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Electrical, Automation and computer engineering (ICEACE), International Conference on

作者： Siyi Chen Jiarui Liang Jionghao Zhu Xiaolin Tian Faculty of Innovation Engineering School of Computer Science and Engineering Macau University of Science and Technology Macau China School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen Guangdong China

The Lunar impact crater is one of the most remarkable geographic features on the surface of the Lunar. The size and shape of the Lunar impact crater are important for future Lunar landings and resource development on the Lunar surface. Hence, detecting impact craters is an essential research orientation. In this paper, we introduced two novel convolutional neural networks that combine YOLO v7 and deformable convolution of Deformable Convolutional Networks, called YOLOv7_DCN and YOLOv7_DCNv2. With the global Lunar CCD data provided by NASA, the experiment has been conducted under the same settings. The result of the experiment showed that the Precision of YOLOv7_DCN is 77.36%, and the Precision of YOLOv7_DCNv2 is 77.19%. Both values exceed the Precision of YOLO v7, which is 75.86%. Improvements have been found in several other key metrics. Demonstrating that YOLOv7_DCN and YOLOv7_DCNv2 have better performance than YOLO V7 in detecting Lunar impact crater.

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Deep learning for macular fovea detection based on ultra-widefield fundus images 2

Deep learning for macular fovea detection based on ultra-wid...

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2nd International Conference on Electrical, Electronics, and Information engineering, EEIE 2023

作者： Wang, Mini Han Huang, Lina Hou, Guanghui Yang, Jie Xing, Lumin Yuan, Qiting Chong, Kelvin Kam-Lung Lin, Zhiyuan Zeng, Peijin Fang, Xiaoxiao Yao, Xiaoping Li, Qingqian Liu, Jiang Lin, Chen Department of Ophthalmology & Visual Sciences Faculty of Medicine Chinese University of Hong Kong Hong Kong999077 Hong Kong The faculty of Data Science City University of Macau 999078 China Zhuhai People's Hospital Zhuhai Hospital Affiliated with Jinan University Zhuhai519000 China The Department of AI and big data application Zhuhai Institute of Advanced Technology Chinese Academy of Sciences Zhuhai519000 China Perspective Technology Group Zhuhai519000 China Jinan University Affiliated Shenzhen Eye Hospital Shenzhen518000 China Aier Eye Hospital of Zhuhai Zhuhai519000 China College of Artificial Intelligence Chongqing Industry & Trade Polytechnic Chongqing408000 China The First Affiliated Hospital Shandong First Medical University Shandong Provincial Qianfoshan Hospital Jinan250014 China Business School Hong Kong University of Science and Technology Hong Kong999077 Hong Kong Company Ltd. Guangdong Shenzhen518000 China School of Computer Beijing Institute of Technology Zhuhai Zhuhai519000 China Union Hospital of Fujian Medical University Fuzhou353000 China Department of Ophthalmology People's Hospital of Yantian District Shenzhen518000 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518000 China Department of Ophthalmology Shenzhen People's Hospital Shenzhen518000 China

ISBN: (纸本)9781510672840

Macula fovea detection is a crucial molecular biological prerequisite for screening and diagnosing macular diseases. Without early detection and proper treatment, any abnormality involving the macula may lead to blindness. However, with the ophthalmologist shortage and time-consuming artificial evaluation, neither the accuracy nor effectiveness of the diagnosis process could be guaranteed. In this project, we proposed a light-weighted deep learning model based on ultra-widefield fundus (UWF) images for macula fovea detection tasks. This study collected 2300 ultra-widefield fundus images from shenzhen Aier Eye Hospital in China. A light-weighted method based on a U-shape network (Unet) and Fully Convolution Network (FCN) approach is implemented on 1800 (before amplifying process) training fundus images, 400 (before amplifying process) validation images, and 100 test images. Three professional ophthalmologists were invited to mark the fovea. A method from the anatomy perspective is investigated. This approach is derived from the spatial relationship between the macula fovea and optic disc center in UWF. A set of parameters of this method is set based on the experience of ophthalmologists and verified to be effective. The ultra-widefield swept-source optical coherence tomography (UWF-OCT) approach is the grounded method. Through a comparison of proposed methods, we conclude that the proposed light-weighted Unet method outperformed other methods on macula fovea detection tasks. © 2024 SPIE.

