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PM₂.₅ Monitoring: Use Information Abundance Measurement and Wide and Deep Learning

IEEE transactions on neural networks and learning systems 2021年第10期32卷 4278-4290页

作者： Ke Gu Hongyan Liu Zhifang Xia Junfei Qiao Weisi Lin Daniel Thalmann Faculty of Information Technology Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing Artificial Intelligence Institute Beijing University of Technology Beijing China School of Computer Science and Engineering Nanyang Technological University Singapore EPFL CH Lausanne Switzerland

This article devises a photograph-based monitoring model to estimate the real-time PM concentrations, overcoming currently popular electrochemical sensor-based PM monitoring methods' shortcomings such as low-density spatial distribution and time delay. Combining the proposed monitoring model, the photographs taken by various camera devices (e.g., surveillance camera, automobile data recorder, and mobile phone) can widely monitor PM concentration in megacities. This is beneficial to offering helpful decision-making information for atmospheric forecast and control, thus reducing the epidemic of COVID-19. To specify, the proposed model fuses Information Abundance measurement and Wide and Deep learning, dubbed as IAWD, for PM monitoring. First, our model extracts two categories of features in a newly proposed DS transform space to measure the information abundance (IA) of a given photograph since the growth of PM concentration decreases its IA. Second, to simultaneously possess the advantages of memorization and generalization, a new wide and deep neural network is devised to learn a nonlinear mapping between the above-mentioned extracted features and the groundtruth PM concentration. Experiments on two recently established datasets totally including more than 100 000 photographs demonstrate the effectiveness of our extracted features and the superiority of our proposed IAWD model as compared to state-of-the-art relevant computing techniques.

关键词： Feature extraction Monitoring Atmospheric modeling COVID-19 Atmospheric measurements Transforms Temperature measurement

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A Cascade Dual-Decoder Model for Joint Entity and Relation Extraction

arXiv

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arXiv 2021年

作者： Cheng, Jian Zhang, Tian Zhang, Shuang Ren, Huimin Yu, Guo Zhang, Xiliang Gao, Shangce Ma, Lianbo The Research Institute of Mine Artificial Intelligence in Chinese Institute of Coal Science State Key Laboratory of Intelligent Coal Mining and Strata Control Tiandi Science and Technology Co. Ltd. Beijing100013 China The College of Software Northeastern University Shenyang China The Institute of Intelligent Manufacturing Nanjing Tech University Nanjing211816 China The School of Intelligent Manufacturing and Control Engineering Shanghai Polytechnic University Shanghai China The Faculty of Engineering University of Toyama Toyama-shi930-8555 Japan Foshan Graduate School of Innovation Northeastern University Foshan China

In knowledge graph construction, a challenging issue is how to extract complex (e.g., overlapping) entities and relationships from a small amount of unstructured historical data. The traditional pipeline methods are to divide the extraction into two separate subtasks, which misses the potential interaction between the two subtasks and may lead to error propagation. In this work, we propose an effective cascade dual-decoder method to extract overlapping relational triples, which includes a text-specific relation decoder and a relation-corresponded entity decoder. Our approach is straightforward and it includes a text-specific relation decoder and a relation-corresponded entity decoder. The text-specific relation decoder detects relations from a sentence at the text level. That is, it does this according to the semantic information of the whole sentence. For each extracted relation, which is with trainable embedding, the relation-corresponded entity decoder detects the corresponding head and tail entities using a span-based tagging scheme. In this way, the overlapping triple problem can be tackled naturally. We conducted experiments on a real-world open-pit mine dataset and two public datasets to verify the method's generalizability. The experimental results demonstrate the effectiveness and competitiveness of our proposed method and achieve better F1 scores under strict evaluation metrics. Our implementation is available at https://***/prastunlp/DualDec. Copyright © 2021, The Authors. All rights reserved.

