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BrMEPS: Review on 3D Body Reconstruction and Measurement Estimation for Personalized Clothing 6th

BrMEPS: Review on 3D Body Reconstruction and Measurement Est...

引用

6th international conference on Recent Innovations in computing, ICRIC 2023

作者： Annamalai, R. Bharathraj, S. Dharaneesh, G.G. Department of Computer Science Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9789819734412

Tailoring remains a timeless technique that guarantees clothing fit each unique body perfectly in an era where precision and personalization are vital. Concurrently, 3D reconstruction proves to be a state-of-the-art addition, utilizing technology to accurately map the human shape. Examining how these disciplines work well together, this paper explores how the bespoke clothes sector may be redefined by their convergence, improving the client experience and the creative process. The role of technology is becoming increasingly important as the digital and physical worlds merge. Its applications range from health care to entertainment, and most importantly, it is used in the meticulous art of tailoring. We examine the strengths and weaknesses of the two main reconstruction strategies: single-camera approaches that are easy to use and accessible and multi-camera systems that offer the highest precision but have scaling issues. From learning-based models to heuristic optimization, our investigation explores a range of approaches and examines how they might transform traditional tailoring by improving accuracy and capturing dynamic movement. We evaluate the difficulties, such as sudden changes in fabric and occlusions, and the contribution of datasets such as Human3.6M to the advancement of this field. The effectiveness of reconstruction is assessed using metrics like PCK and MPJPE. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： 3D reconstruction

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An Introduction to Quantum Reinforcement Learning (QRL)

An Introduction to Quantum Reinforcement Learning (QRL)

引用

international conference on Information and Communication technology convergence (ICTC)

作者： Samuel Yen-Chi Chen Wells Fargo New York NY USA

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

Recent advancements in quantum computing (QC) and machine learning (ML) have sparked considerable interest in the integration of these two cutting-edge fields. Among the various ML techniques, reinforcement learning (RL) stands out for its ability to address complex sequential decision-making problems. RL has already demonstrated substantial success in the classical ML community. Now, the emerging field of Quantum Reinforcement Learning (QRL) seeks to enhance RL algorithms by incorporating principles from quantum computing. This paper offers an introduction to this exciting area for the broader AI and ML community.

关键词： Quantum computing Machine learning algorithms Decision making Reinforcement learning Information and communication technology convergence

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A DNA Coding Design based on Multi-objective Evolutionary Algorithm with Constraint 23

A DNA Coding Design based on Multi-objective Evolutionary Al...

引用

7th international conference on Machine Learning and Soft computing, ICMLSC 2023

作者： Duan, Hengyu Zhang, Kai Zhang, Xinbo School of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430065 China

ISBN: (纸本)9781450398633

Due to the excessive number of objective functions in DNA coding problem, there are dominant impedance between solutions which makes it difficult to evaluate the solutions and the algorithm is hard to converge. And traditional multi-objective evolutionary algorithms tend to fall into premature convergence when dealing with DNA coding problems. We proposed an Improved Nondominated Sorting Genetic Algorithm II with Constraint (ICNSAG-II) to deal with these problem. Firstly, the DNA coding problem and its 6 coding constraints are introduced. Secondly, the constraint function and Block operator are used to reduce the dimensionality of the DNA coding problem, so that the objective function is reduced to two, which make it easy to optimize using multi-objective evolutionary algorithms. Finally, by comparing with the sequences generated by the comparative algorithm, it was verified that the DNA sequences generated by ICNSGA-II have good chemical stability and are able to prevent the of unexpected secondary structures and non-specific hybridization reactions. © 2023 ACM.

关键词： Chemical stability

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Research on Computer Network Routing Based on Granular Computational Intelligence

Research on Computer Network Routing Based on Granular Compu...

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2023 international conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Sun, Yaowen Naval Submarine Academy Shandong Qingdao China

ISBN: (纸本)9798350315455

In today's era, with the continuous development of the network, people have higher and higher requirements for intelligent devices, and traditional computers have been unable to meet these needs. Therefore, a new technology that can solve these problems and has good adaptability and reliability is needed for information management and maintenance work, currently there are mainly server-side, convergence layer and access layer consisting of a three-layer structure system, the core part of which is the routing protocol and IP-bound network link design method, in order to achieve high efficiency and stability, the use of layered architecture design mode, the data is concentrated in different In order to achieve high efficiency and stability, a hierarchical architecture design pattern is used to centralize the data in different layers to process. This paper focuses on the research of computer network routing based on granularity computing intelligence, using server-side and aggregation layer to solve the traditional PC-side routing problem and apply it to various applications. As a result, this method provided Network load balancing 95(%), Network life cycle 92(%), and 0.52 × 106 energy consumption. © 2023 IEEE.

