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Neural Networks, Information and Communication engineering (NNICE), International Conference on

作者： Zhihong Chen Lisha Yao Yue Liu Bingjiang Qiu Chu Han Haoyang Tang Yujia Du Zaiyi Liu Gang Fang Institute of Computing Science and Technology Guangzhou University Guangzhou China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou China School of Medicine South China University of Technology Guangzhou China Department of Radiology Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou China School of Information Engineering Jiangxi College of Applied Technology Ganzhou China Guangdong Cardiovascular Institute Guangdong Provincial People’s Hospital Guangdong Academy of Sciences Guangzhou China Medical Research Institute Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China

Accurate segmentation of polyps in endoscopic images is pivotal for the early detection and diagnosis of colorectal cancer (CRC). Nonetheless, the varied appearance of polyp foregrounds and the intricate background interference significantly diminish the efficacy of pixel-level predictions. To address this issue, we propose a multi-scale perception network with triple-view attention (MPTA-Net) for polyp segmentation. The model primarily revolves around two core principles. The first integrates multiscale spatial information into the network's hierarchical encoding process to better preserve detailed polyp semantic information. The second introduces a triple-view attention mechanism that analyzes feature maps from three distinct perspectives to focus on and localize semantically relevant areas, thereby enhancing segmentation accuracy. Experimental results reveal that the proposed model outperforms existing mainstream segmentation models, effectively addressing under-segmentation, over-segmentation, and edge blurriness issues in polyp segmentation, thereby achieving better identification of challenging areas.

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LINEARIZATION IN MAGNETOELASTICITY

arXiv

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arXiv 2024年

作者： Almi, Stefano Kružík, Martin Molchanova, Anastasia University of Naples Federico II Department of Mathematics and Applications Renato Caccioppoli via Cintia Monte S. Angelo Naples80126 Italy Czech Academy of Sciences Institute of Information Theory and Automation Pod vodárenskou věží 4 Prague 8182 00 Czech Republic Faculty of Civil Engineering Czech Technical University Thákurova 7 Prague 6166 29 Czech Republic University of Vienna Faculty of Mathematics Oskar-Morgenstern-Platz 1 Vienna1090 Austria TU Wien Institute of Analysis and Scientific Computing Wiedner Hauptstraße 8–10 Wien1040 Austria

Starting from a model of nonlinear magnetoelasticity where magnetization is defined in the Eulerian configuration while elastic deformation is in the Lagrangian one, we rigorously derive a linearized model that coincides with the standard one that already appeared in the literature, see, e.g., [19] and where the zero-stress strain is quadratic in the magnetization. The relation of the nonlinear and linear model is stated in terms of the Γ-convergence and convergence of minimizers. © 2024, CC BY.

关键词： Elastic deformation

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Collecting Large Volume Data from Wireless Sensor Network by Drone

SSRN

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SSRN 2022年

作者： Silva, Rone Ilídio da Rezende, Josiane da Costa Vieira Souza, Marcone Jamilson Freitas Department of Technology in Civil Engineering Computation Automation Telematics e Humanities Federal University of São João Del Rei Campus Alto Paraopeba - C.A.P Rod.: MG 443 KM 7 Ouro Branco-MG 36420-000 Brazil Department of Computing FUMEC University Fundação Mineira de Educação e Cultura R. Cobre 200 - Cruzeiro MG Belo Horizonte30310-190 Brazil Departament of Computing Federal University of Ouro Preto Rua Diogo de Vasconcelos 122 Bairro Pilar MG Ouro Preto35400-000 Brazil

