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Multiview Graph Learning Based on Node Perturbation Model

Multiview Graph Learning Based on Node Perturbation Model

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Asilomar conference on Signals, systems & computers

作者： Mohammad Al-Wardat Selin Aviyente Department of Electrical and Computer Engineering Michigan State University East Lansing USA

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

Graph topology inference, i.e., learning graphs from a given set of nodal observations, is a significant task in many application domains. Existing approaches are mostly limited to learning a single graph assuming that the observed data is homogeneous. This is problematic because many modern datasets are heterogeneous or mixed and involve multiple related graphs, i.e., multiview graphs. Recent work proposing to learn multiview graphs ensures the similarity of learned view graphs through either pairwise regularization or consensus-based regularization. In most of the existing approaches, the similarities and differences between networks are assumed to be driven by individual edges. However, a node-based approach provides a more intuitive interpretation of the network differences. In this paper, we introduce a node perturbation model to learn multiview graph Laplacians with the assumption that the differences between the $K$ networks are due to individual nodes that are perturbed across views. An optimization framework is formulated to simultaneously learn the graph Laplacians of $K$ views with the assumption that the observed signals are smooth with respect to the underlying graph Laplacians. The proposed method is compared to existing edge-based multiview Laplacian learning methods as well as joint graphical Lasso based structure inference methods.

关键词： Wiring measurement Learning systems computers Laplace equations Perturbation methods Computational modeling Topology Optimization

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DeepSSM: Deep State-Space Model for 3D Human Motion Prediction

DeepSSM: Deep State-Space Model for 3D Human Motion Predicti...

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IEEE international conference on Robotics and Biomimetics

作者： Xiaoli Liu Jianqin Yin Huaping Liu Jun Liu School of Artificial Intelligence of Beijing University of Posts and Telecommunications Beijing China School of Intelligent Engineering and Automation of Beijing University of Posts and Telecommunications Beijing China Department of Computer Science and Technology Tsinghua University Beijing China Department of Mechanical Engineering City University of Hong Kong Hong Kong China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Predicting future human motion plays a significant role in human-robot interactions for various real-life applications. A unified formulation and multi-order modeling are two critical perspectives for analyzing and representing human motion. In contrast to prior works, we improve the multi-order modeling ability of human motion systems for more accurate predictions by building a deep state-space model (DeepSSM). The DeepSSM utilizes the advantages of both the state-space theory and the deep network. Specifically, we formulate the human motion system as the state-space model of a dynamic system and model the motion system by the state-space theory, offering a unified formulation for diverse human motion systems. Moreover, a novel deep network is designed to parameterize this system, which jointly models the state-state transition and state-observation transition processes. In this way, the state of a system is updated by the multi-order information of a time-varying human motion sequence. Multiple future poses are recursively predicted via the state-observation transition. To further improve the model ability of the system, a novel loss, WT-MPJPE (Weighted Temporal Mean Per joint Position Error), is introduced to optimize the model. The proposed loss encourages the system to achieve more accurate predictions by increasing weights to the early time steps. The experiments on two benchmark datasets (i.e., Human3.6M and 3DPW) confirm that our method achieves state-of-the-art performance with improved accuracy of at least 2.1mm per joint. The code will be available at: https://***/lily21ab/***.

关键词： Analytical models Accuracy Three-dimensional displays Biological system modeling System performance Robot kinematics Human-robot interaction Predictive models State-space methods Time-varying systems

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“Machine Learning-Based Food Edibility Prediction Using Neural Networks”

“Machine Learning-Based Food Edibility Prediction Using Neu...