关键词： Decoding

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Optimizing Neural Network Image Classification with Fractional Order Gradient Methods

SSRN

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SSRN 2023年

作者： Zhao, Haiming Chen, Jiejie Jiang, Ping Zeng, Zhigang Fan, Rui Yang, Honggang School of Computer and Information Engineering Hubei Normal University Huangshi435000 China School of Automation Wuhan University of Technology Wuhan430000 China School of Computer Hubei PolyTechnic University Huangshi435000 China Faculty of Artificial Intelligence Education Central China Normal University Wuhan430000 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430000 China Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education of China Wuhan430000 China

As deep learning technology increasingly permeates various fields, the significance of optimization algorithms in neural network training has become more prominent. Typically, the training of deep learning networks relies on gradient descent optimizers based on calculus computations. Traditional integer-order calculus optimizers often fall short when dealing with complex and subtle data. To overcome these limitations, we innovatively utilized the Grünwald-Letnikov and Caputo definitions of fractional calculus to develop two new optimization algorithms: Fractional Order Adagrad (FAdagrad) and Fractional Order Adam (FAdam). These algorithms have been proven to converge to extremal points.A key advantage of our methods is their capability in handling complex, high-dimensional tasks, such as artistic painting image classification, where they exhibit exceptional performance in recognizing and processing subtle differences. The robustness and adaptability of these algorithms were further validated through experiments on the CIFAR-100 dataset. The results demonstrate the efficacy of fractional order optimizers in enhancing training efficiency and accuracy. © 2023, The Authors. All rights reserved.

关键词： Image classification

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Synergy Analysis of Industrial, Research and University Training Centers Collaboration: A Case Study in Thailand's Eastern Economic Corridor

Synergy Analysis of Industrial, Research and University Trai...

引用

Portland International Conference on Management of engineering and Technology (PICMET)

作者： Udom Lewlomphaisarl Ukrit Pungjitwisut Wassana Pullpol Kumpee Suksomboon Arnuphap Dowrueng Industrial IoT and Automation Research Group (IIARG) Sustainable Manufacturing Center (SMC) Eastern Economic Corridor of Innovation (EECi) Rayong Thailand EEC Automation Park Faculty of Engineering Burapha University Chonburi Thailand Intelligent SCADA Technology Research Team (IST) Advanced Control and Electronics Research Group (ACERG) National Electronics and Computer Technology Center (NECTEC) Pathum Thani Thailand National Electronics and Computer Technology Center (NECTEC) National Science and Technology Development Agency (NSTDA) Pathum Thani Thailand

The Royal Thai Government has outlined a 20-year strategy to achieve high-income status by 2036. “Thailand 4.0” is guided by the concept of “Industry 4.0”, which aims to transform the country into an innovative, value-driven industry, with a focus on 12 areas such as Automation & Robotics, Aviation & Logistics, Biofuel & Biochemicals, and Digital. The development of Eastern Economic Corridor (EEC) lies at the heart of Thailand 4.0 scheme. The EEC is an area-based development initiative aimed at revitalizing the prestigious East Coast, where numerous business developers have experienced a rewarding investment journey and exceptional success over the past 30 *** paper introduces a synergy analysis of the 3-phase collaboration approach between the EEC Automation Park located on the campus of Burapha University as the main university in the EEC and the Sustainable Manufacturing Center (SMC) at Automation Robotics and intelligent System (ARIPOLIS) in Eastern Economic Corridor of Innovation (EECi) with strong partners from industry e.g. Mitsubishi Electric Factory Automation (Thailand) Limited. This 3-phase approach:}{1. Build an island demonstrator, 2. Link production demonstration lines and 3. Transfer to 100 factories in the EEC helps managers realize possible difficulties and systematically prepare solutions in advance.

