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ieee symposium Series on computational intelligence (ieee SSCI)

作者： Hu, Caie Zeng, Sanyou Li, Changhe China Univ Geosci Mech Engn & Elect Informat Wuhan Peoples R China China Univ Geosci Automat & Hubei Key Lab Adv Control & Intelligent Wuhan Peoples R China

ISBN: (纸本)9781728190488

Model management is a critical component in data-driven surrogate-assisted evolutionary optimization. It is mainly used for selecting candidate samples to be evaluated using exact functions to balance the exploration and exploitation of algorithms. However, there are some potential limitations for typical infill sampling criteria. An adaptive model management strategy is proposed to overcome the limitations. It adaptively adjusts the selection probability of candidate samples, and avoids potentially unreliability and the burden of selecting parameters of typical infill sampling criteria. The comparison results on benchmark problems demonstrate the competitiveness of the strategy in solving data-driven optimization problems.

关键词： Evolutionary algorithms Data-driven evolutionary optimization Model management Gaussian process

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Multidisciplinary and Multiphysical Model Represents Complex Research in Applied Informatics

Multidisciplinary and Multiphysical Model Represents Complex...

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International symposium on Applied computational intelligence and Informatics ( SACI)

作者： László Horváth Institute of Applied Mathematics and Doctoral School of Applied Informatics and Applied Mathematics Óbuda University Budapest Hungary

ISBN: (数字)9798350329520

ISBN: (纸本)9798350329537

This paper is about integration of model representations to serve integrated research in applied informatics and applied mathematics. Integration was one of main areas in the engineering purposed software development in a comprehensive effort to serve rushing development of high level automation technologies in aircraft and other leading industries during the past decades. By now, integration of formerly separated model representations provides wide and comprehensive collection of modeling, simulation, organizing, and communication means in in high level multidisciplinary and multiphysical scenario of engineering modeling platform (EMP). This paper focuses on the Virtual Research Laboratory (VRL) where a suitable EMP configuration serves as an all-in-one laboratory for scientific-industrial research. It introduces some of the recent research results in this unusual application of EMP among others the experimental model of research (EMR) which is an all-in-one representation of a research and utilizes services available at a world level EMP. The multidisciplinary and multiphysical EMR is central issue in PhD research program at VRL. Active knowledge background empowers the reactive and autonomous characteristics of EMR. Actual issues at EMP based implementation of EMR at VRL are also discussed in this paper.

关键词： Surveys computational modeling Atmospheric modeling Collaboration Organizations Medical services Mathematical models

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An Intelligent Robot Motion Planning Method and Application via LPPO in Unknown Environment 12

An Intelligent Robot Motion Planning Method and Application ...

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12th International Conference on CYBER Technology in automation, control, and Intelligent Systems, CYBER 2022

作者： Yu, Lingli Luo, Jiawei Zhou, Kaijun School of Automation Central South University Changsha410083 China Hunan Xiangjiang Artificial Intelligence Academy Changsha410083 China School of Computer and Information Engineering Hunan University of Technology and Business Changsha410205 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

Aiming at the problem of motion planning in complex dynamic environment, traditional algorithm which relies on prior map information is unavailable due to the change of environment and suffer from high computational complexity. Proximal policy optimization algorithm suffers from the problem of training failure due to a bad strategy learned in a certain round. In this paper, an improved end-to-end motion planning algorithm with proximal policy optimization is proposed to address this problem by using logarithmic decay strategy dynamically selecting the clipping range and applying the algorithm to robot motion planning. It ensures a larger exploration space for the agent at the beginning of training, and limits the sudden change of the agent's strategy by attenuating the clipping range at the later stage, which improves the stability of the algorithm. We compare our proposed algorithm with several other improved algorithms in different Mujoco environments and our built environment. The results show that our proposed improved algorithm obtains higher reward values and the robot has smoother motion control while navigating obstacle avoidance. © 2022 ieee.

关键词： Intelligent robots

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Using Weka API for creating a custom classification application

Using Weka API for creating a custom classification applicat...

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International symposium on Applied computational intelligence and Informatics ( SACI)

作者： Raul Robu Paul Arseni-Ailoi Dan Ungureanu-Anghel Department of Automation and Applied Informatics Faculty of Automation and Computers Politehnica University of Timisoara Timisoara Romania

In real world applications, classification models can be built by repeatedly going through the steps of data preprocessing, building classification models with different dedicated algorithms, testing the resulted models, until building a good enough classification model is achieved. The model built in this way can be used in order to perform predictions on new data, with a degree of trust resulted by testing the model. All the operations described above can be done with Weka, which is a very powerful machine learning tool. The authors consider that after going through the previous steps, for many real world applications, it would be beneficial to go through a supplementary step, of developing a custom application for data classification, specific to the realized study. The proposed application will use the Weka API, which will allow it to rebuild the selected classification model, save and load it in/from a binary file and then make predictions with the help of a user interface perfectly adapted to the data set used. The proposed application makes it easier to make predictions for a final beneficiary. The paper presents how such a custom classification application can be developed, using the Weka API and Java programming language. In order to validate the proposed solution, the authors have built a custom application for the classification of data of patients who may have heart diseases, starting from the Cleveland heart disease data set, obtained from the UCI Machine Learning Repository. The proposed application allows saving and loading the classification model through serialization and deserialization, so that building it in order to make predictions is necessary only once.

