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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： C.Shobana Nageswari Pramod Pandey Sujitha R Sujatha M S. Senthilkumar S. Sujatha Department of ECE R.M.D. Engineering College Chennai Tamil Nadu India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune Maharashtra India Department of Computer Science and Engineering Annapoorana Engineering College (Autonomous) Salem Tamil Nadu India Department of Electrical and Electronics Engineering Easwari Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Centre for Artificial Intelligence & Data Science (CAI) Vinayaka Mission’s Kirupananda Variyar Engineering College Vinayaka Mission’s Research Foundation ( DU) Salem Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Implementing renewable energy sources, especially solar power, into the electrical grid has distinct difficulties and potential for improving system resilience. This research investigates an adaptive solar energy storage system using deep learning methodologies, particularly Long Short-Term Memory (LSTM) networks, to enhance energy management and grid stability. The proposed system utilizes past solar generating data and usage patterns to forecast future energy needs and generation capacities. LSTM allows the model to efficiently reflect temporal interdependence and variation in solar energy output, facilitating more precise forecasting. The adaptive storage system modifies its charging and discharging processes in real time, optimizing efficiency and reducing energy waste. LSTM-based forecasting improves the storage system’s response to variations in supply and demand, enhancing load balancing and grid resilience. This method facilitates a change in renewable energy sources while enhancing the resilience of grid infrastructure to sustain delays. Findings indicate that integrating flexible energy storage using advanced deep-learning methodologies may significantly improve the efficacy and dependability of solar energy systems. This paper challenges solar energy intermittency with LSTM-based adaptive storage, enhancing forecasting precision, grid resilience, and energy efficiency enhancing stable energy management and optimized battery use.

关键词： Deep learning Renewable energy sources Computational modeling Solar energy Predictive models Real-time systems Robustness Forecasting Long short term memory Resilience

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Optimizing Neural Network Image Classification with Fractional Order Gradient Methods

SSRN

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SSRN 2023年

作者： Zhao, Haiming Chen, Jiejie Jiang, Ping Zeng, Zhigang Fan, Rui Yang, Honggang School of Computer and Information Engineering Hubei Normal University Huangshi435000 China School of Automation Wuhan University of Technology Wuhan430000 China School of Computer Hubei PolyTechnic University Huangshi435000 China Faculty of Artificial Intelligence Education Central China Normal University Wuhan430000 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430000 China Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China Wuhan430000 China

As deep learning technology increasingly permeates various fields, the significance of optimization algorithms in neural network training has become more prominent. Typically, the training of deep learning networks relies on gradient descent optimizers based on calculus computations. Traditional integer-order calculus optimizers often fall short when dealing with complex and subtle data. To overcome these limitations, we innovatively utilized the Grünwald-Letnikov and Caputo definitions of fractional calculus to develop two new optimization algorithms: Fractional Order Adagrad (FAdagrad) and Fractional Order Adam (FAdam). These algorithms have been proven to converge to extremal points.A key advantage of our methods is their capability in handling complex, high-dimensional tasks, such as artistic painting image classification, where they exhibit exceptional performance in recognizing and processing subtle differences. The robustness and adaptability of these algorithms were further validated through experiments on the CIFAR-100 dataset. The results demonstrate the efficacy of fractional order optimizers in enhancing training efficiency and accuracy. © 2023, The Authors. All rights reserved.

关键词： Image classification

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Encoding and Verification of Compound Vortex Beams with Spaced Orbital Angular Momentum

SSRN

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SSRN 2022年

作者： Wu, Pengfei Wang, Xiaodie Wang, Jiao Tan, Zhen kun School of Automation and Information Engineering Xi 'an University of Technology Shaanxi Xi’an710048 China School of Electronic Information and Artificial Intelligence Shaanxi University of Science & Technology Shaanxi Xi'An710021 China Faculty of Optoelectronic Engineering Xi’an Technological University Shaanxi Xi'An710021 China

