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Visual modeling of the Landslide Slopes Stress-Strain State for the computer-Aided Design of Retaining Wall Structures 16

Visual Modeling of the Landslide Slopes Stress-Strain State ...

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IEEE 16th international conference on the Experience of Designing and Application of CAD systems (CADSM)

作者： Voloshkina, Elena Efimenko, Volodymyr Zhukova, Olena Chernyshev, Denis Korduba, Iryna Shovkivska, Viktoriia Kyiv Natl Univ Construct & Architecture Dept Lab Protect & Environm Kiev Ukraine Astron Observ Taras Shevchenko Natl Univ Kyiv Kiev Ukraine

ISBN: (纸本)9781665438940

The report authors have developed a LANDSLIP software package for visual modeling and landslide slopes stress-strain state calculation in JAVA programming language. It has various modifications (flat and spatial versions, various calculation models etc.). The user interacts with the LANDSLIP by means of visual modeling on the Graphics User Interface. The user does not interact with all other LANDSLIP objects, they are completely controlled by the Graphics User Interface. It creates all other objects, modifies, uses, and, if necessary, removes them. The interaction between the user and the Graphics User Interface is performed via the menu system. Using the graphical editor menu, the user can create objects to describe the landslide structure and manipulate the additional sliding surfaces, groundwater lines set and program functions responsible for mathematical calculations and design documentation preparation. The LANDSLIP software package is equipped with interfaces that facilitate collecting data from the MicroStation environment, digital map, and geological database. The geological cross-sections are automatically created, and geotechnical soil parameters are evaluated. The software enables the stability analysis of any slopes or scarps using various calculation methods. It ensures wide opportunities to predict, evaluate and combat natural threats. The LANDSLIP software package results reliability was verified by comparative calculations via similar Canadian SLOPE/W software package at the design office of the "Turow" brown coal mine in Poland. The results differences are within the acceptable limits and depend on the peculiarities of initial-boundary conditions input in various graphic editors, various calculation models, etc.

关键词： visual modeling stress-strain state landslide hazard slopes computer-aided design landslide protection structures

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IT Design for Resiliency Using Extreme Value Analysis 1

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40th international conference on computer Safety, Reliability and Security (SAFECOMP)

作者： Bozoki, Szilard Pataricza, Andras Budapest Univ Technol & Econ Dept Measurement & Informat Syst Magyar Tudosok Krt 2 H-1117 Budapest Hungary

ISBN: (数字)9783030839031

ISBN: (纸本)9783030839031;9783030839024

Safety-critical systems are designed to operate millions of hours without losing or harming life. Millions of hours enable events with small occurrence probability to materialise. Owing to this, rare events have to be factored when designing for millions of safe operating hours. In this paper, we apply a statistical paradigm named Extreme Value analysis for the modeling of the rare events and probabilistic risk assessment. Without loss of generality, our motivation is cyber-physical-systems where the IT infrastructure is frequently shared between functionally independent tasks and the run-time platform, such as Industry 4.0 based on 5G and edge cloud computing. As a practical example, we present our method on a case study on a typical micro-service-based edge computing setup by measuring and analysing the container restart times in Kubernetes. The results can be used to asses and compare resilience mechanism design alternatives.

关键词： Design for resiliency Extreme value analysis Edge computing Statistical modeling Dimensioning Kubernetes

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MIMIC-Pose: Implicit Membership Discrimination of Body joints for Human Pose Estimation

MIMIC-Pose: Implicit Membership Discrimination of Body Joint...

