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2nd International ToothFairy2: Multi-Structure Segmentation in CBCT Volumes, ToothFairy 2024, 3D Teeth Landmarks Detection Challenge, 3DTeethLand 2024, Semi-supervised Teeth Segmentation, STS 2024 held in conjunction with the 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Chen, Liangyu Zhang, Dongping Yan, Tianxu Li, Zheng Wei, Yutong Qian, Luying College of Information Engineering China Jiliang University Hangzhou China College of Computer and Control Engineering Qiqihar University Qiqihar China

ISBN: (纸本)9783031889769

Tooth instance segmentation is a key technology in the field of medical image segmentation, with applications ranging from orthodontic treatment to dental pathology assessment. Although researchers have developed many tooth instance segmentation models, a common drawback is that traditional techniques are not effective in combining global and local background information and fail to make full use of labeled data, resulting in limited performance in different clinical scenarios. This paper proposes a new deep learning method dedicated to the instance segmentation task of dental panoramic images. By introducing the RFEM attention module and the CBAM attention module into the ResNet architecture, we improve the network’s ability to focus on key features by adaptively assigning weights to different channels and further enhance the fusion effect of global and local features. In order to improve the generalization ability of the data, we use a data augmentation algorithm specifically for labeled datasets. Finally, the network is named the fully supervised tooth segmentation model DAE-Net, which performs well on the MICCAI STS 2024 dataset, with a final performance improvement of 32% compared to the original base model, verifying its effectiveness and stability in the tooth instance segmentation task. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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IMC Based PID controller for Spherical Tank Liquid Level control

IMC Based PID Controller for Spherical Tank Liquid Level Con...

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AIML-Applications for engineering & Technology (ICAET), International Conference on

作者： C. B. Kadu V. P. Bhosale P. S. Vikhe V. V. Mandhare Department of Instrumentation & Control Engineering Pravara Rural Engineering College Loni India Department of Computer Engineering Pravara Rural Engineering College Loni India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

In the process industries, it's hard to control a non-linear process. Nonlinear behavior is frequently seen in real processes. The challenging problem of controlling a spherical tank is result of its nonlinearity caused by the continuously varying cross section. An IMC-based PID controller is employed to improve efficiency of controlling the level of spherical tanks which exhibit nonlinear dynamics. For processes up to second-order with dead time and first-order numerator dynamics, a single tuning parameter will be offered. The process model is determined employing mathematical modeling and standard step response-based system identification. The laboratory model of an interacting and non-interacting nonlinear system is introduced, which is capable of performing more complex tasks. To verify the effectiveness of the suggested IMC based PID controller, results of real-time experiments and a simulation study on spherical tank level control are incorporated.

关键词： Process control Real-time systems Mathematical models Regulation Nonlinear dynamical systems System identification Reliability Level control Tuning Standards

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Adaptive Prescribed-Time Neural control of Nonlinear Systems via Dynamic Surface Technique

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 4948-4958页

作者： Wang, Ping Yu, Chengpu Lv, Maolong Zhao, Zilong Beijing Institute of Technology School of Automation Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Air Force Engineering University College of Air Traffic Control and Navigation Xi'an710051 China Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft2628 CD Netherlands

The adaptive practical prescribed-time (PPT) neural control is studied for multiinput multioutput (MIMO) nonlinear systems with unknown nonlinear functions and unknown input gain matrices. Unlike existing PPT design schemes based on backstepping, this study proposes a novel PPT control framework using the dynamic surface control (DSC) approach. First, a novel nonlinear filter (NLF) with an adaptive parameter estimator and a piecewise function is constructed to effectively compensate for filter errors and facilitate prescribed-time convergence. Based on this, a unified DSC-based adaptive PPT control algorithm, augmented with a neural networks (NNs) approximator, is developed, where NNs are used to approximate unknown nonlinear system functions. This algorithm not only addresses the inherent computational complexity explosion associated with traditional backstepping methods but also reduces the constraints on filter design parameters compared to the DSC algorithm that relies on linear filters. The simulation showcases the effectiveness and superiority of the devised scheme by employing a two-degree-of-freedom robot manipulator. © 2024 IEEE.

