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RSI/ISM International Conference on Robotics and Mechatronics (ICROM)

作者： Shima Shademani Shahriyar Kouravand Payam Zarafshan Khalil Aalipour Amir Ghalamzan Esfahani Department of Agro-Technology College of Aburaihan University of Tehran Teltran Iran Department of Mechatronics Engineering AGRicultural INtelligent Systems (AGRINS) Faculty of New Sciences and Technologies University of Tehran Teltran Iran Department of Mechatronics Engineering Advanced Service Robots (ASR) Laboratory Faculty of New Sciences and Technologies University of Tehran Teltran Iran School of Computer Science University of Lincoln United Kingdom

Saffron is one of the most valuable spices in the world, whose origin is attributed to Iran. Iran produces about 90% of the world's saffron annually, however, the harvesting of this product in Iran is mostly done manually. Manual harvest of saffron brings problems. Decreasing the quality of saffron and contamination of saffron with microorganisms are among the problems that arise during manual harvesting. For this purpose, a gripper is recommended for harvesting saffron flowers. One of the special features of the saffron plant is that several flowers may come out from each corm at different time intervals, for this reason, the proposed gripper uses the plucking method to harvest the flowers and separate flowers from the stem so that the next flowers can grow easily. Design, simulation, and control of the gripper have been conducted using conventional software such as SOLIDWORKS, ADAMS, and MATLAB.

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New categorized machine learning models for daily solar irradiation estimation in southern Morocco's, Zagora city

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2024年 10卷

作者： Bounoua, Zineb Chahidi, Laila Ouazzani Mechaqrane, Abdellah SIEDD Laboratory of Intelligent Systems Energy and Sustainable Development Private University of Fez Fez Morocco SIGER Intelligent Systems Georesources and Renewable Energies Laboratory Faculty of Sciences and Technologies Sidi Mohamed Ben Abdellah University Fez PO Box 2202 Morocco LISAC Computer Science Signals Automation and Cognitivism Laboratory Faculty of Sciences Dhar Mehraz Sidi Mohamed Ben Abdellah University PO. Box 1796 Atlas Fez 30003 Morocco

Accurately estimating daily solar irradiation is essential for effectively sizing and simulating solar energy systems. Inaccuracies or discontinuities in solar data can lead to errors in system assessments, potentially resulting in misguided conclusions about their economic feasibility. This study evaluates the performance of various machine learning techniques, including Multilayer Perceptron (MLP), Support Vector Machine Regression (SVMR), and Random Forest (RF), in accurately estimating daily Global and Diffuse Horizontal Irradiations (GHI and DHI), and Direct Normal Irradiation (DNI) in Zagora City, Southern Morocco. The models are categorized based on four classes of data inputs: Temperature-Based Models (GTBMs), Relative Humidity-Based Models (RHBMs) for GHI, Hybrid Parameter-Based Models (HBMs) for GHI, DHI, and DNI, and Clearness Index-Based Models (CIBMs) for DHI and DNI. The results indicate that RF is particularly effective in estimating daily GHI, DHI, and DNI using Hybrid parameters Based Models, achieving validation nRMSEs equal to 8.36 %, 18.55 %, and 10.26 %, respectively. It also performs well in estimating DHI with Clearness index Based Models (nRMSE=20.44 %). The next effective method is MLP, that is the optimal one in estimating daily GHI with Relative Humidity Based Models (nRMSE=11.41 %) and daily DNI using Clearness index Based Models (nRMSE= 12.43 %). Lastly, SVMR is more quitable for estimating daily GHI with Temperature Based Models, with a validation nRMSE of about 9.09 %. The study further demonstrates that empirical models underperform compared to machine learning methods in all included categories. © 2024 The Author(s)

关键词： Clearness index based models (CIBMS) Hybrid parameters based models (HBMS) Machine learning models Relative humidity based models (RHBMs) Solar radiation Temperature based models (GTBMs)

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A Fast and Effective Multiple Kernel Clustering Method on Incomplete Data

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computers, Materials & Continua 2021年第4期67卷 267-284页

作者： Lingyun Xiang Guohan Zhao Qian Li Gwang-Jun Kim Osama Alfarraj Amr Tolba Changsha Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication EngineeringChangsha University of Science and TechnologyChangsha410114China Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems Changsha University of Science and TechnologyChangsha410114China Faculty of Engineering and Information Technology Global Big Data Technologies CentreUniversity of Technology SydneyUltimoNSW2007Australia Department of Computer Engineering Chonnam National UniversityGwangju61186Korea Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Mathematics and Computer Science Faculty of ScienceMenoua UniversityShebin-El-kom32511Egyp