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international conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Narendra Kumar S Ravi KM Arahanth M G Satheesh Pragathi Basavaraj Department of Biotechnology R V College of Engineering BE students Department of Computer Science and Engineering R V College of Engineering Scientific officer cum Assistant Professor R V College of Engineering

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Safety and the edibility of products is a must in the food industry. This research paper introduces a machine learning method for predicting whether food products can be eaten or not. A neural network model was created using various characteristics of the dataset including shelf life, manufacture date, optimum storage temperature and product type (solid, liquid or gas) to predict the edibility of a given item. The performance of the model was measured using accuracy, Mean Squared Error (MSE), and R-squared (R2). These findings demonstrate that neural networks are effective in classification tasks as they offer dependable means of forecasting if food is unsafe to eat.[1] Traditional methods have received a significant boost by this approach since it allows faster scalable automated predictions that could be integrated into larger food safety systems

关键词： Temperature measurement Solid modeling Accuracy Temperature Computational modeling Scalability Neural networks Predictive models Solids Food safety

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NONLINEAR FINITE ELEMENT BASED CONTACT modeling FOR BOLTED jointS IN COMPOSITE LAMINATES 17

NONLINEAR FINITE ELEMENT BASED CONTACT MODELING FOR BOLTED J...

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ASME international Design Engineering Technical conferences / 42nd Annual computers and Information in Engineering conference (IDETC-CIE) / 17th international conference on Multibody systems, Nonlinear Dynamics, and Control (MSNDC)

作者： Wang, Jielong Beijing Aeronaut Technol Res Inst Beijing 100076 Peoples R China

ISBN: (纸本)9780791885468

The paper developed a new numerical joint model based on nonlinear finite element method for the multiple bolted joints in composite laminates. The new joint model consists of the geometrically exact beam and shell elements, as well as the new Hertz contact elements. The geometrically exact elements, beams and shells, are used to model the bolts and laminates with nonlinear deformations, respectively. Meanwhile, the new contact elements apply the Hertz model to simulate the contact, slippage and bolt-laminate interaction. This contact element combined with the multi-point constraints to transfer the shear forces from one shell through beam to another shell element. As an essential part of the bolted joint model, a new type of geometrically exact shell element was developed with second-order accuracy and nine nodes. The strain-displacement relationship of this shell element was formulated according to the rotation-free Green-Lagrange strain tensor. This shell element approximated the higher-order components of strain tensor, ensuring it can accurately describes the real deformation of thin-walled composite laminates. Application of this joint model to a three-bolt, single-shear joint in composite laminates was presented, and its predictions are compared with those of commercial code ABAQUS. Numerical simulation results show that the new joint model is suitable for the design of bolted joints in composite laminates with good accuracy and high efficiency.

关键词： Composite materials Laminates Bolted joints Contact modeling Finite element analysis Shells Tensors Shear (Mechanics) Deformation Rotation computer simulation Finite element methods Displacement Design

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Temperature Prediction of Lithium-Ion Cells Using Transformers and Deep Learning Models

Temperature Prediction of Lithium-Ion Cells Using Transforme...

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Data Science and Business systems (ICDSBS), international conference on

作者： Rohinish Rakshan JVN Ganesh Subramaniyaswamy Vairavasundaram School of Computer Science Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9798331585600

ISBN: (纸本)9798331585617

The accurate prediction of batter temperature is essential for maintaining the safety, longevity, and performance of Panasonic 18650PF lithium-ion cells, especially when used in high-demand appliances like electric vehicles and energy storage systems. This study mainly explores Transformers and Deep Learning Models which can be used to predict the temperature of the Panasonic 18650PF Lithium-ion-cells using the Electrochemical Impedance Spectroscopy (EIS) data gathered at different ambient temperatures. The four main Deep Learning models used are Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), Bidirectional LSTM (Bi-LSTM), and Minimal Gated Unit (MGU) are mainly evaluated for their predictive accuracy and computational efficiency in this paper. Each model's performance is measured using standard metrics ad from the results we see that MGU and transformers perform the best when compared to others. From the results we can see that transformer alone cannot be the best model when compared to other models, hence we need to ensemble other models to get the efficient balance between speed and accuracy. This work establishes a benchmark for machine learningdriven temperature prediction in battery management systems, offering insights into how advanced recurrent neural networks can enhance proactive thermal management.

关键词： Deep learning Temperature measurement Temperature distribution Accuracy Computational modeling Predictive models Transformers Data models Computational efficiency Electrochemical impedance spectroscopy

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Developing Eternal Learning Model Trainer System to Support Continuous Knowledge Integration and Cognitive Growth

Developing Eternal Learning Model Trainer System to Support ...

