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Computing Power Task Placement Method Based on New Energy Generation Power Prediction 14th

Computing Power Task Placement Method Based on New Energy Ge...

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14th international conference on Computer Engineering and networks, CENet 2024

作者： Cai, Junru Wang, Ying Zhang, Manjun Zhuang, Zhihan Wei, Junjie Lin, Lin Wang, Dayang Jiang, Song Zou, Jing State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Information and Communication Branch of State Grid Jiangsu Electric Power Co. Ltd. Jiangsu Nanjing210000 China State Grid Economic and Technological Research Institute Co. Ltd. Beijing102200 China

ISBN: (纸本)9789819642281

Cloud computing resources and energy are gradually showing a trend of geographical distribution, and an innovative computing task placement strategy needs to be designed for a more cost-effective and low-carbon environmental protection cloud deployment. In this paper, from the perspective of optimizing the use of new energy to the maximum extent, a computing power task placement model considering the power difference factors of geographically distributed new energy generation is developed. An effective power prediction model for new energy generation is designed to deal with the volatility and intermittency characteristics of new energy generation. Finally, an intelligent computing task placement algorithm is studied to ensure the maximum use of new energy and sustainable development of the computing network system. the experimental results show that the proposed algorithm can effectively predict the data fluctuation of new energy generation, and the new energy consumption can be increased by 14.9%–36.8% compared with the baseline algorithm. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Computing power

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A wavelet scattering convolutional network for magnetic resonance spectroscopy signal quantitation 14

A wavelet scattering convolutional network for magnetic reso...

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14th international conference on Bio-Inspired systems and Signal Processing, BIOSIGNALS 2021 - Part of the 14th international Joint conference on Biomedical Engineering systems and Technologies, BIOSTEC 2021

作者： Shamaei, Amirmohammad Starčuková, Jana Zenon Starčuk, Z.S. Institute of Scientific Instruments of the CAS Královopolská 147 Brno612 64 Czech Republic Department of Biomedical Engineering Faculty of Electrical Engineering and Communication Brno University of Technology Technická 3058/10 Brno616 00 Czech Republic

ISBN: (纸本)9789897584909

Magnetic resonance spectroscopy (MRS) can provide quantitative information about local metabolite concentrations in living tissues, but in practice the quantification can be difficult. Recently deep learning (DL) has been used for quantification of MRS signals in the frequency domain, and DL combined with time-frequency analysis for artefact detection in MRS. the networks most widely used in previous studies were Convolutional Neural networks (CNN). Nonetheless, the optimal architecture and hyper-parameters of the CNN for MRS are not well understood;CNN has no knowledge about the nature of the MRS signal and its training is computationally expensive. On the other hand, Wavelet Scattering Convolutional Network (WSCN) is well-understood and computationally cheap. In this study, we found that a wavelet scattering network could hopefully be also used for metabolite quantification. We showed that a WSCN could yield results more robust than QUEST (one of quantitation methods based on model fitting) and the same as a CNN while being faster. We used wavelet scattering transform to extract features from the MRS signal, and a superficial neural network implementation to predict metabolite concentrations. Effects of phase, noise, and macromolecules variation on the WSCN estimation accuracy were also investigated. Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

关键词： Magnetic resonance spectroscopy

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Face Emotion Recognition Using Deep Neural Network (DNN)

Face Emotion Recognition Using Deep Neural Network (DNN)

引用

international conference on communication systems and Network Technologies (CSNT)

作者： A Rajendra Babu Mohebbanaaz S. Chandrika G. Rajeswari U. Vidya Pavani Dept of ECE Ravindra College of Engineering for Women Kurnool Andhra Pradesh India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Facial expression recognition is an intelligent human-computer interaction technology that gives a great sense of communication of the expression of our emotions, understanding, and intent between ourselves. Many researchers have attempted to categorize facial expressions, and it has been proven that there are seven universal basic emotions: happiness, sadness, anger, fear, surprise, disgust, and neutrality. Real-time emotion detection is achievable using a webcam. the paper discusses an architecture based on CNN for emotion detection. this model was trained on the FER-2013 dataset after performing some pre-processing steps such as scaling images and color mode correction. A multi-layer CNN model is designed for the classification of emotions as happy, sad, angry, fearful, or surprised, as well as disgusted and neutral. In addition to this, it focuses on real-time analysis to present solutions based on emotions detected, such as automatically playing videos to address sad or angry or fearful emotions. Our developed algorithm attained an highest accuracy of 97.8%. the developed algorithms are very robust, effective and helps in automated seizure detection.

