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检索条件"丛书名=Lecture Notes in Networks and Systems"

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Intelligent Texture Feature-Based Defects Classification of Aircraft Engine Blades

Intelligent Texture Feature-Based Defects Classification of ...

引用

International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Joshi, Soham Kumar, Animesh Lodha, Mokshit Deshmukh, Vaidehi Phadke, Anuradha Dr Vishwanath Karad MIT World Peace Univ Pune Maharashtra India

ISBN: (纸本)9783031671944;9783031671951

This paper proposes an intelligent, automated defect detection system for aircraft engine blades using fuzzy texture features. Common manufacturing defects like cracks, scratches, and coating shedding can pose threats to engine operation. Traditional manual inspection is time-consuming and prone to human error. The proposed method extracts fuzzy texture features using Grey-Level Co-occurrence Matrix (GLCM), Gabor filters, and wavelet transforms. These features are input into a machine learning algorithm to intelligently classify blade images as defective or not. Experimental results demonstrate the effectiveness of the fuzzy texture-based approach for automated defect classification, improving blade quality inspection efficiency in the manufacturing process.

关键词： Aircraft engine blades surface defects texture features GLCM Gabor filter wavelet transform defect classification automated visual inspection

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Virtualization of the Paint Mixing Process Using the Hardware in the Loop Technique 1

引用

Intelligent systems Conference (IntelliSys)

作者： Toapanta, Kelvin F. Pichucho, Katherin G. Ortiz, Jessica S. Varela-Aldas, Jose Univ Indoamer SISAu Res Grp Fac Ingn Ind Prod Ambato Ecuador Univ Indoamer Ctr Invest Ciencias Humanas Educ CICHE Ambato Ecuador Univ Fuerzas Armadas ESPE Latacunga Ecuador

ISBN: (数字)9783031477157

ISBN: (纸本)9783031477140;9783031477157

This paper describes the implementation of the Hardware in the Loop technique, which in the automation area allows communication between a simulated industrial process (software) and a programmable logic controller, PLC, (Hardware). The simulation section consists of TIA PORTAL for the automation of the paint mixing process, while the visualization and monitoring corresponding to the SCADA system is done with the INTOUCH software, and finally the virtualization is done in the Unity 3D software, in order to provide greater realism to the user in the manipulation of the variables that make up the process. The simulations consist of a bilateral communication between INTOUCH and TIA Portal software through tags created in KepServer of the respective variables for the control and monitoring of the process. While the communication with Unity 3D and TIA Portal software will be done through a free Sharp7 library. By means of data blocks, the TIA Portal programming will be related to the variables created in Visual Studio. This will be connected to a real control panel, consisting of a Siemens S7-1200 PLC through the open communication protocol that allows the exchange of data via Industrial Ethernet with TCP/IP protocol (Server-Client), obtaining a digital data sent stored in bits. The Ethernet information frame with a delay time of 220 ms which is considered optimal within the response of an industrial process.

关键词： PLC TIA PORTAL Intouch UNITY 3D Ethernet

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Investigating Role of SVM, Decision Tree, KNN, ANN in Classification of Diabetic Patient Dataset

Investigating Role of SVM, Decision Tree, KNN, ANN in Classi...

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International Conference on Artificial Intelligence: Theory and Applications (AITA)

作者： Kumari, Sarita Upadhaya, Amrita Banasthali Vidiya Peeth Dept Comp Sci & Engn Radha Kishnpura Rajasthan India

ISBN: (纸本)9789819984787;9789819984794

Diabetes, which is a long-term ailment, is characterized primarily by high levels of sugar in the blood. It has been connected to a broad range of different types of complicated disorders, such as heart attack, renal failure, and stroke, among others. Almost 422 million people throughout the world were diagnosed with diabetes in 2014, and according to IDF Atlas 2021 report, 10.5% of the adult population (20-79 years) has diabetes, by 2045, IDF projections show that 1 in 8 adults approx. 783 million will be living with that disease an increment to 46% making it the most common metabolic. Logistic regression was used in traditional research to determine the characteristics that increase a person's likelihood of developing diabetes based on probability value and odds ratio. The authors utilize many classifiers to make predictions about diabetes patients, including NB, DT, AB, and RF. Twenty separate tests were conducted, each using one of three partitioning strategies. These classifier's effectiveness is measured by their accuracy and area under the curve. The overall accuracy rate of ML systems was 90.62% with conventional research. The K10 procedure combined LR-based feature selection with an RF-based classifier to obtain an ACC of 94.25% and an AUC of 0.95. The major goal of this work is to compare the performance of SVM, Decision Tree, KNN, & ANN on a dataset of diabetes patient classifications. The study's goals include improved accuracy and trustworthy results.

关键词： Diabetic patient SVM Decision tree LR KNN ANN Gaussian NB

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High-Speed Quadruped Walking in Constant-Posture/Velocity by Counterweight Legs 27th

High-Speed Quadruped Walking in Constant-Posture/Velocity by...