Multiple kernel clustering is an unsupervised data analysis method that has been used in various scenarios where data is easy to be collected but hard to be ***,multiple kernel clustering for incomplete data is a critical yet challenging *** the existing absent multiple kernel clustering methods have achieved remarkable performance on this task,they may fail when data has a high value-missing rate,and they may easily fall into a local *** address these problems,in this paper,we propose an absent multiple kernel clustering(AMKC)method on incomplete *** AMKC method rst clusters the initialized incomplete ***,it constructs a new multiple-kernel-based data space,referred to as K-space,from multiple sources to learn kernel combination ***,it seamlessly integrates an incomplete-kernel-imputation objective,a multiple-kernel-learning objective,and a kernel-clustering objective in order to achieve absent multiple kernel *** three stages in this process are carried out simultaneously until the convergence condition is *** on six datasets with various characteristics demonstrate that the kernel imputation and clustering performance of the proposed method is signicantly better than state-of-the-art ***,the proposed method gains fast convergence speed.

关键词： Multiple kernel clustering absent-kernel imputation incomplete data kernel k-means clustering

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Departure Counting Process in a Finite-Buffer Queue with Batch Arrivals and Multiple Vacation Policy

SSRN

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SSRN 2024年

作者： Marjasz, Rafal Kempa, Wojciech M. Kovtun, Viacheslav Institute of Theoretical and Applied Informatics Polish Academy of Sciences 5 Baltycka Str. Gliwice44-100 Poland Silesian University of Technology Faculty of Applied Mathematics 23 Kaszubska Str. Gliwice44-100 Poland Vinnytsia National Technical University Faculty of Intelligent Information Technologies and Automation Department of Computer Control Systems 95 Khmelnitske Shose Str. Vinnytsia21000 Ukraine

The study investigates the departure counting process in a finite-buffer queueing system with batch arrivals and multiple vacation policy. A representation for the mixed double transform of the number of departures up to the fixed time moment is obtained in the explicit form applying analytic approach based on integral equations and linear algebra. Numerical study is attached that focuses on understanding the impact of various system parameters on the behaviour of the mean number of packets processed within a fixed time frame. Utilizing numerical experiments, the study examines the influence of vacation duration, initial buffer state, arrival intensity, and processing rate on the departure process. Results reveal significant dependencies between these parameters and the transient behaviour of the queueing system. Notably, the service speed parameter demonstrates the most substantial influence on the mean number of processed packets, followed by the arrival rate. Conversely, variations in vacation duration and initial packet count exhibit comparatively minor effects on system behaviour. Overall, the findings provide valuable insights into the dynamics of departure processes in finite-buffer queue systems with batch arrivals and multiple vacation policies, offering implications for system optimization and performance enhancement strategies. © 2024, The Authors. All rights reserved.

关键词： Integral equations

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Open-SBS: Smart Building Simulator 23

Open-SBS: Smart Building Simulator

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23rd International Arab Conference on Information Technology, ACIT 2022

作者： Degha, Houssem Eddine Laallam, Fatima Zohra Kazar, Okba Khelfaoui, Issam Athamena, Belkacem Houhamdi, Zina University of Ghardaia Faculty of Sciences and Technology Ghardaia Algeria Kasdi Merbah Ouargla University Artificial Intelligence and Information Technologies Laboratory Algeria Biskra University Intelligent Computer Science Laboratory LINFI Biskra Algeria College of Information Technology United Arab Emirate University Department of Information Systems and Security United Arab Emirates School of Insurance and Economics University of International Business and Economics Beijing China College of Business Al Ain University Al Ain United Arab Emirates

ISBN: (纸本)9798350320244

With the rise of machine learning and deep learning techniques in recent years, a representative dataset has become an inspiring source of prediction and knowledge extraction. Furthermore, ambient intelligence environments have emerged as one of the most important research areas. Many complex tasks and challenges face this area to validate and evaluate field research. One of the major challenges in the smart building domain is the lack of a simulation tool capable of simulating complex contexts, executing semantic rules, and collecting representative datasets for experiments. To address these issues, we present in this paper a cross-platform, open-source Smart Building Simulator (Open-SBS). Open-SBS offer an opportunity for researchers in the field of Ambient Intelligence environments to simulate complex contexts and collect representative datasets, save and share their models of experiments. We propose a new simulation process that is divided into four distinct phases: designing, scenario creation, semantic rule addition, and data-set generation. On the System Usability Scale, we conducted a study to assess the ease of use of Open-SBS (SUS). © 2022 IEEE.

