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A Novel Approach for Photo-Realistic High Quality Sign Language Video Synthesis 3

A Novel Approach for Photo-Realistic High Quality Sign Langu...

引用

3rd ieee international conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Pravine Mukesh, R. Natarajan, B. Elakkiya, R. Vaidhya, G.K. Murali, P. Geetha, K. Amrita Vishwa Vidyapeetham Chennai601103 India School of Computer Science and Engineering Vit University Tamilnadu Chennai India Birla Institute of Technology and Science Department of Computer Science Dubai United Arab Emirates Srm Institute of Science and Technology Vadapalani Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Cse Tamilnadu Chennai India Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai601103 India

ISBN: (纸本)9798350361186

This research offers a brand-new framework for producing high-quality, photo-realistic sign language videos using sign language corpora. The framework consists of three main components such as Neural Machine Translation (NMT) using transformer, an open pose estimation approach, and a Generative Adversarial Network (GAN) with VGG-19 classifier. The NMT transformer translates the sign language corpus into a sequence of sign glosses, pose estimation model performs mapping of equivalent signer images and further assists to generate sign images. The GAN with VGG-19 classifier then generates high- quality sign language videos from the sign language images. The proposed method performs at the cutting edge in terms of evaluation on three benchmark dataset using metrics such as PSNR, SSIM, FID, and Inception Score. In addition, the framework offers better translation outcomes with less processing overhead and produces high-quality, photo-realistic sign language videos without the need for animation or avatar techniques. The proposed framework has the potential to offer the uninterrupted communication medium between common people with deaf-mute society. The evaluation scores highlights the improved perfor- mance of the proposed model compared with earlier approaches in terms of visual quality. © 2024 ieee.

关键词： Generative adversarial networks

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Knee Joint Angle Prediction Based on Gait Synergy Mechanism 6

Knee Joint Angle Prediction Based on Gait Synergy Mechanism

引用

6th international conference on Robotics, Control and automation engineering, RCAE 2023

作者： Lin, Zeqiang Yang, Lingling Huang, Yilong School of Automation Guangdong University of Technology Guangzhou China

ISBN: (纸本)9798350315301

Gait trajectory generation plays a pivotal role in designing the rehabilitative lower limb exoskeleton. The exoskeleton can generate personalized gait trajectories for different patients due to the gait synergy mechanism of human lower limbs. A lower limb gait prediction model is constructed based on complementary limb motion estimation (CLME). The model inputs the angle and angular velocity of the hip joint and outputs the angle of the knee joint of the same limb. Principal component analysis (PCA) and continuous relative phase (CRP) are utilized to analyze the synergy mechanism in a single lower limb. The influencing factors for prediction have been determined by examining the collaborative mechanisms among different participants. The experimental results demonstrate that the PCA-based CLME method excels at predicting knee joint angle and offers a straightforward explanation for the gait synergy mechanism. © 2023 ieee.

关键词： Forecasting

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Nonconvex Extended Object Tracking Method Based on Set Inversion 7

Nonconvex Extended Object Tracking Method Based on Set Inver...

引用

7th international conference on Robotics, Control and automation engineering, RCAE 2024

作者： Li, Yiguo Ma, Tianli Yan, Kun Chen, Chaobo Xi'an Technological University School of Electronics and Information Engineering Xi'an China

ISBN: (纸本)9798350355642

To address the problem of target tracking under nonlinear bounded measurements, this paper proposes a nonconvex extended object tracking method based on set inversion. The method first establishes a nonconvex extended object model using the adjacency matrix. A tree-structured constraint propagation algorithm then shrinks the node state prediction set and constructs the set inversion problem based on measurement equation constraints. The shrunk set serves as the initial interval for the set inversion algorithm, which is used to solve the set inversion problem to obtain the optimal state intervals of the extended object nodes. Simulation and experimental results demonstrate the effectiveness of the proposed algorithm in tracking nonconvex extended object. © 2024 ieee.

关键词： Target tracking

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Ant: An Efficient Lossless Compression Algorithm for IoT Time Series Data 21

Ant: An Efficient Lossless Compression Algorithm for IoT Tim...

引用

21st ieee international Symposium on Parallel and Distributed Processing with Applications, 13th ieee international conference on Big Data and Cloud Computing, 16th ieee international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Li, Junhui Xu, Guangping Yang, Hongzhang Wu, Yulei Tianjin University of Technology School of Computer Science and Engineering Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China University of Bristol Faculty of Science and Engineering Bristol United Kingdom

ISBN: (纸本)9798350329223

In various applications in Internet of Things like industrial monitoring, large amounts of floating-point time series data are generated at an unprecedented rate. Efficient compression algorithms can effectively reduce the size of data, enhance transmission performance and storage efficiency, and simultaneously lower storage costs. Therefore, there is a need for lightweight and efficient stream compression algorithms. In this paper, we propose a novel lossless floating-point data compression algorithm called Ant. The main idea is to encode double-precision floating-point numbers into integer form, calculate the delta between adjacent values, and then convert the delta into unsigned integers. This encoding method effectively reduces storage costs and improves data compression efficiency. Extensive experiments on real-world datasets demonstrate that our algorithm achieves compression speeds at least as fast as state-of-the-art streaming methods, and a 63% relative improvement in average compression rate. © 2023 ieee.

