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An Integrated Bifunctional Pressure‒Temperature Sensing System Fabricated on a Breathable Nanofiber and Powered by Rechargeable Zinc–Air Battery for Long‑Term Comfortable Health Care Monitoring

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Advanced Fiber Materials 2024年第4期6卷 1037-1052页

作者： Peng Wang Gengsheng Liu Guifen Sun Chuizhou Meng Guozhen Shen Yang Li School of Mechanical Engineering University of JinanJinan 250022China State Key Laboratory for Reliability and Intelligence of Electrical Equipment Engineering Research Center of Ministry of Education for Intelligent Rehabilitation Device and Detection TechnologyHebei Key Laboratory of Smart Sensing and Human‑Robot InteractionSchool of Mechanical EngineeringHebei University of TechnologyTianjin 300401China School of Integrated Circuits and Electronics Beijing Institute of TechnologyBeijing 100081China Shandong Provincial Key Laboratory of Network Based Intelligent Computing School of Information Science and EngineeringUniversity of JinanJinan 250022China School of Microelectronics Shandong UniversityJinan 250101China

Bulky external power supplies largely limit the continuous long-term application and miniaturization development of smart sensing ***,we fabricate a flexible and wearable integrated sensing system on an electrospun all-nanofiber *** three parts of the sensing system are all obtained by a facile ink-based direct writing *** resistive pressure sensor is realized by decorating MXene sheets on TPU ***,the resistive temperature sensor is prepared by compositing MXene sheets into poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate)(PEDOT:PSS).The thin-film zinc–air battery(ZAB)includes an interdigital zinc–air electrode that is bonded with a gel polymer *** can supply a high open-circuit voltage of 1.39 V and a large areal capacity of 18.2 mAh cm^(-2) for stable and reliable power-supplying sensing parts *** to the hydrophobic nature of TPU and open-ended micropores in the TPU nanofiber,the sensing system is waterproof,self-cleaning,and air and moisture *** application,the above-mentioned functional components are seamlessly integrated into an intelligent electronic wristband,which is comfortably worn on a human wrist to monitor pulse and body temperature in real time with continuous operation of up to 4 *** the novel design and remarkable performance,the proposed integrated all-nanofiber sensing system presents a promising solution for developing advanced multifunctional wearable electronics.

关键词： Pressure‒temperature sensor Rechargeable zinc–air battery Electrospun nanofibers Permeable electronic Integrated sensing system

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Modeling and Clustering of Parabolic Granular Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3728-3742页

作者： Tang, Yiming Gao, Jianwei Pedrycz, Witold Hu, Xianghui Xi, Lei Ren, Fuji Hu, Min Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Hefei University of Technology School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Southeast University School of Computer Science and Engineering Nanjing211189 China University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China

At present, there exist some problems in granular clustering methods, such as lack of nonlinear membership description and global optimization of granular data boundaries. To address these issues, in this study, revolving around the parabolic granular data, we propose an overall architecture for parabolic granular modeling and clustering. To begin with, novel coverage and specificity functions are established, and then a parabolic granular data structure is proposed. The fuzzy c-means (FCM) algorithm is used to obtain the numeric prototypes, and then particle swarm optimization (PSO) is introduced to construct the parabolic granular data from the global perspective under the guidance of principle of justifiable granularity (PJG). Combining the advantages of FCM and PSO, we propose the parabolic granular modeling and optimization (PGMO) method. Moreover, we put forward attribute weights and sample weights as well as a distance measure induced by the Gaussian kernel similarity, and then come up with the algorithm of weighted kernel fuzzy clustering for parabolic granularity (WKFC-PG). In addition, the assessment mechanism of parabolic granular clustering is discussed. In summary, we set up an overall architecture including parabolic granular modeling, clustering, and assessment. Finally, comparative experiments on artificial, UCI, and high-dimensional datasets validate that our overall architecture delivers a good improvement over previous strategies. The parameter analysis and time complexity are also given for WKFC-PG. In contrast with related granular clustering algorithms, it is observed that WKFC-PG performs better than other granular clustering algorithms and has superior stability in handling outliers, especially on high-dimensional datasets. © 2020 IEEE.

关键词： Fuzzy clustering

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A TDOA/AOA Hybrid Positioning Based on Improved Sparrow Search Algorithm for Mobile Position Estimation

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Wireless Communications and Mobile computing 2023年第1期2023卷

作者： Hu, Zhongyi Lu, Dongwan Xu, Minghai Tong, Yinsheng Ma, Hanjie Yue, Yinggao School of Computer Science and Artificial Intelligence Wenzhou University Wenzhou325035 China Intelligent Information Systems Institute Wenzhou University Wenzhou325035 China Key Laboratory of Intelligent Image Processing and Analysis Wenzhou China School of Intelligent Manufacturing and Electronic Engineering Wenzhou University of Technology Wenzhou325035 China