关键词： Convolution

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MFCLIP: Multi-modal Fine-grained CLIP for Generalizable Diffusion Face Forgery Detection

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5888-5903页

作者： Zhang, Yaning Wang, Tianyi Yu, Zitong Gao, Zan Shen, Linlin Chen, Shengyong Qilu University of Technology Shandong Academy of Sciences Faculty of Computer Science and Technology Jinan 250014 China National University of Singapore School of Computing 21 Lower Kent Ridge Rd 119077 Singapore Great Bay University School of Computing and Information Technology Dongguan 523000 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen 518060 China Qilu University of Technology (Shandong Academy of Sciences) Shandong Artificial Intelligence Institute Jinan 250014 China Tianjin University of Technology Key Laboratory of Computer Vision and System Ministry of Education Tianjin 300384 China Shenzhen University Computer Vision Institute College of Computer Science and Software Engineering Shenzhen 518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518129 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen 518060 China

The rapid development of photo-realistic face generation methods has raised significant concerns in society and academia, highlighting the urgent need for robust and generalizable face forgery detection (FFD) techniques. Although existing approaches mainly capture face forgery patterns using image modality, other modalities like fine-grained noises and texts are not fully explored, which limits the generalization capability of the model. In addition, most FFD methods tend to identify facial images generated by GAN, but struggle to detect unseen diffusion-synthesized ones. To address the limitations, we aim to leverage the cutting-edge foundation model, contrastive language-image pre-training (CLIP), to achieve generalizable diffusion face forgery detection (DFFD). In this paper, we propose a novel multi-modal fine-grained CLIP (MFCLIP) model, which mines comprehensive and fine-grained forgery traces across image-noise modalities via language-guided face forgery representation learning, to facilitate the advancement of DFFD. Specifically, we devise a fine-grained language encoder (FLE) that extracts fine global language features from hierarchical text prompts. We design a multi-modal vision encoder (MVE) to capture global image forgery embeddings as well as fine-grained noise forgery patterns extracted from the richest patch, and integrate them to mine general visual forgery traces. Moreover, we build an innovative plug-and-play sample pair attention (SPA) method to emphasize relevant negative pairs and suppress irrelevant ones, allowing cross-modality sample pairs to conduct more flexible alignment. Extensive experiments and visualizations show that our model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. © 2005-2012 IEEE.

关键词： CLIP Diffusion face forgery detection Image-noise fusion Sample pair attention Transformer

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Temporal Information Reconstruction and Non-Aligned Residual in Spiking Neural Networks for Speech Classification

arXiv

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arXiv 2024年

作者： Zhang, Qi Wang, Huamin Shen, Hangchi Duan, Shukai Wen, Shiping Huang, Tingwen College of Artificial Intelligence Southwest University Chongqing400715 China Chongqing Key Laboratory of Brain Inspired Computing and Intelligent Chips Chongqing400715 China College of Information Engineering University of Technology Sydney Australian Institute of Artificial Intelligence Sydney2007 Australia Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518055 China

Recently, it can be noticed that most models based on spiking neural networks (SNNs) only use a same level temporal resolution to deal with speech classification problems, which makes these models cannot learn the information of input data at different temporal scales. Additionally, owing to the different time lengths of the data before and after the sub-modules of many models, the effective residual connections cannot be applied to optimize the training processes of these models. To solve these problems, on the one hand, we reconstruct the temporal dimension of the audio spectrum to propose a novel method named as Temporal Reconstruction (TR) by referring the hierarchical processing process of the human brain for understanding speech. Then, the reconstructed SNN model with TR can learn the information of input data at different temporal scales and model more comprehensive semantic information from audio data because it enables the networks to learn the information of input data at different temporal resolutions. On the other hand, we propose the Non-Aligned Residual (NAR) method by analyzing the audio data, which allows the residual connection can be used in two audio data with different time lengths. We have conducted plentiful experiments on the Spiking Speech Commands (SSC), the Spiking Heidelberg Digits (SHD), and the Google Speech Commands v0.02 (GSC) datasets. According to the experiment results, we have achieved the state-of-the-art (SOTA) result 81.02% on SSC for the test classification accuracy of all SNN models, and we have obtained the SOTA result 96.04% on SHD for the classification accuracy of all models. Furthermore, on the non-spiking dataset GSC, we can achieve a test classification accuracy of 95.63%, which surpasses the original baseline SNN-Delays. Meanwhile, the method proposed in this paper can speed up the model’s processing speed and reduce its energy consumption. Copyright © 2024, The Authors. All rights reserved.