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27th International Conference on Climbing and Walking Robots and Mobile Machine Support Technologies (CLAWAR)

作者： Miura, Yuta Mizuuchi, Ikuo Tokyo Univ Agr & Technol 2-24-16 Naka Cho Koganei Tokyo 1848588 Japan

ISBN: (纸本)9783031713002;9783031713019

Gait styles of quadruped robots can be classified into two main types: static walking and dynamic walking. In both styles, achieving constant-posture/velocity and high speed walking is impossible without additional actuators or joints on the torso side. This is due to the large inertial forces generated by the robot's legs moving at high speed. We propose a method to cancel the inertial translational forces generated by leg motion by attaching counterweights to the upper sides of the legs, and a gait setting method that allows the trunk to maintain constant posture and velocity during high-speed walking. Furthermore, as a result of kinetic analysis and actual machine experiments with the Counter-weight Legs quadruped robot, we demonstrated that it is possible to suppress the influence of inertial translational forces.

关键词： quadruped walkig stability counterweight high speed

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SIM_RL: A New Approach for Integrating Simulation with Reinforcement Learning 1

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Intelligent systems Conference (IntelliSys)

作者： Razy, Elad Herman, Maya Open Univ Israel Raanana Israel

ISBN: (数字)9783031477157

ISBN: (纸本)9783031477140;9783031477157

Computer simulation, the process of mathematical modelling performed on a computer, is designed to predict the behavior of a real-world system. As a system becomes more complex, the simulation engine must run numerous times in response to the increasing complexity of the input and the simulation process. Additionally, an expensive physical experiment needs to be performed to validate the results. This paper demonstrates an innovative, general-purpose simulation approach strengthened by refinement learning (RL), formalized in the SIM_RL algorithm, and using epidemic spread (COVID-19) test data. The main advantages of this approach are computational resource savings, reduced need for physical experiments, and the ability to predict system behavior based on actual results. Moreover, this approach can be used in various disciplines to solve complex simulation problems.

关键词： Computer simulation Machine learning Reinforcement learning

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Ontology-Based Post-Hoc Neural Network Explanations Via Simultaneous Concept Extraction

Ontology-Based Post-Hoc Neural Network Explanations Via Simu...

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Intelligent systems Conference (IntelliSys)

作者： Ponomarev, Andrew Agafonov, Anton Russian Acad Sci St Petersburg Fed Res Ctr St Petersburg 199178 Russia

ISBN: (纸本)9783031477232;9783031477249

Ontology-based explanation techniques provide an explanation on how a neural network came to a particular conclusion using human-understandable terms and their formal definitions, which are encoded in the form of ontology. One of the promising directions in the area of ontology-based neural explanation is based on concept extraction-the process of establishing relationships between internal representations of the network and ontology concepts. Existing algorithms of concept extraction are search-based and require training multiple mapping networks for each concept, which may be time consuming. The paper proposes a method to build post-hoc ontology-based explanations by training a single multi-label concept extraction network, mapping activations of the specified "black box" network to the ontology concepts. The experiments with two public datasets show that the proposed method can generate accurate ontology-based explanations of a given network and requires significantly less time for concept extraction than existing algorithms.

关键词： Ontology Explainable AI Knowledge Black-box Convolutional neural network Interpretability Concept extraction Neuro-symbolic AI

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Virtual Twin for the Smart Factory as a Tool to Enable Robotics Skills Acquisition 14th

Virtual Twin for the Smart Factory as a Tool to Enable Robot...

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14th International Conference on Learning Factories (CLF)

作者： Vazquez-Hurtado, Carlos Altamirano-Avila, Edison Rodriguez-Padilla, Consuelo Ahmad, Rafiq Quinones, Jose I. Gomez Rodriguez-Delgado, Jose Manuel Flores-Ramirez, Man Tecnol Monterrey Sch Engn & Sci Monterrey 64849 NL Mexico Univ Alberta Dept Mech Engn Smart & Sustainable Mfg Syst Lab SMART Lab 9211 116 St Edmonton AB Canada

ISBN: (纸本)9783031654107;9783031654114

This work aims to present the result of the collaboration between Tecnologico de Monterrey (https://***/) and the University of Alberta (https://***/) in terms of the creation of a shared virtual twin platform for digital learning factories. This research explores the integration of a Digital Twin using innovative virtual reality technology such as Oculus and VIVE Pro via Unity Software for the Smart Factory laboratory at Tecnologico de Monterrey. This project, designed and conducted by students with assistance from university researchers, represents significant progress in engineering education and sophisticated manufacturing techniques with an emphasis on Industry 4.0. This paper explores the development, implementation, and pedagogical effect of learning how a Digital Twin works with an immersive learning tool such as the Unity software tool. Leveraging this software, which is a powerful game engine, a virtual replica of the actual laboratory was created. Students could engage with digital models of mobile robots, robotics arms, and machinery with virtual reality goggles. Learning in a risk-free virtual environment and an improved understanding of the industrial processes completely change how students learn. The study examines the advantages of a simulated environment over a genuine one and its educational implications for robotics education.

