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5th International Conference on Big data and artificial intelligence and Software Engineering, ICBASE 2024

作者： Chen, Yiran Yao, Rui Liu, Haoran Liu, Bingqi Liu, Mingzhe Wenzhou University of Technology School of Data Science and Artificial Intelligence Wenzhou China Chengdu University of Technology College of Nuclear Technology and Automation Engineering Chengdu China Norla Institute of Technical Physics Chengdu China

ISBN: (纸本)9798331506612

Automatic fruit detection has greatly reduced labor costs and crop damage rates, contributing to the progress of agricultural modernization. It involves real-time assessment of the surrounding environment and recognition of target fruit categories. However, fruit detection faces challenges due to the similarities in shape, color, and other features among different types of fruits, as well as the impact of environmental factors such as inadequate lighting, occlusion, and background variations on recognition accuracy. This project focuses on the simulation of the robotic fruit harvesting process, which includes environmental image acquisition, image processing, and robot control. The robot utilizes its integrated camera to capture real-time images of the surrounding environment. These images are then processed on a computer, employing color recognition based on traditional algorithms. The images are segmented based on the identified color regions and subjected to object detection using YOLO v5. By discerning the presence of the target objects, the results are transmitted to the robot, which responds accordingly. The real-time performance is enhanced by filtering and narrowing down the target regions, reducing the time required for object recognition. © 2024 IEEE.

关键词： Fruits

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Analysis and Design of Surgical Instrument Localization Algorithm

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Computer Modeling in Engineering & sciences 2023年第10期137卷 669-685页

作者： Siyu Lu Jun Yang Bo Yang Zhengtong Yin Mingzhe Liu Lirong Yin Wenfeng Zheng School of Automation University of Electronic Science and Technology of ChinaChengdu610054China College of Resource and Environment Engineering Guizhou UniversityGuiyang550025China School of Data Science and Artificial Intelligence Wenzhou University of TechnologyWenzhou325000China Department of Geography and Anthropology Louisiana State UniversityBaton Rouge70803USA

With the help of surgical navigation system,doctors can operate on patients more intuitively and *** positioning accuracy and real-time performance of surgical instruments are very important to the whole *** this paper,we analyze and design the detection algorithm of surgical instrument location mark,and estimate the posture of surgical *** addition,we optimized the pose by ***,the algorithm of location mark detection proposed in this paper and the posture analysis data of surgical instruments are verified and analyzed through *** final result shows a high accuracy.

关键词： Surgical navigation system surgical instruments positioning positioning mark detection algorithm matching algorithm posture analysis

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Semi-Supervised Anomaly Detection in the TinyML Domain Through Multi-Target Few-Shot Domain Adaptation 29

Semi-Supervised Anomaly Detection in the TinyML Domain Throu...

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29th IEEE International Conference on Emerging Technologies and Factory automation, ETFA 2024

作者： Kühnel, Johannes Eißmann, Timo Wiede, Christian Schwung, Dorothea Grabmaier, Anton Fraunhofer Ims Duisburg Germany Hochschule Düsseldorf University of Applied Sciences Artificial Intelligence and Data Science in Automation Düsseldorf Germany University of Duisburg-Essen Department of Electronic Circuits and Systems Duisburg Germany

ISBN: (纸本)9798350361230

Tiny ML, defined by its ability to integrate AI into the smallest devices, unlocks the potential for always-on AI solutions at the edge. This capability is essential in predictive maintenance, enabling smart sensors within the Industrial In-ternet of Things (1IoT). In factory automation, the ability to detect anomalies and adapt those anomaly detection models to varying operating conditions is crucial for the efficiency and longevity of machinery. However, the challenge of semi-supervised anomaly detection with domain adaptation across diverse working conditions has not been sufficiently addressed in TinyML. This paper presents Shared Encoder Domain Adaptation (SEDA) to overcome these limitations. SEDA is a multi-target domain adaptation method for anomaly detection tailored to TinyML applications. Our approach facilitates the transfer of knowledge from existing domains to new ones, ensuring reliable anomaly detection in all domains with minimal data under new operational conditions. This is particularly relevant for IloT applications, where sensors must perform condition monitoring in a self-learning manner. The effectiveness of our method is evaluated through an extensive parameter study on the Toy-ADMOS 2 data set and compared to various baseline methods. The results show an improvement of up to 0.212 on the Area Under the Receiver Operating Characteristic Curve (ROC-AUC) score. It demonstrates that our method significantly enhances performance in multi-target domain adaptation. Importantly, it does this while remaining efficient for resource-constrained systems. This method has the potential to enable self-learning sensor systems with adaptation strategies to different working conditions while requiring only a few data points from these new domains. © 2024 IEEE.

关键词： Predictive maintenance

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Towards in X-Ray Induced Acoustic Imaging for Nasopharyngeal Carcinoma Radiotherapy with a Multidimensional Information Fusion Network 3

Towards in X-Ray Induced Acoustic Imaging for Nasopharyngeal...