关键词： Flexible manipulators

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Computing Challenges of UAV Networks: A Comprehensive Survey

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computers, Materials & Continua 2024年第11期81卷 1999-2051页

作者： Altaf Hussain Shuaiyong Li Tariq Hussain Xianxuan Lin Farman Ali Ahmad Ali AlZubi School of Computer Science&Technology Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of EducationChongqing University of Posts and TelecommunicationsChongqing400065China School of Computer Science and Technology Zhejiang Gongshang UniversityHangzhou310018China School of Artificial Intelligence Nanjing University of Information Science&TechnologyNanjing210044China Department of Computer Science and Engineering School of ConvergenceCollege of Computing and InformaticsSungkyunkwan UniversitySeoul03063Republic of Korea Department of Computer Science Community CollegeKing Saud UniversityRiyadh11495Saudi Arabia

Devices and networks constantly upgrade,leading to rapid technological ***-dimensional(3D)point cloud transmission plays a crucial role in aerial computing terminology,facilitating information *** network types,including sensor networks and 5G mobile networks,support this ***,Flying Ad hoc Networks(FANETs)utilize Unmanned Aerial Vehicles(UAVs)as nodes,operating in a 3D environment with Six Degrees of Freedom(6DoF).This study comprehensively surveys UAV networks,focusing on models for Light Detection and Ranging(LiDAR)3D point cloud compression/*** topics covered include autonomous navigation,challenges in video streaming infrastructure,motivations for Quality of Experience(QoE)enhancement,and avenues for future ***,the paper conducts an extensive review of UAVs,encompassing current wireless technologies,applications across various sectors,routing protocols,design considerations,security measures,blockchain applications in UAVs,contributions to healthcare systems,and integration with the Internet of Things(IoT),Artificial Intelligence(AI),Machine Learning(ML),and Deep Learning(DL).Furthermore,the paper thoroughly discusses the core contributions of LiDAR 3D point clouds in UAV systems and their future prediction along with mobility *** also explores the prospects of UAV systems and presents state-of-the-art solutions.

关键词： Autonomous vehicles UAV systems UAV transmission UAV in healthcare future of UAV system security measures

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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A CrossInformer model based on dual-layer decomposition and interpretability for short-term electricity load forecasting

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Alexandria engineering Journal 2025年 129卷 117-127页

作者： Hongjie Li Yirui Tang Dayang Liu College of Computer and Control Engineering Northeast Forestry University Harbin 150040 China College of Economics and Management Northeast Forestry University Harbin 150040 China

Short-term electricity load forecasting is a critical issue in power system management, and its accuracy is essential for the stable operation of the grid and energy dispatch. This paper proposes a CrossInformer model based on dual-layer decomposition and interpretability for predicting short-term electricity load. Firstly, the CrossInformer employs an intelligent optimization algorithm for feature selection, which enhances the model’s training efficiency and prediction accuracy. Secondly, an ICEEMDAN-RLMD dual-layer decomposition method is adopted to denoise the load data, thereby reducing the influence of noise on the prediction results. Subsequently, the CrossInformer architecture integrates multi-granularity patch inputs, ProbSparse Attention, self-attention distillation, and the HiLo attention mechanism to improve the model’s capability in capturing both long-term and short-term dependencies in time series data. Finally, to enhance the model’s interpretability, the SHAP method is used to quantify the contribution of each feature to the prediction outcomes. Experimental results demonstrate that the proposed method outperforms traditional approaches in electricity load forecasting, offering superior prediction accuracy and stability.

关键词： Short-term electricity load forecasting CrossInformer model Interpretability Dual-layer decomposition

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Optimizing Indoor Network Element Layout for Enhanced Signal Coverage and Security in Location-Based Services 7th

Optimizing Indoor Network Element Layout for Enhanced Signa...

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7th International Conference on Attacks and Defenses for Internet-of-Things, ADIoT 2024

作者： Yu, Xiaomin Yu, Xiaokun College of Computer and Control Engineering Qiqihar University Qiqihar China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar University Qiqihar China Heilongjiang Jiaotong Polytechnic Qiqihar China

ISBN: (纸本)9783031855924

Recently, with the rapid development of communication technology, location-based services (LBS) in indoor environments have penetrated into various aspects of people’s daily lives. Wireless local area networks (WLAN) have become the preferred choice for location-based services due to their broad deployment and low cost. However, the typical non-line-of-sight characteristics of indoor environments result in severe fluctuations in received signal strength (RSS), signal coverage vulnerabilities can exacerbate environmental interference, making positioning results more vulnerable to attacks or tampering. The layout of indoor network elements is a key factor that constrains signal coverage rate. This paper focuses on the optimization of indoor network element layout, proposes an indoor network element layout optimization model based on signal coverage and quality in indoor environments and introduces an Adaptive Simulation Annealing Non-inertial Opposite Particle Swarm Optimization algorithm (ASA-NRPSO). Firstly, a reverse strategy is introduced into the particle flight process of the Particle Swarm Optimization (PSO) algorithm, and high-quality particles are selected for the next generation iteration. In the selection of the reverse strategy, the simulated annealing idea is integrated to adaptively select the reverse strategy based on the needs of the particles at different stages, which avoids the particles falling into local optima. The inertia term in the particle swarm velocity update formula is replaced with group information for non-inertial updates. This can more fully utilize the global information of the population to guide the movement of the next generation of particles and improve the convergence speed of the particle swarm. The elite mutation strategy is used to increase the diversity of particles and improve the global search capability of the algorithm. This method ensures the rapid generation of network element layout to improve the signal coverage rate

关键词： Location based services

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Leveraging Data Fitting and Trust Assessment to Improve the Security of Industrial Networks

Leveraging Data Fitting and Trust Assessment to Improve the ...

