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7th International Symposium on Cyber Security, Cryptology, and Machine Learning, CSCML 2023

作者： Liagkou, Vasiliki Nastou, Panagiotis E. Spirakis, Paul Stamatiou, Yannis C. Computer Technology Institute and Press - "Diophantus" University of Patras Campus Patras26504 Greece Department of Computer Science University of Liverpool Liverpool United Kingdom Department of Business Administration University of Patras Patras26504 Greece Department of Informatics and Telecommunications University of Ioannina Kostakioi Arta47100 Greece Department of Mathematics Applied Mathematics and Mathematical Modeling Laboratory University of the Aegean Samos Greece Center for Applied Optimization University of Florida Gainesville United States

ISBN: (纸本)9783031346705

In this paper we investigate phenomena of emergence of highly organized structures in networks. Our approach is based on Kolmogorov complexity of networks viewed as finite size strings. We apply this approach to study the emergence of simple organized, hierarchical, structures based on Sierpinski Graphs and we prove a Ramsey type theorem that bounds the number of vertices in Kolmogorov random graphs that contain Sierpinski Graphs as subgraphs. Moreover, we show that Sierpinski Graphs encompass close-knit relationships among their vertices that facilitate fast spread and learning of information. In the context of our work we can investigate questions related to the emergence or formation of suitably large groups of malicious individuals in a networks of interacting agents, which are able to take control of the networks. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graphic methods

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Applying Layer-Wise Relevance Propagation on U-Net Architectures 27th

Applying Layer-Wise Relevance Propagation on U-Net Architect...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Weinberger, Patrick Fröhler, Bernhard Heim, Anja Gall, Alexander Bodenhofer, Ulrich Senck, Sascha Research Group Computed Tomography University of Applied Sciences Upper Austria Wels Austria School of Informatics Communication and Media University of Applied Sciences Upper Austria Wels Austria Johannes Kepler University Linz Linz Austria Faculty of Computer Science and Mathematics University of Passau Passau Germany Division Development Center X-ray Technology Fraunhofer Institute for Integrated Circuits IIS Erlangen Germany

ISBN: (纸本)9783031781971

For safety critical applications, it is still a challenge to use AI and fulfill all regulatory requirements. Medicine/healthcare and transportation are two fields where regulatory requirements are of fundamental importance. A wrong decision can lead to serious hazards or even deaths. In these fields, semantic segmentation is often utilized to extract features. Especially U-Net architectures are used. This paper shows how to apply layer-wise relevance propagation (LRP) to a trained U-Net architecture. We achieve an efficient explanation of a segmentation by back-propagating the whole resulting image. To tackle the non-linear results of the LRP, we introduce a threshold mechanism in combination with a logarithmic transfer function to preprocess the data for visualization. We demonstrate our method on three use cases: the segmentation of a fiber-reinforced polymer in the field of non-destructive testing, the segmentation of pedestrians in an automotive application, and a lung segmentation example from the medical domain. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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A Convolutional Neural Network for Ultra-Wideband Radar-Based Hand Gesture Recognition

A Convolutional Neural Network for Ultra-Wideband Radar-Base...

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2023 International Technical Conference on Circuits/Systems, computers, and Communications, ITC-CSCC 2023

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Tancharoen, Datchakorn Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Panyapiwat Institute of Management Faculty of Engineering and Technology Nonthaburi Thailand Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350326413

New developments in sensing technology have enabled the creation of improved assistive devices that enhance daily eldercare routines and offer personalized care to users. Wearable or ambient sensors can now detect a person's behavior, but the need for constant recharging due to their high energy consumption makes wearing such devices around the clock difficult. In this paper, a new sensing technique based on deep learning (DL) approaches for hand gesture recognition (HGR) using an ultra-wideband (UWB) radar sensor is introduced. The study examines the recognition performance of a convolutional neural network (CNN) for HGR and utilizes the UWB-gestures dataset to train and test the DL networks in various experiments with different scenarios. The experimental results demonstrate that the CNN achieved the highest F1-score of 97.19%. © 2023 IEEE.

关键词： Convolutional neural networks

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A Hybrid Deep Neural Network with Attention Mechanism for Human Activity Recognition Based on Smartphone Sensors 7

A Hybrid Deep Neural Network with Attention Mechanism for Hu...

