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检索条件"任意字段=7th International Conference on Computing in Engineering and Technology, ICCET 2022"

共 11091 条记录，以下是1001-1010 订阅

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An Improved Lightweight Human Normative Pose Estimation Algorithm 7

An Improved Lightweight Human Normative Pose Estimation Algo...

引用

7th international conference on Computer Information Science and Application technology, CISAT 2024

作者： Li, Danyu Ma, Yunci Zou, Jiajun Xu, Zhiming Guangzhou Xinhua University School of Information and Intelligent Engineering Dongguan China The Fifth Affiliated Hospital Southern Medical University Division of Nephrology Guangzhou China

ISBN: (纸本)9798350375107

Human pose estimation algorithms have high accuracy and speed, but there are still issues of missed and false detections. this article proposed a lightweight human normative posture estimation algorithm for precise target positioning and action norm evaluation in human normative posture recognition. First, the YOLOv4 object detection algorithm and the lightweight OpenPose algorithm were combined to obtain target bone node data. Second, by calculating action angles as features for extraction, an action matching method was constructed to evaluate and correct the training actions. Finally, the extracted training action skeleton image was trained and classified using a deep convolutional neural network (DCNN) model. Relieved the uncertainty and irregularity of human normative movements. Training and validation were conducted on the collected datasets of professional coaches, and the experimental results shown that, our model achieved better performance than other models. © 2024 IEEE.

关键词： Convolutional neural networks

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Image Analysis in Dermatology: A Comprehensive Survey of Disease Classification Methods 7

Image Analysis in Dermatology: A Comprehensive Survey of Dis...

引用

7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Yashasvi Kumar, Kirana Raj, Vijilius Helena Sangeetha, A. Haryana Panchkula India Institute of Aeronautical Engineering Telangana Hyderabad India New Horizon College of Engineering Karnataka Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350350067

One of the most prevalent medical conditions that people have for a long time is skin disease. the diagnosis of skin diseases is primarily based on the doctors' experience and the results of skin biopsies, which is a laborious procedure. the detection and classification of skin diseases using photographs requires an automated computer-based approach in order to handle the lack of human experts and increase diagnosis accuracy. Accurately classifying skin problems from images is a crucial task that mostly depends on the characteristics of the diseases being studied. the visual characteristics of many skin illnesses are quite similar, making it more difficult to extract relevant elements from the image. Correctly identifying these illnesses from the images would speed up the diagnostic process, increase accuracy, and provide patients with more efficient and affordable care. this study provides an overview of various approaches and strategies for classifying skin diseases, including deep learning-based and conventional, manually constructed feature-based approaches. © 2024 IEEE.

关键词： Lung cancer

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Significance detection based on deep convolutional neural network 7

Significance detection based on deep convolutional neural ne...

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7th international conference on Intelligent computing and Signal Processing, ICSP 2022

作者： Ao, Chenggang Zhang, Jing Liu, Yi Wang, Shuyun Zhang, Weixin Zhang, Kai Army Engineering University of Pla Wuhan Ordnance Nco Academy Hubei China

ISBN: (纸本)9781665478571

Aiming at the limitations of the existing saliency detection algorithms in defining saliency features artificially and the poor robustness of the algorithm, a saliency detection algorithm based on deep convolutional neural network was proposed. Firstly, the VGG-16 model used for image classification is modified to make it applicable to significance detection. then, the network is trained with super-pixel segmentation samples and the initial saliency graph is predicted. Finally, on the basis of preliminary significant image to join the regional contrast, increases significantly the difference of target and background region, further improve the quality of significant image. Compared with the existing algorithms, the proposed algorithm avoids the uncertainty of manually defining significant features, and has stronger robustness and generalization. the effectiveness of the proposed algorithm is demonstrated from two aspects of subjective qualitative observation and objective quantitative detection. © 2022 IEEE.

关键词： Deep neural networks

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Contrasting Parallelized with Sequential Sorting 7

Contrasting Parallelized with Sequential Sorting

引用

7th IEEE international conference on Recent Advances and Innovations in engineering, ICRAIE 2022

作者： Dutta, Anurag Saha, Manjari Government College of Engineering and Textile Technology Department of Computer Science Calcutta Serampore India

ISBN: (纸本)9781665489102

Sorting is one of the important Data Management Techniques that is quite commonly operated on Data Structures. It has been proved that best, any sorting algorithm can do, in asymptotic versions is φlog2φ, when the iterations are made to run in sequential manner. But what if we parallelize them? Architectural advancements, like Amdahl's Law have made use of the concept of Parallelism, and have successfully revolutionized the computing power of the microprocessors. In this paper, we will try to push the limit of computing, by making use of Graphical Processing unit and will briefly compare the efficiency of the algorithms acquainted with both the techniques, sequential as well and parallel in terms of running time. In the first section, we have included some of the preexisting works on this topic, and have mentioned about scopes of further improvement. From the next section onwards, we have slowly put together all the pillars required to support our work. © 2022 IEEE.