Data collection is the most important task in wireless sensor networks (WSN). Each sensor node has to send the sensed data to a special node called sink, which is the user interface. The sensor nodes far from the sink send data to intermediate nodes that forward it by multi-hop data paths. This characteristic leads to higher energy consumption in the sensor nodes close to the sink since they have to relay data from all other sensor nodes. The literature presents several studies that use mobile sinks for data collection to reduce the number of hops in the data paths and distributes the energy consumption, considering that the nodes close to the mobile sink change. However, the majority of these studies consider only the network limitation, such as energy. Furthermore, they also consider sensor nodes sending only one data packet to the mobile sink. This work assumes a quad-copter drone as a mobile sink and sensor nodes having several data packets to send to the sink. We propose two GRASP-based heuristics to define drone tours for data collection. Since this vehicle has limited flight time, the primary metric analyzed here is the overall data collection time. Furthermore, they guarantee that the mobile sink will stay a minimal time inside the radio range of each sensor node to ensure that all of them will have enough time to send all data. Simulated experiments showed that the proposed GRASP-based heuristics outperformed the greed algorithm found as state of the art for this type of scenario, mainly when the volume of data stored in each sensor node is high. © 2022, The Authors. All rights reserved.

关键词： Motion planning

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Collecting Large Volume Data from Wireless Sensor Network by Drone

SSRN

引用

SSRN 2022年

关键词： Motion planning

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An Iterative Optimizing Framework for Radiology Report Summarization with ChatGPT

arXiv

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arXiv 2023年

作者： Ma, Chong Wu, Zihao Wang, Jiaqi Xu, Shaochen Wei, Yaonai Zeng, Fang Liu, Zhengliang Jiang, Xi Guo, Lei Cai, Xiaoyan Zhang, Shu Zhang, Tuo Zhu, Dajiang Shen, Dinggang Liu, Tianming Li, Xiang The School of Automation Northwestern Polytechnical University Xi'An710072 China The School of Computer Science Northwestern Polytechnical University Xi'An710072 China The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of Life Science and Technology University of Electronic Science and Technology of China Chengdu611731 China School of Biomedical Engineering ShanghaiTech University Shanghai201210 China Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai200030 China Shanghai Clinical Research and Trial Center Shanghai201210 China The school of computing University of Georgia AthensGA30602 United States Department of Computer Science and Engineering The University of Texas at Arlington Arlington76019 United States The Department of Radiology Massachusetts General Hospital Boston02114 United States

The "Impression" section of a radiology report is a critical basis for communication between radiologists and other physicians. Typically written by radiologists, this part is derived from the "Findings" section, which can be laborious and error-prone. Although deep-learning based models, such as BERT, have achieved promising results in Automatic Impression Generation (AIG), such models often require substantial amounts of medical data and have poor generalization performance. Recently, Large Language Models (LLMs) like ChatGPT have shown strong generalization capabilities and performance, but their performance in specific domains, such as radiology, remains under-investigated and potentially limited. To address this limitation, we propose ImpressionGPT, leveraging the contextual learning capabilities of LLMs through our dynamic prompt and iterative optimization algorithm to accomplish the AIG task. ImpressionGPT initially employs a small amount of domain-specific data to create a dynamic prompt, extracting contextual semantic information closely related to the test data. Subsequently, the iterative optimization algorithm automatically evaluates the output of LLMs and provides optimization suggestions, continuously refining the output results. The proposed ImpressionGPT model achieves superior performance of AIG task on both MIMIC-CXR and OpenI datasets without requiring additional training data or fine-tuning the LLMs. This work presents a paradigm for localizing LLMs that can be applied in a wide range of similar application scenarios, bridging the gap between general-purpose LLMs and the specific language processing needs of various *** Codes 68T50, 68T37, 68T20 Copyright © 2023, The Authors. All rights reserved.

关键词： Radiology

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Simultaneous Prediction of SOH and RUL for Lithium-ion Batteries Using Transferable Knowledge Sharing Network

引用

IEEE Transactions on Power Electronics 2025年

作者： Zhong, Kai Liu, Zhihao Tian, Jiaqiang Fan, Chao Li, Mince Wang, Yujie Liu, Xinghua Wang, Peng Anhui University Institutes of Physical Science and Information Technology Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education Hefei230601 China Anhui University School of Eelectrical Engineering and Automation Anhui230039 China Hefei Comprehensive National Science Center Institute of Energy Hefei230601 China University of Science and Technology of China Department of Automation Hefei230027 China Xi'an University of Technology School of Electrical Engineering Xi'an710048 China Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore

