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3rd International Conference on Computer, Control and Robotics, ICCCR 2023

作者： Wan, Pian Lu, Zhiwei Chen, Chen Teoh, Teik Toe School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Nanyang Technological University NTU Business AI Lab Singapore

ISBN: (纸本)9781665492126

The Coronavirus Disease 2019 (COVID-19) pandemic has dramatically affected the healthcare systems and economy over the world. To help reduce the negative effects of such a contagious malady, the detection time of COVID-19 becomes crucial. While one of the most promising methods to detect whether a patient is infected with this virus is to track his or her lung tissue with chest x-ray (CXR) images, it is considerably time-consuming if all the images are examined by professional radiologists. Besides, the accuracy of detection is determined mainly by the status and stress of the specific radiologist. To tackle these issues, we present a convolutional neural network that automatically detects infections from CXR images. Due to the limited amount of training data, data augmentation methods are used to reduce overfitting. Besides, training improvements such as warmup, cosine scheduler, and label smoothing are also applied during training. The proposed method is validated on the COVID-QU-Ex dataset and has an accuracy of 95.18% and an F1 score of 95.11 % on the validation set, showing its powerful potential in real-world applications for medical diagnostic systems. © 2023 IEEE.

关键词： COVID-19

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A Robust Camera-LiDAR Fusion Framework for 3D Object Detection in High-Dust Environments 22

A Robust Camera-LiDAR Fusion Framework for 3D Object Detecti...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Wang, Mingyuan Liu, Wentao Zhou, Bin Wang, Zhangyu Liu, Runsen Wang, Hanyu School of Transportation Science and Engineering Ministry of Industry and Information Technology Beihang University Beijing China Sate Key Lab of Intelligent Transportation System Ministry of Industry and Information Technology Beihang University Beijing China The Key Laboratory of Autonomous Transportation Technology for Special Vehickes Ministry of Industry and Information Technology Beihang University Beijing China Technology and Development Center of Hefei Metro Research Institute Co. Ltd. Hefei Metro Research Institute Co. Ltd Hefei China

ISBN: (纸本)9798331527471

The fusion of camera and LiDAR features in the Bird's Eye View (BEV) perspective has become a prevalent solution for 3D detection in autonomous driving due to its simplicity and efficiency. However, in challenging environments like mining areas with dusty roads, existing BEV-based fusion networks struggle due to dust occlusion and misidentification of dust as obstacles. To address this, we propose a robust fusion framework that integrates fine-grained depth supervision and channel-wise attention mechanisms. Combined with a temporal multi-frame mechanism, our framework effectively mitigates issues caused by dust occlusion and misidentification. We validated our method's detection accuracy on a self-constructed mining road dataset, achieving 89.6% mAP, surpassing BEVFusion's 88.3% mAP. Tests on sensor occlusion and failure further demonstrated its robustness in adverse conditions typical of unstructured road scenarios. © 2024 IEEE.

关键词： Mine dust

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A multi-source signals separation algorithm for identifying the threatening signals applied in Fiber-Optic Distributed Acoustic Sensor

A multi-source signals separation algorithm for identifying ...

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2023 Optical Fiber Communications Conference and Exhibition, OFC 2023

作者： He, Tao Zhang, Shixiong Li, Hao Yan, Zhijun Liu, Deming Sun, Qizhen School of Cyber Science and Engineering Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Wuhan430074 China Optics Valley Laboratory Huazhong University of Science and Technology School of Optical and Electronic Information National Laboratory for Optoelectronics National Engineering Laboratory for Next Generation Internet Access System Wuhan430074 China

A multi-source signals separation algorithm is proposed to identify the potential threatening signals submerged in the strong background noises. Finally, the recognition rate of the mixed signals is improved from 62.8... 详细信息

ISBN: (纸本)9781957171180

关键词： Source separation

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A Data-Efficient Erbium-Doped Fiber Amplifier Model under Partial Channel Loadings

A Data-Efficient Erbium-Doped Fiber Amplifier Model under Pa...

