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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901-910 订阅

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Research on Classification Label Denoising Algorithm Based on Granular Ball 7

Research on Classification Label Denoising Algorithm Based o...

引用

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

作者： Kong, Weiyu Wu, Yanmin Qi, Jinli Chen, Yanyi Chongqing Key Lab. of Comp. Sci. and Technology Chongqing University of Posts and Telecommunications ChongQing China ChongQing College of Electronic Engineering Department of Artificial Intelligence and Big Data ChongQing China Chongqing University of Posts and Telecommunications Department of Software Engineering ChongQing China

ISBN: (纸本)9781665487115

this paper presents a granular ball denoising method (GBD) which can effectively improve the accuracy and robustness of classification algorithm. GBD method first uses the self-Adaptive hypersphere to cover the data space, then eliminates the data not contained in the hypersphere and the noise data in the sphere, and finally uses only the hypersphere data for training, so as to reduce the sample data and improve the data quality. Experiments show that using the data obtained by this method for training can greatly improve the accuracy of the classification algorithm. In addition, because the hypersphere has good adaptive ability, GBD also has excellent robustness. the experiment shows that GBD sampling data training can still get good results after adding noise to the data set. therefore, GBD is an efficient and robust denoising method. © 2022 IEEE.

关键词： Training Cloud computing Data integrity Noise reduction Big Data Robustness Classification algorithms

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Optical Efficiency Optimization Algorithm for Heliostat Field in Solar Power Tower 7

Optical Efficiency Optimization Algorithm for Heliostat Fiel...

引用

7th IEEE international Electrical and Energy conference, CIEEC 2024

作者： Zhang, Xiaomi Zhang, Hao Yang, Dazhi Shuai, Yong School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350359558

the optical efficiency of a heliostat cluster plays a pivotal role in determining the operational effectiveness of a concentrating solar power system. this study explicates the fundamental principles concerning the radial-staggered layout of heliostat fields and expounds upon the methodology employed in quantifying its optical efficiency. the focus lies in an improved algorithm for assessing shadowing and blocking losses using three-dimensional geometric projection, with a primary objective to ensure simulation accuracy while reducing computational costs. As such, the optical efficiency of individual heliostats can be swiftly assessed, enabling the optimization of hundreds or thousands of heliostat layouts. As a practical case study, simulation analysis and layout optimization of the solar power tower's heliostat field are conducted in Harbin, China, yielding the spatial-temporal distribution pattern of optical efficiency. After optimization, the blocking losses of the heliostat field for a 5-MW solar power tower are reduced by 84.3%, while the annual optical efficiency increased by 8.8%. © 2024 IEEE.

关键词： Solar energy

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Cross-Platform Multimodal Frogeye Leaf Spot Recognition and Classification: Advancing with Cutting-Edge CNN Technologies and Transfer Learning in Diverse Plant Species 7

Cross-Platform Multimodal Frogeye Leaf Spot Recognition and ...

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7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Mohan, G. Usharani, R. Tomar, Vatsala Arthi, A. Kuriyal, Suniti Kumar Yuvaraj, K.P. K.S.Rangasamy College of Technology Department of Mathematics Tamil Nadu India SRM Institute of Science and Technology School of Computing Department of Computational Intelligence Faculty of Engineering and Technology Chennai India IIMT University School of Life Science & Technology Department of Botany UP Meerut India Karpagam College of Engineering Department of Information Technology Coimbatore India Pt.L.M.S.Sridev Suman Uttarakhand University Campus Department of Botany Rishikesh India Sri Krishna College of Engineering and Technology Department of Mechanical Engineering Coimbatore India

ISBN: (纸本)9798350372816

Frogeye Leaf Spot, a common agricultural disease, poses a significant threat to crop yields. Using multimodal data from different platforms successfully has been a hurdle for existing research in leaf spot detection. Using cutting-edge convolutional neural network (CNN) and transfer learning techniques, this research presents a Cross-Platform Multimodal Recognition system to solve this issue. With a recall of 0.88, F1-score of 0.90, accuracy of 0.91, and precision of 0.92, the suggested method demonstrates exceptional performance. Our accuracy is 6% higher, F1-score is 8% higher, and recall is 9% higher than the preceding state-of-the-art, according to comparisons with previous studies. Improved Frogeye Leaf Spot detection on many platforms is a direct result of our method's success in combining picture and sensor data. this study improves farmers' ability to identify diseases and provides a solid answer for precision agriculture. Our Cross-Platform Multimodal Recognition system has shown to be effective in enhancing agricultural disease recognition, and the shown advances highlight its potential influence on crop management tactics. © 2024 IEEE.