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Particle swarm optimization aided calibration of sensor installation errors for MEMS accelerometers

Particle swarm optimization aided calibration of sensor inst...

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International symposium on Applied computational intelligence and Informatics ( SACI)

作者： Richard Pesti Peter Sarcevic Dominik Csík Ákos Odry Doctoral School of Applied Informatics and Applied Mathematics Óbuda University Budapest Hungary Department of Mechatronics and Automation University of Szeged Szeged Hungary

Accelerometers, gyroscopes, and magnetometers, constituting the Inertial Measurement Unit (IMU), are among the most fundamental sensors utilized in localization problems. However, their utilization is a challenging task since the reliability of measurements depends on the precision of the executed calibration method. This paper proposes an industrial robot-based infield calibration process in a numerical optimization aided framework, which does not need turntable nor dedicated expensive laboratory setup. The proposed method utilizes a calibrated industrial robotic arm, which is programmed to execute wide variety of motions with comprehensive dynamical ranges. The end effector of the robotic arm is equipped with IMUs with predefined installation errors. The proposed calibration process records the ground truth accelerations and raw IMU data in a database in Robot Operating System (ROS) framework. Then, realistic sensor models are established, and the Particle Swarm optimization (PSO) algorithm is executed with a custom fitness function to obtain the between sensor misalignment and bias errors. The comprehensive analysis highlights that the proposed method effectively determines the calibration parameters. Mean squared error (MSE) performance metrics shows that the precision of these parameters mainly depends on the complexity of the executed dynamical motions.

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Solar Powered IoT Based Smart Agricultural Lawnmower Using Bluetooth/Android App

Solar Powered IoT Based Smart Agricultural Lawnmower Using B...

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2023 International Conference on Energy, Materials and Communication Engineering, ICEMCE 2023

作者： Rakesh, Yennam Saravanan, S. Irin Loretta, G. Rao Balasani, Srinivasa Naga Malleswara Rao, D.S. Kalaiyarasi, M. B V Raju Institute of Technology Telangana Narsapur India B V Raju Institute of Technology Department of Eee Telangana Narsapur India Saveetha Institute of Medical and Technical Sciences SIMATS Saveetha School of Engineering Chennai India Prasad Institute of Technology Department of Eee Uttar Pradesh Jaunpur India GokarajuRangaraju Institute of Engineering and Technology Telangana Hyderabad India SIMATS Saveetha School of Engineering Department of Computational Intelligence Chennai India

ISBN: (纸本)9798350393378

In India, agriculture is the backbone of nearly 70% of the population, involving a diverse range of tasks spanning from seed sowing to ploughing. The imperative need for automation in agriculture to bolster productivity aligns with the ongoing technological advancements. Conventional fuel-powered lawnmowers require manual assistance. In this paper, an IoT-based solar-powered intelligent lawnmower has been developed. This lawnmower boasts a BLDC motor, four gear motors, an array of sensors, an Arduino-driven charge controller, and a solar panel, making it a sustainable choice. Its user-friendly control interface is seamlessly operated via an Android app for data communication and NodeMCU 8266 for energy and motor management. Notably, the standout feature is its IoT-based motion control, enabling remote operation and achieving an impressive 83% electrical efficiency across diverse weather conditions © 2023 ieee.

关键词： Internet of things

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Continuous Improvement of Process Advisor Optimizing Furnace Model

Continuous Improvement of Process Advisor Optimizing Furnace...

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1st ieee Colombian Conference on Applications in computational intelligence (ColCACI)5th International symposium on Nickel and Cobalt (Ni-Co)

作者： Bjorklund, Peter Korpi, Mikko Grimsey, David Marjakoski, Miikka Outotec Finland Oy Rauhalanpuisto 9 Espoo 02231 Finland BHP Nickel West Kalgoorlie Smelter NKS Smelterman Dr Feysville WA 6431 Australia Boliden Harjavalta Smelter Teollisuuskatu 1 FI-29200 Harjavalta Finland

ISBN: (纸本)9783030656478;9783030656461

The Outotec (R) Process Advisor has successfully been used for automatic control of flash smelting furnace setpoints continuously for 5 years. This is one contribution that has enabled record long furnace campaign life due to the improved matte temperature control, which has prevented matte infiltration into the hearth. Building on this success, the Process Advisor has been enhanced to further utilize the online dynamic heat and material balance model also for other purposes than direct process control. This paper covers some of the latest improvements taken into use after the initial startup. These include, for example, an online maximum feed rate estimator, which continuously updates the maximum feed rate possible for the operator and an online matte level estimator to get a better matte level estimate also between sounding.

