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IEEE Colombian Conference on Applications in Computational Intelligence (ColCACI)

作者： Guillermo Augusto Durán Boneth Rosanna Costaguta Dewar Rico-Bautista Systems Engineering Program University Francisco de Paula Santander Ocaña Colombia Institute for Research in Informatics and Information Systems National University of Santiago del Estero Santiago del Estero Argentina Department of Systems and Informatics University Francisco de Paula Santander Ocaña Colombia

ISBN: (数字)9798331516901

ISBN: (纸本)9798331516918

This paper describes the ECO-LÓGICA prototype, a useful tool for the decision-making process related to air pollution. The ECO-LÓGICA system can generate a wide range of reports, including those based on real data collected through the monitoring of carbon dioxide emissions. Additionally, it employs a specialized neural network to predict future air pollution values resulting from these emissions. The performance of the prototype was validated using data supplied by three Colombian environmental monitoring stations. The applied metrics reach values close to the maximums, demonstrating adequate performance.

关键词： Measurement Decision making Neural networks Prototypes Carbon dioxide Air pollution Solids

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Differentially Private Log-Location-Scale Regression Using Functional Mechanism

arXiv

引用

arXiv 2024年

作者： Sheng, Jiewen Fang, Xiaolei Graduate Program in Operations Research North Carolina State University United States Edward P. Fitts Department of Industrial and Systems Engineering North Carolina State University United States

This article introduces differentially private log-location-scale (DP-LLS) regression models, which incorporate differential privacy into LLS regression through the functional mechanism. The proposed models are established by injecting noise into the log-likelihood function of LLS regression for perturbed parameter estimation. We will derive the sensitivities utilized to determine the magnitude of the injected noise and prove that the proposed DP-LLS models satisfy ϵ-differential privacy. In addition, we will conduct simulations and case studies to evaluate the performance of the proposed models. The findings suggest that predictor dimension, training sample size, and privacy budget are three key factors impacting the performance of the proposed DP-LLS regression models. Moreover, the results indicate that a sufficiently large training dataset is needed to simultaneously ensure decent performance of the proposed models and achieve a satisfactory level of privacy protection. © 2024, CC BY.

关键词： Budget control

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Flow Matching for Accelerated Simulation of Atomic Transport in Materials

arXiv

引用

arXiv 2024年

作者： Nam, Juno Liu, Sulin Winter, Gavin Jun, KyuJung Yang, Soojung Gómez-Bombarelli, Rafael Department of Materials Science and Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Computational and Systems Biology Program Massachusetts Institute of Technology CambridgeMA02139 United States

We introduce LiFlow, a generative framework to accelerate molecular dynamics (MD) simulations for crystalline materials that formulates the task as conditional generation of atomic displacements. The model uses flow matching, with a Propagator submodel to generate atomic displacements and a Corrector to locally correct unphysical geometries, and incorporates an adaptive prior based on the Maxwell–Boltzmann distribution to account for chemical and thermal conditions. We benchmark LiFlow on a dataset comprising 25-ps trajectories of lithium diffusion across 4,186 solid-state electrolyte (SSE) candidates at four temperatures. The model obtains a consistent Spearman rank correlation of 0.7–0.8 for lithium mean squared displacement (MSD) predictions on unseen compositions. Furthermore, LiFlow generalizes from short training trajectories to larger supercells and longer simulations while maintaining high accuracy. With speed-ups of up to 600,000× compared to first-principles methods, LiFlow enables scalable simulations at significantly larger length and time scales. Copyright © 2024, The Authors. All rights reserved.

关键词： Molecular dynamics

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Mammogram Breast Cancer Classification Based on Deep-Convolutional Neural Network

Mammogram Breast Cancer Classification Based on Deep-Convolu...

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International Seminar on Application for Technology of Information and Communication (ISemantic)

作者： Avissa Aurellia Amadea Christy Atika Sari Eko Hari Rachmawanto Wellia Shinta Sari Mohammed S. Noori Mohamed Ayad Alkhafaji Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia Study Program in Information Systems University of Dian Nuswantoro Semarang Indonesia Department of Computer Engineering Technology Northern Technical University Mosul Iraq Department of Computer Science National University of Science and Technology DhiQar Iraq

Based on WHO’s data, breast cancer is one of the most deadly diseases that has claimed many victims, especially women. This disease begins with the presence of an undetected and eventually turns into malignant (cancer). This happens due to ignorance of the importance of having a medical check-up even though in good health. Doctors and researchers can prevent the development of tumor cells through treatment that begins with radiological examinations to identify the possibility of a person being affected by this disease. One of the most frequently used techniques is Mammography. This technique can detect the presence of tumor cells using advanced technology and several methods in displaying the patient’s diagnostic results on low-dose X-rays in the form of mammogram images. The technology is inseparable from the methods used to identify the presence of tumor cells. In this study, we have proposed the CNN method based on the deep-CNN model to identify mammogram images in the detection of breast cancer cells with average evaluation results in terms of accuracy, precision, recall, specificity, and f-measure on mammogram image datasets of 99.52%, 99.72%, 99.31%, 99.72%, and 99.5%. These results showed that this method has a good performance in breast cancer detection.

