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Decentralized Cognitive Swarm-Based Control systems: A Novel Approach for Autonomous Decision-Making and Adaptation

Decentralized Cognitive Swarm-Based Control Systems: A Novel...

引用

international Youth conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Pavel A. Panilov Tatiana Y. Tsibizova Computer science and control systems Bauman Moscow State Technical University Moscow Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

The emergence of decentralized systems has precipitated a paradigm shift in the realm of autonomous control across diverse domains, ranging from robotics to critical infrastructure management. One such approach that is gaining traction is the cognitive swarm-based control system (CSBCS), which applies principles from cognitive science and swarm intelligence to decentralized decision-making processes. The present paper explores the design and application of CSBCS, emphasizing its ability to foster adaptability, robustness, and scalability in dynamic environments. The CSBCS framework integrates cognitive models that simulate human-like thinking, enabling individual agents within the swarm to operate autonomously, collaborate effectively, and adapt to changing conditions without centralized oversight. The experimental findings, derived from simulations and real-world applications demonstrate the potential of CSBCS in augmenting both individual and collective performance across a spectrum of domains, ranging from multi-robot coordination to critical infrastructure protection. CSBCS provides an framework for addressing complex issues that traditional centralized approaches are unable to tackle effectively.

关键词： Adaptation models Scalability Robot kinematics Heuristic algorithms Computational modeling Decision making computer architecture Distance measurement Critical infrastructure Resource management

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AI-Powered Color Restoration of Faded Historical Paintings

AI-Powered Color Restoration of Faded Historical Paintings

引用

international ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Wai Yie Leong Engineering & Quantity Surveying INTI International University Nilai Malaysia

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

The degradation of color in historical paintings poses a significant challenge to art preservation, diminishing the vibrancy and authenticity of masterpieces over time. This paper explores the potential of artificial intelligence (AI) to revolutionize color restoration in historical artworks. By leveraging advanced machine learning techniques, including convolutional neural networks (CNNs) and generative adversarial networks (GANs), AI systems can analyze faded paintings and reconstruct their original hues with unprecedented precision. Using large datasets of high-resolution images, these algorithms learn patterns of pigment degradation and stylistic nuances inherent to specific artists or periods. The study highlights how AI models, trained on multispectral and hyperspectral imaging data, can infer the original color schemes by detecting remnants of pigments invisible to the naked eye. Moreover, AI-driven simulations offer insights into artistic intent and historical contexts, enabling restorers to make informed decisions. This interdisciplinary approach merges art history, chemistry, and computer science, fostering a new era of cultural heritage preservation. The findings underscore AI's transformative role in safeguarding the visual integrity of humanity's artistic legacy while maintaining the ethical considerations of artistic authenticity and conservation standards.

关键词： Ethics Art Image color analysis Computational modeling Imaging Pigments Image restoration Cultural differences Artificial intelligence Painting

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joint Optimization of Wind Farm Layout Considering Yaw Control 8th

Joint Optimization of Wind Farm Layout Considering Yaw Cont...

引用

8th international conference on Life System modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Wang, Yiqun Jiang, Ruolin Chen, Yuanye Fang, Fang School of Control and Computer Engineering North China Electric Power University Beijing102206 China

ISBN: (纸本)9789819602247

As wind farms continue to grow in size, minimizing wake losses between turbines in layout design becomes increasingly important. Current wind farm layout strategies often focus on the performance of individual turbines without considering wake interactions. This study introduces a coordinated control approach that considers wake effects and demonstrates an improved yaw control results to reduce the impact of wake interactions. By implementing cooperative yaw control among turbines and using the NSGA-II algorithm, the study establishes a dual optimization problem of minimizing turbine spacing and maximizing power output, ultimately yielding the optimal layout for wind turbines. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wind turbines

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AI-Driven Real-Time Exercise Repetition Counting System

AI-Driven Real-Time Exercise Repetition Counting System

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international conference on Signal Processing and Communication (ICSC)

作者： Ratan Lal Rikriti Koirala Sai Swathi Thota Khaja Nayab Rasool Shaik School of Computer Science and Information Systems Northwest Missouri State University Maryville MO USA Faculty in the school of computer science and information systems Northwest Missouri State University USA School of computer science and information systems at Northwest Missouri State University USA

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

Real-time exercise detection is a challenging problem. Hence, we present a real-time exercise detection system that addresses the challenge of accurately monitoring exercises without the need for an expensive, real-life coach. Our approach leverages machine learning techniques to detect errors in exercises and provide actionable feedback, making fitness training more accessible and affordable. First, we collect a dataset, capturing multiple video frames with joint landmarks represented as key points in each image. These images are labeled with exercise actions such as curls, presses, and squats. Then, we perform data cleaning and splitting. After that we have trained and evaluated two machine learning models: Long Short-Term Memory (LSTM) networks and Random Forest based on metrics, namely, accuracy, precision, recall, F1 score. From our experimental results, we have found that the LSTM model outperforms the Random Forest, demonstrating its superior effectiveness in real-time exercise detection. Our results highlight the potential of AI-driven systems to provide personalized fitness guidance, opening doors to innovative solutions in the health and fitness industry.

