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2022 Chinese Automation Congress, CAC 2022

作者： Liu, Qingxin Jia, Heming Wu, Di Li, Ni Qi, Qi Huang, Xiaoqing Hainan University School of Computer Science and Technology Haikou China Sanming University School of Information Engineering Sanming China Sanming University School of Education and Music Sanming China Hainan Normal University School of Mathematics and Statistics Hainan Normal University Key Laboratory of Data Science and Intelligence Education Ministry of Education Haikou China Hainan University School of Tourism Haikou China

ISBN: (纸本)9781665465335

Arithmetic Optimization Algorithm (AOA) is a newly proposed meta-heuristics (MAs) inspired by the major arithmetic operators. Although it provides satisfactory solutions to several real-world problems, when facing complex problems, AOA also easily suck into local optimal solution and slow convergence speed. Golden Sine Algorithm (Gold-SA) is another MAs and has an acceptable local search capability with fewer parameters. Considering these situations, this paper we propose a novel hybrid optimization algorithm (HAGA) that integrated both AOA and Gold-SA to achieve superior optimization ability. In HAGA, the search agents are divided into two consistent subgroups and optimized by AOA and Gold-SA, respectively. By this operator, we can exchange useful information and utilize their advantages to find the optimal or near-optimal regions throughout the iterative process. In addition, Lens Opposition- Based Learning (LOBL) is employed to accelerate the convergence speed. We test the proposed method using thirteen benchmark functions and a constrained engineering problem. The results prove the significant superiority of HAGA to those of four counterpart methods in terms of optimization accuracy and convergence speed. © 2022 IEEE.

关键词： Constrained optimization

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FortisAVQA and MAVEN: a Benchmark dataset and Debiasing Framework for Robust Multimodal Reasoning

arXiv

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arXiv 2025年

作者： Ma, Jie Gao, Zhitao Chai, Qi Liu, Jun Wang, Pinghui Tao, Jing Su, Zhou Ministry of Education of Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China Shannxi Provincial Key Laboratory of Big Data Knowledge Engineering School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China Guangdong Guangzhou510000 China

Audio-Visual Question Answering (AVQA) is a challenging multimodal reasoning task requiring intelligent systems to answer natural language queries based on paired audio-video inputs accurately. However, existing AVQA approaches often suffer from overfitting to dataset biases, leading to poor robustness. Moreover, current datasets may not effectively diagnose these methods. To address these challenges, we first introduce a novel dataset, FortisAVQA, constructed in two stages: (1) rephrasing questions in the test split of the public MUSIC-AVQA dataset and (2) introducing distribution shifts across questions. The first stage expands the test space with greater diversity, while the second enables a refined robustness evaluation across rare, frequent, and overall question distributions. Second, we introduce a robust Multimodal Audio-Visual Epistemic Network (MAVEN) that leverages a multifaceted cycle collaborative debiasing strategy to mitigate bias learning. Experimental results demonstrate that our architecture achieves state-of-the-art performance on FortisAVQA, with a notable improvement of 7.81%. Extensive ablation studies on both datasets validate the effectiveness of our debiasing components. Additionally, our evaluation reveals the limited robustness of existing multimodal QA methods. We also verify the plug-and-play capability of our strategy by integrating it with various baseline models across both datasets. Our dataset and code are available at https://***/reml-group/fortisavqa. Copyright © 2025, The Authors. All rights reserved.

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Investigation of Vertically Stacked Horizontal Gate-All-Around SI Nanosheet Ion Sensitive Field Effect Transistor For Detection of C-Reactive Protein

Investigation of Vertically Stacked Horizontal Gate-All-Arou...

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China Semiconductor Technology International Conference (CSTIC)

作者： Yang Liu Qingzhu Zhang Junjie Li Cinan Wu Lei Cao Yanna Luo Zhaohao Zhang Shuhua Wei Qianhui Wei Jiaxin Yao Jiawei Hu Meiyan Qin Enxu Liu Yanchu Han LianLian Li YingLu Li Tao Yang Na Zhou Jianfeng Gao Junfeng Li Key Laboratory of Microelectronic Devices and Integration Technology Institute of Microelectronics Chinese Academy of Sciences Beijing China College of Big Data and Information Engineering Guizhou University Guiyang China School of Information Science and Technology North China University of Technology Beijing China State Key Laboratory of Advanced Materials for Smart Sensing GRINM Group Co. Ltd. Beijing China

In this work, based on advanced gate-all-around (GAA) technology, the extended sensing gate (ESG) GAA Si nanosheet (SiNS) ion sensitive field effect transistor (ISFET) sensor was fabricated and reported for the first time. Due to the GAA structures and the vertically-stacked SiNS channels, the average sensitivity of the ESG GAA SiNS ISFET sensor can be reached 58.8 mV/pH, which provides good gate control and ultra-sensitive detection capabilities. In addition, the actual minimum detection concentration of C-reactive protein (CRP) by ESG GAA SiNS ISFET sensor in 1$\times$ PBS environment is as low as 100pg/mL, which is much lower than the normal concentration of human body.