关键词： Graphics processing unit

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Influence Of Different Feature Calculation Methods On LDA-based Motor Fault Diagnosis 7

Influence Of Different Feature Calculation Methods On LDA-ba...

引用

7th international conference on Intelligent computing and Signal Processing, ICSP 2022

作者： Liang, Li Sun, Zhihan University of New South Wales School of Electrical Engineering and Telecommunications Sydney Australia

ISBN: (纸本)9781665478571

To avoid motor bearing faults affecting the reliability of the overall operation of the motor and to study motor bearing fault diagnosis methods with higher accuracy, it is necessary to strengthen the research on the influence of fault feature calculation methods on motor bearing fault diagnosis methods. Based on the data from the Bearing Data Center of Case Western Reserve University, this paper compares the influence of different feature calculation methods, including time domain, frequency domain feature calculation, wavelet entropy, and distance measurement on motor bearing fault diagnosis based on linear discriminant analysis (LDA). MATLAB software was used to program all feature calculation methods and analyze the results. the results show that the performance of the frequency-domain feature calculation method and distance measurement method in motor bearing fault diagnosis based on LDA is better than that of time-domain and wavelet entropy, and the accuracy is 17.53% higher on average. © 2022 IEEE.

关键词： Failure analysis

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Research Status and Technological Prospects of Scenario Generation Methods for Ship Collision Avoidance Tests 7

Research Status and Technological Prospects of Scenario Gene...

引用

7th IEEE international conference on Transportation Information and Safety, ICTIS 2023

作者： Liu, Donghao Liu, Jialun Li, Shijie Yang, Fan Yang, Zhen School of Transportation and Logistics Engineering Wuhan University of Technology Wuhan China Southern Marine Science and Engineering Zhuhai519000 China National Engineering Research Center for Water Transport Safety Wuhan China State Key Laboratory of Maritime Technology and Safety Wuhan University of Technology Wuhan China Wuhan University of Technology Intelligent Transportation Systems Research Center Wuhan China

ISBN: (纸本)9798350308532

this paper presents a review of the current research on scene generation methods, focusing on ship collision avoidance testing. By analyzing the simulation methods of collision avoidance models and algorithms from domestic and foreign research, a virtual scene generation method based on international maritime collision regulations and AIS ship data is proposed. Furthermore, this paper discusses the emerging research trends and the challenges that need to be considered in the validation of autonomous collision avoidance testing of ships. Additionally, this paper introduces the concept of automated intelligent ships, their various levels, and the relevant policies and plans of the Chinese government for the development of intelligent ships. © 2023 IEEE.

关键词： Ships

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Towards Quantum-inspired Evolutionary Algorithms for Denoising PCG Signals 7

Towards Quantum-inspired Evolutionary Algorithms for Denoisi...

引用

7th international conference on Signal Processing and Information Security, ICSPIS 2024

作者： Siddiqui, Lubna Mani, Ashish Singh, Jaspal Amity University Amity School of Engineering and Technology Noida India Amity University Amity Innovation and Design Centre Noida India Centre for Development of Advanced Computing Chandigarh India

ISBN: (纸本)9798350368673

this document aims to reaffirm the superiority of quantum-inspired evolutionary algorithms(QiEA) over conventional algorithms. the study proposes a novel QiEA with sorted mutation and greedy local restart (SMGLR) algorithm for finding the optimal coefficients of a filter using a multi-objective optimization function. PCG signals sourced from the Washington Hearts Sounds and Murmurs database and the Michigan Heart Sounds database were filtered using this enhanced filter. Comparative analysis was conducted against the proposed algorithm and established techniques such as Wavelet-based denoising and Variable Mode decomposition. Results: the results revealed that the filter designed using QiEA-SMGLR exhibited superior performance in terms of improved Signal-to-Noise Ratio (SNR) in both comparisons. A comprehensive statistical analysis yielded a correlation coefficient of close to 0.99 indicating strong signal integrity. the QiEA-SMGLR algorithm outperforms Wavelet-based and VMD methods by improving the output SNR by 20% to 100%. this variance can be attributed to the intensity of noise introduced. the result reaffirms the hypothesis that the QiEA-SMGLR algorithm is superior in improving signal clarity and precision over traditionally popular techniques. the improvement in the analysis of Phonocardiogram (PCG) signals could help improve patient diagnostics, amidst the fight against global cardiac problems. © 2024 IEEE.

