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Extraction of typical operating scenarios of new power system based on deep time series aggregation

CAAI Transactions on Intelligence Technology 2025年第1期10卷 283-299页

作者： Zhaoyang Qu Zhenming Zhang Nan Qu Yuguang Zhou Yang Li Tao Jiang Min Li Chao Long School of Electrical Engineering Northeast Electric Power UniversityJilinChina Jilin Province Technology Research Center of Power Big Data Intelligent Processing JilinChina State Grid Jiangsu Electric Power Co. Ltd.Nanjing Power Supply BranchNanjingChina State Grid Jilin Electric Power Company Limited ChangchunChina Department of Electrical Engineering and Electronics University of LiverpoolLiverpoolUK

Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system.A novel deep time series aggregation scheme(DTSAs)is proposed to generate typical operational scenarios,considering the large amount of historical operational snapshot ***,DTSAs analyse the intrinsic mechanisms of different scheduling operational scenario switching to mathematically represent typical operational scenarios.A Gramian angular summation field-based operational scenario image encoder was designed to convert operational scenario sequences into highdimensional *** enables DTSAs to fully capture the spatiotemporal characteristics of new power systems using deep feature iterative aggregation *** encoder also facilitates the generation of typical operational scenarios that conform to historical data distributions while ensuring the integrity of grid operational *** studies demonstrate that the proposed method extracted new fine-grained power system dispatch schemes and outperformed the latest high-dimensional feature-screening *** addition,experiments with different new energy access ratios were conducted to verify the robustness of the proposed *** enable dispatchers to master the operation experience of the power system in advance,and actively respond to the dynamic changes of the operation scenarios under the high access rate of new energy.

关键词： convolutional neural networks deep time series aggregation high proportion of new energy new power system operation scenario image encoder power system operation mode

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Learning Structured Compressed Sensing with Automatic Resource Allocation

Learning Structured Compressed Sensing with Automatic Resour...

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2025 IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Wang, Han Pérez, Eduardo Huijben, Iris A.M. van Gorp, Hans van Sloun, Ruud Römer, Florian Applied AI Signal Processing and Data Analysis Fraunhofer Institute for Nondestructive Testing IZFP Germany Department of Electrical Engineering Eindhoven University of Technology Netherlands Electronic Measurements and Signal Processing Ilmenau University of Technology Germany

ISBN: (纸本)9798350368741

Multidimensional data acquisition often requires extensive time and poses significant challenges for hardware and software regarding data storage and processing. Rather than designing a single compression matrix as in conventional compressed sensing, structured compressed sensing yields dimension-specific compression matrices, reducing the number of optimizable parameters. Recent advances in machine learning (ML) have enabled task-based supervised learning of subsampling matrices, albeit at the expense of complex downstream models. Additionally, the sampling resource allocation across dimensions is often determined in advance through heuristics. To address these challenges, we introduce Structured COmpressed Sensing with Automatic Resource Allocation (SCOSARA) with an information theory-based unsupervised learning strategy. SCOSARA adaptively distributes samples across sampling dimensions while maximizing Fisher information content. Using ultrasound localization as a case study, we compare SCOSARA to state-of-the-art ML-based and greedy search algorithms. Simulation results demonstrate that SCOSARA can produce high-quality subsampling matrices that achieve lower Cramér Rao Bound values than the baselines. In addition, SCOSARA outperforms other ML-based algorithms in terms of the number of trainable parameters, computational complexity, and memory requirements while automatically choosing the number of samples per axis. © 2025 IEEE.

关键词： Compressed Sensing Information Theory Subsampling Unsupervised Learning

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A Cross-Domain Ontology Semantic Representation Based on NCBI-BlueBERT Embedding

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Chinese Journal of Electronics 2022年第5期31卷 860-869页

作者： ZHAO Lingling WANG Junjie WANG Chunyu GUO Maozu Faculty of Computing Harbin Institute of Technology Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical University Beijing Key Laboratory of Intelligent Processing for Building Big Data School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture

A common but critical task in biological ontologies data analysis is to compare the difference between ontologies. There have been numerous ontologybased semantic-similarity measures proposed in specific ontology domain, but it still remains a challenge for crossdomain ontologies comparison. An ontology contains the scientific natural language description for the corresponding biological aspect. Therefore, we develop a new method based on natural language processing(NLP) representation model bidirectional encoder representations from transformers(BERT) for cross-domain semantic representation of biological ontologies. This article uses the BERT model to represent the word-level of the ontologies as a set of vectors, facilitating the semantic analysis or comparing the biomedical entities named in an ontology or associated with ontology terms. We evaluated the ability of our method in two experiments: calculating similarities of pair-wise disease ontology and human phenotype ontology terms and predicting the pair-wise of proteins interaction. The experimental results demonstrated the comparative performance. This gives promise to the development of NLP methods in biological data analysis.

