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

共 15864 条记录，以下是461-470 订阅

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Reliable Semantic Understanding for Real World Zero-Shot Object Goal Navigation 27th

Reliable Semantic Understanding for Real World Zero-Shot Ob...

引用

27th international conference on pattern Recognition, ICPR 2024

作者： Unlu, Halil Utku Yuan, Shuaihang Wen, Congcong Huang, Hao Tzes, Anthony Fang, Yi BrooklynNY11201 United States NYU Abu Dhabi Electrical Engineering Abu Dhabi United Arab Emirates Center for Artificial Intelligence and Robotics NYU Abu Dhabi United Arab Emirates Lab NYU Abu Dhabi United Arab Emirates

ISBN: (纸本)9783031781124

We introduce an innovative approach to advancing semantic understanding in zero-shot object goal navigation (ZS-OGN), enhancing the autonomy of robots in unfamiliar environments. Traditional reliance on labeled data has been a limitation for robotic adaptability, which we address by employing a dual-component framework that integrates a GLIP Vision Language Model for initial detection and an InstructionBLIP model for validation. This combination not only refines object and environmental recognition but also fortifies the semantic interpretation, pivotal for navigational decision-making. Our method, rigorously tested in both simulated and real-world settings, exhibits marked improvements in navigation precision and reliability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Visual languages

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Research on the Stability of pattern Recognition Collaborative Algorithms Based on Data Mining 5

Research on the Stability of Pattern Recognition Collaborati...

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5th international conference on Computers, Information Processing and Advanced Education, CIPAE 2024

作者： Fan, Xue College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)9798331527662

The contemporary era and world of big data has seen the stability of advanced pattern recognition and collaborative algorithms that are critical in the practical process of data mining. This research study primarily delves into the factors that significantly enhance algorithms' stability and optimized function. This factor is focused on ensuring reliable and consistent recognition models and patterns. Based on this context, this research primarily focuses on including data mining techniques to bolster collaborative algorithms' optimized functions and robustness. Through a comprehensive analysis of a myriad of data sets, factors that significantly influence the stability of these algorithms are underscored. Findings, therefore, indicate that incorporating pattern recognition and collaborative algorithms based on data mining significantly improves the algorithmic stability of a system. Thus, the research findings validate the extensive and empirical simulation and tests that efficiently maintain stability in a pattern recognition model. © 2024 IEEE.

关键词： Artificial intelligence Attention Mechanism Collaborative Algorithms Data Mining Stability pattern Recognition

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Large Scale Image Data Processing and Content Recognition Based on Deep Learning 3

Large Scale Image Data Processing and Content Recognition Ba...

引用

3rd international conference on Data Analytics, Computing and Artificial intelligence, ICDACAI 2024

作者： Wan, Ruoyan Wei, Xiuzhuo Ma, Rui Zhao, Huinan Wu, Feng Changchun Humanities and Sciences College Jilin Changchun130000 China

ISBN: (纸本)9798331519032

As the cornerstone of computer vision (CV), image recognition is of great importance. It not only profoundly affects daily life fields such as facial recognition, intelligent security, and medical image analysis, but also plays a key role in breakthroughs in high-tech fields such as autonomous driving technology, intelligent robots, and industrial inspection. With the vigorous rise of artificial intelligence (AI) technology, especially the rapid development of deep learning (DL) technology, image recognition has ushered in unprecedented changes. This article focuses on the latest applications of DL in the field of image recognition and proposes an innovative large-scale image data processing and content recognition algorithm. This algorithm effectively captures complex features in images by constructing deep neural network models, and continuously optimizes model parameters using massive training data, thereby improving recognition accuracy and significantly enhancing the algorithm's ability to understand image content. The experimental results show that the algorithm exhibits excellent performance in processing large-scale image datasets, with fast recognition speed and industry-leading recognition accuracy, laying a solid foundation for the further application of image recognition technology. © 2024 IEEE.

关键词： Medical robotics

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Microarchitectural analysis of Pre-Processing Stage in Machine Learning Workloads 7

Microarchitectural Analysis of Pre-Processing Stage in Machi...

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7th international conference on Algorithms, Computing and Artificial intelligence, ACAI 2024

作者： Zhang, Muge Lee, Dae Yeol Janarthanan, Vasudevan Ryoo, Jeeho Fairleigh Dickinson University VancouverBC Canada Dolby Laboratories Inc. SunnyvaleCA United States

ISBN: (纸本)9798331529314

As Machine Learning (ML) has become integral for various applications, ML workloads are now important considerations for deployment across diverse use cases, ranging from data centers to edge devices. ML encompasses diverse application fields, including vision, audio, text, and multimodal areas, each involving specific raw data formats that often needs pre-processing to become more interpretable for the models and to ensure a more balanced and standardized data distribution. This stage can also include data augmentation to improve model robustness and performance. Therefore, most ML workloads incorporate a stage, commonly referred to as pre-processing, prior to processing the actual data in complex ML model. As the amount of data size increases at a drastic rate, the preprocessing stage now requires closer attention given its significant computation time. In this paper, we conduct an in-depth microarchitectural analysis of the ML pipeline's pre-processing stage to uncover bottlenecks and utilize a bottom-up approach to deliver valuable insights by identifying code hotspots. © 2024 IEEE.

