检索结果-内蒙古大学图书馆

EPM: Evolutionary Perception Method for Anomaly Detection in Noisy Dynamic Graphs

IEEE Transactions on Knowledge and Data engineering 2025年第7期37卷 4035-4048页

作者： Wang, Huan Chen, Junyang Wu, Yirui Leung, Victor C. M. Wang, Di King Abdullah University of Science and Technology Division of Computer Electrical and Mathematical Sciences and Engineering Saudi Arabia Huazhong Agricultural University College of Informatics China Shenzhen University College of Computer Science and Software Engineering China Hohai University College of Computer and Information China

With the rapid expansion of interactions across various domains such as knowledge graphs and social networks, anomaly detection in dynamic graphs has become increasingly critical for mitigating potential risks. However, existing anomaly detection methods often assume noise-free dynamic graphs, overlooking the prevalence of noisy dynamic graphs in real-world applications. Specifically, noisy dynamic graphs affected by structural noises-such as spurious and missing nodes and edges-struggle to consistently provide reliable structural evidence for anomaly detection. To tackle this challenge, we propose an Evolutionary Perception Method (EPM) for identifying anomalous nodes in noisy dynamic graphs by resisting the interference of structural noises. EPM primarily consists of two components: a dynamic fitter and a filtering reviser. The dynamic fitter characterizes the interaction dynamics of nodes that removes and generates links at each period as a multiple superposition state, utilizing various link prediction algorithms to fit evolutionary mechanisms. Additionally, the filtering reviser designs evolutional entropies to quantify the evolutional uncertainty in multiple superposition states, further designing the Kalman filter to optimize these entropies. Extensive experiments show that the proposed EPM method surpasses state-of-the-art approaches in detecting anomalous nodes in noisy dynamic graphs. © 1989-2012 IEEE.

关键词： Kalman filters

来源：评论

学校读者我要写书评

暂无评论

Automated segmentation and classification of the atherosclerotic carotid plaque in ultrasound videos 24

Automated segmentation and classification of the atheroscler...

引用

24th International Conference on Digital Signal Processing, DSP 2023

作者： Liapi, Georgia D. Gemenaris, Michalis Loizou, Christos P. Kyriacou, Efthyvoulos Constatninou, Kyriacos P. Nicolaides, Andrew Dept. of Electrical Eng. Computer Eng. and Informatics Cyprus University of Technology Limassol Cyprus Dept. of Computer Science University of Cyprus Nicosia Cyprus Vascular Screening and Diagnostic Centre Nicosia Cyprus

ISBN: (纸本)9798350339598

The automated and reliable delineation of atherosclerotic carotid plaques in ultrasound (CUS) videos is of significant clinical relevance for management of the disease and the prediction of future stroke events. To facilitate stroke risk assessment, in this study, we propose an integrated software system for the automated segmentation and classification of atherosclerotic carotid plaques in longitudinal CUS videos, which was evaluated using 10 CUS videos, from 10 patients (5 Asymptomatic, AS, and 5 Symptomatic, SY). The proposed methodology involves the following steps: a) CUS video frame (VF) resolution and intensity normalization, b) speckle reduction filtering, c) Motion-mode state-based cardiac cycle (CC) identification, d) deep learning (DL)-based plaque segmentation, e) extraction and selection of plaque region of interest (ROI)-specific textural features, and f) machine learning (ML)-based plaque classification. Initially, one CC (cardiac diastole-systole-diastole) was selected per CUS video, and the CC's consecutive VFs were identified and reduced in number to exclude redundant VFs. All standardized VFs per patient were extracted, cropped and resized to mainly accommodate the ROI and were fed into a priorly trained and evaluated 2-dimensional DL plaque segmentation model. For each VF, the DL-based segmented plaque ROI was projected onto its primary resolution-normalized VF counterpart, from which textural and amplitude modulation-frequency modulation (AM-FM) plaque ROI features were extracted. Statistical analysis on the total AS and SY VFs was used for feature selection. We identified 2 plaque-originating AM-FM features, which exhibited statistically significant differences between the AS and SY standardized VFs (p © 2023 IEEE.

