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International Conference on Electrical Energy Systems (ICEES)

作者： S. Avinash S. Sujatha D. Poornima Vedhashree S.K V. Saravanan P. Malathi Department of MBA St. Josephs College of Engineering Chennai Tamil Nadu India Department of EEE Sri Sairam College of Engineering Anekal Bangalore Karnataka Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Department of Electronics and Communication Engineering M.Kumarasamy College of Engineering Chennai Tamil Nadu India Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798350353778

ISBN: (纸本)9798350353785

The incorporation of cutting-edge technology into intelligent power grid management becomes a top priority as the globe keeps searching for effective and environmentally friendly energy solutions. The arrival of 6G wireless networks is expected to completely transform the methods used to track, manage, and enhance energy grids. An effort to provide high data speeds has already been made with the deployment of commercial fifth-generation (5G) wireless communications networks. The advent of IoT applications and the proliferation of smart devices will put a heavy strain on 5G wireless networks since they need extremely dependable and lowlatency connection. So, the massive continuous data traffic boom is probably going to be too much for 5G networks to handle. Research into enhancing current wireless networks in preparation for the 6G cellular systems, the next generation of mobile communication, has been spurred by this. This paper introduces the Neural 6G Wireless Routing Protocol (N6GWRP), a new routing protocol that supports intelligent communication. To evaluate the effectiveness of the suggested approach, it is cross-validated with the Multiple-Access Routing Protocol (MARP), a traditional routing model. Users should not experience any disruptions when mobile power and data transmission systems interact. Another major obstacle is connecting the vast array of devices and people with varying degrees of processing power in a way that is both sustainable and environmentally conscious. This study delves into the smart solution to the aforesaid difficulties and examines how 6G wireless networks might improve smart energy grid resilience and dependability through seamless interaction with other sophisticated technologies like blockchain. A more environmentally friendly and smart vision regarding electrical grid administration is possible thanks to the extensive study that strives to illuminate the revolutionary significance of 6G wireless technologies.

关键词： 6G mobile communication 5G mobile communication Wireless networks Transforms Routing Routing protocols Smart grids Sustainable development Smart devices Strain

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Adaptive Graph Learning-Convolutional Network for Multi-Node Offshore Wind Speed Forecasting

SSRN

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SSRN 2022年

作者： Li, Zhuo-Lin Zhang, Xiao-Lin Xu, Ling-Yu Yu, Jie School of Computer Engineering and Science Shanghai University NO. 333 Nanchen Road Shanghai China School of Information Engineering Inner Mongolia University of Science and Technology Inner Mongolia Baotou China Shanghai Institute for Advanced Communication and Data Science Shanghai University NO. 333 Nanchen Road Shanghai China

Multi-node wind speed forecasting is of great importance for offshore wind power. It is a challenging task due to the unknown complex dependencies among multi-node. Recently, graph neural networks (GNN) have been applied to wind forecasting because of the capability in modeling dependencies, but it requires a pre-defined graph for information propagation. Current GNN methods are to pre-construct a graph based on sensor distances. However it separates the graph from the downstream task and data, which is not optimal for the task and is difficult to construct in many cases. In this paper, we propose a graph learning method that automatically infers hidden associations among multi-node based on the data. In addition, we employ the gated temporal convolutional network to capture the temporal dependencies because of the parallel computational efficiency and gradient stability. We propose Adaptive Graph Learning-Convolutional Networks (AGLCN) to integrate graph learning, temporal and graph convolutional modules in a framework, in which all modules are jointly learned. Experiments are conducted in real-world multi-node wind speed data from the China Sea. The results show that our model achieves state-of-the-art results in all multi-scale wind speed predictions. Moreover, the learned graph can reveal the spatial correlations from a data-driven perspective. © 2022, The Authors. All rights reserved.

