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检索条件"任意字段=International Conference on Pattern Analysis and Intelligence Robotics"

共 15865 条记录，以下是151-160 订阅

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Artificial intelligence-Driven Video Indexing for Rapid Surveillance Footage Summarization and Review 33

Artificial Intelligence-Driven Video Indexing for Rapid Surv...

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33rd international Joint conference on Artificial intelligence (IJCAI)

作者： Jung, Jaemin Park, Soonyong Kim, Harim Lee, Changha Hong, Charmgil Handong Global Univ Sch Comp Sci & Elect Engn Pohang South Korea GMDSOFT Seongnam South Korea

ISBN: (纸本)9781956792041

This paper introduces VIDEX, an advanced tool designed to streamline the analysis of surveillance video through a user-friendly interface. VIDEX achieves high development efficiency and maintainability utilizing the Model-View-ViewModel (MVVM) design pattern. The core feature of VIDEX is a footage summary using object detection and anomaly detection. Its architecture ensures efficient data management by organizing detected objects and anomalies within an indexed database, thus facilitating a more rapid review process. Additionally, multi-threading was used to shorten the processing time. VIDEX provides a video summarization that can be used primarily in the criminal investigation stage using the information stored in a database. Discover more about VIDEX and access its resources at https://***/nth221/videx.

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Phasic Diversity Optimization for Population-Based Reinforcement Learning

Phasic Diversity Optimization for Population-Based Reinforce...

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IEEE international conference on robotics and Automation (ICRA)

作者： Jiang, Jingcheng Piao, Haiyin Fu, Yu Hao, Yihang Jiang, Chuanlu Wei, Ziqi Yang, Xin Dalian Univ Technol Key Lab Social Comp & Cognit Intelligence Dalian Peoples R China Northwestern Polytech Univ Sch Elect & Informat Xian Peoples R China Yangzhou Collaborat Innovat Res Inst Co Ltd Yangzhou Jiangsu Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China

ISBN: (纸本)9798350384581;9798350384574

Reviewing the previous work of diversity Reinforcement Learning, diversity is often obtained via an augmented loss function, which requires a balance between reward and diversity. Generally, diversity optimization algorithms use Multi-armed Bandits algorithms to select the coefficient in the pre-defined space. However, the dynamic distribution of reward signals for MABs or the conflict between quality and diversity limits the performance of these methods. We introduce the Phasic Diversity Optimization (PDO) algorithm, a Population-Based Training framework that separates reward and diversity training into distinct phases instead of optimizing a multi-objective function. In the auxiliary phase, agents with poor performance diversified via determinants will not replace the better agents in the archive. The decoupling of reward and diversity allows us to use an aggressive diversity optimization in the auxiliary phase without performance degradation. Furthermore, we construct a dogfight scenario for aerial agents to demonstrate the practicality of the PDO algorithm. We introduce two implementations of PDO archive and conduct tests in the newly proposed adversarial dogfight and MuJoCo simulations. The results show that our proposed algorithm achieves better performance than baselines.

关键词： Optimization algorithms

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Centralized and Decentralized Federated Learning in Autonomous Swarm Robots: Approaches, Algorithms, Optimization Criteria and Challenges : The Sixth Edition of international conference on pattern analysis and Intelligent Systems (PAIS'24) 6

Centralized and Decentralized Federated Learning in Autonomo...

引用

6th international conference on pattern analysis and Intelligent Systems, PAIS 2024

作者： Hafid, Aicha Hocine, Riadh Guezouli, Lahcene Abdessemed, Mohamed. R University of Batna2 Dept. Math And Computer Science Batna05000 Algeria

ISBN: (纸本)9798350350265

Fault tolerance and energy consumption optimization are critical issues in swarm robotics. This study examines recent approaches to address these challenges, focusing on a comparative analysis between Centralized Federated Learning (CFL) and Decentralized Federated Learning (DFL). CFL requires a centralized access point for model aggregation, while DFL eliminates the need for a central server, enabling aggregation at each node according to a specific architecture. The analysis of the results reveals that other approaches, such as Hybrid Federated Learning (HFL), more effectively meet the needs of intelligent agents (swarm robots). This effectiveness is particularly enhanced when HFL is combined with Deep Reinforcement Learning (DRL), resulting in Deep Hybrid Federated Reinforcement Learning (DHFRL). The results demonstrate that, although DFL eliminates the necessity of a central server, hybrid approaches are more efficient, especially when combined with Deep Reinforcement Learning (DRL), thus forming Deep Hybrid Federated Reinforcement Learning (DHFRL). © 2024 IEEE.