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international joint conference on computer Science and Software Engineering (JCSSE)

作者： Muhammad Irfan Luthfi Wu-Yuin Hwang Graduate Institute of Network Learning Technology National Central University Taiwan Dept. Electronics and Informatics Engineering Education Universitas Negeri Yogyakarta Indonesia College of Science and Engineering National Dong Hwa University Taiwan

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

This study introduces and evaluates the Eternal Learning Model Trainer System (ELMTS), a pioneering approach in data processing and model generation designed to transcend the limitations of human knowledge sharing. ELMTS uses speech-to-text and social media mining together to create detailed digital profiles from real-life and online activities, improving data integration and model accuracy for new ways of continuous knowledge sharing. Moreover, by employing the robust algorithms of GPT 3.5, ELMTS constructs detailed digital profiles, analyzing and interpreting data to recognize individual patterns, preferences, and knowledge areas. This leads to a Question and Answer (QnA) system that acts like a digital twin, reflecting the knowledge and experiences of its human counterpart. The system's performance, evaluated through metrics such as correctness accuracy, relevance, completeness, and linguistic accuracy, displayed significant improvement post-user enhancements, indicating enhanced precision in data handling and improved response quality. User feedback further underscored ELMTS's effectiveness in aspects like adaptability, decision-making, and personal growth while pointing out challenges in information overload and ethical concerns, thus shaping future refinements for the system. ELMTS aims to preserve individual wisdom and contribute to the collective human intellect, offering a novel, sustainable approach to knowledge sharing and learning. This research is crucial to realizing a more efficient, user-centric, and accurate knowledge-sharing platform.

关键词： measurement Knowledge engineering Accuracy Social networking (online) Computational modeling System performance Linguistics

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Deep Model Pruning By Parallel Pooling Attention Block

Deep Model Pruning By Parallel Pooling Attention Block

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2022 IEEE/WIC/ACM international joint conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

作者： Junnan Wu Liming Zhang Computer and Information Science Macau University Macau China

Deep neural network models have achieved great success in various fields, including computer vision and recommendation systems. However, deep models are usually large and computationally time-consuming. Reducing the size of deep models and speeding up the learning process without a sharp drop in accuracy becomes a promising goal, both in research and practice. The channel pruning is one of the most effective methods, which can not only compress deep model size, but also directly speed up inference. In this paper, we propose a novel channel attention block called parallel pooling attention block (PPAB) that is designed on top of the squeeze and excitation (SE) blocks. There are two optimization improvements of PPAB. First, parallel max-pooling branch is added on top of SE blocks. Second, we avoid dimensionality reduction during the excitation phase. Both of these optimizations improve the channel importance measurement capabilities of PPAB. Experiment results show that PPAB outperforms general SE blocks on the channel attention objective. The proposed pruning method could be applied efficiently both in computer vision and recommendation systems.

关键词： Dimensionality reduction Deep learning computer vision Computational modeling Neural networks Libraries Hardware

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Cybersecurity of IoT Devices and Sensors in Industry 4.0

Cybersecurity of IoT Devices and Sensors in Industry 4.0

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international Carpathian Control conference (ICCC)

作者： Jakub Krško Peter Peniak Mário Michálik Gabriel Gašpar Vojtech Šimák Samuel Valter Dpt. of Control and Information Systems FEIT University of Zilina Žilina Slovakia IT Division Continental Tires Puchov Slovakia Research Centre University of Zilina Žilina Slovakia

ISBN: (数字)9798331501273

ISBN: (纸本)9798331501280

The paper discusses cybersecurity issues within the concept of Industry 4.0 as a whole and frequently references its implementation within the specific IoT system in the laboratories of the University of Zilina. The first part deals with an analysis of the aspects concerning study of cybersecurity in Industry 4.0, and more specifically, in IoT, OT and cloud technologies. The next section, in the framework of Industry 4.0, places forward a proposal to classify data in terms of their cybersecurity risks within the various component parts of the system of "Asset Management". In the practical section an IoT system used for educational purposes is designed, built with defined security measures, and then underwent stress testing to establish the efficiency of the built-in measures against cyber threats. The output is an IoT system with tested security features in the laboratory of the University of Žilina, which may be used for the education of students.