关键词： automation Digitalization Industry 4.0 Information management Process control Pyrometallurgy Smelting Expert system Process advisor

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A Novel 2-DOF Cable-Driven Flexible Manipulator Driven by Pneumatic Artificial Muscles

A Novel 2-DOF Cable-Driven Flexible Manipulator Driven by Pn...

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Artificial intelligence and Intelligent Manufacturing (AIIM), International symposium on

作者： Xiaowei Xie Zili Xu Kai Ru Zhiyang Lin School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (数字)9798331541729

ISBN: (纸本)9798331541736

Cable-driven flexible manipulators are highly flexible and adapt well to complex, unstructured environments. Existing cable-driven flexible manipulators typically employ motor-driven actuators, which result in reduced inherent compliance and pose significant safety risks during interaction with the environment. To address these challenges, this study proposes a 2-DOF cable-driven flexible manipulator driven by pneumatic artificial muscles, with the aim of enhancing compliance and improving safety during interaction. Given the complexity and numerous internal components of the articulated joint segment, conventional dynamic analysis methods are difficult to apply effectively. To overcome these challenges and improve computational efficiency, an equivalent inverse dynamics model for the articulated joint segment is developed. To further improve trajectory tracking accuracy, a cable-length control algorithm based on “PID + feedforward compensation” is designed. Finally, a trajectory tracking simulation platform for the cable-driven flexible manipulator is constructed, validating the high-precision trajectory tracking performance of the control algorithm and further confirming the accuracy and effectiveness of the equivalent inverse dynamics model.

关键词： Artificial muscles Accuracy Trajectory tracking Heuristic algorithms Simulation computational modeling 2-DOF Safety Steady-state Manipulator dynamics

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automation of lung ultrasound imaging and image processing for bedside diagnostic examinations

Automation of lung ultrasound imaging and image processing f...

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International symposium on Applied computational intelligence and Informatics ( SACI)

作者： Róbert Zsolt Szabó Gábor Orosz Tamás Ungi Colton Barr Chris Yeung Roland Incze Gabor Fichtinger János Gál Tamás Haidegger Antal Bejczy Center for Intelligent Robotics Óbuda University Budapest Hungary University Research and Innovation Center (EKIK) Óbuda University Budapest Hungary Department of Anesthesiology and Intensive Therapy Semmelweis University Budapest Hungary Laboratory for Percutaneous Surgery Queen’s University Kingston ON Canada Austrian Center for Medical Innovation and Technology Wiener Neustadt Austria John von Neumann Faculty of Informatics Óbuda University Budapest Hungary

The causes of acute respiratory failure can be difficult to identify for physicians. Experts can differentiate these causes using bedside lung ultrasound, but lung ultrasound has a considerable learning curve. We investigate if an automated decision-support system could help novices interpret lung ultrasound scans. The system utilizes medical ultrasound, data processing, and a neural network implementation to achieve this goal. The article details the steps taken in the data preparation, and the implementation of the neural network. The best model’s accuracy and error rate are presented, along with examples of its predictions. The paper concludes with an evaluation of the results, identification of limitations, and suggestions for future improvements.

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A Novel YJQR-LSTM Model for Nonparametric Probabilistic Sustainable Agriculture Wind Power Forecasting Based on Intelligent IoT

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ieee Internet of Things Journal 2024年

作者： Wang, Jie Jiang, Junhui Chen, Xinlong Cai, Defu Wu, Yue Lu, Renzhi School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Harbin Engineering University College of Computer Science and Technology Harbin150001 China State Grid Hubei Electric Power Research Institute Hubei Wuhan430079 China School of Technology State Key Laboratory of Efficient Production of Forest Resources Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation Beijing Forestry University Beijing100083 China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing400065 China State Key Laboratory of Mechanical Transmission for Advanced Equipment Chongqing University Chongqing400044 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

Energy costs associated with the consumption of non-renewable energy sources have become an important issue in improving the international competitiveness of agriculture. Wind power, as a renewable energy source, can replace non-renewable energy sources to reduce energy costs and improve the sustainability of agricultural. However, the inherent intermittency, randomness, and volatility within weather conditions and wind speed presents a substantial challenge in accurately predicting wind power generation. This work proposes a novel YJQR-LSTM algorithm that leverages Yeo-Johnson quantile regression (YJQR) with a long short-term memory (LSTM) network for nonparametric probabilistic forecasting of wind power generation via the Intelligent Internet of Things. First, an improved YJQR model based on the YJ transformation is designed to obtain a more precise characterization of wind uncertainty, providing a more flexible probability density function for wind power generation. Then, utilizing the unique structure of the LSTM network to learn the parameters of the YJQR model, temporal features can be extracted from time series data. To mitigate the impact of outliers in the raw data on accuracy and improve computational efficiency, a novel logarithmic likelihood function is developed as the loss function utilized in the training phase. The effectiveness of the proposed algorithm is validated using a real-world dataset from five wind farms from the Global Energy Forecasting Competition. Numerical results demonstrate that the algorithm provides more accurate wind power prediction results in complex wind power data environments, which is important for making full use of wind energy and thus reducing the consumption of non-renewable energy in agriculture. © 2024 ieee.

关键词： Wind forecasting

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