关键词： Human computer interaction Emotion recognition Accuracy Machine learning algorithms Face recognition Signal processing algorithms Artificial neural networks Real-time systems Data models Classification algorithms

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SUpport for communication with deaf and dumb patients via few-shot machine learning 15

SUpport for communication with deaf and dumb patients via fe...

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14th international conference on ICT, Society, and Human Beings, ICT 2021, 18th international conference on Web Based Communities and Social Media, WBC 2021 and 13th international conference on e-Health, EH 2021 - Held at the 15th Multi-conference on Computer Science and Information systems, MCCSIS 2021

作者： Shovkoplias, Grigorii Tkachenko, Mark Asadulaev, Arip Alekseeva, Olga Dobrenko, Natalia Kazantsev, Daniil Vatian, Alexandra Shalyto, Anatoly Gusarova, Natalia ITMO University Saint Petersburg197101 Russia

ISBN: (纸本)9789898704306

Improving healthcare quality and patient safety for patients with disabilities is one of the most important goals of e-Health. A large percentage of these patients are persons with disabling hearing loss, i.e. partially or completely deaf and dumb. In our work, we discuss the opportunity of fast Sign Language processing having only a small number of examples. We investigated the possibility of classifying datasets with an extremely small number of samples of electromyograms of deaf and dumb gestures using few-shots learning methods. Several such methods have been considered - Matching networks, Model-Agnostic Meta-Learning, and Prototypical networks. the developed methodology makes it possible to train electromyogram classifiers using an extremely small amount of data for other deaf and dumb sign languages. To do this, it is enough to collect a small dataset for re- or additional training of these models for the classification of another language, which is easy to accomplish in practice by means of a small session of recording the gestures of several deaf-mute people - speakers of a particular sign language. In our study, we selected threemain few-shot learning approaches and compared them on small sign language dataset. Based on our results, one can choose the best models and their modifications to adapt to the practice task. © 14th international conference on ICT, Society, and Human Beings, ICT 2021, 18th international conference on Web Based Communities and Social Media, WBC 2021 and 13th international conference on e-Health, EH 2021 - Held at the 15th Multi-conference on Computer Science and Information systems, MCCSIS 2021. All rights reserved.

关键词： Machine learning

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Mitigating Security threats of Bitcoin Network by Reducing Message Broadcasts During Transaction Dissemination

Mitigating Security Threats of Bitcoin Network by Reducing M...

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international conference on Computational Intelligence and communication networks (CICN)

作者： Khaled Tarmissi Atef Shalan Abdullah Al Shahrani Rayan Alsulamy Saud S. Alotaibi Sarah Al-Shareef College of Computer and Info. Systems Umm Al Qura University Macca Saudi Arabia College of Engineering and Computing Georgia Southern University Statesboro GA

ISBN: (纸本)9781665487726

Propagation delay in blockchain networks is a major impairment of message transmission and validation in the bitcoin network. the transaction delay caused by message propagation across long network chains can cause significant threats to the bitcoin network integrity by allowing miners to find blocks during the message consensus process. Potential threats of slow transaction dissemination include double-spending, partitions, and eclipse attacks. In this paper, we propose a method for minimizing propagation delay by reducing non-compulsory message broadcasts during transaction dissemination in the underlying blockchain network. Our method will decrease the propagation delay in the bitcoin network and consequently mitigate the security threats based on message dissemination delay. Our results show improvement in the delay time with more effect on networks with a large number of nodes.

