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Model-Free Adaptive Fault-Tolerant Control for Offshore Wind Turbines 1

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Computing Conference

作者： Lam, Yat Ping Simani, Silvio Univ Ferrara Dept Engn Ferrara Italy Shibaura Inst Technol Toyosu Campus Koto Tokyo 1358548 Japan

ISBN: (数字)9783031622731

ISBN: (纸本)9783031622755;9783031622731;9783031622724

Floating offshore wind turbines have increased in popularity owing to their adaptability for deep water applications and high power generation efficiency. The control of floating offshore wind turbines, on the other hand, is very complex. The main challenges are the difficulty in precisely modeling floating offshore wind turbines and the higher failure rate of components. As a consequence, this study proposes a model-free adaptive fault-tolerant control system for blade root moment sensor failures. A model-free adaptive control approach is used to construct an individual pitch controller and a fault compensation to avoid mathematical modeling of floating offshore wind turbines. The proposed fault-tolerant control technique removes the need for fault detection and isolation by converting the fault dynamic compensation process into a real-time control issue for nonlinear systems. The fatigue, aerodynamics, structures, and turbulence code simulates and tests the proposed control strategy, and the results show that the proposed strategy can not only keep the wheel bearing load balanced, but also reduce the movement of the floating platform and significantly reduce the bearing load of the floating offshore wind turbines. Furthermore, the output power is closer to the rated power, proving the strategy's high fault tolerance in the face of repeated blade root moment sensor faults.

关键词： Floating offshore wind turbines Individual pitch control Fault-tolerant control Model-free adaptive control Blade root moment sensor fault

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The Impact of Cesarean Section Trends and Associated Complications in the Current World: A Comprehensive Analysis Using Machine Learning Techniques 1

引用

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

作者： Rao, K. Mallikharjuna Kaur, Harleen Bedi, Sanjam Kaur Int Inst Informat Technol Naya Raipur Data Sci & Artificial Intelligence Raipur Madhya Pradesh India

ISBN: (数字)9789819984794

ISBN: (纸本)9789819984787;9789819984794

The accurate prediction of the correct mode of delivery is crucial for the safety and well-being of both mother and child. Currently, this decision heavily relies on the subjective judgement of the attending physician, which can introduce risks if an incorrect method is chosen. Many expectant mothers may opt for a cesarean section without fully understanding whether it is the most suitable option for them. Particularly in developing countries, complications during delivery pose significant challenges. This study aims to address these concerns by identifying key features for determining the delivery mode and applying various machine learning algorithms to predict it accurately. The analysis involved five machine learning models, namely K-nearest neighbours (KNN), random forest, decision tree, support vector machine (SVM), and AdaBoost. The dataset utilized in this study consists of 6157 birth records from four different hospitals in Spain, encompassing 161 distinct features. By leveraging regression analysis-based machine learning methods, we strive to enhance the decision-making process and ultimately improve the safety outcomes for mothers and infants.

关键词： Childbirth Vaginal Delivery Supervised machine learning Ensemble methods AdaBoost Random forest Decision tree SVM KNN SMOTE

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Comparison of Software, Programming Language and Framework for System Development in Institute of Higher Learning 13th

Comparison of Software, Programming Language and Framework f...

引用

13th Computer Science Online Conference

作者： Abd Rauf, Muhammad Fairuz Amran, Mohd Fahmi Mohamad Hassan, Wan Azlan Wan Adnan, Zuraidy Osman, Azilayati Wong, Rita Natl Def Univ Malaysia Fac Def Sci & Technol Kuala Lumpur 57000 Malaysia Univ Selangor Bestari Jaya 45600 Malaysia Natl Def Univ Malaysia Language Ctr Kuala Lumpur 57000 Malaysia

ISBN: (纸本)9783031702846;9783031702853

Software or programming language is one of the most crucial aspects in system development, especially in education institutions. Various software need to be considered from each angle, which includes pricing, after sales service, ease of integration and learnability. Model-View-Controller (VC) based software or programming language such as Laravel PHP is being considered since it is widely used by software house. Another software that is considered is Microsoft PowerApps because of its low code approach in system development. Finally, an application development framework that is considered is Corrad. Corrad is a local-based framework that has its own library which can reduce development time. After comparison have been conducted, Laravel PHPhave been chosen as the most suitable development framework. Some of the contributing factors include pricing, integration with existing platform and learnability.

