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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Ma, Hongyao Wang, Zhixue Shen, Zhen Zhang, Hong Li, Chuanfu Wang, Fei-Yue School of Railway Transportation Shandong Jiaotong University Jinan250023 China Intelligent Manufacturing Center Qingdao Academy of Intelligent Industries Qingdao266109 China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing100190 China Shandong Hi-speed Qingdao Development Company Group Qingdao266000 China

ISBN: (纸本)9798350337020

With the exacerbation of bridge aging issues, the demand for efficient and cost-effective bridge inspection solutions becomes increasingly urgent. Currently, most methods for surface damage detection on bridges employ single-modal, image-based object detection models. Despite their overall effectiveness on specific datasets, such methods frequently encounter error detection issues during actual inspection processes. This study proposes a multimodal (image and text) model for surface damage detection in bridge structures by combining the CLIP model with the YOLOv8 model. By conducting tests on the composite concrete bridge of the Jiaozhou Bay Bridge, Qingdao, the effectiveness of bridge detection using drones is validated, and the common issue of false positive detections in traditional object detection models is successfully addressed. © 2023 IEEE.

关键词： bridge detection error detections multimodal object detection UAV

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Real-Time Estimation for the Swimming Direction of Robotic Fish Based on IMU Sensors*

Real-Time Estimation for the Swimming Direction of Robotic F...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Shikun Li Yufan Zhai Chen Wang Guangming Xie State Key Laboratory for Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China Institute of Ocean Research Peking University Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

An increasing number of underwater robots inspired by Carangidae are developed, which is characterized by high efficiency and flexibility. However, estimating the swimming direction of these robotic fish is challenging due to the constant swinging of the head during movement, which complicates precise control. In this study, we installed two low-cost inertial measurement unit (IMU) sensors separately on the head and tail parts of a double-joint robotic fish and presented a method for accurately and timely estimating the swimming direction. Firstly, we effectively compensated for the yaw angle drift of the IMU sensors through a fused Kalman Filter. Furthermore, we propose the Anti-Shake Estimation (ASE) algorithm to calculate the real-time swimming direction using filtered yaw angles at a high updating rate of 100Hz. Finally, we applied the method to swimming direction feedback control for evaluation and comparison. The results show that our ASE method performs better than other existing methods in straight-line swimming experiments. The experiment of S-curve swimming also demonstrates the effectiveness of our method in complex missions.

关键词： Measurement units Estimation Tail Sensor phenomena and characterization Sensor fusion Robot sensing systems Fish

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Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

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Computers, Materials & Continua 2023年第11期77卷 2579-2594页

作者： Abdennasser Dahmani Yamina Ammi Nadjem Bailek Alban Kuriqi Nadhir Al-Ansari Salah Hanini Ilhami Colak Laith Abualigah El-Sayed M.El-kenawy Department of Mechanical Engineering Faculty of Science and TechnologyGIDD Industrial Engineering and Sustainable Development LaboratoryUniversity of RelizaneBourmadiaRelizane48000Algeria Laboratory of Biomaterials and Transport Phenomena(LBMPT) University of MedeaUrban PoleMedea26000Algeria Energies and Materials Research Laboratory Faculty of Sciences and TechnologyUniversity of TamanghassetTamanrasset10034Algeria Sustainable Development and Computer Science Laboratory Faculty of Sciences and TechnologyAhmed Draia University of AdrarAdrarAlgeria CERIS Instituto Superior TécnicoUniversidade de LisboaLisbon1049-001Portugal Civil Engineering Department University for Business and TechnologyPristina10000Kosovo Department of Civil Environmental and Natural Resources EngineeringLulea University of TechnologyLulea97187Sweden Engineering and Architecture Faculty Nisantasi UniversityIstanbulTurkey Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains MalaysiaPulau Pinang11800Malaysia Department of Computing and Information Systems School of Engineering and TechnologySunway University MalaysiaPetaling Jaya27500Malaysia Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt