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international conference on Automatic Face and Gesture Recognition

作者： Ying Huang Shanfeng Hu Institute of Virtual Reality and Intelligent Systems Alibaba Business School Hangzhou Normal University Hangzhou China Department of Computer and Information Sciences Northumbria University Newcastle-upon-Tyne UK

ISBN: (数字)9798350394948

ISBN: (纸本)9798350394955

The objective of human pose estimation is to accurately identify the positions of individual body joints for each person seen in an image for human kinematics modelling and analysis. A majority of current deep-learning approaches focus on utilizing feature learning to regress the coordinates of each individual joint. This can be thought of as a constrained point distribution optimization problem in the image plane. However, in the high-dimensional feature space, the feature distribution of joints as a significant constraint and discriminative condition, has not received enough attention yet. Here we propose a novel approach (MIMIC-Pose) that applies an implicit pair-wise keypoint membership constraint using those features to individual joint regression to ensure that the joints of the same person have higher mutual feature similarities compared with those of different people or the image backgrounds. We propose a novel link prediction integrated learning architecture with high-throughput tokens to learn such similarity feature embeddings to improve the prediction accuracy of individual joints in a single forward inference. Experimental results show that our approach can achieve competitive performance with much fewer model parameters and lower computational cost compared to state-of-the-art methods.

关键词： Representation learning Accuracy Head Computational modeling Pose estimation Kinematics Predictive models

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DeepFuzzLoc Positioning: A Unified Fusion of Fuzzy Clustering and Deep Learning for Scalable Indoor Localization Using Wi-Fi RSSI

DeepFuzzLoc Positioning: A Unified Fusion of Fuzzy Clusterin...

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international Symposium on Soft Computing and Intelligent systems (SCIS)

作者： Muhammad Izzuddin Mahali Jenq-Shiou Leu Nur Achmad Sulistyo Putro Setya Widyawan Prakosa Cries Avian Dept. of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan Dept. of Electronics and Informatics Engineering Education Universitas Negeri Yogyakarta Yogyakarta Indonesia Dept. of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (数字)9798350373332

ISBN: (纸本)9798350373349

This research introduces a novel method for indoor positioning by integrating Fuzzy C-Means clustering with deep learning autoencoders, leveraging Wi-Fi RSSI fingerprinting for precise location prediction and building and floor level classification within a unified framework. The approach combines the comprehensibility of Fuzzy C-Means (FCM) with the robust feature extraction capabilities of deep autoencoders through a dual-phase strategy: extensive offline training utilizing RSSI fingerprint data followed by real-time online position prediction. The model's performance was rigorously tested using 5-Fold cross-validation, examining its effectiveness both with and without the integration of FCM on various cluster numbers. Evaluation metrics, including Euclidean distance error for regression and accuracy, F1-score, precision, and specificity for classification effectiveness, were used for comparative analysis. The results were visualized through regression plots, confusion matrices, and learning curves, supplemented by t-SNE visualizations that provide profound insights into the feature space. These detailed analyses highlight the considerable promise of this integrated approach in enhancing the accuracy and reliability of indoor positioning systems.

关键词： Location awareness Deep learning Training measurement Accuracy Clustering algorithms Fingerprint recognition Real-time systems Reliability Wireless fidelity

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Comparative Analysis of Fraud Detection of Credit Card using Supervised and Unsupervised Learning

Comparative Analysis of Fraud Detection of Credit Card using...

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computer Vision and Machine Intelligence (CVMI), international conference on

作者： Deepthi Sehrawat Yudhvir Singh Harkesh Sehrawat Computer Sc. & Engg. UIET MDU Rohtak India

ISBN: (数字)9798350376876

ISBN: (纸本)9798350376883

The unlawful use of a credit card or other such payment device to obtain cash or property without authorization is known as credit card fraud. Fraudsters steal credit card details from unprotected websites or acquire them through identity theft schemes. A Fraud Detection System (FDS) is implemented to identify fraudulent transactions. With the increasing risk of credit card fraud, where criminals exploit credit cards to access funds from others’ accounts, detecting fraud in credit card transactions has become a critical area of research. In recent years, machine learning has demonstrated positive results when it comes to identifying credit card fraud. In order to evade detection by the present fraud detection systems, fraudsters refine their methods, and a plethora of proposed machine learning models exist that may detect fraud cases using a certain dataset. Though each model functions well to some degree, not all of them are the greatest. This study offers a comprehensive overview of various models by presenting a comparative analysis of credit card fraud detection utilizing both supervised and unsupervised learning approaches, as numerous machine learning models are currently available for this task. This represents the first comparative evaluation of eighteen different models. The performance of all models was assessed using metrics such as precision, recall, and F 1 -score, alongside the plotting and computation of ROC curves and AUC scores for comparison. This paper can be used as a research tool because it compares about eighteen different models.

