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检索条件"任意字段=3rd International Conference on Pattern Recognition and Machine Intelligence"

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A discrete event approach to the simulation of selective laser sintering 3D printing large scale production 3

A discrete event approach to the simulation of selective las...

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3rd international conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding, VIPERC 2024

作者： Isania, Zahra Fanti, Maria Pia Casalino, Giuseppe Polytechnic University of Bari Via Orabona 5 Bari70125 Italy

Various industries, especially construction and building renovation, need custom components in quantities that traditional 3D printing methods are unable to supply. This article addresses the challenge of expanding the Additive Manufacturing (AM) production scale, emphasizing careful consideration in scaling strategies. In particular, it provides insights for optimizing AM factory productivity through discrete event simulation. The article models the setup and processing times for a selective laser sintering case study, examining the variability in machine and operator numbers for cost reduction and increased daily production, in two scenarios. The first scenario is the reference for comparison with the one proposed in this paper. The working hours and days, and the type of machines in the production line are the same in both scenarios. In the proposed scenario, the effect of the number of operators and machines was studied by FlexSim software and compared with the referenced scenario in terms of the cost, daily production count, and average waiting time. It offers a practical roadmap for successful AM production scaling. © 2024 Copyright for this paper by its authors.

关键词： Cost reduction

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Research on short term stock selection strategy based on machine learning 3

Research on short term stock selection strategy based on mac...

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3rd international conference on machine Learning, Big Data and Business intelligence (MLBDBI)

作者： Ren, Yanzheng Beijing Inst Technol Sch Mechatron Beijing Peoples R China

ISBN: (纸本)9781665417907

Based on theoretical analysis, this paper constructs a short-term stock selection strategy based on machine learning. The sample set is constructed based on the closing price trend of individual stocks in the last 20 trading days, and the machine learning algorithms GBDT and GBRank are used for training, and machine learning is used to automatically perform pattern recognition on the processing capabilities of high-dimensional nonlinear data. When forecasting, the former will rank stocks with higher rising probability in the next 3 trading days, and the latter will rank stocks with greater gains in the nest 3 trading days. The stock selection strategy swaps positions every 3 trading days, and each time the top 10 stocks given by the equal-weight buying algorithm are used to construct an investment portfolio. The experimental results show that the short-term quantitative stock selection strategy based on the GBDT algorithm can outperform the market combination, namely the Shanghai and Shenzhen 300 Index, and has certain practicability.

关键词： machine learning pattern recognition stock selection strategy algorithm

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Autoencoder Assisted Cancer Subtyping by Integrating Multi-omics Data 1

引用

9th international conference on pattern recognition and machine intelligence (PReMI)

作者： Madhumita Paul, Sushmita Indian Inst Technol Jodhpur Dept Biosci & Bioengn Jodhpur 342037 Rajasthan India Indian Inst Technol Jodhpur Sch Artificial Intelligence & Data Sci Jodhpur 342037 Rajasthan India

ISBN: (数字)9783031127007

ISBN: (纸本)9783031126994;9783031127007

Cancer subtypes identification can facilitate the subtype-targeted treatments for different cancers. The heterogeneous nature of the disease involves activation of several pathways, significantly affecting the patients' survival and displays distinctive efficacy towards different drugs. Therefore, identification of cancer subtypes using genomic level data is critical. Over the years, several computational methods have been designed for multi-omics data integration and clustering. These methods mainly follow three algorithmic approaches: early, late, or intermediate integration of multi-omics data. Some of them perform clustering on concatenated data, other performs clustering on integrated similarities, whereas other, performs clustering on new representations. In this study, a deep learning framework of the Autoencoder is designed to obtain a low-dimensional representation of the early integrated multi-omics dataset. Spectral clustering is performed on the bottleneck layer of the Autoencoder to predict patients' clusters (cancer subtypes). The Performance of the proposed Autoencoder-assisted workflow is demonstrated on three different cancer data sets taken from The Cancer Genome Atlas database. The performance is also compared with other popular early, late, and intermediate data integration methods. Furthermore, to establish the biological relevance of the identified clusters, a detailed biological analysis of the clusters obtained from the Glioblastoma multiforme dataset (GBM) is also presented.

关键词： Autoencoder Cancer Subtypes Multi-omics Data

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3D Human Pose Estimation with Bone Symmetry and Motion Locality 3

3D Human Pose Estimation with Bone Symmetry and Motion Local...