Increasing global energy consumption has become an urgent problem as natural energy sources such as oil,gas,and uranium are rapidly running *** into renewable energy sources such as solar energy is being pursued to counter *** energy is one of the most promising renewable energy sources,as it has the potential to meet the world’s energy needs *** study aims to develop and evaluate artificial intelligence(AI)models for predicting hourly global *** hyperparameters were optimized using the Broyden-FletcherGoldfarb-Shanno(BFGS)quasi-Newton training algorithm and STATISTICA *** from two stations in Algeria with different climatic zones were used to develop the *** error measurements were used to determine the accuracy of the prediction models,including the correlation coefficient,the mean absolute error,and the root mean square error(RMSE).The optimal support vector machine(SVM)model showed exceptional efficiency during the training phase,with a high correlation coefficient(R=0.99)and a low mean absolute error(MAE=26.5741 Wh/m^(2)),as well as an RMSE of 38.7045 Wh/m^(2) across all ***,this study highlights the importance of accurate prediction models in the renewable energy,which can contribute to better energy management and planning.

关键词： Renewable energy energy prediction global irradiation artificial intelligence BFGS quasi-Newton training algorithm

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RobNODDI: Robust NODDI Parameter Estimation with Adaptive Sampling Under Continuous Representation 15th

RobNODDI: Robust NODDI Parameter Estimation with Adaptive S...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024

作者： Xiao, Taohui Cheng, Jian Fan, Wenxin Yang, Jing Li, Cheng Dong, Enqing Wang, Shanshan School of Mechanical Electrical and Information Engineering Shandong University Weihai264209 China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Zhejiang Hangzhou China Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Peng Cheng Laboratory Guangdong Shenzhen China

ISBN: (纸本)9783031869198

Neurite Orientation Dispersion and Density Imaging (NODDI) is an important imaging technology used to evaluate the microstructure of brain tissue, which is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-based methods perform parameter estimation through diffusion magnetic resonance imaging (dMRI) with a small number of diffusion gradients. These methods speed up parameter estimation and improve accuracy. However, the diffusion directions used by most existing deep learning models during testing needs to be strictly consistent with the diffusion directions during training. This results in poor generalization and robustness of deep learning models in dMRI parameter estimation. In this work, we first verify that the parameter estimation performance of current mainstream methods will significantly decrease when the testing diffusion directions and the training diffusion directions are inconsistent. A robust NODDI parameter estimation method with adaptive sampling under continuous representation (RobNODDI) is proposed. Furthermore, long short-term memory (LSTM) units and fully connected layers are selected to learn continuous representation signals. To this end, we use a total of 100 subjects to conduct experiments based on the Human Connectome Project (HCP) dataset, of which 60 are used for training, 20 are used for validation, and 20 are used for testing. The test results indicate that RobNODDI improves the generalization performance and robustness of the deep learning model, enhancing the stability and flexibility of deep learning NODDI parameter estimatimation applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Predictability performance enhancement for suspended sediment in rivers: Inspection of newly developed hybrid adaptive neuro-fuzzy system model

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International Journal of Sediment research 2022年第3期37卷 383-398页

作者： Rana Muhammad Adnan Zaher Mundher Yaseen Salim Heddam Shamsuddin Shahid Aboalghasem Sadeghi-Niaraki Ozgur Kisi State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering Hohai University Department of Urban Planning Engineering Networks and Systems Institute of Architecture and Construction South Ural State University New Era and Development in Civil Engineering Research Group Scientific Research Center Al-Ayen University Faculty of Science Agronomy Department Hydraulics Division University School of Civil Engineering Faculty of Engineering Universiti Teknologi Malaysia (UTM) Geoinformation Tech.Center of Excellence Faculty of Geomatics Engineering K.N.Toosi University of Technology Department of Computer Science and Engineering Sejong University Civil Engineering Department Ilia State University