关键词： System Development Application for Development Programming Language

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Anomaly Prediction in Real-Time Water Flow Data-Machine Learning Versus Statistical Models 1

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Anand, V. Simha, J. B. Agarwal, Rashmi REVA Univ RACE Bangalore Karnataka India

ISBN: (数字)9789819713134

ISBN: (纸本)9789819713127;9789819713134

Industries are at the vanguard of the looming catastrophe in a time of growing worries about resource depletion and water scarcity. Additionally, the enormous financial outlays needed to secure water sources and treatment methods highlight the underlying cost of water. Industries must optimize their water consumption to reduce this mounting financial burden and the negative environmental effects of excessive water use. This study is highly significant since it takes a novel technique to predict anomalies in industrial water use. This research paper focuses on a thorough comparative study to assess two popular approaches for anomaly prediction in real-time water flow data: statistical models and machine learning models. This research makes use of dynamic machine learning models as opposed to conventional techniques, which concentrate on establishing a single static threshold value. These models adjust to the constantly shifting circumstances in industrial operations. Water usage can vary significantly in dynamic industrial activities, such as those in the Food and Beverage (F&B) sector, depending on output levels, seasonal conditions, or even unplanned equipment failures. As a result, choosing a single threshold number is frequently insufficient since it ignores the subtle patterns and trends in the data. The dataset being looked at comes from an electromagnetic water flow meter that has an Internet of Things (IoT) device attached, allowing for easy data transmission to the cloud. With the help of a high-quality dataset, effectiveness of several models is investigated, including the well-known statistical approaches with Weighted Moving Average (WMA), Simple Moving Average (SMA), Exponential Weighted Moving Average (EWMA), Auto-Regressive Integrated Moving Average (ARIMA), that are known for their skill in identifying time-series anomalies. Parallel to this, we make use of machine learning models like K-Nearest Neighbors (KNNs), Isolation Forest (IF), Support Vector

关键词： Anomaly prediction Real-time data Statistical models Machine learning models IoT

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A Novel Architecture for Long-Text Predictions Using BERT-Based Models

A Novel Architecture for Long-Text Predictions Using BERT-Ba...

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Intelligent systems Conference

作者： Lambruschini, Braulio Cesar Blanco Brorsson, Mats SNT Univ Luxembourg Luxembourg Luxembourg

ISBN: (纸本)9783031664274;9783031664281

The rise in Natural Language Processing usage is driven by the effectiveness of transformer-based models in understanding contextual information. While generative models like GPT have garnered attention, BERT-based models have not received as much focus. Despite their potency, GPT-based models face classification challenges mainly due to hallucinations. Alternatively, BERT-based models prove proficiency in classification, but are restricted to less than 512 words as input. In this context, we propose a BERT-based architecture for long-text predictions, featuring an integration network designed to address the challenge of processing lengthy textual inputs. Within this architecture, an LSTM integration layer demonstrated superior performance for models processing only a few pages, while concatenation layers atop BERT yielded improved results for handling both short and very long text sequences. Two tests were conducted to evaluate the effectiveness of this architecture using company documents sourced from the Luxembourg Business Registers. The first test focused on predicting bankruptcy risk based on extensive text sequences extracted from annexes in Annual Accounts. Our approach exhibited strong predictive abilities in this task, boosting accuracy from 5% to 80% while maintaining a similar precision of 73%. In the second test, which involved analyzing modification reports, the model excelled at predicting page types based on the entire content, achieving a significant increase of approximately 23% in F1 Score compared to the BERT base model. In summary, the proposed BERT-based architecture offers a versatile solution for handling long text sequences in various domains, providing valuable support for categorization and prediction tasks across different industries and applications.

