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robotics, control and automation Engineering (RCAE), international conference on

Presents the front cover, title page, cover page, or splash screen of the proceedings record.

ISBN: (数字)9781665459419

ISBN: (纸本)9781665459426

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Hyperparameter Tuning of Convolution Neural Network for Paddy Leaves Disease Detection

Hyperparameter Tuning of Convolution Neural Network for Padd...

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5th international conference on Emerging Electrical Energy, Electronics and Computing Technologies, ICE4CT 2024

作者： Yusoff, C.M.Y.A. Che Mat Nadzri, N.Z. Yusoff, Y. UniKL Robotics and Industrial Automation Center Universiti Kuala Lumpur Malaysia France Institute Selangor Bangi Malaysia

the traditional approach to identifying paddy leaf diseases in Malaysia's agriculture sector involves visual inspection by farmers or agricultural experts. However, this method heavily relies on human expertise to recognize visual symptoms like discoloration, spots, or lesions on the leaves, leading to subjective assessments and delayed diagnosis. While various deep learning models have been utilized to detect paddy leaf diseases, there is a need for a more comprehensive discussion on the appropriate hyperparameters setting. this study focused on utilizing Convolutional Neural Network (CNN) models, specifically ResNet and VGG architectures, to identify paddy leaf diseases in Sekinchan, Selangor. the models were trained on a dataset consisting of both online and real-world images, which underwent several augmentation processes to enhance the model's robustness. the study has highlighted the optimal hyperparameters of the CNN model, resulting in an impressive accuracy rate of 98.71% and a minimal loss rate of 0.001. the integration of the final model into a specific Graphical User Interface has further enhanced its utility. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Graphical user interfaces

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On Implementing the Sparse Events Closed-Loop control theory for the CartPoleSimulation

On Implementing the Sparse Events Closed-Loop Control Theor...

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16th EAI international conference on Simulation Tools and Techniques, SIMUTools 2024

作者： Díaz-Romero, Santiago Paluch, Marcin Linares-Barranco, Alejandro Rios-Navarro, Antonio Robotics and Technology of Computers Lab. ETSII-EPS I3US SCORE Universidad de Sevilla Seville Spain Institute of NeuroInformatics UZH-ETHZ Zurich Switzerland

ISBN: (纸本)9783031873447

Event-based or neuro-inspired motor control systems communicate information through pulses, instantaneous bursts of electrical current that encode information very efficiently, as occurs in biological organisms. this work proposes a software implementation for sparse events closed-loop neuromorphic motor control theory (SECLOC) in a software tool. the control algorithm has been included in a simulator developed in Python, and publicly available, of a robotic cart controlling an inverted pendulum. the proposed implementations can be easily ported to any FPGA or microcontroller-based board and allow control of the real robotic cart. We have tested and verified the functionality using the algorithm to generate control events for proportional-integral-derivative (PID) and linear-quadratic-regulator (LQR) controllers and obtained the parameters equivalent to the proportionality, integrative, and derivative constants for these pulsing controllers. the empirical results demonstrate the validity of both the theory in simulation and its test in an actual robot in the pulsed domain. In the simulation, for angle control using a PID, with a minimum reaction time of 1 ms from the controller, the pendulum remains in equilibrium along the cart pole travel for more than 5 min, registering the 0-degree passage more than 2800 times. For the real pendulum used, the LQR controller, and the optimal control constants, with a minimum reaction time of 6 ms of the controller, the pendulum remains in equilibrium along the carriage path, as well as under slight and moderate perturbations without dropping during all experiments performed over a simulated period of almost 3.5 days. © ICST Institute for Computer Sciences, Social informatics and Telecommunications Engineering 2025.

关键词： Two term control systems

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the Novel DeepBoost Multi Output Regressor for Household Finance Intelligence

The Novel DeepBoost Multi Output Regressor for Household Fin...

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Advanced Research in Computing (ICARC), international conference on

作者： S. H. S. V. Suwandaratna G. O. Priyankara Department of Computer Science and Engineering Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

this paper forecasts the microeconomic level household expenditures using a novel hybrid deep learning approach. In terms of research significance, household finance control has a major influence on the finance system within the economy. Accurate forecasting of household finances assists in maintaining positive financial behavior among individuals and the economy. the DeepBoost multi-output regressor proposed in this paper is based on the 1D CNN-ANN and the XGBoost. the proposed model in this paper is compared with the R 2 , MSE, and MAE since it’s a regression problem. the experimental results reveal that the proposed DeepBoost multi-output regressor has the best application in forecasting the multiple expenditures of households by outperforming the ANN, 1D CNN-ANN, and Random Forest Regressor models. the proposed DeepBoost multi-output regressor evaluated the housing, food, transportation, healthcare, other necessities, childcare, and tax expenditures that had 0.94, 0.98, 0.83, 0.94, 0.97, 0.97, and 0.99 values for the R 2 , 9037.71, 2692.12, 9788, 15077.33, 1373.93, 13629.36, and 1904.52 values for the MSE, and 66.07, 34.05, 73.17, 87.05, 26.25, 78.74, and 29.47 MAE values than the ANN, RFR, and 1D CNN-ANN models.

