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A Learner Behavior Recognition and Detection Method based on Human Posture Estimation 4

A Learner Behavior Recognition and Detection Method based on...

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4th international conference on Computer Graphics, Image, and Virtualization, ICCGIV 2024

作者： Fu, Chen School of Smart Health Chongqing Polytechnic University of Electronic Technology Chongqing401331 China

ISBN: (纸本)9781510683242

With the rapid development of information technology and smart classroom, the accuracy of learner behavior recognition in classroom has become more and more important. In the process of behavior recognition by means of information technology, including deep learning technology, it is often difficult to accurately identify learners' actions due to limb occlusion, motion blur and camera position. Meanwhile, frequent classroom management with the help of teachers will also lead to class interruption and destroy the overall fluency of teaching. Based on this problem, this work uses an improved pose estimation method based on single-stage key points and pose detection algorithm to classify and judge the movements of learners in the classroom for problems such as limb occlusion and motion ambiguity, so as to achieve the purpose of assisting the management of classroom teaching process. Finally, the results of the behavior recognition detection method proposed in this paper are compared with the marked action image data, and the experimental data show that the proposed method can meet the needs of learners' behavior recognition detection in the classroom to a certain extent. © 2024 SPIE.

关键词： Deep learning

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Film and Television Special Effects Production Based on Modern Technology: from the Perspective of Statistical Machine learning 4

Film and Television Special Effects Production Based on Mode...

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4th international conference on smart Systems and Inventive Technology, ICSSIT 2022

作者： Wang, Yi Department of Digital Creativity and Animation Shenzhen Polytechnic University Shenzhen518055 China

ISBN: (纸本)9781665401180

Based on statistical machine learning, this paper studies film and television special effects production under the background of modern technology. First, it introduces the new challenges of film and television post-production technology, which requires a new development direction. then based on modern technology analysis, the role of special effects production technology in film and television post-production is analyzed, and it is necessary to strengthen related research. therefore, starting with the concept of film and television production, analyzing the technology of film and television post-production special effects according to the specific process and relationship of film and television production, and explaining its application in two-dimensional and three-dimensional animation, it has become the key research content of the current film and television post-production work. © 2022 IEEE

关键词： Special effects

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Behavioral Diversity and Individuality in Simulated Agents 4

Behavioral Diversity and Individuality in Simulated Agents

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4th international conference on Intelligent Technologies, CONIT 2024

作者： Puneet, Nirmith Bali, Rituparna Nissimagoudar, P.C. KLE Technological University School of Electronics and Communication Hubli India KLE Technological University Hubli India

ISBN: (纸本)9798350349900

the project centers on addressing critical challenges in multi-agent co-ordination, resource allocation, and ethical decision-making. the primary focus lies in developing novel algorithms and frameworks that enable simulated agents to dynamically adapt and learn from interactions within multi-agent ecosystems. Emphasis is placed on fostering robust collaboration, efficient resource utilization, and ethical decision-making frame-works tailored for diverse scenarios. the research methodology encompasses a blend of theoretical modeling, algorithmic development, and simulation-based experiments. Leveraging state-of-the-art machine learning techniques, the study aims to create adaptable agents capable of effective decision-making in dynamic and uncertain environments. the outcomes of this research project aim to contribute significantly to the field of artificial intelligence, particularly in advancing the capabilities of simulated agents. By fostering adaptability, collaboration, and ethical reasoning within multi-agent systems, this project endeavors to pave the way for more sophisticated and socially-aware artificial intelligence frame-works. © 2024 IEEE.

关键词： Adversarial machine learning

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Deploying Hybrid Deep learning Methods In Conjunction With One-Class SVM For Anomaly Detection In Crowded Spaces 4

Deploying Hybrid Deep Learning Methods In Conjunction With O...

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4th IEEE international conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Dhingra, Lovish Geetha, B. Mishra, Abhinav Savitha, R. Chavhan, Gajanan H. Vigenesh, M. Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of S&h Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India Karnataka Bangalore India Vishwakarma Institute of Technology Pune India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India

ISBN: (纸本)9798331518981

the importance of public security and smart city initiatives like the proliferation of monitoring networks and the ability to automatically identify suspicious activity in surveillance footage is growing. Modern methods for recognizing moving objects rely on context modeling. Noise and background movement are introduced because current anomaly detection algorithms focus on geographical information while ignoring temporal information, either entirely or partially. there is a lot of visual and behavioral diversity among the many people in a crowded situation, making it challenging to keep track of individual *** get better and quicker tracking results, a strategy that combines strong segmentation with deep learning is suggested. this method involves taking an extracted video frame and then applying a two-dimensional variance plane and a GLCM to the highlighted pixels inside the video. A hybrid deep learning model is trained using one-class support vector machines using spatial data for robust abnormality detection, and Fuzzy C-Means is used to subsequent frames for segmentation. It assesses recall, precision, and accuracy. When compared to existing approaches, the suggested one achieves better results in experiments when measuring accuracy, precision, and recall. the technique is put into action in MATLAB using the PETS2009 and UCSD datasets. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Channel and Spatial Reconstruction for Multi-View Patchmatch Stereo 4

Channel and Spatial Reconstruction for Multi-View Patchmatch...

