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7th IEEE International Conference on Transportation information and Safety, ICTIS 2023

作者： Li, Shuhan Nan, Chunli Ning, Hang Cheng, Xin Zhou, Dan Xidian University School of Computer Science and Technology Xi'an China Chang'an University School of Information Engineering Xi'an China

ISBN: (纸本)9798350308532

Road alignment is one of the main factors affecting traffic safety. The vehicle driving trace is the main basis of road alignment design, which has the geometric characteristics of ride comfort, continuous trace curvature, and continuous curvature variation. Since the geometric characteristics of alignment are less considered in the current abnormal driving trace detection methods, a multi-measure fusion method for abnormal driving trace detection of intelligent vehicles was proposed. The three evaluation indexes of curve similarity, ride comfort, and number of consecutive lane changes were integrated to comprehensively evaluate the abnormal driving trace. A piecewise-weighted-average discrete Fréchet distance (PDFD) algorithm was proposed to calculate the curve similarity. This paper analyzed the two dimensions of ride comfort, i.e., the composition of alignment components and the geometric characteristic parameters of alignment components of the driving trace. The five-point orthogonal fitting curvature method was used to extract the alignment components of the driving trace and the segmentation points of each alignment component. According to the principle of orthogonal least squares, the mathematical model of the alignment component of the driving trace was established, and the geometric characteristic parameters were calculated, such as the starting point, end point, radius of the circular curve, center of the circle, starting radius and ending radius of the transition curve, length of alignment components, and curve trend. The experimental results show that the overall accuracy of the abnormal driving trace detection method is 80.4%;the proportion of anomalies caused by the jump of the curvature variation is 41%;the accuracy of the PDFD algorithm is 81%, which is 14% higher than that of the conventional Fréchet distance algorithm of 67%. © 2023 IEEE.

关键词： Alignment

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Assessing Deep Learning Approaches for Time Series Analysis to Detect Uterine Sarcoma

Assessing Deep Learning Approaches for Time Series Analysis ...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Shukla, Gaurav Dheer, Meenakshi Krishnamoorthy, Ramkumar Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Agriculture Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

This paper aims to evaluate the performance of numerous deep-gaining knowledge of fashions for detecting Uterine Sarcoma via Time series evaluation. Uterine Sarcoma is a malignant tumor that influences the uterus and different parts of the woman's reproductive machine. Time collection analysis techniques have been broadly used in scientific fact mining, specifically for clinical records, because of their capability to capture temporal traits of the data. In this look, quite several deeps getting to know fashions which include Convolutional Neural Networks (CNNs), long brief-time period reminiscence (LSTM), and Self-Organizing Maps (SOMs), were evaluated at the MIMIC-III database-the use of metrics such as accuracy, precision and bear in mind. The results showed that the CNN had the highest accuracy (zero.99%) and precision (zero.75%) and did not forget (0.90%) in predicting Uterine Sarcoma when compared with the opposite models. This examination serves as a starting point for a similar investigation into the potential capabilities of deep mastering for detecting Uterine Sarcoma and other illnesses in medical statistics. This paper evaluates deep learning processes for time series evaluation to hit upon uterine sarcoma. The strategies used in this examination are Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). To assess the performance of the networks, the dataset from the yank university of Radiology (ACR) Uterine Sarcoma Imaging and Research Database changed used. The networks were evaluated for accuracy, sensitivity, and specificity. Moreover, the RNNs and CNNs were compared to evaluate their performance. The results show that the CNN performs better than the RNN with an accuracy of ninety-seven. 50%, a sensitivity of 95.05%, and specificity of ninety-nine. 25%. It is steady with previous studies implementing deep learning techniques for medical photograph evaluation. The outcomes of this observation reveal that both RNN and CNN are appr

关键词： assignment excellent influences records reproductive

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Interrupted-sampling Repeater Jamming Recognition based on the PC-LFM Waveform 16

Interrupted-sampling Repeater Jamming Recognition based on t...