关键词： Frequency modulation

来源：评论

学校读者我要写书评

暂无评论

Decentralized control in active distribution grids via supervised and reinforcement learning

引用

Energy and AI 2024年第2期16卷 137-149页

作者： Stavros Karagiannopoulos Petros Aristidou Gabriela Hug Audun Botterud Laboratory for Information and Decision Systems(LIDS) Massachusetts Institute of TechnologyCambridgeUSA EEH-Power Systems Laboratory ETH Zurichhysikstrasse 38092 ZurichSwitzerland Department of Electrical Engineering Computer Engineering&InformaticsCyprus

While moving towards a low-carbon, sustainable electricity system, distribution networks are expected to host a large share of distributed generators, such as photovoltaic units and wind turbines. These inverter-based resources are intermittent, but also controllable, and are expected to amplify the role of distribution networks together with other distributed energy resources, such as storage systems and controllable loads. The available control methods for these resources are typically categorized based on the available communication network into centralized, distributed, and decentralized or local. Standard local schemes are typically inefficient, whereas centralized approaches show implementation and cost concerns. This paper focuses on optimized decentralized control of distributed generators via supervised and reinforcement learning. We present existing state-of-the-art decentralized control schemes based on supervised learning, propose a new reinforcement learning scheme based on deep deterministic policy gradient, and compare the behavior of both decentralized and centralized methods in terms of computational effort, scalability, privacy awareness, ability to consider constraints, and overall optimality. We evaluate the performance of the examined schemes on a benchmark European low voltage test system. The results show that both supervised learning and reinforcement learning schemes effectively mitigate the operational issues faced by the distribution network.

关键词： Supervised learning Reinforcement learning Deep deterministic policy gradient Decentralized control Active distribution systems

来源：评论

学校读者我要写书评

暂无评论

Comparative Analysis of Time Series and Machine Learning Models for Air Quality Prediction Utilizing IoT Data 13th

Comparative Analysis of Time Series and Machine Learning M...

引用

13th Mining Humanistic Data Workshop, MHDW 2024, 9th Workshop on 5G-Putting Intelligence to the Network Edge, 5G-PINE 2024 and 1st Workshop on AI in Applications for Achieving the Green Deal Targets, AI4GD 2024 held as parallel events of the IFIP WG 12.5 International Workshops on Artificial Intelligence Applications and Innovations, AIAI 2024

作者： Vonitsanos, Gerasimos Panagiotakopoulos, Theodor Kameas, Achilles Computer Engineering and Informatics Department University of Patras Patras Greece School of Technology and Science Hellenic Open University Patras Greece School of Business University of Nicosia Nicosia Cyprus

ISBN: (纸本)9783031632266

Air pollution has been shown to have serious negative effects on people’s health, the environment, and the economy. It is becoming more and more crucial to model, predict, and monitor air quality, particularly in urban areas. Air quality prediction is challenging because of the dynamic nature, instability, and high spatial and temporal variability of particles and pollutants. Internet of things technologies and machine learning offer an efficient way to address these challenges and enables the implementation of effective air quality prediction models. This paper aims to provide a comparative analysis of time series and machine learning methods for air quality prediction based on data collected through IoT sensors. These methods have been evaluated for PM10, PM2.5, and Air Quality Index (AQI) particles. The results indicate that while deep learning models (LSTM) perform better for the air quality index, ARIMA and SVM algorithms best predict the concentrations of the researched air pollutants (PM2.5, PM10). © IFIP International Federation for Information Processing 2024.

关键词： Air quality

来源：评论

学校读者我要写书评

暂无评论

Mathematical Modeling of an Anion Exchange Membrane Electrolyzer for Integration in a Novel Solar Photovoltaic-Battery-Green Hydrogen Nanogrid 3

Mathematical Modeling of an Anion Exchange Membrane Electrol...

