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International Conference on Developments in esystems Engineering (DESE)

作者： Issam Shallal Lamia Rzouga Haddada Najoua Essoukri Ben Amara Université de Sousse Institut Supérieur d’Informatique et des Techniques de Communication de H. Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse Tunisia Université de Sousse Institut Supérieur des Sciences Appliquées et de Technologie de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse Tunisia Université de Sousse Ecole Nationale d’Ingénieurs de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse Tunisia

ISBN: (数字)9798350368697

ISBN: (纸本)9798350368703

With the advancement and widespread use of digital devices, capturing images has become effortless in any location. Images serve as evidence, making the authenticity of digital images increasingly critical. Some individuals alter images by adding or removing elements, rendering the images unreliable. Consequently, detecting image forgery has become essential. The evolution of image editing software has intensified this issue within the realm of computer vision. Recently, a variety of algorithms have been developed to identify image forgery. However, with the progress in digital technology, the ease of image manipulation has led to a surge in forgery cases, presenting significant obstacles when verifying their authenticity. Thus, there is a pressing requirement for effective forgery detection methods. This paper introduces a novel copy-move forgery detection approach based on the fusion of a handcrafted forgery detection system utiliszing the scale-invariant feature transform and the support vector machine with a deep-learning-based forgery detection system using VGG16. The aim is to address the challenges of subtle forgery detection and blending and seamless Integration. We conduct the experiments on the MICC_F2000 image manipulation dataset and assess the efficacy of the proposed approach, achieving $\mathbf{9 6 . 7 5 \%}$ accuracy, $\mathbf{1 0 0 \%}$ precision and $95.5 \%$ F1-score. This research demonstrates superior performance compared to state-of-the-art methods.

关键词： Support vector machines Deep learning Accuracy Digital images Forensics Software algorithms Transforms Feature extraction Forgery Surges

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Safe Efficient Policy Optimization Algorithm for Unsignalized Intersection Navigation

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IEEE/CAA Journal of Automatica Sinica 2024年第9期11卷 2011-2026页

作者： Xiaolong Chen Biao Xu Manjiang Hu Yougang Bian Yang Li Xin Xu State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body the College of Mechanical and Vehicle EngineeringHunan UniversityChangsha 410082China State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body the College of Mechanical and Vehicle EngineeringHunan UniversityChangsha 410082 Wuxi Intelligent Control Research Institute(WICRI)of Hunan University Wuxi 214115China College of Intelligence Science and Technology Institute of Unmanned SystemsNational University of Defense TechnologyChangsha 410073China

Unsignalized intersections pose a challenge for autonomous vehicles that must decide how to navigate them safely and *** paper proposes a reinforcement learning(RL)method for autonomous vehicles to navigate unsignalized intersections safely and *** method uses a semantic scene representation to handle variable numbers of vehicles and a universal reward function to facilitate stable learning.A collision risk function is designed to penalize unsafe actions and guide the agent to avoid them.A scalable policy optimization algorithm is introduced to improve data efficiency and safety for vehicle learning at *** algorithm employs experience replay to overcome the on-policy limitation of proximal policy optimization and incorporates the collision risk constraint into the policy optimization *** proposed safe RL algorithm can balance the trade-off between vehicle traffic safety and policy learning *** intersection scenarios with different traffic situations are used to test the algorithm and demonstrate its high success rates and low collision rates under different traffic *** algorithm shows the potential of RL for enhancing the safety and reliability of autonomous driving systems at unsignalized intersections.

关键词： Autonomous driving decision-making reinforcement learning(RL) unsignalized intersection

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Reducing Injections in Low-Power Electrical Impedance Tomography for Hand Sign Recognition 17

Reducing Injections in Low-Power Electrical Impedance Tomogr...

