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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yuelin Hu Yuejie Zhang Tao Zhang Shang Gao Weiguo Fan School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China School of Information Management and Engineering Shanghai Key Laboratory of Financial Information Technology Shanghai University of Finance and Economics Shanghai China school of Information Technology Deakin University Australia University of Iowa USA

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Deep learning has made significant break through's in image classification, but accurate diagnosis on chest radiography remains challenging due to a variety of potential diseases contained in one scan. Complex relations among diseases have significant clinical meanings, but are always ignored in most of previous work. Thus in this paper, we propose a novel Label Generation Network (LGN) which treats the label sequence as the caption of a radiology image and utilizes RNN to generate the disease labels according to the semantic relations and co-occurrence dependency among them. However, the sequential generation process of RNN makes it hard to capture the complex topological relations among diseases. To mitigate this problem, a Historical information Module (HIM) is especially introduced to LGN, in which all the generated labels are fully considered when generating a new label. Moreover, a specific self-attention mechanism is applied in HIM to learn the topological disease relations and utilize them to select useful historical information which can provide positive guidance to the prediction of new label. Very positive results have been obtained in our experiments on the benchmark dataset of Chest X-ray14, which significantly outperform the state-of-the-art methods.

关键词： Diseases Feature extraction Generators X-ray imaging Diagnostic radiography Computational modeling Training

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Modelling and Optimization of Heating Dispatch in Thermal Power Plant

Modelling and Optimization of Heating Dispatch in Thermal Po...

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第28届中国控制与决策会议

作者： Lixia Xie Yali Wu Xianyong Peng Lei Li Fan Yi School of Automation and Information Engineering Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Emerson Process Management Co.Ltd.

ISBN: (纸本)9781467397155

With the addition of heating in thermal power *** to lack of guidance for heating scheduling in traditional scheduling mode,it has been unable to satisfy the current costs for energy conservation and saving of thermal power *** load distribution(especially heat load dispatch) is an important subject in the operation optimization of a thermal power *** model of heat load dispatch on the condition of economic power dispatch is established and linear programming for optimized heat load distribution among units based on LINGO is *** average distribution and linear programming distribution are contrasted in a thermal power *** is obviously shown by simulation and calculation that linear programming distribution gets more optimal results than current average *** has important significance to reduce energy consumption and save costs.

关键词： Heating Dispatch Thermal Power Plant LINGO

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RetrievalSum: A Retrieval Enhanced Framework for Abstractive Summarization

arXiv

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arXiv 2021年

作者： An, Chenxin Zhong, Ming Geng, Zhichao Yang, Jianqiang Qiu, Xipeng Shanghai Key Laboratory of Intelligent Information Processing Fudan University China School of Computer Science Fudan University China University of Illinois at Urbana-Champaign United States

Existing summarization systems mostly generate summaries purely relying on the content of the source document. However, even for humans, we usually need some references or exemplars to help us fully understand the source document and write summaries in a particular format. But how to retrieve the high-quality exemplars and incorporate them into summarization systems is still challenging and worth exploring. In this paper, we propose RETRIEVALSUM, a novel retrieval enhanced abstractive summarization framework consisting of a dense Retriever and a Summarizer. Retrieved exemplars are not only additional knowledge but also guidance of the writing style of a specific corpus. We validate our method on a wide range of summarization datasets across multiple domains and two backbone models: BERT and BART. Results show that our retrieval enhanced framework can greatly improve baselines with different powerful pre-trained models by 1.38 ∼ 4.66 in ROUGE-1 score. Copyright © 2021, The Authors. All rights reserved.

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A Fusion Shallow and Deep Features Network for Facial StO2 Stress Classification

A Fusion Shallow and Deep Features Network for Facial StO2 S...