关键词： Training measurement Industries Presses Streaming media Signal processing Real-time systems Long short term memory Random forests Monitoring

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Negative Binomial Regression Model for Predicting the Number of Tourists in National Parks of Thailand

Negative Binomial Regression Model for Predicting the Number...

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international ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Sthaporn Thepsumritporn Anuchit Jitpattanakul Sakorn Mekruksavanich Wikanda Phaphan Department of Applied Statistics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Computer Engineering School of Information and Communication University of Phayao Phayao Thailand Research Group in Statistical Learning and Inference

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

This article examines the use of count data models to predict the number of tourists visiting Thailand’s national parks. The dataset, encompassing observations from 2016 to 2022 across 146 national parks, exhibits characteristics such as discreteness, over dispersion, and non-negative integer values. Two models— Poisson (POI), and Negative Binomial (NB)—were compared to identify the most effective approach for modeling and prediction. Model evaluation metrics, including the Akaike Information Criterion (AIC) and log-likelihood, were employed. Results demonstrate that the Negative Binomial regression model best captures the data’s overdispersion compared to the POI model. The NB model achieved the lowest AIC and highest log-likelihood, affirming its superior performance for this application. Our findings underscore the NB model’s utility in predicting tourism-related count data and suggest its broader applicability to other domains characterized by overdispersed count data.

关键词： Natural resources measurement Analytical models Accuracy Predictive models Data models Planning Resource management Dispersion

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Evaluating Large Language Models: Challenges, Limitations, and Future Directions

Evaluating Large Language Models: Challenges, Limitations, a...

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international ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Li-Hua Li Agus Cahyo Nugroho Department of Information Management College of Informatics Chaoyang University of Technology Taichung Taiwan

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

Large Language Models (LLMs) have transformed Natural Language Processing (NLP) by providing previously unheard-of capabilities in text production, translation, and comprehension. Large Language Models have advanced rapidly. Nevertheless, there are several obstacles and restrictions associated with the implementation and assessment of these models. This study offers a thorough analysis of the situation of LLMs today, with an emphasis on the methods for assessing their effectiveness and significance. Key issues like prejudice, moral dilemmas, scalability, and transparency are noted, and the shortcomings of the current frameworks for evaluation are also covered. Our goal in examining these problems is to identify the areas that require improvement. In addition, we suggest possible direction for future investigation, stressing the importance of inclusive datasets, improved interpretability, and stronger assessment criteria. In addition to guiding the creation of more morally and effectively constructed AI systems, our mission is to promote a greater understanding of LLMs.

关键词： measurement Ethics Translation Large language models Scalability Training data Production Natural language processing Safety Testing

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Wellness AI: AI-Driven Personalized Diet and Fitness Recommendation

Wellness AI: AI-Driven Personalized Diet and Fitness Recomme...

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IEEE Students' conference on Electrical, Electronics and computer Science (SCEECS)

作者： Aditya Kothari Utkarsh Khanna Neil Shah Sapna Shah Department of Artificial Intelligence NMIMS MPSTME Mumbai India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

Wellness AI is a health, fitness and lifestyle recommendation platform designed to process direct prompts that contain user information, such as age, gender, dietary preferences, and lifestyle choices. The system integrates real-time health metrics from Apple HealthKit, enabling more comprehensive insights by including user’s vitals. Using advanced fine-tuned large language models (LLMs) with Low-Rank Adaptation (LoRA) and quantization techniques, the platform achieves high efficiency and accuracy. This paper details the technical components, training methods, and model optimization approaches used to streamline personalized health insights.

关键词： measurement Training Technological innovation Quantization (signal) Accuracy Computational modeling Large language models Real-time systems Data models Optimization

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Persian Text-Image Retrieval: A Framework Based on Image Captioning and Scalable Vector Search

Persian Text-Image Retrieval: A Framework Based on Image Cap...