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16th International Conference on Signal Processing Systems, ICSPS 2024

作者： Bu, Yi Yu, Xianxiang Cui, Guolong Yang, Xiaobo School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781510689251

The interrupted-sampling repeater jamming (lSRJ) can cause a series of false targets after pulse compression by quickly sampling and forwarding radar signals, which poses a significant threat to radar performance. This paper proposes a new electronic counter-countermeasures (ECCM) method based on the phase-coded linear frequency modulation (PC-LFM) waveform. In particular, by leveraging the de-chirping operation on the obtained PC-LFM waveform, a novel processing scheme is developed to explore the disparity features between the target and the ISRJ in the range profile. As a result, a target detection method is proposed to identify interference based on these feature differences. Simulation results verify the effectiveness of the proposed target recognition method. © 2025 SPIE.

关键词： Jamming

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Target tracking algorithm based on unbiased conversion of TDOA/FDOA measurements 16

Target tracking algorithm based on unbiased conversion of TD...

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16th International Conference on Signal Processing Systems, ICSPS 2024

作者： Xie, Xinchen Gao, Lin Wei, Ping Li, Wanchun School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781510689251

Due to the nonlinear relationship between radar measurements and Cartesian coordinates, tracking with time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements is a challenging problem. This nonlinearity leads, for three-dimensional (3-D) radar, to a nonellipsoidal measurement uncertainty region in Cartesian coordinates, similar to a thin contact lens, that causes consistency problems for a tracking filter. A solution is proposed by developing an unbiased and statistically consistent conversion of the TDOA/FDOA measurements to Cartesian coordinates, based on third-order Taylor expansion. It is shown that in contrast to the first- and second-order conversion and the best nonlinear conversions (based on the cubature method), only the proposed conversion can cover the true uncertainty. © 2025 SPIE.

关键词： Radar measurement

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Design and optimization of the composition and mechanical properties for non-equiatomic CoCrNi medium-entropy alloys

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Journal of Materials science & technology 2023年第8期139卷 232-244页

作者： J.X.Yan Z.J.Zhang P.Zhang J.H.Liu H.Yu Q.M.Hu J.B.Yang Z.F.Zhang Shi-changxu Innovation Center for Advanced Materials Institute of Metal ResearchChinese Academy of SciencesShenyang 110016China School of Materials Science and Engineering Chongqing Jiaotong UniversityChongqing 400074China chool of Materials Science and Engineering University of Science and Technology of ChinaShenyang 110016China School of Materials Science and Engineering Northeastern UniversityShenyang 110819China School of Information Science and Engineering Shenyang University of TechnologyShenyang 111003China

The development of multi-principal element alloys(MPEAs,also called as high-or medium-entropy al-loys,HEAs/MEAs)provides tremendous possibilities for materials ***,designing MPEAs with desirable mechanical properties confronts great challenges due to their vast composition *** this work,we provide an essential criterion to efficiently screen the CoCrNi MEAs with outstanding strength-ductility *** negative Gibbs free energy difference△E_(FCC-BCC)between the face-centered cubic(FCC)and body-centered cubic(BCC)phases,the enhancement of shear modulus G and the decline of stacking fault energy(SFE)γ_(isf)are combined as three requisites to improve the FCC phase stability,yield strength,deformation mechanisms,work-hardening ability and ductility in the *** effects of chemical composition on△E_(FCC-BCC),G andγisf were investigated with the first principles calculations for Co_(x)Cr_(33)Ni_(67-x),Co_(33)Cr_(y)Ni_(67-y)and Co_(z)Cr_(66-z)Ni_(34)(0≤x,y≤67 and 0≤z≤66)*** on the essential criterion and the calculation results,the composition space that displays the neg-ative Gibbs free energy difference△E_(FCC-BCC),highest shear modulus G and lowest SFEγ_(isf)was screened with the target on the combination of high strength and excellent *** this context,the optimal composition space of Co-Cr-Ni alloys was predicted as 60 at.%-67 at.%Co,30 at.%-35 at.%Cr and 0 at.%-6 at.%Ni,which coincides well with the previous experimental evidence for Co_(55)Cr_(40)Ni_(5)*** valid-ity of essential criterion is further proved after systematic comparison with numerous experimental data,which demonstrates that the essential criterion can provide significant guidance for the quick exploitation of strong and ductile MEAs and promote the development and application of MPEAs.