关键词： Adversarial machine learning

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KIRIGAMI CORRUGATIONS: STRONG, MODULAR, AND PROGRAMMABLE PLATE LATTICES

KIRIGAMI CORRUGATIONS: STRONG, MODULAR, AND PROGRAMMABLE PLA...

引用

ASME international Design Engineering Technical conferences / 43rd Annual Computers and Information in Engineering conference (IDETC-CIE) / 47th Mechanisms and robotics conference (MR)

作者： Rubio, Alfonso Parra Mundilova, Klara Preiss, David Demaine, Erik D. Gershenfeld, Neil MIT Ctr Bits & Atoms 77 Massachusetts Ave Cambridge MA 02139 USA MIT Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (纸本)9780791887363

Plate lattices are high-performance lightweight structures, exhibiting up to twice the yield strength and stiffness compared to truss lattices of similar geometric arrangement and relative density. Although they are of great interest for research and structural engineering applications, their complex manufacturing and assembly processes limit their practical use, with sandwich panels being an exception. This paper presents a novel approach to the design and modular assembly of folded custom 3-dimensional plate lattices as structural corrugations for use in structural engineering and robotics applications. The plate lattice structural corrugation uses a building block strategy and incorporates custom modified unit cells based on the Miura-ori. This transformation involves expanding the top and bottom zig-zag crease lines into facets and orienting them in space. The resulting modified pattern is referred to as the Kirigami Expanded Miura. The unique structure of these lattices not only provides exceptional mechanical performance as static structures, but also allows for the design of anisotropies in their flexural stiffness by alternating between the Maxwell criterion on bending-dominated or stretch-dominated cells. These anisotropies can have value differences of up to 24 with the same geometry, making them ideal for robotic morphing applications. We validate our proposed technology by characterizing the mechanical performance of this new building system and comparing it with state-of-the-art corrugations. We demonstrate the potential of this approach by designing, manufacturing, and modularly assembling multiple structures and robots with single and double curvature.

关键词： Kirigami origami robotics doubly curved structures

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2024 IEEE 11th international conference on E-Learning in Industrial Electronics, ICELIE 2024

2024 IEEE 11th International Conference on E-Learning in Ind...

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11th IEEE international conference on E-Learning in Industrial Electronics, ICELIE 2024

ISBN: (纸本)9798350363654

The proceedings contain 19 papers. The topics discussed include: emerging technologies for water quality monitoring and assessment: a systematic review;experiments and project-based enhancements for STEM learning;an open-source hardware platform to advance educational multi-sensor environmental monitoring;educational high power photovoltaic curve tracer using an IoT DC to DC power converter with smartphone integration;can AI be helpful for teaching engineering subjects?;enhancing educational methods for electric machines and drives through open-source robotics;a comprehensive multi-stage validation approach for development of power electronic systems;and embedded artificial intelligence in education: bibliometric analysis.

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An Efficient analysis of EEG Signals to Perform Emotion analysis 4

An Efficient Analysis of EEG Signals to Perform Emotion Anal...

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4th international conference on Artificial intelligence, robotics and Control, AIRC 2023

作者： Rahman, Ahnaf Akif Kabir, Md Rizwanul Ratul, Rashed Hasan Shamns, Fatin Abrar Nishat, Mirza Muntasir Faisal, Fahim Dhaka Bangladesh

ISBN: (纸本)9798350348248

The analysis of human emotional features is a significant hurdle to surmount on the path to understanding the human mind. Human emotions are convoluted thus making its analysis even more daunting. In this paper, a meticulous and thorough analysis of EEG Brainwave Dataset: Feeling Emotions is performed in order to classify three basic sentiments experienced by people. A Machine Learning (ML) based framework is proposed to execute a multi-class classification process to identify positive, neutral and negative emotional experiences in people. The dataset is analysed in two distinct ways. The first method employs chi-square algorithm to select 500 of the best features from each sample in the dataset which are then employed in classifying multiple emotions utilizing several machine learning models. The second method utilizes the sparsePCA algorithm for feature extraction before conducting a multi-class classification with the help of machine learning models. It is supplemented with a binary classification process of each of the individual sentiments available in the entire dataset to analyze the efficacy of these ML models. The ML algorithms-Support Vector Machines (SVM), Random Forest (RF), Light Gradient Boosting Machines (LGBM) and Multi-Layer Perceptron (MLP) are employed in this investigative study. Maximum accuracy of 99.25% and a precision of 99.25% is obtained from the application of the LGBM model after the optimization of hyper-parameters after sparsePCA is used in feature extraction. © 2023 IEEE.