To address the difficulty in calculating the nonlinear equation of time difference of arrival (TDOA) positioning, as well as the problem of measurement error in the hybrid time difference of arrival/angle of arrival (TDOA/AOA) positioning algorithm, an improved sparrow search algorithm is proposed to optimize positioning, and the optimization mechanism is retained on the basis of improving the performance of the original algorithm. The maximum likelihood estimation method is used to calculate the objective function, and then, the estimated function of the mobile station is used as the fitness function to generate the initial population of sparrows. Then, using particle swarm optimization, optimize the sparrow search algorithm and obtain the population's optimal solution in order to obtain the optimal position. The simulation results show that, when compared to the existing algorithm, increasing the number of base stations increases the average accuracy of the sparrow search algorithm (SSA) positioning method by 18.54% and 4.5%, respectively, and, when compared to the proposed particle swarm optimization (PSO) positioning method, by 13.79% and 11.6% as the radius increases. The SSA hybrid positioning algorithm performs better in terms of positioning accuracy, convergence speed, and robustness. © 2023 Zhongyi Hu et al.

关键词： Time difference of arrival

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ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly Detection

arXiv

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arXiv 2023年

作者： He, Junwei Xu, Qianqian Jiang, Yangbangyan Wang, Zitai Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

Graph anomaly detection is crucial for identifying nodes that deviate from regular behavior within graphs, benefiting various domains such as fraud detection and social network. Although existing reconstruction-based methods have achieved considerable success, they may face the Anomaly Overfitting and Homophily Trap problems caused by the abnormal patterns in the graph, breaking the assumption that normal nodes are often better reconstructed than abnormal ones. Our observations indicate that models trained on graphs with fewer anomalies exhibit higher detection performance. Based on this insight, we introduce a novel two-stage framework called Anomaly-Denoised Autoencoders for Graph Anomaly Detection (ADA-GAD). In the first stage, we design a learning-free anomaly-denoised augmentation method to generate graphs with reduced anomaly levels. We pretrain graph autoencoders on these augmented graphs at multiple levels, which enables the graph autoencoders to capture normal patterns. In the next stage, the decoders are retrained for detection on the original graph, benefiting from the multi-level representations learned in the previous stage. Meanwhile, we propose the node anomaly distribution regularization to further alleviate Anomaly Overfitting. We validate the effectiveness of our approach through extensive experiments on both synthetic and real-world datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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Theme-Based Literature Analysis in the Field of Transportation 16th

Theme-Based Literature Analysis in the Field of Transportati...

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16th International Conference on intelligent information Hiding and Multimedia Signal processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of information technology, Applications and Tools, FITAT 2020

作者： Zhang, Sifan Lu, Hao Zhang, Fuquan Zhu, Yifan Wang, Rongrong Computer School Beijing Institute of Technology Beijing10089 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Min-Jiang University Fuzhou350121 China

ISBN: (纸本)9789813367562

Transportation is an important research field of engineering science, and its related research covers multiple fields and links in policies, methods, technologies, and applications. In order to sort out the research context and development status of the transportation field and provide scientific and technological information support for scientific researchers in the field, this article analyzes the literature published in SCI journals in the transportation field from 2008 to 2018 through the topic analysis method. The analysis results show that the current main research results in the field of transportation have formed different clusters around keywords such as cognitive radio, electric vehicles, optimization and simulation, and intelligent transportation systems, and the research development process of different countries is different. For example, the research results of China in recent years have gone from energy efficiency, automobile emissions to intelligent transportation, while the United States has been focusing on the research of intelligent transportation systems. Over time, China has gradually focused on the cutting-edge technology in the field of transportation, and its research results have greatly promoted the development of research in the field of transportation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Energy efficiency

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A news image captioning approach based on multimodal pointer-generator network

A news image captioning approach based on multimodal pointer...

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作者： Chen, Jingqiang Zhuge, Hai School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computer Science and Network Engineering Guangzhou University Guangzhou China Department of Computer Science School of Engineering and Applied Science Aston University Birmingham United Kingdom Key Laboratory of Intelligent Information Processing Institute of Computing Technology University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing China

News image captioning aims to generate captions or descriptions for news images automatically, serving as draft captions for creating news image captions manually. News image captions are different from generic captions as news image captions contain more detailed information such as entity names and events. Therefore, both images on news and the accompanying text are the source of generating caption of news image. Pointer-generator network is a neural method defined for text summarization. This article proposes the Multimodal pointer-generation network by incorporating visual information into the original network for news image captioning. The multimodal attention mechanism is proposed by splitting attention into visual attention paid to the image and textual attention paid to the text. The multimodal pointer mechanism is proposed by using both textual attention and visual attention to compute pointer distributions, where visual attention is first transformed into textual attention via the word-image relationships. The multimodal coverage mechanism is defined to reduce repetitions of attentions or repetitions of pointer distributions. Experiments on the DailyMail test dataset and the out-of-domain BBC test dataset show that the proposed model outperforms the original pointer-generator network, the generic image captioning method, the extractive news image captioning method, and the LDA-based method according BLEU, METEOR, and ROUGL-L evaluations. Experiments also show that the proposed multimodal coverage mechanisms can improve the model, and that transforming visual attention to pointer distributions can improve the model. © 2020 John Wiley & Sons, Ltd.

