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A trajectory-level robot skill learning method based on fuzzy systems

science China(Information sciences) 2025年第5期68卷 38-52页

作者： Chao ZENG Zhen DENG Boyang TI Yongsheng GAO Department of Computer Science University of Liverpool School of Mechanical Engineering and Automation Fuzhou University State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Key Laboratory of Robotics and System Harbin Institute of Technology

Learning from demonstration(LfD) allows for the effective transfer of human manipulation skills to a robot by building a model that represents these skills based on a limited number of demonstrated ***,a skilllearning model that can comprehensively satisfy multiple requirements,such as computational complexity,modeling accuracy,trajectory smoothness,and robustness,is still ***,this work aims to provide such a model by employing fuzzy ***,we introduce an LfD model named Takagi-Sugeno-Kang fuzzy system-based movement primitives(TSKFMPs),which exploits the advantages of the fuzzy theory for effective robotic imitation learning of human *** work formulates the TSK fuzzy system and gradient descent(GD) as imitation learning models,leveraging recent advancements in GD-based optimization for fuzzy *** study takes a two-step strategy.(ⅰ) The input-output relationships of the model are established using TSK fuzzy systems based on demonstration *** this way,the skill is encoded by the model parameter in the latent space.(ⅱ) GD is used to optimize the model parameter to increase the modeling accuracy and trajectory *** further explain how learned trajectories are adapted to new task scenarios through local *** conduct multiple tests using an open dataset to validate our method,and the results demonstrate performance comparable with those of other ***,we implement it in a real-world case study.

关键词： robot skill learning movement primitives TSK fuzzy systems gradient descent robotics

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Indentation behavior of a semi-infinite piezoelectric semiconductor under a rigid flat-ended cylindrical indenter

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Applied Mathematics and Mechanics(English Edition) 2024年第4期45卷 649-662页

作者： Shijing GAO Lele ZHANG Jinxi LIU Guoquan NIE Weiqiu CHEN State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures Shijiazhuang Tiedao UniversityShijiazhuang 050043China School of Mechanical Engineering Shijiazhuang Tiedao UniversityShijiazhuang 050043China Hebei Key Laboratory for Mechanics of Intelligent Materials and Structures Department of Engineering MechanicsShijiazhuang Tiedao UniversityShijiazhuang 050043China State Key Laboratory of Computer Aided Design and Computer Graphics Key Laboratory of Soft Machines and Smart Devices of Zhejiang ProvinceDepartment of Engineering MechanicsZhejiang UniversityHangzhou 310027China

This paper theoretically studies the axisymmetric frictionless indentation of a transversely isotropic piezoelectric semiconductor(PSC)half-space subject to a rigid flatended cylindrical *** contact area and other surface of the PSC half-space are assumed to be electrically *** the Hankel integral transformation,the problem is reduced to the Fredholm integral equation of the second *** equation is solved numerically to obtain the indentation behaviors of the PSC half-space,mainly including the indentation force-depth relation and the electric potential-depth *** results show that the effect of the semiconductor property on the indentation responses is limited within a certain range of variation of the steady carrier *** dependence of indentation behavior on material properties is also analyzed by two different kinds of *** element simulations are conducted to verify the results calculated by the integral equation technique,and good agreement is demonstrated.

关键词： piezoelectric semiconductor(PSC) insulating indenter electromechanical response singular integral equation finite element simulation

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Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition

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SN computer science 2024年第5期5卷 1-10页

作者： Duhayyim, Mesfer Al Sharif, Mahir Mohammed Munjal, Muskaan Hilal, Anwer A. Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj 16273 Saudi Arabia Department of Computer Science Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Information System Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan

Sleep quality prediction in Internet of Things (IoT) involves leveraging a system of interrelated devices to gather as well as analyse related data. Smart devices like wearable devices or smart mattresses endlessly monitor many parameters such as body movement, heart rate, and environmental situations in bedroom. Real-time recommendations and feedback transported to consumers, allowing them to create informed lifestyle changes for superior sleep hygiene. As IoT ecosystem continues to develop, a combination of various data streams as well as classy analytics holds assurance for further refining sleep quality forecasts and improving overall well-being. The article designs a Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition (BESHDL-SQP) technique. The purpose of the BESHDL-SQP technique is to recognize and classify the quality of the sleep level in the IoT environment. In the presented BESHDL-SQP technique, the preliminary stage of data pre-processing using min–max normalization involved to normalize the input data. For predicting sleep quality, hierarchical long short-term memory (LSTM) technique exploited. Lastly, BES model utilized for optimal hyperparameter selection of LSTM method which helps in attaining enhanced results. To validate better performance of BESHDL-SQP technique, a huge range of simulation analysis is carried out. The extensive results stated that the BESHDL-SQP technique exhibits supreme performance over other models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bald eagle search Internet of things LSTM Sleep quality recognition Smart devices

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Comparative analysis of alkali-treated natural fibres for improved interfacial adhesion in composite materials