关键词： Deep learning Pediatrics Computational modeling Finance Transportation Medical services Predictive models Microeconomics Forecasting Random forests

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Electric Vehicle Charging Demand Prediction using Multiresolution Sinusoidal Neural Network Optimized with Addax Optimization

Electric Vehicle Charging Demand Prediction using Multiresol...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： Rajesh Munirathnam Rohit P. Jadhav Nilesh M. Mahajan Amit Barve M. E. Shashi Kumar Natrayan L Data & Analytics Division TechnoGen Inc Hillsborough New Jersey USA Department of Robotics and Automation D.Y. Patil College of Engineering Akurdi Pune Maharashtra Department of Computer Science and Engineering Parul Institute of Technology Faculty of Engineering and Technology Parul University Limda Gujarat Department of Mechanical Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru Karnataka Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Electric Vehicle (EV) charging demand prediction, while essential for optimizing charging infrastructure and energy management, faces challenges such as data inaccuracies and uncertainties in user behavior patterns. these issues define inaccurate demand forecasts which cause the wrong placement of charging stations and distribution of energy. Also, as the pattern of the EV charging is not static may change due to many factors such as climate, time of day, price preference among others, the prediction models may not handle the dynamism and hence lower the reliability of the forecast results. To overcome these drawbacks, this manuscript proposes an efficient approach for EV charging demand prediction. the data is gathered from a dataset on EV charging. the data is then sent to pre-processing. Using the Maximum Correntropy Quaternion Kalman Filter (MCQKF), the pre-processing section eliminates missing values and normalizes the input. To forecast EV charging demand, the Multiresolution Sinusoidal Neural Network (MSNN) receives the results of the pre-processing data. MSNN’s weight parameter is optimized using Addax Optimization (AO). the proposed MSNN-AO is utilized within the MATLAB platform. the proposed MSNN-AO technique is compared with the existing techniques such as Long Short-Term Memory Neural Network (LSTMNN), Heterogeneous Spatial-Temporal Graph Convolutional Network (HSTGNN) and Artificial Neural Networks (ANN), respectively. the MSNN-AO method achieves an accuracy of 97%, precision of 95%, and a Root Mean Square Error (RMSE) of 2.2%, demonstrating its superior performance in predicting EV charging demand. this highlights the proposed method’s effectiveness in minimizing prediction errors, reducing RMSE by 22.36%, and improving precision by 14.89% compared to existing methods. the MSNN-AO method’s higher accuracy and precision, coupled with its robust performance, make it a reliable and efficient solution for EV charging demand forecasting.

关键词： Accuracy Uncertainty Quaternions Energy resolution Electric vehicle charging Reliability Kalman filters Spatial resolution Root mean square Optimization

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Path planning using discretized equilibrium paths - A robotics example

Path planning using discretized equilibrium paths - A roboti...

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5th international conference on informatics in control, automation and robotics

作者： Sultan, Cornel Virginia Tech Dept Aerosp & Ocean Engn Blacksburg VA 24061 USA

A collision avoidance path planning problem is considered and a simple solution which uses piecewise constant controls generated by discretizing a feasible equilibrium path is presented and investigated.

ISBN: (纸本)9789898111326

关键词： nonlinear control equilibrium path robot path planning

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Shoe grinding cell using virtual mechanism approach

Shoe grinding cell using virtual mechanism approach

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5th international conference on informatics in control, automation and robotics

作者： Nemec, Bojan Zlajpah, Leon Jozef Stefan Inst Ljubljana 1000 Slovenia

ISBN: (纸本)9789898111319

the paper describes the automation of the shoe grinding process using an industrial robot. One of the major problems of flexible automation using industrial robots is how to avoid joint limitations, singular configuration and obstacles. this problem can be solved using kinematically redundant robots. Due to the circular shape of the grinding disc, the robot becomes kinematically redundant. this task redundancy was efficiently handled using Virtual mechanism approach, where the tool is described as a serial mechanism.

关键词： robotics redundant robot control force control shoe grinding

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Call for Papers the 5th IEEE international conference on Cybernetics and Intelligent Systems(CIS 2011) the 5th IEEE international conference on robotics,automation and Mechatronics(RAM 2011)

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控制理论与应用 2011年第2期28卷 142-142,165页

September 17-19,2011,Qingdao,China http://***/the 5th IEEE international Joint conference on Cybernetics and Intelligent Systems and robotics,automation and Mechatronics(CIS-RAM 2011)will be held in Qingdao,China,duri... 详细信息

关键词：《控制理论与应用》期刊摘要编辑部

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Altitude control of small helicopters using a prototype test bed

Altitude control of small helicopters using a prototype test...

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5th international conference on informatics in control, automation and robotics

作者： Vitzilalos, Nikos I. Tsourveloudis, Nikos C. Tech Univ Crete Intelligent Syst & Robot Lab Khania Greece

ISBN: (纸本)9789898111319

In this paper we present an experimental test bed for the development and evaluation of control systems for unmanned helicopters. the test bed consists of a small unmanned helicopter, mounted on a flying stand that permits all possible movements but prevents the helicopter from damaging or crashing. A fuzzy controller is developed in MATLAB and tested in the helicopter using the test bed. the controller is able to perform hovering and altitude control. Experimental results are presented for various test cases.

关键词： unmanned helicopters experimental test bed altitude control fuzzy control aerial robotics

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Real time tracking of an omnidirectional robot - An extended Kalman filter approach

Real time tracking of an omnidirectional robot - An extended...

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5th international conference on informatics in control, automation and robotics

作者： Goncalves, Jose Lima, Jose Costa, Paulo Polytech Inst Braganca Dept Elect Engn Braganca Portugal Univ Porto Deec Fac Engn Porto Portugal

ISBN: (纸本)9789898111319

this paper describes a robust localization system, similar to the used by the teams participating in the Robocup Small size league (SLL). the system, developed in Object Pascal, allows real time localization and control of an autonomous omnidirectional mobile robot. the localization algorithm is done resorting to odometry and global vision data fusion, applying an extended Kalman filter, being this method a standard approach for reducing the error in a least squares sense, using measurements from different sources.

关键词： probabilistic robotics Kalman filter onmidirectional robots

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