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

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Flight Plan Optimization, Scanning and 3D Reconstruction Using an Unmanned Aerial Vehicle

Flight Plan Optimization, Scanning and 3D Reconstruction Usi...

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Computing Conference

作者： Bernik, Andrija Tunjic, Boris Cep, Andrej Univ North Dept Multimedia Varazhdin 42000 Croatia Freelance 3D Artist Varazhdin 42000 Croatia Inpro Doo Cakovec 40000 Croatia

ISBN: (纸本)9783031622717;9783031622694;9783031622687

In the domain of aerial photogrammetry, a prominent challenge arises from the absence of standardized procedures in the data integration phase, which constitutes the initial step in the process of three-dimensional (3D) reconstruction. This study scrutinizes the impact of the camera-to-subject distance during recording, varying overlap percentages in photography, and distinctions among three methodologies for programming flight plans. The study made use of a DJI drone Phantom 3 in conjunction with Pix4Dmapper to conduct scanning, 3D modeling and finding optimal flight plan. The primary emphasis was on the historical town of Medvedgrad, situated near Zagreb, Croatia. The goal of this experiment is to bring awareness of how should large 3D objects be scanned and what should the protocol for 3D remodeling be, with the goal of improving workflow efficiency. This involves maintaining a quantitatively assessed quality determined by both the total count of building elements (2D key points for pictures and 3D for point clouds). The study's findings indicate the feasibility of recording data at various distances without any significant problems in the term of 2D elements. The experiment establishes clear connection between image overlap and 3D key points. As a result, the authors recommend adopting a novel flight plan that incorporates optimized parameters derived from this experiment, potentially enhancing the overall efficiency of the aerial photogrammetry process.

关键词： Unmanned aerial vehicle 3D reconstruction Drone Flight Optimization

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Data Analytics and AI for Quality Assurance in Manufacturing: Challenges and Opportunities 14th

Data Analytics and AI for Quality Assurance in Manufacturing...

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14th International Conference on Learning Factories (CLF)

作者： Catti, Paolo Freitas, Artur Pereira, Eliseu Goncalves, Gil Lopes, Rui Pedro Nikolakis, Nikolaos Alexopoulos, Kosmas Univ Patras Lab Mfg Syst & Automat Patras 26504 Greece Univ Porto SYSTEC ARISE Fac Engn Rua Dr Roberto Frias P-4200465 Porto Portugal Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Braganca Portugal

ISBN: (纸本)9783031654107;9783031654114

Data analytics and Artificial Intelligence (AI) have emerged as essential tools in manufacturing over recent years, providing better insight into production systems. Their importance can be highlighted by the way it can transform quality control, from prescriptive to proactive. Data analytics combined with AI can identify abnormal trends and patterns in huge amounts of data, that could uncover potential defects and allow pre-emptive action to minimize or even prevent these from happening. A direct effect of this is the contribution to waste reduction, as well as saving time and resources. While data in a digital factory is ample and the resources for developing artificial intelligence applications are accessible, the implementation of accurate, robust, standard, and economically viable quality monitoring and assessment approaches is not straightforward. This is also strengthened by the scarce skillset in today's manufacturing companies in this area. In this study, the capabilities and potential of data analytics combined with AI are reviewed with a focus on manufacturing. The implementation challenges posed for a practitioner, as well as the benefits of implementing a solution for a manufacturer using data analytics and AI for quality assessment are discussed, based on real-world experiences from existing production environments. Lastly, a learning approach utilizing a high-fidelity digital twin at its core is presented which a practitioner can utilize to create, test and continuously improve a predictive analytics model.

关键词： Analytics Artificial Intelligence Manufacturing Quality Assurance

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TransX2GAN: Using Self-Attention and Knowledge Transfer to Generate Face Images

TransX2GAN: Using Self-Attention and Knowledge Transfer to G...