关键词： human phenotype ontology cross-domain semantic representation natural language description pair-wise disease ontology data analysis bidirectional encoder representations NLP protein interaction diseases proteins natural language processing ontologies (artificial intelligence) natural language processing representation neural nets bioinformatics semantic-similarity measures cross-domain ontology semantic representation semantic analysis NCBI-BlueBERT embedding biological ontology data analysis

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Enhanced Cross-Modality MRI Segmentation Using Dilated Convolutions and Multi-Scale Gradient Map 28

Enhanced Cross-Modality MRI Segmentation Using Dilated Convo...

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28th International Computer Science and engineering Conference, ICSEC 2024

作者： Murtaza, Ghulam Pluempitiwiriyawej, Charnchai Wangsiripitak, Somkiat Fareed, Mohammad Jawad Khalid, Mudassar Chulalongkorn University Department of Electrical Engineering Bangkok Thailand Chulalongkorn University Multimedia Data Analytics and Processing Research Unit Department of Electrical Engineering Bangkok Thailand King Mongkut's Insittute of Technology Machine Intelligence and Vision Laboratory School of Information Technology Ladkrabang Thailand

ISBN: (纸本)9798350366860

In recent years, convolutional neural networks have significantly advanced image segmentation, particularly for brain images, where important edge features are automatically found. However, accurate segmentation of tumors in a brain remains a challenge across different magnetic resonance modalities, like T1, T2, T1ce, and FLAIR. Using a simple gradient map as an input to the neural networks is not effective due to variations in cross-modality image characteristics. To address this issue, we introduced multi-scale gradient maps that incorporate Holistically Nested Edge Detection (HED) and dilated convolutions into the UNet model. The HED model captures detailed gradient information, enhancing structural feature identification across modalities, while dilated convolutions expand the UNet receptive field for better contextual understanding without increasing parameters. Our method was trained and evaluated on the BraTS2018 dataset. The experimental results demonstrate significant improvements in segmentation accuracy and robustness. Specifically, our method achieved a Dice Similarity Coefficient (DSC) of 0.6902 for T2 to T1ce, 0.6858 for T2 to T1, 0.4329 for FLAIR to T1, and 0.6004 for FLAIR to T1ce, outperforming previous state-of-The-Art methods. This demonstrates the effectiveness of our approach in enhancing segmentation performance across different MR image modalities. © 2024 IEEE.

关键词： Image segmentation

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Automatic extraction of lightweight and efficient neural network architecture of heavy convolutional architectures to predict microsatellite instability from hematoxylin and eosin histology in gastric cancer

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Neural Computing and Applications 2024年第25期36卷 15295-15321页

作者： Rostami, Habib Ashkpour, Maryam Behzadi-Khormouji, Hamed Mokhtari, Maral Khayati, Armin Keshavarz, Ahmad Talatian Azad, Saeed Tabesh, Yahya Computer Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran Artificial Intelligence and Intelligent Healthcare Lab Artificial Intelligence and Data Mining Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Faculty of New Science and Technologies University of Tehran Tehran Iran Department of Computer Science Imec-IDLab University of Antwerp Sint-Pietersvliet 7 Antwerp2000 Belgium Shahid Faghihi Hospital Shiraz University of Medical Sciences Shiraz Iran Department of Computer Science Engineering and IT School of Electrical and Computer Engineering Shiraz University Shiraz Iran Electrical Engineering Department Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr75168 Iran IoT and Signal Processing Research Group Faculty of Intelligent Systems Engineering and Data Science ICT Research Institute Persian Gulf University Bushehr75168 Iran Department of Mathematical Sciences Sharif University of Technology Tehran Iran

Cancers have emerged as a significant concern due to their impact on public health and society. The examination and interpretation of tissue sections stained with Hematoxylin and Eosin (H&E) play a crucial role in disease assessment, particularly in cases like gastric cancer. Microsatellite instability (MSI) is suggested to contribute to the carcinogenesis of specific gastrointestinal tumors. However, due to the nonspecific morphology observed in H&E-stained tissue sections, MSI determination often requires costly evaluations through various molecular studies and immunohistochemistry methods in specialized molecular pathology laboratories. Despite the high cost, international guidelines recommend MSI testing for gastrointestinal cancers. Thus, there is a pressing need for a new diagnostic modality with lower costs and widespread applicability for MSI detection. This study aims to detect MSI directly from H&E histology slides in gastric cancer, providing a cost-effective alternative. The performance of well-known deep convolutional neural networks (DCNNs) and a proposed architecture are compared. Medical image datasets are typically smaller than benchmark datasets like ImageNet, necessitating the use of off-the-shelf DCNN architectures developed for large datasets through techniques such as transfer learning. Designing an architecture proportional to a custom dataset can be tedious and may not yield desirable results. In this work, we propose an automatic method to extract a lightweight and efficient architecture from a given heavy architecture (e.g., well-known off-the-shelf DCNNs) proportional to a specific dataset. To predict MSI instability, we extracted the MicroNet architecture from the Xception network using the proposed method and compared its performance with other well-known architectures. The models were trained using tiles extracted from whole-slide images, and two evaluation strategies, tile-based and whole-slide image (WSI)-based, were employed and comp

