NCM can also be determined by any two of its representative membership functions. This work enriches the Cloud Model's mathematical theoretical foundations, thereby better guiding its practical application.

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Learning Stable nonlinear Dynamics and Interactive Force-Aware Variable Impedance Control for Robotic Contact Tasks

Learning Stable Nonlinear Dynamics and Interactive Force-Awa...

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2023 international conference on Biomimetic Intelligence and Robotics

作者： Wu, Hongmin Zhai, Xueqian Wu, Xinyu Gu, Shichao Liao, Zhaoyang Xu, Zhihao Pan, Jia Zhou, Xuefeng Institute of Intelligent Manufacturing Guangdong Academy of Sciences Guangdong Key Laboratory of Modern Control Technology Guangzhou China Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Shenzhen China Wuyi University Jiangmen China Pazhou Lab Guangzhou China The University of Hong Kong Hong Kong

This paper presents a novel approach for efficient robot programming in tasks such as polishing and grinding, which addresses the challenges of stability and adaptability in robot motion and control. The approach involves learning movement skills and variable impedance control from human demonstrations. Firstly, a non-parametric Gaussian mixture model and globally stable method are proposed for learning robot rhythmic skills with nonlinear dynamics by taking into account the characteristics of robot motion in human-robot physical interaction. Then, adaptive variable impedance control mechanisms are constructed using Gaussian mixture regression, enabling adaptive perception and online control of unknown environments. Finally, a stability-consideration variable impedance controller is designed to minimize the total control-torque while ensuring motion accuracy, which allows efficient adaptation to uncertain environments and complex tasks. The paper provides a novel framework of robot rhythmic skill learning and variable impedance control for robotic complex surface polishing tasks with nonlinear dynamics based on multiple physical human demonstrations, and verifies the proposed methods with a real Franka-robot polishing task. These implementations would be with applications in intelligent manufacturing, robot-assisted rehabilitation. © 2023 The Authors. Published by Elsevier B.V.

关键词： Robot programming

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2nd international conference on Advanced Engineering, Technology and applications, ICAETA 2023

2nd International Conference on Advanced Engineering, Techno...

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2nd international conference on Advanced Engineering, Technology and applications, ICAETA 2023

ISBN: (纸本)9783031509193

The proceedings contain 37 papers. The special focus in this conference is on Advanced Engineering, Technology and applications. The topics include: Leveraging Graph Neural Networks for Botnet Detection;voice Commands with Virtual Assistant in Mixed Reality Telepresence;photovoltaics Cell Anomaly Detection Using Deep Learning Techniques;Anomaly Detection Algorithm with Blockchain to Detect Potential Security Attacks in the IIoT Model of Industry 5.0;COVID-19 Seasonal Effect on Infection Cases and Forecasting Using Deep Learning;PV Output Power Prediction Using Regression Methods;secure Future Healthcare applications Through Federated Learning Approaches;T-SignSys: An Efficient CNN-Based Turkish Sign Language Recognition System;damage Detection on Turbomachinery with Machine Learning Algortihms;image Encryption Using Spined Bit Plane Diffusion and Chaotic Permutation for Color Image Security;Deep Learning for ECG Signal Classification in Remote Healthcare applications;computerized Simulation of a nonlinear Vibration Sandwich Plate Structure with Porous Functionally Graded Materials Core;the Computer Modelling of the Human Gait Cycle for the Determination of Pressure Distribution and Ground Reaction Force Using a Below Knee Sockets;evaluation of the Existing Web Real-Time Signaling Mechanism for Peer-to-Peer Communication: Survey;Autonomous Agent Using AI Q-Learning in Augmented Reality Ludo Board Game;Modelling and Estimating of VaR Through the GARCH Model;Nuclei Instance Segmentation in Colon Histology Images with YOLOv7;evolutionary Approach to Feature Elimination in House Price Estimation;a Hybrid Machine Learning Approach for Brain Tumor Classification Using Artificial Neural Network and Particle Swarm Optimization;optimized KiU-Net: Lightweight Convolutional Neural Network for Retinal Vessel Segmentation in Medical Images;Hardware Implementation of MRO-ELM for Online Sequential Learning on FPGA.