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Intelligent systems Conference (IntelliSys)

作者： Mohammed, Ehsan Ur Rahman Ahmad, Imran Shafiq Univ Windsor Sch Comp Sci Windsor ON Canada

ISBN: (纸本)9783031477232;9783031477249

The success of deep learning relies on huge datasets. However, in some domains, data is not always accessible due to various constraints. Synthetic data generation helps solve the problem. Collecting face images is challenging due to issues such as privacy. Another issue is that models trained on larger datasets are generally not reused for other related tasks. Therefore, the research aims to facilitate the problems in current face image generation models, such as the lack of quality, huge computational burden, and lack of ability to use pre-trained models. The proposedwork aims to utilize aGenerativeAdversarial Network (GAN) model that uses self-attention and knowledge transfer to generate images of faces resembling images from the CelebA dataset. The proposed model is named TransX2GAN. The ramifications are reduced training time, computational burden, and the ability to reuse deep learning models. Due to the use of self-attention, the quality of generated images has also improved considerably when compared to other models. Compared to the other models the Frechet Inception Distance score and training time has been enhanced by 11.90% and 13.40%, respectively. Hence using transfer learning with attention alleviates some of the important problems in face image generation.

关键词： Transfer learning Generative Adversarial networks Attention Image Generation Autoencoders CelebA HQ Dataset Knowledge Transfer Data Generation

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From Pixels to Text: A Comparative Evaluation of Convolution Neural Network and Recurrent Neural Network Models in Image Processing 8th

From Pixels to Text: A Comparative Evaluation of Convolution...

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Ayush, Luhar Arunbhai, Pandya Krishnan Ramde, Karangiya Jash Panchal, Nisha Garg, Dweepna Devang Patel Inst Adv Technol & Res Coll Dept Comp Engn Anand Gujarat India

ISBN: (纸本)9789819713257;9789819713264

In the field of computer vision and natural language processing, the task of generating descriptive and contextually relevant text based on the content of an image vividly known as image captioning has garnered substantial attention. By analyzing the performance of various CNN and RNN models in image captioning, this work presents a comprehensive comparative evaluation of image captioning models, focusing on the interplay between convolutional neural network (CNN) and recurrent neural network (RNN) architectures. With in-depth analysis and results, different model variants show diverse capabilities and their impact on image captioning quality. In this work, we evaluated the VGG16, VGG19, and ResNet model for visual feature extraction and long short-term memory for sequence generation. This research provides valuable insights into the strengths and limitations of each approach. With the findings from our experiment, we tried to shed light on the contributions of these models to image captioning process. This research aims to contribute to the advancement of image captioning technology and offers an insight for selecting the most suitable models for various application scenarios.

关键词： Image captioning Convolutional neural network (CNN) Recurrent neural network (RNN) Comparative evaluation Model analysis Feature xtraction VGG16 VGG19 ResNet Long short-term memory (LSTM)

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SmartHealth: Artificial Intelligence for Lifelong Personalized Health Care

SmartHealth: Artificial Intelligence for Lifelong Personaliz...

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Intelligent systems Conference (IntelliSys)

作者： Santos, Jao Faria, Luiz Martins, Constantino Polytech Porto Res Grp Intelligent Engn & Comp Adv Innovat & Dev Porto Sch Engn ISEPIPP Porto Portugal

ISBN: (纸本)9783031477171;9783031477188

This paper documents the design of a mobile application developed to help people with anxiety disorders or who suffer from mild general anxiety symptoms. The conceived mobile application has several useful features to minimise anxiety disorders, among which we highlight the recommendation of activities, daily recording of self-reflections about the emotional state, analysis of feelings and recommendation of personalized activities. Such tools are available whenever the user needs them, namely, in situations with higher anxiety and stress. The application is intended to help the user to prevent their anxiety disorder, the symptoms associated with it, and the resulting impacts on their daily lives, and to develop strategies to improve their mental wellness. According to psychology experts who collaborate with the project, the activities to be recommended should be those that provide greater satisfaction. In this sense, the activity recommendation algorithm was developed to recommend activities that best suit the user's profile. The features of the recommendation system allow the user to resort to relaxation activities, taking into account the impact of the exercises on improving the quality of life. Feedback provided by the user about the impact of the application's recommendations on reducing disturbances is obtained through questionnaires and daily recordings of emotions.