关键词： Spatio-temporal data

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Explainable Artificial Intelligence Solution for Online Retail

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computers, Materials & Continua 2022年第6期71卷 4425-4442页

作者： Kumail Javaid Ayesha Siddiqa Syed Abbas Zilqurnain Naqvi Allah Ditta Muhammad Ahsan M.A.Khan Tariq Mahmood Muhammad Adnan Khan Department of Computer Science University of South AsiaLahore54000Pakistan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54000Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Division of Science and Technology University of EducationLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea

Artificial intelligence(AI)and machine learning(ML)help in making predictions and businesses to make key decisions that are beneficial for *** the case of the online shopping business,it’s very important to find trends in the data and get knowledge of features that helps drive the success of the *** this research,a dataset of 12,330 records of customers has been analyzedwho visited an online shoppingwebsite over a period of one *** main objective of this research is to find features that are relevant in terms of correctly predicting the purchasing decisions made by visiting customers and build ML models which could make correct predictions on unseen data in the *** permutation feature importance approach has been used to get the importance of features according to the output variable(Revenue).Five ML models i.e.,decision tree(DT),random forest(RF),extra tree(ET)classifier,Neural networks(NN),and Logistic regression(LR)have been used to make predictions on the unseen data in the *** performance of each model has been discussed in detail using performance measurement techniques such as accuracy score,precision,recall,F1 score,and ROC-AUC *** model is the bestmodel among all five chosen based on accuracy score of 90%and F1 score of 79%followed by extra tree ***,our study indicates that RF model can be used by online retailing businesses for predicting consumer buying *** research also reveals the importance of page value as a key feature for capturing online purchasing *** may give a clue to future businesses who can focus on this specific feature and can find key factors behind page value success which in turn will help the online shopping business.

关键词： Explainable artificial intelligence online retail neural network random forest regression

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Follow me droney! Will UWB technology do it right? 12

Follow me droney! Will UWB technology do it right?

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12th Asia Conference on Mechanical and Aerospace engineering, ACMAE 2021

作者： Hanzel, Krzysztof Department of Electronics Electrical Engineering and Microelectronics Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

Object tracking for consumer drones is currently a rapidly developing technology. Its implementation is currently carried out with the use of object-in-image detection, which often limits the latest possibilities of this technology. The disadvantage of such a solution is also the increase in energy demand and the need to provide additional computing power, or delays resulting from the transfer of this operation to the control device. The proposed solution to this problem in this article is the use of UWB (Ultra-wideband) technology, which is increasingly appearing in consumer devices. The possible yield related to the accuracy of determining the position of the object as well as the experimentally determined frequency of receiving information about the position was presented. Additional advantages, such as resistance to NLOS (non-line of sight) conditions, were also presented. © Published under licence by IOP Publishing Ltd.

关键词： Object detection

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Fence Theorem: Towards Dual-Objective Semantic-Structure Isolation in Preprocessing Phase for 3D Anomaly Detection

arXiv

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arXiv 2025年

作者： Liang, Hanzhe Zhou, Jie Chen, Xuanxin Dai, Tao Wang, Jinbao Gao, Can College of Computer Science and Software Engineering Shenzhen University China Shenzhen Audencia Financial Technology Institute Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Faculty of Education Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

3D anomaly detection (AD) is prominent but difficult due to lacking a unified theoretical foundation for preprocessing design. We establish the Fence Theorem, formalizing preprocessing as a dual-objective semantic isolator: (1) mitigating cross-semantic interference to the greatest extent feasible and (2) confining anomaly judgements to aligned semantic spaces wherever viable, thereby establishing intra-semantic comparability. Any preprocessing approach achieves this goal through a two-stage process of Semantic-Division and Spatial-Constraints stage. Through systematic deconstruction, we theoretically and experimentally subsume existing preprocessing methods under this theorem via tripartite evidence: qualitative analyses, quantitative studies, and mathematical proofs. Guided by the Fence Theorem, we implement Patch3D, consisting of Patch-Cutting and Patch-Matching modules, to segment semantic spaces and consolidate similar ones while independently modeling normal features within each space. Experiments on Anomaly-ShapeNet and Real3D-AD with different settings demonstrate that progressively finer-grained semantic alignment in preprocessing directly enhances point-level AD accuracy, providing inverse validation of the theorem’s causal logic. Copyright © 2025, The Authors. All rights reserved.

关键词： Fences

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A Consumer Electronics-Enhanced UAV System for Agricultural Farm Tracking With Fuzzy SMO and Actuator Fault Detection control Algorithms

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IEEE Transactions on Consumer Electronics 2025年

作者： Bilal, Hazrat Aslam, Muhammad Shamrooz Tian, Yibin Ullah, Inam Ayouni, Sarra Vasilakos, Athanasios V. College of Mechatronics and Control Engineering College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China China University of Mining and Technology Artificial Intelligence Research Institute Xuzhou2211106 China Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Gachon University Department of Computer Engineering Seongnam13120 Korea Republic of Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan Princess Nourah bint Abdulrahman University Department of Information Systems College of Computer and Information Sciences Saudi Arabia Grimstad Norway

