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2023 International Conference on Intelligent Communication and Networking, ICN 2023

作者： Li, Xueru Zhang, Yan Liu, Lingjie Tian, Ye Zhang, Yue Harbin Engineering University College of Computer Science and Technology Harbin China China Industrial Control Systems Cyber Emergency Response Team Beijing China Environment and Food and Drug Crime Investigation Detachment of Hangzhou Public Security Bureau Hangzhou China Xidian University Hangzhou Institute of Technology Hangzhou China Key Laboratory of Peace-Building Big Data of Zhejiang Province Zhejiang China

ISBN: (纸本)9798350357677

With the continuous growth in demand for meat and frozen products in the cold chain market, illegally smuggled frozen goods have quickly entered the market due to their low prices, leading to a series of food safety issues. This article proposes a method for identifying the risks of smuggled frozen meat for urban public safety awareness using the YOLOv7 model. The YOLOv7 model is used to detect data captured at various traffic checkpoints, obtaining spatiotemporal information of frozen goods vehicles. This information is then combined with map data to generate vehicle trajectory information. The trajectory similarity between the frozen vehicle trajectories and the specialized database of frozen transportation routes is analyzed using the EDwP distance algorithm. The EDwP algorithm employs a threshold-free approach, dynamically interpolating using the concept of projection, and automatically adapting to non-uniform sampling rates to address the issue of inconsistent trajectory data sampling rates. The experimental results of the YOLOv7-EDwP algorithm were verified by verifying the alerts generated in the recent period through on-site verification by a third-party inspection agency, with a recognition accuracy of 90.77%. This holds significant significance in the detection and warning of smuggled frozen meat. © 2023 IEEE.

关键词： Transportation routes

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Image Enhancement using linear filters for Rectal Cancer Diagnosis using MR Images

Image Enhancement using linear filters for Rectal Cancer Dia...

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International Conference on Biosignals, Images and Instrumentation (ICBSII)

作者： Sonal Patreena D’Almeida Surekha Kamath K.V Rajagopal K.N Manjunath Department of Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Radiodiagnosis and Imaging Kasturba Medical College Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (数字)9798331513863

ISBN: (纸本)9798331513870

computer-aided diagnosis (CAD) is an efficient tool in analyzing medical images for the diagnosis and staging of rectal cancer. Magnetic Resonance Imaging (MRI) plays a crucial role in the detection and evaluation of rectal cancer due to its high-resolution imaging capabilities. This paper discusses the pre-processing methods for MR images of rectal cancer, which facilitate further image analysis for diagnosing the stage of cancer. Pre-processing is a critical step for MR images, aiding in subsequent steps of segmentation and classification. This paper investigates the impact of widely used pre-processing techniques. Initially, pelvic DICOM images are provided as input. By combining three filters—mean, median, and adaptive median filters—the noise is removed from the image. Image enhancement techniques such as Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) are used to enhance the quality of the image. Statistical parameters like Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Structural Similarity Index Measure (SSIM) are used to evaluate performance. Results indicate that CLAHE provides superior outcomes.

关键词： Image quality Maximum likelihood detection PSNR Magnetic resonance imaging Noise Nonlinear filters computer aided diagnosis Medical diagnostic imaging Image enhancement Cancer

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Dynamic Quasi-Hyperbolic Momentum Iterative Attack With Small Perturbation for 4D-Flight Trajectory Prediction

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IEEE Internet of Things Journal 2025年第12期12卷 22533-22553页

作者： Zhao, Zhengyang Wang, Buhong Yao, Xuan Tian, Jiwei Dong, Ruochen Zhu, Peican Air Force Engineering University School of Information and Navigation Xi’an710082 China University of Manchester Department of Computer Science ManchesterM13 9PL United Kingdom Air Force Engineering University Air Traffic Control and Navigation College Xi’an710005 China Xi’an Jiaotong University School of Cyber Science and Engineering Xi’an710049 China Northwestern Polytechnical University School of Artificial Intelligence Optics and Electronics Xi’an China