关键词： Threat modeling Technological innovation Standards organizations Laboratories Organizations Fourth Industrial Revolution Internet of Things computer security Stress measurement Testing

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Enhanced Camouflaged Object Detection for Agricultural Pest Management: Insights from Unified Benchmark Dataset Analysis

Enhanced Camouflaged Object Detection for Agricultural Pest ...

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international Bhurban conference on Applied Sciences & Technology, IBCAST

作者： Syed Muhammad Hussain Samiya Ali Zaidi Afsah Hyder Syed Muhammad Ali Rizvi Muhammad Farhan School of Science and Engineering Habib University Karachi Pakistan

ISBN: (数字)9798331516680

ISBN: (纸本)9798331516697

In recent decades, the severity of climate change has led to a rise in the frequency of agricultural pest attacks on farms causing significant economic damage and food shortages. Effective management of pests, specifically Camouflaged pests, poses significant challenges in agriculture, requiring accurate automated detection and segmentation. In this study, we leverage state-of-the-art object detection and segmentation models, specifically the single-stage YOLOv8 model, fine-tuned on a large-scale Unified Benchmark Camouflaged Object Detection Dataset (UBCODD) consisting of 52,447 images. Furthermore, we extend our analysis to benchmark agricultural pest datasets such as IP-102 and the Locust-Mini Dataset, showcasing competitive performance metrics. This integrated approach allows us to capture agricultural camouflaged pests with greater detail and accuracy. Our findings lay the groundwork for the advancement of single-stage object detectors and segmentation models in the field of agriculture. Moreover, we contribute to open-source initiatives in agricultural technology by generating bounding box annotations for the entire IP-102 and binary masks for the Agricultural Pests Image Dataset. This research signifies a significant advancement in agricultural pest recognition and segmentation using cutting-edge computer vision technologies.

关键词： measurement Image segmentation computer vision Accuracy Biological system modeling Computational modeling Object detection Benchmark testing Agriculture Real-time systems

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Efficient Image Super-Resolution via Symmetric Visual Attention Network

Efficient Image Super-Resolution via Symmetric Visual Attent...

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international joint conference on Neural Networks (IJCNN)

作者： Qinrui Fan Chengxu Wu Shu Hu Xi Wu Xin Wang Jing Hu School of Computer Chengdu University of Information Technology Chengdu China Department of Computer Information and Graphics Technology Indiana University–Purdue University Indianapolis IN USA School of Public Health University at Albany SUNY USA

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In recent years, efficient super-resolution research has focused on reducing model complexity and improving efficiency by leveraging deep small-kernel convolution, but it has the problem of a small receptive field, which leads to a limited ability of the network to reconstruct details. Large kernel convolution can provide a large receptive field and lead to a substantial enhancement in the quality of image reconstruction, but its computational cost is too high. To minimize the model’s parameter count and achieve efficient super-resolution reconstruction, this study introduces a symmetric visual attention network. The network decomposes the large kernel convolution into three different lightweight and efficient convolutions. It then forms a bottleneck structure by leveraging the varied receptive field sizes of these convolutions in combination. The attention mechanism is integrated to create a bottleneck attention module, enhancing the network’s feature awareness. Furthermore, the bottleneck attention modules are symmetrically arranged to construct a symmetric large kernel attention block, thereby further enhancing the network’s capability to extract deep features. The experimental results demonstrate that the proposed model achieves competitive quantitative metrics when compared to other lightweight super-resolution methods, and the details of the reconstructed images are enhanced. With only 183K parameters, the model achieves a lightweight yet high-quality super-resolution model, offering a novel solution approach for efficient super-resolution.

关键词： Training measurement Visualization Convolution Computational modeling Superresolution Neural networks

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