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Allocation of Active Power Filter in Distribution Network Considering nonlinear Loads 2

Allocation of Active Power Filter in Distribution Network Co...

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2nd IEEE international conference on Smart Technologies for Power, Energy and Control, STPEC 2021

作者： Brahmachary, Rupali Bhattacharya, Aniruddha Ahmed, Irfan National Institute of Technology Durgapur Department of Electrical Engineering Durgapur India

ISBN: (纸本)9781665443197

In this paper, a Harmonics Mitigation Algorithm (HM-Algorithm) is proposed to minimize the Total Harmonic Distortion (THD) by optimizing the size and location of Active Power Filters (APF) with non-linear loads being present. To find out fundamental voltage and current, Backward Forward Sweep (BFS) load flow analysis is performed, whereas Decoupled Harmonic Load Flow (De-HLF) analysis along with BFS is used for the harmonic voltage and current analysis. Differential Evolution (DE) algorithm is used for optimization purpose. Later, a comparative analysis to mitigate THD is performed in IEEE 33 bus Radial Distribution Network (RDN) to establish its efficiency under the influence of Six-Pulse Converter and Pulse-width modulated (PWM) nonlinear loads. The performance of HM-Algorithm is verified with and without integrating the APFs. The result shows a significant improvement after integrating APFs in optimal way. © 2021 IEEE.

关键词： Optimization

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Iterative parameter estimation of nonlinear systems 2

Iterative parameter estimation of nonlinear systems

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2021 2nd international conference on Computer Information and Big Data applications, CIBDA 2021

作者： Yin, Miao Zhao, Li School of Automation and Electronic Engineering Qingdao University of Science and Technology Qingdao266061 China Information Section Qingdao Hospital of Traditional Chinese Medicine Qingdao266033 China

This paper discusses the identification methods for nonlinear systems. Using the negative gradient search, the gradient based iterative algorithm is derived to determine the parameters of the nonlinear system. In addition, in order to improve the computational efficiency, the gradient-based iterative algorithm based on the model transformation is derived. The basic principle is to transform a complex nonlinear system into a linear or simple nonlinear system. Finally, a numerical example is given to verify the effectiveness of the proposed methods. © Published under licence by IOP Publishing Ltd.

关键词： nonlinear systems

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Autonomous and Adaptive Communications Systems in Internet of Things

Autonomous and Adaptive Communications Systems in Internet o...

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Artificial Intelligence and Machine Learning applications Theme: Healthcare and Internet of Things (AIMLA), international conference on

作者： Deeplata Sharma Anila Bajpai Sweta Kumari Department of Chemistry Vivekananda Global University Jaipur India Department of Management School of Mangement - UG JAIN (Deemed to be University) Bangalore Karnataka India Department of Law ARKA JAIN University Jamshedpur Jharkhand India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

Self-sustaining and adaptive communications structures within the Internet of Factors (IoT) are essential to allow a hit deployment of various sensing, evaluation, and choice-making processes. Autonomous structures are characterised by systems that may execute tasks independently from any outside human or physical manipulation, and adaptive structures react and alter their environment to emerge as greener through the years. The capacity to adjust to environmental modifications and respond autonomously permits extra efficient usage of assets, quicker communication, and more green management of sensor networks. Self-sustaining networks are often characterised by an expanded complexity related to extra problems in programming and preservation. Therefore, the choice assist architecture needs to be well designed for a successful implementation. This design should remember scalability, range, and protection requirements as a way to ensure the nice of the device's performance. Inside the context of IoT, self-sufficient and adaptive networks so that you can self-prepare to be able to locate and manage pollution, temperature manipulation, and energy resources. IoT networks should also be able to make decisions based totally on statistics from the network's environment.

关键词： Adaptive systems Scalability Green products programming Routing Sensors Internet of Things

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On the Current and Emerging Challenges of Developing Fair and Ethical AI Solutions in Financial Services 2

On the Current and Emerging Challenges of Developing Fair an...