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17th International Workshop on Impedance Spectroscopy, IWIS 2024

作者： Ghoul, Bilel Barioul, Rim Hafsa, Mariem Sahnoun, Salwa Fakhfakh, Ahmed Kanoun, Olfa Chemnitz University of Technology Faculty of Electrical Engineering and Information Technology Measurement and Sensor Technology Chemnitz09126 Germany National School of Electronics and Telecommunications of Sfax Technopole of Sfax Laboratory of Signals Systems Artificial Intelligence and Networks Digital Research Centre of Sfax Ons City 3021 Tunisia Université de Sousse Ecole Nationale d'Ingénieurs de Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia

ISBN: (纸本)9798331507626

Electrical Impedance Tomography (EIT) is increasingly being used to recognize hand signs due to its internal sensing capabilities. EIT systems operate by injecting a lowamplitude electrical current into the forearm through a set of electrodes and recording the resulting voltages for signal processing. However, this method can result in high power consumption in wearable hand gesture recognition applications due to the current injection-based principle. To overcome this limitation, our investigation focuses on reducing the power consumption of EIT systems by minimizing the number of injected currents. In this study, we propose an injection reduction approach based on ambiguity analysis and the reduction of non-relevant voltage readings. As a result, the proposed method eliminates 3 out of 8 current injections, leading to a 37.5 % reduction in power consumption. This decreases energy usage by 22.5 mW per measurement for a current amplitude of 150 μ A over 10 trials. Further, acquisition time was reduced from 1 second to 0.6 seconds. The classification accuracy using the KNN classifier reached 99 % with full measurements and 96 % with reduced injections. Moreover, a comparative analysis of the reduced injections to forearm anatomy is performed. It demonstrates that bones and high-density muscle areas can affect the current flow and result in inaccurate measurements. These findings demonstrate the potential for reducing current injections in EIT to produce more energy-efficient wearable hand sign recognition systems. © 2024 IEEE.

关键词： Nearest neighbor search

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Thermal and Magnetic Dual-Responsive Catheter-Assisted Shape Memory Microrobots for Multistage Vascular Embolization

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Research 2024年第3期2024卷 529-543页

作者： Qianbi Peng Shu Wang Jianguo Han Chenyang Huang Hengyuan Yu Dong Li Ming Qiu Si Cheng Chong Wu Mingxue Cai Shixiong Fu Binghan Chen Xinyu Wu Shiwei Du Tiantian Xu Guangdong Provincial Key Lab of Robotics and Intelligent Systems Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhenChina University of Chinese Academy of Sciences BeijingChina Department of Neurosurgery South China HospitalMedical SchoolShenzhen University.ShenzhenChina The Key Laboratory of Biomedical Imaging Science and System Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhenChina.

Catheters navigating through complex vessels,such as sharp turns or multiple U-turns,remain challenging for vascular ***,we propose a novel multistage vascular embolization strategy for hard-to-reach vessels that releases untethered swimming shape-memory magnetic microrobots(SMMs)from the prior catheter to the vessel ***,made of organo-gel with magnetic particles,ensure biocompatibility,radiopacity,thrombosis,and fast thermal and magnetic *** SMM is initially a linear shape with a 0.5-mm diameter at 20°C inserted in a *** transforms into a predetermined helix within 2 s at 38°C blood temperature after being pushed out of the catheter into the *** enable agile swimming in confined and tortuous vessels and can swim upstream using helical propulsion with rotating magnetic ***,we validated this multistage vascular embolization in living rabbits,completing 100-cm travel and renal artery embolization in 2 *** 4 weeks,the SMMs maintained the embolic position,and the kidney volume decreased by 36%.

