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Optimizing vibratory energy harvesters with differentiable data-driven control

Chaos, Solitons and Fractals 2025年 198卷

作者： Al-zahrani, Mohammed S. Alsaade, Fawaz W. Alsaadi, Fuad E. Department of Computer Networks and Communications College of Computer Sciences and Information Technology King Faisal University Al-Ahsa31982 Saudi Arabia Department of Computer Science College of Computer Sciences and Information Technology King Faisal University Al-Ahsa31982 Saudi Arabia Communication Systems and Networks Research Group Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia

Nonlinear vibratory energy harvesters often exhibit multiple coexisting attractors, making their control challenging and energy-intensive. Ensuring an effective transition between these attractors while minimizing control effort is crucial for maximizing power generation and maintaining system stability. This paper introduces a differentiable data-driven control strategy that optimizes energy harvester performance by leveraging a neural network (NN)-based control framework. Unlike traditional fixed-gain or surrogate modeling approaches, the proposed method directly integrates system dynamics and control power consumption into the gain adaptation process, ensuring real-time adaptability. By exploiting the differentiability of neural networks, the controller employs gradient-based optimization to continuously refine control parameters in response to changes in operating conditions. The control framework consists of an offline training phase, where the neural network learns an energy-efficient control strategy through differentiable simulations. During this phase, the neural network is trained using a large dataset of system responses to different control inputs, allowing it to learn the most energy-efficient control strategy. This trained network is then used in the online deployment phase, where the trained controller dynamically adjusts control parameters in real-time. The proposed approach minimizes control energy consumption and efficiently guides the system toward the high-energy attractor, avoiding unnecessary control effort. Our approach significantly outperforms conventional PID-based sliding mode controllers. Simulation results confirm that the differentiable controller considerably enhances energy harvesting efficiency and reduces chattering, ensuring a smooth transition to the high-energy orbit while maintaining robust system stability. The study also highlights the necessity of an adaptive control strategy, stressing the urgency of its implementation for o

关键词： Lyapunov methods

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Synthesizing realistic images for improved early detection of novel pandemics: A GAN enabled approach

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Heliyon 2025年第6期11卷

作者： Rounaq, Sumera Shah, Shahid Munir Aljawarneh, Mahmoud Mohammad Khan, Sarah Muhammad, Ghulam Department of Computer Science DHA Suffa University Karachi Pakistan Faculty of Engineering Sciences and Technology Hamdard University Karachi Pakistan Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman Jordan System Architect Low Code Solutions London United Kingdom Department of Computer Science Bahria University Karachi Pakistan

Infectious diseases like the novel Coronavirus (COVID-19) affect millions of individuals if not managed well in time. Thus, to reduce the transmission rate, effective diagnostic techniques must be identified. Early detection and diagnosis may be aided by Artificial Intelligence (AI) based automated diagnostics;however, these algorithms need a lot of pertinent data to be trained. In the early stages of a pandemic, gathering a lot of data is never an easy task. This presents challenges for accurate automatic diagnosis of such infectious diseases. To address this issue, a customized Generative Adversarial Networks (GAN) based architecture i. e., CoroWGAN-GP is proposed to synthesize images and to expand datasets. A dataset of 588 images with COVID-19, Normal, and Viral Pneumonia categories (taken from Cohen's repository [1]) was expanded using the proposed CoroWGAN-GP approach and used to train a proposed customized deep CNN architecture i. e., DeepCNN-CovNet19. Experimental results demonstrate that the proposed model achieved a considerable detection accuracy. i. e., 98.0 % in detecting the disease. The effectiveness of the proposed method was further evaluated by comparing its results with the state-of-the-art methods in the literature. Comparative analyses indicate that the proposed approach offers a promising solution for the accurate diagnosis of COVID-19-like pandemics. © 2025 The Authors

关键词： Chest X-ray Convolutional neural networks COVID-19 CXR GAN Pandemic Wasserstein GAN

