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Joint Energy and Frequency Regulation Market Clearing Considering Wind Power Uncertainty

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Joint Energy and Frequency Regulation Market Clearing Consid...

IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)

作者： Yemin Wu Jiyao Wang Chenyu Peng Chunyu Chen Xuemei Dai Jianxiao Wang School of Electrical Engineering China University of Mining and Technology Xuzhou China School of Electrical Engineering Shanghai Electric Power University Shanghai China National Engineering Laboratory of Big Data Analytics and Applied Technologies Peking University Beijing China

Efficient integration of renewable power such as wind energy in electricity markets is among the highest priorities. However, the deterministic market designs may not effectively adapt to the uncertainty of renewable power. In this paper, we investigate a joint energy and frequency regulation market clearing considering wind power uncertainty. By introducing the chance-constrained market design, we propose a joint market-clearing strategy for both conventionally deterministic units and uncertain wind farms. By the simulation based on real market data, we show that as the confidence level increases, system stability improves, but this comes at the expense of increased wind abandonment losses and higher operating costs in the market. Simultaneously, a higher penetration of wind power leads to lower market operating costs and energy shadow prices.

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Antigen epitopes of animal coronaviruses:a mini-review

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Animal Diseases 2024年第1期4卷 19-26页

作者： Mingjun Su Guanghui Zheng Xiangwen Xu Houhui Song Key Laboratory of Applied Technology On Green-Eco-Healthy Animal Husbandry of Zhejiang Province Zhejiang Provincial Engineering Research Center for Animal Health Diagnostics&Advanced TechnologyZhejiang International Science and Technology Cooperation Base for Veterinary Medicine and Health ManagementChina-Australia Joint Laboratory for Animal Health Big Data AnalyticsCollege of Animal Science and Technology&College of Veterinary Medicine of ZhejiangA&F University666 Wusu StreetLin'an DistrictHangzhou 311300Zhejiang ProvinceChina

Coronaviruses are widespread in nature and can infect mammals and poultry,making them a public health ***,prevention and control of emerging and re-emerging animal coronaviruses is a great *** mecha-nisms of virus-mediated immune responses have important implications for research on virus prevention and *** antigenic epitope is a chemical group capable of stimulating the production of antibodies or sensitized lympho-cytes,playing an important role in antiviral immune ***,it can shed light on the development of diagnos-tic methods and novel ***,we have reviewed advances in animal coronavirus antigenic epitope research,aiming to provide a reference for the prevention and control of animal and human coronaviruses.

关键词： Animal coronavirus Antigen epitope B-cell epitope T-cell epitope Immune responses Vaccines

GPTR: Gestalt-Perception Transformer for Diagram Object Detection

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arXiv 2022年

作者： Hu, Xin Zhang, Lingling Liu, Jun Fan, Jinfu You, Yang Wu, Yaqiang Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering School of Computer Science and Technology Xi’an Jiaotong University China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University China Department of Control Science and Engineering Tongji University Shanghai China Department of Computer Science National University of Singapore Singapore Lenovo Research Beijing China

Diagram object detection is the key basis of practical applications such as textbook question answering. Because the diagram mainly consists of simple lines and color blocks, its visual features are sparser than those of natural images. In addition, diagrams usually express diverse knowledge, in which there are many low-frequency object categories in diagrams. These lead to the fact that traditional data-driven detection model is not suitable for diagrams. In this work, we propose a gestalt-perception transformer model for diagram object detection, which is based on an encoder-decoder architecture. Gestalt perception contains a series of laws to explain human perception, that the human visual system tends to perceive patches in an image that are similar, close or connected without abrupt directional changes as a perceptual whole object. Inspired by these thoughts, we build a gestalt-perception graph in transformer encoder, which is composed of diagram patches as nodes and the relationships between patches as edges. This graph aims to group these patches into objects via laws of similarity, proximity, and smoothness implied in these edges, so that the meaningful objects can be effectively detected. The experimental results demonstrate that the proposed GPTR achieves the best results in the diagram object detection task. Our model also obtains comparable results over the competitors in natural image object detection. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

Analysis of Interpretable Spiking Neural Networks for Motor Imagery Recognition Tasks

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SSRN

SSRN 2023年

作者： Wang, Xiuqing Yang, Yunpeng Li, Qingru Li, Qiuting An, Yang Ye, Xiaoya Key Laboratory of Network & Information Security College of Computer and Cyber Security Hebei Normal University Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Hebei Normal University Shijiazhuang China