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A PCNN model for infrared pedestrian segmentation in complex environments

引用

Infrared Physics and Technology 2025年 149卷

作者： Cao, Honggui Ye, Bo Zou, Yangkun Zhu, Zhizhen Wan, Zijie Yin, Shsoda Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Intelligent Control and Application Kunming650500 China Faculty of Civil Aviation and Aeronautics Kunming University of Science and Technology Kunming650500 China The First Military Representative Office of the Chongqing Military Representative Bureau of the Army Equipment Department Kunming650500 China

Pulse Coupled Neural Network (PCNN) is widely used in infrared pedestrian image segmentation. Due to the complex background, heat source interference, uneven brightness distribution of human targets and easy aliasing with the background in infrared pedestrian images, the segmentation of human targets is incomplete and the segmentation effect is poor. An improved PCNN algorithm for segmenting infrared pedestrian targets in complex environments and a new idea of preferentially segmenting background regions are proposed. The infrared pedestrian image is represented as a graph structure, and the superpixels in the infrared image are represented as nodes of the graph. The score of each node becoming the background region is evaluated by analyzing the structure of the graph, and the score is mapped to the original image as the connection strength of the model. Correspondingly, the dynamic threshold is set as the clustering center of the image background, and the background region of the image is prioritized for classification and output. Infrared pedestrian images with complex environment can be effectively segmented by the model, especially for infrared images with more heat source interference in the background. © 2025 The Authors

关键词： Image segmentation

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A Survey for Large Language Models in Biomedicine

arXiv

引用

arXiv 2024年

作者： Wang, Chong Li, Mengyao He, Junjun Wang, Zhongruo Darzi, Erfan Chen, Zan Ye, Jin Li, Tianbin Su, Yanzhou Ke, Jing Qu, Kaili Li, Shuxin Yu, Yi Liò, Pietro Wang, Tianyun Wang, Yu Guang Shen, Yiqing School of Medical Engineering Xinxiang Medical University Xinxiang China Engineering Technology Research Center of Neurosense and Control of Henan Province Xinxiang China Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design Xinxiang China Shanghai AI Laboratory Shanghai China Amazon Palo AltoCA United States Boston Children's Hospital MA United States Harvard Medical School Harvard University MA United States Toursun Synbio Shanghai China Department of Data Science & AI Faculty of IT Monash University Melbourne Australia School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China School of Computer Science and Engineering University of New South Wales Sydney Australia Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom School of Basic Medical Sciences Xinxiang Medical University Xinxiang China Institute of Natural Sciences Shanghai Jiao Tong University Shanghai China School of Mathematics and Statistics University of New South Wales Sydney Australia Department of Computer Science Johns Hopkins University MD United States

Recent breakthroughs in large language models (LLMs) offer unprecedented natural language understanding and generation capabilities. However, existing surveys on LLMs in biomedicine often focus on specific applications or model architectures, lacking a comprehensive analysis that integrates the latest advancements across various biomedical domains. This review, based on an analysis of 484 publications sourced from databases including PubMed, Web of science, and arXiv, provides an in-depth examination of the current landscape, applications, challenges, and prospects of LLMs in biomedicine, distinguishing itself by focusing on the practical implications of these models in real-world biomedical contexts. Firstly, we explore the capabilities of LLMs in zero-shot learning across a broad spectrum of biomedical tasks, including diagnostic assistance, drug discovery, and personalized medicine, among others, with insights drawn from 137 key studies. Then, we discuss adaptation strategies of LLMs, including fine-tuning methods for both uni-modal and multi-modal LLMs to enhance their performance in specialized biomedical contexts where zero-shot fails to achieve, such as medical question answering and efficient processing of biomedical literature. Finally, we discuss the challenges that LLMs face in the biomedicine domain including data privacy concerns, limited model interpretability, issues with dataset quality, and ethics due to the sensitive nature of biomedical data, the need for highly reliable model outputs, and the ethical implications of deploying AI in healthcare. To address these challenges, we also identify future research directions of LLM in biomedicine including federated learning methods to preserve data privacy and integrating explainable AI methodologies to enhance the transparency of LLMs. As this field of LLM rapidly evolves, continued research and development are essential to fully harness the capabilities of LLMs in biomedicine while ensuring their respon