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international CSI computer conference, CSICC

作者： Rasoul Asadian Alireza Akhavanpour Shenasa AI Tehran Iran

ISBN: (数字)9798331523114

ISBN: (纸本)9798331523121

Text-image retrieval systems have seen significant advancements with CLIP-based models and Vision-Language models, which rely on vast datasets and powerful computational resources. However, these advancements primarily focus on English and other high-resource languages, leaving a substantial gap for languages like Persian. This paper introduces a pioneering Persian Text-Image Retrieval (PTIR), the first comprehensive framework designed to address Persian's unique challenges. We present three key innovations: (1) a diverse corpus of 1.2 million Persian image-caption pairs from multiple sources; (2) training a vision-language model combining a vision-encoder and a pre-trained Persian text-decoder to optimize cross-modal alignment and generate detailed captions; and (3) a scalable retrieval pipeline using sentence embedding models and vector databases for efficient Persian query-image matching. Evaluations on the Persian-adapted COCO benchmark dataset demonstrate PTIR achieves a 30-40% improvement over existing Persian image captioning models in standard metrics and a 9% mean improvement in Hit@K compared to the CLIP baseline. The framework's modular design enables domain-specific adaptations, offering a blueprint for low-resource language retrieval systems. A subset of our implementation, including code and datasets, is available at: https://***/PTIR.

关键词： Training measurement Adaptation models Technological innovation Databases Computational modeling Pipelines Diversity reception Vectors Standards

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Co-Design of Network Topology and Qubit Allocation for Distributed Quantum Computing

Co-Design of Network Topology and Qubit Allocation for Distr...

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Quantum Communications, Networking, and Computing (QCNC), international conference on

作者： Jiyao Liu Lei Fan Yuanxiong Guo Zhu Han Yu Wang Department of Computer and Information Sciences Temple University Philadelphia USA Dept. of Engineering Technology University of Houston Houston USA Dept. of Electrical and Computer Engineering University of Houston Houston USA Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio USA

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

Distributed Quantum Computing (DQC) enables the execution of quantum circuits across multiple interconnected quantum processing units (QPUs), but requiring efficient qubit allocation and network topology design to optimize computational performance. Proper qubit allocation minimizes entanglement costs across QPUs, balances computational workload, and ensures efficient execution of quantum computing tasks. Meanwhile, network topology plays a crucial role in reducing entanglement routing complexity and communication overhead for remote quantum gate operations. In this paper, we propose a joint optimization framework for network topology design and qubit allocation in DQC to minimize the communication overhead. We formulate the problem as a tractable integer nonlinear programming model that explicitly incorporates entanglement routing, thereby ensuring a more tractable optimization process. To further improve computational efficiency, we present a partially linearized version of the problem, making it solvable using any classical optimization solver. Extensive simulations on both random and real-world quantum circuits validate the effectiveness of our proposed approach, demonstrating its capability to handle complex quantum circuits while reducing communication costs in DQC.

关键词： Costs Network topology Computational modeling Qubit Topology Computational efficiency Resource management Quantum circuit Integrated circuit modeling Optimization

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Machine Learning for Phase Estimation in Satellite-to-Earth Quantum Communication

Machine Learning for Phase Estimation in Satellite-to-Earth ...

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Quantum Communications, Networking, and Computing (QCNC), international conference on

作者： Nathan K. Long Robert Malaney Kenneth J. Grant School of Electrical Engineering and Telecommunications University of New South Wales Kensington NSW Australia Sensors and Effectors Division of Defence Science and Technology Group Edinburgh SA Australia

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

A global continuous-variable quantum key distribution (CV-QKD) network can be established using a series of satellite-to-Earth channels. Increased performance in such a network is provided by performing coherent measurement of the optical quantum signals using a real local oscillator, calibrated locally by encoding known information on transmitted reference pulses and using signal phase error estimation algorithms. The speed and accuracy of the signal phase error estimation algorithm are vital to practical CV-QKD implementation. Our work provides a framework to analyze long short-term memory neural network (NN) architecture parameterization, with respect to the quantum Cramér-Rao uncertainty bound of the signal phase error estimation, with a focus on reducing the model complexity. More specifically, we demonstrate that signal phase error estimation can be achieved using a low-complexity NN architecture, without significantly sacrificing accuracy. Our results significantly improve the real-time performance of practical CV-QKD systems deployed over satellite-to-Earth channels, thereby contributing to the ongoing development of the Quantum Internet.

关键词： Accuracy Uncertainty Error analysis Atmospheric modeling computer architecture Optical variables measurement Real-time systems Complexity theory Long short term memory Quantum communication

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