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3rd International Conference on Computer Applications Technology, CCAT 2024

作者： Jiang, Xin Chen, Zhi-Chao Sun, Zheng Liu, Ming-Zhe Chongqing University of Technology School of Artificial Intelligence Chongqing401135 China Chongqing University Cancer Hospital Department of Radiology Chongqing400030 China Chengdu University of Technology College of Nuclear Technology and Automation Engineering Chengdu610000 China Wenzhou University of Technology School of Data Science and Artificial Intelligence Wenzhou325000 China

ISBN: (纸本)9798331539795

X-ray-induced acoustic computed tomography (XACT) imaging is an emerging noninvasive imaging technique with wide applications in various clinical medicine fields. However, traditional XACT imaging methods often suffer from low imaging quality and increased noise artifacts, particularly notable under sparse sampling conditions. To overcome these challenges, we propose a hybrid supervised reconstruction method based on deep learning. Specifically, we extract and integrate information from XACT thermoacoustic signals and XACT images reconstructed by traditional methods to leverage complementary feature information from both modalities. To adapt to imaging under sparse sampling, we define multiple sparse conditions based on real-world imaging environments and validate them using a real radiotherapy dataset of head and neck cancer patients. The results demonstrate the adaptability of our method to sparse conditions and its capability to enhance XACT imaging quality under such circumstances. Additionally, we validate the feasibility of X-ray thermoacoustic imaging for 3D dose monitoring. © 2024 IEEE.

关键词： Radiotherapy

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Transverse Velocity Field Measurements in High-resolution Solar Images Based on Deep Learning

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Research in Astronomy and Astrophysics 2023年第6期23卷 86-97页

作者： Zhen-Hong Shang Si-Yu Mu Kai-Fan Ji Zhen-Ping Qiang Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and TechnologyKunming 650500China Yunnan Observatories Chinese Academy of SciencesKunming 650216China College of Big Data and Intelligent Engineering Southwest Forestry UniversityKunming 650224China

To address the problem of the low accuracy of transverse velocity field measurements for small targets in highresolution solar images,we proposed a novel velocity field measurement method for high-resolution solar images based on *** method transforms the transverse velocity field measurements into an optical flow field prediction *** evaluated the performance of the proposed method using the Hαand TiO data sets obtained from New Vacuum Solar Telescope *** experimental results show that our method effectively predicts the optical flow of small targets in images compared with several typical machine-and deeplearning *** the Hαdata set,the proposed method improves the image structure similarity from 0.9182 to0.9587 and reduces the mean of residuals from 24.9931 to 15.2818;on the TiO data set,the proposed method improves the image structure similarity from 0.9289 to 0.9628 and reduces the mean of residuals from 25.9908 *** optical flow predicted using the proposed method can provide accurate data for the atmospheric motion information of solar *** code implementing the proposed method is available on https://***/lygmsy123/transverse-velocity-field-measurement.

关键词： methods:data analysis techniques:image processing Sun:fundamental parameters

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Dimensionality Reduction for Classification Using Divide-and-Conquer Based Genetic Programming 13

Dimensionality Reduction for Classification Using Divide-and...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Wang, Peng Xue, Bing Liang, Jing Zhang, Mengjie School of Electrical and Information Engineering Zhengzhou University Zhengzhou China Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand School of Electrical Engineering and Automation Henan Institute of Technology Xinxiang China

ISBN: (纸本)9798350308365

Dimensionality reduction (DR) is to obtain meaningful low-dimensional representation concealed within high-dimensional data. Genetic programming (GP) has been used to achieve DR for classification because of the flexible representations and promising performance. However, one drawback of many current GP-based DR methods is the high computational demands, potentially limiting their utility in high-dimensional scenarios. To tackle this issue, this work develops a new GP algorithm to achieve DR for classification. A divide-and-conquer strategy based on feature clustering is proposed to split the original feature set into multiple small feature groups. Each tree in the multi-tree GP is used to learn one high-level feature from each feature group. The performance of the proposed method is examined on 10 classification datasets with varying difficulty. The results indicate that the new GP method achieves significantly better classification performance than commonly used feature construction and feature selection methods. More importantly, the proposed GP method uses fewer nodes but achieves better or comparable classification accuracy than the baseline multi-tree GP method. © 2024 IEEE.