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3rd international conference on Electronics and Information Technology, EIT 2024

作者： Zhang, Xinyu Hua, Chunsheng Liaoning University Institute of Intelligent Robots and Pattern Recognition College of Information Liaoning Shenyang110036 China

ISBN: (纸本)9798350369151

The bone symmetry and motion locality are two important priors for 3D human pose estimation. For bone symmetry, symmetric bones typically have equal length and higher motion correlation. In terms of motion locality, the motion of the human body conforms to its structure and is temporally continuous. These two priors align with human intuition, but are challenging for Transformer to directly abstract them from data. We propose novel modules, Bone Symmetry Temporal Transformer (BSTT) and Spatial-Temporal Locality Fusion (STLF), to utilize these two priors, and design a new Transformer-based BSMLFormer model. Specifically, BSTT considers the temporal sequences of two symmetric bones as a single new sequence and calculates its global relationships by self-attention mechanism. STLF performs local computations by GCN and convolutions to capture the local dependencies in adjacent joints and adjacent parts. The local-global alternating structure of BSMLFormer allows for consideration of the local information during global calculations. More remarkably, BSMLFormer achieves the state-of-the-art performance with 39.0mm MPJPE on Human3.6M dataset. © 2024 IEEE.

关键词： Motion estimation

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Artificial intelligence-Enabled Fault Detection and Diagnosis for Improved Power Quality in Hybrid Renewable Energy Systems 3

Artificial Intelligence-Enabled Fault Detection and Diagnosi...

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3rd international conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Kumawat, Hitesh Tundwal, Prerna Dave, Vikramaditya Ctae Mpuat Department of Electrical Engineering Rajasthan India

ISBN: (纸本)9798350330724

Hybrid renewable energy systems (HRES) have drawn a lot of interest as a viable answer to the world's rising energy needs. On the other hand, combining many renewable energy sources into HRES makes it challenging to maintain excellent power quality. The optimization methodology for improving power quality in HRES using clever control tactics is proposed in this research. The suggested framework successfully manages and controls the power flow, voltage stability, and frequency control in HRES by utilizing cutting-edge control approaches including artificial intelligence and machine learning. To reduce power fluctuations and maintain a dependable and stable power supply, artificial intelligence dynamically alters the operation of RES, energy storage systems, and power converters. According to simulation results, the suggested optimization framework is effective at enhancing power quality in HRES, making it a promising strategy for the deployment of HRES in the future. © 2023 IEEE.

关键词： Power quality

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Computer Simulation of Sports and Design of Auxiliary Training System 3rd

Computer Simulation of Sports and Design of Auxiliary Traini...

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3rd EAI international conference on Application of Big Data, Blockchain, and Internet of Things for Education Informatization, BigIoT-EDU 2023

作者： Meng, Xianfeng Yang, Chaofeng Wang, Donghui Lin, Baisong Tang, Weiming Wei, Bingtao Xinjiang Vocational and Technical College of Communications Xinjiang Urumqi831401 China School of Artificial Intelligence Wenshan University Wenshan663000 China

ISBN: (纸本)9783031631351

Motion analysis technology is a very broad field, which involves many fields such as computer vision, artificial intelligence and pattern recognition. It is a highly interdisciplinary subject. Because of its high application value, it has always been a research hotspot. Computer simulation of sports is a computer-based method used to study the effect of training and performance. It allows researchers to assess how different training methods affect an individual’s performance in a particular sport. Coaches, athletes, coaches, and researchers use computer simulations to determine whether various techniques help improve their performance. Computer simulation of design system is a modeling technology, which helps designers and engineers create models of real objects using computer software. Designers use certain parameters (such as size, weight) to create models so that they can be tested according to the actual design without building the model first. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Sports

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2023 3rd international conference on Theoretical and Applicative Aspects of Computer Science, ICTAACS 2023

2023 3rd International Conference on Theoretical and Applica...

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3rd international conference on Theoretical and Applicative Aspects of Computer Science, ICTAACS 2023

ISBN: (纸本)9798350385854

The proceedings contain 13 papers. The topics discussed include: an educational supply chain management model as basis for a big data-driven decision support framework for the university;automatic summarization for Arabic scientific articles using combined approaches;HazOp scenarios modelling for digital factories;deep learning for self-adaptive system analyzer improvement;NorAGR: a normative agent Groupe role model for organizational centered open multiagent system;fault tolerance in the IoT: a taxonomy based on techniques;automatic face recognition system based on data augmentation and transfer learning;using artificial intelligence for flexible job shop scheduling problem solving;a novel method for solving the university course timetabling problem based on the grey wolf optimizer algorithm;and project assignment based on the analysis of emotional and social relationships between learners in an educational social network.