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2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Li, Yuqi Zhang, Mingming Hu, Zihe Song, Zhuoxuan Yan, Siqi Tang, Ming School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Optics Valley Laboratory Wuhan China School of Optical and Electronic Information Huazhong University of Science and Technology National Engineering Laboratory for Next Generation Internet Access System Wuhan China

We proposed a modified spectral gain model for the Erbium-doped fiber amplifier based on singular value decomposition. More than 93% of predicted loading channel gain errors are less than 0.2 dB under partial channel ... 详细信息

ISBN: (纸本)9798350312614

关键词： Broadband Amplification EDFA Modeling Partial Channel Loadings Singular Value Decomposition

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Complex scene fall detection method based on improved YOLOv5 24

Complex scene fall detection method based on improved YOLOv5

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13th International Conference on Computing and Pattern Recognition, ICCPR 2024

作者： Li, Yifan Liu, Pengyu Kong, Lili Deng, Yiyang Liu, Weilong Chen, Shanji Chen, Jiali Yuan, Bowen School of Information Science and Technology Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing China Hebei Hongyi Environmental Protection Technology Co. Ltd Dingzhou Hebei China Beijing-Dublin International College at BJUT Beijing University of Technology Beijing China Information Technology Co. Ltd Dingzhou Hebei China College of Physics and Electronic Information Engineering Qinghai Minzu University Qinghai China

ISBN: (纸本)9798400717482

Aiming at the existing deep learning-based fall detection model in multiple complex scenes such as multi-person scenes, long-distance scenes, and occlusion scenes, there are problems such as leakage detection and false detection. In this paper, we take the falling person as the research object, construct a dataset containing multiple complex scenes such as multi-person scenes, long-distance scenes, and occlusion scenes, and propose a fall detection model based on improved YOLOv5. By improving the backbone network and neck network structure of the detection model, the problem of low accuracy of fall detection due to incomplete target deformation features and target detail features in complex scenes is solved. The proposed algorithm in this paper achieves an average accuracy of 92.78% for fall detection in complex scenes, and the detection speed reaches 36 frames/second. © 2024 Copyright held by the owner/author(s).

关键词： Fall detection

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A Comparison Study of Measures to Quantify the Evolution of Prolific Research Teams

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Data and information Management 2021年第1期5卷 56-64页

作者： Zou, Bentao Wang, Yuefen Management Science and Engineering School of Economics and Management Nanjing University of Science and Technology Nanjing China Information Management and Information System School of Economics and Management Nanjing University of Science and Technology Nanjing China

Scientific research teams play an increasingly significant role in scientific activities. To better understand the dynamic evolution process of research teams, we explored measures that quantify the evolution of prolific research teams. We collected our data from the Web of science in the field of artificial intelligence, and applied the label propagation algorithm to identify research teams in the co-authorship network. The Top 1‰ prolific teams were selected as our research object, whose node stability and two types of edge stabilities were measured. The results show that prolific teams are much more stable during the evolution process, in terms of both member and membership stability. The measure of stability has varying degrees of impact on teams with different sizes, and small-sized teams get considerably different stability results by different measures. © 2021 Bentao Zou et al., published by Sciendo

关键词： Stability

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BHJO: A Novel Hybrid Metaheuristic Algorithm Combining the Beluga Whale, Honey Badger, and Jellyfish Search Optimizers for Solving engineering Design Problems

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Computer Modeling in engineering & sciences 2024年第10期141卷 219-265页

作者： Farouq Zitouni Saad Harous Abdulaziz S.Almazyad Ali Wagdy Mohamed Guojiang Xiong Fatima Zohra Khechiba Khadidja Â Kherchouche Department of Computer Science and Information Technology Kasdi Merbah UniversityP.O.Box 30000OuarglaAlgeria Department of Computer Science College of Computing and InformaticsUniversity of SharjahP.O.Box 27272SharjahUnited Arab Emirates Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization *** approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization *** this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)*** proposed hybrid algorithm will be referred to as *** this fusion,the BHJO algorithm aims to leverage the strengths of each *** this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and *** meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization *** addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid ***,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem ***,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation *** rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedd

关键词： Global optimization hybridization of metaheuristics beluga whale optimization honey badger algorithm jellyfish search optimizer chaotic maps opposition-based learning

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Metal-organic polyhedra as dynamic cross-linker for recyclable mixed matrix membranes with promising dimension stability