关键词： turns maintained travel

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Active learning attraction basins of dynamical system

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Chinese Physics B 2025年第05期 118-126页

作者：曹小尾茹小磊严钢 MOE Key Laboratory of Advanced Micro-Structured Materials and School of Physical Science and Engineering Tongji University National Key Laboratory of Autonomous Intelligent Unmanned Systems MOE Frontiers Science Center for Intelligent Autonomous Systemsand Shanghai Research Institute of Intelligence Science and Technology Tongji University

Dynamical systems often exhibit multiple attractors representing significantly different functioning conditions. A global map of attraction basins can offer valuable guidance for stabilizing or transitioning system states. Such a map can be constructed without prior system knowledge by identifying attractors across a sufficient number of points in the state space. However, determining the attractor for each initial state can be a laborious task. Here, we tackle the challenge of reconstructing attraction basins using as few initial points as possible. In each iteration of our approach, informative points are selected through random seeding and are driven along the current classification boundary, promoting the eventual selection of points that are both diverse and enlightening. The results across various experimental dynamical systems demonstrate that our approach requires fewer points than baseline methods while achieving comparable mapping ***, the reconstructed map allows us to accurately estimate the minimum escape distance required to transition the system state to a target basin.

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Lightweight Hybrid Model Combining MobilNetV2 and PCA for Copy-Move Forgery Detection

Lightweight Hybrid Model Combining MobilNetV2 and PCA for Co...

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International Conference on Developments in esystems Engineering (DESE)

作者： Issam Shallal Lamia Rzouga Haddada Najoua Essoukri Ben Amara LATIS- Laboratory of Advanced Technology and Intelligent Systems Université de Sousse Institut Supérieur d’Informatique et des Techniques de Communication de H. Sousse Sousse Tunisia LATIS- Laboratory of Advanced Technology and Intelligent Systems Université de Sousse Institut Supérieur des Sciences Appliquées et de Technologie de Sousse Sousse Tunisie LATIS- Laboratory of Advanced Technology and Intelligent Systems Université de Sousse Ecole Nationale d’Ingénieurs de Sousse Sousse Tunisie

ISBN: (数字)9798350368697

ISBN: (纸本)9798350368703

MobileNet is a lightweight convolutional neural network optimized for resource-limited environments. However, its use of depthwise separable convolutions to minimize parameters and computation can reduce accuracy due to oversimplified channel interactions. Principal Component Analysis (PCA) can address this issue by reducing the dimensionality of weight matrices while preserving key features. Applying PCA can help maintain accuracy and compress the model simultaneously. Based on this, we propose a novel copy-move forgery detection approach based on MobilNetV2 and PCA for feature extraction, and a random forest for classification. This allows us to enhance MobileNet accuracy while keeping its model size compact. The results of our experiments conducted on the MICC-F2000 dataset reveal that the proposed hybrid lightweight model outperforms the individual transfer learning structures and the existing literature, achieving 96.37% accuracy.

关键词： Accuracy Computational modeling Splicing Transfer learning Computer architecture Feature extraction Forgery Convolutional neural networks Random forests Principal component analysis

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Spatial Resolution Enhancement in OFDR Using a Modified Cohen's Class Analysis

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Journal of Lightwave technology 2025年第12期43卷 5915-5922页

作者： Liang, Changshuo Wang, Yu Gao, Yan Zhang, Hongjuan Jin, Baoquan Bai, Qing Taiyuan University of Technology College of Electrical and Power Engineering Taiyuan030024 China Taiyuan University of Technology Key Laboratory of Advanced Transducers and Intelligent Control Systems Ministry of Education and Shanxi Province Taiyuan030024 China Shanxi Transportation Technology Research & Development Co. Ltd Shanxi Taiyuan030006 China

We proposed a demodulation algorithm for optical frequency domain reflectometry (OFDR) sensing system which utilizes a Cohen's class analysis with a modified kernel function to improve spatial resolution and hold a reliable sensing accuracy. With the enhanced time-frequency concentration of Cohen's class analysis compared to conventional short-time Fourier transform (STFT), the trade-off between spatial resolution and sensing accuracy is mitigated. The procedure of the proposed Cohen's class analysis algorithm and the demodulation method with this algorithm are expounded in steps with the advantages explained in theory. The experiment results demonstrate that the OFDR system with the proposed algorithm is capable of sensing strain ranging from 0 to 1000 μϵ at 14 m sensing distance. As a comparison to conventional OFDR demodulation using STFT with window length of 200 points, the spatial resolution has improved from 6.05 mm to 0.16 mm, while the demodulation root mean square error (RMSE) reaches 2.91 × 10-3 nm in the strained area. © 1983-2012 IEEE.