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Improving Solar Efficiency via CNN-LSTM and Cloud-Integrated IoT Prediction 3

Improving Solar Efficiency via CNN-LSTM and Cloud-Integrated...

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3rd international conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Varun, P. Sunitha, T. Nagalingam, Mythili Nithiya, P. Selvakumaran, S. Mohanaprakash, T.A. School of Computing SRM Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur India St. Joseph's Institute of Technology OMR Department of Computer Science and Engineering Chennai600119 India Rajalakshmi Institue of Technology Department of Artificial Intelligence and Data Science Chennai India Department of Artificial Intelligence and Data Science Velammal Engineering College Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Institue of Technology Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350396157

Photovoltaic panel used in solar power generation is an environmentally beneficial and sustainable energy source that has been used to transform sunlight into electrical power. Arranged in large solar facilities, these panels are connected to a central inverter, which converts Direct Current (DC) to alternating current (AC) electricity with a small amount of energy loss. Clean surfaces, unhindered light exposure, and high solar irradiance are all necessary for optimal panel performance. It's critical to assess the inverter's efficiency by comparing its AC to DC power. Large-scale installations with sensor-equipped panels and inverters track performance to help with maintenance and forecasting of power generation. The Internet of Things (IoT) facilitates data accessibility and remote monitoring, which helps choose the best location for solar power generation. Smart system continuous monitoring expedites site inspections, which supports urban smart grid integration. In this research study, a hybrid machine learning model is presented by combining the attention processes, long short-term memory (LSTM) networks, and clustering approaches. This model is separated into different phases for forecasting, training, and cloud data clustering, finds pertinent historical data, builds a hybrid machine learning model, and chooses the best training model. In comparison to conventional approaches, this method significantly improves prediction accuracy, which is important for integrating photovoltaic systems into smart grids, particularly in smart cities. © 2024 IEEE.

关键词： Solar power generation

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Self-supervised Image Classification Using Convolutional Neural Network 3

Self-supervised Image Classification Using Convolutional Neu...

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3rd IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2023

作者： Kong, Shiyun Shanghai United International School Shanghai China

ISBN: (纸本)9781665472784

Image classification has been a trendy research topic in the field of pattern recognition and computer vision, which extracts different features of images and predict the category of images. Thanks to the development of deep learning, powerful convolutional neural networks can be used in the field of image recognition. However, existing deep learning-based image recognition research mostly follows the framework of supervised learning, and model learning relies on a large number of accurate labels. Providing a large amount of label data will undoubtedly require laborious human effort and expensive costs. Therefore, image recognition based on unsupervised learning (without using any image category labels to achieve classification) has become a spotlight for research. In this paper, explorations on the image classification by self-supervised framework SimCLR on image classification successfully clusters a large number of images into an optimum amount categories. Qualitative results have show SimCLR is particularly effective in recognizing the colors of images;both qualitative and quantitative results shows SimCLR is great at identifying simple contours. However, when the colors are similar, and contour lines are complex, SimCLR does not obtain satisficing results. The accuracy on classifying Mnist dataset is 32%. © 2023 IEEE.

关键词： Image recognition

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Data Characterization for Reliable AI in Medicine 5th

Data Characterization for Reliable AI in Medicine

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5th international conference on Recent Trends in Image Processing and pattern recognition (RTIP2R)

作者： Rajaraman, Sivaramakrishnan Zamzmi, Ghada Yang, Feng Xue, Zhiyun Antani, Sameer K. NIH Bethesda MD 20894 USA

ISBN: (纸本)9783031235986;9783031235993

Research in Artificial intelligence (AI)-based medical computer vision algorithms bear promises to improve disease screening, diagnosis, and subsequently patient care. However, these algorithms are highly impacted by the characteristics of the underlying data. In this work, we discuss various data characteristics, namely Volume, Veracity, Validity, Variety, and Velocity, that impact the design, reliability, and evolution of machine learning in medical computer vision. Further, we discuss each characteristic and the recent works conducted in our research lab that informed our understanding of the impact of these characteristics on the design of medical decision-making algorithms and outcome reliability.

关键词： Data characteristics Artificial intelligence machine learning Deep learning Medical imaging Data-driven design Reliability Generalizability Robustness

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