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Tracking the carbon footprint of global generative artificial intelligence

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Innovation 2025年第5期6卷 100866页

作者： Ding, Zhaohao Wang, Jianxiao Song, Yiyang Zheng, Xiaokang He, Guannan Chen, Xiupeng Zhang, Tiance Lee, Wei-Jen Song, Jie State Key Lab of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing102206 China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing100871 China Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong999077 China Department of Industrial Engineering and Management College of Engineering Peking University Beijing100871 China Jan C. Willems Center for Systems and Control University of Groningen Groningen9700AB Netherlands Energy Systems Research Center University of Texas at Arlington ArlingtonTX76019 United States

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Kinetics of copper ion leaching from cement-solidified body of blast furnace slag adsorbing copper ion 21st

Kinetics of copper ion leaching from cement-solidified body ...

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21st Chinese Materials Conference, CMC 2020

作者： Wang, Yali Li, Zimo Yang, Ning Wei, Qi Hao, Liwei Faculty of Materials and Manufacturing Beijing University of Technology Beijing100124 China State Key Laboratory of Solid Waste Reuse for Building Materials Beijing Building Materials Academy of Science Research Beijing100041 China National Engineering Laboratory for Industrial Big-data Application Technology Beijing100124 China

ISBN: (纸本)9783035717389

Blast furnace slag has good adsorption performance and can be used to adsorb heavy metal ions in waste liquid. It is worth to study whether the blast furnace slag absorbing heavy metal ions will pose a potential threat to the environment during the process using the cement-based materials. The leaching amount of copper ions and the leaching kinetics in the blast furnace slag-cement system were analyzed. The results showed that the leaching amount of copper ions in the blast furnace slag-cement system basically met the national standard, and the solidified period was 28 days. The blast furnace slag content 30% is the smallest condition for the amount of copper ion leaching. The leaching model of copper ions in the blast furnace slag-cement system is the Elovich equation, which is a heterogeneous diffusion process. The longer the curing period is the slower the leaching process is completed and it does not cause environmental pollution during long-term use. © 2021 Trans Tech Publications Ltd, Switzerland.

关键词： Slags

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Are we there yet? An Industrial Viewpoint on Provenance-based Endpoint Detection and Response Tools

arXiv

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arXiv 2023年

作者： Dong, Feng Li, Shaofei Jiang, Peng Li, Ding Wang, Haoyu Huang, Liangyi Xiao, Xusheng Chen, Jiedong Luo, Xiapu Guo, Yao Chen, Xiangqun Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science Peking University China Arizona State University United States Sangfor Technologies Inc. China The Hong Kong Polytechnic University Hong Kong

Provenance-Based Endpoint Detection and Response (P-EDR) systems are deemed crucial for future APT defenses. Despite the fact that numerous new techniques to improve P-EDR systems have been proposed in academia, it is still unclear whether the industry will adopt P-EDR systems and what improvements the industry desires for P-EDR systems. To this end, we conduct the first set of systematic studies on the effectiveness and the limitations of P-EDR systems. Our study consists of four components: a one-to-one interview, an online questionnaire study, a survey of the relevant literature, and a systematic measurement study. Our research indicates that all industry experts consider P-EDR systems to be more effective than conventional Endpoint Detection and Response (EDR) systems. However, industry experts are concerned about the operating cost of P-EDR systems. In addition, our research reveals three significant gaps between academia and industry: (1) overlooking client-side overhead;(2) imbalanced alarm triage cost and interpretation cost;and (3) excessive server-side memory consumption. This paper's findings provide objective data on the effectiveness of P-EDR systems and how much improvements are needed to adopt P-EDR systems in industry. Copyright © 2023, The Authors. All rights reserved.