关键词： Mean square error

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An Error Correcting Output Code Algorithm Based on Hierarchical Clustering for Lithology Identification 43

An Error Correcting Output Code Algorithm Based on Hierarchi...

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43rd Chinese Control Conference, CCC 2024

作者： Li, Ao Chen, Xi Hunan University of Technology School of Rail Transit Zhuzhou412007 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China

ISBN: (纸本)9789887581581

Lithology identification serves as a crucial foundation for the precise exploration and safe exploitation of geological resources. However, challenges arise from the inherent imbalance and overlap present in logging data, making the accurate identification of lithology a complex task. Addressing these challenges effectively, this paper introduces a novel algorithm, the Error Correcting Output Code algorithm based on Hierarchical Clustering(HC-ECOC). The proposed algorithm initially formulates a coding matrix using distance and overlap measures between class clusters. Subsequently, it extends the length of the coding matrix by considering both the accuracy of the integrated classifier and the diversity among base classifiers. To evaluate the efficacy of the proposed method, comparisons were conducted with alternative algorithms using UCI datasets and an authentic lithology dataset. The results unequivocally demonstrate the superior performance of our algorithm. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Lithology

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Design of Load Frequency Controller for Power System Based on Equivalent Input Disturbance Method 4

Design of Load Frequency Controller for Power System Based o...

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4th IEEE New Energy and Energy Storage System Control Summit Forum, NEESSC 2024

作者： Zhang, Jianrong Xu, Hang Wang, Hongzhang Zhang, Chuanke China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation Wuhan China

ISBN: (纸本)9798350389012

In the power system, due to the instability of the external environment, safe and stable operation cannot always be guaranteed, as it frequently encounters varying degrees of external disturbances. In order to mitigate the effects of these disturbances on the system, the power system can operate safely and stably, In this paper, a power system load frequency control (LFC) based on Equivalent Input Disturbance (EID) method is proposed from the perspective of active disturbance suppression. Firstly, the disturbances present in the environment are unified as external disturbances, and then a single area the electric power system model with external disturbance was constructed. Then, a controller algorithm based on EID was designed using EID equivalent processing method and linear matrix inequality method to address the issue of external interference in the power system. Finally, a numerical example is used to validate the effectiveness and accuracy of the controller by simulating with two aspects, fixed perturbation and random perturbation, respectively. © 2024 IEEE.

关键词： Linear matrix inequalities

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Few-shot Pose-free View Synthesis with 3D Gaussian Splatting

Few-shot Pose-free View Synthesis with 3D Gaussian Splatting

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2024 China Automation Congress, CAC 2024

作者： Huang, Yuan Xu, Chi School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9798350368604

Recent advancements in Novel View Synthesis using neural radiance fields and 3D Gaussian splatting have demonstrated promising results. However, these methods conventionally rely on accurate initial poses and point clouds from the model of structure from motion, which poses challenges in sparse input views. To addressing these drawbacks, we introduces an novel method for view synthesis based on 3D Gaussian splatting. Our approach operates on sparse input views without the prerequisite of pose priors, achieving reconstruction and real-time rendering of novel perspectives. For the deficiency of camera poses and point cloud, we employ an end-to-end deep learning model to recover the 3D structure from a few of 2D images to initialize the 3D Gaussian point cloud. In addressing the issue with few-shot, we utilize the differentiable rasterization module to render depth map and apply regulation on it. Additionally, we incorporate supervision between input views by interpolating to generate novel perspectives during training. Experimental results demonstrate that our proposed method significantly enhances the capabilities of 3D Gaussian splatting for novel view synthesis in sparse views and without the necessity of camera pose priors. © 2024 IEEE.

关键词： 3D reconstruction

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