关键词： digital twin integration virtual reality educational innovation higher education learning factories

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A Lesion Feature Engineering Technique Based on Gaussian Mixture Model to Detect Cervical Cancer 1

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4th Congress on Intelligent systems (CIS)

作者： Mukku, Lalasa Thomas, Jyothi CHRIST Deemed Univ Kengeri Bangalore 560074 Karnataka India

ISBN: (数字)9789819990375

ISBN: (纸本)9789819990368;9789819990375

Latest innovations in technology and computer science have opened up ample scope for tremendous advances in the healthcare field. Automated diagnosis of various medical problems has benefitted from advances in machine learning and deep learning models. Cancer diagnosis, prognosis prediction and classification have been the focus of an immense amount of research and development in intelligent systems. One of the major concerns of health and the reason for mortality in women is cervical cancer. It is the fourth most common cancer in women, as well as one of the top reasons of mortality in developing countries. Cervical cancer can be treated completely if it is diagnosed in its early stages. The acetowhite lesions are the critical informative features of the cervix. The current study proposes a novel feature engineering strategy called lesion feature extraction (LFE) followed by a lesion recognition algorithm (LRA) developed using a deep learning strategy embedded with a Gaussian mixture model with expectation maximum (EM) algorithm. The model performed with an accuracy of 0.943, sensitivity of 0.921 and specificity of 0.891. The proposed method will enable early, accurate diagnosis of cervical cancer.

关键词： Cervical cancer Deep learning Gaussian mixture modelling Expectation maximization Lesion feature extraction Segmentation

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Safeguarding SaaS Through HoneyPots: Strategies for Data Privacy and Security Compliance

Safeguarding SaaS Through HoneyPots: Strategies for Data Pri...

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2024 International Conference on Computing, Communication, Cybersecurity and AI

作者： Vaishnav, Pulkit Raikwar, Sunny Prajapat, Shaligram TechRitzy Pvt Ltd Indore India Dr P J Abdul Kalam Univ Indore India Devi Ahilya Univ Indore India

ISBN: (纸本)9783031744426;9783031744433

In the present era, the Internet is expanding at an unimaginable rate where Software security and Privacy have become necessary for protecting the Intellectual Property from Various Attacks and Reverse Engineering. Data Cryptography is the most commonly used security mechanism but has its pitfalls like key storage and computation costs. Unauthorized access and prevent unwanted exposure of proprietary code. HoneyPot techniques can detect and monitor unauthorized access at the application level. The application itself can act as a honeypot by incorporating decoy instructions that trigger security measures if deviated from. This approach allows SaaS applications to function without encryption or performance impact while remaining secure and lightweight. The honeypot mechanism enables code deployment on remote systems without risking data leakage, providing an effective and impactful security solution for SaaS applications.

关键词： Data Privacy Security Honeypot Cloud Computing

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Feature Selection Techniques to Enhance Prediction of Clinical Appointment No-Shows Using Neural Network

Feature Selection Techniques to Enhance Prediction of Clinic...

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International Conference on Artificial Intelligence: Theory and Applications (AITA)

作者： Joseph, Jeffin Senith, S. Kirubaraj, A. Alfred Ramson, S. R. Jino Karunya Inst Technol & Sci Coimbatore Tamil Nadu India GlobalFoundries US LL2 Essex Jct VT USA

ISBN: (纸本)9789819984787;9789819984794

The issue of no-shows is a significant concern, as it results in many patients missing their appointments at outpatient clinics worldwide, often without any prior cancelation. This leads to inefficiencies in terms of idle resources and wasted capacity. Hence prediction models are needed to anticipate whether a patient will attend their scheduled appointment. The effectiveness of a predictive model in predicting clinical appointment no-shows is heavily influenced by the features used in the model. To address this issue, the present study compares various feature selection techniques in order to enhance the accuracy of prediction. Univariate Selection, Recursive Feature Elimination, Random Forest Classifier, and Reciprocal Ranking are the feature selection techniques utilized in the current study. These techniques are applied prior to building a neural network model using a Multilayer Perceptron to predict clinical appointment no-shows. The study employed the scikit-learn library in python for model implementation, and the performance of each model is evaluated using performance measures, including Accuracy, Specificity, Sensitivity, Precision, F-measure, Matthews Correlation, Log Loss, and Area under the Curve. Feature selection methods demonstrated excellent performance by reducing the number of variables while maintaining the predictive accuracy across all models. Consistently, the most critical features across all models were the Patient Trust, Appointment Type, Reminder Message, Lead Time, and Missed Appointment History.

关键词： Hospital management Appointment no-shows Neural network Predictive analytics Feature selection methods

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