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A Clustering-Based Routing Protocol Using Path Pattern Discovery Method to Minimize Delay in VANET

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Xie, Xiaoyun Navaei, Yahya Dorostkar Einy, Sajad School of Electronic Information Engineering Gannan University of Science and Technology Jiangxi Ganzhou341000 China Ganzhou Key Laboratory of Cloud Computing and Big Data Jiangxi Ganzhou341000 China Computer and Information Technology Engineering Qazvin Branch Islamic Azad University Qazvin Iran Istanbul Aydin University Department of Application and Research Center for Advanced Studies Istanbul Turkey

In vehicular ad hoc networks (VANETs), vehicle-to-vehicle (V2V) communications can link vehicles to each other, and vehicle-to-infrastructure (V2I) messaging and communications can link roadside infrastructure such as routers. The vehicles in these networks act as relays that transmit critical messages in the network. Due to the high-speed movement of vehicles on the road, real-time messaging and minimizing the delay in sending messages is one of the most important objectives of VANET developers. On the other hand, the high mobility of vehicles causes communication interruptions and decreases the data delivery rate in VANET. To overcome this issue, predicting the path of vehicles can play an important role in sending data from the source to the destination. When an accident occurs on the road, the messages that are sensed by the imbedded sensors in the vehicles need to be sent, and if they are sent by the vehicles that change their route, these messages will not be sent to the destination and the performance of the network will be disturbed. Previous methods in the literature for data transmission in intervehicular networks have focused more on reliability and trust, and little attention has been paid to the prediction of vehicle movement paths in these types of networks. Therefore, for fast and reliable data transmission in VANET, accurate prediction of vehicle movement and creation of movement patterns can be effective in message transmission delay and data delivery rate. In this paper, we present an approach using a combination of cluster-based routing protocols and pattern discovery methods to minimize latency in VANETs. The outline of the proposed method has four modules: primary data collection and analysis, primary data preparation and analysis, pattern extraction and vehicle route discovery, and vehicle clustering and data/information transmission routing. The simulation results show that the proposed method with a delivery rate of 88.56% has significantly i

关键词： Vehicular ad hoc networks

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Animal Shapes, Modal Analysis, and Visualization of Motion (III): Giraffe, Duck, Goose, T-Rex Dinosaur, and the Flying Modes of Eagle

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Journal of Geometric Analysis 2023年第10期33卷 1-28页

作者： Yang, Jing Chen, Goong Wei, Chunqiu Sergeev, Alexey Huang, Jingtong Scully, Matthew M. Krantz, Steven G. Yao, Pengfei Guo, Tiexin Wang, Junmin Chen, Ming-Chieh Corti, Darrell School of Mathematics and Statistics Beijing Institute of Technology Beijing 102488 China Department of Mathematics Texas A &M University College Station 77843 TX United States Institute for Quantum Science and Engineering Texas A &M University College Station 77843 TX United States Hsinchu Branch National Center for Theoretical Sciences National Tsing Hua University Hsinchu Taiwan Department of Mathematics and Data Science Beijing University of Civil Engineering and Architecture Beijing 100044 China Department of Mathematics Washington University at St. Louis St. Louis 63130 MO United States Key Laboratory of Complex Systems and Data Science of Ministry of Education School of Mathematics Shanxi University Shanxi Taiyuan 030006 China Institute of Systems Science Academy of Mathematics and Systems Science Academia Sinica Beijing 100190 China School of Mathematics and Statistice Central South University Hunan Changsha 410075 China Department of Mechanical Engineering Cheng Shiu University Kaohsiung Taiwan Corti Brothers 5810 Folsom Blvd. Sacramento 95819 CA United States

This paper contains more varieties of animals, including the giraffe, bird species duck, goose and eagle, and the T-Rex dinosaur. They range across mammals, birds and reptiles. For each of them, we present their first one hundred modes of motion. Three objectives of this paper are as follows: (i)To see more different types of modes of motion from a larger varieties of animals so as to make visual comparisons between interspecies behavioral patterns.(ii)To illustrate the wing-flapping flying modes of an eagle.(iii)To show the modes of motions of an extinct animal, the T-Rex dinosaur. © 2023, Mathematica Josephina, Inc.