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Interactions 2024年第1期245卷 1-13页

作者： Bhagat, Madhuri S. Jadhav, Varsha D. Kulkarni, Sumant Khanderao Satishkumar, P. Saminathan, Rajasekaran Department of Civil Engineering Yeshawantrao Chavan College of Engineering Maharashtra Wanadongari Nagpur India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Maharashtra Pune India School of Civil Engineering REVA University Rukmini Knowledge Park Karnataka Kattigenahalli Bengaluru India Department of Mechanical Engineering Rathinam Technical Campus Tamilnadu Coimbatore India Department of Mechanical Engineering College of Engineering and Computer Science Jazan University Jazan Saudi Arabia

Natural fibers such as bamboo, kenaf, flax, ramie and hemp were chemically treated to improve their adherence with hydrophobic matrices. One common chemical process for altering the surface of these natural fibers is alkali treatment (AT). The experiment involves soaking bamboo fibers (BF) in solutions of 1, 10, and 30 wt % sodium hydroxide (NaOH) for one day, half an hour, and twenty minutes at room temperature. These alkali-treated and untreated bamboo fibers was compared with their physical and mechanical qualities. Using scanning electron microscopy (SEM) and the circular fiber assumption, the CSA of bamboo fibers both before and after AT were compared. Improving the AT procedures for BF at room temperature using varying NaOH concentrations is the focus of this effort. Additionally, the tensile characteristics of both untreated and variously treated AT Bamboo fibres are examined using a two-parameter Weibull distribution. Instead of just showing the average tensile strength, probabilistic tensile strength has shown to be a more effective method. This study proves without a reasonable doubt that adding 1 wt % NaOH to bamboo fiber makes natural fiber reinforced composites mechanically better. According to the SEM, the CSA of bamboo fibers decreases by about 13.62–20.24% and about 18.42–22.63%, respectively, after alkali treatment. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Tensile strength

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Improved Sparrow Search Algorithm with Support Vector Machine for Wind Turbine Fault Detection 2

Improved Sparrow Search Algorithm with Support Vector Machin...

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2nd International Conference on Data science and Information system, ICDSIS 2024

作者： Nagendar, Y. Alabdeli, Haider Shruthi, B.S. Kamesh, M.R. Sudha, N. Sr University School Of Computer Science And Engineering Telangana Warangal India The Islamic University Department Of Computers Techniques Engineering College Of Technical Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of Civil Engineering Bengaluru India Dayananda Sagar College Of Engineering Department Of Mechanical Engineering Bengaluru India Angel College Of Engineering And Technology Embedded System Technologies Tamil Nadu India

ISBN: (纸本)9798350381665

Wind turbines are widely used across the globe as a primary means of renewable energy by using the power of wind, it converts kinetic energy into electrical energy, contributing to generating electricity. However, turbines are positioned far from control centers, operating in environments that continuously change and have uncontrollable factors and conditions lead to wind turbine failures. The proposed method using an Improved Sparrow Search Algorithm (ISSA) optimizer along with also used a Support Vector Machine (SVM) classifier helps to detect the faults in turbines. This method uses the Supervisory Control and Data Acquisition (SCADA) dataset for wind turbine fault detection, the optimization using ISSA which is improved by Uniformity Diversity Orientation Strategy (UDOS) to optimize model's internal parameters. This helps to learn pattern during training period and helps to classify the normal and fault patterns. Then in the SVM classification process using the parameters this compares the obtained values with the current fitness value to classify patterns. The performance of the ISSA is analyzed by using accuracy, precision, f1-score, and recall. This shows that the proposed method outperforms existing methods such as K Nearest Neighbors (KNN), stacking classifier, and Feature Simplification Random Forest (FS-RF) in terms of accuracy 98.78%, precision 98.97%, recall 98.24%, and f1-score 98.00%. © 2024 IEEE.

关键词： Wind turbines

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Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

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computers, Materials & Continua 2024年第4期79卷 857-873页

作者： Amardeep Singh Hamad Ali Abosaq Saad Arif Zohaib Mushtaq Muhammad Irfan Ghulam Abbas Arshad Ali Alanoud Al Mazroa School of Computer Information Sciences University of CumberlandsWilliamsburgKentucky40769USA Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Electrical Engineering The University of LahoreLahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware *** study provides a new approach for RaaS attack detection which uses an ensemble of deep learning *** this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is *** the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are ***,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested *** proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%*** empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual *** expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

关键词： Cloud encryption RaaS ensemble threat detection deep learning cybersecurity

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Android Based Arduino Learning Model by Implementing Multi Marker Augmented Reality

Android Based Arduino Learning Model by Implementing Multi M...