引用

3rd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Arsalis, Alexandros Papanastasiou, Panos Georghiou, George E. University of Cyprus PV Technology Laboratory Department of Electrical and Computer Engineering Nicosia Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus University of Cyprus Department of Civil and Environmental Engineering Nicosia Cyprus

ISBN: (纸本)9798350375923

The Mediterranean area is an ideal location for harnessing solar energy due to the high solar irradiation availability. Utilization of innovative solutions address renewable energy instabilities and optimize the utilization of local clean energy resources. A promising technology is the utilization of green hydrogen subsystem to these solar systems. Such an integration could add significant capabilities, namely, provision of seasonal energy storage, increase of solar photovoltaic capacity, and export of the generated green hydrogen for other uses, such as fueling of hydrogen electric vehicles. A detailed model is developed in this work to assess the performance of the proposed green hydrogen subsystem configuration with a view to utilizing it as part of a future, larger, wholistic energy system. At full-load the model performs at an efficiency of 62.4%, while performance at a 10% part-load is 82.3%. These performance results suggest a very promising potential for this technology which can be harnessed in the future multi-scale energy systems, provided that the economic performance is also promising. © 2024 IEEE.

关键词： Clean energy

来源：评论

学校读者我要写书评

暂无评论

Design and Modeling of a Proton Exchange Membrane Electrolyzer-Compressed Hydrogen Storage-Proton Exchange Membrane Fuel Cell Green Hydrogen Subsystem for Integration to a Solar Photovoltaic-Battery Nanogrid 3

Design and Modeling of a Proton Exchange Membrane Electrolyz...

引用

3rd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Arsalis, Alexandros Kourougianni, Fanourios Olympios, Andreas V. Papanastasiou, Panos Georghiou, George E. Konstantinou, Charalampos University of Cyprus PV Technology Laboratory Department of Electrical and Computer Engineering Nicosia Cyprus Efficient and Sustainable Energy Solutions PHAETHON Centre of Excellence for Intelligent Nicosia Cyprus University of Cyprus Department of Civil and Environmental Engineering Nicosia Cyprus

ISBN: (纸本)9798350375923

A Photovoltaic-Battery-Green Hydrogen nanogrid system is designed and modeled to investigate its potential as a decentralized energy solution able to satisfy the load profile of Near Zero Energy Buildings. Such a system could potentially maximize self-consumption through onsite power generation, which also achieves minimization of dependence on the central power grid. The proposed system includes battery and hydrogen energy storage for various purposes. Such a combination could succeed in satisfying both the need for rapid power supply when needed (through battery energy storage), but it can also provide seasonal energy storage when the weather pattern of available solar energy changes between summer and winter (through hydrogen energy storage). The Green Hydrogen subsystem includes electrolyzer and fuel cell units, both based on proton exchange membrane technology, a hydrogen storage unit, and auxiliary components. The proposed system generates 110 kg of hydrogen per year, with electrolyzer and fuel cell efficiencies (at full load design conditions) at 58.4% and 45.8%, respectively. Self-sufficiency rate is close to 100%, while self-consumption rate is slightly below 48%. © 2024 IEEE.

关键词： Hydrogen storage

来源：评论

学校读者我要写书评

暂无评论

Insights into Household Energy Consumption: Disaggregating Cooling and Heating Profiles using Principal Component Analysis 3

Insights into Household Energy Consumption: Disaggregating C...

引用

3rd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Loizidis, Stylianos Kourougianni, Fanourios Olympios, Andreas V. Kyprianou, Andreas Georghiou, George E. University of Cyprus PV Technology Laboratory Department of Electrical and Computer Engineering Nicosia Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus University of Cyprus Department of Mechanical and Manufacturing Engineering Nicosia Cyprus

ISBN: (纸本)9798350375923

This paper develops a methodology based on Principal Component Analysis (PCA) to disaggregate distinct baseline electricity, heating and cooling components from the daily total electricity consumption data of 68 houses in cyprus over the course of one year. The results show that the electricity data involves several principal components, with the two main ones accounting for 52.27% and 14.83% of the total variance, representing cooling and heating, respectively. The findings highlight the potential of using PCA for effective energy disaggregation, which could be extended to various building types and communities. By analyzing energy consumption patterns, targeted energy-saving and improved energy efficiency measures can be identified. Future research will compare PCA results with physics-driven building models, explore alternative disaggregation methods, and apply the methodology to broader datasets. © 2024 IEEE.