关键词： Finance BERT LSTM Long-Text analysis Unstructured dataset NLP

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A Simulation Based Staff Scheduling Analysis for Call Centers in Cargo Industry

A Simulation Based Staff Scheduling Analysis for Call Center...

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International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Kandaz, Omer Faruk Ozcan, Tuncay Istanbul Tech Univ TR-34467 Istanbul Turkiye

ISBN: (纸本)9783031671913;9783031671920

Call centers play a crucial role in terms of customer satisfaction. In the cargo industry, it holds even more importance than many others, not only for B2B but also for B2C deliveries. Long waiting times in queues cause re-dials or call abandonments, negatively affecting overall service delivery. The term "call center staff scheduling" mainly refers to the effective use of resources while meeting customer demands to prevent customer dissatisfaction. The aim of this study is to illustrate the existing state of the operating system in a cargo call center to ultimately manage staff scheduling effectively and efficiently, using the ARENA simulation program. Within this context, related data on call center operations for different processes is collected. After conducting pre-processing of the data to identify relevant best-fit distributions, a simulation model is formed using the ARENA simulation program, employing discrete event simulation techniques. Additionally, bottleneck analysis is carried out aiming to further improve the system. Moreover, based on the results, scenario analysis is implemented to compare and propose different alternative solutions that improve the system.

关键词： Simulation Staff Scheduling Call Center Bottleneck Analysis Cargo Industry

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Enhancing the Immersion of Augmented Reality Through Haptic Feedback 21st

Enhancing the Immersion of Augmented Reality Through Haptic ...

引用

21st International Conference on Smart Technologies and Education (STE) on Smart Technologies for a Sustainable Future

作者： Grodotzki, Joshua Mueller, Benedikt Tobias Tekkaya, A. Erman TU Dortmund Univ Inst Forming Technol & Lightweight Components D-44227 Dortmund Germany

ISBN: (纸本)9783031618901;9783031618918

Mobile multi-actoric force feedback gloves (bHaptics Tactgloves) have been coupled with a mobile holographic display device (HoloLens 2). The communication is realized using open programmable interfaces, while the gloves are paired with a laptop. The holographic device, which must be connected to the internet, sends information via a network protocol to the cloud. This information is read by a program on the laptop and converted into actions sent to the gloves. The intensity and duration of the force-feedback can be modulated depending on the action the user is currently performing. Clicking a holographic button triggers a low-intensity, short-duration pulse at the fingertip while moving the entire hand into an area of potential danger causes all fingers and wrist motor to vibrate at maximum intensity as long as the user does not remove the hand from that area. First tests have shown that for novice users and users familiar with Augmented Reality, the additional sensory feedback through haptics offers a significantly higher level of immersion. While previously holograms could only be interacted with by hands without any haptic feedback, they now report that they have a sense of being able to touch and feel the holograms. The feedback gloves are viewed to be bulky and stiff, hence further development is needed on the hardware side, similar to the holographic devices themselves. Overall, connecting force-feedback gloves with Augmented Reality shows large potential in the fields of manufacturing, maintenance and education, as the level of immersion is further increased.

关键词： Haptic Feedback Augmented Reality Immersion Immersivity

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Exploring the Performance of Large Language Models for Data Analysis Tasks Through the CRISP-DM Framework 12th

Exploring the Performance of Large Language Models for Data ...