关键词： Federated learning

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Passivity properties for regulation of DC networks with stochastic load demand ⁎

引用

IFAC-PapersOnLine 2020年第2期53卷 13113-13118页

作者： Amirreza Silani Michele Cucuzzella Jacquelien M.A. Scherpen Mohammad Javad Yazdanpanah Jan C. Wilems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Nijenborgh 4 9747 AG Groningen the Netherlands. Control & Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

In this paper we present new (stochastic) passivity properties for Direct Current (DC) power networks, where the unknown and unpredictable load demand is modelled by a stochastic process. More precisely, the considered power network consists of distributed generation units supplying ZIP loads, i.e., nonlinear loads comprised of impedance (Z), current (I) and power (P) components. Differently from the majority of the results in the literature, where each of these components is assumed to be constant, we consider time-varying loads whose dynamics are described by a class of stochastic differential equations. Finally, we prove that an existing distributed control scheme achieving current sharing and (average) voltage regulation ensures the asymptotic stochastic stability of the controlled network.

关键词： DC power networks Nonlinear systems Stochastic modelling Passivity theory

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A New Terminal Sliding Mode control for Bridge Cranes

A New Terminal Sliding Mode Control for Bridge Cranes

引用

Smart City Challenges & Outcomes for Urban Transformation (SCOUT)

作者： Tianlei Wang Zhaoming Zheng Ziyang Chen Jingling Zhang Jinzhao Xu Hongmin Wang Kenneth Teo Tze Kin Yongwen Xie Faculty of Intelligent Manufacturing Wuyi University Jiangmen Guangdong China School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing China School of Mathematics and Computational Science Wuyi University Jiangmen Guangdong China Faculty of Engineering University Malaysia Sabah Kota Kinabalu Sabah Malaysia Jinming Machinery Manufacturing Co. LTD Jiangmen Guangdong China

ISBN: (数字)9781665407670

ISBN: (纸本)9781665407687

In different transportation tasks of bridge cranes, the mass of the load and the cable length are different. To solve this problem, this paper designs a model-free adaptive non-singular terminal sliding mode controller. Specifically, the bridge crane model is changed first, and then a suitable non-singular terminal sliding surface is selected. The adaptive rate is introduced in the design of the controller, so that the driving force is continuous and does not require information of system parameters. The Lyapunov theorem proves that the system is stable. Finally, simulation experiments verify the effectiveness of the algorithm proposed in this paper.

关键词： Bridges Adaptation models Cranes Adaptive systems Force Transportation Task analysis

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Defect engineering approaches for metal oxide semiconductor-based chemiresistive gas sensing

引用

Coordination Chemistry Reviews 2025年 541卷

作者： Kumar, Amit Mazumder, Julaiba Tahsina Joyen, Kenza Favier, Frédéric Mirzaei, Ali Kim, Jin-Young Kwoka, Monika Bechelany, Mikhael Jha, Ravindra Kumar Kumar, Mahesh Kim, Hyoun Woo Kim, Sang Sub Department of Electrical Engineering Indian Institute of Technology Jodhpur Jodhpur 342030 India Department of Electronics Assam Skill University Assam Mangaldai 784125 India European Institut of Membranes (IEM) – UMR 5635 University of Montpellier ENSCM CNRS Montpellier 34090 France Institut Charles Gerhardt Montpellier (ICGM) – UMR 5253 University of Montpellier ENSCM CNRS Montpellier 34090 France Department of Materials Science and Engineering Shiraz University of Technology Shiraz 715557-13876 Iran Customized manufacturing R&D department Korea Institute of Industrial Technology Gyeonggi-do 15014 South Korea Department of Cybernetics Nanotechnology and Data Processing Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice 44-100 Poland Functional Materials Group Gulf University for Science and Technology (GUST) Mubarak Al-Abdullah 32093 Kuwait Department of Electronics and Electrical Engineering & Centre for Intelligent Cyber-Physical Systems Indian Institute of Technology Guwahati Guwahati 781039 India Division of Materials Science and Engineering Hanyang University Seoul 04763 South Korea Department of Materials Science and Engineering Inha University Incheon 22212 South Korea