关键词： Anxiety disorder Generalized anxiety disorder (GAD) Recommendation system

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Human-Centered Programming: The Design of a Robotic Process Automation Language 20th

Human-Centered Programming: The Design of a Robotic Process ...

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20th International Workshop on Combinatorial Image Analysis (IWCIA)

作者： Gago, Piotr Voitenkova, Anna Jablonski, Daniel Debelyi, Ihor Skorupska, Kinga Grzeszczuk, Maciej Kopec, Wieslaw Polish Japanese Acad Informat Technol Warsaw Poland

ISBN: (纸本)9783031665936;9783031665943

RPA (Robotic Process Automation) helps automate repetitive tasks performed by users, often across different software solutions. Regardless of the RPA tool chosen, the key problem in automation is analyzing the steps of these tasks. This is usually done by an analyst with the possible participation of the person responsible for the given activity. However, currently there exists no one-size-fits-all description language, which would allow to record, process, and easily automate steps of specific tasks. Every RPA solution uses a different notation, which is not easily human-readable, editable, and which cannot be applied to a different automation platform. Therefore, in this paper, we propose a new eXtensible Robotic Language (XRL) that can be understood by both programmers and non-programmers to automate repetitive business processes.

关键词： RPA HCI robotic process automation human-centered computing business processes software robots

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Map Merge and Accurate Localization in Multi-robot systems in Real Environments 6th

Map Merge and Accurate Localization in Multi-robot Systems i...

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6th Iberian Robotics Conference (Robot)

作者： Martinelli, Dieisson Kalempa, Vivian Cremer de Oliveira, Andre Schneider Fed Univ Technol Parana UTFPR Ave Sete Setembro 3165 Curitiba Parana Brazil Univ Estado Santa Catarina UDESC Luiz Fernando Hastreiter St 180 Sao Bento Do Sul SC Brazil

ISBN: (纸本)9783031586750;9783031586767

This article presents an approach to map merged and precise position determination in multi-robot systems operating in real environments, using the matrix transformation technique and Particle Swarm Optimization (PSO). The primary objective is to combine information from multiple maps, represented by occupancy matrices, into a unique and comprehensive map. This map can be employed in applications that require cooperation and coordination among robots. To achieve the proposed objective, the PSO technique is applied to find the optimal values of rotation (psi), translation along the x-axis (dx), and translation along the y-axis ( dy) to optimize the map merged. The map merged is performed based on identified correspondences between the maps, using the Jaccard Similarity algorithm. The PSO approach is employed in this work to determine the best possible overlap between the maps. After obtaining the transformation values, they are used to find and update the real positions of the robots on the resulting fused map. Consequently, the proposed technique provides an advanced and efficient solution for map merged and robot positioning in real environments. This approach opens doors for the application of effective cooperation techniques and intelligent navigation for multiple robots in complex and dynamically changing scenarios. An experiment video has been produced and can be accessed through the following link https://***/RLQsJhlnMuQ.

关键词： Mobile robots multi-robot systems merge maps

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Toward Detecting and Explaining Stress of Nurses Using Wearable Devices and LLMs 1

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16th International Conference on Ubiquitous Computing and Ambient Intelligence

作者： Miyake, Naoya Kaneko, Haru Ronando, Elsen Garcia, Christina Inoue, Sozo Kyushu Inst Technol Grad Sch Life Sci & Syst Engn Fukuoka Japan