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12th World Conference on Information systems and Technologies (WorldCIST)

作者： Musazade, Nurlan Mezei, Jozsef Wang, Xiaolu Abo Akad Univ Fac Social Sci Business & Econ Turku Finland

ISBN: (纸本)9783031602269;9783031602276

This paper investigates the impact of Large Language Models (LLMs), specifically GPT, on data analysis tasks within the framework of CRISP-DM (Cross-Industry Standard Process for Data Mining). In order to assess the efficiency of text-to-code language models in data-related tasks, we systematically examine the performance of LLMs in the stages of the data mining process. GPT models are tested against a series of Python programming and SQL tasks derived from a Master's program's curriculum. The tasks focus on data exploration, visualization, preprocessing, and advanced analytical tasks like association rule mining and classification. The findings show that GPT models exhibit proficiency in Python programming across various CRISP-DM stages, particularly in Data Understanding, Preparation, and Modeling. They adeptly utilize Python libraries for data manipulation and visualization, demonstrating potential as effective tools in data science. However, the study also uncovers areas where the GPT Text-to-code model shows partial correctness, highlighting the need for human oversight in complex data analysis scenarios. This research contributes to understanding how AI can augment traditional data analysis methods, particularly under the CRISP-DM framework. It reveals the potential of LLMs in automating stages of data analysis, suggesting an acceleration in analytical processes and decision-making. The study provides valuable insights for organizations integrating AI into data analysis, balancing AI strengths with human expertise.

关键词： Large Language Models CRISP-DM GPT Decision Support

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Designer Face Mask Detection Using Marker-Based Watershed Transform and YOLOv2 CNN Model

Designer Face Mask Detection Using Marker-Based Watershed Tr...

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

作者： Vyas, Arpita Sharma, Jankiballabh Rajasthan Tech Univ Dept Elect Engn Kota India

ISBN: (纸本)9789819984787;9789819984794

Face mask detection using artificial intelligence (AI) has become more challenging due to high variability in modern designer masks such as single color, multicolor, textile, and printed. This paper presents a designer face mask detection using marker-controlled watershed transform and YOLOv2 CNN model. In the first stage, the face images are preprocessed and segmented using marker-controlled watershed transform by setting mask color as foreground and face color as background marker. The segmented image is applied to the YOLOv2 CNN model for the detection of the face mask. Marker-controlled watershed transform is employed for segmentation and highlights the multicolor mask texture, to improve the classification efficiency of the YOLOv2 CNN model. Simulation performed using different types of designer masks gives an accuracy of 86.66% and an F1-score of about 0.91, which verifies the efficiency of the proposed scheme. The technique deployed in this paper can be used to develop automated systems for face mask detection, classification, and alarm systems.

关键词： Face mask detection YOLOv2 Watershed segmentation

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Share Spell - From Fantasy to Reality: A Collaboration Platform with Learning Analytics for a Dynamic Online Learning Environment System

Share Spell - From Fantasy to Reality: A Collaboration Platf...

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2024 International Conference on Computing, Communication, Cybersecurity and AI

作者： Soni, Priyanshi Prajapat, Shaligram Int Inst Profess Studies DAVV Indore India

ISBN: (纸本)9783031744426;9783031744433

Growth of online learning and the need for interactions has increased globally and in India. The use of social media and online chat in educational environments became the backbone for this learning. Traditional classroom communication lacks freedom due to moderator control and time limitations. To support this, the Share Spell platform aims here as a secure, moderated communication model with a resource-sharing mechanism. It uses role-based access control to enhance information sharing and promote effective discussions in professional learning settings. Share Spell model here is for users to post and manage content within predefined categories, participate in discussions, and report inappropriate content, while administrators manage user access, categories, and reported content through a comprehensive dashboard. This work contributes to the development process of Massive online open courses for self-paced learners. The collaborative nature of MOOCs (Massive Open Online Courses), and ability to simulate professional environments to enhance communication and knowledge sharing among learners. The proposed model enhances learner performance, contributing to their success and growth using discussion forums. The prototype accommodates diverse learning styles, encourages professional behavior, and advanced analytics to continuously improve user experience and the effectiveness of knowledge sharing.

关键词： Communication Platform Knowledge Sharing Role-Based Access Control Content Moderation User Engagement Analysis

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