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Energy generation in a multi-sided photovoltaic system: Analysis of the prediction effectiveness

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Renewable Energy 2025年 251卷

作者： Zator, Slawomir Osuchowski, Jakub Kasana, Singara Singh Shelke, Nitin Arvind Tomaszewski, Michal Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh India School of Computer Science and Engineering Technology Bennett University UP Greater Noida India

This article investigates the role of multi-oriented photovoltaic (PV) systems in enhancing energy self-sufficiency and reducing greenhouse gas emissions. Focusing on the evaluation of energy production prediction performance, the study utilizes data from four multi-oriented PV installations from 2022, standardized to 1 kWp capacity for comparative analysis. A custom-built measurement system, incorporating single-phase and three-phase meters, and solar irradiance data from the Solcast API, facilitated the creation of a dataset for predictive modeling. The Random Forest machine learning algorithm was employed to develop predictive models, enabling a comprehensive analysis of different installation orientations, including feature importance, prediction errors, and self-consumption and self-sufficiency rates. The study introduces new indicators alongside typical metrics to assess the accuracy of energy production predictions, aiming to address the surplus or deficit in energy forecasting. The results offer valuable insights into optimizing energy generation and grid integration of PV systems, highlighting the potential for improved prediction reliability and precision in multi-oriented installations. © 2025

关键词： Random forests

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AI and computer science

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KI - Kunstliche Intelligenz 2012年第1期26卷 1-4页

作者： Schmid, Ute Cognitive Systems Faculty Information Systems and Applied Computer Science University of Bamberg Bamberg Germany

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Maximizing revaccination effectiveness under resource constraints in the SIRVS model

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Physica A: Statistical Mechanics and its Applications 2025年 671卷

作者： Zbik, Bartosz Dybiec, Bartlomiej Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University ul. St. Lojasiewicza 11 Krakow30–348 Poland Institute of Theoretical Physics and Mark Kac Center for Complex Systems Research Jagiellonian University ul. St. Lojasiewicza 11 Krakow30–348 Poland

Discrete compartmental epidemiological models are widely used in epidemiological modeling because they allow the study of relationships between microscopic dynamics and macroscopic properties of the epidemic. They can also be used to optimize control strategies. Adequate planning of epidemic control methods is particularly important in situations where the disease may be endemic or where repeated vaccination is required due to new mutations or loss of immunity. Using the agent-based SIRVS model, we show that despite limited resources, it is still possible to design the most effective control strategy by finding the optimal time interval between successive booster doses. Importantly, the optimal time between successive vaccinations exists even in the presence of limited resources. Its value depends on the proportion of the population willing to take the vaccine and is also influenced by the clustering of individuals with respect to their attitudes toward vaccination. Finally, we show that from an individual perspective, willingness to vaccinate, coupled with successful vaccination, are key components in reducing the number of reinfections of the same individual. © 2025 Elsevier B.V.

关键词： Malaria control

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Exploring Contextualized Embeddings for C++ Code Search and Algorithms Classification

Exploring Contextualized Embeddings for C++ Code Search and ...

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International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE)

作者： Robert R. Khazhiev Darya A. Skvortsova Sergey N. Golubev Lev R. Barabanshchikov Industrial Logistic Department Bauman Moscow State Technical University Moscow Russian Federation Dept. of Information Processing and Management Systems Bauman Moscow State Technical University Moscow Russian Federation Institute of Applied Computer Science ITMO University Saint-Petersburg Russian Federation

ISBN: (数字)9798331531836

ISBN: (纸本)9798331531843

Contextualized embeddings of words (such as BLOOM and BERT) outperform static embeddings (such as Word2vec, Fasttext, and GloVe) for many natural language processing tasks. In this article, an exploration is conducted of the use of contextualized embeddings for the purposes of searching for C++ code and classifying the algorithms that are utilized. Furthermore, a novel approach is proposed for reducing the dimensionality of the resulting embeddings, leveraging a multilayer perceptron trained with Multi Similarity Loss. The experiments performed for the methods of multilabel classification of algorithms and for the code similarity search show that the proposed method not only effectively reduces the dimension of embeddings, but also increases their representativeness.