With the rapid growth of global air traffic, 4D-flight trajectory prediction (4D-FTP) using deep learning (DL) methods has become essential for applications such as flight delay prediction, fuel consumption analysis, and traffic management. However, the adversarial attacks pose significant security threats to DL-based 4D-FTP systems reliant on automatic dependent surveillance-broadcast (ADS-B) sensors. Furthermore, existing vulnerabilities in time series prediction (TSP) models for 4D-FTP remain underexplored due to the lack of effective and stealthy attack methods. To address this gap, we propose the Dynamic Quasi-Hyperbolic Momentum Iterative Attack with Small Perturbation (DQM-Attack). This method leverages quasi-hyperbolic momentum (QHM) and dynamic step sizes to optimize gradient utilization in 4D-FTP models. In addition, attack stealth is enhanced through the Manifold Smooth Module (MSM) and Sparse Smooth Reinforcement Learning (SS-RL). Experimental results demonstrate that DQM-Attack effectively disrupts predictions with minimal perturbations across four state-of-the-art TSP models. This study appears to be the first to address stealthy adversarial attacks on 4D-FTP systems, revealing a critical security vulnerability in air traffic management (ATM). © 2014 IEEE.

关键词： Flight paths

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SIAVC: Semi-Supervised Framework for Industrial Accident Video Classification

arXiv

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arXiv 2024年

作者： Li, Zuoyong Lin, Qinghua Fan, Haoyi Zhao, Tiesong Zhang, David The Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China The School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China The School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou45000 China The Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information Fuzhou University Fuzhou350108 China Shenzhen518172 China

Semi-supervised learning suffers from the imbalance of labeled and unlabeled training data in the video surveillance scenario. In this paper, we propose a new semi-supervised learning method called SIAVC for industrial accident video classification. Specifically, we design a video augmentation module called the Super Augmentation Block (SAB). SAB adds Gaussian noise and randomly masks video frames according to historical loss on the unlabeled data for model optimization. Then, we propose a Video Cross-set Augmentation Module (VCAM) to generate diverse pseudo-label samples from the high-confidence unlabeled samples, which alleviates the mismatch of sampling experience and provides high-quality training data. Additionally, we construct a new industrial accident surveillance video dataset with frame-level annotation, namely ECA9, to evaluate our proposed method. Compared with the state-of-the-art semi-supervised learning based methods, SIAVC demonstrates outstanding video classification performance, achieving 88.76% and 89.13% accuracy on ECA9 and Fire Detection datasets, respectively. The source code and the constructed dataset ECA9 will be released in https://***/AlchemyEmperor/SIAVC. Copyright © 2024, The Authors. All rights reserved.

关键词： Classification (of information)

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Key Feature Selecting in the Clean Oil Refinery Process Based on a Two-Stage Data Mining Framework 2

Key Feature Selecting in the Clean Oil Refinery Process Base...

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2nd International Conference on Green Energy, Environment and Sustainable Development, GEESD 2021

作者： Lin, X. Ma, J.Y. Lin, F.X. Wang, D.Q. Xiao, X.S. School of Management Zhejiang University Hangzhou310058 China College of Computer Science and Technology Zhejiang University Hangzhou310027 China College of Control Science and Engineering Zhejiang University Hangzhou310027 China Bigdata Technology Institute Guizhou Light Industry Polytechnic College Guiyang550025 China

ISBN: (纸本)9781643682228

Maintaining the ratio of octane number and reducing the proportion of harmful substances in the heavy oil fluid catalytic cracking process meets both environmental and economic benefits. Through collecting tremendous processing data by digital hardware, gasoline refiners are still hard to do well data analytical work in production process control due to the large scale of ambiguous intermediate operating variables. This paper proposes a two-stage data mining framework integrates the strengths of Ridge regression and Person correlation analysis to extract a scale limited group of key features. Different with traditional recursive feature elimination methods, we pay more attention to the correlation analysis between every couple of features in the result. Two stop criterions guarantee to fulfil refining standards and limit the computational work in finite steps. A real word case study contains 325 samples, 13 quality indicators and 354 operating variables which testifies the validity and practicality of our algorithm. The result shows only 13 features (operating variables) are significant to the rationality of process design and the improvement of process control. © 2021 The authors and IOS Press.