关键词： Animal shapes Duck, Goose, Dinosaur, and the flying modes of an eagle Motion patterns

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Image reconstruction from photoacoustic projections

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Photonics Insights 2024年第3期3卷 145-209页

作者： Chao Tian Kang Shen Wende Dong Fei Gao Kun Wang Jiao Li Songde Liu Ting Feng Chengbo Liu Changhui Li Meng Yang Sheng Wang Jie Tian Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefeiChina School of Engineering Science University of Science and Technology of ChinaHefeiChina Department of Anesthesiology the First Affiliated Hospital of USTCDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiChina Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing HefeiChina College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjingChina School of Information Science and Technology ShanghaiTech UniversityShanghaiChina Shanghai Clinical Research and Trial Center ShanghaiChina CAS Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijingChina School of Precision Instruments and Optoelectronics Engineering Tianjin UniversityTianjinChina Academy for Engineering and Technology Fudan UniversityShanghaiChina Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhenChina Department of Biomedical Engineering College of Future TechnologyPeking UniversityBeijingChina National Biomedical Imaging Center Peking UniversityBeijingChina Departments of Ultrasound State Key Laboratory of Complex Severe and Rare DiseasesPeking Union Medical College HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina School of Engineering Medicine Beihang UniversityBeijingChina Key Laboratory of Big Data-Based Precision Medicine Beihang UniversityMinistry of Industry and Information TechnologyBeijingChina

Photoacoustic computed tomography(PACT)is a rapidly developing biomedical imaging modality and has attracted substantial attention in recent *** reconstruction from photoacoustic projections plays a critical role in image formation in *** we review six major classes of image reconstruction approaches developed in the past three decades,including delay and sum,filtered back projection,series expansion,time reversal,iterative reconstruction,and deep-learning-based *** principal ideas and implementations of the algorithms are summarized,and their reconstruction performances under different imaging scenarios are *** challenges,future directions,and perspectives for the development of image reconstruction algorithms in PACT are also *** review provides a self-contained reference guide for beginners and specialists in the photoacoustic community,to facilitate the development and application of novel photoacoustic image reconstruction algorithms.

关键词： optical imaging photoacoustic tomography image reconstruction deep learning

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MACCoM: A multiple attention and convolutional cross-mixer framework for detailed 2D biomedical image segmentation

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Computers in Biology and Medicine 2024年 179卷 108847-108847页

作者： Ejiyi, Chukwuebuka Joseph Qin, Zhen Ejiyi, Makuachukwu Bennedith Ukwuoma, Chiagoziem Ejiyi, Thomas Ugochukwu Muoka, Gladys Wavinya Gyarteng, Emmanuel S.A. Bamisile, Olusola O. Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu China Pharmacy Department University of Nigeria Nsukka Enugu State Nsukka Nigeria Department of Pure and Industrial Chemistry University of Nigeria Nsukka Enugu State Nsukka Nigeria School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Research Center Chengdu University of Technology Chengdu China

The UNet architecture, which is widely used for biomedical image segmentation, has limitations like blurred feature maps and over- or under-segmented regions. To overcome these limitations, we propose a novel network architecture called MACCoM (Multiple Attention and Convolutional Cross-Mixer) – an end-to-end depthwise encoder-decoder fully convolutional network designed for binary and multi-class biomedical image segmentation built upon deeperUNet. We proposed a multi-scope attention module (MSAM) that allows the model to attend to diverse scale features, preserving fine details and high-level semantic information thus useful at the encoder-decoder connection. As the depth increases, our proposed spatial multi-head attention (SMA) is added to facilitate inter-layer communication and information exchange, enabling the network to effectively capture long-range dependencies and global context. MACCoM is also equipped with a convolutional cross-mixer we proposed to enhance the feature extraction capability of the model. By incorporating these modules, we effectively combine semantically similar features and reduce artifacts during the early stages of training. Experimental results on 4 biomedical datasets crafted from 3 datasets of varying modalities consistently demonstrate that MACCoM outperforms or matches state-of-the-art baselines in the segmentation tasks. With Breast Ultrasound Image (BUSI), MACCoM recorded 99.06 % Jaccard, 77.58 % Dice, and 93.92 % Accuracy, while recording 99.50 %, 98.44 %, and 99.29 % respectively for Jaccard, Dice, and Accuracy on the Chest X-ray (CXR) images used. The Jaccard, Dice, and Accuracy for the High-Resolution Fundus (HRF) images are 95.77 %, 74.35 %, and 95.95 % respectively. The findings here highlight MACCoM's effectiveness in improving segmentation performance and its valuable potential in image analysis. © 2024 Elsevier Ltd

关键词： Mixers (machinery)

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