关键词： Support vector machines

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UTP: A Unified Term Presentation Tool for Clinical Textual Data Using pattern-Matching Rules and Dictionary-Based Ontologies 15th

UTP: A Unified Term Presentation Tool for Clinical Textual D...

引用

15th international conference on Agents and Artificial intelligence (ICAART)

作者： Hatoum, Monah Bou Charr, Jean Claude Ghaddar, Alia Guyeux, Christophe Laiymani, David Univ Franche Comte F-90000 Belfort France Int Univ Beirut Dept Comp Sci POB 146404 Beirut Lebanon

ISBN: (纸本)9783031553257;9783031553264

Clinical textual data such as discharge summaries and chief complaints summarize the patient's medical history and treatment plan. These unstructured complex data include ambiguous medical terms, abbreviations, diagnostic investigation values and dates which pose significant challenges for human and machine learning tasks to process them. This paper proposes a novel approach that transforms clinical text with different writing styles into a uniform and standard presentation using pattern-matching rules and JSON dictionarybased ontologies. The main goal of the proposed approach is to improve the communication between healthcare parties or professionals by improving the quality of the clinical textual data and reducing its heterogeneity and ambiguity. In addition, this data quality improvement enhances the performance of machine learning downstream tasks. Our approach identifies the abbreviations, medical terms, negations, dates, and investigation values from the unstructured textual data. Then, it replaces the detected entities with their corresponding unified and normalized presentation based on pattern-matching rules that relies on the linguistic features, pattern-matching rules, and JSON dictionaries. The inductive content analysis method was followed to generate the pattern-matching rules with the help of a medical team. Its role is to validate the accuracy of the detected entities. Finally, the proposed approach was applied to a massive real-world dataset in order to evaluate its impact on the performance of various machine learning models. The results show a significant improvement in performance after preprocessing the clinical textual data using our approach.

关键词： Deep learning Natural Language Processing (NLP) Computer-Aid diagnosis Chief complaints Text mining Abbreviations Medical phrases ChatGPT 4

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analysis of external factors influencing the calibration of binocular cameras

Analysis of external factors influencing the calibration of ...

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2023 international conference on Computer Graphics, Artificial intelligence, and Data Processing, ICCAID 2023

作者： Zhang, Fan Sun, Liying Gao, Xiangdong Shi, Yunting School of Control and Mechanical Engineering Tianjin Chengjian University Tianjin300384 China School of Management Hebei University Baoding071002 China

ISBN: (纸本)9781510677685

This study utilizes regression analysis to examine the external factors affecting the calibration outcomes of binocular cameras. The experiment employs a binocular camera with fixed focal length and baseline, coupled with a commonly used checkerboard calibration pattern. A series of calibration pattern images were captured and subjected to image processing and parameter computation. Employing factorial experiments, variance analysis was conducted to assess the impact of factors such as black and white spacing between checkerboard patterns, the quantity of calibration images, the number of internal corner points, and the distance between calibration boards on calibration errors. Results indicate that all aforementioned factors influence baseline errors. Findings suggest that optimizing the quantity of calibration images, adjusting black and white spacing in checkerboard patterns, increasing internal corner points, and reducing the distance between calibration boards enhance the precision and performance of binocular cameras in 3D vision applications. © 2024 SPIE.

关键词： Calibration

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Revolutionizing Mathematics Learning: Exploring the Potential of robotics and AI as Interactive Tools for Personalized and Engaging Mathematical Education 6

Revolutionizing Mathematics Learning: Exploring the Potentia...

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Anand, Monika Srivastava, V.K. Rayal, Ashish Pandey, Shweta Kumar, Bura Vijay Pachouri, Vikrant School of Life and Applied Sciences Uttaranchal University Dehradun India Law College Dehradun Uttaranchal University Dehradun India School of Computer Science and Artificial Intelligence S. R. University Warangal Andhra Pradesh India Uttaranchal Institute of Technology Uttaranchal University Dehradun India

ISBN: (纸本)9798350304480

Mathematical learning comprehends mathematical ideas in the past few years. By using the digitalization of technology, resources improved in terms of interactivity, interest, and accessibility. Rapid growth in digitalization leads to the efficient use of mathematical learning tools. There are vast ranges of resources available for mathematical education. Advanced technologies such as robotics and AI inclusion in mathematical learning revolutionize learning experiences, developing learning environments and emphasizing creativity. Various applications of AI & robotics in mathematical learning have been evaluated. Mathematical learning tools setbacks the revolution in the education sector for the growing interest of students and faculties. Logo programming & Lego Mindstorm which is a hardware and software have been evaluated for mathematical games. Finally, based on the thorough analysis various challenges faced by the technology have been discussed and future suggestion has been provided. © 2023 IEEE.

关键词： Artificial intelligence (AI) Lego Mindstorm Logo Programming Mathematics learning robotics

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