关键词： Dimensionality reduction

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Federated intelligence for Intelligent Vehicles

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第5期9卷 1-5页

作者： Zhang, Weishan Zhang, Baoyu Jia, Xiaofeng Qi, Hongwei Qin, Rui Li, Juanjuan Tian, Yonglin Liang, Xiaolong Wang, Fei-Yue School of China University of Petroleum Qingdao China Department of Data Management Big Data Centre Beijing Beijing China Technology Company Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Faculty of Innovation Engineering Macau University of Science and Technology Macao China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

This letter is a brief summary of a series of IEEE TIV's decentralized and hybrid workshops (DHWs) on Federated intelligence for Intelligent Vehicles. The discussed results are: 1) Different scales of large models (LMs) can be federated and deployed on IVs, and three types of federated collaboration between large and small models can be adopted for IVs. 2) Federated fine-tuning of LMs is beneficial for IVs data security. 3) The sustainability of IVs can be improved through optimizing existing models and continuous learning using federated intelligence. 4) LM-enhanced knowledge can make IVs smarter. IEEE

关键词： Intelligent vehicle highway systems

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Effective and Robust Detection of Adversarial Examples via Benford-Fourier Coefficients

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Machine intelligence Research 2023年第5期20卷 666-682页

作者： Cheng-Cheng Ma Bao-Yuan Wu Yan-Bo Fan Yong Zhang Zhi-Feng Li National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China School of Data Science The Chinese University of Hong KongShenzhen 518172China Shenzhen Research Institute of Big Data Shenzhen 518172China AI Lab Tencent Inc.Shenzhen 518057China

Adversarial example has been well known as a serious threat to deep neural networks(DNNs).In this work,we study the detection of adversarial examples based on the assumption that the output and internal responses of one DNN model for both adversarial and benign examples follow the generalized Gaussian distribution(GGD)but with different parameters(i.e.,shape factor,mean,and variance).GGD is a general distribution family that covers many popular distributions(e.g.,Laplacian,Gaussian,or uniform).Therefore,it is more likely to approximate the intrinsic distributions of internal responses than any specific ***,since the shape factor is more robust to different databases rather than the other two parameters,we propose to construct discriminative features via the shape factor for adversarial detection,employing the magnitude of Benford-Fourier(MBF)coefficients,which can be easily estimated using ***,a support vector machine is trained as an adversarial detector leveraging the MBF *** experiments in terms of image classification demonstrate that the proposed detector is much more effective and robust in detecting adversarial examples of different crafting methods and sources compared to state-of-the-art adversarial detection methods.

关键词： Adversarial defense adversarial detection generalized Gaussian distribution Benford-Fourier coefficients image classification

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FedDGL: Federated Dynamic Graph Learning for Temporal Evolution and data Heterogeneity 16

FedDGL: Federated Dynamic Graph Learning for Temporal Evolut...

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16th Asian Conference on Machine Learning, ACML 2024

作者： Xie, Zaipeng Li, Likun Chen, Xiangbin Yu, Hao Huang, Qian Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China College of Artificial Intelligence and Automation Hohai University Nanjing China

Federated graph learning enhances federated learning by enabling privacy-preserving collaborative training on distributed graph data. While traditional methods are effective in managing data heterogeneity, they typically assume static graph structures, overlooking the dynamic nature of real-world graphs. Integrating federated graph learning with dynamic graph neural networks addresses this issue but often fails to retain previously acquired knowledge, limiting generalization for both global and personalized models. This paper proposes FedDGL, a novel framework designed to address temporal evolution, and data heterogeneity in federated dynamic graph learning. Unlike conventional approaches, FedDGL captures temporal dynamics through a global knowledge distillation technique and manages client heterogeneity using a global prototype-based regularization method. The framework employs contrastive learning to generate global prototypes, enhancing feature representation while utilizing a prototype similarity-based personalized aggregation strategy for effective adaptation to local and global data distributions. Experiments on multiple benchmark datasets show that FedDGL achieves significant performance improvements over state-of-the-art methods, with up to 9.02% and 8.77% gains in local and global testing, respectively, compared to FedAvg. These results highlight FedDGL’s effectiveness in improving personalized and global model performance in dynamic, heterogeneous federated graph learning scenarios. © 2024 Z. Xie, L. Li, X. Chen, H. Yu & Q. Huang.

关键词： Dynamic Graph Learning Federated Graph Learning Knowledge Distillation Personalized Aggregation Prototype Learning Temporal Evolution

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Recursive Dynamic inner PrincipalComponent Analysis for Adaptive ProcessModeling ⁎

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IFAC-PapersOnLine 2024年第14期58卷 682-687页

作者： Qilin Qu Yining Dong Ying Zheng School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan 430074 China School of Data Science City University of Hong Kong Hong Kong China

Dynamic latent variable (DLV) methods, represented by dynamic-inner principal component analysis (DiPCA), take into account the high dimensionality and auto-correlation of industrial process data to successfully extract and model the dynamic components. Meanwhile, the time-varying dynamics involved in industrial processes motivate us to explore adaptive DLV methods. In this paper, we propose a recursive DiPCA (RDiPCA) for time-varying dynamic process modeling. Specifically, a recursive autocovariance matrices updating method and the corresponding deflation method are given to achieve low computational costs. The computational efficiency is further improved by a recursive parameter initialization approach in the iterative optimization algorithm solving procedure. Finally, the effectiveness of the proposed algorithm is demonstrated with experiments on a numerical dataset and a wastewater treatment plant dataset.

关键词： Dynamic latent variable methods Recursive dynamic-inner principal component analysis Adaptive dynamic process modeling

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