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science China Chemistry 2025年第2期68卷 705-713页

作者： Linjie Lan Mingxin Zhang Haiyan Xiao Wei Liu-Fu Yuyan Lai Jiadong Chen Jia-Fu Yin Panchao Yin State Key Laboratory of Luminescent Materials and Devices&South China Advanced Institute for Soft Matter Science and Technology Guangdong Basic Research Center of Excellence for Energy and Information Polymer MaterialsSouth China University of TechnologyGuangzhou 510641China State Key Laboratory of Marine Resource Utilization in South China Sea Hainan Provincial Key Lab of Fine ChemistrySchool of Chemical Engineering and TechnologyHainan UniversityHaikou 570228China

Mixed matrix membranes(MMMs)are designed by incorporating microporous inorganic fillers into polymer matrix for the integration of inorganics’excellent gas separation performance with polymers’promising ***,due to the complex interphase interaction,the prevention of long-term aging is still challenging and the poor processability/recyclability of MMMs hinders their extensive ***,a coordination assembly protocol is developed for the fabrication of MMMs with both excellent processability/recyclability upon thermal annealing and promising dimension stability even under high gas *** bearing isophthalic acid(IPA)as side chains are applied to coordinate with Cu^(2+)and the assembled 2.5 nm microporous metal-organic polyhedra(MOP),{Cu_(24)IPA_(24)},serves as multi-functional cross-linkers for the polymer network *** MOPs’well-defined microporosity grants MMMs the capability for O_(2)/N_(2) separation while their activated dynamics at high temperatures(>120℃)endows polymer networks with excellent *** contrast to conventional supramolecular units,the MOPs possess unique logarithmic ligand-exchange kinetics and they show nearly frozen bonding dynamics at room temperature,ensuring promising dimensional stability to overcome the long-term aging and high gas pressure *** work not only provides fundamental understanding of the unique relaxation dynamics of complex systems with intertwined dynamics of supramolecular bonds and polymer chains,but also paves new avenues for the fabrication of robust and recyclable MMMs and elastomers.

关键词： dynamic bonding metal-organic polyhedra mixed matrix membrane polymer recycling gas separation

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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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Mobile and Simulation-based Approach to reduce the Dyslexia with children Learning Disabilities 10

Mobile and Simulation-based Approach to reduce the Dyslexia ...

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10th IEEE Region 10 Humanitarian technology Conference, R10-HTC 2022

作者： Muthumal, S.A.D.M. Neranga, K.T. Harshanath, S.M.B. Sandeepa, V.D.R.P. Lihinikaduwa, D.N.R. Rajapaksha, U.U.S.K. Sri Lanka Institute of Information Technology Department of Information System Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (数字)9781665401562

ISBN: (纸本)9781665401562

Learning disabilties are frequently overlooked while treating various limitations. The primary causes for such a dropout might be a lack of awareness of the risks and access to adequate medical care. Because Learning Disabilities are neurological illnesses, their etiology is unknown. Learning Disabilities do not have a therapy or a cure. Dyslexia, Dysgraphia and Dyscalculia are the most common types of Learning Dis-abilities among school children. There are numerous approaches for diagnosing and treating this ailment available today. The optimal method is to make use of a mobile application. Existing applications either do not adequately handle the problem or have minor limitations in terms of meeting genuine expectations. We can mitigate this dysfunction by utilizing mobile and simulation-based technologies. Furthermore, earlier research has not given contact and curiosity among children a significant emphasis and children have not interacted with robots. Therefore, this paper, introduces interactive and collaborative mobile appli-cation called 'Helply' with a robotic based simulation that may foster learning and help children improve and encour-age Color identification skills, Reading skills and Short-term memory skills the learning process while reducing their other dyslexic disorders. Also, NAO Robot is used to taking inputs as voice clip using voice recognition technology and images using image processing technology which embedded in NAO robot. We constructed three models for three distinct disorders and obtained accurate findings. CNN model for colour disorder had a training accuracy of 93%, FFNN model for short-term memory disorder had a training accuracy of 98%, and CNN model for reading disorder had a training accuracy of 94%. © 2022 IEEE.

关键词： Diagnosis

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