A novel encoding method based on orbital angular momentum (OAM) mode and radial mode of composite vortex beams is proposed, with a detection rate of 98.8% at the receiving end of OAM communication system. The superposition of the two beams generate 32 different types of composite vortex beams, one of which is laguerre-Gaussian (LG) beam with a fixed OAM mode and radial mode, and the other is LG beam contains four radial modes ( p = 0, 1, 2, 3) and eight OAM modes with the same interval ( l = ±3, ±5, ±7, ±9). We innovatively completed 5-bit binary coding using the above mentioned composite vortex beams and verified the correctness of the encoding by calculating the autocorrelation of the received data through employing the same encoding mode for multiple transmissions. The proposed concept in this study can greatly improve the channel capacity of the communication system, and then it can be potentially utilized for free-space optical communication in the future. © 2022, The Authors. All rights reserved.

关键词： Vortex flow

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Management-Oriented Operating Systems: Harnessing the Power of DAOs and Foundation Models

Management-Oriented Operating Systems: Harnessing the Power ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Rui Qin Juanjuan Li Xiaolong Liang Fei-Yue Wang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Faculty of Innovation Engineering Macau University of Science and Technology Macau China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

This paper presents Management-Oriented Operating Systems (M2OS) that leverages the power of parallel intelligence theory, Decentralized Autonomous Organizations (DAOs) and foundation models to revolutionize the manner of management in Cyber-Physical-Social Systems (CPSS). The parallel architecture on M2OS is proposed, including the parallel interactive actual M2OS and artificial M2OS. Among them, the artificial M2OSs provide digital infrastructures for organizations to operate, collaborate, and make decisions in the virtual space, and conduct computational experiments to evaluate management decisions and predict future states of the actual M2OS. Through parallel execution and closed-loop feedback between the artificial and actual M2OSs, the management and control, experimentation and evaluation, as well as learning and training of the actual M2OS can be realized. Moreover, the functional layers of M2OS, including the infrastructure layer, data layer, scenario layer, modeling layer, decision layer, and application layer, are discussed. These layers work together to support the intelligent, autonomous, collaborative, and adaptive nature of the M2OS, and facilitate data-driven decision-making, optimize business operations, and empower managers with real-time actionable insights. The proposed M2OS paradigm has great potential to transform the management paradigm and opens up new possibilities for intelligent and collaborative decision-making.

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Validation and Verification of Remaining Useful Life Prediction for Lithium-Ion Batteries based on AM-CNN Approach

Validation and Verification of Remaining Useful Life Predict...

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Self Sustainable artificial intelligence Systems (ICSSAS), International Conference on

作者： Prasanna Kumara S G Asheesh Kumar Shaik Fakruddin Babavali Chetan Shankar Patil M J Murali T Ch Anil Kumar Department of Physics Government Science College (Autonomous) Hassan Karnataka India Department of Mechanical Engineering Mahatma Gandhi Institute of Technology Hyderabad India Department of Physics V.R.Siddhartha Engineering College Vijayawada Andhra Pradesh India Artificial Intelligence and Data Science Angadi Institute of Technology and Management Belagavi India Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of Mechanical Engineering VFSTR Deemed to be University Vadlamudi Guntur Andhra Pradesh India

An empirical model decomposition algorithm, Elman neural networks, and long-term memory are employed to predict the remaining battery life. The method for deconstructing empirical models is given the data on battery capacity vs cycle number that has been acquired. After that, recurrent long short-term memory and Elman neural networks are used to make predictions for the high-frequency and low-frequency sub-layers, respectively. data gathered at an unprecedented scale during battery testing has aided in model parameterization and performance evaluation. The first three steps of the proposed process are processing the data, choosing the features to use, and training the model. The proposed approach uses a Denoising Transformer (De-Transformer) network to clean up the raw data. The extended Kalman Particle Filter (EKPF) is a crucial sample density function that the proposed system employ in feature selection. AM-CNN is then used to train the models after the features have been chosen. The proposed method is superior to AM and CNN, two common substitutes.

