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检索条件"任意字段=IEEE International Conference on Engineering of Complex Computer Systems"

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Safely Knowledge Transfer from Source Models via an Iterative Pruning Based Learning Approach

Safely Knowledge Transfer from Source Models via an Iterativ...

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2024 ieee international conference on systems, Man, and Cybernetics, SMC 2024

作者： Lu, Xiaoyu Sean Zhang, Jianan Yao, Siya Huang, Bo School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing210000 China College of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou310018 China Nanjing University of Science and Technology Computer Science and Engineering Nanjing210000 China

ISBN: (纸本)9781665410205

Transfer learning has become a key technique in deep learning, widely adopted in the industry and academia for developing customized models, especially for specific and downstream tasks solving. Despite the prevalence of transfer learning, the target model can easily inherit defects from the source model during the learning process, such as vulnerability to backdoor attacks and adversarial attacks. Thus, this work proposes a novel approach, iterative pruning learning approach (IPLA), that reduces the inheritance of potential defects during the transfer learning process. In order to reduce the vulnerability to attacks and improve the robustness of target model, IPLA evaluates the importance of weights from the source model and retains ones that are critical to the target task, then prunes the redundant weights through an iterative pruning process. Experiments are performed on 4 datasets over 2 backbone source models. Results demonstrate the satisfactory performance of our proposed method. © 2024 ieee.

关键词： Adversarial machine learning

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Adaptive Game Design Using Machine Learning Techniques: A Survey

Adaptive Game Design Using Machine Learning Techniques: A Su...

引用

2024 ieee international conference on Internet of Things and Intelligence systems, IoTaIS 2024

作者： Hossan, Md Mosharaf Fouda, Mostafa M. Eishita, Farjana Z. Department of Computer Science Idaho State University Pocatello United States Department of Electrical and Computer Engineering Idaho State University Pocatello United States

ISBN: (纸本)9798331521219

Adaptive game design is a dynamic gamification approach that changes game elements such as challenges, feedback mechanisms, and rewards based on players’ preferences, behaviors, and needs. It is an emerging research field that aims to improve classic gamification techniques by adapting a game environment to meet the particular needs of various users and contexts. Machine learning (ML) techniques have the ability to predict future actions based on historical data to progress toward an ideal outcome. In digital gaming, adaptation of different gaming elements such as levels, difficulty, and feedback to match players’ skills is a fundamental requirement for an enjoyable experience. In this scenario, ML techniques can come into play. These techniques can help to advance the enjoyment of the gameplay toward players’ maximum satisfaction and engagement by anticipating the necessary changes in the game based on their previous behaviors. In this paper, we have conducted a systematic review of 17 papers to investigate the game balancing technology adopted recently. The fundamental goal of this survey is to provide a comprehensive overview of the latest developments in game balancing technologies that use ML techniques. ©2024 ieee.

关键词： Adversarial machine learning

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A Novel Approach for Lightweight Sign Language Recognition Leveraging Google Mediapipe and Deep Neural Net 1

A Novel Approach for Lightweight Sign Language Recognition L...

引用

1st international conference on Software, systems and Information Technology, SSITCON 2024

作者： Siju, Ijas Selvam, Prabu Vellore Institute of Technology School of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tiruchirappalli India

ISBN: (纸本)9798350352931

The advent of technologies like Deep Learning has revolutionized human interaction, transcending language and disability barriers. Sign Language Recognition (SLR) systems have emerged as vital tools, facilitating seamless communication between individuals and between humans and machines through Interfaces. However, existing SLR systems pose significant challenges, including the demand for high accuracy, low latency, simplicity in development and deployment, lightweight design for edge compatibility, and efficient resource utilization during training. Addressing these challenges necessitates developing a system that balances training data accuracy, ease of training, and resource efficiency. Conventional Deep learning approaches often rely on high-dimensional image datasets for training HGR models, resulting in trade-offs between latency, accuracy, complexity, and resource requirements. To mitigate these challenges, this paper harnesses the capabilities of Google Mediapipe to extract a 21-point hand landmark vector from each training image. By leveraging this low-dimensional data, we train model that are accurate, lightweight, less complex, and exhibit low latency, making them compatible with edge devices. The hand gesture images were collected for several gestures, such as peace, okay, and stop. These images will be converted to landmark vectors using Mediapipe. These landmark points were used to train the Deep Neural Network architecture developed in Tensorflow. The model was trained with each hand gesture assigned with a common word in English and the proposed model was tested with a live webcam stream. The proposed model is developed in Python in the Google Colab IDE with the help of libraries and frameworks like OpenCV, Tensorflow and Google Mediapipe. The proposed model outperforms current state-of-the-art approaches by achieving an accuracy of 9 4. 8 8 %. © 2024 ieee.

关键词： Convolutional neural networks

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Does Real-Time Feedback Improve User Performance in SSVEP-based Brain-computer Interfaces? 44

Does Real-Time Feedback Improve User Performance in SSVEP-ba...