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Economic Feasibility and Sensitivity Analysis on Integration of Solar PV and Electric Vehicles in Renewable Energy Community

Economic Feasibility and Sensitivity Analysis on Integration...

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Compatibility in Power Electronics (CPE)

作者： Shoaib Ahmed Amjad Ali Sikandar Abdul Qadir Antonio D’Angola Department of Information and Electrical Engineering and Applied Mathematics (DIEM) University of Salerno Via Giovanni Paolo II 132 Fisciano SA Italy Department of Engineering University of Basilicata Via dell’Ateneo Lucano 10 Potenza PZ Italy Interdisciplinary Research Centre for Sustainable Energy Systems King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia Electrical Engineering Department King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia

ISBN: (数字)9798331515171

ISBN: (纸本)9798331515188

Renewable Energy Communities (RECs) have emerged recently because of numerous advantages, as these are characterized by locally generated electricity from renewable energy sources (RESs) controlled by individuals, enterprises, and public administrations. This article focuses on the development of a mathematical model to investigate the time-dependent behavior of RECs with an overview of the technical, economic, and environmental scenario. The economic viability and overall profitability of incorporating electric vehicles (EVs) into a photovoltaic (PV)-based REC that consists of consumers and prosumers are assessed in this study. Key economic parameters, for instance Net Present Value (NPV), Levelized cost of energy (LCOE), and payback period (PP), are included in the mathematical model, and, additionally, CO 2 emission analysis has been considered. A sensitivity analysis of the REC has been carried out to focus on the impact of various parameters considered in our configuration. Moreover, the saving of CO 2 emissions and economic benefits have been evaluated. The mathematical and numerical model could represent a useful tool for policymakers, the Government, stakeholders, and participants to analyze the advantages of building a REC.

关键词： Photovoltaic systems Renewable energy sources Power engineering Sensitivity analysis Profitability Mathematical models Power electronics Numerical models Stakeholders Carbon emissions

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On the Consistency of Renewable-to-Hydrogen Pricing

引用

CSEE Journal of Power and Energy systems 2022年第2期8卷 392-402页

作者： Litao Zheng Jianxiao Wang Yang Yu Gengyin Li Ming Zhou Qing Xia Guizhi Xu State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China Institute for Interdisciplinary Information Sciences Tsinghua UniversityBeijing 100084China State Key Lab of Power Systems Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Global Energy Interconnection Research Institute co. Ltd.State Grid Corporation of ChinaBeijing 102211China

With the proposal of carbon neutrality goals and hydrogen energy development strategies in various countries,the development and construction of hydrogen supply chains have become important ***,existing research has paid little attention to the hydrogen market and ***,a hydrogen pricing method based on marginal pricing theory is proposed in this paper,which adapts to hydrogen systems with renewable-to-hydrogen as a major source,in the future.A hydrogen energy market is established to define the industrial chain of hydrogen and the hydrogen trading *** hydrogen market-clearing model is formulated considering a dynamic line *** to its nonconvexity,the model is equivalently converted into mixed-integer second-order cone programming,and the optimality gap is minimized by introducing a penalty *** on the clearing solution,the concept and calculation method of the locational marginal hydrogen price(LMHP)are proposed with respect to the locational marginal price(LMP)in electricity *** studies based on a modified Belgium 20-node gas network and Pennsylvania,New Jersey,and Maryland(PJM)market operation data demonstrate the consistency between LMHP and LMP.

关键词： Hydrogen market locational marginal price renewable-to-hydrogen second-order cone programming Weymouth equation

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Quantitative Analysis of Ankle Spasticity Through Electromyography Signal

Quantitative Analysis of Ankle Spasticity Through Electromyo...

引用

Medical Technologies National Conference (TIPTEKNO)

作者： Ayse Gurluk Hafiz Muhammad Bilal Elif Hocaoglu Living Robotics Laboratory Graduate School of Health Sciences Physiotherapy and Rehabilitation Program Istanbul Medipol University Istanbul Türkiye Living Robotics Laboratory Graduate School of Engineering and Natural Sciences Electrical-Electronics Engineering and Cyber-Systems Program Istanbul Medipol University Istanbul Türkiye Department of Electrical and Electronics Engineering Living Robotics Laboratory Istanbul Medipol University Istanbul Türkiye

ISBN: (数字)9798331529819

ISBN: (纸本)9798331529826

This study presents a more accurate evaluation method for assessing ankle spasticity compared to conventional goniometric techniques by utilizing measurements obtained from Inertial Measurement Units (IMU) and surface electromyography (sEMG) devices. Integrating IMU and EMG sensors provides substantial benefits for accurately determining the range of motion (ROM) associated with spasticity during targeted movements. In this study, the proposed method provides precise measurement and an objective evaluation of onset time detection of antagonistic muscle co-contraction, and accordingly the ROM of the actuated joint. Moreover, the implementation of cost-effective wearable sensors enables the monitoring of muscle spasticity in clinical environments and can be particularly valuable in regions with limited access to experienced therapists, providing a more efficient approach to tracking spasticity levels.