关键词： computer networks granular computing network routing research quotient space granularity routing protocol

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Aptamer-Protein Interaction Prediction using Transformer

Aptamer-Protein Interaction Prediction using Transformer

引用

IEEE international conference on Big Data and Smart computing (BigComp)

作者： Shin, Incheol Song, Giltae Pusan Natl Univ Dept Informat Convergence Engn Busan South Korea Pusan Natl Univ Sch Comp Sci & Engn Busan South Korea

ISBN: (纸本)9781665421973

Traditional drug discovery has focused on antibody which costs a lot of time and efforts to produce it. To speed up drug discovery processes, various new biomaterial has been developed such as aptamers that are short single-strand oligonucleotide with three-dimensional structure. While aptamers show similar binding affinity to antibody, it is cheaper and faster than antibody. Systematic evolution of ligands by exponential enrichment (SELEX) is an in vitro experimental method to discover aptamers that bind a target protein. It takes several months to perform the full round of the SELEX experiment. To reduce time and cost for SELEX, there are several studies that find aptamers in silico, but most focus on the analysis of SELEX experiment results. There are some studies that apply machine learning to predict the interaction of aptamers and a target protein, but they feed only primary structure of aptamers and proteins into their machine model while apatmers and proteins are in three-dimension. This causes information loss. In this study, we propose a new machine learning model based on Transformer, which inputs aptamers and proteins in secondary structure. We validate our model using benchmark datasets and compare it with four existing methods. Our model outperforms in this evaluation. We believe that our model can improve the efficiency of SELEX experiments.

关键词： Aptamer Protein Aptamer-Protein Interaction drug discovery binary classification pre-trained Transformer

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A Way for Diagnosing Motor Faults Utilizing Deep Learning Techniques

A Way for Diagnosing Motor Faults Utilizing Deep Learning Te...

引用

2024 international conference on New Power System and Power Electronics, NPSPE 2024

作者： Gong, Yi Gong, Ruyue Qingdao University of Science and Technology School of Information Science and Technology Qingdao China Neusoft Institute Guangdong School of Computing Foshan China

ISBN: (纸本)9798350389869

Considering motors' widespread application in manufacturing, energy production, transportation, and numerous industrial areas, it's vital to diagnose malfunctions to ensure the reliability of the equipment. In recent years, techniques for diagnosing faults using Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) have become more mature. The conventional approach overly depends on expert knowledge, which facilitates the convergence of training outcomes to a locally optimal solution in the training process, leading to an exponential increase in computational demands due to the expansion of time series data. Addressing these issues, the research suggests a motor fault detection approach utilizing Swin-Transformer and Global-Attention-BiGRU parallel techniques. Initially, the one-dimensional fault signal underwent decomposition via VMD and transformation through FFT, and the final results were stacked;Next, the gathered data was inputted into the two-dimensional convolutional structure of the Swin-Transformer model to identify and extract the fault signal's local spatial characteristics, meanwhile the BiGRU network, refined through Global-Attention, is employed to extract the Global time domain characteristics of the fault signal;Ultimately, by merging local and global characteristics, the motor's present fault mode was estimated. Furthermore, the research employed t-SNE to simplify the data samples for visual representation, thereby improving the model's clarity. Confirmed through the CWRU public dataset, the findings indicate the technique's efficacy in diagnosing and identifying motor-bearing breakdown. © 2024 IEEE.

关键词： Recurrent neural networks

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Optimal Joint Trajectory and Caching with Graph-based Federated Knowledge Distillation in UAV-assisted Networks

Optimal Joint Trajectory and Caching with Graph-based Federa...

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2023 international conference on Networks, Communications and Intelligent computing, NCIC 2023

作者： Maale, Gerald Tietaa Kuadey, Noble Arden Elorm Sun, Guolin Liu, Guisong School of Computer Science and Engineering UESTC Natl. Key Lab. for Intelligent Collab. Computing Intelligent Term. Key Lab. of Sichuan Prov. Yibin Chengdu China School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Chengdu China

ISBN: (纸本)9798350371949

The use of unmanned aerial vehicles (UAVs) as mobile cache nodes can enhance data traffic throughput and reduce user latency in urban areas. However, jointly optimizing UAV trajectories for efficient content delivery and onboard caching strategies presents significant challenges stemming from the tight coupling between these two problems, as well as issues related to the non-independent and non-identically distributed (non-IID) nature of real-world content popularity and wireless channel dynamics. To address these challenges, we propose KDLMGCN, a deep learning framework that employs graph convolutional networks with LSTM to capture intricate relationships between UAV locations and ground user content demand patterns. Through empirical evaluation, we demonstrate that KDLMGCN surpasses benchmark methods in terms of convergence, cache hit rates, and average transmission, with real-world trajectory dataset and Zipf-based content popularity data. © 2023 IEEE.