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44th Annual international conference of the ieee-engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Mu, Jing Liu, Po-Chen Grayden, David B. Tan, Ying Oetomo, Denny Univ Melbourne Dept Mech Engn Parkville Vic 3010 Australia Univ Melbourne Dept Biomed Engn Parkville Vic 3010 Australia

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

Offline and online experiments are both widely used in SSVEP-based BCI research and development for different purposes. One of the major differences between offline and online experiments is the existence of real-time feedback to the user while they are using the interface. However, the role of feedback in SSVEP-based BCIs has not yet been well studied. This work focuses on understanding the effect of feedback in SSVEP-based BCIs and if there exists any relationship between offline and online BCI performance. An experiment was designed to compare directly the accuracies of the BCI with and without feedback for participants. Results showed that feedback can improve performance in a complex task, but no clear improvement was observed in a simple task.

关键词： Sociology Focusing Real-time systems Brain-computer interfaces Biology Task analysis Statistics

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Machine Vision Applications in Robotics and Automation 9

Machine Vision Applications in Robotics and Automation

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9th international conference on Science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Bharathi, G.P. Kavitha, P. Abhang, Laxman Baburao Rathinasabapathi, G. Sharma, Vipin Sathish, T. Sri Sai Ram Institute of Technology Department of Computer And Communication Engineering West Tambaram Tamil Nadu Chennai600045 India R.M.K Engineering College Department of Electrical And Electronics Engineering Tamil Nadu Kavaraipettai601206 India Pravara Rural Engineering College Department of Automation And Robotics Maharashtra Loni413736 India Panimalar Engineering College Department of Mechanical Engineering Tamil Nadu Chennai600123 India Medi-Caps University Department of Mechanical Engineering Madhya Pradesh Indore453331 India Saveetha School of Engineering Simats Department of Mechanical Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350365092

The extensive incorporation of machine vision into the fields of robotics and automation in a variety of different ways. The various uses of machine vision and the revolutionary impact it has on the capabilities of robotic systems. This investigation is fueled by improvements in imaging technology and artificial intelligence. In the abstract, major features such as object detection, navigation, and human-robot interaction are discussed in depth. The abstract also demonstrates how machine vision algorithms contribute to enhanced precision, efficiency, and adaptability in automation processes. The purpose of thisis to shed light on the role that machine vision plays in revolutionising industrial automation, collaborative robotics, and autonomous systems by conducting an examination of real-world implementations and case studies. This all-encompassing investigation not only highlights the current status of machine vision applications but also forecasts the future trajectory of this technology, thereby establishing it as a foundational component for the development of robotics and automation in a variety of fields. © 2024 ieee.

关键词： Navigation systems

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Internet of Things Based Agribot for Smart Farming and farm Monitoring

Internet of Things Based Agribot for Smart Farming and farm ...

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2024 international conference on Intelligent systems for Cybersecurity, ISCS 2024

作者： Srivastava, Vivek Singh, Gulbir Aggarwal, Ritu Kumar, Suneet Haryana Ambala India Graphic Era Hill University Haldwani Campus Department of Computer Science and Engineering Uttarakhand Nanital India Seth Jai Parkash Mukand Lal Institute of Engineering and Technology Department of Computer Science and Engineering Haryana Yamunanagar India Mullana Department of Computer Science and Engineering Haryana Ambala India

ISBN: (纸本)9798350375237

The agricultural business of today is data-centered, precise, and wiser than it has ever been before. This is in contrast to the impression that some people may have about the agricultural process. Almost every sector, including 'smart farming,' was rebuilt as a result of the fast rise of technology built on the Internet of Things (IoT), which shifted the industry away from statistical techniques and towards quantitative ones. These revolutionary developments are shaking up the many agricultural practices that are now in use and bringing forth new possibilities of Agribots along with a variety of obstacles. Within the context of agriculture, this article present the promise of IoT Agribot, as well as the obstacles that are expected to be encountered when integrating it with the conventional agricultural operations. This paper conduct an in-depth analysis of IoT Agribot methods related with wireless sensors that are used in smart farming and farm monitoring. The explanation of how Agribot technology assists producers throughout the many phases of agricultural production, beginning with planting and continuing through harvesting, packaging, shipping and monitoring. In addition, this paper takes into consideration the use of Unmanned Aerial Vehicles (UAV) for the purpose of farm monitoring as well as other advantageous applications such as the crop yield optimization. On the basis of this comprehensive assessment, it conclude by identifying present and future trends of the IoT Agribot and highlighting prospective research problems. © 2024 ieee.