Defect engineering in metal oxides presents a promising approach for tailoring material properties. This strategy enhances gas sorption, catalysis, and control over key physical characteristics such as bandgap, magnetic behavior, and electrical conductivity. Despite its potential, the role of defect engineering in advancing metal oxide semiconductor (MOS) gas-sensing performance remains underexplored. This review introduces defect engineering strategies, emphasizing their applications in gas sensing. Gas sensors play a vital role in environmental monitoring, industrial safety, healthcare, and in improving energy efficiency. The demand for advanced gas sensors has never been more critical, given the need for real-time, accurate, and cost-effective detection of pollutant gases. MOS-based gas sensors are widely used in air quality monitoring, industrial safety, and health diagnostics. Vacancies and defect architectures in MOS have been widely studied for their role in sensing performance, as they fundamentally influence sensor efficiency. The effectiveness of MOS sensors largely depends on the type and concentration of defects. This review introduces vacancies and defects in MOS, followed by an in-depth discussion of defect types, factors influencing defect formation, and their role in charge transport. Additionally, it examines the correlation between elemental chemical properties and defect chemistry. Special attention is given to the defect chemistry of metal oxides, including TiO₂, ZnO, Co₃O₄, ZrO₂, WO₃, and CeO₂. It concludes with an examination of surface engineering methods for defect control to improve gas-sensing capabilities. © 2024

关键词： Chemiresistive gas sensing Defect engineering Metal-oxide semiconductor

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Heat Treatment Induced Microstructural Evolution and Strength Enhancement of Al-Cr-Fe-Ni-V High-Entropy Alloy Fabricated by Laser Powder Bed Fusion

SSRN

引用

SSRN 2022年

作者： Liu, Ziwei Tan, Zhen Yao, Haili Chen, Chao Zhou, Zheng Xue, Yunfei Shao, Wei Guo, Xingye Yao, Haihua Chen, Lijia He, Dingyong Faculty of Materials and Manufacturing Beijing University of Technology Beijing100124 China State Key Laboratory of Powder Metallurgy Central South University Changsha10 410083 China School of Materials Science and Engineering Beijing Institute of Technology Beijing100081 China Institute of Intelligent Manufacturing Guangdong Academy of Sciences Guangdong Key Laboratory of Modern Control Technology Guangzhou510070 China Beijing Engineering Research Center of Eco-materials and LCA Beijing100124 China

Herein, different heat treatment protocols are adopted to further improve the mechanical properties of the Al-Cr-Fe-Ni-V high-entropy alloy (HEA) fabricated by laser powder bed fusion (LPBF) additive manufacturing (AM). The initial LPBF-processed HEA rendered a combination of high strength (ultimate tensile strength (UTS) of 1088.48 MPa) and ductility (fracture elongation (FE) of 29.4%), which could be attributed to the hierarchical microstructure with fine grains, cellular dislocation structure, and L12 nano-precipitation. When the annealing temperature is increased from 573 K to 1473 K, the cellular dislocation structure is disappeared, and both L12 and B2 phases are progressively precipitated in the FCC matrix. Simultaneously, the tensile strength dramatically increased at the expense of ductility reduced and LPBF-processed HEA demonstrated a high UTS of 1369.28 MPa and FE of 14.26% after annealing at 1173 K. The results reveal that precipitation strengthening plays a significant role in improving the mechanical properties of LPBF-processed HEAs during annealing. For instance, L12 and B2 precipitates hindered the dislocation movement and, in turn, enhanced the pinning force of boundary migration. Moreover, the highly coherent and low misfit FCC-L12 interfaces minimized the strain accumulation due to the dislocation shear and, thereby, prevented the crack initiation. © 2022, The Authors. All rights reserved.

关键词： Microstructural evolution

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