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Optimal Control in the Problem With Two Gas Fields 17

Optimal Control in the Problem With Two Gas Fields

引用

17th International conference on Management of Large-Scale System Development, MLSD 2024

作者： Skiba, Alexander Federal Research Center 'Computer Science and Control' of Ras Department of Mathematical Methods of Regional Programming Moscow Russia

ISBN: (纸本)9798350375718

We explore a continuous aggregated dynamic model for developing two gas fields. The new borehole commissioning rates are the control parameters. Changes in the average flow rate of producing boreholes and current natural gas production are proportional. It is necessary to solve the problem of maximizing discounted accumulated income for two gas fields. We analyze the optimal control problem with an unrestricted right end and a fixed time. The Pontryagin maximum principle is a main tool for solving the problem. The special optimal control mode is of particular interest. We highlight the critical aspects of all possible optimal controls. © 2024 IEEE.

关键词： Gas industry

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Crafting a Machine Learning Model for Discerning Synthetic and Authentic Images 2

Crafting a Machine Learning Model for Discerning Synthetic a...

引用

2nd International conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Shah, Misha Jaiswal, Gautam Sharma, Dolly Amity School of Engineering and Technology Amity University Noida India Department of Computer Science and Engineering Amity University Noida India

ISBN: (纸本)9798331541217

This paper shows the development of a Convolutional Neural Network (CNN) model with an attention mechanism and data augmentation for discerning synthetic and authentic images. Given the increase in AI-generated content there is a need for robust methods to identify an image's authenticity. The model employs ensemble learning by using ResNet50 and VGG16 architectures which are combined with a custom CNN. The project has detailed explanations of CNNs, data augmentation, TensorFlow, CUDA, ensemble models and the mathematical concepts underlying these techniques. With a dataset of 2.7 million images, the model aims for high accuracy in classification tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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Propagation Dynamics and Connectivity Assurance in Sensor Radio Networks

Propagation Dynamics and Connectivity Assurance in Sensor Ra...

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2024 International conference on Communication, computer sciences and Engineering, IC3SE 2024

作者： Sharma, Smita Sharma, Tarunika Praveena, K. Kumar, Manish Syambabu, V. Hameed, Ali Abduhussien Amity University Department Of Electronics & Communication Engineering Greater Noida India New Horizon College Of Engineering Department Of Applied Sciences Bangalore India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India Lovely Professional University Phagwara India Mlr Institute Of Technology Department Of Electronics And Communication Engineering Telangana Hyderabad India College Of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq

ISBN: (纸本)9798350366846

This piece talks about research that has been done on sending signals over sensor radio networks and making sure that links are safe. Connections and sharing info must be effective in a world that is always changing. To make this happen, we show a full plan that speeds up the network by using three important methods, each backed by a statistical model. Dynamic Propagation Modeling (DPM) guesses how signals will behave as they happen. Network managers can make better decisions when they use dynamic mathematical models that consider changes in the environment and the movement of nodes. The second method, called the Disturbance Mitigation technique (IMA), changes the broadcast power and channel selection on the fly to stop close devices from interfering. In busy WiFi networks, this makes sure that joining is effective. Algorithm 3: ARPC changes route paths based on how the network is working right now. It consistently links, even when the network changes. Using all three of these methods together helps us consider differences in propagation, get the most out of transmission power, and choose communication lines on the fly. This makes the network work better and be more reliable. mathematical models help us figure out things like throughput, packet loss rate, end-to-end latency, network life, energy saves, and the dependability of connections. © 2024 IEEE.

关键词： Wireless sensor networks

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SNIP: BRIDGING mathematical SYMBOLIC AND NUMERIC REALMS WITH UNIFIED PRE-TRAINING 12

SNIP: BRIDGING MATHEMATICAL SYMBOLIC AND NUMERIC REALMS WITH...

引用

12th International conference on Learning Representations, ICLR 2024

作者： Meidani, Kazem Shojaee, Parshin Reddy, Chandan K. Farimani, Amir Barati Department of Mechanical Engineering Carnegie Mellon University United States Department of Computer Science Virginia Tech United States Machine Learning Department Carnegie Mellon University United States