关键词： Internet of things

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Research on Gait Planning for Elderly Biomimetic Robot Dogs 4

Research on Gait Planning for Elderly Biomimetic Robot Dogs

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4th international conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Wang, Wenting Huang, Xuanzhi Pan, Xiang He, Pengfei Guangxi Vocational University School of Computer and Information Engineering Nanning China

ISBN: (纸本)9798331531225

This study conducts an in-depth exploration of the gait issues of bionic robotic dogs for elderly care. The main objective of this research is to improve the model predictive control (MPC) algorithm through deep learning technology to enhance the gait control capabilities of bionic robotic dogs. Specifically, we aim to address the limitations of traditional control methods when dealing with dynamic environments. enable robots to walk more naturally in complex scenarios and interact effectively with humans. To this end, we will explore the combination of deep learning and MPC algorithms, aiming to achieve greater flexibility and adaptability. A deep neural network is used for learning and predicting gait data, which is integrated with the MPC algorithm to adjust gait control strategies through real-time feedback. Through a large number of simulation experiments and practical tests, we have verified the effectiveness of the proposed method. The contribution of this research lies in the proposal of a deep learning-based MPC algorithm, which significantly enhances the gait control performance of bio-mimetic robotic dogs. © 2024 ieee.

关键词： Predictive control systems

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FPGA Implementation of CNN Accelerator with Pruning for ADAS Applications 9

FPGA Implementation of CNN Accelerator with Pruning for ADAS...

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9th ieee international conference for Convergence in Technology, I2CT 2024

作者： Jose, Aimy Alense, K.T. Gijo, Liya Jacob, Jobymol Apj Abdul Kalam Technological University Department of Electronics Engineering Model Engineering College Thrikkakara Kerala India Apj Abdul Kalam Technological University Department of Electronics Engineering College of Engineering Poonjar Kerala India

ISBN: (纸本)9798350394474

Convolutional neural network plays a prominent role in computer vision applications such as advanced driver assistance systems which demand high accuracy and low latency. The need for low latency, resource limitation and power constraints are the major complexities that are associated with the deployment of the computationally intensive convolutional neural networks in ADAS applications. In this paper, an efficient CNN accelerator is implemented on Xilinx PYNQ-Z2 FPGA Board for traffic sign recognition. The CNN model is further accelerated by pruning the weights of the filters which has very little effect on accuracy. The experimental results show that the proposed method effectively achieves 5.65 times speed in terms of execution time with only 0.33 times drop in accuracy on LeNet-5 architecture. © 2024 ieee.

关键词： Advanced driver assistance systems

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Unraveling the Impact of Density and Noise on Symbol Recognition in engineering Drawings 12

Unraveling the Impact of Density and Noise on Symbol Recogni...

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12th ieee international conference on Intelligent systems, IS 2024

作者： Shteriyanov, Vasil Dzhusupova, Rimma Bosch, Jan Olsson, Helena Holmström Engineering McDermott The Hague Netherlands Chalmers University of Technology Computer Science and Engineering Gothenburg Sweden Malmö University Computer Science and Media Technology Malmö Sweden

ISBN: (纸本)9798350350982

Applied Artificial Intelligence (AI) in engineering is gaining significant traction. AI object detection methods can be applied in the engineering industry to extract information from engineering drawings, offering immense benefits to engineers. A promising application of AI in industrial engineering is symbol recognition applied to engineering drawings. However, these drawings often exhibit areas with a high density of symbols, as well as noise in the form of markups, indicating revisions. These factors could cause symbol misclassification or omission, impacting applications reliant on accurate symbol recognition. This study evaluates the accuracy of a symbol recognition model on engineering drawings called Piping and Instrumen-tation Diagrams (P&IDs) exhibiting varying levels of density and markups causing noise. Despite the assumption that density poses a challenge for accurate symbol recognition in engineering drawings, our study reveals that density has no significant impact on recognition performance when a dense detector is employed. In addition, we quantitatively show that markup-induced noise on engineering drawings negatively influences recognition accuracy. Finally, we provide recommendations regarding the applicability of symbol recognition in engineering applications. The study's findings and recommendations apply to any P&IDs, regardless of the standard used, as they were evaluated on various worldwide projects. Moreover, the research not only contributes to the advancement of symbol recognition on P&IDs, but also can be applied to other types of engineering drawings. Thus, it holds the potential for enhancing symbol recognition in various real-world industrial applications and research. © 2024 ieee.

关键词： Industrial research

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Fault Estimation for T-S Fuzzy systems Based on Iterative Learning Observer

Fault Estimation for T-S Fuzzy Systems Based on Iterative Le...

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2024 international Annual conference on complex systems and Intelligent Science, CSIS-IAC 2024

作者： Zhao, Qiankun Fu, Shasha Xu, Bing School of Electronic Engineering University of Heilongjiang Harbin China

ISBN: (纸本)9798331504755

The fault estimation problem for T-S fuzzy systems including actuator and sensor faults is studied in this article. The sensor fault is reconstructed by introducing a linear transformation matrix and a new measurable variable, and two observers are designed to estimate actuator and sensor faults separately. One observer is an adaptive observer that can accurately estimate actuator fault, while the other uses an iterative learning method to estimate the reconstructed sensor fault. Meanwhile, it has also been proven that the average estimation errors of the iterative learning observer converges to zero. Finally, the simulation results of numerical example validate the effectiveness of this method. © 2024 ieee.

关键词： Linear transformations

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