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AUTOMATIC SLEEP SCORING: A DEEP LEARNING ARCHITECTURE FOR PATIENTS WITH SLEEP DISTURBANCE

AUTOMATIC SLEEP SCORING: A DEEP LEARNING ARCHITECTURE FOR PA...

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亚洲睡眠医学会2021年学术大会暨第十三届中国睡眠研究会年会

作者： Rui Yan Fan Li Fengyu Cong School of information science and engineering Hebei University of Science & Technology Faculty of Information Technology University of Jyv?skyl? School of Biomedical Engineering Faculty of Electronic Information and Electrical EngineeringDalian University of Technology School of Artificial Intelligence Faculty of Electronic Information and Electrical EngineeringDalian University of Technology

Objective: Due to our hectic lifestyle,complaints about sleep problems increase dramatically among people,which burdens sleep scoring. Automatic sleep scoring is a great way to alleviate the growing unmet needs. However,the sleep patterns of patients with sleep disturbance are varied,and there exist huge population difference and individual difference. How to build a generalized model for patients' sleep scoring is still an open question. This paper aims to develop a versatile deep-learning architecture to automate sleep scoring for patients with diverse sleep disturbances. Methods: The proposed model applies the 2-dimensional convolutional unit and the long-short time memory unit to explore the spatial and temporal information among three types of sleep signals(namely electroencephalograms,electrooculogram and electromyograms). An improved inception module is used to extract features from input signals in multiple resolutions. Then,the learnt features are fed to the long-short time memory unit to exploit long-range contextual relation among sleep stages. Finally,the decision layer distinguishes features and gives the labels of sleep segments. In this article,model performance is tested on the ISRUC dataset,in which subgroup 1 records 100 sleep recordings from 11 types of patients with sleep disturbance. Result: After 5-fold cross-validation,the proposed model achieves an accuracy of 0.86 with a kappa value of 0.82,and the highest classification accuracy is achieved by a fusion of multiple sleep signals. Compared with state-of-the-art methods that use the same dataset,the proposed model exhibits compact structure and high precision. Conclusion: The proposed model obtains good performance on patients with diverse sleep disturbances,which demonstrates good model generalizability. Due to the proven availability and versatility,the proposed method can be integrated with polysomnography systems,thereby facilitating sleep monitoring in clinical or routine care.

关键词： automatic sleep scoring deep learning patients with sleep disturbance multi-modality analysis

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Enhancing sugarcane leaf disease classification using vision transformers over CNNs

Discover Artificial Intelligence

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Discover artificial intelligence 2025年第1期5卷

作者： Miryala, Saritha Rasane, Krupa Department of Artificial Intelligence and Data Science S.G. Balekundri Institute of Technology Belagavi India Department of Electronics and Communication Engineering Jain College of Engineering Belagavi India

Sugarcane is a globally significant crop facing threats from leaf diseases that impact its productivity. Traditional detection methods are often inefficient and time-consuming. This study explores the use of Vision Transformers (ViT) for classifying sugarcane leaf diseases and compares their performance with traditional CNNs. A dataset of 19,926 images across six classes was used to fine-tune both ViT and CNN models. The optimized ViT model achieved a test accuracy of 96.53%, outperforming the CNN models (ResNet50 and VGG16) with accuracies of 91.92% and 92.30%, respectively. These findings demonstrate the superior performance of ViTs over CNNs in early disease detection for sustainable crop management. Future work will focus on expanding the dataset and optimizing model parameters for further improvements in disease classification accuracy. © The Author(s) 2025.

关键词： Sugar cane

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Perceptive self-supervised learning network for noisy image watermark removal

arXiv

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arXiv 2024年

作者： Tian, Chunwei Zheng, Menghua Li, Bo Zhang, Yanning Zhang, Shichao Zhang, David School of Software Northwestern Polytechnical University Xi’an710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’an710129 China School of Electronics and Information Northwestern Polytechnical University Xi’an710129 China School of Computer Science Northwestern Polytechnical University National Engineering Laboratory for Integrated AeroSpace-Ground-Ocean Big Data Application Technology Xi’an710129 China Guangxi Key Lab of Multisource Information Mining & Security College of Computer Science & Engineering Guangxi Normal University Guilin541004 China Shenzhen518172 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China