Reliable modeling of river sediments transport is important as it is a defining factor of the economic viability of dams, the durability of hydroelectric-equipment, river susceptibility to pollution, suitability for navigation, and potential for aesthetics and fish habitat. The capability of a new machine learning model, fuzzy c-means based neuro-fuzzy system calibrated using the hybrid particle swarm optimization-gravitational search algorithm（ANFIS-FCM-PSOGSA） in improving the estimation accuracy of river suspended sediment loads（SSLs） is investigated in the current study. The outcomes of the proposed method were compared with those obtained using the fuzzy c-means based neuro-fuzzy system calibrated using particle swarm optimization（ANFIS-FCM-PSO）, ANFIS-FCM, and sediment rating curve（SRC） models. Various input combinations involving lagged river flow（Q） and suspended sediment（S） values were used for model development. The effect of Q and S on the model's accuracy also was assessed by including the difference between lagged Q and S values as inputs. The model performance was assessed using the root mean square error（RMSE）, mean absolute error（MAE）, Nash-Sutcliffe Efficiency（NSE）, and coefficient of determination（R2） and several graphical comparison methods. The results showed that the proposed model enhanced the prediction performance of the ANFIS-FCM-PSO（or ANFIS-FCM） models by 8.14%（1.72%）, 14.7%（5.71%）, 12.5%（2.27%）, and 25.6%（1.86%）,in terms of the RMSE, MAE, NSE and R2, respectively. The current study established the potential of the proposed ANFIS-FCM-PSOGSA model for simulation of the cumulative sediment load. The modeling results revealed the potential effects of the river flow lags on the sediment transport quantification.

关键词： Suspended sediment load Adaptive neuro-fuzzy system Particle swarm optimization Gravitational search algorithm

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Core-shell structured BN/SiO_(2)nanofiber membrane featuring with dual-effect thermal management and flame retardancy for extreme space thermal protection

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Science Bulletin 2025年第5期70卷 722-732页

作者： Duo Pan Ziyuan Han Junting Lei Yutao Niu Hu Liu Sunmi Shin Chuntai Liu Zhanhu Guo State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial EquipmentNational Engineering Research Center for Advanced Polymer Processing TechnologyZhengzhou UniversityZhengzhou 450001China Key Laboratory of Multifunctional Nanomaterials and Smart Systems Advanced Materials DivisionSuzhou Institute of Nano-Tech and Nano-BionicsChinese Academy of SciencesSuzhou 215123China Department of Mechanical Engineering College of Design and EngineeringNational University of SingaporeSingapore 117575Singapore Mechanical and Construction Engineering Faculty of Engineering and EnvironmentNorthumbria UniversityNewcastle Upon TyneNE18STUK

With the rapid progress of aerospace frontier engineering,the extreme space thermal environment has brought severe challenges to astronauts’space suits,putting forward higher requirements for thermal protection *** this basis,a unique core-shell structured hexagonal boron nitride(h-BN)/silicon dioxide(SiO_(2))nanofiber membrane(HS)was prepared using the coaxial electrospinning method,of which both the thermal insulation SiO_(2)nanofiber cortex and the passive radiation cooling(PRC)h-BN nanofiber core make it a promising dual-effect thermal management ***,when the amount of h-BN is 0.9 g,the resultant HS(HS0.9)exhibits astonishing low thermal conductivity of 0.026 W m−1 K−1 and high reflectivity and emissivity of exceeding 90%over an extremely wide *** expected dual-effect thermal management performance enables the HS to have an ideal cooling effect under both high sunlight intensity and strong light *** addition,HS also shows excellent flame retardant performance arising from the excellent high-temperature stability of h-BN and SiO_(2).What is more,the tensile strength of HS0.9 was also significantly increased from 0.42 to 7.2 MPa by encapsulating polyimide through vacuum ***,the research results of this work provide innovative highlights for high-temperature protection in daily life and even extreme space environments.