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4th IEEE international conference on Electronic Technology, Communication and Information, ICETCI 2024

作者： Zhang, Xinji Tian, Lianfang Du, Qiliang South China University of Technology School of Automation Science and Engineering Guangzhou China South China University of Technology Guangdong Engineering Research Center of Cloud-Edge-End Collaboration Technology for Smart City School of Automation Science and Engineering Guangzhou China

ISBN: (纸本)9798350361643

During the process of multi-view 3D reconstruction, there may be redundant features that can affect the reconstruction results. To address this issue, we have applied an improved S-shaped feature pyramid network to capture connections at different scales. We have also incorporated a spatial redundancy removal module (SRU) and a channel redundancy removal module (CRU) to eliminate redundant information during feature extraction. Furthermore, we have developed a new patchmatch-based network structure using an improved cascade structure. By training on the publicly available DTU dataset, we have achieved good results in terms of completeness and accuracy, the results are better than the benchmark network and our approach has good performers in 3D reconstruction using deep learning methods. the results of the point cloud which we reconstructed from the depth map have also shown significant improvement. © 2024 IEEE.

关键词： Redundancy

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Enhancing Locational FDIA Detection in smart Grids: A Hyperparameter Optimization Analysis 4

Enhancing Locational FDIA Detection in Smart Grids: A Hyperp...

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4th international conference on smart Grid and Renewable Energy, SGRE 2024

作者： Ibraheem, Rawan Eddin, Maymouna Ez Massaoudi, Mohamed Abu-Rub, Haitham Texas A&M University Department of Electrical and Computer Engineering College Station United States Texas A&M University at Qatar Department of Electrical and Computer Engineering Doha Qatar

ISBN: (纸本)9798350306262

In cyber-physical power systems, mitigating the detrimental effects associated with the stealthy nature of False Data Injection Attacks (FDIAs) is imperative. this paper introduces a deep learning model based on a 1-D Convolutional Neural Network (1-D CNN) for simultaneously localizing and detecting FDIAs. To enhance the predictive accuracy of the CNN model, the hyperparameters must be tweaked. An analysis focused on hyperparameter optimization is performed by comparing various optimizers, including random search, hill climbing, and Bayesian Optimization (BO). To this end, the number of filters, kernel size, and number of layers of the CNN structure are optimally tuned using Graphics Processing Unit (GPU) resources. the findings of this study present evidence that the BO method is perfectly tailored for the hyperparameter tuning task with minimum error in comparison with competitive models. the efficiency of the CNN-BO method is demonstrated with two benchmark representative examples from the IEEE 14-bus system and the IEEE 118-bus system. For the IEEE 14-bus system, BO uses 256 filters, five kernels, and four layers;the IEEE 118-bus system uses 128 filters, three kernels, and eight layers. the CNN-BO method outperforms other optimization algorithms by achieving an impressive locational detection accuracy of 96.67% for the IEEE 14-bus system. © 2024 IEEE.

关键词： Cyber Physical System

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Double Deep Q Networks Reinforcement learning based Controller for Grid-Connected 7-Level PUC Inverter 4

Double Deep Q Networks Reinforcement Learning based Controll...

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4th international conference on smart Grid and Renewable Energy, SGRE 2024

作者： Alquennah, Alamera Nouran Chida, Melanie Zamzam, Tassneem Trabelsi, Mohamed Ghrayeb, Ali Khatri, Sunil Texas A&M University Department of Electrical and Computer Engineering College StationTX United States Texas A&M University at Qatar Qatar Foundation Department of Electrical and Computer Engineering Doha Qatar Kuwait College of Science and Technology Electronics and Communications Engineering Department Kuwait City Kuwait