关键词： Convolutional neural networks

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A Fast Cross-Band Spectrum Anomaly Detection Algorithm Based on Meta-Learning 7

A Fast Cross-Band Spectrum Anomaly Detection Algorithm Based...

引用

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

作者： Peng, Chung Zhang, Mengbo Hu, Weilin Wang, Lunwen National University of Defense Technology College of Electronic Engineering Hefei China

ISBN: (纸本)9781665478571

Spectrum anomaly detection is an important research topic in cognitive radio. It can detect anomalies by predicting differences between the actual data. Existing deep learning-based spectral anomaly detection algorithms use a lot of training data. Due to the difference in frequency band, the detection model cannot be used directly across frequency bands. In order to solve this problem, a cross-band spectral anomaly detection method based on meta-learning is studied in this paper. Firstly, the data of different frequency bands are analyzed by using the pre-training of InceptionV3 to clarify the differences between different frequency bands. Secondly, a meta-learning data set is constructed and the optimal distribution of model parameters is found through the meta-learning training model. Finally, a small amount of target band data is used to fine-tune the model to detect anomalies in the target band. the experimental findings suggest that the proposed method is more stable than transfer learning and can detect cross-band anomalies with less target band data. © 2022 IEEE.

关键词： Cognitive radio

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Migration strategy of edge computing tasks based on Particle Swarm Optimization 7

Migration strategy of edge computing tasks based on Particle...

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2022 7th international conference on Electronic technology and Information Science, ICETIS 2022

作者： Ma, Wei Yang, Jun Su, Xiuli School of Information Engineering Ningxia University Yinchuan China

ISBN: (纸本)9783800757794

edge computing is to migrate terminal tasks to the edge server for execution, and reduce task execution delay and terminal energy consumption through reasonable migration decision. In view of the fact that a large number of literatures ignore the balance of computing tasks between servers, a multi-task multi-server joint optimization strategy based on Particle Swarm Optimization (PSO) algorithm is proposed. Taking the delay and energy consumption as the optimization objectives, and considering the queuing delay and the balance of the amount of computation of the edge computing server (MEC). Simulation results show that, compared with other algorithms, the average task cost of the proposed algorithm based on Particle Swarm Optimization is the smallest, and the idle MEC server can be fully utilized. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Edge computing

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Integrating Mechanistic Knowledge with Deep Learning Models for Wellbore Integrity Risk Prediction 7

Integrating Mechanistic Knowledge with Deep Learning Models ...

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7th IEEE international conference on Big Data and Artificial Intelligence, BDAI 2024

作者： Zhan, Kaichao Zhang, Xingquan Liu, Yupei Yin, Zhiming Li, Yonghua Beijing University of Posts and Telecommunications Information and Communication Engineering Beijing China Cnooc China Limited Beijing Research Center Beijing China

ISBN: (纸本)9798350352009

As global oil resources gradually deplete, the development of deepwater oil and gas resources becomes particularly important. However, the exploitation of deepwater oil and gas resources is characterized by high technology, high investment, and high risk, especially in terms of production safety. this study aims to accurately predict wellbore annular pressure by analyzing the time series of wellbore production data using advanced Transformer models and a multi-scale mechanism. By incorporating a multi-scale mechanism, the model can pay attention to both short-term subtle changes and long-term trend changes. Experimental results show that compared to traditional LSTM models and Transformer, our Multi-Scale Transformer model demonstrates advantages in prediction accuracy and the ability to handle long sequence data. this will contribute to enhancing the safety and economic efficiency of deepwater oil and gas development. © 2024 IEEE.

关键词： Oil field development

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7th international conference on Innovative computing, IC 2024

7th International Conference on Innovative Computing, IC 202...

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7th international conference on Innovative computing, IC 2024