关键词： h-BN/SiO_(2)nanofiber membrane Core-shell structure Passive radiative cooling Thermal insulation Flame retardancy

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L-Lipschitz Gershgorin ResNet Network

arXiv

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arXiv 2025年

作者： Juston, Marius F.R. Norris, William R. Nottage, Dustin Soylemezoglu, Ahmet The Grainger College of Engineering Industrial and Enterprise Systems Engineering Department University of Illinois Urbana-Champaign UrbanaIL61801-3080 United States Construction Engineering Research Laboratory U.S. Army Corps of Engineers Engineering Research and Development Center IL61822 United States

Deep residual networks (ResNets) have demonstrated outstanding success in computer vision tasks, attributed to their ability to maintain gradient flow through deep architectures. Simultaneously, controlling the Lipschitz bound in neural networks has emerged as an essential area of research for enhancing adversarial robustness and network certifiability. This paper uses a rigorous approach to design L-Lipschitz deep residual networks using a Linear Matrix Inequality (LMI) framework. The ResNet architecture was reformulated as a pseudo-tri-diagonal LMI with off-diagonal elements and derived closed-form constraints on network parameters to ensure L-Lipschitz continuity. To address the lack of explicit eigenvalue computations for such matrix structures, the Gershgorin circle theorem was employed to approximate eigenvalue locations, guaranteeing the LMI’s negative semi-definiteness. Our contributions include a provable parameterization methodology for constructing Lipschitz-constrained networks and a compositional framework for managing recursive systems within hierarchical architectures. These findings enable robust network designs applicable to adversarial robustness, certified training, and control systems. However, a limitation was identified in the Gershgorin-based approximations, which over-constrain the system, suppressing non-linear dynamics and diminishing the network’s expressive capacity. © 2025, CC BY.

关键词： Eigenvalues and eigenfunctions

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1-Lipschitz Network Initialization for Certifiably Robust Classification Applications: A Decay Problem

arXiv

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arXiv 2025年

This paper discusses the weight parametrization of two standard 1-Lipschitz network structure methodologies, the Almost-Orthogonal-Layers (AOL) and the SDP-based Lipschitz Layers (SLL), and derives their impact on the initialization for deep 1-Lipschitz feedforward networks in addition to discussing underlying issues surrounding this initialization. These networks are mainly used in certifiably robust classification applications to combat adversarial attacks by limiting the effects of perturbations on the output classification result. An exact and an upper bound for the parameterized weight variance was calculated assuming a standard Normal distribution initialization;additionally, an upper bound was computed assuming a Generalized Normal Distribution, generalizing the proof for Uniform, Laplace, and Normal distribution weight initializations. It is demonstrated that the weight variance holds no bearing on the output variance distribution and that only the dimension of the weight matrices matters. Additionally, this paper demonstrates that the weight initialization always causes deep 1-Lipschitz networks to decay to zero. © 2025, CC BY.

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Simulation to predict the behaviour of a new seaport gate: An application in the Sines container terminal

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Procedia Computer Science 2025年 253卷 2409-2418页

作者： Raquel Gil Pereira Rui Borges Lopes Leonor Teixeira Center for Research & Development in Mathematics and Applications (CIDMA) Department of Economics Management Industrial Engineering and Tourism (DEGEIT) University of Aveiro Aveiro Portugal Institute of Electronics and Informatics Engineering of Aveiro (IEETA) Intelligent Systems Associate Laboratory (LASI) Department of Economics Management Industrial Engineering and Tourism (DEGEIT)University of Aveiro Aveiro Portugal

With rapid technological changes and globalized supply chains, intelligent and adaptable logistics systems are crucial, particularly at seaports. Container terminals face increasing volumes and demands, leading to potential truck congestion and long queues. Automation offers a solution by optimizing port performance and adapting to dynamic conditions, though port digitalization is complex due to diverse stakeholders and information flows. This work is part of the digital transformation of the Port of Sines - Portugal, specifically in the Smart Gate concept context. For this purpose, the Port of Sines Administration, together with other partners, has established the prerequisite of a pre-gate, which is currently being analysed from the point of view of its processes, the technologies to be included and the information models to be supported. This work aims to dynamically analyse the pre-gate model by testing different scenarios to propose improvements to the inherent processes using simulation. The study culminated in a discrete-event simulation model with Anylogic software to predict pre-gate behaviour and foresee potential issues. By testing various scenarios and evaluating their effects on key performance indicators, this model supports the continuous improvement of pre-gate processes.