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2nd ACM international conference on AI in Finance (ICAIF)

作者： Kurshan, Eren Chen, Jiahao Storchan, Victor Shen, Hongda Columbia Univ New York NY 10027 USA Parity New York NY USA JP Morgan Palo Alto CA USA Uni Alabama Huntsville AL USA JP Morgan AI Res New York NY USA

ISBN: (纸本)9781450391481

AI has found a wide range of application areas in the financial services industry. As the number and the criticality of the applications continue to increase, fair and ethical AI has emerged as an industry-wide objective. In recent years, numerous ethical principles, guidelines and techniques have been proposed. However, the model development organizations face serious challenges in building ethical AI solutions. This paper focuses on the overarching issues model development teams face, which range from the design and implementation complexities, to the shortage of tools, and the lack of organizational constructs. It argues that focusing on the practical considerations is an important step in bridging the gap between the high-level ethics principles and the deployed AI applications, as well as starting industry-wide conversations toward solution approaches.

关键词： Computer programming

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Characterizing Boundedness in Chase Variants

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THEORY And PRACTICE OF LOGIC programming 2021年第1期21卷 51-79页

作者： Delivorias, Stathis Leclere, Michel Mugnier, Marie-Laure Ulliana, Federico Univ Montpellier LIRMM CNRS INRIA Montpellier France

Existential rules are a positive fragment of first-order logic that generalizes function-free Horn rules by allowing existentially quantified variables in rule heads. This family of languages has recently attracted significant interest in the context of ontology-mediated query answering. Forward chaining, also known as the chase, is a fundamental tool for computing universal models of knowledge bases, which consist of existential rules and facts. Several chase variants have been defined, which differ on the way they handle redundancies. A set of existential rules is bounded if it ensures the existence of a bound on the depth of the chase, independently from any set of facts. Deciding if a set of rules is bounded is an undecidable problem for all chase variants. Nevertheless, when computing universal models, knowing that a set of rules is bounded for some chase variant does not help much in practice if the bound remains unknown or even very large. Hence, we investigate the decidability of the k-boundedness problem, which asks whether the depth of the chase for a given set of rules is bounded by an integer k. We identify a general property which, when satisfied by a chase variant, leads to the decidability of k-boundedness. We then show that the main chase variants satisfy this property, namely the oblivious, semi-oblivious (aka Skolem), and restricted chase, as well as their breadth-first versions.

关键词： knowledge representation and nonmonotonic reasoning logic programming methodology and applications databases and semantic web reasoning theory

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Resource Optimisation Study Based on Multi-objective Particle Swarm Optimisation and Monte Carlo Simulation

Resource Optimisation Study Based on Multi-objective Particl...

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Electrical, Automation and Computer Engineering (ICEACE), international conference on

作者： Ying Shi Guoyue Liu Suzhou City University Suzhou China Insnex Technologies Co. Ltd Suzhou China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368215

This study focuses on the application of multi-objective optimisation algorithms in complex environments, aiming at algorithmically solving resource utilisation and revenue optimisation problems. For resource allocation under specific constraints, we adopt a linear programming framework to model the problem, focusing on exploring the dynamic relationship between output, cost and market price. To this end, this paper proposes a solution based on a multi-objective particle swarm optimisation (PSO) algorithm and uses the Matlab platform to solve the linear programming model and generate the optimal decision-making strategy for the period 2024-2030. To cope with parameter uncertainty, we further combine conditional value-at-risk (CVaR) and robust optimisation methods to ensure that the optimisation model is adaptive and robust under uncertainty. The performance of the algorithm in dealing with uncertain scenarios is enhanced by Monte Carlo simulation, which improves the model's responsiveness to market fluctuations. In addition, the paper analyses the interactions between different parameters (e.g., production, pricing, cost structure) through Pearson's correlation coefficient, which provides a scientific basis for the constraints in the model. This algorithm-driven optimisation-based approach demonstrates the potential for a wide range of applications in the fields of resource allocation, supply chain management, etc., and exhibits significant advantages in terms of computational efficiency and resilience.

关键词： Adaptation models Uncertainty Monte Carlo methods Heuristic algorithms Decision making Linear programming Mathematical models Resource management Particle swarm optimization Optimization

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