ISBN: (纸本)9789819741243

the proceedings contain 157 papers. the special focus in this conference is on Innovative computing. the topics include: Proactive Auto Scaling Based on Marginal Request Change Analysis for Reducing Tail Latency in Kubernetes Cluster;Integration of Generative Adversarial Networks (GAN) and AI Drawing in Criminal Sketches—Applied in Crime Scene Investigation by Law Enforcement;implementation of a High Quality Cardiopulmonary Resuscitation Teaching Intelligence Training and Assessment System;Predicting Twitter Posts from Fake Accounts Using XGBoost Model;the Interplay Between Self-regulated Learning and Programming Learning Achievement;a Study on the Continued Viewing Intention of YouTuber Videos: Experiential Marketing, Social Capital, and Habit Perspectives;an Exploratory Study on the Design of Human-Computer Interaction Interfaces for Augmented Reality Integrated Museum Exhibitions;communication-Aware Optimization of Microservice Scheduling on Edge computing;Design of Computer Model for Raw Material Supplier Selection Based on Entropy Weight TOPSIS Method;exploring How Cybersecurity Competitions Can Help Improve Students’ Practical Abilities in Cybersecurity;Adversarially Residual U2Net for COVID-19 Lung Infection Segmentation from CT Images;partnerships Analysis Based on Measurements of Common Research Interest;System Design of Operating Costs of Intelligent Healthcare Services for the AHP Algorithm;a Multi-party Private Set Union Protocol Against Malicious Adversary;analysis of the Current Status of Research on Unmanned Collaborative Equipment Above and Below Water;data Mining Analysis of New Energy Vehicles Based on Cluster Analysis technology;Two-Layer Minimum Variance FIR Filter-Based DPLL Design;fuzzy Proximity Two-Dimension Inductance Gesture Recognition System Analysis and Implement;proximity Capacitive Gesture Recognition for Recursive Neighbor Memory Neural Network;application of Genetic Algorithms in Automated Mechanical Design.

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EEG Signals Recognition of Active Brain-Computer Interface Based on Sequential Encoding 7

EEG Signals Recognition of Active Brain-Computer Interface B...

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

作者： Huang, Xuewen Wang, Li Ren, Lingling Zhan, Qianqian Guangzhou University School of Electronic and Communication Engineering Guangzhou China

ISBN: (纸本)9781665478571

To complement the existing experimental paradigm of motor imagery, an active brain-computer interface (BCI) experimental paradigm based on the sequential encoding of speech imagery and motor imagery is proposed. At the same time, a sub-time window filter bank common spatial pattern (STWFBCSP) algorithm is proposed to overcome the deficiency of common spatial pattern (CSP). In this algorithm, the imagery period is divided into five overlapping time windows and each time window is filtered by multi-frequency. Feature extraction and selection are achieved by CSP and mutual information, respectively. the final decision output is determined by the voting results of five support vector machines (SVMs). Using STWFBCSP, the average classification accuracy of electroencephalogram (EEG) signals of 12 subjects is 84.87%. Compared with CSP, filter bank common spatial pattern (FBCSP), and common time-frequency-spatial pattern (CTFSP) algorithm, the result of the proposed algorithm is improved by 14.38%, 9.02%, and 3.97%, respectively. the results show that the EEG signals of this experimental paradigm can be better extracted and classified by STWFBCSP, and the practicability of BCI is also improved. © 2022 IEEE.

关键词： Signal encoding

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A High Efficiency Bandwidth-Improved Transmitarray Antenna with Phase-Shifting Elements 7

A High Efficiency Bandwidth-Improved Transmitarray Antenna w...

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7th IEEE international conference on Electronic Information and Communication technology, ICEICT 2024

作者： Wang, Xiaoxue Luo, Wei Liu, Meilin Chongqing University of Posts and Telecommunications School of Optoelectronic Engineering Chongqing China Chongqing Institute of Digital Arena Chongqing China Shanghai Jiao Tong University College of Aeronautics and Astronautics Shanghai China

ISBN: (纸本)9798350384437

this paper presents a high efficiency and bandwidth-improved transmitarray antenna. the antenna employs a phase-shifting element with three metallic layers. this element is based on the principle of polarization conversion, which enables a phase coverage of approximately 300° by adjusting the relevant parameters in the phase-shift element. this element features a low transmission loss throughout the entire working frequency band. An improved phase compensation scheme is proposed to enhance the efficiency of the array antenna. this scheme incorporates the introduction of a reference phase and a more precise evaluation function to calculate the weighted phase errors corresponding to different schemes. A transmitarray comprising 20 × 20-element is designed with the phase-shifting elements. the simulated results show that the 1-dB and 3-dB bandwidth are 16.8% and 28.8% respectively. Within the 1-dB gain bandwidth, the aperture efficiency is better than 31%, and the peak aperture efficiency and peak gain are 47% and 28.4 dBi. © 2024 IEEE.

关键词： Bandwidth

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7th international conference on Innovative computing, IC 2024

7th International Conference on Innovative Computing, IC 202...

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7th international conference on Innovative computing, IC 2024

ISBN: (纸本)9789819741816

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