Brain-computer interface (BCI) is an important aspect of human-computer interaction (HCI). Motor imagery (MI) EEG-based BCI has great application potential for assisting disabled people in motor function reconstruction and restoration. To fulfill the given tasks based on MI-EEG signals successfully, decoding MI-EEG signals accurately is important. Although deep learning (DL) can be used to analyze EEG signals, it still lacks interpretability. Compared with traditional artificial neural networks (ANNs), spiking neural networks (SNNs) can use spiking neurons which are bio-plausible for the neurons in the brain to communicate with each other, and analyze EEG signals by spike trains with better bio-interpretability. We propose a deep spiking convolutional neural network based on a self-attention (DSCNN-SA) mechanism for EEG-based MI recognition. The DSCNN-SA model is first used to extract the high-level spatio-temporal features of the EEG signal and transform the extracted features into spike trains as input to the deep spiking convolutional neural networks (DSCNNs), and analyze the EEG signal for MI according to the firing patterns of spiking neurons. The classification of 2-class motor imagery tasks is evaluated by the BCI Competition IV-2a and IV-2b datasets, and the average accuracy of the DSCNN-SA model is 85.53% and 81.52% respectively, which is better than that of the comparison models, such as KNN, MLP, DCNN-SA, ECCSP-TB2B, CNN-SAE, EEGNet, KLD and STNN, etc. The experimental results show that the DSCNN-SA model is effective for EEG-based MI recognition. © 2023, The Authors. All rights reserved.

关键词： Brain computer interface

Mitigating Object Hallucinations in Large Vision-Language Models with Assembly of Global and Local Attention

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arXiv 2024年

作者： An, Wenbin Tian, Feng Leng, Sicong Nie, Jiahao Lin, Haonan Wang, Qianying Chen, Ping Zhang, Xiaoqin Lu, Shijian Xi’an Jiaotong University China National Engineering Laboratory for Big Data Analytics China Nanyang Technological University Singapore Lenovo Research University of Massachusetts Boston United States Zhejiang University of Technology China

Despite great success across various multimodal tasks, Large Vision-Language Models (LVLMs) often encounter object hallucinations with generated textual responses being inconsistent with the actual objects in images. We examine different LVLMs and pinpoint that one root cause of object hallucinations lies with deficient attention on discriminative image features. Specifically, LVLMs often predominantly attend to prompt-irrelevant global features instead of prompt-relevant local features, undermining their visual grounding capacity and leading to object hallucinations. We propose Assembly of Global and Local Attention (AGLA), a training-free and plug-and-play approach that mitigates hallucinations by assembling global features for response generation and local features for visual discrimination simultaneously. Specifically, we introduce an image-prompt matching scheme that captures prompt-relevant local features from images, leading to an augmented view of the input image where prompt-relevant content is highlighted while irrelevant distractions are suppressed. Hallucinations can thus be mitigated with a calibrated logit distribution that is from generative global features of the original image and discriminative local features of the augmented image. Extensive experiments show the superiority of AGLA in LVLM hallucination mitigation, demonstrating its wide applicability across both discriminative and generative tasks. Our code is available at https://***/Lackel/AGLA. © 2024, CC BY.

关键词： Visual languages

EFFICIENTLY PARAMETERIZED NEURAL METRIPLECTIC SYSTEMS

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arXiv 2024年

作者： Gruber, Anthony Lee, Kookjin Lim, Haksoo Park, Noseong Trask, Nathaniel Center for Computing Research Sandia National Laboratories AlbuquerqueNM United States School of Computing and Augmented Intelligence Arizona State University TempeAZ United States Big Data Analytics Laboratory Yonsei University Seoul Korea Republic of Big Data Analytics Laboratory Korea Advanced Institute of Science and Technology Daejeon Korea Republic of School of Engineering and Applied Science University of Pennsylvania PhiladelphiaPA United States

Metriplectic systems are learned from data in a way that scales quadratically in both the size of the state and the rank of the metriplectic data. Besides being provably energy conserving and entropy stable, the proposed approach comes with approximation results demonstrating its ability to accurately learn metriplectic dynamics from data as well as an error estimate indicating its potential for generalization to unseen timescales when approximation error is low. Examples are provided which illustrate performance in the presence of both full state information as well as when entropic variables are unknown, confirming that the proposed approach exhibits superior accuracy and scalability without compromising on model expressivity. Copyright © 2024, The Authors. All rights reserved.