关键词： Support vector machines measurement Computational modeling Nearest neighbor methods Credit cards Boosting Real-time systems Fraud Random forests Unsupervised learning

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Sensory Modulation of Gait Balance in Human Locomotion: A Neuromusculoskeletal modeling Study

Sensory Modulation of Gait Balance in Human Locomotion: A Ne...

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IEEE/RAS-EMBS international conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Vahid Firouzi Omid Mohseni Oskar Von Stryk Andre Seyfarth Maziar A. Sharbafi Laufiabor Locomotion Laboratory Centre for Cognitive Science Technische Universität Darmstadt Darmstadt Germany Department of Computer Science Technische Universität Darmstadt Darmstadt Germany Department of Electrical Engineering and Information Technology Measurement and Sensor Technology Group Technische Universität Darmstadt Darmstadt Germany

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Maintaining balance during gait relies on intricate sensorimotor processes, engaging compensatory muscle actions and reflex modulations to counteract external perturbations and prevent falls. This study aims to identify possible muscle reflex parameters that are modulated to maintain balance in the face of external perturbations through neuromechanical simulations. A reflex-based neuromusculoskeletal model is utilized and subjected to four distinct sagittal-plane perturbation types (two whole body linear momentum (WBLM) and two whole body angular momentum (WBAM) applied to the torso at five different timings. Analysis of the correlation between reflex parameters and their impact on perturbation recovery indicates seven key reflex parameters as strong candidates for modulating gait balance. For WBLM perturbations, our findings indicated the contribution of both stance and swing phase strategies by modulating Soleus positive force feedback and Iliopsoas activity. In WBAM perturbations, Vasti positive force feedback emerged as a crucial reflex contributing to knee joint stability in backward torque perturbation. The study suggests leg adjustments as a primary strategy for recovering from forward torque perturbations, achieved by modulating hamstring positive force feedback and increasing Iliopsoas muscle activation during the swing phase. These findings provide new insights into the underlying mechanisms of reflex modulation and its significance in balancing and recovering from perturbations during locomotion.

关键词： Torso Legged locomotion Torque Perturbation methods Force feedback Modulation Muscles Robot sensing systems Stability analysis Timing

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Research on SOH Prediction of Lithium Battery Based on Improved SSA-BP Neural Network

Research on SOH Prediction of Lithium Battery Based on Impro...

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computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI), IEEE international conference on

作者： Yannan Sun Jiaman Gu Ziliang An Kun Xie School of Rail Transportation Shanghai Institute of Technology Shanghai Art and Design College Shanghai Normal University Tianhua College Shanghai Faculty of Urban Operations and Management Shanghai Urban Construction Vocational College Shanghai

ISBN: (数字)9798350378542

ISBN: (纸本)9798350378559

The rapid growth of electric vehicles has led to widespread adoption of lithium batteries in energy systems, valued for their high energy density and extended cycle life. Nonetheless, frequent charge-discharge cycles and environmental conditions contribute to capacity loss, which ultimately reduces vehicle longevity. As a vital metric, the state of health (SOH) of lithium batteries is challenging to measure directly, and traditional methods often lack precision in SOH prediction. This study gathers experimental data on battery aging under various charge-discharge conditions and incorporates NASA's B05 battery data to identify health metrics. Correlation analysis is used to select optimized input variables. A BP neural network model, enhanced by the sparrow search algorithm (SSA), is developed. Furthermore, combining Levy flight and the sine cosine algorithm (SCA) improves prediction accuracy for SOH.

关键词： measurement Accuracy Input variables Computational modeling Neural networks Predictive models Prediction algorithms Lithium batteries Optimization Convergence

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Research on Semantic Segmentation of Remote Sensing Images Based on Improved U-net

Research on Semantic Segmentation of Remote Sensing Images B...