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IEEE information Technology, Networking, Electronic and Automation Control Conference

作者： Haolin Xia Sirui Sun Xinyu Liu Ju Zhou Hanpu Wang Tong Chen School of Electronic and Information Engineering Southwest University Chongqing China Key Laboratory of Non-Linear Circuit and Intelligent Information Processing of Chongqing Chongqing China Institute of Legal Psychology and Intelligent Computing Chongqing China

Stress is an emotional state that affects our lives, so it is vital to detect it accurately. Numerous studies have shown that tissue oxygen saturation (StO2) is an effective physiological signal for identifying different stress states. However, previous StO2 feature extraction methods relied too heavily on baselines to be realized in practice. In this paper, a two-stream network combining shallow and deep features, called CRNet, is proposed for baseline-independent StO2 stress classification. In CRNet, shallow features are extracted using a CNN network and deep features are extracted using a ResNet network. For the extracted high-dimensional deep features, MLP module is employed for dimensionality reduction, which can effectively combine the two levels of features and improve the feature extraction capability of the network. Experimental results show that CRNet can significantly improve the performance of StO2 stress classification to the state-of-the-art (SOTA) even when using only raw imbalanced data.

关键词： Dimensionality reduction Automation Anxiety disorders Feature extraction Physiology Stress

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Tokenize Anything via Prompting

arXiv

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arXiv 2023年

作者： Pan, Ting Tang, Lulu Wang, Xinlong Shan, Shiguang Key Laboratory of Intelligent Information Processing ICT CAS China Beijing Academy of Artificial Intelligence China University of Chinese Academy of Sciences Beijing China

We present a unified, promptable model capable of simultaneously segmenting, recognizing, and captioning anything. Unlike SAM, we aim to build a versatile region representation in the wild via visual prompting. To achieve this, we train a generalizable model with massive segmentation masks, e.g., SA-1B masks, and semantic priors from a pre-trained CLIP model with 5 billion parameters. Specifically, we construct a promptable image decoder by adding a semantic token to each mask token. The semantic token is responsible for learning the semantic priors in a predefined concept space. Through joint optimization of segmentation on mask tokens and concept prediction on semantic tokens, our model exhibits strong regional recognition and localization capabilities. For example, an additional 38M-parameter causal text decoder trained from scratch sets a new record with a CIDEr score of 164.7 on the Visual Genome region captioning task. We believe this model can be a versatile region-level image tokenizer, capable of encoding general-purpose region context for a broad range of visual perception tasks. Code and models are available at https://***/baaivision/tokenize-anything. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Loop-based topology maintenance in Wireless Sensor Networks

Loop-based topology maintenance in Wireless Sensor Networks

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Third International Conference on Computer Network and Mobile Computing, ICCNMC 2005

作者： Li, Yanping Wang, Xin Baueregger, Florian Xue, Xiangyang Toh, C.K. Software School Fudan University Shanghai 200433 China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai 200433 China Department of Computer Sciences Friedrich-Alexander University Erlangen-Nuremberg Germany Department of Computer Sciences Fudan University Shanghai 200433 China Department of Electronic Engineering Queen Mary University of London United Kingdom

Clustering is recognized as an effective topology maintenance framework to provide better scalability for Wireless Sensor Networks. When loop structures are deployed to topology maintenance, it can provide better robustness and convenient route recovery, for the topology information is disseminated within a loop instead of a single node and the nature of the loop that there are two paths between each pair of nodes within a loop provides backup routes. In this paper, we propose a novel topology maintenance approach based on loop structures. The algorithm is a distributed, on-demand and configurable. It prefers a low-mobility network which is typically suitable for wireless sensor networks. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Wireless telecommunication systems

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Relay selections for cooperative underlay CR systems with energy harvesting

Relay selections for cooperative underlay CR systems with en...

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作者： Ye, Jia Liu, Zhedong Zhao, Hui Pan, Gaofeng Ni, Qiang Alouini, Mohamed-Slim Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Southwest University Chongqing400715 China Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia School of Computing and Communications Lancaster University LancasterLA1 4WA United Kingdom

In this paper, we consider a cooperative underlay cognitive radio system in which one secondary user (S) transmits its data to a secondary receiver with the assistance of multiple relays. In the considered underlay spectrum sharing scenario, the transmit power levers at S and forwarding relays are adjusted simultaneously according to a given threshold interference power that the primary user can tolerate. We also consider that relays adopt threshold decode-and-forward relay scheme and harvest energy from the primary transmitter with finite energy storage capacity under time switching scheme. Three best relay selection (RS) schemes, optimal source-relay link with wireless energy harvesting (WEH), optimal relay-destination link with WEH and optimal source-relay-destination link with WEH schemes, are proposed and studied. Closed-form analytical expressions for the outage probability of these three best RS schemes are derived, respectively. Finally, our analytical results are verified by Monte-Carlo simulations. © 2015 IEEE.

