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ASME 2019 International Mechanical engineering Congress and Exposition, IMECE 2019

作者： Wang, Yuxiang Chen, Zhangwei Zu, Hongfei Zhang, Xiang State Key Laboratory of Fluid Power and Mechatronics Systems Zhejiang University Hangzhou China Faculty of Mechanical Engineering&Automation Zhejiang Sci-Tech University Hangzhou China Institute of Computer Science Hangzhou Dianzi University Hangzhou China

ISBN: (纸本)9780791883518

This paper proposes an intelligent technique by combining multilayer feed-forward neural networks (MLFNN) and beetle antennae search (BAS) algorithm for the inverse calibration of a 7R manipulator. First of all, the MLFNN is optimized by BAS. Then, the optimized BAS-MLFNN method is employed for the nonlinear mapping from the true angles of the joint to the errors of the joint. The true angle values of the joints are selected as the inputs and the joint errors as the outputs. The simulation of calibration is implemented on a Kuka’s LWR manipulator. Numerical simulation results show that compared with the commonly used BPNN, the proposed BAS-MLFNN method possesses better performance in terms of higher precision. Copyright © 2019 ASME.

关键词： Manipulators

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Multi-modal remote sensory learning for multi-objects over autonomous devices

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Frontiers in bioengineering and biotechnology 2025年 13卷 1430222页

作者： Aysha Naseer Naif Almudawi Hanan Aljuaid Abdulwahab Alazeb Yahay AlQahtani Asaad Algarni Ahmad Jalal Hui Liu Department of Computer Science Air University Islamabad Pakistan. Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia. Department of Computer Science College of Computer Science and Information System Najran University Najran Saudi Arabia. Department of Informatics and Computer Systems King Khalid University Abha Saudi Arabia. Department of Computer Sciences Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia. Department of Computer Science and Engineering College of Informatics Korea University Seoul South Korea. Guodian Nanjing Automation Co. Ltd. Nanjing China. Jiangsu Key Laboratory of Intelligent Medical Image Computing School of Future Technology Nanjing University of Information Science and technology Nanjing China. Cognitive Systems Lab University of Bremen Bremen Germany.

Introduction:There has been an increasing focus on object segmentation within remote sensing images in recent years due to advancements in remote sensing technology and the growing significance of these images in both military and civilian realms. In these situations, it is critical to accurately and quickly identify a wide variety of objects. In many computer vision applications, scene recognition in aerial-based remote sensing imagery presents a common issue. Method:However, several challenging elements make this work especially difficult: (i) Different objects have different pixel densities; (ii) objects are not evenly distributed in remote sensing images; (iii) objects can appear differently depending on viewing angle and lighting conditions; and (iv) there are fluctuations in the number of objects, even the same type, in remote sensing images. Using a synergistic combination of Markov Random Field (MRF) for accurate labeling and Alex Net model for robust scene recognition, this work presents a novel method for the identification of remote sensing objects. During the labeling step, the use of MRF guarantees precise spatial contextual modeling, which improves comprehension of intricate interactions between nearby aerial objects. By simultaneously using deep learning model, the incorporation of Alex Net in the following classification phase enhances the model's capacity to identify complex patterns in aerial images and adapt to a variety of object attributes. Results:Experiments show that our method performs better than others in terms of classification accuracy and generalization, indicating its efficacy analysis on benchmark datasets such as UC Merced Land Use and AID. Discussion:Several performance measures were calculated to assess the efficacy of the suggested technique, including accuracy, precision, recall, error, and F1-Score. The assessment findings show a remarkable recognition rate of around 97.90% and 98.90%, on the AID and the UC Merced Land datasets,

关键词： autonomous devices deep learning computer vision likelihood estimation multi-modal multi-objects posterior probability remote sensing scene analysis

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Intellectual control system for unmanned energy crop combine 1

Intellectual control system for unmanned energy crop combine

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1st International Conference on Intellectual Systems and Information Technologies, ISIT 2019

作者： Gunchenko, Yurii Shvorov, Sergey Lukin, Volodymyr Mezhuyev, Vitalii Dept. of Mathematical Support of Computer Systems Odessa I.I.Mechnikov National University Odessa Ukraine Dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Gambang Malaysia