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7th International Conference on Information technology, InCIT 2023

作者： Mekruksavanich, Sakorn Tancharoen, Datchakorn Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Panyapiwat Institute of Management Faculty of Engineering and Technology Nonthaburi Thailand Faculty of Applied Science King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350358698

Human activity recognition (HAR) is an area of study that seeks to automatically and precisely detect an individual's behavior by analyzing bio-signal data. Bio-signal data can be acquired using sensing technology integrated into smartphones and other worn intelligent gadgets. Nevertheless, collecting sensor data tagged with activity information employing individual smartphones can result in data being analyzed in varying contexts, potentially compromising the accuracy of machine learning prediction methodologies. This paper introduces a novel HAR approach via smartphone sensors. The proposed method incorporates a hybrid deep neural network architecture enhanced with an attention mechanism. The deep learning model under consideration is referred to as the Att-CNN-BLSTM network. This particular network can autonomously extract significant features from smartphone sensor data. This feature extraction aims to effectively classify different human actions with a high degree of accuracy. In order to assess the efficacy of the hybrid model, a series of investigations were undertaken utilizing a publically accessible HAR dataset. This dataset was employed for training and testing purposes, employing a 5-fold cross-validation approach. We additionally carried out an analytical comparison utilizing state-of-the-art deep learning models from prior research. The findings from our experimentation demonstrate superior performance of the Att-CNN-BLSTM model over other advanced AI algorithms, with the highest accuracy rate of 90.54% and top F1-score of 86.88% attained. © 2023 IEEE.

关键词： Smartphones

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Day-Ahead Forecasting of Solar Irradiance & PV Power Output Through Statistical Machine Learning Methods

Day-Ahead Forecasting of Solar Irradiance & PV Power Output ...

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2022 Saudi Arabia Smart Grid Conference, SASG 2022

作者： Kamarianakis, Yiannis Pantazis, Yannis Kalligiannaki, Evangelia Katsaounis, Theodoros D. Kotsovos, Konstantinos Gereige, Issam Abdullah, Marwan Jamal, Aqil Tzavaras, Athanasios Foundation for Research and Technology-Hellas Institute of Applied and Computational Mathematics Data Science Group Greece University of Crete Dept. of Mathematics and Applied Mathematics Greece Saudi Aramco RandD Center Carbon Management Division Dhahran Saudi Arabia Computer Electrical and Mathematical Sciences and Engineering Division Thuwal Saudi Arabia

ISBN: (纸本)9781665475891

Energy production from solar photovoltaic (PV) plants is unpredictable, mainly due to the stochastic formation and movement of clouds or aerosol - dust particles which scatter or disperse solar radiation. Accurate forecasts of PV output are essential to Distribution and Transportation System Operators as they assist efficient solar energy trading and management of electricity grids. This work evaluates an autoregressive, computationally-light KNN-regression scheme (TSFKNN) for hourly, day-ahead forecasts of solar irradiance and energy yield of various PV technologies. The model is being tested and validated using data measured in Thuwal, Saudi Arabia. The available measured records span a 60-month period. The developed forecasting models are designed for online systems and provide increased levels of accuracy and low computational cost. Several parametric and nonparametric specifications, coupled with conventional versus outlier-robust estimation procedures are tested, in order to derive an optimal month-specific daily profile (MDP). Current results demonstrate that including intraday variability to the monthly-based irradiance models achieve improved predictive accuracy between 10% and 25% on average. © 2022 IEEE.

关键词： Forecasting

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Sensor-Based Cattle Behavior Classification Using Deep Learning Approaches

Sensor-Based Cattle Behavior Classification Using Deep Learn...

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2023 International Technical Conference on Circuits/Systems, computers, and Communications, ITC-CSCC 2023

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Tancharoen, Datchakorn Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Panyapiwat Institute of Management Faculty of Engineering and Technology Nonthaburi Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350326413

The usage of precision livestock has grown due to the need for higher efficiency and productivity in response to the high demand for food. To ensure sustainable development and quality control of the inputs required by the industry, it is essential to monitor and classify the behavior of cattle. Sensor-based monitoring systems provide accurate information by capturing raw data and identifying behavior through machine learning and deep learning algorithms. This approach has allowed farmers to better understand the individual needs of their animals. This study presents a deep residual neural network for the classification of cattle behavior. The performance of the ResNeXt model was evaluated using a public real-world dataset collected from sensors attached to the neck of six different Japanese black beef cows. The experimental results showed that the presented ResNeXt model achieved the highest average accuracy of 94.96% and the highest average F1-score of 93.66%. Compared to other baseline deep learning models and the current state-of-the-art model for cattle behavior classification, the presented model outperformed them and achieved better performance. © 2023 IEEE.

关键词： Quality control

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Human Activity Recognition in Logistics Using Wearable Sensors and Deep Residual Network 38

Human Activity Recognition in Logistics Using Wearable Senso...

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38th IEEE Region 10 Conference, TENCON 2023

作者： Mekruksavanich, Sakorn Tancharoen, Datchakorn Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Panyapiwat Institute of Management Faculty of Engineering and Technology Nonthaburi Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350302196

Human action identification is a practical area of study with broad applicability in various domains, such as medical care, sport science, and manufacturing management. In logistics, it is essential to identify and examine individual actions, enabling machines to perceive and comprehend human motions for non-verbal interaction. This study specifically fo-cuses on efficiently classifying working activities in the logistics industry using wearable sensors, particularly in the context of human activity recognition. To achieve the research objective, a deep residual neural network was introduced, integrating convolutional layers, shortcut connections, and aggregated transformation for human activity recognition in logistics. The authors evaluated the effectiveness of their proposed deep learning model using the publicly accessible LARa dataset. The LARa dataset comprises a diverse range of human actions in the logistics domain, including standing, walking, cart handling, and synchronization. The details of activity were captured using wearable sensors affixed to different anatomical sites of the study participants. The experimental findings indicate that the model achieved a maximum F-measure of 85.30%. © 2023 IEEE.