关键词： Storage efficiency

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Incorporating Knowledge Graph Embeddings into Graph Neural Networks for Sequential Recommender Systems 9

Incorporating Knowledge Graph Embeddings into Graph Neural N...

引用

9th international conference on computer science and engineering, UBMK 2024

作者： Yüksel, Kazim Emre Üsküdarli, Susan Bogazici University Department of Computer Engineering Istanbul Turkey

ISBN: (数字)9798350365887

ISBN: (纸本)9798350365887

Traditional recommendation methods often struggle to capture the evolving nature of user interactions and the rich contextual information available in external data sources. This paper presents an innovative approach to sequential recommendations by integrating knowledge graph embeddings into a graph neural network (GNN) architecture. We utilize the MovieLens dataset as our primary data source and enrich it with additional contextual information from Wikidata, an open-source comprehensive knowledge base by incorporating the translation-based knowledge graph embeddings. Experimental results demonstrate that our approach improves recommendation accuracy, particularly in terms of Hit@K metrics, by leveraging the enriched movie embeddings. This study highlights the potential of utilizing open-source knowledge bases to enhance the predictive capabilities of recommendation systems, providing a more nuanced and comprehensive understanding of user preferences. © 2024 ieee.

关键词： Knowledge graph

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Numerical Calculation of Steel Pipe Quenching Aims to Provide Support for AI Models 4

Numerical Calculation of Steel Pipe Quenching Aims to Provid...

引用

4th ieee international conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Wang, Shang Zhang, Yajuan Sun, Zhe Fan, Yibing Beijing Polytechnic School of Automotive Engineering Beijing China Beijing Polytechnic College of Automation Engineering Beijing China University of Science and Technology Beijing Institute of Engineering Technology Beijing China

ISBN: (纸本)9798350363586

To provide data support for the quenching AI model, this paper employs modeling and numerical analysis to investigate the temperature field evolution during the quenching of steel pipes. A finite element model was established, and numerical calculations were conducted, focusing on the influence of the medium temperature and film coefficient. The results indicate that the developed model can effectively simulate the quenching process of steel pipes. The region with the fastest cooling of the steel pipe is located on the outer side of the transition zone. After the quenching process, the temperature of the nodes along the radial direction gradually decreases. As the medium temperature value increases, the temperature of the steel pipe rises correspondingly, exhibiting a nearly linear relationship. Higher film coefficient values result in a more pronounced temperature drop, with the trend gradually stabilizing. The findings of this study provide theoretical and data support for the quenching AI model and algorithms, and also offer valuable references for the intelligent transformation of heat treatment processes for other metallic materials. © 2024 ieee.

关键词： Quenching

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Implementing DevOps Practices in CPPS Using Microservices and GitOps 28

Implementing DevOps Practices in CPPS Using Microservices an...

引用

28th ieee international conference on Emerging Technologies and Factory automation, ETFA 2023

作者： Koren, István Rinker, Felix Meixner, Kristof Kröger, Moritz Zeng, Michael RWTH Aachen University Process and Data Science Germany Technische Universität Wien CDL SQI Institute of Information Systems Engineering Austria RWTH Aachen University Laser Technology Germany RWTH Aachen University Laboratory for Machine Tools and Production Engineering Germany

ISBN: (纸本)9798350339918

As digitalization shapes Industry 4.0, integrating software engineering practices into the manufacturing domain becomes a key focus. DevOps is such a practice and methodology that enhances collaboration and automates processes in software development and IT operations. In previous work, we discussed challenges and opportunities of applying the DevOps paradigm in Cyber-Physical Production Systems (CPPS) engineering, which are complex systems that integrate physical components with computer-controlled operations. This paper instantiates our DevOps4CPPS framework, spotlighting the application of GitOps and microservices in a CPPS environment. A real-world case study from laser ultra short pulse manufacturing illustrates how the approach effectively addresses the inherent challenges of CPPS, boosting agility and operational performance, while reducing downtime. The paper provides guidance to researchers and practitioners on applying DevOps practices to CPPS, and suggests future research directions. © 2023 ieee.

关键词： Software design

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Latent Graph Neural Networks for Human Skeleton-based Action Recognition 2

Latent Graph Neural Networks for Human Skeleton-based Action...