关键词： Graphic methods

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Deconfound Semantic Shift and Incompleteness in Incremental Few-shot Semantic Segmentation 39

Deconfound Semantic Shift and Incompleteness in Incremental ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wu, Yirui Xia, Yuhang Li, Hao Yuan, Lixin Chen, Junyang Liu, Jun Lu, Tong Wan, Shaohua Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computing and Communication Lancaster University Lancaster United Kingdom National Key Lab for Novel Software Technology Nanjing University Nanjing China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China

ISBN: (纸本)157735897X

Incremental few-shot semantic segmentation (IFSS) expands segmentation capacity of the trained model to segment new-class images with few samples. However, semantic meanings may shift from background to object class or vice versa during incremental learning. Moreover, new-class samples often lack representative attribute features when the new class greatly differs from the pre-learned old class. In this paper, we propose a causal framework to discuss the cause of semantic shift and incompleteness in IFSS, and we deconfound the revealed causal effects from two aspects. First, we propose a Causal Intervention Module (CIM) to resist semantic shift. CIM progressively and adaptively updates prototypes of old class, and removes the confounder in an intervention manner. Second, a Prototype Refinement Module (PRM) is proposed to complete the missing semantics. In PRM, knowledge gained from the episode learning scheme assists in fusing features of new-class and old-class prototypes. Experiments on both PASCAL-VOC 2012 and ADE20k benchmarks demonstrate the outstanding performance of our method. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Semantic Segmentation

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Unsupervised Community Discovery Algorithm via Reconstructed Graph Neural NEtwork

Unsupervised Community Discovery Algorithm via Reconstructed...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Jinghong Wang Hui Wang Jiateng Yang Xizhao Wang College of Computer and Cyber Security Hebei Normal University Shijiazhuang China Hebei Key Laboratory of Network and Information Security Hebei Normal University Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security (Hebei Normal University) Shijiazhuang China College of Artificial Intelligence and Big Data Hebei Polytechnic Institute Shijiazhuang China College of Computer Science and Software Engineering ShenZhen University Shenzhen China

Community discovery can help discover potential community structures in the network, which is a fundamental and important issue in network science. Graph neural network-based algorithms are receiving increasing attention. However, there is relatively little work on community discovery directly using graph neural networks in reconstruction design. This paper presents an Unsupervised Community Discovery Algorithm based on reconstructed graph neural network (UCDA) to resolve the above issues. To be specific, this paper proposes a framework that learns the representation based on network structure and attribute information. By designing input network features, encoders, decoders, this algorithm uses an unsupervised loss function to train neural networks to extract communities in an integrated way. These designs integrate higher-order modularity information with network features and use variational graph auto-encoders to train data end-to-end under the graph neural network framework. Based on a series of experiments with a wide range of datasets and advanced algorithms, UCDA has achieved superior performance and shows strong competitiveness. Finally, the last section reports the results analysis, t-SNE visualization and hyperparameter analysis, which depicts stable performance and powerful network modularity ability.

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Vulnerability of Machine Learning Approaches Applied in IoT-based Smart Grid: A Review

arXiv

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arXiv 2023年

作者： Zhang, Zhenyong Liu, Mengxiang Sun, Mingyang Deng, Ruilong Cheng, Peng Niyato, Dusit Chow, Mo-Yuen Chen, Jiming The State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550000 China The Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS10 2TN United Kingdom The State Key Laboratory of Industrial Control Technology the College of Control Science and Engineering Zhejiang University Hangzhou310027 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The UM SJTU Joint Institute Shanghai Jiao Tong University Shanghai200240 China