关键词： Statistical tests

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Lesion-Aware Open Set Medical Image Recognition with Domain Shift

Lesion-Aware Open Set Medical Image Recognition with Domain ...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yiqian Xu Rui-Wei Zhao Rui Feng School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China National Children’s Medical Center Children’s Hospital of Fudan University Shanghai China Shanghai Collaborative Innovation Center of Intelligent Visual Computing Shanghai China

Medical image diagnosis in real clinical scenario faces the challenging open set recognition problem with domain shift. Based on the observation that it is often merely the local lesion areas in the whole image decide or cause the true seen or potential unseen disease labels, we propose a unified deep learning framework to address it by establishing a lesion-aware mask generator to guide the model towards focusing on the lesion areas for both seen and unseen diseases, followed by a multi-task feature learning module to extract the lesion image features; and an open set recognizer with improved capability to reject unseen classes. Last but not least, we propose tailored training strategies with mixed up data and mask augmentation for improved OSR and multi-clue cross-domain feature alignment for domain generalization. Through comparative experiments and ablation studies on the representative skin lesion benchmarks, we successfully validate the effectiveness of our proposed framework.

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Exploring Data Geometry for Continual Learning

arXiv

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arXiv 2023年

作者： Gao, Zhi Xu, Chen Li, Feng Jia, Yunde Harandi, Mehrtash Wu, Yuwei Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China Department of Electrical and Computer Systems Eng. Monash University Data61 Australia

Continual learning aims to efficiently learn from a non-stationary stream of data while avoiding forgetting the knowledge of old data. In many practical applications, data complies with non-Euclidean geometry. As such, the commonly used Euclidean space cannot gracefully capture non-Euclidean geometric structures of data, leading to inferior results. In this paper, we study continual learning from a novel perspective by exploring data geometry for the non-stationary stream of data. Our method dynamically expands the geometry of the underlying space to match growing geometric structures induced by new data, and prevents forgetting by keeping geometric structures of old data into account. In doing so, making use of the mixed curvature space, we propose an incremental search scheme, through which the growing geometric structures are encoded. Then, we introduce an angular-regularization loss and a neighbor-robustness loss to train the model, capable of penalizing the change of global geometric structures and local geometric structures. Experiments show that our method achieves better performance than baseline methods designed in Euclidean space. Copyright © 2023, The Authors. All rights reserved.

关键词： Geometry

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Enhancing Multi-turn Dialogue Modeling with Intent information for E-Commerce Customer Service 1

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9th CCF International Conference on Natural Language processing and Chinese computing, NLPCC 2020

作者： Liu, Ruixue Chen, Meng Liu, Hang Shen, Lei Song, Yang He, Xiaodong JD AI Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (数字)9783030604509

ISBN: (纸本)9783030604493

Nowadays, it is a heated topic for many industries to build intelligent conversational bots for customer service. A critical solution to these dialogue systems is to understand the diverse and changing intents of customers accurately. However, few studies have focused on the intent information due to the lack of large-scale dialogue corpus with intent labelled. In this paper, we propose to leverage intent information to enhance multi-turn dialogue modeling. First, we construct a large-scale Chinese multi-turn E-commerce conversation corpus with intent labelled, namely E-IntentConv, which covers 289 fine-grained intents in after-sales domain. Specifically, we utilize the attention mechanism to extract Intent Description Words (IDW) for representing each intent explicitly. Then, based on E-IntentConv, we propose to integrate intent information for both retrieval-based model and generation-based model to verify its effectiveness for multi-turn dialogue modeling. Experimental results show that extra intent information is useful for improving both response selection and generation tasks. © 2020, Springer Nature Switzerland AG.

关键词： Electronic commerce

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Lifting the Answer: Reranking Candidates on Data Augmented Text-to-SQL

Lifting the Answer: Reranking Candidates on Data Augmented T...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Guang-Yu Cheng Shuo Yu Wen-Bin Jiang Shuai Ma Xiang Ao Qing He Henan Institutes of Advanced Technology Zhengzhou University Zhengzhou P.R. China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China Baidu inc Beijing China

The text-to-SQL task is a realistic and challenging job, which aims to translate natural language questions into corresponding SQL queries. When attempting to generate SQL queries in the text-to-SQL task, prevailing semantic parsing models employ beam search to generate several candidates. Based on our pilot study, we observe that the gold-standard SQL answer may exist in the n-best candidate list produced by the decoder, rather than the first. Hence, we aim to lift the correct answer given several candidates generated by semantic parsing models for text-to-SQL. To this end, we propose a reranking module that reorders the n-best list of candidate SQL queries by pair-wise hinge loss. Meanwhile, a data augmentation module is leveraged to enrich the inadequate training instances for providing candidates with better qualities. These two modules can be easily grafted onto text-to-SQL backbone networks, and extensive experiments on the cross-domain text-to-SQL benchmark Spider demonstrate that our method achieves 73.8% in accuracy on the Spider dataset, surpassing the base model by up to 3.5%.

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