关键词： Deep learning

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Text Mining in the Domain of Plant Genetic Resources 10

Text Mining in the Domain of Plant Genetic Resources

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10th IEEE International Conference on intelligent systems, IS 2020

作者： Popchev, Ivan Orozova, Daniela Bulgarian Academy of Sciences Intelligent Systems Department Institute of Information and Communication Technologies Sofia Bulgaria Burgas Free University Institute of Information and Communication Technologies Bulgarian Academy of Sciences Faculty of Computer Science Sofia Bulgaria

ISBN: (纸本)9781728154565

Upcoming is the construction of informational and social spaces which integrate heterogeneous technologies and various approaches to the collection, storage and processing of information related to the plant genetic resources of Bulgaria. In the present paper a multistage algorithm for automatic analysis of documents from the area of plant genetic resources is proposed. Determination of the significant words in the document and the type of its content is carried out based on the object-oriented ontologies in the field. By applying software instruments for classification, testing and evaluation, the documents are classified into related and not related to the studied subject area- A griculture and plant genetic resources of Bulgaria. © 2020 IEEE.

关键词： intelligent systems

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Additive Manufacturing of Ti6Al4V Alloy Through Laser Metal Deposition (LMD): An Elebotative Approach of Process Modelling Via Computational Fluid Dynamics

SSRN

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SSRN 2023年

作者： Rehman, Asif Ur Azher, Kashif Rehman, Aqsa Shah, Mussadiq Mahmood, Muhammed Arif Tüfekci, Celal Sami Salamci, Metin U. ERMAKSAN Bursa Turkey Department of Mechanical Engineering Faculty of Engineering Gazi University Ankara Turkey Additive Manufacturing Technologies Application and Research Center-EKTAM Ankara Turkey Department of Computer Science NFC-IET Punjab Multan60000 Pakistan Advanced Manufacturing Technologies Center of Excellence-URTEMM Ankara Turkey Intelligent Systems Center Missouri University of Science and Technology RollaMO65409 United States

Additive manufacturing (AM) has revolutionized many industries. Laser Melting Deposition (LMD) offers remarkable utility in size and is an effective method in terms of energy efficiency. The interaction of powder with laser before coming in contact with the substrate helps to attain fair melting and deposition. However, there is a problem associated with non-uniformity and accumulation of metallic melt on the edges of the manufactured parts which needs to be understood. Herein, LMD process of Ti6Al4V alloy is presented. The forces defining the melt pool flow have been elaborated. The phenomenon of recoil pressure and Marangoni actions along the single tracks of the melt pool is defined via the computational fluid dynamics (CFD) analysis. The behaviour of the melt pool in conduction mode and to check whether it will form a keyhole region or not. The main factor which influences the melt pool dynamics are found to be surface tension and density differences. Two modes of heat transfer conduction and depression are discussed with the Marangoni actions. Conduction mode is mainly governed by Marangoni actions while the depression mode is by recoil pressure. The stream traces and velocity vectors are observed for single tracks LMD process. A similar Marangoni actions experimental investigation is compared with the phenomenon observed under CFD analysis. The Discrete Element Modelling (DEM) adds value to the study as variable particle diameters are considered which are near to the actual powder size distribution. Moreover, considering the conduction mode flow dynamics to avoid the depression mode and generation of keyhole region in the melt pool will lead to porosity. © 2023, The Authors. All rights reserved.

关键词： Computational fluid dynamics

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Integrating Local STDP and Global Backpropagation Learning Using Spike-Time and Spike-Rate Representations for Improved Classification of Spatiotemporal Data in the Neucube Brain Inspired Evolving Spiking Neural Network Architecture: A Case Study on EEG Brain Data

SSRN

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SSRN 2023年

作者： Zhou, Xuanle Doborjeh, Maryam Kasabov, Nikola Doborjeh, Zohreh Knowledge Engineering and Discovery Research Innovation School of Engineering Computer & Mathematical Sciences Auckland University of Technology Auckland New Zealand School of Population Health Faculty of Medical and Health Sciences The University of Auckland Auckland New Zealand Intelligent Systems Research Centre Ulster University Londonderry United Kingdom Institute for Information and Communication Technologies Bulgarian Academy of Sciences Bangladesh