关键词： Reinforcement learning

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CMIB: A Cross Modal Interaction with Information Bottleneck Framework for Multimodal Survival analysis 11

CMIB: A Cross Modal Interaction with Information Bottleneck ...

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11th IEEE international conference on Cybernetics and Intelligent Systems (CIS) / 11th IEEE international conference on robotics, Automation and Mechatronics (RAM)

作者： Lai Zhouyou Long Lifan Cui Jiaqi Xiong Deng Liu Yuanjun Yin Hao Wang Yan Sichuan Univ Sch Software Chengdu Peoples R China Sichuan Univ Sch Comp Sci Chengdu Peoples R China Suining Cent Hosp Dept Hepatobiliary Surg Suining Peoples R China Stevens Inst Technol Hoboken NJ 07030 USA

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200;9798350364194

Recently, multimodal survival analysis that integrates histology images and genomic data has become a hot topic. Existing multimodal survival analysis methods have evolved from direct fusion strategies to cross-modal attention mechanisms to incorporate multimodal features. However, these methods ignore the redundancy and noise in the fusion features. To solve this problem, we introduced a Cross Modal Interaction with Information Bottleneck (CMIB) framework for multimodal survival analysis, which filters out redundancy and noise while exploring the latent complementary information across modalities. Specifically, CMIB uses the Private Feature Extraction Block (PFEB) and Common Feature Extraction Block (CFEB) to extract the private and the common features of different modalities, respectively. Subsequently, it captures the deep interactions between these features through Co-Attention (CA). Additionally, a Multimodal Information Bottleneck (MIB) is employed to yield a robust representation of the fused features. To verify the effectiveness of CMIB, we conducted extensive experiments on three public TCGA datasets. The results show that CMIB outperforms the current state-of-the-art methods.

关键词： Survival analysis Computational pathology Multimodal Learning Information Bottleneck

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A Case Series in Position-Aware Myoelectric Prosthesis Control using Recurrent Convolutional Neural Network Classification with Transfer Learning

A Case Series in Position-Aware Myoelectric Prosthesis Contr...

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international conference on Rehabilitation robotics (ICORR)

作者： Williams, Heather E. Hebert, Jacqueline S. Pilarski, Patrick M. Shehata, Ahmed W. Univ Alberta Dept Biomed Engn Edmonton AB T6G 2V2 Canada Univ Alberta Div Phys Med & Rehabil Dept Med Edmonton AB T6G 2R3 Canada Alberta Machine Intelligence Inst Amii Edmonton AB T5J 3B1 Canada

ISBN: (纸本)9798350342758

Position-aware myoelectric prosthesis controllers require long, data-intensive training routines. Transfer Learning (TL) might reduce training burden. A TL model can be pretrained using forearm muscle signal data from many individuals to become the starting point for a new user. A recurrent convolutional neural network (RCNN)-based classifier has already been shown to benefit from TL in offline analysis (95% accuracy). The present real-time study tested whether an RCNNbased classification controller with TL (RCNN-TL) could reduce training burden, offer improved device control (per functional task performance metrics), and mitigate what is known as the "limb position effect". 27 participants without amputation were recruited. 19 participants performed wrist/hand movements across multiple limb positions, with resulting forearm muscle signal data used to pre-train RCNN-TL. 8 other participants donned a simulated prosthesis, retrained (calibrated) and tested RCNN-TL, plus trained and tested a conventional linear discriminant analysis classification controller (LDA-Baseline). Results confirmed that TL reduces user training burden. RCNNTL yielded improved task performance durations over LDABaseline (in specific Grasp and Release phases), yet other metrics worsened. Overall, this work contributes training condition factors necessary for TL success, identifies metrics needed for comprehensive control analysis, and contributes insights towards improved position-aware control.