ISBN: (纸本)9798350306262

the Packed-U-Cell (PUC) topology has been widely employed as a single DC-source multilevel inverter (MLI) in various applications, with different control techniques. In grid-connected applications, the two main control objectives are the minimization of the total harmonic distortion (thD%) of the generated current and the regulation of the flying capacitor voltage at its reference value. this paper proposes a reinforcement learning-based control (RLC) using the off-policy value function-based double deep Q networks (DDQN) reinforcement learning algorithm for a grid-tied single-phase 7-level PUC inverter. the agent consists of two DQNs that estimate the value function and determine the control action at each time step. the reinforcement learning-based control using DDQN (RLC-DDQN) is trained by interacting with the PUC7 system on three different pairs of current and voltage references. In the testing phase, the trained agent was tested on different operating conditions to validate its performance. the presented results show that the current thD has been kept below 3% for different reference signals from 4A to 11A, whereas the maximum deviation of the capacitor voltage from its volts reference value is 0.5V. the trained RLC-DDQN algorithm shows a high dynamic response under disturbed operating conditions (variations in the reference signals including the grid current, the DC-source voltage, and the voltage-current phase-shift). Moreover, the robustness of the trained agent when applied on systems with different component design (capacitance and inductance variations) is verified. © 2024 IEEE.

关键词： Reinforcement learning

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A Snapshot of University Students’ Perceptions About Online learning During the Covid-19 Pandemic 6th

A Snapshot of University Students’ Perceptions About Online...

引用

6th international conference on smart learning ecosystems and regional development, SLERD 2021

作者： Nikou, Stavros A. School of Education University of Strathclyde Glasgow United Kingdom

ISBN: (纸本)9789811639296

Gaining a better understanding of how the Covid-19 pandemic has affected students’ attitudes towards online education is important in order to pave the way from education disruption to education recovery. the current study aims to investigate how Covid-19 pandemic has changed University students’ perceptions about online education. the study used a localised version of a questionnaire developed by the Association for smart learning Ecosystem and regional development on different aspects of distance education. Participants were 90 University students. the results emerging from the investigation demonstrate a positive overall attitude of University students about online distance education, with relatively high levels of perceived ease of use, perceived usefulness, perceived interest and digital competences increase and a preference towards blended modes of delivery. the findings can help education professionals to better plan and design future online courses in the post-Covid-19 landscape. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： ecosystems

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Pattern Recognition in Apple Orchards During Dormancy and Bud development 4th

Pattern Recognition in Apple Orchards During Dormancy and ...

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4th international conference on smart Multimedia, ICSM 2024

作者： Sehgal, Bhavya Gandhi, Ritik Cheng, Irene Department of Computing Science University of Alberta EdmontonABT6G 2R3 Canada

ISBN: (纸本)9783031824746

Effective management of apple orchards during dormancy and bud development stages is crucial for optimizing fruit production and tree health. Automation using computer vision and deep learning techniques offers a promising solution for tree segmentation and labeling in complex outdoor environments. However, existing methods face challenges in accurately segmenting trees with bare branches and adapting to changing perspectives. this paper presents innovative solutions for bare tree segmentation and labeling, combining depth sensing and advanced image processing with deep learning approaches. the proposed methods address the limitations of current techniques, offering improved accuracy and robustness. We also introduce a novel tree labeling algorithm that utilizes spatial information and YOLOv4 for accurate tree identification and labeling across video frames. Experimental results demonstrate the effectiveness of our approach in handling complex outdoor conditions and bare tree segmentation. Our research contributes to advancing automated orchard management and precise monitoring during dormancy and bud development stages, enhancing fruit quality and overall orchard productivity. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Self-Adaptive Service Deployment for Resilience of smart Manufacturing Architectures 4

Self-Adaptive Service Deployment for Resilience of Smart Man...

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4th IEEE international conference on Automatic Computing and Self-Organizing Systems Companion, ACSOS-C 2023

作者： Van, Hoai My Da Silva, Alexandre Sawczuk Knissel, Tim Weiss, Gereon Fraunhofer Iks Fraunhofer Institute for Cognitive Systems Iks Munich Germany

ISBN: (纸本)9798350337464

Recent advances in the manufacturing sector - including edge-to-cloud continuum, machine learning, and digitalization - can enable smart manufacturing solutions, such as control optimization and predictive maintenance. One challenge in new system architectures is the efficient resource management under changing conditions while meeting process requirements, such as latency, when deploying software services. To address this, we propose an approach for self-adaptive service deployment that increases the resilience of smart manufacturing systems. We combine self-adaptation principles with run-time models - that describe the system in the form of the standardized Asset Administration Shell - to enable flexible software architectures for manufacturing. the proposed solution comprises the continuous adaptation of the service deployment in response to system changes, such as resource exhaustion or failure, to ensure an optimized operation. An evaluation of an example manufacturing use case shows that the proposed solution leads to lower execution latency and continuation of production in situations with low resources, e.g., through failures, compared to less flexible deployment approaches. © 2023 IEEE.

关键词： Flow control

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