The state of health (SOH) and remaining useful life (RUL) prediction of lithium-ion batteries are critical for the safe operation and maintenance optimization of electrical systems. However, existing methods face challenges in generalization ability and prediction accuracy, including the limitations of single-task learning for joint SOH and RUL prediction, sensitivity to complex operating conditions, and the impact of cross-domain data distribution differences. To address these challenges, this paper proposes a dual-representation transferable knowledge sharing network (DRTKSN) to predict SOH and RUL for batteries. The proposed algorithm is based on the framework of multi task learning (MTL) and Transfer Learning. By extracting and fusing dual-representation data features, the framework effectively captures the latent patterns of battery aging characteristics. It employs parameter and network structure sharing mechanisms to enhance the adaptability and generalization of cross-domain features. The proposed model achieves information collaboration between SOH and RUL tasks through a shared network, effectively reducing computational costs and improving prediction accuracy. Experimental results demonstrate that the proposed framework exhibits excellent robustness and transferability in complex degradation scenarios, significantly outperforming baseline models. © 1986-2012 IEEE.

关键词： Multi-task learning

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An IoT Edge computing System Architecture and its Application

An IoT Edge Computing System Architecture and its Applicatio...

引用

2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020

作者： Chen, Shichao Li, Qijie Zhang, Hua Zhu, Fenghua Xiong, Gang Tang, Ying Macau University of Science and Technology State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences China Beijing University of Technology State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Aerospace Smart Manufacturing Technology Development Co. Ltd Platform Research and Development Department Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Engineering Research Center of Intelligent Systems and Technology Institute of Automation Cloud Computing Center Chinese Academy of Sciences Beijing China Rowan University Department of Electrical and Computer Engineering GlassboroNJ United States Institute of Smart Education Qingdao Academy of Intelligent Industries Qingdao China

ISBN: (纸本)9781728168531

As the number of devices connected to the Internet of things (IoT) surges, the amount of data explodes. Therefore it not only increases the bandwidth load of data transmission but also aggravates the computing and storage load of a cloud platform. At the same time, the traditional computing paradigm centered on cloud computing cannot meet the real-time requirements in many application scenarios. The emergence of edge computing can solve the problems of realtime data processing and network bandwidth occupation in the current IoT scene. In this paper, according to the characteristics of IoT, such as fragmented data, heterogeneous network, and limited energy consumption, the architecture of an IoT edge computing system is constructed to suit better an IoT scene. In addition, the application of edge computing key technologies such as virtualization, edge intelligence, computing offload, collaborative scheduling and micro-services in resource-constrained IoT scenarios is analyzed in detail. Finally, the functions and application of energy consumption monitoring and optimization to a central air-conditioning system are analyzed and summarized, which is a typical application of edge computing in the context of the IoT. © 2020 IEEE.

关键词： Edge computing

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When brain-inspired AI meets AGI

Meta-Radiology

引用

Meta-Radiology 2023年第1期1卷

作者： Zhao, Lin Zhang, Lu Wu, Zihao Chen, Yuzhong Dai, Haixing Yu, Xiaowei Liu, Zhengliang Zhang, Tuo Hu, Xintao Jiang, Xi Li, Xiang Zhu, Dajiang Shen, Dinggang Liu, Tianming School of Computing The University of Georgia Athens 30602 United States Department of Computer Science and Engineering The University of Texas at Arlington Arlington 76019 United States MOE Key Laboratory for Neuroinformation School of Life Science and Technology University of Electronic Science and Technology of China Chengdu 611731 China School of Automation Northwestern Polytechnical University Xi'an 710072 China Department of Radiology Massachusetts General Hospital and Harvard Medical School Boston 02115 United States School of Biomedical Engineering ShanghaiTech University Shanghai 201210 China Shanghai United Imaging Intelligence Co. Ltd. Shanghai 200230 China Shanghai Clinical Research and Trial Center Shanghai 201210 China