This paper proposes a method for improved learning and classification of spatio-temporal data in one of the most popular brain-inspired spiking neural network (SNN) architectures, NeuCube. The Spike-timing-dependent plasticity (STDP) local learning on spike-time data, that captures spatiotemporal patterns from the data, is now extended with global backpropagation supervised learning on spike-rate data to modify connectivity of the SNN, that reinforces the spatio-temporal association of the input variables to pre-defined classes for classification tasks and improves the discrimination between the learned data patterns that belong to different classes. This results in a significantly improved classification of spatio-temporal data and interpretability and explainability of the SNN model, that is demonstrated on two benchmark EEG datasets. A new Leaky Integrate-and-Fire model is proposed for the backpropagation learning, which reinforces the relationship in both spatial and temporal information domains of the input data variables with the pre-defined classes. For the non-differentiable spike sequences, a novel approximation function is defined to calculate the derivatives using spike-rate representation. The proposed here novel NeuCubeBP framework, while demonstrated on brain EEG data, is applicable to other types of spatio-temporal brain data and beyond. © 2023, The Authors. All rights reserved.

关键词： Backpropagation

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Emergency Vehicle Navigation in Connected Autonomous systems Using Enhanced Traffic Management System

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IEEE Internet of Things Journal 2025年

作者： Raja, Gunasekaran Anbalagan, Sudha Gurumoorthy, Sugeerthi Jegathesan, Darshini Girivel, Niveditha Subramanian Sundaram, Varsha Mani Shanmuga Khowaja, Sunder Ali Dev, Kapal Anna University NGNLab Department of Computer Technology MIT Campus Chennai600044 India Vellore Institute of Technology Chennai Centre for Smart Grid Technologies School of Computer Science and Engineering Chennai600127 India Citicorp Services Private Ltd Software Development Chennai600113 India Dublin City University School of Computing Faculty of Engineering and Computing Glasnevin Campus DublinD09 V209 Ireland Munster Technological University Department of Computer Science Bishopstown CorkT12 P928 Ireland University of Johannesburg Department of Institute of Intelligent Systems Johannesburg2006 South Africa Chitkara University Institute of Engineering and Technology Centre for Research Impact Outcome Chitkara University Punjab Rajpura140401 India

Autonomous Vehicle (AV) usage has become predominant in the rapidly evolving landscape of urban transportation. Integrating AVs and non-AVs in the existing traffic infrastructure has significantly increased the complexity of traffic patterns. This research work primes the enhanced Traffic Management System (e-TMS), a solution implemented to expedite Emergency Vehicle (EV) travel in the context of Connected AVs (CAVs) and ensured secured communication employing Public Key Infrastructure (PKI) among the Internet of Vehicles (IoV) framework. When an EV is detected, the IoV system verifies the EV's signal using PKI, ensuring its authenticity and integrity by encrypting the communication between the EV and Road Side Units (RSUs). Further, the e-TMS activates the platooning process where CAVs in the EV's lane shift to the adjacent lanes and dynamically form platoons to create a dedicated lane for the EV. To further optimize the platooning process, an Adaptive Smart Leader Selection (ASLS) algorithm is employed to select a leader vehicle spontaneously among the AVs based on the proximity to the EV, communication reliability, and lane position. Radio Detection and Ranging devices aid this platooning process by providing distance and target velocity, which are needed to maintain the required distance between the AVs and ensure safe platoon formation. The e-TMS enhances EV response times and overall traffic flow efficiency and resulted in 27.8% increase in mean speed compared to the traditional methods. © 2014 IEEE.

关键词： Emergency vehicles

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Meta-RegGNN: Predicting Verbal and Full-Scale Intelligence Scores using Graph Neural Networks and Meta-Learning

arXiv

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arXiv 2022年

作者： Jegham, Imen Rekik, Islem Université de Sousse Ecole Nationale d’Ingénieurs de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Horizon School of Digital Technologies Sousse4023 Tunisia BASIRA Lab Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey

Decrypting intelligence from the human brain construct is vital in the detection of particular neurological disorders. Recently, functional brain connectomes have been used successfully to predict behavioral scores. However, state-of-the-art methods, on one hand, neglect the topological properties of the connectomes and, on the other hand, fail to solve the high inter-subject brain heterogeneity. To address these limitations, we propose a novel regression graph neural network through meta-learning namely Meta-RegGNN for predicting behavioral scores from brain connectomes. The parameters of our proposed regression GNN are explicitly trained so that a small number of gradient steps combined with a small training data amount produces a good generalization to unseen brain connectomes. Our results on verbal and full-scale intelligence quotient (IQ) prediction outperform existing methods in both neurotypical and autism spectrum disorder cohorts. Furthermore, we show that our proposed approach ensures generalizability, particularly for autistic subjects. Our Meta-RegGNN source code is available at https://***/basiralab/Meta-RegGNN. © 2022, CC BY-NC-ND.

关键词： Forecasting

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