Machine learning (ML) sees an increasing prevalence of being used in the internet-of-things (IoT)-based smart grid. However, the trustworthiness of ML is a severe issue that must be addressed to accommodate the trend of ML-based smart grid applications (MLsgAPPs). The adversarial distortion injected into the power signal will greatly affect the system’s normal control and operation. Therefore, it is imperative to conduct vulnerability assessment for MLsgAPPs applied in the context of safety-critical power systems. In this paper, we provide a comprehensive review of the recent progress in designing attack and defense methods for MLsgAPPs. Unlike the traditional survey about ML security, this is the first review work about the security of MLsgAPPs that focuses on the characteristics of power systems. We first highlight the specifics for constructing the adversarial attacks on MLsgAPPs. Then, the vulnerability of MLsgAPP is analyzed from both the aspects of the power system and ML model. Afterward, a comprehensive survey is conducted to review and compare existing studies about the adversarial attacks on MLsgAPPs in scenarios of generation, transmission, distribution, and consumption, and the countermeasures are reviewed according to the attacks that they defend against. Finally, the future research directions are discussed on the attacker’s and defender’s side, respectively. We also analyze the potential vulnerability of large language model-based (e.g., ChatGPT) power system applications. Overall, we encourage more researchers to contribute to investigating the adversarial issues of MLsgAPPs. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Rotatable Antennas for Integrated Sensing and Communications

arXiv

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arXiv 2025年

作者： Zhou, Chao You, Changsheng Zheng, Beixiong Shao, Xiaodan Zhang, Rui Shenzhen518055 China School of Microelectronics South China University of Technology Guangzhou511442 China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

In this letter, we propose to deploy rotatable antennas (RAs) at the base station (BS) to enhance both communication and sensing (C&S) performances, by exploiting a new spatial degree-of-freedom (DoF) offered by array rotation. Specifically, we formulate a multi-objective optimization problem to simultaneously maximize the sum-rate of multiple communication users and minimize the Cramér-Rao bound (CRB) for target angle estimation, by jointly optimizing the transmit beamforming vectors and the array rotation angle at the BS. To solve this problem, we first equivalently decompose it into two subproblems, corresponding to an inner problem for beamforming optimization and an outer problem for array rotation optimization. Although these two subproblems are non-convex, we obtain their high-quality solutions by applying the block coordinate descent (BCD) technique and one-dimensional exhaustive search, respectively. Moreover, we show that for the communication-only case, RAs provide an additional rotation gain to improve communication performance;while for the sensing-only case, the equivalent spatial aperture can be enlarged by RAs for achieving higher sensing accuracy. Finally, numerical results are presented to showcase the performance gains of RAs over fixed-rotation antennas in integrated sensing and communications (ISAC). Copyright © 2025, The Authors. All rights reserved.

关键词： Cramer-Rao bounds

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IDENTIFYING THROUGH FLOWS FOR RECOVERING LATENT REPRESENTATIONS 8

IDENTIFYING THROUGH FLOWS FOR RECOVERING LATENT REPRESENTATI...

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8th International Conference on Learning Representations, ICLR 2020

作者： Li, Shen Hooi, Bryan Lee, Gim Hee Institute of Data Science NUS Graduate School for Integrative Sciences and Engineering National University of Singapore Singapore Department of Computer Science National University of Singapore Singapore

Identifiability, or recovery of the true latent representations from which the observed data originates, is de facto a fundamental goal of representation learning. Yet, most deep generative models do not address the question of identifiability, and thus fail to deliver on the promise of the recovery of the true latent sources that generate the observations. Recent work proposed identifiable generative modelling using variational autoencoders (iVAE) with a theory of identifiability. Due to the intractablity of KL divergence between variational approximate posterior and the true posterior, however, iVAE has to maximize the evidence lower bound (ELBO) of the marginal likelihood, leading to suboptimal solutions in both theory and practice. In contrast, we propose an identifiable framework for estimating latent representations using a flow-based model (iFlow). Our approach directly maximizes the marginal likelihood, allowing for theoretical guarantees on identifiability, thereby dispensing with variational approximations. We derive its optimization objective in analytical form, making it possible to train iFlow in an end-to-end manner. Simulations on synthetic data validate the correctness and effectiveness of our proposed method and demonstrate its practical advantages over other existing methods. © 2020 8th International Conference on Learning Representations, ICLR 2020. All rights reserved.