Artificial General Intelligence (AGI) has been a long-standing goal of humanity, with the aim of creating machines capable of performing any intellectual task that humans can do. To achieve this, AGI researchers draw inspiration from the human brain and seek to replicate its principles in intelligent machines. Brain-inspired artificial intelligence is a field that has emerged from this endeavor, combining insights from neuroscience, psychology, and computer science to develop more efficient and powerful AI systems. In this article, we provide a comprehensive overview of brain-inspired AI from the perspective of AGI. We begin with the current progress in brain-inspired AI and its extensive connection with AGI. We then cover the important characteristics for both human intelligence and AGI (e.g., scaling, multimodality, and reasoning). We discuss important technologies toward achieving AGI in current AI systems, such as in-context learning and prompt tuning. We also investigate the evolution of AGI systems from both algorithmic and infrastructural perspectives. Finally, we explore the limitations and future of AGI. © 2023 The Authors

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Line Drawing Guided Progressive Inpainting of Mural Damage

arXiv

引用

arXiv 2022年

作者： Li, Luxi Zou, Qin Zhang, Fan Yu, Hongkai Chen, Long Song, Chengfang Huang, Xianfeng Wang, Xiaoguang Li, Qingquan Department of Computer Science Technology United International College of Beijing Normal University HongKong Baptist University Zhuhai China Machine Vision and Robotics Laboratory School of Computer Science Wuhan University Wuhan China State Key Laboratory of Surveying Mapping and Remote Sensing Information Engineering Wuhan University Wuhan China Department of Electrical Engineering and Computer Science Cleveland State University OH United States Institute of Automation Chinese Academy of Sciences Beijing China Cultural Heritage Intelligent Computing Laboratory Wuhan University Wuhan China Guangming Laboratory Shenzhen University Shenzhen China

Mural image inpainting is far less explored compared to its natural image counterpart and remains largely unsolved. Most existing image-inpainting methods tend to take the target image as the only input and directly repair the damage to generate a visually plausible result. These methods obtain high performance in restoration or completion of some pre-defined objects, e.g., human face, fabric texture, and printed texts, etc., however, are not suitable for repairing murals with varying subjects and large damaged areas. Moreover, due to discrete colors in paints, mural inpainting may suffer from apparent color bias. To this end, in this paper, we propose a line drawing guided progressive mural inpainting method. It divides the inpainting process into two steps: structure reconstruction and color correction, implemented by a structure reconstruction network (SRN) and a color correction network (CCN), respectively. In structure reconstruction, SRN utilizes the line drawing as an assistant to achieve large-scale content authenticity and structural stability. In color correction, CCN operates a local color adjustment for missing pixels which reduces the negative effects of color bias and edge jumping. The proposed approach is evaluated against the current state-of-the-art image inpainting methods. Qualitative and quantitative results demonstrate the superiority of the proposed method in mural image inpainting. The codes and data are available at https://***/qinnzou/mural-image-inpainting . © 2022, CC BY-NC-ND.

关键词： Restoration

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A novel SVM parameter tuning method based on advanced whale optimization algorithm 4

A novel SVM parameter tuning method based on advanced whale ...

引用

2019 4th International Conference on Intelligent computing and Signal Processing, ICSP 2019

作者： Yin, Xuehao Hou, Yandong Yin, Jiabao Li, Chao Department of Computing Jiangsu Automation Research Institute Lian Yungang Jiangsu222000 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin Heilongjiang150000 China

The classification performance of support vector machine (SVM) algorithm is highly dependent on the careful tuning of hyper-parameters and penalty coefficient. This paper introduces a novel SVM parameter optimization method by using the advanced whale optimization algorithm (AWOA) that is an improved whale of algorithm (WOA) with external archiving strategy. A new framework for SVM parameter optimization based on AWOA is built. To demonstrate the performance of our proposed method, six typical data sets are chosen to evaluate the effect of SVM classification problem. Experimental results show that the higher accuracy and better convergence can be achieved by AWOA compared with other three usual parameter optimization methods (WOA, PSO, and DE). © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Support vector machines

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