In an era where symbolic mathematical equations are indispensable for modeling complex natural phenomena, scientific inquiry often involves collecting observations and translating them into mathematical expressions. Recently, deep learning has emerged as a powerful tool for extracting insights from data. However, existing models typically specialize in either numeric or symbolic domains, and are usually trained in a supervised manner tailored to specific tasks. This approach neglects the substantial benefits that could arise from a task-agnostic multi-modal understanding between symbolic equations and their numeric counterparts. To bridge the gap, we introduce SNIP, a Symbolic-Numeric Integrated Pre-training model, which employs contrastive learning between symbolic and numeric domains, enhancing their mutual similarities in the embeddings. By performing latent space analysis, we observe that SNIP provides cross-domain insights into the representations, revealing that symbolic supervision enhances the embeddings of numeric data and vice versa. We evaluate SNIP across diverse tasks, including symbolic-to-numeric mathematical property prediction and numeric-to-symbolic equation discovery, commonly known as symbolic regression. Results show that SNIP effectively transfers to various tasks, consistently outperforming fully supervised baselines and competing strongly with established task-specific methods, especially in the low data regime scenarios where available data is limited. © 2024 12th International conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Embeddings

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Design and Implementation of a Document Encryption Convergence Program Selecting Encryption methods, and Integrating the Program into the Existing Office System

Design and Implementation of a Document Encryption Convergen...

引用

12th International conference on Data science, Technology and Applications, DATA 2023

作者： Ryu, Hong-Jin Lee, Samuel Sangkon Dept. of Computer Engineering Jeonbuk National University Chonbuk Jeonju Korea Republic of Dept. of Computer Science and Engineering Jeonju University Chonbuk Jeonju Korea Republic of

ISBN: (纸本)9789897586644

The article explores the limitations of current encryption methods and proposes a new encryption system that combines ancient and modern cryptography through software engineering. The article also discusses the history of cryptography, from simple substitution encryption methods to more complex mathematical algorithms. The proposed encryption system fuses several ancient ciphers with AES-256 encryption, creating a more secure and stronger password that is difficult to decrypt. This paper suggests the usefulness and reliability of cryptographic algorithms, such as virtual currency and blockchain fields, and could be used easily by the general public in electronic devices. © 2023 by SCITEPRESS–science and Technology Publications, Lda.

关键词： Ciphertext

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Evaluating Machine Learning methods for Bangla Text Emotion Analysis

Evaluating Machine Learning Methods for Bangla Text Emotion ...

引用

2024 Asia Pacific conference on Innovation in Technology, APCIT 2024

作者： Akter, Tahmina Akter, Mst. Sharmin Mahmud, Tanjim Islam, Dilshad Hossain, Mohammad Shahadat Andersson, Karl Port City International University Dept. of Computer Science and Engineering Chittagong Bangladesh Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati4500 Bangladesh Chattogram Veterinary and Animal Sciences University Dept. of Physical and Mathematical Sciences Bangladesh University of Chittagong Dept. of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Cybersecurity Laboratory Luleå97187 Sweden

ISBN: (纸本)9798350361537

Text-based emotion identification goes beyond simple sentiment analysis by capturing emotions in a more nuanced way, akin to shades of gray rather than just positive or negative sentiments. This paper details our experiments with emotion analysis on Bangla text. We collected a corpus of user comments from various social media groups discussing socioeconomic and political topics to identify six emotions: sadness, disgust, surprise, fear, anger, and joy. We evaluated the performance of four widely used machine learning algorithms - RF, DT, k-NN, and SVM - alongside three popular deep learning algorithms - CNNs, LSTM, and Transformer Learning - using TF-IDF feature extraction and word embedding techniques. The results showed that among the machine learning algorithms, DT, RF, k-NN, and SVM achieved accuracy scores of 82%, 84%, 73%, and 83%, respectively. In contrast, the deep learning models CNN and LSTM both achieved higher performance with an accuracy of 85% and 86% respectively. These findings highlight the effectiveness of traditional ML and DL approaches in detecting emotions from Bangla social media texts, indicating significant potential for further advancements in this area. © 2024 IEEE.

关键词： Contrastive Learning

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On a General Method for Modeling the Controlled Dynamics of Manipulators with Parallel Kinematics as Systems with Geometrical Constraints 10

On a General Method for Modeling the Controlled Dynamics of ...

引用

10th International conference on Advances in Machinery, Material science and Engineering Application, MMSE 2024

作者： Krasinskiy, Aleksandr Ya Dept. of Computer Modeling and Probability Theory Moscow Aviation Institute National Research UniversityInformation and Computing Center Rosbiotech Moscow Russia

ISBN: (纸本)9781643685465

An adequate mathematical model including all components of an automatic system is a prerequisite for the most cost-effective implementation of the device's potential capabilities. Such a strict nonlinear mathematical model must make it possibility to minimize the actuator drives number, reduce the measurement information amount (the number of measuring sensors) and analyze transient processes in actuator drives. The traditional application of Lagrange equations with constraint multipliers in dynamics models of non-free systems leads to an increase in their dimensionality. The variables of such models are all coordinates, all (including dependent) velocities and also constraint multipliers. The article develops the application of the theoretical mechanics rigorous methods non-free systems to minimize the dimensionality of nonlinear mathematical models of parallel manipulators as the systems with geometric constraints, taking into account the dynamics of actuator electric drives. The transition to equations in redundant coordinates, free from constraint multipliers, excludes from consideration not only multipliers, but also dependent velocities. The application of the proposed approach to modeling the stand Ball and Beam dynamics is considered. © 2024 The Authors.