The purpose of the work is to develop methodical bases for the construction of an intelligent control system (ISC) for unmanned combines (UMs) of energy crops (EC) for biogas plants (BP). In order to achieve this goal, the functional structure of the ISC was substantiated in the work, the method of recognition and determination of the volume of biomass EC in fields with unmanned aerial vehicles (UAVs) was developed, the method of synthesis of compromise-optimal routes of the BP with the minimum length of their routes in the process of biomass collection was proposed. and taking into account passive (unmoving) and active (moving) obstacles. With the help of the proposed ISC, the following tasks are solved: monitoring of the EC cultivation process and the determination of EC volumes based on the use of UAVs;determination of the density of changes in the yield of EC, the coordinates of active and passive obstacles in the way of the UMs movement;distribution of UMs by fields and planning of optimal routes and speed parameters of their movement for EC collection. A prerequisite for efficient harvesting combine driving is the constant control of the technological process of harvesting energy crops. Deviations from the work plan in most cases occur due to malfunction of technical equipment or adverse weather conditions. However, there are situations in which the deviation of the "plan-fact" is influenced by other factors, which may lead to a failure of the plan, and vice versa. Failure to complete the plan may be due to a reduction in the intensity of the work or the assumption by the user of errors when entering the initial data into the system, which reduces the adequacy of the model to the real process. The increase in planned indicators may be due to an increase in the speed of technological operations, which may lead to additional losses of biomass or a decrease in its quality. To solve the tasks listed, the ISC is divided into a subsystem of monitoring, planning and

关键词： Biomass

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Possibilities of vehicle state estimation using big data approaches 16

Possibilities of vehicle state estimation using big data app...

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16th Mini Conference on Vehicle System Dynamics, Identification and Anomalies, VSDIA 2018

作者： Fényes, Dániel Németh, Balázs Gáspár, Péter Asszonyi, Máté Systems and Control Laboratory Computer and Automation Research Institute Kende u. 13-17 Budapest Hungary Department of Control for Transportation and Vehicle Systems Faculty of Transportation Engineering and Vehicle Engineering Budapest University of Technology and Economics Stoczek u. 2 BudapestH-1111 Hungary

Nowadays, the development of the autonomous vehicles is one of the most important challenges for the automotive industry. In general, these vehicles are equipped with numerous sensors, such as onboard-camera, lidar, radar, ultrasonic sensor, accelerometer and gyroscope. These sensors provide information about the environment and other vehicles. This information is not only processed and used directly on the car but also can be stored on internal or cloud-based memory. This collected data might contain hidden information about the motion and the dynamical behaviour of the car. This hidden information can be brought out of the datasets by using big data-based data mining approaches. The aim of the paper is to create a vehicle state estimation model based on the collected sensor data and using data mining tools. In the paper, the sensor data is collected from a high-fidelity simulation software, CarSim. The estimation model is created by the machine-learning software Weka. Finally, the created model is validated through several CarSim simulations. © 2019 Budapest University of Technology and Economics. All rights reserved.

关键词： computer software

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Peer-to-Peer Interpretability and Communication: Simple Language Design Using Model-Based Systems engineering with Lifecycle-Oriented Strategies and Innovative Practices for Career Readiness

Peer-to-Peer Interpretability and Communication: Simple Lang...

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Frontiers in Education (FIE) Conference

作者： Lena Spiller Etahe Johnson LaKeisha Harris Tiara Cornelius Bede Nnebedum Weiwei Zhu-Stone Theresa Queenan Marea De Koning Willie L. Brown Dinesha Sharma Urban Wiggins Roland Wescott Ibibia Dabipi Lanju Mei Jason Cornelius Enrique M. Jackson Lei Zhang Cynthia Cravens Rasheed Graham Laurence Price-Webb Department of Engineering and Aviation Sciences University of Maryland Eastern Shore Princess Anne Maryland USA Department of Computer Science and Engineering Technology University of Maryland Eastern Shore Princess Anne Maryland USA Dean of Graduate Studies University of Maryland Eastern Shore Princess Anne Maryland USA Department of Mathematics University of Maryland Eastern Shore Princess Anne Maryland USA Director of Career Services University of Maryland Eastern Shore Princess Anne Maryland USA Engineering and Natural Sciences Automation Technology and Mechanical Engineering Tampere University Tampere Finland Vice Provost for Faculty Affairs University of Maryland Eastern Shore Princess Anne Maryland USA Department of Business Management and Accounting University of Maryland Eastern Shore Princess Anne Maryland USA Vice Provost for Decision Science and Visualization University of Maryland Eastern Shore Princess Anne Maryland USA Wallops Flight Facility Goddard Space Flight Center National Aeronautics and Space Administration Wallops Island Virginia USA Marshall Space Flight Center National Aeronautics and Space Administration Huntsville Alabama USA Department of English and Modem Language University of Maryland Eastern Shore Princess Anne Maryland USA Chief Technology Officer Norsou360 Fort Lauderdale Florida USA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This work in progress examines how outcomes and solutions from peer-to-peer engineering education can propel innovative practices to address gaps in industry requirements and design specifications with simple language. engineering education often grapples with understanding activities and their practical applications, prompting the need for innovative approaches. Peer-to-peer learning emerges as a solution, offering opportunities to apply model-based systems engineering (MBSE) concepts to environmental conditions and delve into relevant industry requirements and design specifications. These learning outcomes encompass the necessity to foster sustainability in innovation processes and collaborative team approaches for analysis. The framework considers functional outcomes, assessing environmental conditions, attributes, and lifecycle relationships while emphasizing innovative approaches. The peer-to-peer learning environment prioritizes engineering and educational outcomes, cultivating a mindset for setting requirement objectives and roadmaps, considering constraints and conditions, and evaluating peer-to-peer findings against established innovative practices. Consequently, implementing strategies to explain life cycle-oriented designed industry requirements can acknowledge norms, expectations, and formalities, ensuring effective functioning within a peer-to-peer learning context. Process communication design standards align with peer-to-peer workplaces' values, promoting learning and performance lifecycle-oriented designation through recommended analysis methodologies. In this study, a multidisciplinary team of STEM learners spanning engineering, aviation, aerospace, business, and non-STEM disciplines designed how relationships are determined by categorizing business and system use cases. By mapping out these relationships, learners gain enhanced insights into industry requirements for career development. The study focuses on developing career strategies for success