关键词： Distillation

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Convolutional Neural Networks Parameter Training for SCM Algorithm Based on Hausdorff Difference 25th

Convolutional Neural Networks Parameter Training for SCM Al...

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25th international conference on Parallel and Distributed computing, Applications and Technologies, PDCAT 2024

作者： Jian, Jing Gao, Zhe Xiao, Jianyu Zhang, Haibin Beijing University of Technology Beijing100022 China Liaoning University Shenyang110036 China

ISBN: (纸本)9789819642069

In this paper, a convolutional neural network parameter training method based on Hausdorff difference is proposed to solve the problems of gradient vanishing and local optimum in the momentum algorithm. A momentum algorithm is proposed based on Hausdorff difference, introduced similar to the conception of Hausdorff derivative. Furthermore, the proposed algorithm is improved by using two new methods for adaptive nonlinear adjustment of the order. We analyse the influence of the order on the training results of the network parameters and verify the effectiveness of the proposed methods in improving the recognition accuracy and convergence speed of convolutional neural networks by the fashion-MNIST dataset and the CIFAR-10 dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Multi-objective Wolf Pack Algorithm Based on Random Scouting and Hierarchical Learning 15th

Multi-objective Wolf Pack Algorithm Based on Random Scouting...

引用

15th international conference on Genetic and Evolutionary computing, ICGEC 2023

作者： Dong, Wenfei Wu, Runxiu Lv, Feng Zhao, Jia Nanchang Institute of Technology Nanchang330099 China

ISBN: (纸本)9789819700677

Since the wolf pack algorithm has the advantages of fast convergence and good robustness in solving single-objective problems, we propose a multi-objective wolf pack algorithm based on random scouting and hierarchical learning (MOWPA-RH) for solving multi-objective optimization problems by combining the excellent hunting habits of wolves. Firstly, a random scouting strategy is used to help the population spread its search range and enhance the global search ability of the people. Secondly, the population is stratified by non-dominated sorting. The individuals in the first layer carry out the Levy-Flight strategy to enhance the ability of the algorithm to jump out of the local optimum, and the individuals in each layer except the first layer are guided by the dominant individuals in the former layer, which is conducive to searching for higher-quality solutions. Finally, the current population is merged with the previous generation, and then the population update is accomplished through the screening mechanism so that the algorithm has good convergence and distribution. Comparing MOWPA-RH with five multi-objective optimization algorithms on 12 different benchmarking problems, the experimental results validate the effectiveness of MOWPA-RH. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multiobjective optimization

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Multi-Information Fusion Online Recognition of Diamond Tool Grinding Direction Based on IPSO-RBF 8th

Multi-Information Fusion Online Recognition of Diamond Tool ...

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8th international conference on computing, Control and Industrial Engineering, CCIE 2024

作者： Feng, Xuewen Zhao, Bin Ma, Haitao Wu, Jiayu Changchun University of Technology Jilin Province Changchun City130102 China

ISBN: (纸本)9789819769360

In order to improve the online recognition accuracy of the grinding direction of single crystal diamond tools and thereby enhance the efficiency of single crystal diamond tool grinding, a method based on multi-information fusion and improved particle swarm optimization radial basis function (IPSO-RBF) neural network is proposed to online recognize the tool’s grinding direction. By collecting the vibration signals and acoustic emission (AE) signals of the tool during the grinding process, feature parameters are extracted through signal feature processing. The feature parameters are used as inputs to the IPSO-RBF neural network model for fusion, and the grinding direction of the tool is recognized online. Compared with the drawbacks of the slow convergence speed and easy trapping in local optima of the back propagation (BP) neural network, the IPSO-RBF neural network has a simple structure and fast training. Simulation results show that compared with the BP neural network and the standard RBF neural network, the IPSO-RBF neural network has higher recognition accuracy, faster convergence speed, shorter simulation time, and effectively improves the recognition accuracy of the tool’s grinding direction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Radial basis function networks

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