关键词： Convolution

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Obstacle-surmounting analysis of a Novel Deformable Wheel-track Composite Walking Platform 9

Obstacle-surmounting Analysis of a Novel Deformable Wheel-tr...

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9th international conference on Automation, robotics and Applications (ICARA)

作者： Kun, Wang Huawei, Liang Chinese Acad Sci Hefei Inst Phys Sci Hefei 230031 Peoples R China Univ Sci & Technol China Hefei 230026 Peoples R China Anhui Engn Lab Intelligent Driving Technol & Appl Hefei 230088 Peoples R China Chinese Acad Sci Innovat Res Inst Robot & Intelligent Mfg Hefei 230088 Peoples R China

ISBN: (纸本)9781665489218

Based on high mobility requirements of unmanned ground platforms in various environments and combining the advantages of traditional wheeled, crawler and legged walking mechanisms, a novel deformable wheel-track composite walking mechanism with multi-movement modes is proposed,obtaining better obstacle-surmounting performances than the famous reconfigurable wheel-track mechanism(RWT type) in recent *** walking mechanism has two stable forms of round wheel configuration and prolate polygon configuration with three modes of wheel rolling, track movement and gait rotation, which are respectively suitable for travelling on hard roads at high speeds, moving on soft earth with low contact pressure and crossing *** of the innovative structure and a platform equipped with the novel machines is carried ***,the platform's obstacle surmounting performance is analyzed through dynamic modelling and *** to verify the advantages of the machine and its design rationality, comparative simulation experiments of obstacle-surmounting performances is implemented in the multibody simulation environment,setting up obstacle models and building experimental platform models integrating this machine and the RWT type machine respectively.

关键词： deformable wheel-track composite multi-movement modes obstacle-surmounting

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Automating Forensic analysis: Integrating Machine Learning Algorithms for Forensic Evidence and Serial Killer Profiling 4

Automating Forensic Analysis: Integrating Machine Learning A...

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4th international Mobile, Intelligent, and Ubiquitous Computing conference

作者： Hassan, Hana Ahmed Mohamed Abouelwafa, Mariam Mohamed Saber, Amira Alaa Ibrahim, Ahmed Ehab Shorim, Nada AbdElminaam, Diaa Salama Misr Int Univ Fac Comp Sci Cairo Egypt Benha Univ Fac Comp & Artificial Intelligence Banha Egypt

ISBN: (纸本)9798350367782;9798350367775

Serial murder investigations pose significant challenges due to the complexity of criminal patterns and behaviors. This study presents an integrated forensic analysis system using advanced artificial intelligence (AI) techniques to enhance the efficiency and accuracy of criminal investigations, particularly in serial murder cases. By applying various machine learning algorithms, including logistic regression, support vector machines (SVM), K-nearest neighbors (KNN), convolutional neural networks (CNN), and generative adversarial networks (GAN), the system analyzes key forensic evidence such as serial killer patterns, fingerprint recognition, face sketching, bloodstain analysis, and autopsy reports. Each module in the system contributes to narrowing down suspect profiles and linking potential cases using AI-driven pattern recognition and data analysis. The system demonstrated high performance across multiple forensic tasks, with logistic regression and SVM achieving 85% accuracy in serial killer pattern recognition, GAN achieving 99% accuracy in face recognition, and LSTM achieving 99.3% accuracy in bloodstain analysis. The integration of these AI techniques ensures robust, unbiased, and efficient forensic analysis, significantly reducing the time and labor required in traditional investigative methods. The findings of this study underscore the transformative potential of AI in forensic science, providing law enforcement with powerful tools to profile serial killers, analyze crime scenes, and identify critical evidence with precision. This unified AI-based approach holds promise for improving investigative outcomes and enhancing public safety by assisting in the timely resolution of complex murder cases.

关键词： Serial killer Artificial intelligence Forensic analysis Fingerprint face sketching Autopsy Report Bloodstain Glass Identification

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Research on the Current Situation and Trends of Artificial intelligence in Exhibition Design 11th

Research on the Current Situation and Trends of Artificial I...