关键词： Bitcoin Delays Blockchains Security communication networks Computational intelligence Propagation delay

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A New Method to Evaluate the Engineering Practice Ability of Young University Teachers 14

A New Method to Evaluate the Engineering Practice Ability of...

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14th international conference on Education Technology and Computers, ICETC 2022

作者： Zhao, Kaina Wang, Zhiping School of Science Dalian Maritime University China

ISBN: (纸本)9781450397766

the engineering practice ability of young teachers directly affects the quality of cultivation of applied talents, so it becomes especially important to objectively evaluate the engineering practice ability of teachers and adopt targeted teacher training methods based on the evaluation results. this paper establishes an index system with subject teaching, practical ability, scientific research and innovation, communication and expression and personality quality as the first-level indicators, uses the improved AHP method to determine the weights of the indicators, and establishes the IF-TOPSIS model to evaluate the engineering practice ability of young teachers. the cases show that the model can effectively and reasonably evaluate the engineering practice ability of young teachers in colleges and universities, and provides a basis for scientific evaluation of the engineering practice ability of young teachers. © 2022 ACM.

关键词： Hierarchical systems

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SARSA-Based Voltage Regulation of PV-Buck Converter System Under Multi-Nature Load Conditions 14

SARSA-Based Voltage Regulation of PV-Buck Converter System U...

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14th IEEE international conference on communication systems and Network Technologies, CSNT 2025

作者： Maurya, Rinki Srivastava, Ankit Nishad, Dashrath Singh, Ashutosh Kumar Electronics & Communication Department IIIT Allahabad India

ISBN: (纸本)9798331531935

DC microgrids are experiencing rapid evolution due to increasing renewable energy integration and diverse load requirements, necessitating advanced control strategies for stable operation. this paper presents a novel voltage regulation approach utilizing policy gradient reinforcement learning with SARSA algorithm for a PV-fed buck converter system. the proposed methodology addresses the complex dynamics of multiple load types through a hierarchical control framework that combines actor-critic networks for continuous control actions. the control strategy employs a comprehensive seven-parameter state space encompassing voltage error, current measurement, solar irradiance, duty cycle history, voltage dynamics, error integration, and average voltage deviation. the experimental validation employs a buck converter maintaining 48V DC output from a 100V PV source, tested under challenging scenarios including 100W constant power load, 50W pulsed power load, and variable linear load conditions. the results demonstrate significant advantages over traditional PI control: 83% reduction in steady-state error, 75% improvement in voltage stability, and 73% faster load transition response. the controller successfully maintains voltage variations within +-1V across diverse load combinations, effectively addressing the destabilizing effects of constant power loads and sudden pulsed load changes. the integration of policy-based learning with on-policy SARSA implementation enables real-time adaptation without prior system modeling requirements. this approach showcases exceptional performance in both steady-state maintenance and transient response, particularly in scenarios involving mixed load characteristics. the framework provides a robust solution for modern DC microgrid applications requiring stable voltage regulation under complex load dynamics. © 2025 IEEE.

关键词： Transient analysis

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Exploring Deep Learning Paradigms for Handwritten Digit Recognition: Insights and Advances

Exploring Deep Learning Paradigms for Handwritten Digit Reco...

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international conference on communication systems and Network Technologies (CSNT)

作者： Amit Kumar Gupta Priya Mathur Nysa Maheshwari Ayushi Mathur Computer Science and Engineering Manipal University Jaipur Jaipur Rajasthan India Department of Mathematics Poornima Institute of Engineering and Technology Jaipur Rajasthan India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