关键词： Quantum computers

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Extended Finite State Machine based Fault Tolerance in WSN 7

Extended Finite State Machine based Fault Tolerance in WSN

引用

7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Lekha, D. Ahsan Shariff, M. Lakshmi, S. Venkata Anwar Basha, H. Department of Computer Science and Engineering RMK College of Engineering and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Aalim Muhammed Salegh College of Engineering Tamil Nadu Chennai India Artificial Intelligence and Data Science Sri Krishna College of Engineering and Technology Tamil Nadu Coimbatore India School of Computer Science and Engineering REVA University Karnataka Bengaluru India

ISBN: (纸本)9781665464086

Wireless Sensor Network (WSN) suffers from different malware attacks. Several traditional approaches are proposed for detecting fault nodes in WSN. It is necessary to redistribute the nodes and detect the fault nodes while modifying the WSN parameters. Faults can occur in the nodes interrupting the continuous communication process of the nodes in the WSN. the main reason for a fault is also the induction of the energy drain in a node to the maximum possible level, failure of links in communicating nodes due to bandwidth constraints, and attacks induced by the malicious nodes. To solve this problems, Extended Finite State Machine based Fault Tolerance (EFSM_FT) in the WSN is used to detect the fault sensor nodes in the WSN. In this approach, called the finite state machine (FSM), state prompted is modified while a set of situations are true. Every node can be absolutely to be faulty or not establishing on the sensor node states. Using sensor energy, drop rate, forward sensor rate, sensor obtained rate, bandwidth, and transmission delay, this method finds the node with the fault. As a result, this approach accurately detects the faulty nodes in the WSN. the simulation outcomes illustrate the proposed method has a better fault detection ratio and minimizes the false negative ratio in the WSN. © 2023 IEEE.

关键词： Finite automata

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Avoidance of no-fly zone for Hypersonic Vehicle based on Adaptice Radau Pseudospectral Method 7

Avoidance of no-fly zone for Hypersonic Vehicle based on Ada...

引用

7th international conference on Intelligent computing and Signal Processing, ICSP 2022

作者： Tian, Ruocen Zhang, Qingzhen Cui, Langfu Yu, Yue Beihang University School of Automation Science and Electrical Engineering Beijing China Beijing Aerospace Automatic Control Institute Beijing China

ISBN: (纸本)9781665478571

Aiming at the reentry trajectory planning of hypersonic vehicle, considering the constraints of no fly zone in the process, the reentry guidance scheme is designed based on the adaptive Radau pseudospectrum method;Firstly, the kinematic equations and various constraint models of the reentry section are established. Taking the minimum heat flow and trajectory oscillation in the reentry process as the optimization objective, the control and state variables are discretized by radau pseudo spectrum method, and the reentry trajectory planning problem is transformed into a nonlinear programming problem. Numerical simulation is carried out to verify the effectiveness of the method. the simulation results show that the method can plan the reentry trajectory that meets the constraints of process and no fly zone, and can provide useful support for reentry guidance of hypersonic vehicles. © 2022 IEEE.

关键词： Nonlinear programming

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Generalized Undersampling of Center Points Based on Granular Ball 7

Generalized Undersampling of Center Points Based on Granular...

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7th international conference on Cloud computing and Big Data Analytics, ICCCBDA 2022

作者： Bai, Hao Wu, Yanmin Qi, Jinli Chen, Yanyi Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Computer Science and Technology ChongQing China ChongQing College of Electronic Engineering Department of Artificial Intelligence and Big Data ChongQing China ChongQing College of Electronic Engineering Department of General Education and International Studies ChongQing China Chongqing University of Posts and Telecommunications Department of Software Engineering ChongQing China

ISBN: (纸本)9781665487115

In this paper, we propose a new generic sampling classification method based on granular Ball sampling because of good robust noise immunity and possessing properties that can be classified as an undersampling method, called Granular Ball Center-based Common Under-sampling. the method uses adaptively generated Ball to cover the data space, and the sampled data are supported by the centroid properties of the Ball. Unlike the boundary sampling of granular ball sampling, this method mainly uses the center points to smooth out the noise, which not only can clearly represent the attributes, but also can reduce some computational effort, and in most of the time, it can obtain almost the same accuracy as the original model, or even can perform better. Compared with undersampling, it can be better adapted to the task of unbalanced classification as well as non-unbalanced classification. the above can show that the method is able to be generalized without the restriction of specific classifiers and also has a strong adaptability to the dataset. the method can also be effectively used as an undersampling method for unbalanced classification because it is supported by the centroid property, and the method has a speedup effect for most classifiers. these advantages form a strong advantage of the method in improving classifier performance. © 2022 IEEE.

关键词： Cloud computing Adaptation models Computational modeling Big Data Task analysis

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