关键词： Measurement Power engineering Codes Source coding Large language models C++ languages Multi label classification Multilayer perceptrons Natural language processing

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Performance upper bound of a Grover-mixer quantum alternating operator ansatz

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Physical Review A 2025年第1期111卷 012401-012401页

作者： Ningyi Xie Jiahua Xu Tiejin Chen Xinwei Lee Yoshiyuki Saito Nobuyoshi Asai Dongsheng Cai Graduate School of Science and Technology University of Tsukuba 1 Chome-1-1 Tennodai Tsukuba Ibaraki 305-8577 Japan School of Computing and Augmented Intelligence Arizona State University Tempe Arizona 85281 USA Graduate School of Systems and Information Engineering University of Tsukuba 1 Chome-1-1 Tennodai Tsukuba Ibaraki 305-8577 Japan Present address: Singapore Management University Singapore. Graduate School of Computer Science and Engineering University of Aizu Aizuwakamatsu Fukushima 965-8580 Japan School of Computer Science and Engineering University of Aizu Aizuwakamatsu Fukushima 965-8580 Japan Faculty of Engineering Information and Systems University of Tsukuba 1 Chome-1-1 Tennodai Tsukuba Ibaraki 305-8577 Japan

The quantum alternating operator ansatz (QAOA) represents a branch of quantum algorithms for solving combinatorial optimization problems. A specific variant, the Grover-mixer (GM) QAOA, ensures uniform amplitude across states that share equivalent objective values. This property makes the algorithm independent of the problem structure, focusing instead on the distribution of objective values within the problem. In this work we provide an alternative proof for the upper bound on the probability of measuring a computational basis state from a GM QAOA circuit with a given depth, which is a critical factor in QAOA cost. Using this, we derive the upper bounds for the probability of sampling an optimal solution and for the approximation ratio of maximum optimization problems, both dependent on the objective value distribution. Through numerical analysis, we link the distribution to the problem size and build the regression models that relate the problem size, QAOA depth, and performance upper bound. Our results suggest that the GM QAOA provides a quadratic enhancement in sampling probability and requires circuit depth that scales exponentially with problem size to maintain consistent performance.

关键词： Combinatorial optimization

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Adaptive computer Vision Solutions for Individuals with Special Needs

Adaptive Computer Vision Solutions for Individuals with Spec...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Issam Jebreen Shadi Nashwan Ahmad Al-Qerem Ali Mohd Ali Mohammad A Al Khaldy Mohammed Rajab Faculty of Information Technology Middle East University Jordan Computer Science Department Zarka University Jordan College of Engineering Technology Al-Balqa Applied University Jordan Business Intelligence Data Analytics University of Petra Amman Jordan University Headquarter University Of Anbar Iraq

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

With the rapid evolution of artificial intelligence (AI) and computer vision, assistive technologies have become increasingly adept at supporting individuals with special needs. This paper provides a comprehensive examination of two critical areas: AI-powered visual assistance systems for the visually impaired and sign language recognition and generation frameworks for the Deaf and hard-of-hearing communities. We review the latest research trends, algorithms, and tools, highlighting emerging deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Transformers in both domains. We then propose a unified framework that leverages edge computing, multimodal learning, and advanced human-computer interaction (HCI) techniques to address challenges such as real-time object recognition, complex gesture capture, data scarcity, and cultural variations in sign languages. We further detail an experimental design and set of results that demonstrate the feasibility and benefits of these approaches, followed by a discussion of ethical considerations, limitations, and future directions. Our goal is to illustrate a path toward more inclusive AI systems that offer robust solutions for accessibility, ultimately bridging communication gaps and fostering independence for individuals with special needs [1]–[11].