关键词： Fluid catalytic cracking

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YOLO-based Semantic Communication with Generative AI-aided Resource Allocation for Digital Twins Construction

arXiv

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arXiv 2023年

作者： Du, Baoxia Du, Hongyang Liu, Haifeng Niyato, Dusit Xin, Peng Yu, Jun Qi, Mingyang Tang, You School of Electrical and Information Engineering JiLin Agricultural Science and Technology University Jilin132101 China School of Information and Control Engineering Jilin Institute of Chemical Technology Jilin132022 China School of Computer Science and Engineering Nanyang Technological University Singapore College of Agriculture Yanbian University Yanji133002 China

Digital Twins play a crucial role in bridging the physical and virtual worlds. Given the dynamic and evolving characteristics of the physical world, a huge volume of data transmission and exchange is necessary to attain synchronized updates in the virtual world. In this paper, we propose a semantic communication framework based on You Only Look Once (YOLO) to construct a virtual apple orchard with the aim of mitigating the costs associated with data transmission. Specifically, we first employ the YOLOv7-X object detector to extract semantic information from captured images of edge devices, thereby reducing the volume of transmitted data and saving transmission costs. Afterwards, we quantify the importance of each semantic information by the confidence generated through the object detector. Based on this, we propose two resource allocation schemes, i.e., the confidence-based scheme and the artificial intelligence-generated scheme, aimed at enhancing the transmission quality of important semantic information. The proposed diffusion model generates an optimal allocation scheme that outperforms both the average allocation scheme and the confidence-based allocation scheme. Moreover, to obtain semantic information more effectively, we enhance the detection capability of the YOLOv7-X object detector by introducing new Efficient Layer Aggregation Network-HorNet (ELAN-H) and SimAM attention modules, while reducing the model parameters and computational complexity, making it easier to run on edge devices with limited performance. The numerical results indicate that our proposed semantic communication framework and resource allocation schemes significantly reduce transmission costs while enhancing the transmission quality of important information in communication services. © 2023, CC BY.

关键词： Object detection

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Image Visibility Graph Analysis of the Eye Disease Images

Image Visibility Graph Analysis of the Eye Disease Images

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International Congress on Image and Signal Processing, BioMedical engineering and Informatics (CISP-BMEI)

作者： Dandan Wu Zhiyi Ma Xiaodong Yang Guangkang Jiang Chen Yan Lu Ma School of Computer Science and Technology China University of Mining and Technology Xuzhou China School of Information and Control Engineering China University of Mining and Technology Xuzhou China Suzhou Vocational and Technical College Suzhou China

Visibility graph algorithm is a widely used time series complex network transformation algorithm. It has been mainly applied to one-dimensional or multidimensional time series, while many real-life objects, such as images, cannot be represented by sequences. In this paper, we focus on two-dimensional images and propose a novel method of image visibility graph network (IVGN) with redefined node-degree features. We use the staining images of corneal ulcer of eye as the research object and demonstrate the application of IVGN to medical images. Our method consists of the following steps: (1) We construct a novel IVGN by treating each pixel as a node and connecting the visible pixels. We compute the average node degree of the IVGN for ocular pathology image analysis. (2) Based on the IVGN, we extract the node-degree features of the visible images and map them to a new degree topology, which is used for image enhancement. (3) By doing so, we build a gray co-occurrence matrix based on the degree topology and extract its four parameters, showing the role of the degree topology in image texture extraction. (4) As a result, we use the proposed contrast-based descriptors to achieve effective detection of eye staining images with different categories and severity levels.

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A novel high-through cell micronucleus detection algorithm based on improved YOLOv5

A novel high-through cell micronucleus detection algorithm b...