引用

2nd ieee international conference on Sensors, Electronics and computer engineering, ICSECE 2024

作者： Wei, Xianlong Li, Ce Zhao, Kaipan Jiang, Zhongbo School of Artificial Intelligence China University of Mining & Technology Beijing Computer Science and Technology Xueyuan Rd Beijing Haidian District100089 China

ISBN: (纸本)9798350373646

The exploration of spatial-temporal graph neural networks has been extensive, yet it remains a challenge due to the intricate variations in spatiotemporal dependencies across skeleton joint flows. In response, we propose a novel guidance mechanism within the latent 3D spatial-temporal space, termed Latent Graph Neural Networks (LGNN). Our approach involves designing a long-term spatio-temporal model with a high k-value to capture action information effectively within the latent spatial-temporal domain, thereby representing the spatiotemporal dynamics of skeleton sequences. To enhance feature information between distant nodes and address redundant information, we introduce a pair distance normalization operation in the latent space. This operation facilitates the formation of LGNN, where two streams of base LGNN seamlessly merge into a single network, enabling end-to-end training. Notably, our method significantly improves performance compared to state-of-the-art approaches and enhances accuracy across three widely used and challenging datasets for action recognition. © 2024 ieee.

关键词： Graph neural networks

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Enhancement of Agriculture Productivity and automation of Agriculture using IoT 6

Enhancement of Agriculture Productivity and Automation of Ag...

引用

6th international conference on Inventive Computation Technologies, ICICT 2023

作者： Seetha, J. Senthil Kumar, S. Nruthya, K. Rao, Nynalasetti Kondala Kameswara Leela Rani, D. Department of Computer Science and Business Systems Panimalar Engineering College Chennai India Department of Mechanical Engineering R.M.K College of Engineering and Technology Thiruvallur India Department of Civil Engineering Indian Institute of Science Bengaluru India Department of Computer Science and Engineering Sagi Rama Krishnam Raju Engineering College Bhimavaram India Department of Electronics and Communication Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9798350398496

The utilisation of sensor networks in agriculture monitoring is the invention that benefits Indians the most. Given that agriculture is the primary source of revenue, it is essential to boost production while decreasing expenses, inputs of time, and labour. Information gathered from agricultural areas is stored in a database, which may be monitored via the Internet of Things (IoT). Connecting Raspberry Pi with IoT and sensors to increase agricultural activity productivity is the challenging task in the Farm Management System for secure agriculture. Temperature, humidity, soil moisture content, and any field fires are tracked as a challenge's outcome. Specifications like temperature, moisture content, and fire are currently detected manually in farm systems. This technique automates the completion of watering. It used to lighten the workload for farmers. Any type of crop productivity is feasible. This method's drawbacks include an increase in labour costs, increased time requirements, and the inability to do continuous monitoring. © 2023 ieee.

关键词： Wages

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Automated semantic segmentation and Diagnosis of Coconut Tree Infection Using Drone Imagery and Machine Learning approach for smart agriculture

Automated semantic segmentation and Diagnosis of Coconut Tre...

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2024 ieee international conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Vasumathy, M. Devakanth, J. Jude Moses Anto Thirugnanam, Mythili Roselinkiruba, R. Hamsaveni, R. Nandhini, R. Kingston Engineering College Computer Science and Engineering Tamilnadu Vellore India Madanapalle Institute of Technology & Science Department ofComputer Applications Andhra Pradesh Madanapalle India Vellore Institute of Technology School of Computer Science and Engineering Tamilnadu Vellore India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Computer Science and Engineering Tamilnadu Chennai India Computer Science and Engineering Andhra Pradesh Chittoor India D.K.M College for Women Computer Science and Applications Tamilnadu Vellore632001 India

ISBN: (纸本)9798331539948

The coconut industry of our Indian nation contributes an extensive range of economy to the national GDP. About 19,247 million coconuts, or 31.45% of the world's production, were produced in India in the year 2021-2022 making it the top producer. However, the coconut farmers face major challenges from a plethora of pests and diseases of coconut trees, and yield loss, which affects the farmers economically, standing around at 10-15%. Major pests, such as Rhinoceros beetles and red palm weevils, are highly destructible, in some scenarios leading even to the mortality of crops. In most of the cases, when trees that are affected need a cure, farmers have to climb the trees to put chemicals in crowns infested with the pests, exposing the farmers to harmful chemicals and risking severe injuries or permanent disabilities. As a solution, the proposed methodology involves the collection of extensive drone imagery from coconut plantations, followed by the annotation and preprocessing of the data. This work utilizes state-of-the-art deep learning models, including Unet, convolutional neural networks (CNNs) for semantic segmentation, to differentiate coconut trees from the background and other vegetation. Subsequently, machine learning classifiers are employed to diagnose diseases by analyzing visual symptoms present in the segmented trees. The proposed approach offers significant improvements in accuracy and efficiency compared with other traditional manual methods, providing real-time insights into tree health and disease management. This system has the potential to enhance crop monitoring, optimize resource allocation, and ultimately increase agricultural productivity. © 2024 ieee.

关键词： Drones

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