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11th international conference on Culture and Computing (C&C) part of the 25th international conference on Human-Computer Interaction (HCI)

作者： Yi, Xinyue Liu, Zhizheng Guangxi Arts Univ Nanning 530009 Peoples R China Beijing Inst Technol Beijing 102488 Peoples R China

ISBN: (纸本)9783031347313;9783031347320

This study combined VOSviewer and CiteSpace to conduct a knowledge mapping analysis of the Web of Science research record on artificial intelligence exhibition design from 1991-2022. The results show that: (1) international research is more influential in western developed countries;(2) higher education institutions are the main research institutions;(3) international research focuses on environments, augmented reality and virtual-reality;(4) social robotics, deep learning, emotional computing, cultural display and cultural heritage have become international research hotspots in recent years;(5) RAI-ED can be divided into four main categories: #1 Theoretical framework, #2 Optimization design, #3 Application themes and #4 Scientific analysis. In this paper, Citespace and VOSviewer are combined in the research of artificial intelligence exhibition design, which can effectively visualize the existing data and provide reference meaning and research path for similar themes.

关键词： Artificial intelligence Exhibition design Bibliometric analysis Current state of research Emerging trends

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Harnessing Artificial intelligence and robotics to Combat Water Hyacinth Invasion for Sustainable Environment : A Review 3

Harnessing Artificial Intelligence and Robotics to Combat Wa...

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3rd IEEE international conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Pawar, Mrunal Ranjan, Nihar Patil, Vaibhav Naik, Sanika Shetty, Rutika Divekar, Rajiv JSPM's Rajarshi Shahu College of Engineering Department of Information Technology Pune India Symbiosis Institute of Management Studies Pune India

ISBN: (纸本)9798350354348

Water hyacinth (Eichhornia crassipes), an aquatic free-floating plant, is highly invasive and can now be found in most freshwater bodies in the world's subtropical and tropical climates. Because of the potential harm these plants do to the environment and civilization, it is critical to track their seasonality and spatial distribution in order to develop effective prevention techniques at an early stage. With an emphasis on the application of machine learning algorithms and image analysis techniques for weed detection, this study investigates and examines the usage of artificial intelligence (AI) to overcome these difficulties. Sophisticated algorithms such as the Single Shot Detector (SSD) approach are used for real-time object detection, as well as the integration of machine learning techniques such as CART, RF, and SVM, CNN, and KNN helps for object detection. Modern advances in robotics and artificial intelligence (AI) have shown promise in addressing issues related to the environmental challenges. This review examines prior studies, innovative methodologies, and case studies to identify gaps, challenges, and opportunities for future study and application for controlling the growth of water hyacinth. © 2024 IEEE.

关键词： Convolutional neural networks

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Spatial and Temporal analysis of Normal and Shear Forces During Grasping, Manipulation and Social Activities

Spatial and Temporal Analysis of Normal and Shear Forces Dur...

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international conference on Rehabilitation robotics (ICORR)

作者： Castaneda, Theophil Spiegeler Matos, Joana Capsi-Morales, Patricia Piazza, Cristina Tech Univ Munich TUM Dept Comp Engn Sch Computat Informat & Technol Munich Germany Tech Univ Munich TUM Munich Inst Robot & Machine Intelligence Munich Germany Univ Porto U Porto Fac Engn Porto Portugal

ISBN: (纸本)9798350342758

Extensive research has established and widely acknowledged the important contribution of human hand sensory receptors in providing tactile feedback. The absence of these receptors results in a poor perception of the environment, impairing our efficient manipulation skills. Although the literature emphasizes the significance of normal forces in human grasping, further investigations should point toward the role of shear forces in this process. This paper presents an analysis of human everyday grasping activities through the use of 20 three-axis magnetic soft skin force sensors, in the form of rings and bands, that measure both normal and shear forces. Our study includes twelve tasks that cover various grasping requirements. Results highlight the importance of spatial information and the usefulness of shear forces in the prediction of unexpected changes that can not be always observed in normal forces. Tactile sensing can ultimately be integrated into prosthetic and rehabilitation devices for improved control and potentially provide sensory feedback to the user.

关键词： Sensory feedback

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