关键词： Network architecture

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SDN-Based Multicast Traffic engineering for Radio Telescope data Networks

IEEE Networking Letters

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IEEE Networking Letters 2024年第1期6卷 65-69页

作者： Slabber, Martin Ventura, Neco Mwangama, Joyce Science Data Processing Team South African Radio Astronomy Observatory Cape Town7925 South Africa Tsholo Storage Systems Cape Town7785 South Africa University of Cape Town Department of Electrical Engineering Cape Town7701 South Africa

Modern radio telescopes utilize IP multicast in their data networks because multicasting offers enhanced flexibility. Telescope data networks carry high volumes of real-time data and need to support network reconfigurability. Hence, they stand to gain from optimized traffic management. Software-Defined Networking (SDN) simplifies and adds dynamism to multicast routing. We reference the MeerKAT radio telescope data network and investigate methods to route the multicast streams in an SDN context. Such route planning is influenced by the telescope control system along with architectural and dynamic (e.g., link or device failure) properties of the network. This letter shows that SDN is a practical option for radio telescope data networks and that the system is flexible, easy to understand, and scalable. Additionally, we present two basic algorithms which can serve as foundational templates for more tailored solutions. © 2019 IEEE.

关键词： Control systems

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Least Squares Projection Onto the Behavior for SISO LTI Models ⁎

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IFAC-PapersOnLine 2024年第15期58卷 336-341页

作者： S. Lagauw B. De Moor Center for Dynamical Systems Signal Processing and Data Analytics (STADIUS) Department of Electrical Engineering (ESAT) KU Leuven Belgium

We consider the least squares projection onto the behavior for linear time-invariant (LTI) single-input single-output (SISO) models, in which the observed input-output data are modified in a least squares (LS) sense by subtracting so-called misfits, so that the modified data satisfy a given linear dynamic relation. We show that the LS-criterion of the projection problem induces an orthogonal decomposition of the ambient data space and we characterize this decomposition by means of banded block-Toeplitz matrices, the elements of which are the coefficients of the difference equation describing the SISO LTI dynamics. We thereby generalize earlier results in the literature on autonomous LTI models to the more complicated SISO case. Additionally, we illustrate that the novel characterization is equivalent (up to a change of model representation) to results derived using (isometric) state-space representations in the literature on behavioral systems theory.

关键词： Modeling identification linear systems errors in variables identification

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Seeing the light: Hyperspectral retinal imaging as a surrogate marker for early stage Alzheimer's disease

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ACTA OPHTHALMOLOGICA 2025年第S284期103卷

作者： Ghesquiere, Michiel Christinaki, Eirini Van Eijgen, Jan Vandenbulcke, Mathieu Vandenberghe, Rik De Vos, Maarten Stalmans, Ingeborg Department of Neurosciences – Research Group Ophthalmology KU Leuven Leuven Belgium Department of Neurosciences – Research Group Psychiatry KU Leuven Leuven Belgium Department of Neurosciences – Research Group Experimental Neurology KU Leuven Leuven Department of Electrical Engineering (ESAT) – Dynamical Systems Signal Processing and Data Analytics (STADIUS) KU Leuven Leuven Belgium