关键词： Medium-entropy alloys First-principles calculations Phase stability Stacking-fault energy Strength Ductility

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Deep learning-based classification,detection,and segmentation of tomato leaf diseases:A state-of-the-art review

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Artificial Intelligence in Agriculture 2025年第2期15卷 192-220页

作者： Aritra Das Fahad Pathan Jamin Rahman Jim Md Mohsin Kabir M.F.Mridha Department of Computer Science and Engineering American International University-BangladeshDhaka 1229Bangladesh Institute of Information and Communication Technology Shahjalal University of Science and TechnologySylhet 3114Bangladesh Advanced Machine Intelligence Research Lab Dhaka 1207Bangladesh Bangladesh University of Business and Technology DhakaBangladesh

The early identification and treatment of tomato leaf diseases are crucial for optimizing plant productivity,efficiency and *** by the farmers poses the risk of inadequate treatments,harming both tomato plants and *** of disease diagnosis is essential,necessitating a swift and accurate response to misdiagnosis for early *** regions are ideal for tomato plants,but there are inherent concerns,such as weather-related *** diseases largely cause financial losses in crop *** slow detection periods of conventional approaches are insufficient for the timely detection of tomato *** learning has emerged as a promising avenue for early disease *** study comprehensively analyzed techniques for classifying and detecting tomato leaf diseases and evaluating their strengths and *** study delves into various diagnostic procedures,including image pre-processing,localization and *** conclusion,applying deep learning algorithms holds great promise for enhancing the accuracy and efficiency of tomato leaf disease diagnosis by offering faster and more effective results.

关键词： Deep learning Tomato leaf disease Classification Detection Segmentation Imaging

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An Automated System Pre-Trained to Identify Cardiovascular Disorders from ECG Pictures 2

An Automated System Pre-Trained to Identify Cardiovascular D...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Bhaskar, Nuthanakanti Aelgani, Vivekanand Kumar, Sheo Devi, Bigul Sunitha Kumar, Voruganti Naresh Divya, G. CMR Technical Campus Department of Computer Science and Engineering Hyderabad India CMR College of Engineering & Technology Department of Computer Science and Engineering Hyderabad India CMR Engineering College Department of Computer Science and Engineering Hyderabad India CMR Institute of Technology Department of Computer Science and Engineering Hyderabad India CMR Technical Campus Department of Information Technology Hyderabad India

ISBN: (纸本)9798331519056

Globally, heart disorders, often recognized as cardiovascular diseases, are among the major causes of death. Additional lives might be protected the earlier they are identified and anticipated. Cardiovascular disease can be identified with an electrocardiogram (ECG), a simple, low-cost, non-invasive method of detecting the electrical functioning of the heart. Employing a public dataset of ECG images from heart patients, deep learning algorithms were utilized in this study to calculate the four main cardiac abnormalities: abnormal heartbeat, myocardial infarction history, coronary artery disease, and typical individual categories. Initially, the transfer method for learning was studied using the previously learned deep neural networks, VGG16, RESNET50, and InceptionV3. Classical methods of machine learning such as Support Vector Machine, K-nearest relatives, Random Forest, and Naïve Bayes were able to identify features employing the models that were already trained previously stated. For the study, ECG images from people who had or did not have a diagnosis of CVD were collected. Using these preprocessed pictures, relevant features including time-domain, frequency-domain, including nonlinear features were extracted. The collected features were then used to train various machine learning computations, which helped identify the prevalence of CVD. The efficiency of each algorithm was evaluated using metrics such as precision, specificity, and consciousness. In addition, the research employed Flask technology in Python to create an online user experience for the model, facilitating patients' and healthcare practitioners' easy access. ECG pictures can be uploaded through the interface, and the model analyzes them to determine whether CVD is present. These algorithms can also offer a reliable diagnosis, which lowers the possibility of human error. © 2024 IEEE.