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第43届中国控制会议

作者： Heng Zhang Yinchu Wang Yawen Xue Ruilong Deng Hongran Li Jian Zhang School of Computer Engineering Jiangsu Ocean University College of Control Science and Engineering Zhejiang University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Industrial network control systems(INCSs) are easy to be targeted by attackers due to their high economic *** of the existing defense methods are deployed at the network boundary,which causes high-security risks to INCS once an attacker enters the *** addition,many existing intrusion detection systems(IDSs) build detection models based on historical *** INCS lacks sufficient historical data,it is difficult to build detection models with high *** this paper,we propose a trust assessment model for INCS based on Fourier series fitting employing concept of zero trust to assess the real-time trust of *** model alerts when INCS has low *** test two data injection attacks in the experiment to prove the effectiveness of our approach.

关键词： Zero Trust Fourier Series Curve Fitting Sliding Window Industrial control System

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Improved Prediction of Metamaterial Antenna Bandwidth Using Adaptive Optimization of LSTM

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computers, Materials & Continua 2022年第10期73卷 865-881页

作者： Doaa Sami Khafaga Amel Ali Alhussan El-Sayed M.El-kenawy Abdelhameed Ibrahim Said H.Abd Elkhalik Shady Y.El-Mashad Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Systems Engineering Faculty of Engineering at ShoubraBenha UniversityEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

The design of an antenna requires a careful selection of its parameters to retain the desired ***,this task is time-consuming when the traditional approaches are employed,which represents a significant *** the other hand,machine learning presents an effective solution to this challenge through a set of regression models that can robustly assist antenna designers to find out the best set of design parameters to achieve the intended *** this paper,we propose a novel approach for accurately predicting the bandwidth of metamaterial *** proposed approach is based on employing the recently emerged guided whale optimization algorithm using adaptive particle swarm optimization to optimize the parameters of the long-short-term memory(LSTM)deep *** optimized network is used to retrieve the metamaterial bandwidth given a set of *** addition,the superiority of the proposed approach is examined in terms of a comparison with the traditional multilayer perceptron(ML),Knearest neighbors(K-NN),and the basic LSTM in terms of several evaluation criteria such as root mean square error(RMSE),mean absolute error(MAE),and mean bias error(MBE).Experimental results show that the proposed approach could achieve RMSE of(0.003018),MAE of(0.001871),and MBE of(0.000205).These values are better than those of the other competing models.

关键词： Metamaterial antenna long short term memory(LSTM) guided whale optimization algorithm(Guided WOA) adaptive dynamic particle swarm algorithm(AD-PSO)

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Hgo-Yolo: Advancing Anomaly Behavior Detection with Hierarchical Features and Lightweight Optimized Detection

SSRN

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SSRN 2024年

作者： Dong, Guanghui Zheng, Qizhi Luo, Zhongze Guo, Meiyan Wang, Xinzhu Wu, Renqimuge Meng, Qiu College of Computer and Control Engineering Northeast Forestry University Harbin150040 China

Accurate and real-time object detection is crucial for anomaly behavior detection, especially in scenarios constrained by hardware limitations, where achieving a balance between accuracy and speed is imperative to improve detection performance. In this study, we propose a model called HGO-YOLO, which integrates the HGNetv2 network structure into YOLOv8. This architectural combination expands the receptive field and captures a wider range of features while also simplifying the model complexity by replacing some convolutions with GhostConv. We introduce a new lightweight detection head, OptiConvDetect, which utilizes parameter sharing concepts to construct the detection head more efficiently. We evaluate the performance of the HGO-YOLO model on a collected dataset. Extensive experimental results show that the proposed algorithm achieves a mAP@0.5 of 87.4% and a recall rate of 81.1% with a remarkably compact model size of only 4.6 MB. When executed on the CPU, the algorithm achieves a frame rate of 56 FPS. Compared to the original YOLOv8n algorithm, our approach not only improves accuracy by 3.0% but also reduces the computational load by 51.69% (from 8.9 GFLOPs to 4.3 GFLOPs), while increasing the frame rate by a factor of 1.7. These results underline the excellent overall performance of the HGO-YOLO model in detecting anomalous behavior. © 2024, The Authors. All rights reserved.

关键词： Object detection

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