关键词： cooling disaggregation Energy demand heating principal component analysis

来源：评论

学校读者我要写书评

暂无评论

A MLP-Mixer and mixture of expert model for remaining useful life prediction of lithium-ion batteries

引用

Frontiers of computer Science 2024年第5期18卷 1-10页

作者： Lingling ZHAO Shitao SONG Pengyan WANG Chunyu WANG Junjie WANG Maozu GUO Faculty of Computing Harbin Institute of TechnologyHarbin 150001China School of Electrical Engineering Liaoning University of TechnologyJinzhou 121004China School of Computer Science Northeast Electric Power UniversityJilin 132000China Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical UniversityNanjing 211166China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing 100044China

Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management *** learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series ***,the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be *** address this challenge,this paper proposes a novel deep learning model,the MLP-Mixer and Mixture of Expert(MMMe)model,for RUL *** MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level ***,we devise an ensemble predictor based on a Mixture-of-Experts(MoE)architecture to generate reliable RUL *** experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods,providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation *** code and dataset are available at the website of github.

关键词： lithium-ion battery remaining useful life deep learning MLP-Mixer mixture-of-experts

来源：评论

学校读者我要写书评

暂无评论

Integrating Data Protection at Interface of RISC-V Processor Core

Integrating Data Protection at Interface of RISC-V Processor...

引用

2024 Panhellenic Conference on Electronics and Telecommunications, PACET 2024

作者： Mach, Jan Kohutka, Lukas Cicak, Pavel Slovak University of Technology Institute of Computer Engineering and Applied Informatics Slovakia Information Systems and Software Engineering Slovak University of Technology Institute of Informatics Slovakia

ISBN: (纸本)9798350318845

Fulfilling increasing performance demands of space and automotive applications can be problematic as high dependability is required. Memory is one of the most radiationsensitive parts, so it is often protected with information redundancy. This paper describes an integration of error correction and detection techniques into the interface of RISC-V processor to protect the data stored in the memory. We also provide a hardware-software interface for reporting errors and software routines for correcting data in memory. Our solution has a negligible impact (-3.9%) on the overall performance of the core with a small area and power consumption overhead. © 2024 IEEE.

关键词： Error correction

来源：评论

学校读者我要写书评

暂无评论

Sound of Reason: Using AI to build a Digital Twin after the Kanon of Pythagoras in school settings 10

Sound of Reason: Using AI to build a Digital Twin after the ...

引用

10th IEEE Smart World Congress, SWC 2024

作者： Anthis, Zach Basiakoulis, Alexandros Efstathiou, Kyriakos Efstathiou, Marianna Department of Communication Culture and Media London United Kingdom Aristotle University of Thessaloniki Laboratory for Machine Tools and Manufacturing Engineering Department of Mechanical Engineering Thessaloniki Greece Cyprus University of Technology Digital Heritage Research Lab Department of Electrical Engineering Computer Engineering and Informatics Limassol Cyprus

ISBN: (纸本)9798331520861

This paper presents ongoing work on kanON, an AI-driven EdTech tool designed to foster interactive learning and support Digital Cultural Heritage (DCH) preservation. kanON facilitates the digital reconstruction of the infamous Pythagorean monochord, by allowing users to seamlessly manipulate parameters such as origin (location and epoch), materials, and shape/dimensions. Through the integration of Machine Learning (ML) algorithms, the tool predicts missing archaeological data and generates real-time 3D renderings based on user input, accompanied by corresponding confidence metrics. A mixed-methods (pilot) study was conducted in classroom environments to evaluate its educational impact via quantitative Likert scale assessments and qualitative content analysis of educator feedback. Preliminary results demonstrate high levels of student engagement and reflect positive educator responses, highlighting its potential to enhance the in-class accessibility and understanding of complex historical concepts, as well as to enable the preservation and exploration of cultural artifacts through digital means. Future work will focus on refining kanON and expanding its applicability within both educational and DCH contexts, by introducing the capability of outputting a fully operational digital twin, that integrates mixed reality functionalities. The idea is to enable users to later engage with their (personalized) model dynamically (e.g., readjusting string length and tension), thus allowing them to produce different soundscapes in real-time. This advancement aims to deepen the pedagogical impact by merging tactile interaction with visual and auditory feedback, further enhancing the educational experience via immersive, experiential learning. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：