关键词： Deep neural networks

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DeepSVDNet:A Deep Learning-Based Approach for Detecting and Classifying Vision-Threatening Diabetic Retinopathy in Retinal Fundus Images

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computer Systems science & engineering 2024年第2期48卷 511-528页

作者： Anas Bilal Azhar Imran Talha Imtiaz Baig Xiaowen Liu Haixia Long Abdulkareem Alzahrani Muhammad Shafiq College of Information Science and Technology Hainan Normal UniversityHaikou571158China Department of Creative Technologies Air UniversityIslamabadPakistan School of Life Science and Technology University of Electronic Science and Technology of China UESTCChengduChina School of Science and Technology University of Management and Technology UMTLahorePunjabPakistan Faculty of Computer Science&Information Technology Al Baha UniversityAl BahaSaudi Arabia School of Information Engineering Qujing Normal UniversityQujingChina

Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness ***,previous automated VTDR detection methods have mainly relied on manual feature extraction and classification,leading to *** paper proposes a novel VTDR detection and classification model that combines different models through majority *** proposed methodology involves preprocessing,data augmentation,feature extraction,and classification *** use a hybrid convolutional neural network-singular value decomposition(CNN-SVD)model for feature extraction and selection and an improved SVM-RBF with a Decision Tree(DT)and K-Nearest Neighbor(KNN)for *** tested our model on the IDRiD dataset and achieved an accuracy of 98.06%,a sensitivity of 83.67%,and a specificity of 100%for DR detection and evaluation tests,*** proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection.

关键词： Diabetic retinopathy(DR) fundus images(FIs) support vector machine(SVM) medical image analysis convolutional neural networks(CNN) singular value decomposition(SVD) classification

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A Robust Low-Rank Tensor Decomposition and Quantization based Compression Method 40

A Robust Low-Rank Tensor Decomposition and Quantization base...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Ouyang, Yudian Xie, Kun Wen, Jigang Xie, Gaogang Li, Kenli College of Computer Science and Electronics Engineering Hunan University Changsha China Hangzhou Institute of Advanced Technology Hangzhou China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Institute of Computer Network Information Center Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350317152

Tensor data is widely used in fields such as smart grids, cloud systems, and deep learning. As the scale of this data increases, storage and transmission costs rise significantly. Many tensor data exhibit low-rank structures, offering the potential for data compression through low-rank decomposition techniques. Tucker decomposition, a typical low-rank decomposition technique, achieves data compression and interpretability by capturing complex data correlations and representing the original tensor with a compact core tensor and factor matrices. However, the compression ratio provided by Tucker decomposition is often insufficient, particularly for large-scale tensors. To tackle this issue, we propose a robust low-rank tensor compression method that leverages Tucker decomposition with quantization and coding. Initially, we establish a robust Tucker decomposition framework that decomposes the low-rank tensor into a core tensor and factor matrices using well-designed Tucker rank-setting rules. This framework effectively handles noise and missing values. Subsequently, we conduct an in-depth analysis of the numerical characteristics of the core tensor and factor matrices within the Tucker decomposition framework. Based on their distinct characteristics, we design tailored quantization and coding schemes to compress the core tensor and factor matrices, respectively, thereby significantly improving the compression ratio of Tucker decomposition while maintaining high accu-racy. Through extensive experiments on four publicly available datasets (which can form 3 or 4 order tensors), we demonstrate that our approach can achieve compression ratios 4×-10× higher than the best competitor, with recovery errors improved by 8% -42%. © 2024 IEEE.

关键词： Tensors

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Quantum spin systems: Toroidal classification and geometric duality

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Physical Review B 2024年第14期110卷 L140403页

作者： Vahid Azimi-Mousolou Anders Bergman Anna Delin Olle Eriksson Manuel Pereiro Danny Thonig Erik Sjöqvist Department of Applied Mathematics and Computer Science Faculty of Mathematics and Statistics Department of Physics and Astronomy Department of Applied Physics School of Engineering Sciences (SeRC) Wallenberg Initiative Materials Science for Sustainability (WISE) Wallenberg Initiative Materials Science WISE School of Science and Technology

We demonstrate a toroidal classification for quantum spin systems, revealing an intrinsic geometric duality within this structure. Through our classification and duality, we reveal that various bipartite quantum features in magnon systems can manifest equivalently in both bipartite ferromagnetic and antiferromagnetic materials, based upon the availability of relevant Hamiltonian parameters. Additionally, the results highlight the antiferromagnetic regime as an ultrafast dual counterpart to the ferromagnetic regime, both exhibiting identical capabilities for quantum spintronics and technological applications. Concrete illustrations are provided, demonstrating how splitting and squeezing types of two-mode magnon quantum correlations can be realized across ferro- and antiferromagnetic regimes.

关键词： Magnons Quantum correlations in quantum information Quantum information processing Quantum information theory Spintronics

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