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Industrial IoT, Big Data and Supply Chain (IIoTBDSC), International Conference on

作者： Weiyi Wei Jingyu Li Xiaoqin Wu Hangjian Zhang College of Computer Science and Engineering Northwest Normal University Lanzhou China Radiology Department Gansu Provincial Center For Disease Control And Prevention Lanzhou China Business school Northwest Normal University Lanzhou China

ISBN: (数字)9798350341690

ISBN: (纸本)9798350341706

In response to the challenges associated with cell micronucleus image recognition, namely small target sizes, high class similarity, and limited sample data, A novel cell micronucleus detection algorithm named YOLOv5-ECA is proposed based on YOLOv5. The proposed algorithm gets hyper-accurate micronucleus identification through a series of improvements. To begin with, the anchor frame size is recalibrated based on a meticulously constructed dataset. This process serves to expedite convergence speed while bolstering accuracy. Subsequently, in C3 module, a coordinate attention (CA) is introduced, which enlarging the receptive field and enabling the model to allocate greater focus to crucial elements within processed images. Ultimately, a superficial detection layer is incorporated, thereby enhancing the precision of cell micronucleus identification through multi-scale detection. Comparative analysis with the original YOLOv5 which demonstrates a 2% increase in average precision, accuracy, and a 1.3% enhancement in recall for the YOLOv5-ECA algorithm based on experimental results.

关键词： YOLO Accuracy Heuristic algorithms Supply chains Real-time systems Detection algorithms Optimization

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Optical Evanescent Field for Direct Observation of Sub-100nm Particle Motion on Glass Surface

Optical Evanescent Field for Direct Observation of Sub-100nm...

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Robotics, engineering, science, and Technology (RESTCON), International Conference on

作者： Panart Khajornrungruang Thitipat Permpatdechakul Keisuke Suzuki Aran Blattler Yutaka Terayama Daiki Goto Pipat Phaisalpanumas Natthaphon Bun-Athuek Amornphun Phunopas Dept. of Intelligent and Control System Faculty of Computer Science and Systems Engineering Mechanical Science and Technology Division Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Interdisciplinary Informatics Graduate School of Computer Science and Systems Engineering Mechanical Information Science and Technology Division Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Production and Robotics Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Dept. of Intelligent and Control System Graduate School of Computer Science and Systems Engineering Mechanical Science and Technology Course Kyushu Institute of Technology Iizuka-Shi Fukuoka Japan Dept. of Industiral Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Dept. of Mechanical Engineering Technology College of Industrial Technology King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand

To directly investigate the dynamic nanoscale phenomenon on the surface being processed in wet conditions such as precision polishing, and cleaning in semiconductor industrial, an optical method for visualization and observation of each sub-100 nm sized particle that is moving on an interface such as a silica glass surface by applying an evanescent field have been proposing. Subsequently, we developed an experimental apparatus equipped with an optical microscopy system for verifying the moving particle observation method in a laboratory scale. This article introduces some experimentally direct observation results of duplicated wet processes.

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NFV-Enabled Service Recovery in Space-Air-Ground Integrated Networks: A Matching Game Based Approach

arXiv

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arXiv 2025年

作者： Jia, Ziye Cao, Yilu He, Lijun Li, Guangxia Zhou, Fuhui Wu, Qihui Han, Zhu College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing211106 China State Key Laboratory of ISN Xidian University Xian710071 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

To achieve ubiquitous connectivity of the sixth generation communication, the space-air-ground integrated network (SAGIN) is a popular topic. However, the dynamic nodes in SAGIN such as satellites and unmanned aerial vehicles, may be fragile and out of operation, which can potentially cause service failure. Therefore, the research on service recovery in SAGIN under situations of resource failure is critical. In order to facilitate the flexible resource utilization of SAGIN, the network function virtualization technology (NFV) is proposed to be employed. Firstly, the task management is transformed into the deployment of service function chains (SFCs). Then, we design an NFV-based SFC recovery model in SAGIN in the face of resource failure, so that tasks can quickly select alternative resources to complete deployments. Moreover, the problem of SFC recovery is formulated to minimize the total time consumption for all completed SFCs. Since it is an NP-hard integer linear programming problem, we propose the efficient recovery algorithm based on the matching game. Finally, via various simulations, the effectiveness of the proposed algorithm and its advantages are verified, where the total time consumption is optimized by about 25%, compared with other benchmark methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Network function virtualization

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