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Impact of energy efficiency on the morphology and behaviour of evolved robots

arXiv

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arXiv 2021年

作者： Rebolledo, Margarita Zeeuwe, Daan Bartz-Beielstein, Thomas Eiben, A.E. Institute for Data Science Engineering and Analytics TH Köln Gummersbach Germany Department of Computer Science Vrije Universiteit Amsterdam Netherlands

Most evolutionary robotics studies focus on evolving some targeted behavior without taking the energy usage into account. This limits the practical value of such systems because energy efficiency is an important property for real-world autonomous robots. In this paper, we mitigate this problem by extending our simulator with a battery model and taking energy consumption into account during fitness evaluations. Using this system we investigate how energy awareness affects the evolution of robots. Since our system is to evolve morphologies as well as controllers, the main research question is twofold: (i) what is the impact on the morphologies of the evolved robots, and (ii) what is the impact on the behavior of the evolved robots if energy consumption is included in the fitness evaluation? The results show that including the energy consumption in the fitness in a multi-objective fashion (by NSGA-II) reduces the average size of robot bodies while at the same time reducing their speed. However, robots generated without size reduction can achieve speeds comparable to robots from the baseline set. © 2021, CC BY-NC-SA.

关键词： Health

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Passive Reflection Codebook Design for IRS-Integrated Access Point

arXiv

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arXiv 2023年

作者： Huang, Yuwei Zhu, Lipeng Zhang, Rui The NUS Graduate School National University of Singapore Singapore119077 Singapore The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

Intelligent reflecting surface (IRS) has emerged as a promising technique to control wireless propagation wireless environment for improving the communication performance cost-effectively and extending the wireless signal coverage of access point (AP). In order to reduce the path-loss of the cascaded user-IRS-AP channels, the IRS-integrated AP architecture has been proposed to deploy the antenna array of the AP and the IRSs within the same antenna radome. To reduce the pilot overhead for estimating all IRS-involved channels, in this paper, we propose a novel codebook-based IRS reflection design for the IRS-integrated AP to enhance the coverage performance in a given area. In particular, the codebook consisting of a small number of codewords is designed offline by employing an efficient sector division strategy based on the azimuth angle. To ensure the performance of each sector, we optimize its corresponding codeword for IRS reflection pattern to maximize the sector-min-average-effective-channel-power (SMAECP) by applying the alternating optimization (AO) and semidefinite relaxation (SDR) methods. With the designed codebook, the AP performs the IRS reflection training by sequentially applying all codewords and selects the one achieving the best communication performance for data transmission. Numerical results show that our proposed codebook design can enhance the average channel power of the whole coverage area, as compared to the system without IRS. Moreover, the proposed codebook-based IRS reflection design is compared with several benchmark schemes, which achieves significant performance gain in both single-user and multi-user transmissions. © 2023, CC0.

关键词： Antenna arrays

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Machine Learning Designed identification on cervical cancers in patient

Machine Learning Designed identification on cervical cancers...

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Artificial Intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： M. Sangeetha G. Sasikala K. Anitha S D Prabu Ragavendiran K R Senthil Kumar M. Deivakani Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Tamil Nadu Department of Mathematics SRM Valliammai Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Saveetha Engineering College Thandalam Tamil Nadu India Department of Computer Science and Engineering RVS Technical campus - Coimbatore Coimbatore Tamil Nadu India Department of Mechanical Engineering R. M K. Engineering College Kavaraipettai Tamil Nadu India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul Tamil Nadu India

In this paper, we develop an automatic diagnosis model that aims to screen and detect the presence of cervical cancer in women patients. The diagnosis model consists of a series of stages that involves pre-processing, feature extraction and classification of cancer using bees swarm optimization (BSO). The BSO helps to classify the instances from the extracted features in an effective way that does not fall into premature convergence. The fully grown solution provides effective classification of images from the pre-defined medical datasets. The simulation is conducted on python in a high-end computing system to test the efficacy of BSO in classifying the cervical cancer. The validation shows an increasing precision of classifying the instances than other state-of-art methods.

关键词： Computational modeling Machine learning Feature extraction Knowledge discovery Concurrent engineering Particle swarm optimization Cervical cancer

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