关键词： Manipulators

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Multi-Modal Medical Image Fusion via 3D Manifold Fitting and Dual-Domain Cross-Attention

Multi-Modal Medical Image Fusion via 3D Manifold Fitting and...

引用

2025 IEEE International conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Zeyu Wang, Jiayu Song, Haiyu Feng, Jiawei Duan, Haoran College of Computer Science and Engineering Dalian Minzu University Dalian China College of Computer Science Durham University Durham United Kingdom

ISBN: (纸本)9798350368741

Medical image fusion (MIF) aims to extract complementary features from multi-modal source images and fuse them into a single image to assist in clinical diagnostics. Despite its importance, MIF faces two primary challenges: the lack of tailored paradigms for CMSF extraction and insufficient dual exploration of multi-modality and multi-frequency domains. To address these challenges, we propose a novel MIF model in this study. From the perspective of image manifolds, we reformulate CMSF extraction as a 3D manifold fitting problem and introduce a paradigm that uses mathematical fitting methods to generate CMSF. This approach achieves accurate feature extraction without the need for carefully designed loss functions as constraints, significantly reducing the number of parameters. Additionally, we introduce Cross-Modality Co-Frequency (CM-CoF) and Cross-Frequency Co-Modality (CF-CoM) attention modules, which explore implicit relationships between modalities and frequency domains. Experimental results demonstrate that the proposed model outperforms many state-of-the-art MIF algorithms. © 2025 IEEE.

关键词： Cross Attention Feature Extraction Frequency Domain Manifold Fitting Multi-Modal Medical Image Fusion

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Automatic Mathematic In-Context Example Generation for LLM Using Multi-Modal Consistency 31

Automatic Mathematic In-Context Example Generation for LLM U...

引用

31st International conference on Computational Linguistics, COLING 2025

作者： Lee, Jaeseong Yang, Wei Gupta, Gopal Wei, Shiyi Department of Computer Science The University of Texas at Dallas RichardsonTX United States

ISBN: (纸本)9798891761964

Large Language Models (LLMs) have advanced Natural Language Processing (NLP) tasks but are limited in mathematical reasoning. To address this, few-shot examples are used in prompts for in-context learning. However, existing methods require annotated datasets, resulting in higher computational costs and lower quality examples. To mitigate these limitations, we propose AutoMathIC, a framework that automatically generates high-quality in-context examples to enhance LLMs' mathematical reasoning. AutoMathIC ensures consistency across different modalities (e.g., Chain-of-Thought (CoT), code snippets, and equations) by generating and selecting mutations that improve response consistency. Evaluated on four math problem datasets, AutoMathIC outperforms six baselines, with LLM accuracy ranging from 87.0% to 99.3% for GPT-3.5 and 93.1% to 98.7% for GPT-4o-mini. It surpasses the state-of-the-art in-context example retrieval method in three of the four datasets by 0.3% to 11.8%, without relying on an annotated dataset. © 2025 Association for Computational Linguistics.

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Minimization of Nonlinear Energies in Python Using FEM and Automatic Differentiation Tools 1

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15th International conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Béreš, Michal Valdman, Jan Institute of Geonics of the Czech Academy of Sciences Studentská 1768 Ostrava708 00 Czech Republic Department of Applied Mathematics VSB - Technical University of Ostrava 17. listopadu 15/2172 Ostrava708 00 Czech Republic Institute of Information Theory and Automation of the Czech Academy of Sciences Pod vodárenskou věží 4 Prague18208 Czech Republic Department of Computer Science Faculty of Science University of South Bohemia Branišovská 31 České Budějovice37005 Czech Republic

ISBN: (数字)9783031857034

ISBN: (纸本)9783031857027

This contribution examines the capabilities of the Python ecosystem to solve nonlinear energy minimization problems, with a particular focus on transitioning from traditional MATLAB methods to Python’s advanced computational tools, such as automatic differentiation. We demonstrate Python’s streamlined approach to minimizing nonlinear energies by analyzing three problem benchmarks - the p-Laplacian, the Ginzburg-Landau model, and the Neo-Hookean hyperelasticity. This approach merely requires the provision of the energy functional itself, making it a simple and efficient way to solve this category of problems. The results show that the implementation is about ten times faster than the MATLAB implementation for large-scale problems. Our findings highlight Python’s efficiency and ease of use in scientific computing, establishing it as a preferable choice for implementing sophisticated mathematical models and accelerating the development of numerical simulations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Python

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