Popular methods usually use a degradation model in a supervised way to learn a watermark removal model. However, it is true that reference images are difficult to obtain in the real world, as well as collected images by cameras suffer from noise. To overcome these drawbacks, we propose a perceptive self-supervised learning network for noisy image watermark removal (PSLNet) in this paper. PSLNet depends on a parallel network to remove noise and watermarks. The upper network uses task decomposition ideas to remove noise and watermarks in sequence. The lower network utilizes the degradation model idea to simultaneously remove noise and watermarks. Specifically, mentioned paired watermark images are obtained in a self-supervised way, and paired noisy images (i.e., noisy and reference images) are obtained in a supervised way. To enhance the clarity of obtained images, interacting two sub-networks and fusing obtained clean images are used to improve the effects of image watermark removal in terms of structural information and pixel enhancement. Taking into texture information account, a mixed loss uses obtained images and features to achieve a robust model of noisy image watermark removal. Comprehensive experiments show that our proposed method is very effective in comparison with popular convolutional neural networks (CNNs) for noisy image watermark removal. Codes can be obtained at https://***/hellloxiaotian/PSLNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Image denoising

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TAO-Based data Governance in Intelligent Transportation Systems

TAO-Based Data Governance in Intelligent Transportation Syst...

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International Conference on Intelligent Transportation

作者： Xiaolong Liang Juanjuan Li Rui Qin Wenwen Ding Fei-Yue Wang Faculty of Innovation Engineering Macau University of Science and Technology Macao China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex Systems and State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences and Collaborative Laboratory for Intelligent Science and Systems Beijing China

Intelligent Transportation Systems (ITS) are heavily dependent on private user data. However, most ITS fails to harness the potential of this invaluable data due to the absence of effective data governance mechanisms promoting users' contributions. Moreover, data contributors face security risks such as privacy preservation, data leakage, etc., as well as high costs in data sharing, while benefits are disproportionately reaped by system operators and interaction. This systemic imbalance could indirectly incentivize a surge in inactions and even malicious actions. In response to these challenges, we propose the design of a True Autonomous Organization (TAO) for ITS, namely ITS TAO. Utilizing the newly designed decision models with decentralized organization structures and the three-power structure, ITS TAO aims to realize the fair distribution of rights and benefits for ITS data contributors. Furthermore, we design a real-time evaluation system based on parallel intelligence capable of identifying potential hazards.

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DT-GRU Approach for Developing Remotely Monitored Central Nervous System Using Wearable Sensors

DT-GRU Approach for Developing Remotely Monitored Central Ne...

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Self Sustainable artificial intelligence Systems (ICSSAS), International Conference on

作者： Sreejith L Das Aaftab Alam B. Anjanee Kumar K. K. Sunalini Vidhya P Sakthivel M ECE Department Sri Venkateswara College of Engineering and Technology Chittoor Andhra Pradesh India Department of CSE Integral University Lucknow India Department of ECE Dadi Institute of Engineering and Technology Anakapalle Visakhapatnam India Department of English Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Andhra Pradesh India Department of Artificial Intelligence and Data Science M. Kumarasamy College of Engineering Karur India Department of CSE-AIML Malla Reddy College Of Engineering Hyderabad Telangana India

The capacity to evaluate numerous bio- behavioral factors concurrently is a potential strength of RMT for patients with neurological diseases. Although the benefits of RMT have been demonstrated in a large body of research, there is still some debate over its usefulness among medical experts. Providing doctors with visual summaries of data in the patient record is one way that RMT could enhance the system of care for patients with illnesses of the central nervous system. It's important to think about the constraints on the benefits these technologies can provide and the difficulties of using them effectively. The proposed strategy makes use of pre-processing, feature selection, and model training. data is preprocessed in LMS. Feature selection employs PCA. The DT -GRU algorithm is used to train the model. Two additional models, GRU and DT, are contrasted with the suggested method. The proposed method is quite effective.

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