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Fault Diagnosis for Rolling Bearing Based on RF-LSTM

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Fault Diagnosis for Rolling Bearing Based on RF-LSTM

Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Qiuting Li Xiuqing Wang Yunpeng Yang Ruiyi Wang Feng Lv College of Computer and Cyber Security Hebei Normal University Shijiazhuang China Hebei Provincial Key Laboratory of Network & Information Security Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Shijiazhuang China

ISBN: (纸本)9781665401166

In the era of big data, it is of great significance to study data-driven fault diagnostic methods for equipment in complex industrial environments. In this paper, a data-driven fault diagnostic method for rolling bearing based on RF-LSTM is proposed, which combines random forest (RF) algorithm with long and short term memory (LSTM) networks. RF is used to select sensor data’s features, which have great influence on rolling bearing fault diagnosis, and LSTM is used for prediction rolling bearing’s working states. The proposed method is used for fault diagnosis of the Case Western Reserve University (CWRU) bearing dataset and the experimental results validated the effectiveness of the proposed RF-LSTM model. Experimental results also show that the performance of the RFLSTM model is better than that of RF, LSTM, RF-RF, PCA-RF, PCA-LSTM models.

关键词： Fault diagnosis Radio frequency Fault detection Time series analysis Rolling bearings Forestry Predictive models

C-Net: A reliable convolutional neural network for biomedical image classification

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arXiv 2020年

作者： Barzekar, Hosein Yu, Zeyun Big Data Analytics and Visualization Laboratory Department of Computer Science University of Wisconsin-Milwaukee MilwaukeeWI53211 United States Department of Biomedical Engineering University of Wisconsin-Milwaukee MilwaukeeWI53211 United States

Cancers are the leading cause of death in many countries. Early diagnosis plays a crucial role in having proper treatment for this debilitating disease. The automated classification of the type of cancer is a challenging task since pathologists must examine a huge number of histopathological images to detect infinitesimal abnormalities. In this study, we propose a novel convolutional neural network (CNN) architecture composed of a Concatenation of multiple Networks, called C-Net, to classify biomedical images. The model incorporates multiple CNNs including Outer, Middle, and Inner. The first two parts of the architecture contain six networks that serve as feature extractors to feed into the Inner network to classify the images in terms of malignancy and benignancy. The C-Net is applied for histopathological image classification on two public datasets, including BreakHis and Osteosarcoma. To evaluate the performance, the model is tested using several evaluation metrics for its reliability. The C-Net model outperforms all other models on the individual metrics for both datasets and achieves zero misclassification. This approach has the potential to be extended to additional classification tasks, as experimental results demonstrate utilizing extensive evaluation metrics. Copyright © 2020, The Authors. All rights reserved.

关键词： Computer aided diagnosis

PFD-Net: Pyramid Fourier Deformable Network for medical image segmentation

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Computers in Biology and Medicine 2024年 172卷 108302-108302页

作者： Yang, Chaorong Zhang, Zhaohui College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Shijiazhuang050024 China Hebei Provincial Key Laboratory of Network & Information Security Hebei Normal University Shijiazhuang050024 China

Medical image segmentation is crucial for accurately locating lesion regions and assisting doctors in diagnosis. However, most existing methods fail to effectively utilize both local details and global semantic information in medical image segmentation, resulting in the inability to effectively capture fine-grained content such as small targets and irregular boundaries. To address this issue, we propose a novel Pyramid Fourier Deformable Network (PFD-Net) for medical image segmentation, which leverages the strengths of CNN and Transformer. The PFD-Net first utilizes PVTv2-based Transformer as the primary encoder to capture global information and further enhances both local and global feature representations with the Fast Fourier Convolution Residual (FFCR) module. Moreover, PFD-Net further proposes the Dilated Deformable Refinement (DDR) module to enhance the model's capacity to comprehend global semantic structures of shape-diverse targets and their irregular boundaries. Lastly, Cross-Level Fusion Block with deformable convolution (CLFB) is proposed to combine the decoded feature maps from the final Residual Decoder Block (DDR) with local features from the CNN auxiliary encoder branch, improving the network's ability to perceive targets resembling the surrounding structures. Extensive experiments were conducted on nine publicly medical image datasets for five types of segmentation tasks including polyp, abdominal, cardiac, gland cells and nuclei. The qualitative and quantitative results demonstrate that PFD-Net outperforms existing state-of-the-art methods in various evaluation metrics, and achieves the highest performance of mDice with the value of 0.826 on the most challenging dataset (ETIS), which is 1.8% improvement compared to the previous best-performing HSNet and 3.6% improvement compared to the next-best PVT-CASCADE. Codes are available at https://***/ChaorongYang/PFD-Net. © 2024 Elsevier Ltd

关键词： Convolution