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Electronic Information Engineering and computer Communication (EIECC), international conference on

作者： Hong Ding Fu Lei Wenqing Zhang Lipeng Liu School of Physical Science and Technology Nantong University Nantong China Independent Researcher Tech Lead San Jose CA Electrical & Systems Engineering Washington University Missouri USA College of Engineering Peking University Beijing China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

This study explores the important role of attention mechanism in enhancing the performance of U-Net model for remote sensing image segmentation. By introducing ECA (Efficient Channel Attention) and GACM (Global Attention Context Module), the model performance is significantly improved, as evidenced by the significant increase in MIoU, accuracy and F1 *** ECA module improves the MIoU by 6.51 percentage points, the accuracy by 11.84 percentage points and the F1 scores by 14.03 percentage points, respectively, while the use of the GACM module further improved MIoU by 9.11 percentage points, accuracy by 16.03 percentage points and F1 score by 17.73 percentage points. The strategy of combining ECA with GACM results in the improvement of MIoU, accuracy and F1 value by 22.21, 22.84 and 25.71 percentage points, respectively, which proves the complementary nature of the two mechanisms and the potential of the present method in improving the accuracy and robustness of the model, which is of significant practical application value and provides an effective performance enhancement scheme for remote sensing image segmentation.

关键词： measurement Accuracy Attention mechanisms Semantic segmentation Focusing Network architecture Robustness Graph neural networks Remote sensing Context modeling

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Adaptive Skill Selection for Effective Exploration of Action Space

Adaptive Skill Selection for Effective Exploration of Action...

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international joint conference on Neural Networks (IJCNN)

作者： Haoke Zhang Yiyong Huang Wei Han Dan Xiong Chuanfu Zhang Yanjie Yang School of Systems Science and Engineering Sun Yat-Sen University Guangzhou China National Innovation Institute of Defense Technology Academy of Military Science Beijing China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Skill-based deep reinforcement learning (DRL) typically embeds skills extracted from expert demonstrations into a large latent space. However, it is difficult for agents to explore the latent skill space, because only a small portion of the space is relevant to a given robot state. To this end, we propose a three-layer algorithm to realize that the agent can adaptively select skills and directly explore the skill region associated with the state. The high-level policy outputs skill categories with a given state; then, a conditional probability model is constructed using the state and skill categories to guide the agent’s exploration. We first learn high- and mid-level skill prior models and a low-level controller with offline data. Next, we learn high-level and mid-level policies to construct two different action spaces. The high-level action is used to guide the mid-level policy to explore skill-related regions; the mid-level is used to guide low-level models to generate skill-related robot actions. Finally, we validate our approach in three challenging manipulation tasks, and the experimental results show that our approach can significantly speed up exploration and cross-task learning.

关键词： Adaptation models Computational modeling Neural networks computer architecture Aerospace electronics Deep reinforcement learning Data models

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Research on Radio Propagation Model Based on Deep Learning and Feature Images

Research on Radio Propagation Model Based on Deep Learning a...

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Electronic Information Engineering and computer Technology (EIECT), international conference on

作者： Ran Wang Jidong Cui Fang Lai Liang Yao Weiran Li Junjie Ai Jiayin Lv Henan Provincial Forestry and Ecological Construction and Development Center Zhengzhou China School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798331528850

ISBN: (纸本)9798331528867

The radio propagation model based on deep learning can extract features from a large amount of relevant data and learn from them to adapt to the real-time computing requirements of complex environmental scenes. In this study, we construct a multi-dimensional feature dataset and develop a U-Net-based propagation model. To enhance the performance of the model, we introduce a feature classification preprocessing module before the model input to distinguish features with different physical meanings. Additionally, Atrous Spatial Pyramid Pooling is incorporated in the encoder to improve the model's ability to learn multi-scale features. Experiments are conducted on a dataset consisting of 275 cells in an urban area, using MAE, RMSE, and MAPE as evaluation metrics. The results demonstrate that our proposed model outperforms the original U-Net architecture in terms of all evaluation metrics for overall, indoor, and outdoor areas.

关键词： Deep learning measurement Adaptation models Computational modeling Urban areas Radio propagation Predictive models Feature extraction Data models Real-time systems

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