关键词： port logistics container terminal smart gate truck congestion simulation Anylogic

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Study on indoor pollutants emission in Akure, Ondo State, Nigeria

Arab Gulf Journal of Scientific Research

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Arab Gulf Journal of Scientific research 2024年第4期42卷 1643-1663页

作者： Abulude, Francis Olawale Suriano, Domenico Oluwagbayide, Samuel Dare Akinnusotu, Akinyinka Abulude, Ifeoluwa Ayodeji Awogbindin, Emmanuel Environmental and Sustainable Research Group Science and Education Development Institute Akure Nigeria Department of Sustainable Development Research Center of Brindisi ENEA-Italian National Agency for New Technologies Energy and Environment Brindisi Italy Department of Agricultural and Bio-Environmental Engineering Federal Polytechnic Idah Nigeria Department of Science Laboratory Technology Rufus Giwa Polytechnic Owo Nigeria Department of Agricultural Economics Bioeconomy and Food Systems Justus – Liebig University (JLU) Giessen Germany Department of Computer Science Bamidele Olumilua University of Education Science and Technology Ekiti Ikere Nigeria

Purpose: This study aimed to characterize the concentrations of indoor pollutants (such as carbon dioxide (CO2), ozone (O3), nitrogen dioxide (NO2) and sulfur dioxide (SO2), as well as particulate matter (PM) (PM1, PM... 详细信息

Purpose: This study aimed to characterize the concentrations of indoor pollutants (such as carbon dioxide (CO₂), ozone (O₃), nitrogen dioxide (NO₂) and sulfur dioxide (SO₂), as well as particulate matter (PM) (PM₁, PM_2.5 and PM₁₀) in Akure, Nigeria, as well as the relationship between the parameters’ concentrations. Design/methodology/approach: The evaluation, which lasted four months, used a low-cost air sensor that was positioned two meters above the ground. All sensor procedures were correctly carried out. Findings: CO₂ (430.34 ppm), NO₂ (93.31 ppb), O₃ (19.94 ppb), SO₂ (40.87 ppb), PM₁ (29.31 µg/m3), PM_2.5 (43.56 µg/m³), PM₁₀ (50.70 µg/m³), temperature (32.4°C) and relative humidity (50.53%) were the average values obtained. The Pearson correlation depicted the relationships between the pollutants and weather factors. With the exception of April, which had significant SO₂ (18%) and low PM₁₀ (49%) contributions, NO₂ and PM₁₀ were the most common pollutants in all of the months. The mean air quality index (AQI) for NO₂indicated that the AQI was “moderate” (51–100). In contrast to SO₂, whose AQI ranged from “moderate” to “very unhealthy,” O₃'s AQI ranged from “good” (50) to “unhealthy” (151–200). Since PM₁, PM_2.5 and PM₁₀ made up the majority of PC1’s contribution, both PM_2.5 and PM₁₀ were deemed “hazardous.” Practical implications: The practical implication of indoor air pollution is long-term health effects, including heart disease, lung cancer and respiratory diseases such as emphysema. Indoor air pollution can also cause long-term damage to people’s nerves, brain, kidneys, liver and other organs. Originality/value: Lack of literature in terms of indoor air qu

关键词： Air quality index Indoor air quality Nigeria Pollutants United States environmental protection agency (USEPA) World health organization (WHO)

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