关键词： Industries Technological innovation engineering profession Collaboration Peer-to-peer computing Stakeholders engineering education Sustainable development Modeling Hip

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MACHINE LEARNING APPROACHES FOR COVID-19 DETECTION FROM CHEST X-RAY IMAGING: A SYSTEMATIC REVIEW

arXiv

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arXiv 2022年

作者： Arteaga-Arteaga, Harold Brayan delaPava, Melissa Mora-Rubio, Alejandro Bravo-Ortíz, Mario Alejandro Alzate-Grisales, Jesus Alejandro Arias-Garzón, Daniel López-Murillo, Luis Humberto Buitrago-Carmona, Felipe Villa-Pulgarín, Juan Pablo Mercado-Ruiz, Esteban Orozco-Arias, Simon Hassaballah, M. de la Iglesia-Vaya, Maria Cardona-Morales, Oscar Tabares-Soto, Reinel Department of Electronics and Automation Universidad Autónoma de Manizales Manizales Colombia Department of Chemical Engineering Universidad Nacional de Colombia Manizales Colombia Department of Computer Science Universidad Autónoma de Manizales Manizales Colombia Department of Systems and Informatics Universidad de Caldas Manizales Colombia Faculty of Computers and Information South Valley University Qena Egypt Unidad Mixta de Imagen Biomédica FISABIO-CIPF Fundación para el Fomento de la Investigación Sanitario y Biomédica de la Comunidad Valenciana Valencia Spain

There is a necessity to develop affordable, and reliable diagnostic tools, which allow containing the COVID-19 spreading. Machine Learning (ML) algorithms have been proposed to design support decision-making systems to assess chest X-ray images, which have proven to be useful to detect and evaluate disease progression. Many research articles are published around this subject, which makes it difficult to identify the best approaches for future work. This paper presents a systematic review of ML applied to COVID-19 detection using chest X-ray images, aiming to offer a baseline for researchers in terms of methods, architectures, databases, and current limitations. © 2022, CC BY.

关键词： COVID-19

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Continuous authentication of smartphones based on application usage

IEEE Transactions on Biometrics, Behavior, and Identity Scie...

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IEEE Transactions on Biometrics, Behavior, and Identity Science 2019年第3期1卷 165-180页

作者： Mahbub, Upal Komulainen, Jukka Ferreira, Denzil Chellappa, Rama Department of Electrical and Computer Engineering University of Maryland at College Park College ParkMD20742 United States Center for Automation Research UMIACS University of Maryland at College Park College ParkMD20742 United States Qualcomm Technologies Inc. San DiegoCA United States Faculty of Information Technology and Electrical Engineering University of Oulu Oulu90014 Finland Visidon Ltd. Oulu Finland

An empirical investigation of active/continuous authentication for smartphones is presented by exploiting users' unique application usage data, i.e., distinct patterns of use, modeled by a Markovian process. Specifically, variations of hidden Markov models (HMMs) are evaluated for continuous user verification, and challenges due to the sparsity of session-wise data, an explosion of states, and handling unforeseen events in the test data are tackled. Unlike traditional approaches, the proposed formulation utilizes the complete app-usage information to achieve low latency. Through experimentation, empirical assessment of the impact of unforeseen events, i.e., unknown applications and unforeseen observations, on user verification is done via a modified edit-distance algorithm for sequence matching. It is found that for enhanced verification performance, unforeseen events should be considered. For validation, extensive experiments on two distinct datasets, namely, UMDAA-02 and Securacy, are performed. Using the marginally smoothed HMM a low equal error rate (EER) of 16.16% is reached for the Securacy dataset and the same method is found to be able to detect an intrusion within 2.5 min of application use. © 2019 IEEE.