ISBN: (数字)9783031775710

ISBN: (纸本)9783031775703;9783031775710

In this paper, we improve nurse stress detection using wearable device data by balancing the dataset through Large Language Models (LLMs). The rise in wearable technologies has spurred stress detection studies in both daily life and healthcare settings. However, integrating an LLM on stress detection with wearable devices is still emerging. Data imbalance and faulty sensor readings, especially from EDA sensors in devices like the E4, can significantly impact the accuracy of stress detection models. To address this, we utilize LLMs to mitigate data imbalance by generating relevant synthetic samples through varied contextual prompting strategies. A Random Forest (RF) classifier is trained using physiological sensor datasets including acceleration, electrodermal activity (EDA), heart rate (HR), and skin temperature (Temp). We compared Zero-shot, Few-shot, Zero-shot CoT, and Few-shot CoT prompting ability on data generation. We benchmark the LLM approach against SMOTE and Random Sampling comparing overall performance on stress detection. Furthermore, we employ the RACCCA (Relevance, Accuracy, Completeness, Clarity, Coherence, Appropriateness) framework to assess the quality of prompting and output. Overall, the proposed data augmentation using LLMs improved performance compared to the baseline. Specifically, Zero-Shot prompting increased the overall F1-score by 10% (from 88.12% to 98.11%). This study underscores the potential of LLM-generated synthetic data in addressing data imbalance and enhancing stress detection models.

关键词： Large Language Models stress detection wearable devices data augmentation

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Real-Time Inferencing Using Transfer Learning for a Screening of Depression Detection Using Actigraphy

Real-Time Inferencing Using Transfer Learning for a Screenin...

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

作者： Ghate, Rajanikant Walambe, Rahee Kalnad, Nayan Kotecha, Ketan Avegen Ltd London England Symbiosis Int Univ Symbiosis Ctr Appl Artificial Intelligence Symbiosis Inst Technol Pune India

ISBN: (纸本)9789819984756;9789819984763

Automated depression screening and diagnosis is a highly relevant problem today. There are several limitations of the traditional depression detection methods, namely high dependence on clinicians and biased self-reporting. In recent years, research has suggested strong potential in machine learning (ML)-based methods that make use of the user's passive data collected via wearable devices. However, ML is data-hungry. Especially in the healthcare domain primary data collection is challenging. In this work, we present an approach based on transfer learning, from a model trained on a secondary dataset, for the real-time deployment of the depression screening tool based on the actigraphy data of users. This approach enables real-time inferencing on a trained model for a limited primary data. A modified version of leave-one-out cross-validation approach performed on the primary set resulted in mean accuracy of 0.96, where in each iteration one subject's data from the primary set was set aside for testing. Such an approach can be useful in understanding the capability of a trained model on secondary data in situations where primary data is limited.

关键词： Machine learning Actigraphy Depression detection and screening Digital phenotyping Transfer learning

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IoT-Based Weather Monitoring System Using NodeMCU ESP8266 1

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Phadke, Maitreyi Korde, Monali AP Shah Inst Technol Dept Artificial Intelligence & Machine Learning Thana India

ISBN: (数字)9789819713202

ISBN: (纸本)9789819713196;9789819713202

The IoT-based weather monitoring system using NodeMCU ESP8266 represents a pioneering approach to weather data acquisition, analysis, and dissemination. Confronting the limitations of conventional weather monitoring systems such as high costs and restricted real-time data accessibility, this project leverages NodeMCU ESP8266 technology to offer an economical and efficient solution. The system integrates a network of sensors to capture crucial weather parameters like temperature, humidity, and light intensity. Through the NodeMCU ESP8266 micro-controller and Wi-Fi module, the collected data is processed and transmitted to a central server. A user-friendly interface empowers users to access real-time weather conditions, and forecasts, all presented through intuitive graphical representations. This report comprehensively details the system's architecture, hardware, software components, implementation steps, results, challenges, and future possibilities. By combining IoT and NodeMCU ESP8266, this system unlocks fresh possibilities for data-informed decision-making in various fields.

关键词： IoT-based NodeMCU ESP8266 Sensors Real-time data visualization Arduino cloud

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