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2023 International Conference on Informatics engineering, science and Technology, INCITEST 2023

作者： Adler, John Setiyadi, Dianto Br Ginting, Selvia Lorena Rinaldy Ginting, Yogie Wicaksono, Mochamad Fajar Universitas Komputer Indonesia Department Computer Engineering Bandung Indonesia Universitas Komputer Indonesia Department of Computer System Bandung Indonesia Universitas Komputer Indonesia Department Computer System Bandung Indonesia Universitas Riau Pekanbaru Faculty of Engineering Department of Mechanical Engineering Indonesia

ISBN: (纸本)9798350317862

This research aims to develop an interactive learning system based on Arduino Uno basic material. To realize this system, Augmented Reality technology is used which will explain each Arduino Uno pin function along with an explanation of each example of the basic circuit provided. This started with the Arduino Uno learning process which often caused damage to the Arduino or the components that were connected to the Arduino itself during the process of making the circuit. This problem can occur due to a lack of basic understanding of theory. The use of Augmented Reality technology is able to display objects in the form of Arduinos in virtual 3D, so that 3D objects can appear, markers are used to mark each object, the introduction of the Arduino Uno itself can be simulated by providing a real picture using the multi marker method where there is a main marker containing 3D objects in the Arduino series and supporting markers containing explanations of the series. Thus, learning is not limited to conventional learning and minimizes errors in trials. The method used to develop the system is the waterfall method. The waterfall method itself is part of implementing a design with a good and reasonable target. Based on the tests obtained, the Arduino Uno learning application is able to run well using multiple markers, 3D or 2D objects can be run on a smartphone, and black box testing and marker reading testing are carried out with different lighting levels and reading from different sides. © 2023 IEEE.

关键词： Smartphones

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Artificial intelligence for sustainable internet research

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International Journal of computers and Applications 2022年第6期44卷 501-502页

作者： Haldorai, Anandakumar Sharif, Muhammad Ravana, Sri Devi Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering COMSATS University Islamabad Wah Cantt Pakistan Department of Information System Faculty of Computer Science & Information Technology University of Malaya Malaysia

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Deep Learning with a Novel Concoction Loss Function for Identification of Ophthalmic Disease

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computers, Materials & Continua 2023年第9期76卷 3763-3781页

作者： Sayyid Kamran Hussain Ali Haider Khan Malek Alrashidi Sajid Iqbal Qazi Mudassar Ilyas Kamran Shah Department of Computer Science TIMES InstituteMultan60000Pakistan Department of Software Engineering Faculty of Computer ScienceLahore Garrison UniversityLahore54000Pakistan Department of Computer Science Applied CollegeUniversity of TabukTabukSaudi Arabia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversityAl Hofuf31982Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl HofufSaudi Arabia

As ocular computer-aided diagnostic(CAD)tools become more widely accessible,many researchers are developing deep learning(DL)methods to aid in ocular disease(OHD)*** eye diseases like cataracts(CATR),glaucoma(GLU),and age-related macular degeneration(AMD)are the focus of this study,which uses DL to examine their *** imbalance and outliers are widespread in fundus images,which can make it difficult to apply manyDL algorithms to accomplish this analytical *** creation of efficient and reliable DL algorithms is seen to be the key to further enhancing detection *** the analysis of images of the color of the retinal fundus,this study offers a DL model that is combined with a one-of-a-kind concoction loss function(CLF)for the automated identification of *** study presents a combination of focal loss(FL)and correntropy-induced loss functions(CILF)in the proposed DL model to improve the recognition performance of classifiers for biomedical *** is done because of the good generalization and robustness of these two types of losses in addressing complex datasets with class imbalance and *** classification performance of the DL model with our proposed loss function is compared to that of the baseline models using accuracy(ACU),recall(REC),specificity(SPF),Kappa,and area under the receiver operating characteristic curve(AUC)as the evaluation *** testing shows that the method is reliable and efficient.

关键词： Deep learning multi-classification focal loss CNN eye disease

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Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification

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computers, Materials & Continua 2024年第10期81卷 867-883页

作者： Mahesh Thyluru Ramakrishna Kuppusamy Pothanaicker Padma Selvaraj Surbhi Bhatia Khan Vinoth Kumar Venkatesan Saeed Alzahrani Mohammad Alojail Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India School of Computer Science and Engineering VIT-AP UniversityAmaravati522241India Department of Computer Science&Technology Madanapalle Institute of Technology&ScienceMadanapalle517325India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK School of Computer Science Engineering&Information Systems(SCORE) Vellore Institute of Technology(VIT)Vellore632014India Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh11531Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpuraPunjab140401India

Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient *** different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI *** approaches predominantly rely on traditional machine learning and basic deep learning methods for image *** methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI *** the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor *** approach highlights a major advancement in employing sophisticated machine learning techniques within computer science and engineering,showcasing a highly accurate framework with significant potential for healthcare *** model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification *** successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current *** integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider *** research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.

关键词： Deep learning MRI brain tumor cassification EfficientNetB3 computational engineering healthcare technology artificial intelligence in medical imaging tumor segmentation neuro-oncology

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