Aims/Purpose: Alzheimer's disease (AD), the leading cause of dementia, affects millions, underscoring the need for early diagnostic methods. The retina, which can exhibit AD-related pathological processes, offers a promising diagnostic site. This study aims to utilize Hyperspectral Retinal Imaging (HSRI) to differentiate between positive (+) and negative (-) PET-amyloid beta (Aβ) status. Within this cohort, a subset is Aβ+ while remaining cognitively healthy, indicating preclinical amyloid accumulation. Unlike most research targeting later symptomatic stages, our approach could facilitate interventions before clinically apparent neurodegeneration occurs. Methods: data was used from 37 Aβ- and 25 Aβ+ individuals of which 8 with mild cognitive impairment (MCI). HSRI were captured using an easy-to-use hyperspectral snapshot camera, covering a spectral range of 460–630 nm. Images were taken from primary, inferior, and superior views of the retina, with further regions of interest (ROI) defined in the primary view. After preprocessing, these spectra were used to train machine learning classifiers [1]. Results: The best classifier performance was observed using XGBoost with the superior spectra (AUC = 0.72), and using a Linear Discriminant Analysis classifier in superior quadrants (S1 AUC = 0.74, S2 AUC = 0.68). Lower performance in other ROI underscored the variability in amyloid deposition across the retina, correlating with previous findings that retinal Aβ deposits in AD patients are frequently concentrated in the superior quadrants. Conclusions: We were able to achieve similar performance of previous research in AD patients [1], but in a preclinical cohort of cognitively normal participants up to MCI patients. Consistent with the literature, the superior retinal regions seem most discriminative for Aβ status. Further exploration of the specificity of the HSRI signal will pave the way for early AD detection, potentially improving outcomes through earlier therapeutic i

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Multimedia Classification via Tensor Linear Discriminant Analysis

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IEEE Transactions on Broadcasting 2024年第4期70卷 1139-1152页

作者： Chang, Shih-Yu Wu, Hsiao-Chun Yan, Kun Chih-Hao Huang, Scott Wu, Yiyan San Jose State University Department Of Applied Data Science San JoseCA95192 United States Louisiana State University School Of Electrical Engineering And Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center For Ai Applications Taoyuan32003 Taiwan Guilin University Of Electronic Technology Guangxi Key Laboratory Of Wireless Wideband Communication And Signal Processing Department Of Information And Telecommunication Guilin541004 China National Tsing Hua University Department Of Electrical Engineering Hsinchu300 Taiwan Western University Department Of Electrical And Computer Engineering LondonONN6A 3K7 Canada

Linear discriminant analysis (LDA) is a well-known feature-extraction technique for data analytic and pattern classification. As the dimensionality of multimedia data has increased in this big era, it is often to characterize data by tensors. Over the past two decades, researchers have thus explored to extend LDA to the general tensor space, especially in two common ways: LDA of tensors using tensor decomposition methods (by conversion of tensors to matrices) and LDA of tensors built upon the T-product. However, both of the aforementioned approaches have restrictions thereby. A critical problem about how to carry out LDA of arbitrary scatter tensors based on the Einstein product still remains unsolved by the existing methods. Therefore, we propose a novel tensor LDA (a.k.a. TLDA) approach, which can carry out the LDA of arbitrary-dimensional scatter-tensors without any need of tensor decomposition. Besides, for reducing the computation time, we also design a parallel paradigm to execute our proposed TLDA in this work. Numerical experiments conducted over real multimedia data demonstrate the efficacy of our proposed new TLDA in terms of classification accuracy. Moreover, the comparison of the classification accuracies, computational-complexities, and memory-complexities of our proposed novel TLDA scheme and other existing tensor-based LDA methods is made. By leveraging TLDA for high-dimensional feature extraction, segmentation, and user-item interaction data processing, future multimedia recommendation systems can facilitate more accurate, engaging, and satisfactory user experience over the Internet. © 1963-12012 IEEE.

关键词： Tensors

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Multi-View Graph Embedding Learning for Image Co-Segmentation and Co-Localization

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IEEE Transactions on Circuits and Systems for Video Technology 2024年第6期34卷 4942-4956页

作者： Huang, Aiping Li, Lijian Zhang, Le Niu, Yuzhen Zhao, Tiesong Lin, Chia-Wen Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou350108 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou350108 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Fuzhou University Fujian Key Laboratory for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering The Fujian Science and Technology Innovation Laboratory for Optoelectronic Information Fuzhou350108 China Institute of Communications Engineering National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan

Image co-segmentation and co-localization exploit inter-image information to identify and extract foreground objects with a batch mode. However, they remain challenging when confronted with large object variations or complex backgrounds. This paper proposes a multi-view graph embedding (MV-Gem) learning scheme which integrates diversity, robustness and discernibility of object features to alleviate this phenomenon. To encourage the diversity, the deep co-information containing both low-layer general representations and high-layer semantic information is generated to form a multi-view feature pool for comprehensive co-object description. To enhance the robustness, a multi-view adaptive weighted learning is formulated to fuse the deep co-information for feature complementation. To ensure the discernibility, the graph embedding and sparse constraint are embedded into the fusion formulation for feature selection. The former aims to inherit important structures from multiple views, and the latter further selects important features to restrain irrelevant backgrounds. With these techniques, MV-Gem gradually recovers all co-objects through optimization iterations. Extensive experimental results on real-world datasets demonstrate that MV-Gem is capable of locating and delineating co-objects in an image group. © 1991-2012 IEEE.

关键词： Semantics

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