关键词： Sign language computer vision Visualization Ethics Visual impairment Assistive technologies Transformers Real-time systems Object recognition Artificial intelligence

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Loss Functions in Deep Learning: A Comprehensive Review

arXiv

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arXiv 2025年

作者： Elharrouss, Omar Mahmood, Yasir Bechqito, Yassine Serhani, Mohamed Adel Badidi, Elarbi Riffi, Jamal Tairi, Hamid Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Department of Information Systems College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Informatics Faculty of Sciences Dhar El Mahraz Sidi Mohamed Ben Abdellah University Fez Morocco

Loss functions are at the heart of deep learning, shaping how models learn and perform across diverse tasks. They are used to quantify the difference between predicted outputs and ground truth labels, guiding the optimization process to minimize errors. Selecting the right loss function is critical, as it directly impacts model convergence, generalization, and overall performance across various applications, from computer vision to time series forecasting. This paper presents a comprehensive review of loss functions, covering fundamental metrics like Mean Squared Error and Cross-Entropy to advanced functions such as Adversarial and Diffusion losses. We explore their mathematical foundations, impact on model training, and strategic selection for various applications, including computer vision (Discriminative and generative), tabular data prediction, and time series forecasting. For each of these categories, we discuss the most used loss functions in the recent advancements of deep learning techniques. Also, this review explore the historical evolution, computational efficiency, and ongoing challenges in loss function design, underlining the need for more adaptive and robust solutions. Emphasis is placed on complex scenarios involving multi-modal data, class imbalances, and real-world constraints. Finally, we identify key future directions, advocating for loss functions that enhance interpretability, scalability, and generalization, leading to more effective and resilient deep learning models. © 2025, CC BY.

关键词： Deep learning

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Deep learning techniques for sentiment analysis in code-switched Hausa-English tweets

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International Journal of information Management Data Insights 2025年第1期5卷

作者： Aliyu, Yusuf Sarlan, Aliza Danyaro, Kamaluddeen Usman Sani abd Rahman, Abdullahi Muazu, Aminu Aminu Abubakar, Mustapha Yusuf Department of Computer and Information Science Universiti Teknologi PETRONAS Perak Seri Iskandar 32610 Malaysia Center for Foundation Studies Universiti Teknologi PETRONAS Perak Seri Iskandar 32610 Malaysia Centre for Cyber-Physical Systems (C2PS) Universiti Teknologi PETRONAS Perak Seri Iskandar 32610 Malaysia Computer Science Department Faculty of Natural and Applied Science Umaru Musa Yar'adua University Katsina Nigeria Computer Science Department School of Technology Kano State Polytechnic Kano 700231 Nigeria

Social media serve as a crucial platform for expressing opinions and perspectives. Its texts often characterised by code-switching or mixed languages in multilingual setting. This results in a diverse and complex linguistic context, which can negatively affect the accuracy of sentiment analysis for low-resource languages such as Hausa. Prior research has predominantly concentrated on sentiment analysis within single-language data rather than code-switched data. This paper proposes an efficient hyperparameter tuning framework and a novel stemming algorithm for the Hausa language. The framework leverages word embeddings to determine the polarity scores of code-mixed tweets and enhances the accuracy of sentiment analysis models in low-resource language. The extensive experiments demonstrate the framework's efficiency and reveal a superior performance of transformer models over conventional deep learning models. The framework achieves a balance between accuracy and computational efficiency, making it suitable for deployment in practical applications. Compared to state-of-the-art transformer models, our framework significantly reduces computational costs while maintaining competitive performance. Notably, the AfriBERTa model achieves outstanding results, with an F1-score of 0.92 and an accuracy of 0.919, surpassing current baseline standards. These findings have broad implications for social media monitoring, customer feedback analysis, and public sentiment tracking, enabling more inclusive and accessible NLP tools for underrepresented linguistic communities. © 2025 The Author(s)

关键词： Code-switched Hausa language Low-resource Sentiment analysis Transformer Word-embedding

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