In the application area of postal automation to signature verification, the Handwritten digit recognition (HRD) is major challenging field in the era of pattern recognition and machine learning. the convolutional neural networks (CNNs) becoming most emerging technology in the field of deep learning. the CNN made significant advancement in landscape of digit recognition, surpassing traditional methods in accuracy and performance. Motivated by the significance of accurate digit classification, this research presents a comprehensive review and synthesis of seminal works in the field. through a meticulous examination of pioneering studies, we identify trends, challenges, and emerging paradigms characterizing contemporary research in handwritten digit recognition. Five deep learning methods, including a Baseline MLP, Simple CNN Model, Larger CNN Model, Basic ResNet Architecture, and Inception V3, are evaluated for their performance in digit recognition tasks. Results demonstrate that the Inception V3 model achieves the highest accuracy of 0.99, with a precision of 0.9929 and a recall of 0.9995. the review encompasses foundational works laying the groundwork for digit recognition algorithms, as well as cutting-edge research leveraging transfer learning, hybrid models, and advanced deep learning architectures. By synthesizing insights from these studies, our aim is to provide researchers and practitioners with a comprehensive understanding of state-of-art techniques, enabling informed decision-making and inspiring future advancements in HRD.

关键词： Deep learning Handwriting recognition Technological innovation Accuracy Reviews communication systems Decision making Transfer learning Market research Convolutional neural networks

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An efficient network operation automation scheme using network status information for local 5G networks

An efficient network operation automation scheme using netwo...

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international conference on Information and communication Technology Convergence (ICTC)

作者： Mahnsuk Yoon Jihun Seo Kyucheol Cho Future Mobile Communication Research Center GERI(Gumi Electronics & Information Technology Research Institute) Gumi Korea Center for Connected & Automated Driving Research Department of The Fourth Industrial Revolution & Transport Korea Transport Institute Sejong Korea School of Computer Secience and Engineering Software Education Center Kyungpook National University Daegu Korea

5G SA communication technology is being recognized as a crucial technology shaping the future of wireless communication. Compared to previous generations of networks, 5G offers ultra-high-speed, low latency, and massive connectivity, presenting both enterprises and individual users with opportunities for new business models and an enhanced quality of life. One of the most significant changes between the conventional mobile communication environment and 5G is the activation of various application cases in B2B services based on the local 5G network. the local 5G network, tailored to specific sites, requires effective management of high-volume data generated in dense areas and necessitates minimizing manual intervention by administrators. the ZSM (Zero-touch Service Management) technology is gaining attention to meet these requirements. ZSM focuses on automated operations for managing large systems like commercial mobile communication networks. It is anticipated that ZSM will also be applied to the local 5G network for efficient management and operation of tailored services. this paper proposes a network management technique that minimizes administrator intervention in the local 5G network environment. the proposed network management technique collects statistical logs from local 5G network base stations and core systems, supports real-time network status checks as well as traffic predictions using machine learning. Additionally, it offers automated operation techniques for the local 5G network by incorporating functions such as disconnecting network access for devices that are factors of malfunction within the specialized network.

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Hybrid Beamforming-based Beam Tracking using Angular speed in Massive MIMO systems 14

Hybrid Beamforming-based Beam Tracking using Angular speed i...

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14th international conference on Information and communication Technology Convergence, ICTC 2023

作者： Sin, Seungseok Cho, Jihun Liu, Huaping Moon, Sangmi Sim, Yuna Kim, Kyunam Kim, Jihyung Hwang, Intae Chonnam National University Deptartment of Ict Convergences System Engineering Gwangju Korea Republic of Oregon State University United States Korea Nazarene University Department of It Artificial Intelligence Cheonan Korea Republic of Alps Electric Korea Co. Ltd Gwangju Korea Republic of Electronics and Telecommunications Research Institute Spatial Wireless Transmission Research Section Daejeon Korea Republic of Chonnam National University Department of Electronic Engineering and Department of Ict Convergence System Engineering Gwangju Korea Republic of

ISBN: (纸本)9798350313277

this paper proposes an angular speed 3D-based beam tracking method using hybrid beamforming. We propose a new beam tracking algorithm that enables dynamic pilot insertion based on closed loop feedback control by utilizing the Line of Sight (LoS) path of the channel between T-BS and UAV-UE. Simulations show that the proposed beam tracking method has improved overall performance in terms of beamforming gain and spectral efficiency (SE). © 2023 IEEE.

关键词： 3D beam tracking hybrid beamforming multiple-input multiple-output orthogonal frequency division multiplexing

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