The adoption of agricultural robots, or agrobots, has revolutionized modern farming operations, ranging from crop monitoring to automated harvesting, significantly boosting productivity. Motivated by the rapid advancements in agrobots and their integration into smart agricultural practices, this study proposes an autonomous trajectory tracking system for wheat farms using quadcopter UAVs. To address actuator fault detection, including stuck faults and partial loss of efficiency, a TSF-H∞-SMO (Takagi-Sugeno Fuzzy-based H∞ Sliding Mode Observer) fault detection framework is introduced. The approach initiates with the derivation of a TSF (Takagi-Sugeno Fuzzy) attitude control model that integrates an uncertainty term, constructed from the original nonlinear dynamics of the UAV and approximated through local linear models at four equilibrium positions. An actuator fault model is subsequently integrated to develop a comprehensive TSF-UAV model, accounting for actuator faults. The TSF-H∞-SMO is then designed using matrix coordinate transformation to enable precise fault detection. The fault detection capabilities of the TSF-H∞-SMO are evaluated through simulations on the TSF-UAV model under SISO (single-input single-output) actuator fault scenarios. The experimental results validate the proposed system, demonstrating its ability to detect a range of actuator faults accurately and promptly. The analysis reveals a proportional relationship between the amplitude of the state change and the severity of the fault, attributed to the interaction between system states and actuator flaps. This approach underscores the potential for deploying autonomous UAV-based fault detection and trajectory tracking systems in agricultural applications. Furthermore, integrating such advanced fault-tolerant control algorithms holds promise for consumer technology applications, where precision, reliability, and robustness are critical to enhancing system performance and operational efficiency. © 1

关键词： Agricultural robots

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Predictive maintenance for electrical motors: Current approach and usage of artificial intelligence algorithms

Predictive maintenance for electrical motors: Current approa...

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International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Tomasz Herzog Krzysztof Bartecki Faculty of Electrical Engineering Automatic Control and Computer Science Opole University of Technology Opole Poland Rockwell Automation Power Control Business Katowice Poland

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

The article presents the current approach in artificial intelligence methods used in predictive maintenance for electrical motors. The study focuses on presenting data classification methods for fault detection and isolation using data from condition monitoring systems. We discuss the advantages and disadvantages of using machine learning methods like perceptron and Support Vector Machine (SVM) for fault classification problems. Our conclusion shows great interest in machine learning methods combined with condition monitoring systems. Our future research will focus on comparing SVM, perceptron networks, and other methods, such as K-nearest neighbors, with their possibility for implementation in embedded firmware systems.

关键词： Support vector machines Condition monitoring Accuracy Fault detection Artificial neural networks Machine learning Prediction algorithms

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A Unified Analysis for Finite Weight Averaging

arXiv

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arXiv 2024年

作者： Wang, Peng Shen, Li Tao, Zerui Sun, Yan Zheng, Guodong Tao, Dacheng School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan China School of Cyber Science & Technology Shenzhen Campus of Sun Yat-sen University Shenzhen China RIKEN Center for Advanced Intelligence Project Tokyo Japan Faculty of Engineering The University of Sydney Australia School of Computer and Data Science Nanyang Technological University Singapore

Averaging iterations of Stochastic Gradient Descent (SGD) have achieved empirical success in training deep learning models, such as Stochastic Weight Averaging (SWA), Exponential Moving Average (EMA), and LAtest Weight Averaging (LAWA). Especially, with a finite weight averaging method, LAWA can attain faster convergence and better generalization. However, its theoretical explanation is still less explored since there are fundamental differences between finite and infinite settings. In this work, we first generalize SGD and LAWA as Finite Weight Averaging (FWA) and explain their advantages compared to SGD from the perspective of optimization and generalization. A key challenge is the inapplicability of traditional methods in the sense of expectation or optimal values for infinite-dimensional settings in analyzing FWA’s convergence. Second, the cumulative gradients introduced by FWA introduce additional confusion to the generalization analysis, especially making it more difficult to discuss them under different assumptions. Extending the final iteration convergence analysis to the FWA, this paper, under a convexity assumption, establishes a convergence bound O(log (T/k) /√T), where k ∈ [1, T/2] is a constant representing the last k iterations. Compared to SGD with O(log(T)/√T), we prove theoretically that FWA has a faster convergence rate and explain the effect of the number of average points. In the generalization analysis, we find a recursive representation for bounding the cumulative gradient using mathematical induction. We provide bounds for constant and decay learning rates and the convex and non-convex cases to show the good generalization performance of ***, experimental results on several benchmarks verify our theoretical results. © 2024, CC BY.

关键词： Stochastic systems

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