关键词： Hidden Markov models

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Agent DEVS simulation of the evacuation process from a commercial building during a fire 12th

Agent DEVS simulation of the evacuation process from a comme...

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12th International Scientific and Technical Conference computer Science and Information Technologies, CSIT 2017

作者： Kulakowski, Andrzej Rogala, Bartosz Faculty of Electrical Engineering Automation Control and Computer Science Kielce University of Technology Kielce Poland

ISBN: (纸本)9783319705804

The paper describes a simulation application for the process of evacuating people from a building in the event of a fire. This simulation was developed as an agent system using the DEVS formalism. The following sections show the design and implementation of such a system using the Adevs library. A series of simulation tests were then performed for different simulation scenarios. © 2018, Springer International Publishing AG.

关键词： Office buildings

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Corrigendum to “Optimization and control of solar-wind islanded hybrid microgrid by using heuristic and deterministic optimization algorithms and fuzzy logic controller” [Energy Rep. 10 (2023) 3272–3288]

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Energy Reports 2023年 10卷 3847-3847页

作者： Sk.A. Shezan Md. Fatin Ishraque GM Shafiullah Innobent Kamwa Liton Chandra Paul SM Muyeen Ramakrishna NSS Mohammed Zeehan Saleheen Polamarasetty P. Kumar School of Industrial Automation and Electrical Engineering Engineering Institute of Technology Melbourne VIC Australia School of Engineering and Energy Murdoch University Australia Department of Electrical Electronic and Communication Engineering Pabna University of Science and Technology Pabna Bangladesh Department of Electrical Engineering and Computer Engineering Laval University Quebec QC G1V 0A6 Canada Department of Electrical Engineering Qatar University Doha 2713 Qatar Department of Electrical and Electronics GMR Institute of Technology Rajam Andhra Pradesh 532127 India Department of Data Science & AI Faculty of IT Monash University Australia

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MCOA: A Multistrategy Collaborative Enhanced Crayfish Optimization Algorithm for engineering Design and UAV Path Planning

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International Journal of Intelligent Systems 2025年第1期2025卷

作者： Yaning Xiao Hao Cui Ruba Abu Khurma Abdelazim G. Hussien Pedro A. Castillo School of Automation and Intelligent Manufacturing Southern University of Science and Technology Shenzhen 518055 China *** College of Mechanical and Electrical Engineering Northeast Forestry University Harbin 150040 China *** Information Technology Department Al-Huson University College Al-Balqa Applied University Irbid 19117 Jordan bau.edu.jo Department of Computer and Information Science Linköping University Linköping 58183 Sweden liu.se Faculty of Science Fayoum University Faiyum 63514 Egypt fayoum.edu.eg Department of Computer Engineering Automatics and Robotics University of Granada Granada 18071 Spain ugr.es

The crayfish optimization algorithm (COA) is a recent bionic optimization technique that mimics the summer sheltering, foraging, and competitive behaviors of crayfish. Although COA has outperformed some classical metaheuristic (MH) algorithms in preliminary studies, it still manifests the shortcomings of falling into local optimal stagnation, slow convergence speed, and exploration–exploitation imbalance in addressing intractable optimization problems. To alleviate these limitations, this study introduces a novel modified crayfish optimization algorithm with multiple search strategies, abbreviated as MCOA. First, specular reflection learning is implemented in the initial iterations to enrich population diversity and broaden the search scope. Then, the location update equation in the exploration procedure of COA is supplanted by the expanded exploration strategy adopted from Aquila optimizer (AO), endowing the proposed algorithm with a more efficient exploration power. Subsequently, the motion characteristics inherent to Lévy flight are embedded into local exploitation to aid the search agent in converging more efficiently toward the global optimum. Finally, a vertical crossover operator is meticulously designed to prevent trapping in local optima and to balance exploration and exploitation more robustly. The proposed MCOA is compared against twelve advanced optimization algorithms and nine similar improved variants on the IEEE CEC2005, CEC2019, and CEC2022 test sets. The experimental results demonstrate the reliable optimization capability of MCOA, which separately achieves the minimum Friedman average ranking values of 1.1304, 1.7000, and 1.3333 on the three test benchmarks. In most test cases, MCOA can outperform other comparison methods regarding solution accuracy, convergence speed, and stability. The practicality of MCOA has been further corroborated through its application to seven engineering design issues and unmanned aerial vehicle (UAV) path planning tasks

关键词： crayfish optimization algorithm engineering optimization expanded exploration strategy Lévy flight specular reflection learning vertical crossover operator

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