关键词： Energy harvesting

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A New pattern recognition Algorithm Based on Fuzzy Association Degree

A New Pattern Recognition Algorithm Based on Fuzzy Associati...

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International Conference on Neural Networks and Brain (ICNNB)

作者： Shifei Ding Zhongzhi Shi College of Information Science and Engineering Shandong Agricultural University Taian China Key Laboratory of Intelligent Information Processing Chinese Academy of Sciences Institute for Computing Technology Beijing China

Based on fuzzy association degree, a new pattern recognition algorithm is set up. First, some new concepts of fuzzy association coefficient (FAC), fuzzy association degree (FAD) and fuzzy relative weight (FRW) have been proposed for surveying data information. Second, on the basis of the concepts proposed here, a new pattern recognition algorithm has been set up. At last, the algorithm set up here is applied to surveying data. The results of simulation application show that the recognition algorithm presented here is feasible and effective

关键词： pattern recognition Fuzzy sets Laboratories Fuzzy neural networks Educational institutions information science Agricultural engineering History Artificial neural networks pattern analysis

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TMFIF:Transformer-based multi-focus image fusion 5

TMFIF:Transformer-based multi-focus image fusion

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5th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2024

作者： Li, Rui Geng, Shengling Zhang, Dan Zhou, Mingquan Qinghai Normal University School of Computer Science Xining China Nanyang Institute of Technology School of Computer and Software Nanyang China People's Government of Qinghai Province & Beijing Normal University Academy of Plateau Science and Sustainability Xining China Qinghai Normal University The State Key Laboratory of Tibetan Intelligent Information Processing and Application China

ISBN: (纸本)9798350373820

Multi-focus image fusion is a hot topic in the field of image processing, and it is a fundamental problem in the fields of image editing, image synthesis, and target retrieval. In previous fusion methods, although feature-rich datasets, models, and algorithms have been provided, there are still many problems with the effective fusion of distant and near views in complex backgrounds. To solve the challenging multi-focus image fusion problem more accurately, we introduce the Transformer Network based on Encoder Decoder (TMFIF), which can extract more generalized features based on the features of multi-focused images to achieve image fusion in complex backgrounds for better visual effects. In this work, we achieve fusion by inputting two images, near and far view. We compare the performance with other multifocal image fusion algorithms by conducting experiments on publicly available datasets and illustrated using existing evaluation methods and evaluation metrics;the results of the experiments show that our method visualizes the fusion effect better through the encoder and the decoder and the evaluation metrics are also relatively good. © 2024 IEEE.

关键词： Image fusion

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Prediction of Drug-Disease Associations Based on Long Short-Term Memory Network and Gaussian Interaction Profile Kernel 15th

Prediction of Drug-Disease Associations Based on Long Short-...

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15th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2020

作者： Jiang, Han-Jing Wang, Yan-Bin Huang, Yabing Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China College of Computer Science and Technology Zhejiang University Hangzhou315000 China Department of Pathology Renmin Hospital of Wuhan University Wuhan430060 China

ISBN: (纸本)9789811613531

Drug-disease association depicts the accessibility landscape of the association at the molecular level, thereby revealing the intermolecular reaction in the process of drug reposition. However, the scarcity of drug-disease association data often complicates the drug reposition. In this work, we present a method for predicting drug-disease association, which is called drug-disease association prediction via Gaussian interaction profile kernel and long short-term memory network (G-LSTM). G-LSTM combines a recurrent network framework with a Gaussian interaction profile kernel to learn underlying features that accurately characterize drug-disease-association data. We have verified G-LSTM models on the two benchmark datasets Fdataset and Cdataset, respectively. The experimental results show that the predictive performance was significantly improved, with AUC of 0.9527 and 0.9705, respectively. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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