关键词： Ankle Measurement units Inertial navigation Muscles Electromyography Sensors Reliability Motion control Read only memory Monitoring

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Implementation of AI-Based Support Vector Machine Algorithm for Fraud Detection in Digital Payment systems

Implementation of AI-Based Support Vector Machine Algorithm ...

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International Seminar on Research of Information Technology and Intelligent systems (ISRITI)

作者： Edy Prayitno Rikie Kartadie Uning Lestari Munengsih Sari Bunga Accounting Information Systems Faculty of Information Technology Universitas Teknologi Digital Indonesia Yogyakarta Indonesia Computer Engineering Study Program Faculty of Information Technology Universitas Teknologi Digital Indonesia Yogyakarta Indonesia Informatics Study Program Faculty of Science and Information Technology Universitas Akprind Indonesia Yogyakarta Indonesia Department of Computer Engineering Smart City Information Systems Politeknik Negeri Indramayu Indramayu Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

The increasing use of digital payment systems has led to a rise in fraudulent activities, presenting a significant challenge in ensuring secure transactions. This research focuses on implementing the Support Vector Machine (SVM) algorithm with a Radial Basis Function (RBF) kernel to detect fraud in digital payment systems. One of the main challenges addressed in this study is the severe class imbalance in the dataset, where fraudulent transactions account for only 0.17% of total transactions. To overcome this, the SMOTE (Synthetic Minority Over-sampling Technique) method was applied to balance the dataset, allowing the model to better recognize fraudulent patterns. The results indicate that the SVM model achieved an accuracy of 99.93%, with a precision of 86.23% and a recall of 75.51%. These results demonstrate that SVM, combined with SMOTE and RBF kernel, is highly effective in detecting fraudulent transactions while minimizing false positives. This research provides a strong foundation for improving fraud detection models in the context of digital payment systems, offering enhanced security and trust for users. Further research could explore hybrid models and real-time data analysis to improve performance.

关键词： Support vector machines Seminars Adaptation models Accuracy Data models Real-time systems Fraud Security Kernel Intelligent systems

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Learning from 2D: machine learning of 3D effective properties of heterogeneous materials based on 2D microstructure sections

arXiv

引用

arXiv 2023年

作者： Hu, Guangyu Latypov, Marat I. Department of Materials Science and Engineering University of Arizona TucsonAZ85721 United States Graduate Interdisciplinary Program in Applied Mathematics University of Arizona TucsonAZ85721 United States

Microstructure–property relationships are key to effective design of structural materials for advanced applications. Advances in computational methods enabled modeling microstructure-sensitive properties using 3D models (e.g., finite elements) based on microstructure representative volumes. 3D microstructure data required as input to these models are typically obtained from either 3D characterization experiments or digital reconstruction based on statistics from 2D microstructure images. In this work, we present machine learning (ML) approaches to modeling effective properties of heterogeneous materials directly from 2D microstructure sections. To this end, we consider statistical learning models based on spatial correlations and convolutional neural networks as two distinct ML strategies. In both strategies, models are trained on a dataset of synthetically generated 3D microstructures and their properties obtained from micromechanical 3D simulations. Upon training, the models predict properties from 2D microstructure sections. The advantage of the presented models is that they only need 2D sections, whose experimental acquisition is more accessible compared to 3D characterization. Furthermore, the present models do not require digital reconstruction of 3D microstructures. © 2023, CC BY.

关键词： Microstructure

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Interactive Facial Expression Editing with Non-linear Blendshape Interpolation

Interactive Facial Expression Editing with Non-linear Blends...

引用

43rd Annual Conference on European Association for Computer Graphics, EUROGRAPHICS 2022

作者： Roh, Ji Hyun Kim, Seong Uk Jang, Hanyoung Seol, Yeongho Kim, Jongmin Department of Computer Science and Engineering Kangwon National University Korea Republic of Interdisciplinary Graduate Program in Medical Bigdata Convergence Kangwon National University Korea Republic of Game AI Lab NC Korea Republic of NVIDIA Korea Republic of

ISBN: (纸本)9783038681694

The ability to manipulate facial animations interactively is vital for enhancing the productivity and quality of character animation. In this paper, we present a novel interactive facial animation editing system that can express the naturalness of non-linear facial movements in real-time. The proposed system is based on a fully automatic algorithm that maintains all positional constraints while deforming the facial mesh as realistic as possible. Our method is based on direct manipulation with non-linear blendshape interpolation. We formulate the facial animation editing as a two-step quadratic minimization and solve it efficiently. From our results, the proposed method produces the desired and realistic facial animation better compared to existing mesh deformation methods, which are mainly based on linear combination and optimization. © 2022 The Author(s).

关键词： Interpolation

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