关键词： Wearable sensors

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MHD Nanofluid Flow Past a Vertical Plate Embedded in a Rotating Porous Medium with Chemical Reaction

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Journal of applied mathematics and Physics 2024年第12期12卷 4242-4273页

作者： Waheed A. Ahmed Edward R. Onyango David Theuri Faiz Awad Institute for Basic Sciences Technology and Innovation Pan African University Nairobi Kenya Department of Mathematics Faculty of Mathematical and Computer Sciences University of Gezira Wad Medani Sudan Department of Pure and Applied Mathematics Jomo Kenyatta University of Agriculture and Technology Juja Kenya Department of Mathematics Omdurman Islamic University Omdurman Sudan

In this paper, the problem of an unsteady free convection of MHD nanofluid flow past a semi-infinite vertical rotating plate embedded in a porous medium subject to a first-order chemical reaction has been studied numerically. The fluid flow has been mathematically modelled under the assumption that the viscosity of the fluid is temperature dependent. The governing dimensional partial differential equations were transformed into their equivalence non-dimensional form using a suitable variable. The resulting nonlinear partial differential equations were solved numerically using the bivariate interpolated spectral relaxation method (BI-SRM). The impacts of our physical parameters on the velocity components, temperature, concentration, and axial components of the induced magnetic field, as well as skin friction, are investigated using graphical and tabular forms. This establishes that as the rotation parameter increases, the secondary velocity field is rising, however when the Reynolds magnetic number increases, the opposite pattern is demonstrated.

关键词： Temperature-Dependent Viscosity Variable Magnetic Field Rotating Frame Permeability Bivariate Spectral Relaxation Method

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Free-electron crystals for enhanced X-ray radiation

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Light(Science & Applications) 2024年第7期13卷 1299-1309页

作者： Lee Wei Wesley Wong Xihang Shi Aviv Karnieli Jeremy Lim Suraj Kumar Sergio Carbajo Ido Kaminer Liang Jie Wong School of Electrical and Electronic Engineering Nanyang Technological University50 Nanyang AvenueSingapore 639798Singapore. Solid State Institute and Faculty of Electrical and Computer Engineering Technion–Israel Institute of TechnologyHaifa 3200003Israel. School of Electrical Engineering Fleischman Faculty of EngineeringTel Aviv UniversityTel Aviv 69978Israel Department of Applied Physics Stanford UniversityStanfordCA 94305USA. Science Mathematics and TechnologySingapore University of Technology and Design8 Somapah RoadSingapore 487372Singapore. Electrical and Computer Engineering Department UCLA420 WestwoodLos AngelesCA 90095USA. Physics and Astronomy Department UCLA475 Portola PlazaLos AngelesCA 90095USA. SLAC National Accelerator Laboratory Stanford University2575 Sand Hill RoadMenlo ParkCA 94025USA

Bremsstrahlung—the spontaneous emission of broadband radiation from free electrons that are deflected by atomic nuclei—contributes to the majority of X-rays emitted from X-ray tubes and used in applications ranging from medical imaging to semiconductor chip ***,we show that the bremsstrahlung intensity can be enhanced significantly—by more than three orders of magnitude—through shaping the electron wavefunction to periodically overlap with atoms in crystalline ***,we show how to shape the bremsstrahlung X-ray emission pattern into arbitrary angular emission profiles for purposes such as unidirectionality and ***,we find that these enhancements and shaped emission profiles cannot be attributed solely to the spatial overlap between the electron probability distribution and the atomic centers,as predicted by the paraxial and nonrecoil theory for free electron light *** work highlights an unprecedented regime of free electron light emission where electron waveshaping provides multi-dimensional control over practical radiation processes like *** results pave the way towards greater versatility in table-top X-ray sources and improved fundamental understanding of quantum electron-light interactions.

关键词： overlap attributed arbitrary

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Numerical Analysis of a Functional Equation for the Stochastic Optimal Control Problem 15

Numerical Analysis of a Functional Equation for the Stochast...

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15th International Conference Management of Large-Scale System Development, MLSD 2022

作者： Chernov, Aleksey Zhukova, Aleksandra MIPT-School of Applied Mathematics and Computer Science Moscow Institute of Physics and Technology Moscow Russia Frc Csc Ras Dept. of Mathematical Modelling of Economic Systems Moscow Russia

This work presents the approach to solving the functional equation of non-local type that appears in the study of a stochastic optimal control problem. This experience demonstrates the challenges and possible solution... 详细信息

ISBN: (数字)9781665497015

ISBN: (纸本)9781665497015

关键词： Numerical methods

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